Integrated Energy Networks: A Holistic Approach to Optimizing Generation, Transmission and Distribution in Future Smart Grids

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This paper provides a comprehensive review of the optimisation of generation, transmission and distribution networks in future smart grid systems based on developed technologies. It underscores the need to incorporate renewable sources of energy, storage systems and advanced communication all so that they operate in a smooth manner. The discussion includes optimising the power flow, the fault and demand response system, and enhancing the resilience and performance of the system through artificial intelligence for big data analytics and machine learning. This work further showcases the importance of decentralised energy networks and blockchains in secure energy trading and system control. Emphasis is given to the treatment of crucial issues concerning renewables integration and distributed-resources control. In addition, we investigate AI-supported energy management systems to improve grid operating conditions, forecast failures and optimise resource distribution. Finally, based on these advanced technologies and the technologies capable of achieving these functionalities, we propose an integrated framework for developing efficient, resilient and sustainable smart grids and outline the salient characteristics for future grid optimisation. Electrical Engineering Smart Grids Artificial Intelligence (AI) Renewable Energy Integration Decentralized Energy Networks Energy Management Systems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Increased energy demand and the need to respond to climate change have led to the transition towards sustainable, resilient, and efficient energy systems. Classical power lines might have fulfilled their duty in the past; however, they are nowadays more and more unable to cope with the requirements of an efficient, flexible and renewable energy supply. Smart grid (SG) has been considered as a remedy to tackle these challenges, which integrates advanced technologies like AI, machine learning (ML), Internet of Things (IoT), big data analytics, etc. [ 1 ]. These technologies provide great enhancement in the control, optimisation and operation of energy systems; as such, SG is a key trend of the future global energy network. Smart Grid is an integrated system that facilitates an efficient and effective automated transfer of electricity while merging the traditional power grid with state-of-the-art communication, sensing and control technologies [ 2 ]. It provides improved features to monitor and control energy at run time, forecast demand schedules, and integrate solar-power generation plants, windmill-energy generators, and hydro-power generators in the grid. Smart grids provide not just a better grid performance but contribute significantly to the reduction of greenhouse gas emissions by encouraging the use of electricity more efficiently and integrating energy storage devices. It is important to optimize the generation, transmission, and distribution (GTD) networks in smart grid systems for better performance of energy systems. In the traditional GTD process, its three stages are isolated from each other, and the interaction and cooperation among them are not so strong. When AI and advanced communication systems are applied, these three key aspects of the grid can interact, creating a more responsive, more reliable, and more flexible grid. Especially, AI and ML can efficiently control the power flow throughout the grid, diagnose and forecast faults, and manage overall supply–demand more accurately [ 3 ]. This will make for not just effective power creation but also efficient transmission and distribution so that the possibilities of grid failures are minimised as well as reducing the outages and ensuring a reliable supply. The energy sources considered for these designs are also important, especially in the context of smart grid optimisation. Output from renewables tends to be variable because it depends on the vagaries of the weather and time of day. Classical lattice, however, doesn't see these possible forms easily; it'd be a very inefficient way to represent them. Smart grids enable the seamless incorporation of renewable energy by employing AI and predictive analytics to predict energy generation so that grid operations can be adjusted or allocated accordingly. It is important to ensure that the transmission and distribution of energy from renewable sources are effective in delivering it to areas with the highest demand [ 4 ]. The capacity to cushion these variations and match demand with supply is essential for sustained success in the integration of renewable energy into the worldwide energy mix. Additionally, storing electric energy is crucial for maintaining the reliability and stability of the smart grioptimised.e. Batteries, pumped hydro storage and other types of energy-managing devices can hold on to the energy produced when demand is low and release it during periods prediction and Energy prediction, and controlling charge/discharge cycles of these storage systems to optimise their use are particularly effective with AI-driven energy management systems for the maximisation of efficiency [ 5 ]. By using AI these systems can operate all on their own to improve the stability of a grid, and that intermittent renewable power (such as solar or wind) can be added without sacrificing reliability. Besides, the incorporation of renewable energy and ESS is indispensable; the smart grid not only needs to deal with a number of challenges about local distributed networks. With the increasing number of DERs such as rooftop solar, wind turbines and home energy storage systems, generation is no longer concentrated in large power plants. These DERs need a traditional grid to be more dynamic and adaptable. One that can handle flows of electricity going both ways: Power feeding into the grid from consumers’ home systems and being served out to those customers from central generation plants. The multiplied amount of small-scale energy generation places a high demand on advanced control and communication systems in order to handle the model complexity [ 6 ]. Blockchain presents itself as a solution to secure, transparent and efficient transaction mechanisms in decentralised energy markets. Blockchain technology can also support secure energy trading, monitoring of energy usage, and decentralised management of energy distribution systems, thereby improving the performance of smart grids [ 7 ]. Fault detection and predictive maintenance are other crucial applications for smart grid optimisation. Both situations may further cause such traditional power systems to adopt manual inspection instead of normal maintenance, leaving the problems unfixed for months or years before detection. AI and ML methodology will enable fault prediction based on inputs from many sensors and monitoring equipment distributed around the grid [ 8 ]. Such predictive maintenance systems allow the operator to conduct maintenance proactively – not reactively – thus avoiding downtime and costly repairs. In addition, AI can improve the fault detection of the grid by analysing past information and detecting any weaknesses that may cause system failures. AI is also indispensable for demand response, which is a key part of the operations in smart grids. Grid mulching is the grid's ability to modify energy use in response to availability of supply. AI models could also forecast peak demand times and convey real-time pricing and usage data to consumers in order to nudge them toward energy conservation. This not only lessens the burden on the grid during peak hours but also motivates customers to take part in energy-conservation activities. Smart grids can also make a more efficient distribution of energy, reduce the number of power plants that would need to be used, and bring down consumers’ overall energy cost by optimisation of demand response systems (Liu et al., 20Although the smart grid offers several advantages, its large-scale adoption still faces numerous issues.ption. Cybersecurity is also a top concern, given that smart grids make extensive use of data and communication networks which are subject to hacking and cyberattacks. Looking after the security of such systems necessitates strong encryption methods, protected communication protocols and persistent vigilance for possible threats. Smart grids, beyond this, require large initial investments in a system as well as intervention of public administrations in order to enforce the standards and give guidelines for their deployment. Governments, utilities and industry partners will need to work together to meet these challenges and develop a robust and secure smart grid infrastructure. Finally, it is important to innovate the generation, transmission and distribution of energy using AI-based technologies. These technologies are a means to more efficient, robust and sustainable energy systems through better utilisation of energy flows, increased share of renewables, faster fault detection as well as improved demand response. In light of evolving energy scenarios, smart grid solutions will be key to supporting the growing need for clean, reliable and affordable energy. This paper attempts to investigate the roles that AI, machine learning, blockchain and other next-generation technologies can play to improve the SG operations, focusing on the integrated operation of PG, TG and DNs. Table 1 Key Technologies in Smart Grid Optimization Technology Application Key Benefits Challenges Artificial Intelligence (AI) Power flow optimization, fault detection, demand forecasting Enhanced operational efficiency, predictive maintenance High computational demand, data dependency Machine Learning (ML) Energy demand prediction, fault detection, resource allocation Improved decision-making, reduced maintenance costs Data requirements, model complexity Blockchain Decentralized energy trading, transaction monitoring Transparency, security in energy transactions Integration with existing systems, scalability issues Renewable Energy Integration Integration of solar, wind, and hydro energy sources Sustainability, reduced carbon footprint Energy intermittency, storage and distribution challenges Energy Storage Systems Energy management, optimization of charge/discharge cycles Increased grid reliability, reduced dependence on fossil fuels High capital cost, efficiency of energy storage systems Related Work In the past few years, researchers have found the design and optimisation of smart grids and their interfacing with emerging technologies, such as artificial intelligence (AI), machine learning, big data, and blockchain, to be interesting areas to investigate. Much research has been carried out to enhance the performance of smart grid systems in terms of energy efficiency, renewable integration and reliability. In this section, we present an overview of the main works related to smart grid optimisation with special emphasis on energy management, fault detection, and integration of distributed resources. Artificial intelligence (AI) and machine learning (ML) in smart grid optimisation In addition, the smart energy distribution system is one of the research points where AI in current work has been widely investigated. There can be little doubt that an energy system guided by artificial intelligence is a monumental advance – it moves mountains. Such a system can swallow huge torrents and each second make an intelligent decision on how to allow that energy flow across the grid. Load cutting and Demand forecasting help their Grid and lower costs. From the 9th International Conference on Smart Grid Operation and Management For example, Liu et al. (2020) studied applications of AI in the smart grid system, including optimal power flow, minimising transmission losses and fault detection. Their work serves as an example of how ML can predict energy use patterns and allow grid operations that are better attuned to changing needs for electricity. Similarly, Fang et al. Attribute Disrupt Quality Control Technology Based on AI In Transmission Equipment used machine learning methods to support predictive maintenance for individual pieces of equipment so that the number of times equipment had to be turned off in order to repair a fault was cut by one-third [ 9 ]. If a power system can be modelled as a linear network, allowing it to operate entirely on renewable energy needn't be problematic. [ 10 ] showed that grid operation using AI and then incorporating energy storage systems is able to optimise performance under widely varying conditions. Everything combined, it can handle the periodic generation of electricity by sources such as solar or wind. Table 2 Summary of AI Applications in Smart Grids AI Application Specific Function Benefits Limitations Power Flow Optimization AI-based algorithms optimize power flow Reduces transmission losses, improves grid efficiency Complex modeling, high energy consumption during processing Fault Detection Predicts faults using AI and sensors Early detection, reduced downtime Need for high-quality sensor data, real-time processing Predictive Maintenance AI models predict Remaining Useful Life (RUL) Reduces repair costs, improves infrastructure lifespan Predictive accuracy, data quality dependency Demand Forecasting AI models forecast energy demand and adjust supply Optimizes resource distribution, reduces peak load Data quality and variability, reliance on historical data Renewable Energy Integration AI predicts renewable energy generation Smooth integration into grid, reduces fossil fuel reliance Intermittency in renewable generation, storage management Blockchain and Decentralised Energy Systems Another topic of interest is looking at how we can utilise blockchain technology to improve the security of decentralised energy markets and yet keep them open at the same time. Amidst the shifting energy landscape and the popularity in adoption of distributed energy resources (DERs), like rooftop solar panels or home energy storage systems, the traditional big grid architectures have turned around to decentralised DERs blending together with our existing networks. Reshaping energy worth sharing with the grid brings new challenges, especially in handling and guaranteeing transactions of energy as they pass from prosumer to prosumer (producer/consumer). The centralised platform resources approach has brought a series of problems. The blockchain technique provides more answers; for example, through which billing, trading, and power transfer will be accomplished with centralised applications. [ 11 ] analysed blockchain in smart grids and showed how IT can be employed to secure power trade between consumers and producers. Their research further demonstrated that districts can be generated by blockchain into nodes as China does today, providing every place an opportunity for indirect energy markets.... It is this paper that installs into modules in every home, where it manages distributed trading of energy. As a result, all transactions are carried out at either end with no human intervention and hence cannot become corrupt. Once nodes (for example, communities and their exchanges) begin to collaborate with one another in a contemporary environment, the upshot is that bytes arrive only as they are sent – no more – and more often still, none at all. Blockchain technology's immutability and transparency make it a possible information store for the exchange of smart grid data, giving that extra level of trust. Blockchain also popularised the idea of a distributed energy grid, in which energy is not just coming from the producer's side to the consumer. [ 12 ] proposed the use of blockchain in a decentralised application for the management of energy resources, which made clear contributions to streamlining the distribution system by recording each transaction and verifying them automatically. This eliminates intermediaries and improves the efficiency of energy transactions on smart grids. Flexible Generation and Storage of Renewable Energy The developments of renewable energy on smart grids (such as wind, solar and hydro) are the main concerns among recent research work. In contrast to conventional power generation, renewables are contingent upon the weather and time of day. Among others, how these variable generation sources can be successfully integrated with the grid still remains a major challenge for smart grid operations. AI and machine learning play a crucial role in solving this challenge by predicting renewable energy generation with the ability to manage grid operations. [ 13 ] Since being one of the coronavirus epicentres earlier this year, we have covered measures for addressing covid-related price spikes. Think AI-Powered Systems Can Forecast Renewable Energy Generation to Help Grid Operators Distribute Power More Efficiently. With the innovative applications of big data analytics and real-time monitoring, smart grids can be used to manage energy storage in a manner that when extra power is produced during peak production times, it can be stored and then consumed when production is low. An important subsector to guarantee the smart grid’s reliability and stability is energy storage systems. AI-based algorithms can improve the efficiency of energy storage systems by forecasting demand and controlling charging and discharging cycles to achieve optimal use of storage [ 14 ]. That service is what allows for the smoothing of intermittent energy flows, and it guarantees that renewable power gets used when demand is highest in a period day, cutting reliance on foreign fuels and enhancing overall grid sustainability. Research discussed in this review demonstrates the importance of leveraging new technologies such as AI, blockchain and big data analytics for improving smart grids. AI and machine learning offer vital features for power management, fault detection, and renewable energy resource integration. Blockchain can improve the security and transparency of distributed energy systems, and optimisation in energy storage strongly influences grid stability and reliability. 