Sizing and Location of Distributed Generations in Multi-Microgrid Environment using Jaya optimization technique

Sizing and Location of Distributed Generations in Multi-Microgrid Environment using Jaya optimization technique | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2025 V1 Latest version Share on Sizing and Location of Distributed Generations in Multi-Microgrid Environment using Jaya optimization technique Authors : Sri Suresh Mavuri 0000-0002-9040-9023 [email protected] and Jayaram Nakka Authors Info & Affiliations https://doi.org/10.22541/au.173639727.79006108/v1 208 views 108 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Distributed Generation (DGs) are emerging as a favorable and eco-friendly solution for energy production. DG systems typically consist of small to medium-sized power-generating units, such as solar panels, wind turbines, fuel cells, or microturbines, which are integrated into the local power grid or used independently. However, because renewable energy sources are inherently variable, they pose challenges and operational difficulties when used as the sole energy source. To overcome these issues, it’s essential to incorporate energy storage systems and carefully manage the uncertainties associated with both energy demand and generation. This paper proposes a structure of system that connects wind energy, solar (PV), Fuel cell (FC) and Battery Energy Storage System (BESS) in a Multi-Microgrid (MMG) structure. This study gives the analysis to address the uncertainties in energy demand, weather conditions, and cost of energy, optimizing the arrangement of DGs and BESS within the MMGs. To find the optimal location and sizing of DGs for the MMG system, the Jaya optimization algorithm is employed. The use of Jaya optimization has resulted in a reduction of the Net Present Cost from $451.354 million to $434.256 million and the Levelized Cost of Energy (LCOE) to $0.267 per kWh, taking into account the uncertainties in energy demand, generation data, and fluctuating energy prices. The effectiveness of this approach is confirmed by comparing the results with those obtained using the GWO algorithm and CCPSO algorithm,. The Jaya algorithm shows superior performance, achieving lower total NPC, reduced system size, and a lower LCOE, while also exhibiting the fastest convergence, making it more accurate and reliable than the GWO and CCPSO, PSO algorithms. Sizing and Location of Distributed Generations in Multi-Microgrid Environment using Jaya optimization technique Mr.Sri Suresh Mavuri 1 Research Scholar, Electrical Engineering Dept. National Institute of Technology Andhra Pradesh, India Dr. Jayaram Nakka 2 Assistant Professor,Electrical Engineering Dept. National Institute of Technology Andhra Pradesh, India [email protected] Abstract: Distributed Generation (DGs) are emerging as a favorable and eco-friendly solution for energy production. DG systems typically consist of small to medium-sized power-generating units, such as solar panels, wind turbines, fuel cells, or microturbines, which are integrated into the local power grid or used independently. However, because renewable energy sources are inherently variable, they pose challenges and operational difficulties when used as the sole energy source. To overcome these issues, it’s essential to incorporate energy storage systems and carefully manage the uncertainties associated with both energy demand and generation. This paper proposes a structure of system that connects wind energy, solar (PV), Fuel cell (FC) and Battery Energy Storage System (BESS) in a Multi-Microgrid (MMG) structure. This study gives the analysis to address the uncertainties in energy demand, weather conditions, and cost of energy, optimizing the arrangement of DGs and BESS within the MMGs. To find the optimal location and sizing of DGs for the MMG system, the Jaya optimization algorithm is employed. The use of Jaya optimization has resulted in a reduction of the Net Present Cost from $451.354 million to $434.256 million and the Levelized Cost of Energy (LCOE) to $0.267 per kWh, taking into account the uncertainties in energy demand, generation data, and fluctuating energy prices. The effectiveness of this approach is confirmed by comparing the results with those obtained using the GWO algorithm and CCPSO algorithm,. The Jaya algorithm shows superior performance, achieving lower total NPC, reduced system size, and a lower LCOE, while also exhibiting the fastest convergence, making it more accurate and reliable than the GWO and CCPSO, PSO algorithms. Key words: Distributed Generation, Net present cost, Multi-Microgrid system, Levlized Cost of Energy, Jaya algorithm I. INTRODUCTION Distributed Generation (DG) refers to the production of electricity at or near the point of use, rather than relying on large, centralized power plants and long-distance transmission networks. DG systems typically consist of small to medium-sized power-generating units, such as solar panels, wind turbines, fuel cells, or microturbines [1] which are integrated into the local power grid or used independently. The incorporation of distributed generators, or small-scale energy sources, has greatly risen due to the restructuring of power systems. Distributed Generation (DG) can serve as backup power and maintain a stable electricity supply during outages or disruptions in the central grid, enhancing the dependability and longevity of the distribution network. By generating power close to where it is utilized, DG decreases transmission and distribution losses, making the system more efficient. It also facilitates the integration of renewable energy sources such as wind, solar and small-scale hydro promoting a more sustainable energy mix. Additionally, DG can assist avoid the need for costly modifications to transmission and distribution facilities by lessening the load on the existing grid and minimizing the demand for long-distance power transfer.Distributed generation (DG) decreases dependence on centralized power plants, enhancing the resilience of the energy system against outages [2] and interruptions. Distributed Generation (DG) can contribute to ensuring a reliable power supply during emergencies by producing electricity in close proximity to the point of consumption. Strategically positioning Distributed Generation (DG) can provide auxiliary power in the event of power failures, therefore improving the robustness and dependability of the energy distribution, especially in crucial or isolated areas. Distributed Generation (DG) can alleviate strain on the current power grid and mitigate the necessity for costly enhancements to transmission and distribution infrastructure by producing electricity in close proximity to where it is consumed. Strategic positioning of renewable energy-dependent distributed generation (DG), such as solar or wind, maximizes the efficient usage of resources and guarantees the seamless integration of power into the grid. Furthermore, the installation of distributed generation (DG) can effectively reduce voltage drops and fluctuations, hence enhancing the overall quality of power supplied to consumers. Optimal size of a multi-microgrid requires setting the components within a network of interconnected microgrids to achieve the best efficiency and cost-effectiveness. This includes determining the right capacities for power generation units like solar panels, wind turbines, and fuel cells to fulfill energy demands while optimizing for efficiency, dependability, and renewable energy integration. It also entails finding the optimal size for energy storage equipment, such as batteries[3] or flywheels, to manage extra energy during low-demand periods and deliver power during peak times or outages. Additionally, it needs balancing load requirements among the microgrids to provide dependable and efficient energy supply and properly sizing distribution and transmission infrastructure to regulate power flows and preserve system stability. The main objective in constructing microgrid (MG) configurations is to guarantee long-term economic viability. Strategic positioning and appropriate dimensions of Distributed Energy Resources (DERs) [4] are crucial for achieving optimal cost-effectiveness and ensuring technological viability. The objective of this approach to reduce the overall expenses through the strategic deployment of distributed energy resources (DER) components. The research emphasizes important elements of MG planning such as the strategic selection and placement of DERs to decrease the overall cost of investments, limiting power losses during implementation, and optimizing scheduling to save operational expenses. In order to handle periods of high electricity demand, the integration of Battery Energy Storage Systems (BESS) is used to supply the power demand. These energy storage devices contribute to store energy during periods of low demand