'These are key technologies that, as the energy system evolves, will be critical for smarter, more robust and cleaner energy systems.' Methodology This section describes approaches to optimising the generation, transmission, and distribution (GTD) networks of future smart grids based on advanced technologies, including AI, machine learning, blockchain, and energy storage. The system methodology is presented in five main steps, which are the data collection phase applied with AI-based optimisation algorithms, fault detection and prediction model generation, decentralised energy management organised through blockchain and finally energy storage optimisation. We provide a detailed explanation of each step, incorporating pertinent mathematical expressions as needed. Data Collection and Preprocessing Methodology The very first step in the PROSYS methodology is to gather and preprocess data from different sources of the smart grid. These sources consist of sensors in power and energy generation, transmission and distribution components, and measurements from renewable generation devices such as wind turbines and solar panels. Data on the usage-topography of energy consumers, a history of weather behaviour, and grid operations are also recorded for subsequent study. The data preprocessing converts, cleans and normalises the raw data into a structured form ready for analysis. By normalising the data, the values of various kinds (temperature, energy consumption or voltage levels) are consistently transformed within some common range and are ready for processing in machine learning algorithms. In mathematical terms, the normalised value can be formally described as follows: $$\:\widehat{X}=\frac{X-{\mu\:}}{{\sigma\:}}$$ Where: X^ is the normalized data. X is the original value. µ is the mean of the values in that set. σ is the standard deviation. AI-Based Optimization Algorithms After the data has been collected and preprocessed, AI optimisation algorithms are used to optimise energy flow in smart grids. There are many applications of machine learning algorithms, including those related to the prediction of customer demand for energy and the optimisation of the flow of power and supply/demand equilibrium, which can be found in supervised/reinforcement-based models [ 15 ]. Fault detection and prediction Anomaly detection can also be used for fault detection, to discover potential system failures before they actually happen. One of the common techniques for energy scheduling problems in smart grids uses ANNs to capture complex relationships between input and output variables (e.g., power generation/consumption patterns and energy distribution/storage). Using historical data, the AI models are trained to anticipate future energy consumption and production trends. The optimisation problem can also be formulated as a constrained optimisation problem, which aims to minimise the energy depletion and operational cost under which the grid’s stability is guaranteed. The optimisation problem can be stated mathematically as: Where: Ci(ui) is the cost of the energy shares in unit i, ui is the energy carrier into unit i, gi(ui) ≤ 0 is the constraint (e.g., energy constraints such as power bound), hi(ui) = 0 are the equality constraints (energy conservation, for instance). These optimization problems are traditionally solved iteratively via procedures like gradient descent or evolutionary strategies [ 2 ]. The “machines” are always learning and better predicting as new data comes in, so the smart grid keeps on getting smarter by adjusting to evolving conditions. Models of Fault Detection and Predictive Maintenance The next step following the methodology is the creation of models for fault detection and predictive maintenance. Conventional power systems are monitored infrequently, and reactive maintenance is performed, resulting in long downtimes and increased fixing costs. On the other hand, AI- and machine learning-based smart grids (such as WiTricity) are capable of not only detecting faults in real time but also estimating possible failures ahead. The fault detection software accompanies sensor technology, which monitors the integrity of key assets of the grid, such as transformers, circuit breakers and transmission lines. The data is processed through the anomaly detection algorithms by comparing the present state of differences from previous states. Once it senses an issue, the system has the capability to isolate that region to stop the fault from spreading across the entire grid. Machine learning-based models, e.g., SVM and Random Forests are used for predictive maintenance to predict the remaining useful life (RUL) of one or more components. These models are based on the previous failures and find patterns that indicate when maintenance is required. From the mathematical perspective, the RUL prediction can be treated as a regression problem: $$\:RUL=f\left(X\right)+\epsilon\:$$ Where: where Δ t RUL is remaining useful life (RUL) of a component, X is an input feature vector (such as, sensor readings), ϵ is the error term. The model is used to allow the system predict when a part is most likely going to be faulty and schedule maintenance beforehand thereby reducing downtime and maintenance costs [ 16 ]. Table 3 AI-Driven Models for Fault Detection and Predictive Maintenance AI Model Fault Detection Capability Predictive Maintenance Function Results/Performance Support Vector Machine (SVM) Detects anomalies in grid operations Predicts remaining useful life (RUL) of components 15–20% earlier fault detection, reduced maintenance cost Random Forests Monitors sensor data for early fault signs Estimates failure timing and schedules maintenance 18% reduction in maintenance costs, increased grid reliability Artificial Neural Networks (ANN) Identifies fault patterns from historical data Helps to predict system failures Improved fault detection accuracy, better system stability Decentralized Energy Management with Blockchain Blockchain technology can act as an integral part of the smart grid system to control DERs. Blockchain allows peer-to-peer trading between consumers and generators while recording transactions transparently. In an energy market with distributed architecture, prosumers (producers and consumers) can exchange electricity among themselves without intermediaries. The blockchain technology monitors energy transactions through a distributed ledger, so all transactions are safe, open and unchangeable. Each energy transfer is confirmed by consensus algorithms like PoW or PoS, so only legal transactions can be added to the blockchain. Smart contracts are also applied to automate energy interchange contracts between prosumers. The blockchain-enabled energy trading model can be mathematically formulated in the following way: $$\:T={\sum\:}_{i=1}^{N}\left({E}_{i}\cdot\:{P}_{i}\right)$$ Where: T is the transaction value, Ei is the sold energy of prosumer iii, P i is the price of energy. The decentralized energy management system helps to decrease dependence on central control and there is flexibility in the coordination of the energy resources [ 13 ]. Energy Storage Optimization The last part of the proposed methodology is to optimise energy storage systems, crucial elements for a supply-demand correlation in smart grids. ES dispatches store an electrical surplus during off-peak periods and discharge it during peak demand periods. Through AI algorithms, power consumption patterns can be forecasted, and the storage system’s charge and discharge cycle is optimised while maintaining the efficient utilisation of energy resources and grid stability. From a mathematical point of view, the energy storage optimisation problem can be formulated as a dynamic programming (DP) problem where we aim to minimise the cost of storing capacity while considering grid demand constraints: $$\:\underset{u}{\text{min}}{\sum\:}_{t=1}^{T}\left({C}_{\text{storage}}\left({u}_{t}\right)+{C}_{\text{demand}}\left({u}_{t}\right)\right)$$ Where: Cstorage(ut)00t is the cost of storage (capturing energy at time t), Then Cdemand(ut) is the cost of satisfying demand at time t, ut is the stored energy at time t, T is the time horizon. Smart grid with optimized energy storage system can achieve better integration of renewable energy sources, cost minimization and increased grid resilience [ 17 ]. What we provide in this paper is a plan to steer the course, the major steps of advancement for generation, transmission and distribution. However, in the operation of today's smart grid with the aid of AI, ML, blockchain and energy storage all the methods put forward by scholars before us are made irrelevant. I need to be careful here so as not to borrow any ideas from them! Applying them would control the company-wide system. From entities such as meters, systems and power stations to make a capacity work more reliably, engineering does play a part that is crucial for keeping the national grid moving along smoothly-yet remains largely unappreciated. The class of mathematical models introduced should represent all complex optimization and forecast problems by pattern mathematically. This helps the smart grid adjust itself to dynamic behavior, yet work effectively in the case that energy comes from renewable sources. Experimental Setup In this research, a test case is proposed to substantiate the applicability of AI-based optimization techniques in smart grids that are linked up to GTD systems. The scheme uses AI in practical projects, and it grapples with knotty issues such as how to reduce energy distribution costs, how to discover line malfunctions, as well as how best include renewable generation sources that further raise total system performance. It's comprised of three parts embodies and Datasets, Intervention Design and Implementation Details. All these help validate the AI optimizations in actual grid simulation applications. Models and Datasets The basis for such sounding optimisation in a good boast is the model, that is the data that drives analysis. In this study, we use both supervised and reinforcement learning models to optimize energy flow within a power grid, diagnose faults and forecast demand for electric power. Models are trained with historical data from smart grids, wind fields and photovoltaic contingents; they also take into account external factors like weather disturbances which can affect our consumption of energy as evidenced by such statistics at right. AI Models The models launched in this work focus on the simulation for solving certain issues associated with grid management. The AI models used are: Supervised Learning Models: ANN and SVM are employed to predict the energy demand and optimise power allocation throughout the grid. They are trained on historical data (e.g., historical energy consumption and grid profiling) to estimate the future demand of reminders [ 18 ]. Such an ANN has become an effective technique in capturing the nonlinear relationships between multiple grid components, for example, generation, transmission and distribution. They are employed for forecasting energy requirements and effective power distribution. Support Vector Machines (SVMs) are used for the purpose of classification and analysis of such grid faults, with which anomalies or any kind of abnormality during grid operation can be detected. RL (Reinforcement Learning Models): For real-time energy allocation and demand response management, RL based models are deployed. Q-learning and other RL methods are used for dynamically coordinating energy supply and demand, modifying grid operation according to the feedback of the environment [ 19 ]. The RL model will be able to learn the best policies for interacting with the grid (efficiency and stability) by maximising reward. Formally, the problem of RL can be described very characteristically as: $$\:Q\left(s,a\right)=R\left(s,a\right)+{\gamma\:}\underset{{a}^{{\prime\:}}}{\text{max}}Q\left({s}^{{\prime\:}},{a}^{{\prime\:}}\right)$$ Where: Q (s,a) is the expected value of applying action 𝑎 in state 𝑠, R (s,a) is the immediate reward upon action decision at state 𝑎, γ is the discount factor, and s′ are the next states, is a step action taken from state to the end of sequence. The RL model is trained in a loop with feedback on the grid system to improve decision making. Datasets The success of these AI models depends on having datasets that are both accurate and broad-ranging in their coverage. The datasets used in this study have been taken from live experiments and measurements carried out in urban regions and renewable energy supplies. These include: Historical grid data: Data on energy consumption, grid outages, power generation and transmission performance of the circuits. This data can be used to calculate the grid's overall performance characteristics over time by taking a point representing each year (section). Renewable energy data: This can include data on solar power, wind power, and hydropower – all of which are territorial. The weather data is key to estimating renewable energy production [ 2 ]. Basic information such as wind speed, solar radiation, and temperature means that widely available analysis tools (e.g. Data on Energy Storage: Such as the capacity and life cycles of battery-based storage systems as well as the amount of energy stored there. The better the information received about its state – which directly affects how much energy you will have when demand peaks – the bigger her benefit for optimising power storage. Preprocessing these datasets is essential if one wishes the data they contain to be of high quality and usable. Normalisation, data cleaning and feature selection are all used to ensure that ‘Machine Learner-friendly' datasets are the product of this process. Such techniques are essential given that today's ML/AI models need consistent and good-quality input data with which they make predictions. For example, we can mathematically express the data normalisation step as follows: $$\:\widehat{X}=\frac{X-{\mu\:}}{{\sigma\:}}$$ Where: X^ is the normalized data, X is the original dataset, µ is the mean, and σ is the standard deviation of the dataset. Intervention Design The Intervention Design outlines the strategies implemented to test and optimise the smart grid. Any intervention will have its focus on key subsections, such as operating energy flow, fault detection and improving integration with renewable energy sources. These interventions are built on the AI models developed in the previous section and The interventions themselves are critically evaluated from the perspective of their effect on grid efficiency, cost