and providing the discharge of power at it high power demand. Several optimization strategies have been suggested to tackle various issues in MG planning. An example of this is the utilization of the Firefly Algorithm (FA) [5] to minimize operational expenses in remote microgrids by implementing an economically optimized schedule that takes into account the ideal capacity of battery storage. The Equilibrium Optimizer (EO) [6] has been suggested as a means of improving the dimensions of independent photovoltaic/fuel cell/battery energy storage system (PV/FC/BESS) microgrids in order to decrease expenses. The JAYA algorithm has been utilized to optimize the setup of system and improve the dependability of standalone MGs, with comparisons performed against PSO, TLBO and other optimization techniques. The Particle Swarm Optimization based solution for optimal DG size takes into account, modeling unpredictability in load and resources using a fuzzy logic controller. In [6] a hybrid MG model was studied to minimize costs and boost efficiency using the Improved Heap-Based Optimizer (IHBO), albeit it did not include uncertainty in resources and demand. In [7] Hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO) has been presented to identify the best sizing of MG components at the lowest cost, albeit it did not account for uncertainties or compare with other robust algorithms. In [8] Honey Badger Algorithm (HBA) has been applied to optimize DG sizing, with its effectiveness compared with Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). Fig.1 Structure of the Proposed System A model has been proposed for optimal planning of Multi-Microgrids (MMGs), taking into account uncertainties in load and DGs like PV, Wind. Similarly, to reduce the energy cost and to identify the optimal sizing of DGs by using Nash equilibrium game theory. In [9] a stochastic planning for DER-based MMGs, which analyzes uncertainty in meteorological resources, has employed Quantum PSO (QPSO) to cut costs, exhibiting an average cost reduction of 8% due to the involvement in the energy market. In [10] a leader-multi-follower optimization method has been developed for energy transactions within MMGs, although it does not account uncertainties in demand and energy costs, which could effect operations and profitability. In [11] a novel algorithm of Hybrid Simulated Annealing Particle Swarm (SAPS) algorithm, has been presented to identify the appropriate MG scale, ensuring economic feasibility and reliable operation in both off grid and on-grid. Uncertainty [12] in DGs resources is considered throughout the improvement in design of Distributed Generation (DG) systems, largely to reduce operational costs. A table summarizes the past studies on optimal location of DGs and sizing of DG that have addressed uncertainty management using stochastic analysis. Table-I: Literature review of optimal sizing and allocation of Distributed Generations [11] Considers planning and operation cost optimizations for RES penetrations SQP Planning of RES COE is ignored, System computational time complexity is higher [12] network cost reduction and maximize availability of AC/DC MOPSO, MOGA Operating cost reduction and reliability improvement MG Uncertainty of generation and load profiles are not considered [13] DER optimum sizing has achieved using energy trading Game theory, Nash equilibrium Optimum DER sizing and EMS Reliability of the system is ignored. Uncertainties of RES and load are not considered [14] Showed stochastic analysis is better than deterministic. UT-MJAYA Cost & emission reduction real time power flow is not considered [15] Total cost reduction and system performance improvement EO Cost reduction and voltage stability Energy market uncertainty is ignored [16] Each MG gained maximum profit & their participation is increased Game theory Maximize profit No cooperation attained [17] Daily operating cost and power loss reduction ANN Cost reduction and maximize RES utilization Trade-off among the MGs is not considered. [18] enhanced peak load and load factor Hybrid Lexicography Operating cost reduction & DR coordination energy market profile is not considered [19] verified objectives and effectively modeled uncertainties MOEA operating cost and emission reduction ESS is not considered for further efficacy From Table-I reveals the literature revirew on the part of the microgrid that minimizes the operating cost [13] through various optimization techniques and their limitations and research findings are represented. In addition to the previously discussed goals, some research has concentrated on finding the optimal sizing for various components within a multi-microgrid (MMG) [14], including solar PV, wind turbines (WT), fuel cells (FC), hydrogen tanks, and electrolyzers. For example, the study referenced in [15] examined how to optimally size an MMG by considering the microgrid’s influence as a price-maker in the electricity market, with the objective of lowering operating costs. The study found that higher market clearing prices (MCP) [16] lead to increased electricity sales to the grid, whereas lower MCP result in more electricity being purchased. However, this analysis did not account for the uncertainties associated with Distributed Energy Resources (DERs) and energy prices. Another study mentioned in [17] underscored the importance of designing and sizing Distributed Generation (DG) systems optimally. It highlighted several critical techno-economic factors, including Net Present Cost (NPC), reliability, Levelized Cost of Energy (LCOE) [18], capacity shortage limitations, the State of Charge (SOC) of Battery Energy Storage Systems (BESS), dispatch strategies, and DG power generation. None of the studies previously discussed have taken into account the elastic energy market model and its interactions with the main grid. In [19] gives the constantly evolving dynamics and price sensitivity of the electricity market, simulating these conditions is essential, as they significantly affect the performance of stochastic energy planning algorithms. One of the significant challenges in developing MMG systems is the high initial cost of DERs it was discussed in [20]. This challenge can be addressed by optimally sizing DERs while accounting for energy prices, which can also reduce the total cost of the Multi-Microgrid system. Furthermore, robust algorithms that effectively balance exploration and exploitation are needed to optimize DER sizing and minimize costs. Therefore, comparing the most effective algorithms is necessary to validate their performance on this particular objective. In addition to the above described purposes, some research has centered on identifying the appropriate sizing for various components within a multi-microgrid (MMG), including solar PV, wind turbines (WT), fuel cells (FC), hydrogen tanks, and electrolyzers. For example, the study described in [21] studied how to best size an MMG by considering the microgrid’s role as a price-maker in the power market, with the purpose of minimizing operational costs. The study indicated that higher market clearing prices (MCP) lead to more energy export to the grid, whereas MCP got lower which results in more electric energy has to be imported. Therefore, this research did not account for the uncertainty associated with Distributed Energy Resources (DERs) and energy costs. Another study stated in [22] underscored the need of designing and sizing Distributed Generation (DG) systems optimally. It emphasized numerous crucial techno-economic variables, including Net Present Cost (NPC), reliability, Levelized Cost of Energy (LCOE), capacity shortage constraints, Battery SOC and Battery Energy Storage Systems (BESS), dispatch techniques, and generation of power through DGs. Earlier presented studies have not taken into account the elastic energy market model and its interactions with the main grid. [23] has given the constantly fluctuating dynamics and price sensitivity of the electrical market, simulating these conditions is vital, since they greatly affect the performance of stochastic energy planning algorithms. In this [24] work introduces a stochastic planning approach that tries to maximize the sizing of MMGs while factoring in real-time energy pricing. The uncertainties are controlled utilizing the Adaptive Neuro-Fuzzy Inference System [25] which helps to eliminate mismatches between DER generation and demand during MMG operations. One of the key problems in building MMG systems is the high initial cost of DERs. This difficulty can be solved by properly sizing DERs while accounting for uncertainties and energy trading mechanisms, which can also minimize the overall cost of the MMG. Furthermore, strong algorithms that successfully balance exploration and exploitation are essential to maximize DER sizing and minimize expenses. Therefore, comparing the most effective algorithms is required to validate their effectiveness on this particular target. The literature research indicates that a limited number of studies have utilized Pareto-based multi-objective optimization approaches compared to the traditional weighted sum approach. Furthermore, only a restricted number of these investigations have identified the optimal compromised solution from the Pareto set. This study introduces Jaya algorithm that incorporates a Pareto set and clustering method to determine the optimal placement and capacities of PVDG [22] units. This seeks to simultaneously lower total active power loss, limit yearly economic loss, and enhance the voltage profile. Additionally, fuzzy set theory is applied to find the most perfect compromise alternative from the Pareto optimum set. In contrast to the standard Jaya optimization [18] algorithm, which considers local and global best particles as discrete entities, the proposed approach sees them as sets. The success of the strategy is strongly dependent upon the size of these sets, which may be modified by trial and error. The following are contributions of the proposed work: 1. A Jaya optimization technique is proposed to improve the power generation through Distributed Generation in Multi-Microgrid system . 2. The Proposed Jaya optimization technique is introduced to decrease the Net Present Cost of the entire Multi-Microgrid system 3. The proposed Jaya optimization technique is effectively utilized to determine the optimal location and sizing of DGs in the Multi-Microgrid Environment. 4. Emphasize the necessity of integrating DGs into power distribution networks: The study’s results suggest that correctly installed DGs may decrease power losses, eliminate economic losses, and enhance voltage profile while adhering to all applicable network construction and DG limits. 5. This work introduces the load growth and uncertainities in DGs and loads are accounted in to the consideration for proper planning and operation of DGs. II. STRUCTURE OF MULTI-MICROGRID SYSTEM When operating in grid-connected mode, all Distributed Energy Resources (DERs)—both existing and newly added are carefully controlled to maximize economic returns through the provision of behind-the-meter (BTM) or grid-related services, except those that are constrained by emission restrictions. This research focuses on two popular BTM services: employing DERs to lower energy consumption expenses and demand charges. Furthermore, the technique may be modified to provide grid services such as energy arbitrage, frequency control, and deferring important asset improvements. During an outage, the microgrid functions autonomously in island mode, drawing on all available DERs to fulfill local power needs. The goal, taking into account the technical and economic characteristics of various DER choices, is to reduce net costs while assuring the microgrid’s survival in the event of an unforeseen outage, while also fulfilling system-wide and component-specific limits in both grid-connected and island modes. Survivability is described here as a microgrid’s ability to continue running during an outage. Investment expenditures encompass installation and ongoing costs, whereas economic advantages stem from cost reductions achieved through BTM services. Because the operational cost of a microgrid without additional DERs remains constant, the focus switches to lowering the investment costs for new DERs as well as the operating costs of a microgrid that uses all DERs. Fig.1 shows the structure of Multi-microgrid system which consists of distribution system with microgrids[ ]. The system consists of three individual microgrids namely MG1,MG2, MG3. Every microgrid consists of Diesel Generators, PV, Wind, Battery Energy storage system, Loads. These are connected on modified 33-bus radial distribution system as shown in Fig.2 Fig.2 modified 33 bus system with division of microgrid zones 2.1 Modeling of Distributed Generation: Modeling of Distributed producing (DG) [5] involves the modeling of small-scale power producing units situated in proximity to the loads they provide. Distributed Generation often pertains to renewable energy sources, including solar, wind, small hydropower, and biomass, alongside non-renewable sources such as diesel generators and natural gas turbines. Distributed generation systems are progressively included into electricity networks to enhance efficiency, dependability, and sustainability. This pertains to the modeling and analysis of decentralized power production technologies situated near the load they provide, including solar panels, wind turbines, microturbines, and combined heat and power (CHP) systems. 2.1.1 Modeling of PV: Photovoltaic modeling is crucial for the design, optimization, and evaluation of solar power plants. Precise modeling guarantees that photovoltaic systems can satisfy energy requirements while reducing expenses and enhancing efficiency. The power output from these arrays is affected by sun irradiation and can potentially be calculated using the following equation: Where P Solar is the generation of power from photovoltaic panels, P mSolar is the power output of each array at that instant of S t =1000, S t is the solar irradiance which is incident on the panel and K is the number of PV modules. 2.1.2 Modeling of Fuel Cell: Fuel Cell Modeling includes modeling and assessing the behavior, performance, and efficiency of fuel cells under different operating instances. Accurate modeling is critical for improving their design, control techniques, and integration with energy systems like power grids or automobiles. Proton Exchange Membrane (PEM) fuel cells are widely used in the technologies of fuel cell. In this study, natural gas is utilized to generate the hydrogen required for the fuel cell units. As a result, it’s crucial to determine the amount of electrical energy produced by these units. Assuming a 50% fuel cell efficiency, each cubic meter of hydrogen gas generates 2.083 kWh, as calculated using the following equation [19]: 2.1.3 Modeling of Wind: It involves simulating the behavior and performance of wind turbines and wind farms to predict energy output, efficiency, and the impact of environmental conditions. Wind models are used to optimize the design of wind turbines, assess the feasibility of wind power projects, and integrate wind energy into larger power grids. The performance of a wind turbine depends on factors like wind speed, air density, and turbine design. Wind energy, obtained from renewable resources, is a good alternative owing to its extensive availability around the world. As such, a wind generator is applied in this research as a clean method for producing electrical power. The wind turbine’s power, as a function of wind speed, is given by the following equation [32]: Where V cut-in is the cut-in wind speed, V cut-off is the cut-out wind speed,V is the wind speed, V rated rated wind speed, P wind-max is the maximum power of wind turbine. 2.2 Load growth modeling: It refers to the process of predicting the increase in electrical demand over time in a given area or system. Accurate load growth models are essential for effective planning and management of electrical infrastructure, ensuring that supply can meet future demand. Several causes, including population expansion and the creation of new industrial facilities, contribute to rising power needs within networks. Therefore, it is vital to account for load increase early in the network design process. To solve this, we examine the load increase for current loads in the distribution system. As a consequence, additional distributed generation (DG) units need to be integrated to satisfy this demand growth. The DG units installed in the j th year will generate electricity for (K − j + 1) years, where K indicates the project’s lifespan. Additionally, these units incur yearly charges for the network. To assess these charges, the net current cost of the extra units must be computed. The load growth coefficient for each load type for that specific year, as shown below: Where is the load duration cycle at j th year, C j is the coefficient of load growth,is the starting load curve. 