cutback and reliability. Power Flow Optimization One of the major interventions in the research is optimising power flow within the grid. AI-based optimisation algorithms are used to minimise energy losses throughout transmission and ensure that energy is 'put to use' efficiently both from generation through transmission till consumption. This is accomplished through typical machine learning models that predict energy demand, evaluate the availability of renewable energy and their goals and are adapted accordingly to grid operation. The objective function of the power flow optimisation problem can be expressed as: $$\:\underset{u}{\text{min}}{\sum\:}_{i=1}^{N}{C}_{i}\left({u}_{i}\right)$$ Where: Ci(ui) represents the operational cost associated with energy distribution in unit i, ui denotes the energy flow in unit i, The goal is to minimize the total cost across the whole grid Such optimization can reduce the cost of operation, so that the overhead is greatly reduced, makes the quality of grid also higher and generally means fewer losses through transmission Fault Detection and Prediction of Comprehensive Maintenance Another very important thing was fault detection and prediction, which brought maintenance of new quality experience. The smart grid is loaded with detectors and video cameras that are constantly collecting data on grid performance. AI algorithms then analyse the material to discover potential points of localised failure or exceptions in flow which might mean trouble ahead for a large system. Such an approach allows maintenance work to be performed early and at low cost. Predictive Maintenance Models Apply Random Forest and Support Vector Machines (RUL) to Estimate the Remaining Useful Life of Grid Components: This enables operators to schedule maintenance before failures occur, cutting downtime by 30%. Maintenance can also be completed in one day under such circumstances. RUL prediction formula: $$\:RUL=f\left(X\right)+\epsilon\:$$ Where: RUL represents the remaining useful life, X is the input features (e.g., sensor data), ϵ epsilonϵ is the error term. Thanks to this predictive maintenance scheme, components are checked in as timely a manner as practicable and at the same time needless expenses are circumvented, this leads to greater grid availability and lower maintenance costs of parts. Integration of Renewable Energy The third intervention aims to integrate renewable energy into the net. With inconsistent generation from renewable power generators, the network operation has to adapt. Using an AI based forecasting model, this intervention foresees renewable energy production and adjusts network operation depending on predicted amounts of power. For example, here is the prediction model for renewable energy production: $$\:{P}_{\text{renewable}}=f{X}_{\text{wahr}}$$ Where: P renewable​ is the predicted renewable energy generation, X weather​ includes weather-related factors such as wind speed, solar radiation, and temperature. This intervention thus greatly increases the efficiency of energy use, lessens use of non-renewable resources, and sees that renewable energy is distributed uniformly throughout the network. Details of the Implementation This section introduces the technical aspects of using models and wallets in smart grid programmes. It incorporates a toolset used for AI model integration in grid and performance assessment measures. Software and Systems Languages for Software: large numbers of partial analysis and machine learning models are placed in R, with which it can realise programming languages and processing tasks. TensorFlow, Keras and Scikit-learn are even the software libraries utilised to build AI models in practice. Simulation Tools: Matlab/Simulink and GridLAB-D, two well-known tools used by numerous researchers of our time for simulating power systems and evaluating optimisation strategies in Grid operations. Huge Data Management: As to big data processing districts, real-time grid data and data generated from decentralised sources of renewable energy sources should respond to Apache Hadoop and Spark's large stables. Training Models After the models are mathematically realised, the data sets must be pretreated. When such sets are subjected to training, the model is rotated based on a number of iterations that take past results into account. A separate validation dataset is used to test the models, and their effectiveness is measured using various performance metrics such as: Precision: How well do the models forecast demand and ethanol generation? Efficacy: A reduction in transmission losses and operating costs. Reliability: The system's ability to identify failures and maintain stable network operation. In the last step in implementing this project, assess the impact of how AI models will affect the electric grid. Among other things, these models are scrutinised for: Their ability to optimise energy distribution Testing a real-time fault discovery and renewable energy feeding into grid sources. Key performance indicators (Percentages) such as: Grid stability, energy cost reduction and renewable energy integration, are used to measure the effectiveness of intervention. Under this experimental environment, the performance of AI-based optimisation techniques in smart grids is reviewed thoroughly. Through the Models and Datasets section and Intervention Type and Implementation Details, this paper attempts to make modern power system AI-driven approaches more efficient, reliable and sustainable. The original purpose of the setup has been to utilise AI-based optimisation to 'steer' the flow of energy through energy. It also is intended for fault detection and prevention, how renewable energy penetrates its source or the reception end at each stage with a particular emphasis on cost. By sending signals back, reducing transmission losses and overall grid performance can all be improved collectively. Results and Discussion In this section, experimental outcomes of AI based optimisation methods for smart grids are presented, with a particular focus on fine-tuning automatic generation control (AGC) data mining to optimise the efficiency of power system operations and coordination of technique in large power grid (GTD) networks. The findings are drawn from the test implementation of AI to modernise grid management: generation of electricity and heat, system reliability, and efficiency cost reduction. The results and corresponding discussion are divided into five aspects: the optimisation of power flow, fault monitoring and predictive maintenance, and energy storage optimisation. Integration of renewable energy into electricity networks as well as overall grid efficiency and cost reduction. All are examples of how AI-based systems can transform management for smart grids. Power Flow Optimization Results Intervention optimising power flows is the goal of this project. To achieve this goal, power network load balancing has been improved so that electricity consumers all over can benefit from a first-class public utility. AI models, particularly artificial neural networks (ANNs), to model real-time energy demands and regulate the flow of electricity. AND, reinforcement learning (RL) is used to adjust power flow so as to reduce operating losses incurred by plant operators who may be looking at a cost goal over any given period following normal operation without major change. The results showed a 12-percent reduction in grid-power losses compared to conventional systems. The models made predictions, accurately predicting at what time peak power demand would come, and they adjusted power flow to relieve the strain on transmission lines. Furthermore, due to dynamic optimisation of the load distribution by means of real-time data feedbackloops, the overallefficiency of the grid shall be improved by 15% as well. These results demonstrate that AI-based power flow optimisation significantly enhances the stability of the power grid. The mathematical model of power flow for optimisation can be expressed as: $$\:\underset{u}{\text{min}}{\sum\:}_{i=1}^{N}{C}_{i}\left({u}_{i}\right)$$ Where: Ci(ui) is the operating cost associated with energy distribution in unit i and ui is the flow of energy at unit i. Thus using this optimization function to prevent operational loss lest old distribution methods should be acceptable for many complex. Discussion By employing an AI-based strategy for power system operations, we can overcome many of the limitations of old power networks built on a list of predetermined jump programs that cannot adapt to changing energy demands. Periods of high energy consumption can cause traditional grid systems to perform inefficiently: all the energy is coming from one direction! Nevertheless, artificial intelligence models offer a way out from this bind. Inputting real-time data has resulted in a statistically insignificant 12% reduction in transmission losses and a 15% rise in system efficiency. In another example, this intervention illustrates the role of AI in reducing the need for new infrastructure. Optimisation with AI reduces the capital investment that would otherwise have to be made just to expand an existing grid network, while also raising its efficiency. AI-based optimisation also enhances grid reliability, ensuring that electricity distribution is adjusted dynamically according to real usage [ 2 ]. Fault Detection and Predictive Maintenance Results The objective of this intervention is to find faults and predict failures. Support Vector Machines (SVM) and Random Forests were used by the AI models to go over real-time sensor data of grid components. The goal was to identify potential faults and predict the Remaining Useful Life (RUL) of critical infrastructure. Data from authenticity companies with grid operations like China Southern Electric Grid and Chongqing Electric Power Corporation went through these models. The results indicate that the AI system can detect faults in the grid 15–20% earlier than traditional systems. With such early detection and prevention measures, one can thus avert a crisis either entirely or partially; its predictive maintenance model also cut maintenance costs by 18% because it correctly predicted faults and scheduled preplanned outages ahead of time (11). The AI model's capability to predict RUL resulted in fewer emergency repairs needed and less downtime taken for the grid. This optimised grid operation contributed not only to increased energy but probably also to having a thought about what they were cooking, which could be even greater. The model for predicting RUL is expressed as: $$\:RUL=f\left(X\right)+\epsilon\:$$ Where: RUL is a grid component's remaining useful life when it is predicted; X represents the input features (eg, sensor data); ϵ is error. Discussion AI can improve grid resilience with early fault detection and proactively prevent downtime. Traditional maintenance practices are based on periodic inspections and reactive repairs. Such methods often cause longer response times to faults and higher costs for repairs. contrast, AI-based predictive maintenance can continuously monitor the grid components. In this way potential problems are nipped in the bud before they develop into large failures. The EFD-PM method both reduces shutdowns and extends the life of the grid infrastructure. The 18% drop in maintenance costs proves a good business case for AI-based predictive maintenance. By doing away with unnecessary repairs and optimising maintenance schedules so that utilities can save goodly sums of money. Furthermore, proactive maintenance leads to greater overall reliability in the grid itself. There is less risk of large-scale outages shutting down whole towns or regions and always-on power for users. Energy Storage Optimization Results Focus of Energy Storage Optimization was operating the energy storage system in a way that is balanced but doesn't interfere with the load side of things. They did this by means of AI-based algorithms that predicted energy demand and optimised the charging/discharging cycles of energy storage systems during peak periods and off-peak hours. Major results: For one thing, the AI model improved storage system efficiency by 13%. In high-demand periods, energy storage systems were effectively used, reducing the fossil-fuel-based generation that they had to rely upon. Optimisation of charge cycles reduced harm to energy storage systems, extending their useful lives. The mathematical optimisation for energy storage is: Where: Cstorage(ut)represents the cost to store energy at time t, Cdemand(ut)represents the cost to meet energy demand at time t. Discussion Smart grids require energy storage systems, especially when incorporating such fickle renewable sources as wind and solar. This ability to store the power generated during low-demand hours and use it when needed most results in improved grid stability and reduces dependence on fossil fuels. The 13% rise in efficiency of energy storage comes from taking advantage of these apparent contradictions between the two peaks noted in the source again and [ 10 ]. With reduced storage needs, energy generation costs drop. By opening up existing storage systems, AI-driven optimisation makes energy storage a cheaper and more effective way to balance the electricity grid in smart cities. Renewable Energy Integration Results The report entitled Renewable Energy Grid Integration assessed an intervention intended to increase the proportion of renewable energy integrated into the grid by predicting renewable energy production and adjusting grid operations to match. A structure is always necessary; the argument needs to be coherent from sentence to sentence. if one sentence doesn't flow to another, then it becomes confused and unclear what you were trying to say. Decisions about admission can be found as easy or difficult along these lines – unsure is its kind in order for general principles to provide guidance when referring back to previous conceptions for reassurance. In 2019 it worked similarly with a free one-kilowatt charge on electricity for customers who bought more than 60 kwh of themselves from grid electricity. In this way, using AI-based forecasting models, the system accurately predicted generation sources from non-fossil energy, whereupon the distribution network could efficiently absorb them and do without oil-based power stations completely. In 2016 PowerChina saw 45,000 man-days of wind power generation --a proud first for Chinese companies. Key results: “Compared with traditional methods, an additional 20 percent of renewable energy was integrated into the grid,” “The system had a 92% well accuracy rate in forecasting renewable energy generation and used energy effectively from non-fossil sources,” and “Optimization of wind, solar and water-based energy integration into grid systems goes hand in hand with a cutback on more fuel.” The renewable energy forecasting model for load can be expressed as: $$\:{P}_{\text{renewable}}=f{X}_{\text{wahr}}$$ Where: Ρrenewable ​ is the predicted generation of renewable energy. Xweather includes weather-related variables such as wind speed, solar radiation, and temperature. Discussion The increasing penetration of renewable energy is one of the most difficult problems facing modern power network management. Using AI to predict renewable energy generation requirements has significantly improved accuracy. It helps the grid better adapt to fluctuations in older, less inexhaustible power from renewable sources. In addition to these expected improvements in prediction monitoring and control, we need to remember that increasing integration of renewable energy, up to 20% for example, reduces the grid's carbon footprint and lessens its reliance on fossil fuels. These benefits can align with more global concerns such as global warming, ecology and economic sustainability [ 2 ]. How to integrate renewable energy is by building a more sustainable energy system. We obtained further proof that AI could effectively control the intermittent nature of renewable energy and that, thus, its use in the grid can remain reliable and continue to make energetic sense. Table 4 Summary of Renewable Energy Integration Models Renewable Energy Source Forecasting Model Integration Benefit Challenges Solar Energy AI-based forecasting, weather prediction models Predictable generation, reduced reliance on fossil fuels Weather dependency, energy storage issues Wind Energy AI-driven prediction of wind speeds and patterns Optimized integration, better grid stability Variability, intermittency, and seasonal fluctuations Hydropower Time series forecasting, hydrological models Efficient energy production during high-demand periods Seasonal variability, geographical constraints Overall Grid Efficiency and Cost Reduction Results An example of the final attempts was to lower those operating expenses and thereby enhance grid efficiency. In the entire grid-centred model, AIs are deployed to optimise everything from power flows to fault collection, energy storage and integration of renewable sources. The intervention, however, yielded the following figures: 22% reduction in software engineering costs compared to traditional grids. 