2.4 Uncertainty modeling: Uncertainty in distributed generation involves investigating the unpredictability and possible errors associated with the performance and integration of distributed energy resources. This involves analyzing elements like as generating output, load demands, and the influence of environmental conditions. Understanding these uncertainties is crucial for optimal planning, operation, and integration of DG into the electricity system. Predicting the output power from PV and wind entails different uncertainties deriving from the unpredictable nature of DGs like PV radiation and wind speed. Thus, to guarantee efficient planning and maximum exploitation of renewable resources, it is vital to include these uncertainties into the forecasting process. To calculate the uncertainties, predict the PV radiation, wind and demand of the load. After that, the power generated through solar and wind can be determined using Equations (1) and (2). It’s crucial to remember that there will always be deviations between the anticipated values and the true readings. To account for these differences, the following deviations of PV, wind, load demand given by [34] : These deviations intrinsically show random properties. To capture this aspect, deviations in P wind , deviations in P PV deviations in P Load are multiplied by random variables (noise).Finally, the deviations are added to the anticipated values as follows: Where P un-PV , P un-wind and P un-load are the power generation from PV array units and wind, load demand respectively. III. PROBLEM FORMULATION One of the most significant problem comes in establishing the best scale and positioning and sizing of DGs within the Multi-Microgrid system to reduce overall system costs while satisfying different limitations. Key economic variables include lowering overall losses in the Multi-microgrid system. On the technical side, major restrictions are maintaining load balance, voltage constraints and power flow constraints and ensuring the power generation through DGs fulfills the system’s demands. These factors are defined as follows: Net Present Cost (NPC) is a fundamental capital planning measure used to quantify the difference between the present value of a project’s cash inflows and expenditures. In this research, Net Present Cost is applied to determine the entire cost associated with DGs inside the Multi-Microgrid system. The cost for each DG unit comprises of investment cost, maintenance cost, operating cost and replacement cost. The Net Present Cost for each DG unit is defined as follows: 3.1 Expenses of overall power losses: One of the most important factor atffecting the overall cost is the power loss which has to be included in the objective function. The total cost consumed for power loss (NPC Loss ) is represented by the following equation: Where C loss is penalty constant for overall losses. 3.2 Penalties for greenhouse gases: Penalties cost for gas emissions is considered as objective function. The following equation is considered as the penalty factor of pollution from carbon gas emissions: 3.3 Fuel Cost: In this study, natural Gas is used as fuel for the fuel cell to produce electricity. The net present cost of natural gas purchased from the grid is as follows: 3.4 Objective function: The objective function aims to minimize the total net present costs as follows: 3.5 Constraints: The generated power from renewable energy sources, along with the input power from the main bus, must meet the required loads and account for power losses in the distribution system. Accordingly, the load balance constraint is expressed as follows: DG Constraints: DG units generate power between maximum and minimum limits The configuration with the highest cost-effectiveness and reliability is selected based on the Levelized Cost of Energy (LCOE). IV. METHODOLOGY Fig.3 shows that flowchart for optimal location and sizing of DGs in Multi-microgrid system. The objective function of this multi-microgrid system consists of various DGs for optimal sizing and location and levelized cost of Energy (LCOE) and Net Present cost (NPC) is solved by Jaya optimization algorithm. The Jaya algorithm, proposed by Rao in 2019, is a population-based optimization approach meant to tackle multiple sorts of optimization problems, including both restricted and unconstrained scenarios. Unlike many other optimization approaches, the Jaya algorithm does not require algorithm-specific parameters. Instead, it relies exclusively on two regulating parameters: population size and the total number of repetitions. Fig.3 Flowchart for optimal sizing of DG for proposed algorithm Jaya is notable for its ability to obtain optimal outcomes with fewer function evaluations by utilizing the greatest and worst values of the objective function. The algorithm aims to achieve ”victory” by selecting an ideal option while concurrently moving away from the worst solution. The position update equation for the Jaya method, as illustrated in equation (3.23), eliminates learning variables and inertia weight, including the worst value of the function to boost solution quality. The JAYA algorithm continually works toward optimum answers while avoiding the worst ones. One of its primary characteristics is its ability to handle both confined and unconstrained optimization problems well. A noteworthy feature of JAYA is its potential to obtain exact and optimum answers in fewer iterations, all without relying on algorithm-specific parameters. Fig.4 Flowchart for power balance through DG Fig.4. shows that flowchart that represents the power balance through DG whenever microgrid enters into island mode the desired amount of power will be supplied inorder to meet the demand this was done by using neighboring microgrid. The algorithm for optimal allocation of DG and sizing of DG through Jaya optimization algorithm was discussed in the further section i.e.,4.1. 4.1 Implementation of Jaya Algorithm for optimal location and sizing of DGs in Multi-microgrid system Step 1: Define problem Objective: Minimize/optimize objective function (e.g., system losses, cost, reliability, etc.) Constraints: Power balance, voltage limits, DG size and capacity limits, etc. Step 2: Initialization Initialize population size (n) Initialize maximum number of iterations (T) Initialize bounds for decision variables (locations and sizes of DGs) Generate initial population of solutions (each candidate contains DG locations and sizes) Step 3: Evaluate initial fitness For each candidate in the population: Compute fitness using the objective function Step 4: Main optimization loop For iteration = 1 to T: Step 5: Identify the best and worst solutions Find X_best (solution with the best fitness) Find X_worst (solution with the worst fitness) Step 6: Update each candidate solution For each candidate X_i in the population: For each decision variable j in X_i: r 1 = Random number between 0 and 1 r 2 = Random number between 0 and 1 For each decision variable j in X’_i: If X’_i[j] Upper Bound[j]: X’_i[j] = Upper Bound[j] Step 7:Evaluate fitness of updated solution Compute fitness of X’_i Accept improved solution If fitness(X’_i) is better than fitness(X_i): Replace X_i with X’_i Check convergence criteria (optional) If stopping criteria met: Break loop Step 8: Return results Output X_best (optimal DG locations and sizes) END V. SIMULATION RESULTS This work divides into five category scenarios proposes an optimal method for sizing and placing of Distributed Generation (DG) units using Jaya algorithm. To validate the proposed analysis and assess the effectiveness of the presented optimization methods, the study was applied to a modified 33-bus system distinguished into different microgrid zones, the optimal location and sizing can be determined for the following test cases under different load category scenarios: 1. Category scenario-I: Constant load 2. Category scenario-II: Industrial load 3. Category scenario-III: Residential load 4. Category scenario-IV: Commercial load 5. Category scenario-V: Mixture of all loads. Under all these load category scenarios the following test cases are to be validated using proposed optimization and to assess the effectiveness of the proposed optimization algorithm, it was compared with several optimization algorithms. All category scenarios simulations are carried out in MATLAB 2021a. The following are the test cases: Case-i: Without DG Case-ii: With one DG integration with the system Case-iii: With two DG integration with the system. Case-iv: With three DGs integration with the system. Case-v: With three DGs integration (Type-I) Case-vi: With three DGs integration (Type-II) Case-vii: With three DGs integration (Type-III) Category Scenario-I: Constant load: Case-i: Without DG For constant load, without DG integration to the Multi-Microgrid system the active power loss is 205.31kW. An increase in load leads to higher power losses and a decline in the system’s voltage profile. From Table-II for constant power load without DG and with integration of DGs are given for three years planning of DGs. In the first year, the minimum voltage at bus 18 was recorded at 0.9161 p.u., which dropped to 0.8937 p.u. by the fourth year. This decline is attributed to the higher load demand in the fourth year, causing a more significant voltage drop. Consequently, for simplicity, only the voltage profile results of the fourth year are presented in the discussion. I st year: The system has load of P and Q is 3725.5 kW and 1849.7 kVar. Before the integration of DG, the active power loss is 210.3 kW, and the recorded value of voltage about 0.9154 p.u. at 18 bus. II nd year: The system has load of P and Q is 4123.9 kW and 2131.5 kVar respectively. In this year, the power loss without integration of DG is 215.3 kW and the recorded value of voltage about 0.9017 p.u at 18 bus. III rd year: The system has load of P and Q is 4228.98 kW and 2189.72 kVar respectively. Prior to the integration of DG, the power loss amounts to 228.35 kW, and the recorded value of voltage about 0.9013 p.u.at 18 bus. Case-ii: With one DG integration with the system By using single DG, the DG size is determined optimally by the jaya algorithm was about 2.55 MW, and the location of DG is at bus 6. Fig. 5 shows the voltage variations of the Multi-Microgrid system under integration of single DG, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with PSO. Under these conditions, the system has loss of P and Q was 111.1 kW, 73.91 kVar. Using a single DG, the loss can be minimized up to 57.22% with respect to the base year and the following three years. By using a single DG integration with the system, the maximum improvement in the voltage profile is 5.0% from the base year through the next three years. Case-iii: With two DG integration with the system With the integration of a two DGs, the DG sizes are of 0.8567 MW and 1.3532 MW are installed at buses 13 and 30, respectively, resulting in losses of P and Q are 85.2 kW, 59.9 kVar. Fig. 6 shows the voltage variations of the Multi-Microgrid system under integration of two DGs, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with PSO and GWO. With one DG, the loss reduction was obtained as 57.22%. However, with the integration of two DGs, the loss can be decreased to 37.22% over the base year and the following three years. Case-iv: With three DGs integration with the system With the integration of a three DGs, the DG sizes are of 0.7595 MW, 1.204 MW, and 1.0584 MW are placed at buses 14, 24, and 30, respectively. Fig. 7 shows the voltage variations of the Multi-Microgrid system under integration of three DGs, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with CCPSO, GWO, PSO. This configuration results in in losses of P and Q are 73.4 kW and 51.2 kVar. With one DG, the loss reduction was obtained as 72.25% .However, with three DG units, the reduction increases to about 93.3% over the base year and the subsequent three years. Table-II: For constant load the optimal allocation and sizing of DG and % reduction in power loss Constant Power Load First Year I - 3.5 140.5 - II 6 2.59 111.1 57.12 III 13 30 0.8567 1.3532 88.2 37.22 IV 14 24 30 0.7095 1.2604 1.1184 73.4 47.75 V 14 24 30 0.801 1.09 1.053 72.78 48.19 VI 14 24 30 0.901 1.1 1.2 50.03 64.39 VII 14 24 30 0.78 0.95 1.02 7.811 94.44 Constant Power Load Second Year I - 3.8 153.2 - II 6 2.85 129.2 15.66 III 13 30 0.9546 1.523 97.5 36.35 IV 14 24 30 0.895 1.35 1.13 84.5 44.84 V 14 24 30 1.12 1.23 1.32 76.25 50.22 VI 14 24 30 1.02 1.32 1.52 55.45 63.80 VII 14 24 30 0.84 0.99 1.12 17.5 88.44 Constant Power Load Third Year I - 4.2 162.2 - II 6 3.12 132.12 18.54 III 13 30 1.12 1.956 101.5 37.42 IV 14 24 30 0.975 1.46 1.25 96.8 40.32 V 14 24 30 1.27 1.35 1.42 78.52 51.59 VI 14 24 30 1.12 1.41 1.64 57.25 64.70 VII 14 24 30 0.95 1.13 1.25 18.1 88.84 Case-v: With three DGs integration (Type-I) In DG (Type-II) configurations, each DG installation involves two decision variables: one is the size of either “P” or “Q” and its location. When using multiple DG installations, the total number of decision variables are six this will appear in case-v and case-vi, However, for the case-vii where both “P” and “Q” of each DG are accounted, then it increases to a total nine decision variables. The Multi-Microgrid system has a total load of both “P” and “Q” are 3.685 MW and 2.1 MVAR, with a base voltage of 11.66 kV. In Scenario 1, which represents the condition without any DG integration, a power flow analysis revealed that “P” and “Q” losses are 209.98 kW and 142.34 kVar. To reduce these losses, optimal location and sizing of DGs are determined through optimization algorithms. The Jaya algorithm has identified location of three DGs and their DG sizes of 821.35 kW, 1064.31 kW, and 1035.7 kW, respectively. By using Jaya algorithm power losses are reduced from 214.92 kW to 71.32 kW, achieving a reduction in loss of 65.45%. Additionally, for minimizing the power losses the Jaya algorithm was outperformed by comparing with other optimization methods. Case-vi: With three DGs integration (Type-II) To reduce the power losses, various DGs are integrated with the Multi-Microgrid system were optimally located within the system by using different optimization techniques. The Jaya algorithm effectively identifies the optimal sizes and placements of DGs, leading to a substantial reduction in loss by 138.25 kW. This Jaya optimization technique achieves with 33.45% highest reduction in loss of comparing with other optimization techniques. These findings highlight the efficiency of the Jaya optimization technique in addressing DG location and sizing challenges. Case-vii: With three DGs integration (Type-III) With the integration of three DGs (Type-III) to the Multi-Microgrid system, the size of DGs are determined optimally by the Jaya algorithm was found to be 0.84 MW, 0.99 MW, 1.12 MW. These results of Jaya algorithm are compared with various optimization techniques. The comparison reveals that Jaya optimization technique achieves with 93.45% highest loss reduction in comparing with other optimization techniques. Category Scenario-II: Industrial load: Case-i: Without DG The system has load of P and Q are 3759.3 kW and 1645.7 kVar. From Table-III for industrial load without DG and with integration of DGs are given for three years planning of DGs. Before integrating DG, the power loss is 157.7 kW, and the recorded value of voltage about 0.91578 p.u. at 18 bus. I st year: The system has load of P and Q are 3853.3 kW and 1720.52 kVar. Prior to DG integration, the power loss is 194.23 kW, and the recorded value of voltage about of 0.9124 p.u. at 18 bus. II nd year: The system has load of P and Q are 4135.40 kW and 1855.78 kVar. Before integrating DG, the power loss is 214.25 kW, and the recorded value of voltage about 0.9091 p.u. at 18 bus. III rd year: The system has load of P and Q are 4356.39 kW and 1956.45 kVar. Before integrating DG, the power loss amounts to 261 kW, and the recorded value of voltage about 0.901 p.u. at 18 bus. Case-ii: With one DG integration with the system By using single DG, the DG size is determined optimally by the jaya algorithm was found to be 2.65 MW, placed at bus 6. Fig. 5 shows the voltage variations of the Multi-Microgrid system under integration of single DG, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with PSO. In this configuration, the total P and Q power losses are 112.1 kW and 70.91 kVar. Using a single DG, the loss can be minimized up to 55.45% with respect to the base year and the following three years. Case-iii: With two DG integration with the system With the integration of a two DGs, DG sizes are of 0.8379 MW and 1.45 MW installed at buses 13 and 30, resulting in losses of P and Q are 82.17 kW and 60.9 kVar. Fig. 6 shows the voltage variations of the Multi-Microgrid system under integration of two DGs, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with PSO and GWO. Using a single DG, the loss can be minimized up to 55.45%. However, with the integration of two DG units the loss can be minimized up to 82.31% over the base year and the following three years. Case-iv: With three DGs integration with the system With the integration of a three DGs, the DG sizes are of 0.715 MW, 1.1604 MW, and 1.1284 MW are placed at buses 14, 24, and 30. Fig. 7 shows the voltage variations of the Multi-Microgrid system under integration of three DGs, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with CCPSO, GWO, PSO. This configuration results in total active power losses of 73.4 kW and reactive power losses of 51.2 kVar. Using a single DG, the loss can be minimized up to 73.95% with the integration of a single DG. However, with three DG units, the reduction increases to about 93.3% over the base year and the subsequent three years.Table-III: For Industrial load the optimal allocation and sizing of DG and % reduction in power loss Industrial Load First Year I - 2.6 140.5 - II 6 2.5 71.3 49.25 III 13 30 0.837 1.125 49.1 65.05 IV 14 24 30 1.82 0.77 1.05 35.4 74.80 V 14 24 30 1.94 0.65 0.98 30.7 78.14 VI 14 24 30 1.75 0.56 0.78 29.6 78.93 VII 14 24 30 1.89 0.54 0.65 27.2 80.64 Industrial Load Second Year I - 2.7 151.4 - II 6 2.6 82.9 45.24 III 13 30 0.709 0.4381 57 62.35 IV 14 24 30 0.8096 1.1487 1.102 41 72.91 V 14 24 30 0.915 1.02 0.985 35.6 76.48 VI 14 24 30 1.14 1.01 0.87 34.9 76.94 VII 14 24 30 1.92 0.45 0.56 32.14 78.59 Industrial Load Third Year I - 3.11 161.3 - II 6 2.43 96.4 40.23 III 13 30 0.658 1.125 66.1 59.02 IV 14 24 30 0.715 1.125 1.025 50.2 68.87 