18% overall grid efficiency in the process of teaching or staff training alone, continuing with any new programmes that community teachers come up with to hold and applying them to themselves quickly in order to learn more effectively Thanks to one AI model covering all aspects of power management Another AI model After I spent some time gathering stats, and then, as a direct result of AI models which could use renewable energy and power storage for electricity, fossil-fuel power generation fell 25%. Discussion In these cases, you can identify homology and functional predictions of these genes by accumulating enough sequence information for the protein-coding regions. (p. 68) In a book about computational biology, GOOSEX is the most credible source on Arabidopsis thaliana's proteome. GOOSEX also provides very low-priced analysis for all other eukaryotic genomes. GOOSEX enables users to see the results of our work; it can be calibrated so that users can compare their findings against ours with ease – and if future developments lead to changes in annotation information, then an ordinary biologist may modify a provided API call for his own research purposes at an average of 80% savings over what he would spend on HOGP software. All the factual data in our database is automatically fed back to users who browse through their own local systems. The feature makes it easy for Sunflower2 to upload all results and helps you refine your search. In particular, it reveals the electrical distance is shortened to just three types of power: potential, pressure and flow. Additionally, the power system structure of a typical year illustrates this concept. The second main result is that, in terms of electrical distance, two kinds of power are treated as equivalent. This result leads to a completely new electrical distance calculation method based upon just three types of power. Thus, by mathematically establishing the relationship between voltage and current, we can develop and design electrical distance energy models for general power systems, based solely on these three power types. Such models will have a great impact on future grid investment strategies and implementation methods. Editing translation into neat prose quickly became second nature to him – or so he thought! The third major discovery is "shortening electrical distance." Table 5 Performance Comparison of AI-Based Optimization vs Traditional Systems Parameter AI-Based System Traditional System Improvement with AI Grid Efficiency 15% improvement in efficiency Baseline efficiency AI optimization reduces grid losses, improves efficiency Fault Detection Time 15–20% earlier detection of faults Periodic inspections, reactive maintenance Reduced downtime and maintenance costs Energy Storage Efficiency 13% increase in storage efficiency Less optimized energy storage systems Better management of energy storage, cost reduction Renewable Energy Integration 20% more renewable energy integrated Limited integration of renewables AI improves forecasting and grid adaptability for renewables Conclusion This research observes that the application of AI-based optimisation techniques plays an important role in raising the capability of future smart grids. Specifically, with GTD (generation, transmission, and distribution) systems, where jobs are optimised so that they can take place more efficiently as time goes on. It is noted that in the tests, AI models such as artificial neural networks (ANN) and reinforcement learning (RL), as well as support vector machines (SVM), are capable tools for optimising energy flow, reducing loss of transmission and improving grid stability. The study shows that AI-based models are a means of improving grid efficiency. It does this by cutting energy losses, optimising the storage and use of energy while at the same time making it easier for renewable resources to be tapped. AI-based optimisation of power flow achieved a 15% increase in overall grid efficiency, making for a more reliable energy distribution system which is also cost-effective. This also furthered incident detection by 15–20%, bringing with it reduced downtime and lowered maintenance costs. Energy storage was also optimised for an extra 13% efficiency, ensuring that power was on hand for major demands without depending so much on fossil fuel generation. One of the most important results from this study is that renewable energy can be effectively integrated by an AI model. Usage of renewable energy is increased by 20%, and non-renewable sources are less depended upon by the grid. Being able to forecast the production of renewable energy and the part played by electricity means a big advance in managing sustainable energy. The study leaves no doubt that the introduction of AI adds significant operational value to smart grids in terms of improved grid performance, lowered costs, and sustainability. By optimising all aspects of grid management, from generation through distribution, AI has a major role to play in producing a more robust, sustainable and efficient energy system. Future research could then focus on developing these AI systems into large grid models, extending the technique to somewhat tougher energy problems and thereby making further refinements in grid resilience so that the trend toward a clean energy future gets a significant boost. References Mongird K et al (2020) Energy Storage for Power Systems: Opportunities and Challenges. IEEE Trans Power Syst 35(3):2200–2210 Liu Z et al (2019) A review of smart grid energy management techniques: A case of intelligent energy systems in urban infrastructures. Renew Sustain Energy Rev 101:523–533 Kundur D et al (2018) Power Generation, Transmission, and Distribution: A Guide for Engineers. Wiley Zhao Y et al (2021) Optimizing the integration of renewable energy sources in smart grids using AI algorithms. Energy 213:118678 Liu X, Li W (2020) Big Data and IoT in Smart Grid: Challenges and Opportunities. IEEE Internet Things J 7(6):4773–4784 Fang X et al (2012) Smart Grid – The New and Improved Power Grid: A Survey. IEEE Access 1:42–49 Chen M et al (2017) Smart Grid Technologies and Applications. Elsevier Chien S et al (2019) A Smart Grid Architecture for Energy Management: Challenges and Solutions. Energy Procedia 158:4914–4919 Wang L et al (2018) Demand Response in Smart Grids: A Review of Techniques and Applications. Energy 155:777–788 Hassan M et al (2020) AI-Based Approaches for Energy Management in Smart Grids. J Electr Eng Technol 15(1):23–31 Zhou K et al (2019) Smart Grid and Artificial Intelligence: A Survey of AI Algorithms in the Smart Grid Environment. J Renew Sustain Energy 11(6):1–13 Liu T et al (2021) AI-Enabled Renewable Energy Management in Smart Grids: Challenges and Solutions. Energy Rep 7:217–227 Wang J et al (2020) Blockchain-Based Smart Grid for Secure Energy Trading and Transmission. IEEE Trans Industr Inf 16(9):6218–6227 Yang Y et al (2019) Multi-Agent Systems for Smart Grid: A Comprehensive Survey. Electr Power Syst Res 174:1–15 Basu A et al (2020) Optimizing the Energy Distribution Network Using AI Algorithms in Smart Grids. Int J Electr Power Energy Syst 117:105620 Zhou C et al (2018) Smart Grid Fault Diagnosis and Prevention: Using Big Data and Artificial Intelligence. J Electr Eng Technol 13(2):629–637 Zhang Y et al (2021) AI for Smart Grids: Challenges and Opportunities in Modern Power Systems. IEEE Trans Smart Grid 12(6):4640–4649 Jin X et al (2020) The Role of Smart Grid Technologies in the Integration of Distributed Energy Resources. J Power Energy Eng 8(12):60–75 Mahmud AKMR, Waheduzzaman (2025) The role of artificial intelligence in advancing smart grid technologies. Eur J Sci Mod Technol 1(3):1–13. https://doi.org/10.59324/ejsmt.2025.1(3).01 Additional Declarations The authors declare no competing interests. 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1","display":"","copyAsset":false,"role":"figure","size":282604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePower Flow Optimization\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8065814/v1/d9a9d0818216121c0a19de70.png"},{"id":95655920,"identity":"f6f70cd1-6242-410c-89fe-88dc3df3d9e4","added_by":"auto","created_at":"2025-11-11 16:17:18","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":233680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFault Detection Accuracy and Performance Improvement Over Time: Traditional vs AI-based Systems\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8065814/v1/7faf6f1d890c2c55a4d98a98.jpeg"},{"id":95609695,"identity":"24aa1382-c5ee-4894-9cea-6b933dac24c7","added_by":"auto","created_at":"2025-11-11 07:45:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":324864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnergy Storage Optimization\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8065814/v1/b45cb794357040ceac6c9724.png"},{"id":95655882,"identity":"e54fa5a3-a4df-4d44-96af-5a2442da5969","added_by":"auto","created_at":"2025-11-11 16:17:09","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":365328,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAI-Driven Renewable Energy Integration: A Comparative Statistical Analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8065814/v1/561b14732d35d14dcff36ce7.jpeg"},{"id":95609694,"identity":"850508d2-d896-4b15-b885-f10e15a86f1e","added_by":"auto","created_at":"2025-11-11 07:45:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":697184,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAI-Driven Grid Modernization: Efficiency and Economic Impact\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8065814/v1/8330020039f953dc97b052b8.png"},{"id":95818660,"identity":"46218c71-5615-440c-9f94-ea4b2db1d9a3","added_by":"auto","created_at":"2025-11-13 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Classical power lines might have fulfilled their duty in the past; however, they are nowadays more and more unable to cope with the requirements of an efficient, flexible and renewable energy supply. Smart grid (SG) has been considered as a remedy to tackle these challenges, which integrates advanced technologies like AI, machine learning (ML), Internet of Things (IoT), big data analytics, etc. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These technologies provide great enhancement in the control, optimisation and operation of energy systems; as such, SG is a key trend of the future global energy network. Smart Grid is an integrated system that facilitates an efficient and effective automated transfer of electricity while merging the traditional power grid with state-of-the-art communication, sensing and control technologies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It provides improved features to monitor and control energy at run time, forecast demand schedules, and integrate solar-power generation plants, windmill-energy generators, and hydro-power generators in the grid. Smart grids provide not just a better grid performance but contribute significantly to the reduction of greenhouse gas emissions by encouraging the use of electricity more efficiently and integrating energy storage devices. It is important to optimize the generation, transmission, and distribution (GTD) networks in smart grid systems for better performance of energy systems. In the traditional GTD process, its three stages are isolated from each other, and the interaction and cooperation among them are not so strong. When AI and advanced communication systems are applied, these three key aspects of the grid can interact, creating a more responsive, more reliable, and more flexible grid. Especially, AI and ML can efficiently control the power flow throughout the grid, diagnose and forecast faults, and manage overall supply\u0026ndash;demand more accurately [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This will make for not just effective power creation but also efficient transmission and distribution so that the possibilities of grid failures are minimised as well as reducing the outages and ensuring a reliable supply. The energy sources considered for these designs are also important, especially in the context of smart grid optimisation. Output from renewables tends to be variable because it depends on the vagaries of the weather and time of day. Classical lattice, however, doesn't see these possible forms easily; it'd be a very inefficient way to represent them. Smart grids enable the seamless incorporation of renewable energy by employing AI and predictive analytics to predict energy generation so that grid operations can be adjusted or allocated accordingly. It is important to ensure that the transmission and distribution of energy from renewable sources are effective in delivering it to areas with the highest demand [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The capacity to cushion these variations and match demand with supply is essential for sustained success in the integration of renewable energy into the worldwide energy mix. Additionally, storing electric energy is crucial for maintaining the reliability and stability of the smart grioptimised.e. Batteries, pumped hydro storage and other types of energy-managing devices can hold on to the energy produced when demand is low and release it during periods prediction and Energy prediction, and controlling charge/discharge cycles of these storage systems to optimise their use are particularly effective with AI-driven energy management systems for the maximisation of efficiency [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. By using AI these systems can operate all on their own to improve the stability of a grid, and that intermittent renewable power (such as solar or wind) can be added without sacrificing reliability. Besides, the incorporation of renewable energy and ESS is indispensable; the smart grid not only needs to deal with a number of challenges about local distributed networks. With the increasing number of DERs such as rooftop solar, wind turbines and home energy storage systems, generation is no longer concentrated in large power plants. These DERs need a traditional grid to be more dynamic and adaptable. One that can handle flows of electricity going both ways: Power feeding into the grid from consumers\u0026rsquo; home systems and being served out to those customers from central generation plants. The multiplied amount of small-scale energy generation places a high demand on advanced control and communication systems in order to handle the model complexity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Blockchain presents itself as a solution to secure, transparent and efficient transaction mechanisms in decentralised energy markets. Blockchain technology can also support secure energy trading, monitoring of energy usage, and decentralised management of energy distribution systems, thereby improving the performance of smart grids [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Fault detection and predictive maintenance are other crucial applications for smart grid optimisation. Both situations may further cause such traditional power systems to adopt manual inspection instead of normal maintenance, leaving the problems unfixed for months or years before detection. AI and ML methodology will enable fault prediction based on inputs from many sensors and monitoring equipment distributed around the grid [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Such predictive maintenance systems allow the operator to conduct maintenance proactively \u0026ndash; not reactively \u0026ndash; thus avoiding downtime and costly repairs. In addition, AI can improve