V 14 24 30 0.875 1.01 0.885 41.3 74.39 VI 14 24 30 1.025 1.015 0.87 40 75.2 VII 14 24 30 1.83 0.41 0.53 37.5 76.75 Case-v: With three DGs integration (Type-I) . In Scenario 1, which represents the condition without any DG integration, a power flow analysis revealed that “P” and “Q” losses are 208.14 kW and 123.54 kVar. To reduce these losses, various DGs are integrated with the Multi-Microgrid system were optimally located within the system by using different optimization techniques. The Jaya algorithm has identified location of three DGs and their DG sizes of 758.15 kW, 1035.36 kW, and 1012.35 kW, respectively. By using Jaya algorithm power losses are reduced from 204.45 kW to 72.15 kW, achieving reduction in loss of 63.35%. Additionally, for minimizing the power losses the Jaya algorithm was outperformed by comparing with other optimization methods. Case-vi: With three DGs integration (Type-II) To reduce these losses, various DGs are integrated with the Multi-Microgrid system were optimally located within the system by using different optimization techniques. The Jaya algorithm effectively identifies the optimal sizes and placements of DGs, leading to a substantial reduction in loss by 135.45 kW. It surpasses other optimization methods, achieving the highest loss reduction percentage (LR) of 34.47%. These findings highlight the efficiency of the Jaya optimization technique in addressing DG location and sizing challenges. Case-vii: With three DGs integration (Type-III) With the integration of three DGs (Type-III) to the Multi-Microgrid system, the size of DGs are determined optimally by the Jaya algorithm was found to be 0.789 MW, 1.105 MW, 1.19 MW. These results of Jaya algorithm are compared with various optimization techniques. The comparison reveals that Jaya optimization technique achieves with 84.51% highest loss reduction in comparing with other optimization techniques. . Category Scenario-III: Residential load: Case-i: Without DG The system has load of P and Q are 3478.98 kW and 1794.63 kVar, respectively. From Table-IV for residential without DG and with integration of DGs are given for three years planning of DGs. Before integrating DG, the power loss is, the power loss is 145.96 kW, and the recorded value of voltage about 0.9175 p.u. at 18 bus. I st year: The system has load of P and Q are 3725.63 kW and 1946.7 kVar, respectively. Prior to DG integration, the power loss is 173.65kW, and the recorded value of voltage about 0.9132 p.u. at 18 bus. II nd year: The system has load of P and Q are 1256.35 kW and 2156.36 kVar, respectively. Before DG integration, the power loss is 216.65 kW, and the recorded value of voltage about 0.9056 p.u. at 18 bus. III rd year: The system’s active and reactive power loads are 4394.8 kW and 2264.7 kVar, respectively. Prior to DG integration, the power loss is 232.65 kW, and the recorded value of voltage about 0.9012 p.u. at 18 bus. Case-ii: With one DG integration with the system By using single DG, the DG size is determined optimally by the jaya algorithm was found to be 2.654 MW, placed at bus 6. Fig. 5 shows the voltage variations of the Multi-Microgrid system under integration of single DG, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with PSO. In this configuration, the total P and Q power losses are 114.19 kW and 72.45 kVar, respectively. Using a single DG, the loss can be minimized up to 55.24% over the base year and the following three years. Table-IV: For Residential load the optimal allocation and sizing of DG and % reduction in power loss Residential Load First Year I - 2.59 159.1 - II 6 2.45 75.58 52.49 III 13 30 0.7897 1.035 56.63 64.4 IV 14 24 30 0.594 1.15 0.98 46.7 70.6 V 14 24 30 0.784 1.02 0.78 39.8 74.98 VI 14 24 30 0.814 0.905 0.75 37.7 76.3 VII 14 24 30 0.802 1.05 0.789 35.4 77.74 Residential Load Second Year I - 2.73 162.5 - II 6 2.45 87.7 46.03 III 13 30 0.871 1.12 65.3 59.81 IV 14 24 30 0.71 1.01 0.94 49.9 69.293 V 14 24 30 0.741 1.08 0.96 44.9 72.36 VI 14 24 30 0.817 0.905 0.804 43.8 73.04 VII 14 24 30 0.786 1.05 0.725 41.2 74.64 Residential Load Third Year I - 2.95 171.5 - II 6 2.45 118.6 30.84 III 13 30 0.7897 1.035 88 48.68 IV 14 24 30 0.594 1.15 0.98 67.2 60.81 V 14 24 30 0.784 1.02 0.78 60.4 64.78 VI 14 24 30 0.814 0.905 0.75 59.8 65.13 VII 14 24 30 0.802 1.05 0.789 53.4 68.86 Case-iii: With two DG integration with the system With the integration of a two DGs, the DG sizes are of 0.837 MW and 1.425 MW are installed at buses 13 and 30, resulting in losses of P and Q are 84.32 kW and 60.9 kVar. Fig. 6 shows the voltage variations of the Multi-Microgrid system under integration of two DGs, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with PSO and GWO. Using a single DG, the loss can be minimized up to 55.45%. However, with the integration of two DG units the loss can be minimized up to 82.31% over the base year and the following three years. Case-iv: With three DGs integration with the system With the integration of a three DGs, the DG sizes are of 0.7095 MW, 1.134 MW, and 1.1045 MW are placed at buses 14, 24, and 30. Fig. 7 shows the voltage variations of the Multi-Microgrid system under integration of three DGs, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with CCPSO, GWO, PSO. This configuration results in losses of P and Q are 73.4 kW and 51.2 kVar. Using a single DG, the loss can be minimized up to 72.65%. However, with three DG units, the reduction increases to about 93.3% over the base year. Case-v: With three DGs integration (Type-I) . In Scenario 1 which represents the condition without any DG integration, a power flow analysis revealed that “P” and “Q” losses are 209.36 kW and 126.35 kVar. To reduce these losses, various DGs are integrated with the Multi-Microgrid system were optimally located within the system by using different optimization techniques. The Jaya algorithm has identified location of three DGs and their DG sizes of 738.25 kW, 1018.36 kW, and 1004.35 kW, respectively. By using Jaya algorithm power losses are reduced from 204.45 kW to 72.15 kW, achieving a reduction in loss of 64.50%. Additionally, for minimizing the power losses the Jaya algorithm was outperformed by comparing with other optimization methods. Case-vi: With three DGs integration (Type-II) To reduce these losses, various DGs are integrated with the Multi-Microgrid system were optimally located within the system by using different optimization techniques. The Jaya algorithm effectively identifies the optimal sizes and placements of DGs, leading to a substantial reduction in loss by 112.45 kW. It surpasses other optimization methods, achieving a reduction in loss of 33.74%. These findings highlight the efficiency of the Jaya optimization technique in addressing DG location and sizing challenges. Case-vii: With three DGs integration (Type-III) With the integration of three DGs (Type-III) to the Multi-Microgrid system, the size of DGs are determined optimally by the Jaya algorithm was found to be 0.802 MW, 1.05 MW, 0.789 MW. These results of Jaya algorithm are compared with various optimization techniques. The comparison reveals that Jaya optimization technique achieves with 68.51% highest loss reduction in comparing with other optimization techniques. Category Scenario-IV: Commercial load Case-i: Without DG The system has load of P and Q are 3329.15 kW and 1756.3 kVar. From Table-V for commercial load without DG and with integration of DGs are given for three years planning of DGs. Before integrating DG, the power loss is 145.65 kW, and the recorded value of voltage about 0.9245 p.u. at 18 bus. I st year: The system has load of P and Q are 3456.2 kW and 1856.3 kVar. Before DG integration, the power loss is 156.35 kW, and the recorded value of voltage about 0.9145 p.u. at 18 bus. II nd year: The system has load of P and Q are 3635.21kW and 1956.24 kVar. Prior to DG integration, the power loss is 185.65 kW, and the recorded value of voltage about 0.9125 p.u. at 18 bus. III rd year: The system has load of P and Q are 3956.45kW and 2014.56 kVar. Before DG integration, the power loss amounts to 194.5 kW, and the recorded value of voltage about 0.9106 p.u. at 18 bus. Case-ii: With one DG integration with the system By using single DG, the DG size is determined optimally by the jaya algorithm was about 2.78 MW, placed at bus 6. Fig. 5 shows the voltage variations of the Multi-Microgrid system under integration of single DG, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with PSO. In this configuration, the total P and Q power losses are 126.19 kW and 81.56 kVar, respectively. Using a single DG, the loss can be minimized up to 45.24% over the base year. Table-V: For commercial load the optimal allocation and sizing of DG and % reduction in power loss Commercial Load First Year I - 3.4 171.5 - II 6 3.1 75.7 55.86 III 13 30 2.95 58.6 65.83 IV 14 24 30 0.8391 0.9782 45.6 73.41 V 14 24 30 0.6586 1.0095 0.9454 41.8 75.62 VI 14 24 30 0.7572 0.5506 0.9234 40.8 76.20 VII 14 24 30 0.5476 0.6774 0.55 38.5 77.55 Commercial Load Second Year I - 3.5 174.5 - II 6 3.3 87.9 49.62 III 13 30 1.0205 1.1085 68 61.03 IV 14 24 30 1.6388 0.728 0.9315 53.1 69.57 V 14 24 30 1.5253 0.8954 0.9456 49.6 71.57 VI 14 24 30 1.4563 0.9258 0.9874 47.7 72.66 VII 14 24 30 1.0256 0.7896 0.9568 45.6 73.86 Commercial Load Third Year I - 3.2 175.8 - II 6 2.89 91.6 47.9 III 13 30 1.0481 1.221 71.6 59.27 IV 14 24 30 0.8512 1.2904 1.1331 65.2 62.91 V 14 24 30 0.9607 0.6792 1.1346 63.6 63.82 VI 14 24 30 0.8445 0.2136 0.4973 61.3 65.13 VII 14 24 30 0.7546 0.3145 0.5986 59.4 66.21 Case-iii: With two DG integration with the system With the integration of a two DGs, the DG sizes are of 0.8445 MW and 0.947 MW are installed at buses 13 and 30, resulting in losses of P and Q are 94.32 kW and 73.9 kVar. Fig. 6 shows the voltage variations of the Multi-Microgrid system under integration of two DGs, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with PSO and GWO. Using a single DG, the loss can be minimized up to 53.45%. However, with the integration of two DG units the loss can be minimized up to 73.21% over the base year and the following three years. Case-iv: With three DGs integration with the system With the integration of a three DGs, the DG sizes are of 0.6586 MW, 0.6774 MW, and 1.012 MW are placed at buses 14, 24, and 30. Fig. 7 shows the voltage variations of the Multi-Microgrid system under integration of three DGs, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with CCPSO, GWO, PSO. This configuration results in losses of P and Q are 84.35 kW and 65.24 kVar. Using a single DG, the loss can be minimized up to 72.65%. However, with three DG units, the reduction increases to about 72.35% over the base year. Case-v: With three DGs integration (Type-I) In Scenario 1 which represents the condition without any DG integration, a power flow analysis revealed that “P” and “Q” losses are 211.33 kW and 129.65 kVar. To reduce these losses, various DGs are integrated with the Multi-Microgrid system were optimally located within the system by using different optimization techniques. The Jaya algorithm has identified location of three DGs and their DG sizes of 1.25 MW, 1018.36 MW, and 1004.35 MW, respectively. By using Jaya algorithm power losses are reduced from 204.45 kW to 72.15 kW, achieving a reduction in loss of 64.50%. Additionally, for minimizing the power losses the Jaya algorithm was outperformed by comparing with other optimization methods. Case-vi: With three DGs integration (Type-II) To reduce these losses, various DGs are integrated with the Multi-Microgrid system were optimally located within the system by using different optimization techniques. The Jaya algorithm effectively identifies the optimal sizes and placements of DGs, leading to a substantial reduction in loss by 112.45 kW. It surpasses other optimization methods, achieving a reduction in loss of 33.74%. These findings highlight the efficiency of the Jaya optimization technique in addressing DG location and sizing challenges. Case-vii: With three DGs integration (Type-III) With the integration of three DGs (Type-III) to the Multi-Microgrid system, the size of DGs are determined optimally by the Jaya algorithm was found to be 0.802 MW, 1.05 MW, 0.789 MW. These results of Jaya algorithm are compared with various optimization techniques. The comparison reveals that Jaya optimization technique achieves with 68.51% highest loss reduction in comparing with other optimization techniques. V) Category Scenario-IV: Mixture of all loads. Case-i: Without DG The system has load of P and Q are 3435.63.0 kW and 1735.69 kVar, respectively. From Table-VII for Mix load without DG and with integration of DGs are given for three years planning of DGs. Before DG integration, the power loss is 157.69 kW, and the recorded value of voltage about 0.9135 p.u. at 18 bus. I st year: The system has load of P and Q are 3732.56 kW and 1859.63 kVar, respectively. Before integrating DG, the power loss is 178.96 kW, and the recorded value of voltage about 0.9114p.u. at 18 bus II nd year: The system has load of P and Q are 3963.56 kW and 1987.69 kVar, respectively. Before DG integration, the power loss is 204.56 kW, and the recorded value of voltage about 0.9099 p.u. at 18 bus. III rd year: The system has load of P and Q are 4132.56 kW and 2105.63 kVar, respectively. Prior to DG integration, the power loss is 232.63 kW, and the recorded value of voltage about 0.9012 p.u. at 18 bus. Case-ii: With one DG integration with the system By using single DG, the DG size is determined optimally by the jaya algorithm was about 2.78 MW, placed at bus 6. Fig. 5 shows the voltage variations of the Multi-Microgrid system under integration of single DG, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with PSO. In this configuration, the total P and Q power losses are 126.19 kW and 81.56 kVar, respectively. Using a single DG, the loss can be minimized up to 45.24% over the base year. Case-iii: With two DG integration with the system With the integration of a two DGs, the DG sizes are of 0.8445 MW and 0.947 MW are installed at buses 13 and 30, resulting in losses of P and Q are 94.32 kW and 73.9 kVar. Fig. 6 shows the voltage variations of the Multi-Microgrid system under integration of two DGs, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with PSO and GWO. Using a single DG, the loss can be minimized up to 53.45%. However, with the integration of two DG units the loss can be minimized up to 73.21% over the base year and the following three years. Case-iv: With three DGs integration with the system With the integration of a three DGs, the DG sizes are of 0.7095 MW, 1.134 MW, and 1.1045 MW are placed at buses 14, 24, and 30. Fig. 7 shows the voltage variations of the Multi-Microgrid system under integration of three DGs, the obtained voltage profile is optimized through the proposed jaya algorithm was compared with CCPSO, GWO, PSO. This configuration results in losses of P and Q are 73.4 kW and 51.2 kVar. Using a single DG, the loss can be minimized up to 72.65%. However, with three DG units, the reduction increases to about 93.3% over the base year. Table-VI: For Mixing of load the optimal allocation and sizing of DG and % reduction in power loss Mixing of Load First Year I - 2.78 159.6 - II 6 2.65 76.1 52.31 III 13 30 0.8752 1.0319 57.3 64.09 IV 14 24 30 0.7306 1.038 0.9684 43.3 72.86 V 14 24 30 0.8956 0.5892 0.9715 39 75.56 VI 14 24 30 0.7065 0.4892 0.932 38 76.19 VII 14 24 30 0.689 0.689 1.12 37.4 76.56 Mixing of Load Second Year I - 2.89 161.5 - II 6 2.71 88.5 45.20 III 13 30 0.9428 1.11 65.6 59.38 IV 14 24 30 0.7867 1.1165 1.0421 50.2 68.91 V 14 24 30 0.952 0.6361 1.0028 45.3 71.95 VI 14 24 30 0.9514 0.743 0.642 44.1 72.69 VII 14 24 30 0.978 0.664 0.512 42.3 73.80 Mixing of Load Third Year I - 2.94 163.24 - II 6 2.83 102.8 37.02 III 13 30 1.0148 1.1942 76.1 53.38 IV 14 24 30 0.846 1.2003 1.1206 58.3 64.28 V 14 24 30 0.8926 0.6706 1.0884 52.4 67.9 VI 14 24 30 0.9675 1.2893 1.2046 51 68.75 VII 14 24 30 0.9845 1.136 1.023 48.5 70.28 Fig.8 shows that convergence characteristics between the total power loss and iterations, the proposed Jaya algorithm is compared with GWO and PSO. By using Jaya optimization with low values of iteration the power loss value was decreased. During 20 th iteration the power loss optimized through Jaya algorithm was decreased to 110.11 kW, whereas by using GWO algorithm the power loss convergence at the 20 th iteration was found to be 115.35 kW and from PSO the power loss obtained at the 20 th iteration was found to be113.65kW. The Jaya algorithm gives better optimal performance in comparison with the other optimization algorithms. Case-v: With three DGs integration (Type-I) In Scenario 1 which represents the condition without any DG integration, a power flow analysis revealed that “P” and “Q” losses are 209.36 kW and 126.35 kVar. To reduce these losses, various DGs are integrated with the Multi-Microgrid system were optimally located within the system by using different optimization techniques. The Jaya algorithm has identified location of three DGs and their DG sizes of 0.7385 MW, 1.0136 MW, and 1.0035 MW, respectively. By using Jaya algorithm power losses are reduced from 204.45 kW to 72.05 kW, achieving a reduction in loss of 67.50%. Additionally, for minimizing the power losses the Jaya algorithm was outperformed by comparing with other optimization methods. Case-vi: With three DGs integration (Type-II) To reduce these losses, various DGs are integrated with the Multi-Microgrid system were optimally located within the system by using different optimization techniques. The Jaya algorithm effectively identifies the optimal sizes and placements of DGs, leading to a substantial reduction in loss by 112.45 kW. It surpasses other optimization methods, achieving a reduction in