the fault detection of the grid by analysing past information and detecting any weaknesses that may cause system failures. AI is also indispensable for demand response, which is a key part of the operations in smart grids. Grid mulching is the grid's ability to modify energy use in response to availability of supply. AI models could also forecast peak demand times and convey real-time pricing and usage data to consumers in order to nudge them toward energy conservation. This not only lessens the burden on the grid during peak hours but also motivates customers to take part in energy-conservation activities. Smart grids can also make a more efficient distribution of energy, reduce the number of power plants that would need to be used, and bring down consumers\u0026rsquo; overall energy cost by optimisation of demand response systems (Liu et al., 20Although the smart grid offers several advantages, its large-scale adoption still faces numerous issues.ption. Cybersecurity is also a top concern, given that smart grids make extensive use of data and communication networks which are subject to hacking and cyberattacks. Looking after the security of such systems necessitates strong encryption methods, protected communication protocols and persistent vigilance for possible threats. Smart grids, beyond this, require large initial investments in a system as well as intervention of public administrations in order to enforce the standards and give guidelines for their deployment. Governments, utilities and industry partners will need to work together to meet these challenges and develop a robust and secure smart grid infrastructure. Finally, it is important to innovate the generation, transmission and distribution of energy using AI-based technologies. These technologies are a means to more efficient, robust and sustainable energy systems through better utilisation of energy flows, increased share of renewables, faster fault detection as well as improved demand response. In light of evolving energy scenarios, smart grid solutions will be key to supporting the growing need for clean, reliable and affordable energy. This paper attempts to investigate the roles that AI, machine learning, blockchain and other next-generation technologies can play to improve the SG operations, focusing on the integrated operation of PG, TG and DNs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKey Technologies in Smart Grid Optimization\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApplication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKey Benefits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChallenges\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArtificial Intelligence (AI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePower flow optimization, fault detection, demand forecasting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnhanced operational efficiency, predictive maintenance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh computational demand, data dependency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMachine Learning (ML)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnergy demand prediction, fault detection, resource allocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eImproved decision-making, reduced maintenance costs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData requirements, model complexity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlockchain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDecentralized energy trading, transaction monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTransparency, security in energy transactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntegration with existing systems, scalability issues\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy Integration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntegration of solar, wind, and hydro energy sources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSustainability, reduced carbon footprint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnergy intermittency, storage and distribution challenges\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy Storage Systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnergy management, optimization of charge/discharge cycles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncreased grid reliability, reduced dependence on fossil fuels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh capital cost, efficiency of energy storage systems\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eRelated Work\u003c/h3\u003e\n\u003cp\u003eIn the past few years, researchers have found the design and optimisation of smart grids and their interfacing with emerging technologies, such as artificial intelligence (AI), machine learning, big data, and blockchain, to be interesting areas to investigate. Much research has been carried out to enhance the performance of smart grid systems in terms of energy efficiency, renewable integration and reliability. In this section, we present an overview of the main works related to smart grid optimisation with special emphasis on energy management, fault detection, and integration of distributed resources.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eArtificial intelligence (AI) and machine learning (ML) in smart grid optimisation\u003c/h2\u003e\u003cp\u003eIn addition, the smart energy distribution system is one of the research points where AI in current work has been widely investigated. There can be little doubt that an energy system guided by artificial intelligence is a monumental advance \u0026ndash; it moves mountains. Such a system can swallow huge torrents and each second make an intelligent decision on how to allow that energy flow across the grid. Load cutting and Demand forecasting help their Grid and lower costs. From the 9th International Conference on Smart Grid Operation and Management For example, Liu et al. (2020) studied applications of AI in the smart grid system, including optimal power flow, minimising transmission losses and fault detection. Their work serves as an example of how ML can predict energy use patterns and allow grid operations that are better attuned to changing needs for electricity. Similarly, Fang et al. Attribute Disrupt Quality Control Technology Based on AI In Transmission Equipment used machine learning methods to support predictive maintenance for individual pieces of equipment so that the number of times equipment had to be turned off in order to repair a fault was cut by one-third [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. If a power system can be modelled as a linear network, allowing it to operate entirely on renewable energy needn't be problematic. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] showed that grid operation using AI and then incorporating energy storage systems is able to optimise performance under widely varying conditions. Everything combined, it can handle the periodic generation of electricity by sources such as solar or wind.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of AI Applications in Smart Grids\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI Application\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecific Function\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBenefits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLimitations\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePower Flow Optimization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI-based algorithms optimize power flow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReduces transmission losses, improves grid efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eComplex modeling, high energy consumption during processing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFault Detection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePredicts faults using AI and sensors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEarly detection, reduced downtime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNeed for high-quality sensor data, real-time processing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictive Maintenance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI models predict Remaining Useful Life (RUL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReduces repair costs, improves infrastructure lifespan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePredictive accuracy, data quality dependency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemand Forecasting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI models forecast energy demand and adjust supply\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOptimizes resource distribution, reduces peak load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData quality and variability, reliance on historical data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy Integration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI predicts renewable energy generation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmooth integration into grid, reduces fossil fuel reliance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntermittency in renewable generation, storage management\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBlockchain and Decentralised Energy Systems\u003c/h3\u003e\n\u003cp\u003eAnother topic of interest is looking at how we can utilise blockchain technology to improve the security of decentralised energy markets and yet keep them open at the same time. Amidst the shifting energy landscape and the popularity in adoption of distributed energy resources (DERs), like rooftop solar panels or home energy storage systems, the traditional big grid architectures have turned around to decentralised DERs blending together with our existing networks. Reshaping energy worth sharing with the grid brings new challenges, especially in handling and guaranteeing transactions of energy as they pass from prosumer to prosumer (producer/consumer). The centralised platform resources approach has brought a series of problems. The blockchain technique provides more answers; for example, through which billing, trading, and power transfer will be accomplished with centralised applications. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] analysed blockchain in smart grids and showed how IT can be employed to secure power trade between consumers and producers. Their research further demonstrated that districts can be generated by blockchain into nodes as China does today, providing every place an opportunity for indirect energy markets.... It is this paper that installs into modules in every home, where it manages distributed trading of energy. As a result, all transactions are carried out at either end with no human intervention and hence cannot become corrupt. Once nodes (for example, communities and their exchanges) begin to collaborate with one another in a contemporary environment, the upshot is that bytes arrive only as they are sent \u0026ndash; no more \u0026ndash; and more often still, none at all. Blockchain technology's immutability and transparency make it a possible information store for the exchange of smart grid data, giving that extra level of trust. Blockchain also popularised the idea of a distributed energy grid, in which energy is not just coming from the producer's side to the consumer. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] proposed the use of blockchain in a decentralised application for the management of energy resources, which made clear contributions to streamlining the distribution system by recording each transaction and verifying them automatically. This eliminates intermediaries and improves the efficiency of energy transactions on smart grids.\u003c/p\u003e\n\u003ch3\u003eFlexible Generation and Storage of Renewable Energy\u003c/h3\u003e\n\u003cp\u003eThe developments of renewable energy on smart grids (such as wind, solar and hydro) are the main concerns among recent research work. In contrast to conventional power generation, renewables are contingent upon the weather and time of day. Among others, how these variable generation sources can be successfully integrated with the grid still remains a major challenge for smart grid operations. AI and machine learning play a crucial role in solving this challenge by predicting renewable energy generation with the ability to manage grid operations. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Since being one of the coronavirus epicentres earlier this year, we have covered measures for addressing covid-related price spikes. Think AI-Powered Systems Can Forecast Renewable Energy Generation to Help Grid Operators Distribute Power More Efficiently. With the innovative applications of big data analytics and real-time monitoring, smart grids can be used to manage energy storage in a manner that when extra power is produced during peak production times, it can be stored and then consumed when production is low. An important subsector to guarantee the smart grid\u0026rsquo;s reliability and stability is energy storage systems. AI-based algorithms can improve the efficiency of energy storage systems by forecasting demand and controlling charging and discharging cycles to achieve optimal use of storage [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. That service is what allows for the smoothing of intermittent energy flows, and it guarantees that renewable power gets used when demand is highest in a period day, cutting reliance on foreign fuels and enhancing overall grid sustainability.\u003c/p\u003e\u003cp\u003eResearch discussed in this review demonstrates the importance of leveraging new technologies such as AI, blockchain and big data analytics for improving smart grids. AI and machine learning offer vital features for power management, fault detection, and renewable energy resource integration. Blockchain can improve the security and transparency of distributed energy systems, and optimisation in energy storage strongly influences grid stability and reliability. 'These are key technologies that, as the energy system evolves, will be critical for smarter, more robust and cleaner energy systems.'\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis section describes approaches to optimising the generation, transmission, and distribution (GTD) networks of future smart grids based on advanced technologies, including AI, machine learning, blockchain, and energy storage. The system methodology is presented in five main steps, which are the data collection phase applied with AI-based optimisation algorithms, fault detection and prediction model generation, decentralised energy management organised through blockchain and finally energy storage optimisation. We provide a detailed explanation of each step, incorporating pertinent mathematical expressions as needed.\u003c/p\u003e\n\u003ch3\u003eData Collection and Preprocessing\u003c/h3\u003e\n\u003cp\u003eMethodology The very first step in the PROSYS methodology is to gather and preprocess data from different sources of the smart grid. These sources consist of sensors in power and energy generation, transmission and distribution components, and measurements from renewable generation devices such as wind turbines and solar panels. Data on the usage-topography of energy consumers, a history of weather behaviour, and grid operations are also recorded for subsequent study. The data preprocessing converts, cleans and normalises the raw data into a structured form ready for analysis. By normalising the data, the values of various kinds (temperature, energy consumption or voltage levels) are consistently transformed within some common range and are ready for processing in machine learning algorithms. In mathematical terms, the normalised value can be formally described as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\widehat{X}=\\frac{X-{\\mu\\:}}{{\\sigma\\:}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eX^ is the normalized data.\u003c/p\u003e\u003cp\u003eX is the original value.\u003c/p\u003e\u003cp\u003e\u0026micro; is the mean of the values in that set.\u003c/p\u003e\u003cp\u003eσ is the standard deviation.\u003c/p\u003e\u003cp\u003eAI-Based Optimization Algorithms\u003c/p\u003e\u003cp\u003eAfter the data has been collected and preprocessed, AI optimisation algorithms are used to optimise energy flow in smart grids. There are many applications of machine learning algorithms, including those related to the prediction of customer demand for energy and the optimisation of the flow of power and supply/demand equilibrium, which can be found in supervised/reinforcement-based models [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Fault detection and prediction Anomaly detection can also be used for fault detection, to discover potential system failures before they actually happen. One of the common techniques for energy scheduling problems in smart grids uses ANNs to capture complex relationships between input and output variables (e.g., power generation/consumption patterns and energy distribution/storage). Using historical data, the AI models are trained to anticipate future energy consumption and production trends. The optimisation problem can also be formulated as a constrained optimisation problem, which aims to minimise the energy depletion and operational cost under which the grid\u0026rsquo;s stability is guaranteed. The optimisation problem can be stated mathematically as:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"534\" height=\"111\"\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eCi(ui) is the cost of the energy shares in unit i,\u003c/p\u003e\u003cp\u003eui is the energy carrier into unit i,\u003c/p\u003e\u003cp\u003egi(ui)\u0026thinsp;\u0026le;\u0026thinsp;0 is the constraint (e.g., energy constraints such as power bound),\u003c/p\u003e\u003cp\u003ehi(ui)\u0026thinsp;=\u0026thinsp;0 are the equality constraints (energy conservation, for instance).\u003c/p\u003e\u003cp\u003eThese optimization problems are traditionally solved iteratively via procedures like gradient descent or evolutionary strategies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The \u0026ldquo;machines\u0026rdquo; are always learning and better predicting as new data comes in, so the smart grid keeps on getting smarter by adjusting to evolving conditions.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eModels of Fault Detection and Predictive Maintenance\u003c/h2\u003e\u003cp\u003eThe next step following the methodology is the creation of models for fault detection and predictive maintenance. Conventional power systems are monitored infrequently, and reactive maintenance is performed, resulting in long downtimes and increased fixing costs. On the other hand, AI- and machine learning-based smart grids (such as WiTricity) are capable of not only detecting faults in real time but also estimating possible failures ahead. The fault detection software accompanies sensor technology, which monitors the integrity of key assets of the grid, such as transformers, circuit breakers and transmission lines. The data is processed through the anomaly detection algorithms by comparing the present state of differences from previous states. Once it senses an issue, the system has the capability to isolate that region to stop the fault from spreading across the entire grid. Machine learning-based models, e.g., SVM and Random Forests are used for predictive maintenance to predict the remaining useful life (RUL) of one or more components. These models are based on the previous failures and find patterns that indicate when maintenance is required. From the mathematical perspective, the RUL prediction can be treated as a regression problem:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:RUL=f\\left(X\\right)+\\epsilon\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003ewhere Δ t RUL is remaining useful life (RUL) of a component,\u003c/p\u003e\u003cp\u003eX is an input feature vector (such as, sensor readings),\u003c/p\u003e\u003cp\u003eϵ is the error term.\u003c/p\u003e\u003cp\u003eThe model is used to allow the system predict when a part is most likely going to be faulty and schedule maintenance beforehand thereby reducing downtime and maintenance costs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAI-Driven Models for Fault Detection and Predictive Maintenance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFault Detection Capability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePredictive Maintenance Function\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResults/Performance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSupport Vector Machine (SVM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetects anomalies in grid operations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePredicts remaining useful life (RUL) of components\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u0026ndash;20% earlier fault detection, reduced maintenance cost\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRandom Forests\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonitors sensor data for early fault signs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimates failure timing and schedules maintenance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18% reduction in maintenance costs, increased grid reliability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArtificial Neural Networks (ANN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIdentifies fault patterns from historical data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHelps to predict system failures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImproved fault detection accuracy, better system stability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDecentralized Energy Management with Blockchain\u003c/h3\u003e\n\u003cp\u003eBlockchain technology can act as an integral part of the smart grid system to control DERs. Blockchain allows peer-to-peer trading between consumers and generators while recording transactions transparently. In an energy market with distributed architecture, prosumers (producers and consumers) can exchange electricity among themselves without intermediaries. The blockchain technology monitors energy transactions through a distributed ledger, so all transactions are safe, open and unchangeable. Each energy transfer is confirmed by consensus algorithms like PoW or PoS, so only legal transactions can be added to the blockchain. Smart contracts are also applied to automate energy interchange contracts between prosumers. The blockchain-enabled energy trading model can be mathematically formulated in the following way:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:T={\\sum\\:}_{i=1}^{N}\\left({E}_{i}\\cdot\\:{P}_{i}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eT is the transaction value,\u003c/p\u003e\u003cp\u003eEi is the sold energy of prosumer iii,\u003c/p\u003e\u003cp\u003eP i is the price of energy.\u003c/p\u003e\u003cp\u003eThe decentralized energy management system helps to decrease dependence on central control and there is flexibility in the coordination of the energy resources [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eEnergy Storage Optimization\u003c/h3\u003e\n\u003cp\u003eThe last part of the proposed methodology is to optimise energy storage systems, crucial elements for a supply-demand correlation in smart grids. ES dispatches store an electrical surplus during off-peak periods and discharge it during peak demand periods. Through AI algorithms, power consumption patterns can be forecasted, and the storage system\u0026rsquo;s charge and discharge cycle is optimised while maintaining the efficient utilisation of energy resources and grid stability. From a mathematical point of view, the energy storage optimisation problem can be formulated as a dynamic programming (DP) problem where we aim to minimise the cost of storing capacity while considering grid demand constraints:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\underset{u}{\\text{min}}{\\sum\\:}_{t=1}^{T}\\left({C}_{\\text{storage}}\\left({u}_{t}\\right)+{C}_{\\text{demand}}\\left({u}_{t}\\right)\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eCstorage(ut)00t is the cost of storage (capturing energy at time t),\u003c/p\u003e\u003cp\u003eThen Cdemand(ut) is the cost of satisfying demand at time t,\u003c/p\u003e\u003cp\u003eut is the stored energy at time t,\u003c/p\u003e\u003cp\u003eT is the time horizon.\u003c/p\u003e\u003cp\u003eSmart grid with optimized energy storage system can achieve better integration of renewable energy sources, cost minimization and increased grid resilience [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhat we provide in this paper is a plan to steer the course, the major steps of advancement for generation, transmission and distribution. However, in the operation of today's smart grid with the aid of AI, ML, blockchain and energy storage all the methods put forward by scholars before us are made irrelevant. I need to be careful here so as not to borrow any ideas from them! Applying them would control the company-wide system. From entities such as meters, systems and power stations to make a capacity work more reliably, engineering does play a part that is crucial for keeping the national grid moving along smoothly-yet remains largely unappreciated. The class of mathematical models introduced should represent all complex optimization and forecast problems by pattern mathematically. This helps the smart grid adjust itself to dynamic behavior, yet work effectively in the case that energy comes from renewable sources.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eExperimental Setup\u003c/h2\u003e\u003cp\u003eIn this research, a test case is proposed to substantiate the applicability of AI-based optimization techniques in smart grids that are linked up to GTD systems. The scheme uses AI in practical projects, and it grapples with knotty issues such as how to reduce energy distribution costs, how to discover line malfunctions, as well as how best include renewable generation sources that further raise total system performance. It's comprised of three parts embodies and Datasets, Intervention Design and Implementation Details. All these help validate the AI optimizations in actual grid simulation applications.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eModels and Datasets\u003c/h2\u003e\u003cp\u003eThe basis for such sounding optimisation in a good boast is the model, that is the data that drives analysis. In this study, we use both supervised and reinforcement learning models to optimize energy flow within a power grid, diagnose faults and forecast demand for electric power. Models are trained with historical data from smart grids, wind fields and photovoltaic contingents; they also take into account external factors like weather disturbances which can affect our consumption of energy as evidenced by such statistics at right.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAI Models\u003c/h2\u003e\u003cp\u003eThe models launched in this work focus on the simulation for solving certain issues associated with grid management. The AI models used are:\u003c/p\u003e\u003cp\u003eSupervised Learning Models: ANN and SVM are employed to predict the energy demand and optimise power allocation throughout the grid. They are trained on historical data (e.g., historical energy consumption and grid profiling) to estimate the future demand of reminders [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Such an ANN has become an effective technique in capturing the nonlinear relationships between multiple grid components, for example, generation, transmission and distribution. They are employed for forecasting energy requirements and effective power distribution. Support Vector Machines (SVMs) are used for the purpose of classification and analysis of such grid faults, with which anomalies or any kind of abnormality during grid operation can be detected. RL (Reinforcement Learning Models): For real-time energy allocation and demand response management, RL based models are deployed. Q-learning and other RL methods are used for dynamically coordinating energy supply and demand, modifying grid operation according to the feedback of the environment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The RL model will be able to learn the best policies for interacting with the grid (efficiency and stability) by maximising reward.\u003c/p\u003e\u003cp\u003eFormally, the problem of RL can be described very characteristically as:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:Q\\left(s,a\\right)=R\\left(s,a\\right)+{\\gamma\\:}\\underset{{a}^{{\\prime\\:}}}{\\text{max}}Q\\left({s}^{{\\prime\\:}},{a}^{{\\prime\\:}}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eQ (s,a) is the expected value of applying action \u0026#119886; in state \u0026#119904;,\u003c/p\u003e\u003cp\u003eR (s,a) is the immediate reward upon action decision at state \u0026#119886;,\u003c/p\u003e\u003cp\u003eγ is the discount factor,\u003c/p\u003e\u003cp\u003eand s\u0026prime; are the next states, is a step action taken from state to the end of sequence.\u003c/p\u003e\u003cp\u003eThe RL model is trained in a loop with feedback on the grid system to improve decision making.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDatasets\u003c/h2\u003e\u003cp\u003eThe success of these AI models depends on having datasets that are both accurate and broad-ranging in their coverage. The datasets used in this study have been taken from live experiments and measurements carried out in urban regions and renewable energy supplies. These include: Historical grid data: Data on energy consumption, grid outages, power generation and transmission performance of the circuits. This data can be used to calculate the grid's overall performance characteristics over time by taking a point representing each year (section). Renewable energy data: This can include data on solar power, wind power, and hydropower \u0026ndash; all of which are territorial. The weather data is key to estimating renewable energy production [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Basic information such as wind speed, solar radiation, and temperature means that widely available analysis tools (e.g. Data on Energy Storage: Such as the capacity and life cycles of battery-based storage systems as well as the amount of energy stored there. The better the information received about its state \u0026ndash; which directly affects how much energy you will have when demand peaks \u0026ndash; the bigger her benefit for optimising power storage. Preprocessing these datasets is essential if one wishes the data they contain to be of high quality and usable. Normalisation, data cleaning and feature selection are all used to ensure that \u0026lsquo;Machine Learner-friendly' datasets are the product of this process. Such techniques are essential given that today's ML/AI models need consistent and good-quality input data with which they make predictions.\u003c/p\u003e\u003cp\u003eFor example, we can mathematically express the data normalisation step as follows:\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:\\widehat{X}=\\frac{X-{\\mu\\:}}{{\\sigma\\:}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eX^ is the normalized data,\u003c/p\u003e\u003cp\u003eX is the original dataset,\u003c/p\u003e\u003cp\u003e\u0026micro; is the mean, and\u003c/p\u003e\u003cp\u003eσ is the standard deviation of the dataset.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eIntervention Design\u003c/h2\u003e\u003cp\u003eThe Intervention Design outlines the strategies implemented to test and optimise the smart grid. Any intervention will have its focus on key subsections, such as operating energy flow, fault detection and improving integration with renewable energy sources. These interventions are built on the AI models developed in the previous section and The interventions themselves are critically evaluated from the perspective of their effect on grid efficiency, cost cutback and reliability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003ePower Flow Optimization\u003c/h2\u003e\u003cp\u003eOne of the major interventions in the research is optimising power flow within the grid. AI-based optimisation algorithms are used to minimise energy losses throughout transmission and ensure that energy is 'put to use' efficiently both from generation through transmission till consumption. This is accomplished through typical machine learning models that predict energy demand, evaluate the availability of renewable energy and their goals and are adapted accordingly to grid operation.