loss of 33.74%. These findings highlight the efficiency of the Jaya optimization technique in addressing DG location and sizing challenges. Case-vii: With three DGs integration (Type-III) With the integration of three DGs (Type-III) to the Multi-Microgrid system, the size of DGs are determined optimally by the Jaya algorithm was found to be 0.802 MW, 1.05 MW, 0.789 MW. These results of Jaya algorithm are compared with various optimization techniques. The comparison reveals that Jaya optimization technique achieves with 68.51% highest loss reduction in comparing with other optimization techniques. Fig.5 Voltage Variations of MMG system with single DG Fig. 6. Voltage Variations of MMG system with integration of two DGs Fig.8. Total power loss of the MMG through optimization algorithms Fig.7. Voltage Variations of MMG system with three DGs Fig. 9. Optimal DG sizes through different optimization algorithms Fig.10. Power loss of MMG through different optimization algorithms From Fig. 9 shows the optimal DG sizes from different optimization algorithms, the proposed Jaya algorithm gives better optimal sizes of DGs in comparing with other optimization algorithms. Fig. 10 shows the optimal power loss of the Multi-Microgrid system through different optimization algorithms, at each bus the obtained power loss through Jaya optimization algorithm gives lesser value the remaining optimization techniques. This shows that Jaya algorithm reduces the power loss of the system in comparing with other optimization techniques. Table-VII shows that optimal location and size of the DGs in Multi-Microgrid system with respect to integration of DGs, with integration of three DGs gives better optimal location and sizes of the DGs because by integration of three DGs into the overall system losses are reduced. Fig.11 shows that overall power loss of the Multi-Microgrid system with respect to integrating DGs, without integration of DG the power loss the Multi-Microgrid system was quite high. In order to decrease the power loss of the Multi-Microgrid System, DGs are integrated into the Multi-Microgrid system. With single DG integration to Multi-Microgrid system the overall power loss is reduced to 10MW, it was observed that by integrating DGs into Multi-Microgrid the total powerloss was reduced. Fig.11 shows that with the integration of three DGs into the Multi-Microgrid system, the overall power loss was quite reduced when comparing with other topologies.Fig.12 shows that voltage profile of the Multi-Microgrid system with respect to integration of DGs. With integration of DGs the voltage profile of the Multi-Microgrid system was improved. Fig.12 it was observed that with the integration three DGs gives better improvement in the voltage profile in comparing with integration of one DG and integration of two DGs. Fig.11.Overall power loss under integration of DGs Fig.12. Voltage profile of the MMG system under integration of DGs Table-VII: Comparison of optimal DG sizes and active power loss under different cases P loss (Kw) 211.2 111.1 88.2 73.4 Percentage P loss reduction (%LR) - 45.40 57.24 63.25 Q loss (kVar) 140.5 75.91 62.83 51.6 Percentage Q loss reduction (%LR) - 45.97 55.28 63.26 Minimum voltage 0.904 0.948 0.9716 0.9676 Minimum voltage at bus location 18 18 18 18 Optimal DG sizes (MW) - 2.5910 0.8667 1.3232 0.7095 1.2604 1.1180 Optimal DG location - 6 13 20 14 24 30 Table –VIII shows that comparison of different optimization techniques for optimal location and size of DGs under integration of DGs into Multi-Microgrid system. Without DG integration into the Multi-Microsystem, the power loss are quite high. Inorder to decrease the overall power loss of the system DGs are integrated into the system, the location and size of the DGs are optimized through Jaya, GWO, CCPSO and PSO algorithms. With single DG integration, the Jaya algorithm gives the superior performance with reduction in active power loss of 52.59% and reduction in reactive powerloss of 45.60%. With two DGs integration, the Jaya algorithm gives the superior performance with reduction in active power loss of 57.11% and reduction in reactive power loss of 46.62%. With three DGs integration into Multi-Microgrid system, the Jaya algorithm gives the superior performance with reduction in active power loss of 57.39% and reduction in reactive powerloss of 47.54%. Table-VIII: Comparison of optimal location and sizing of DG in MMG through Proposed Jaya optimization algorithm with different algorithm under integration of DGs P Loss (Kw) Q Loss (kVar) P Loss Reduction in % Q Loss Reduction in % Minimum Voltage Value in p.u Minimum Voltage at the Bus With one DG Jaya 6 2.59 111.1 73.9 52.59 45.60 0.948 18 GWO 6 3.13 120.42 79.43 48.34 41.53 0.948 18 CCPSO 6 3.249 128.69 84.12 45.09 38.07 0.936 18 PSO 6 3.54 131.75 89.56 43.78 34.07 0.9312 18 With two DGs Jaya 13 20 0.8752 1.0319 100.52 72.5 57.11 46.62 0.956 18 GWO 13 20 1.35 1.456 105.23 75.12 55.09 44.699 0.956 18 CCPSO 13 20 1.55 1.958 110.45 81.36 52.86 40.106 0.943 18 PSO 13 20 2.05 1.98 115.25 85.23 50.81 59.38 0.9345 18 With three DGs Jaya 14 24 30 0.8956 0.5892 0.9715 98.56 71.25 57.39 47.54 0.9645 18 GWO 14 24 30 1.145 0.987 1.256 101.25 81.36 56.79 40.106 0.9645 18 CCPSO 14 24 30 1.245 1.025 1.548 108.25 85.14 53.80 37.32 0.9546 18 PSO 14 24 30 1.536 1.254 1.98 113.45 87.35 51.58 35.69 0.9546 18 With three DGs (Type-I) Jaya 14 24 30 0.9856 1.1235 0.9756 95.25 70.32 59.35 48.23 0.9453 18 GWO 14 24 30 1.1564 1.897 1.258 103.26 81.35 55.933 40.11 0.946 18 CCPSO 14 24 30 1.369 2.025 1.569 113.15 85.35 51.71 37.16 0.9345 18 PSO 14 24 30 1.89 2.35 1.98 115.26 88.32 50.81 34.98 0.9345 18 With three DGs (Type-II) Jaya 14 24 30 1.1235 1.9684 1.8963 94.23 65.32 59.787 51.91 0.947 18 GWO 14 24 30 1.3695 2.1365 1.9654 113.25 70.32 51.67 48.23 0.943 18 CCPSO 14 24 30 1.659 2.365 2.136 115.15 81.35 50.85 62.56 0.9346 18 PSO 14 24 30 1.789 2.456 2.236 121.31 84.32 48.23 37.92 0.9312 18 With three DGs (Type-III) Jaya 14 24 30 1.3689 2.4565 2.1235 103.25 63.41 55.93 53.32 0.9315 18 GWO 14 24 30 1.4569 2.569 2.236 115.26 75.25 50.81 44.611 0.9345 18 CCPSO 14 24 30 1.789 2.656 2.362 123.25 84.32 47.40 37.92 0.9456 18 PSO 14 24 30 2.325 2.756 2.654 125.14 88.32 46.59 34.98 0.9573 18 Conclusion: This work offers a systematic planning for the optimal location and sizing of Distributed Generators (DGs) in radial distribution systems using the Jaya optimization technique. The key aims of the study are to decrease power losses and increase the profile of the voltage at the buses in the Multi-microgrid system. The proposed approach is evaluated on the modified 33-bus radial distribution system with a constant power load model. Four scenarios are examined: the base case (without DG), with integration of one DG, with integration of two DGs, with integration of three DGs. Each scenario considers one to three DG units. Additionally, the results are examined for five different types of loads namely constant power load, industrial load, residential load, commercial load, and mixed load are examined, with a 7.5% annual load growth for each category. The results gives that the combination of P and Q DGs are more successful in lowering losses and improving the profile of voltage in compared to the other cases. Furthermore, it is noted that increasing the number of DG units boosts the system’s performance, with five DGs offering the highest decrease in power losses and the most substantial voltage profile improvement compared to a single DG. Moreover, the strategy is adaptable to diverse load types in the distribution system, enabling utilities in prioritizing optimal DG planning based on specified parameters. Another portion of the study focuses on the optimal scaling of a multi-microgrid (MMG) system by minimizing the Net Present Cost (NPC) while accounting for uncertainties in demand, power generation through DGs, and their cost of energy. The Jaya optimization technique is applied to construct a mathematical model for calculating the appropriate size of DGs to satisfy load needs. 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Information & Authors Information Version history V1 Version 1 09 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords distributed generation jaya algorithm levlized cost of energy multi-microgrid system net present cost Authors Affiliations Sri Suresh Mavuri 0000-0002-9040-9023 [email protected] National Institute of Technology Andhra Pradesh View all articles by this author Jayaram Nakka National Institute of Technology Andhra Pradesh View all articles by this author Metrics & Citations Metrics Article Usage 208 views 108 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sri Suresh Mavuri, Jayaram Nakka. Sizing and Location of Distributed Generations in Multi-Microgrid Environment using Jaya optimization technique. Authorea . 09 January 2025. 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