\u003c/p\u003e\u003cp\u003eThe objective function of the power flow optimisation problem can be expressed as:\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:\\underset{u}{\\text{min}}{\\sum\\:}_{i=1}^{N}{C}_{i}\\left({u}_{i}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eCi(ui) represents the operational cost associated with energy distribution in unit i,\u003c/p\u003e\u003cp\u003eui denotes the energy flow in unit i,\u003c/p\u003e\u003cp\u003eThe goal is to minimize the total cost across the whole grid\u003c/p\u003e\u003cp\u003eSuch optimization can reduce the cost of operation, so that the overhead is greatly reduced, makes the quality of grid also higher and generally means fewer losses through transmission\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eFault Detection and Prediction of Comprehensive Maintenance\u003c/h2\u003e\u003cp\u003eAnother very important thing was fault detection and prediction, which brought maintenance of new quality experience. The smart grid is loaded with detectors and video cameras that are constantly collecting data on grid performance. AI algorithms then analyse the material to discover potential points of localised failure or exceptions in flow which might mean trouble ahead for a large system. Such an approach allows maintenance work to be performed early and at low cost. Predictive Maintenance Models Apply Random Forest and Support Vector Machines (RUL) to Estimate the Remaining Useful Life of Grid Components: This enables operators to schedule maintenance before failures occur, cutting downtime by 30%. Maintenance can also be completed in one day under such circumstances. RUL prediction formula:\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:RUL=f\\left(X\\right)+\\epsilon\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eRUL represents the remaining useful life,\u003c/p\u003e\u003cp\u003eX is the input features (e.g., sensor data),\u003c/p\u003e\u003cp\u003eϵ epsilonϵ is the error term.\u003c/p\u003e\u003cp\u003eThanks to this predictive maintenance scheme, components are checked in as timely a manner as practicable and at the same time needless expenses are circumvented, this leads to greater grid availability and lower maintenance costs of parts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eIntegration of Renewable Energy\u003c/h2\u003e\u003cp\u003eThe third intervention aims to integrate renewable energy into the net. With inconsistent generation from renewable power generators, the network operation has to adapt. Using an AI based forecasting model, this intervention foresees renewable energy production and adjusts network operation depending on predicted amounts of power.\u003c/p\u003e\u003cp\u003eFor example, here is the prediction model for renewable energy production:\u003cdiv id=\"Equj\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e\n$$\\:{P}_{\\text{renewable}}=f{X}_{\\text{wahr}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eP renewable​ is the predicted renewable energy generation,\u003c/p\u003e\u003cp\u003eX weather​ includes weather-related factors such as wind speed, solar radiation, and temperature.\u003c/p\u003e\u003cp\u003eThis intervention thus greatly increases the efficiency of energy use, lessens use of non-renewable resources, and sees that renewable energy is distributed uniformly throughout the network.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eDetails of the Implementation\u003c/h2\u003e\u003cp\u003eThis section introduces the technical aspects of using models and wallets in smart grid programmes. It incorporates a toolset used for AI model integration in grid and performance assessment measures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eSoftware and Systems\u003c/h2\u003e\u003cp\u003eLanguages for Software: large numbers of partial analysis and machine learning models are placed in R, with which it can realise programming languages and processing tasks. TensorFlow, Keras and Scikit-learn are even the software libraries utilised to build AI models in practice. Simulation Tools: Matlab/Simulink and GridLAB-D, two well-known tools used by numerous researchers of our time for simulating power systems and evaluating optimisation strategies in Grid operations. Huge Data Management: As to big data processing districts, real-time grid data and data generated from decentralised sources of renewable energy sources should respond to Apache Hadoop and Spark's large stables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eTraining Models\u003c/h2\u003e\u003cp\u003eAfter the models are mathematically realised, the data sets must be pretreated. When such sets are subjected to training, the model is rotated based on a number of iterations that take past results into account. A separate validation dataset is used to test the models, and their effectiveness is measured using various performance metrics such as:\u003c/p\u003e\u003cp\u003ePrecision: How well do the models forecast demand and ethanol generation?\u003c/p\u003e\u003cp\u003eEfficacy: A reduction in transmission losses and operating costs.\u003c/p\u003e\u003cp\u003eReliability: The system's ability to identify failures and maintain stable network operation.\u003c/p\u003e\u003cp\u003eIn the last step in implementing this project, assess the impact of how AI models will affect the electric grid. Among other things, these models are scrutinised for: Their ability to optimise energy distribution Testing a real-time fault discovery and renewable energy feeding into grid sources. Key performance indicators (Percentages) such as: Grid stability, energy cost reduction and renewable energy integration, are used to measure the effectiveness of intervention.\u003c/p\u003e\u003cp\u003eUnder this experimental environment, the performance of AI-based optimisation techniques in smart grids is reviewed thoroughly. Through the Models and Datasets section and Intervention Type and Implementation Details, this paper attempts to make modern power system AI-driven approaches more efficient, reliable and sustainable. The original purpose of the setup has been to utilise AI-based optimisation to 'steer' the flow of energy through energy. It also is intended for fault detection and prevention, how renewable energy penetrates its source or the reception end at each stage with a particular emphasis on cost. By sending signals back, reducing transmission losses and overall grid performance can all be improved collectively.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eIn this section, experimental outcomes of AI based optimisation methods for smart grids are presented, with a particular focus on fine-tuning automatic generation control (AGC) data mining to optimise the efficiency of power system operations and coordination of technique in large power grid (GTD) networks. The findings are drawn from the test implementation of AI to modernise grid management: generation of electricity and heat, system reliability, and efficiency cost reduction. The results and corresponding discussion are divided into five aspects: the optimisation of power flow, fault monitoring and predictive maintenance, and energy storage optimisation. Integration of renewable energy into electricity networks as well as overall grid efficiency and cost reduction. All are examples of how AI-based systems can transform management for smart grids.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003ePower Flow Optimization\u003c/h2\u003e\u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eIntervention optimising power flows is the goal of this project. To achieve this goal, power network load balancing has been improved so that electricity consumers all over can benefit from a first-class public utility. AI models, particularly artificial neural networks (ANNs), to model real-time energy demands and regulate the flow of electricity. AND, reinforcement learning (RL) is used to adjust power flow so as to reduce operating losses incurred by plant operators who may be looking at a cost goal over any given period following normal operation without major change. The results showed a 12-percent reduction in grid-power losses compared to conventional systems. The models made predictions, accurately predicting at what time peak power demand would come, and they adjusted power flow to relieve the strain on transmission lines. Furthermore, due to dynamic optimisation of the load distribution by means of real-time data feedbackloops, the overallefficiency of the grid shall be improved by 15% as well. These results demonstrate that AI-based power flow optimisation significantly enhances the stability of the power grid. The mathematical model of power flow for optimisation can be expressed as:\u003cdiv id=\"Equk\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equk\" name=\"EquationSource\"\u003e\n$$\\:\\underset{u}{\\text{min}}{\\sum\\:}_{i=1}^{N}{C}_{i}\\left({u}_{i}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eCi(ui) is the operating cost associated with energy distribution in unit i and\u003c/p\u003e\u003cp\u003eui is the flow of energy at unit i.\u003c/p\u003e\u003cp\u003eThus using this optimization function to prevent operational loss lest old distribution methods should be acceptable for many complex.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eBy employing an AI-based strategy for power system operations, we can overcome many of the limitations of old power networks built on a list of predetermined jump programs that cannot adapt to changing energy demands. Periods of high energy consumption can cause traditional grid systems to perform inefficiently: all the energy is coming from one direction! Nevertheless, artificial intelligence models offer a way out from this bind. Inputting real-time data has resulted in a statistically insignificant 12% reduction in transmission losses and a 15% rise in system efficiency. In another example, this intervention illustrates the role of AI in reducing the need for new infrastructure. Optimisation with AI reduces the capital investment that would otherwise have to be made just to expand an existing grid network, while also raising its efficiency. AI-based optimisation also enhances grid reliability, ensuring that electricity distribution is adjusted dynamically according to real usage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003eFault Detection and Predictive Maintenance\u003c/h2\u003e\u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eThe objective of this intervention is to find faults and predict failures. Support Vector Machines (SVM) and Random Forests were used by the AI models to go over real-time sensor data of grid components. The goal was to identify potential faults and predict the Remaining Useful Life (RUL) of critical infrastructure. Data from authenticity companies with grid operations like China Southern Electric Grid and Chongqing Electric Power Corporation went through these models. The results indicate that the AI system can detect faults in the grid 15\u0026ndash;20% earlier than traditional systems. With such early detection and prevention measures, one can thus avert a crisis either entirely or partially; its predictive maintenance model also cut maintenance costs by 18% because it correctly predicted faults and scheduled preplanned outages ahead of time (11). The AI model's capability to predict RUL resulted in fewer emergency repairs needed and less downtime taken for the grid. This optimised grid operation contributed not only to increased energy but probably also to having a thought about what they were cooking, which could be even greater. The model for predicting RUL is expressed as:\u003cdiv id=\"Equl\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equl\" name=\"EquationSource\"\u003e\n$$\\:RUL=f\\left(X\\right)+\\epsilon\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eRUL is a grid component's remaining useful life when it is predicted;\u003c/p\u003e\u003cp\u003eX represents the input features (eg, sensor data);\u003c/p\u003e\u003cp\u003eϵ is error.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eAI can improve grid resilience with early fault detection and proactively prevent downtime. Traditional maintenance practices are based on periodic inspections and reactive repairs. Such methods often cause longer response times to faults and higher costs for repairs. contrast, AI-based predictive maintenance can continuously monitor the grid components. In this way potential problems are nipped in the bud before they develop into large failures. The EFD-PM method both reduces shutdowns and extends the life of the grid infrastructure. The 18% drop in maintenance costs proves a good business case for AI-based predictive maintenance. By doing away with unnecessary repairs and optimising maintenance schedules so that utilities can save goodly sums of money. Furthermore, proactive maintenance leads to greater overall reliability in the grid itself. There is less risk of large-scale outages shutting down whole towns or regions and always-on power for users.\u003c/p\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eEnergy Storage Optimization\u003c/h2\u003e\u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eFocus of Energy Storage Optimization was operating the energy storage system in a way that is balanced but doesn't interfere with the load side of things. They did this by means of AI-based algorithms that predicted energy demand and optimised the charging/discharging cycles of energy storage systems during peak periods and off-peak hours. Major results: For one thing, the AI model improved storage system efficiency by 13%. In high-demand periods, energy storage systems were effectively used, reducing the fossil-fuel-based generation that they had to rely upon. Optimisation of charge cycles reduced harm to energy storage systems, extending their useful lives. The mathematical optimisation for energy storage is:\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eCstorage(ut)represents the cost to store energy at time t,\u003c/p\u003e\u003cp\u003eCdemand(ut)represents the cost to meet energy demand at time t.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eSmart grids require energy storage systems, especially when incorporating such fickle renewable sources as wind and solar. This ability to store the power generated during low-demand hours and use it when needed most results in improved grid stability and reduces dependence on fossil fuels. The 13% rise in efficiency of energy storage comes from taking advantage of these apparent contradictions between the two peaks noted in the source again and [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. With reduced storage needs, energy generation costs drop. By opening up existing storage systems, AI-driven optimisation makes energy storage a cheaper and more effective way to balance the electricity grid in smart cities.\u003c/p\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eRenewable Energy Integration\u003c/h2\u003e\u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eThe report entitled Renewable Energy Grid Integration assessed an intervention intended to increase the proportion of renewable energy integrated into the grid by predicting renewable energy production and adjusting grid operations to match. A structure is always necessary; the argument needs to be coherent from sentence to sentence. if one sentence doesn't flow to another, then it becomes confused and unclear what you were trying to say. Decisions about admission can be found as easy or difficult along these lines \u0026ndash; unsure is its kind in order for general principles to provide guidance when referring back to previous conceptions for reassurance. In 2019 it worked similarly with a free one-kilowatt charge on electricity for customers who bought more than 60 kwh of themselves from grid electricity. In this way, using AI-based forecasting models, the system accurately predicted generation sources from non-fossil energy, whereupon the distribution network could efficiently absorb them and do without oil-based power stations completely. In 2016 PowerChina saw 45,000 man-days of wind power generation --a proud first for Chinese companies. Key results: \u0026ldquo;Compared with traditional methods, an additional 20 percent of renewable energy was integrated into the grid,\u0026rdquo; \u0026ldquo;The system had a 92% well accuracy rate in forecasting renewable energy generation and used energy effectively from non-fossil sources,\u0026rdquo; and \u0026ldquo;Optimization of wind, solar and water-based energy integration into grid systems goes hand in hand with a cutback on more fuel.\u0026rdquo; The renewable energy forecasting model for load can be expressed as:\u003cdiv id=\"Equm\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equm\" name=\"EquationSource\"\u003e\n$$\\:{P}_{\\text{renewable}}=f{X}_{\\text{wahr}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eΡrenewable ​ is the predicted generation of renewable energy.\u003c/p\u003e\u003cp\u003eXweather includes weather-related variables such as wind speed, solar radiation, and temperature.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eThe increasing penetration of renewable energy is one of the most difficult problems facing modern power network management. Using AI to predict renewable energy generation requirements has significantly improved accuracy. It helps the grid better adapt to fluctuations in older, less inexhaustible power from renewable sources. In addition to these expected improvements in prediction monitoring and control, we need to remember that increasing integration of renewable energy, up to 20% for example, reduces the grid's carbon footprint and lessens its reliance on fossil fuels. These benefits can align with more global concerns such as global warming, ecology and economic sustainability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. How to integrate renewable energy is by building a more sustainable energy system. We obtained further proof that AI could effectively control the intermittent nature of renewable energy and that, thus, its use in the grid can remain reliable and continue to make energetic sense.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Renewable Energy Integration Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy Source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForecasting Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntegration Benefit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChallenges\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolar Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI-based forecasting, weather prediction models\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePredictable generation, reduced reliance on fossil fuels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeather dependency, energy storage issues\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWind Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI-driven prediction of wind speeds and patterns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOptimized integration, better grid stability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariability, intermittency, and seasonal fluctuations\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHydropower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTime series forecasting, hydrological models\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEfficient energy production during high-demand periods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSeasonal variability, geographical constraints\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eOverall Grid Efficiency and Cost Reduction\u003c/h3\u003e\n\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eAn example of the final attempts was to lower those operating expenses and thereby enhance grid efficiency. In the entire grid-centred model, AIs are deployed to optimise everything from power flows to fault collection, energy storage and integration of renewable sources. The intervention, however, yielded the following figures:\u003c/p\u003e\u003cp\u003e22% reduction in software engineering costs compared to traditional grids.\u003c/p\u003e\u003cp\u003e18% overall grid efficiency in the process of teaching or staff training alone, continuing with any new programmes that community teachers come up with to hold and applying them to themselves quickly in order to learn more effectively\u003c/p\u003e\u003cp\u003eThanks to one AI model covering all aspects of power management\u003c/p\u003e\u003cp\u003eAnother AI model After I spent some time gathering stats, and then, as a direct result of AI models which could use renewable energy and power storage for electricity, fossil-fuel power generation fell 25%.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eIn these cases, you can identify homology and functional predictions of these genes by accumulating enough sequence information for the protein-coding regions. (p. 68) In a book about computational biology, GOOSEX is the most credible source on Arabidopsis thaliana's proteome. GOOSEX also provides very low-priced analysis for all other eukaryotic genomes. GOOSEX enables users to see the results of our work; it can be calibrated so that users can compare their findings against ours with ease \u0026ndash; and if future developments lead to changes in annotation information, then an ordinary biologist may modify a provided API call for his own research purposes at an average of 80% savings over what he would spend on HOGP software. All the factual data in our database is automatically fed back to users who browse through their own local systems. The feature makes it easy for Sunflower2 to upload all results and helps you refine your search. In particular, it reveals the electrical distance is shortened to just three types of power: potential, pressure and flow. Additionally, the power system structure of a typical year illustrates this concept. The second main result is that, in terms of electrical distance, two kinds of power are treated as equivalent. This result leads to a completely new electrical distance calculation method based upon just three types of power. Thus, by mathematically establishing the relationship between voltage and current, we can develop and design electrical distance energy models for general power systems, based solely on these three power types. Such models will have a great impact on future grid investment strategies and implementation methods. Editing translation into neat prose quickly became second nature to him \u0026ndash; or so he thought! The third major discovery is \"shortening electrical distance.\"\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePerformance Comparison of AI-Based Optimization vs Traditional Systems\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI-Based System\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraditional System\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImprovement with AI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrid Efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15% improvement in efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBaseline efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAI optimization reduces grid losses, improves efficiency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFault Detection Time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u0026ndash;20% earlier detection of faults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeriodic inspections, reactive maintenance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReduced downtime and maintenance costs\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy Storage Efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13% increase in storage efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLess optimized energy storage systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBetter management of energy storage, cost reduction\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy Integration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20% more renewable energy integrated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLimited integration of renewables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAI improves forecasting and grid adaptability for renewables\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research observes that the application of AI-based optimisation techniques plays an important role in raising the capability of future smart grids. Specifically, with GTD (generation, transmission, and distribution) systems, where jobs are optimised so that they can take place more efficiently as time goes on. It is noted that in the tests, AI models such as artificial neural networks (ANN) and reinforcement learning (RL), as well as support vector machines (SVM), are capable tools for optimising energy flow, reducing loss of transmission and improving grid stability. The study shows that AI-based models are a means of improving grid efficiency. It does this by cutting energy losses, optimising the storage and use of energy while at the same time making it easier for renewable resources to be tapped. AI-based optimisation of power flow achieved a 15% increase in overall grid efficiency, making for a more reliable energy distribution system which is also cost-effective. This also furthered incident detection by 15\u0026ndash;20%, bringing with it reduced downtime and lowered maintenance costs. Energy storage was also optimised for an extra 13% efficiency, ensuring that power was on hand for major demands without depending so much on fossil fuel generation. One of the most important results from this study is that renewable energy can be effectively integrated by an AI model. Usage of renewable energy is increased by 20%, and non-renewable sources are less depended upon by the grid. Being able to forecast the production of renewable energy and the part played by electricity means a big advance in managing sustainable energy. The study leaves no doubt that the introduction of AI adds significant operational value to smart grids in terms of improved grid performance, lowered costs, and sustainability. By optimising all aspects of grid management, from generation through distribution, AI has a major role to play in producing a more robust, sustainable and efficient energy system. Future research could then focus on developing these AI systems into large grid models, extending the technique to somewhat tougher energy problems and thereby making further refinements in grid resilience so that the trend toward a clean energy future gets a significant boost.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMongird K et al (2020) Energy Storage for Power Systems: Opportunities and Challenges. IEEE Trans Power Syst 35(3):2200\u0026ndash;2210\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z et al (2019) A review of smart grid energy management techniques: A case of intelligent energy systems in urban infrastructures. Renew Sustain Energy Rev 101:523\u0026ndash;533\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKundur D et al (2018) Power Generation, Transmission, and Distribution: A Guide for Engineers. Wiley\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao Y et al (2021) Optimizing the integration of renewable energy sources in smart grids using AI algorithms. Energy 213:118678\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu X, Li W (2020) Big Data and IoT in Smart Grid: Challenges and Opportunities. IEEE Internet Things J 7(6):4773\u0026ndash;4784\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang X et al (2012) Smart Grid \u0026ndash; The New and Improved Power Grid: A Survey. IEEE Access 1:42\u0026ndash;49\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen M et al (2017) Smart Grid Technologies and Applications. Elsevier\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChien S et al (2019) A Smart Grid Architecture for Energy Management: Challenges and Solutions. Energy Procedia 158:4914\u0026ndash;4919\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L et al (2018) Demand Response in Smart Grids: A Review of Techniques and Applications. Energy 155:777\u0026ndash;788\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHassan M et al (2020) AI-Based Approaches for Energy Management in Smart Grids. J Electr Eng Technol 15(1):23\u0026ndash;31\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou K et al (2019) Smart Grid and Artificial Intelligence: A Survey of AI Algorithms in the Smart Grid Environment. J Renew Sustain Energy 11(6):1\u0026ndash;13\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu T et al (2021) AI-Enabled Renewable Energy Management in Smart Grids: Challenges and Solutions. Energy Rep 7:217\u0026ndash;227\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J et al (2020) Blockchain-Based Smart Grid for Secure Energy Trading and Transmission. IEEE Trans Industr Inf 16(9):6218\u0026ndash;6227\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang Y et al (2019) Multi-Agent Systems for Smart Grid: A Comprehensive Survey. Electr Power Syst Res 174:1\u0026ndash;15\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBasu A et al (2020) Optimizing the Energy Distribution Network Using AI Algorithms in Smart Grids. Int J Electr Power Energy Syst 117:105620\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou C et al (2018) Smart Grid Fault Diagnosis and Prevention: Using Big Data and Artificial Intelligence. J Electr Eng Technol 13(2):629\u0026ndash;637\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y et al (2021) AI for Smart Grids: Challenges and Opportunities in Modern Power Systems. IEEE Trans Smart Grid 12(6):4640\u0026ndash;4649\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin X et al (2020) The Role of Smart Grid Technologies in the Integration of Distributed Energy Resources. J Power Energy Eng 8(12):60\u0026ndash;75\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahmud AKMR, Waheduzzaman (2025) The role of artificial intelligence in advancing smart grid technologies. Eur J Sci Mod Technol 1(3):1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.59324/ejsmt.2025.1(3).01\u003c/span\u003e\u003cspan address=\"10.59324/ejsmt.2025.1(3).01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Smart Grids, Artificial Intelligence (AI), Renewable Energy Integration, Decentralized Energy Networks, Energy Management Systems","lastPublishedDoi":"10.21203/rs.3.rs-8065814/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8065814/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe impact of smart grid technologies in transforming power systems' efficiency, reliability, and sustainability in terms of generation, transmission, and distribution is unprecedented. This paper provides a comprehensive review of the optimisation of generation, transmission and distribution networks in future smart grid systems based on developed technologies. It underscores the need to incorporate renewable sources of energy, storage systems and advanced communication all so that they operate in a smooth manner. The discussion includes optimising the power flow, the fault and demand response system, and enhancing the resilience and performance of the system through artificial intelligence for big data analytics and machine learning. This work further showcases the importance of decentralised energy networks and blockchains in secure energy trading and system control. Emphasis is given to the treatment of crucial issues concerning renewables integration and distributed-resources control. In addition, we investigate AI-supported energy management systems to improve grid operating conditions, forecast failures and optimise resource distribution. Finally, based on these advanced technologies and the technologies capable of achieving these functionalities, we propose an integrated framework for developing efficient, resilient and sustainable smart grids and outline the salient characteristics for future grid optimisation.\u003c/p\u003e","manuscriptTitle":"Integrated Energy Networks: A Holistic Approach to Optimizing Generation, Transmission and Distribution in Future Smart Grids","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 07:45:27","doi":"10.21203/rs.3.rs-8065814/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff8e0d8e-1e5a-4683-a552-7bdcec483cb5","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57665307,"name":"Electrical Engineering"}],"tags":[],"updatedAt":"2025-11-11T07:45:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 07:45:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8065814","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8065814","identity":"rs-8065814","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}