Fuzzy Logic and HOMER: Innovations in Distributed Generation for System Stability and Performance

Fuzzy Logic and HOMER: Innovations in Distributed Generation for System Stability and Performance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fuzzy Logic and HOMER: Innovations in Distributed Generation for System Stability and Performance Nimisha Tiwari, Kaustubh Dwivedi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4634514/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract The HOMER software is described in this article to provide the efficient distributed generation configurations. This is based on nature of the area and its renewable energy availability. This software is used to develop and assess alternatives for stand-alone and grid-connected power distributed generation applications, both technically and financially. It enables the consideration of a huge range of technological alternatives. It’s a powerful tool for simulating both the traditional and sustainable energy techniques. Furthermore, in distributed generation electric networks, a fuzzy-based strategy is suggested to choose the suitable power source and ascribe it to the appropriate load. Fuzzy Logic Control is an effective approach to resolve complex problems using algebraic calculations Moreover, sustainable energy distributed generation has grown rapidly as large central power plants have become financially unviable because of substantial decreases in fossil fuels, rising fuel costs, global energy crunch and the emergence of environment degradation problems. Nevertheless, the integration of distributed power generation into conventional electric power networks has influence on their consistency, safeness, and power feature. As a result, inoculation of distributed power generation necessitates the use of adequate controllers to preserve system stability and performance. Distributed generation Fuzzy HOMER Wind Solar Energy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Renewable energy sources (RES) like wind and solar energy are pure, budget - friendly, household and limitless. These are non-polluting energy sources and safer sources of energy for everyone. These benefits prompted inoculating RESs into electrical networks to address the ongoing energy needs and to incorporate alternative solutions to the global energy crunch. In addition to these reasons, interest in distributed generation (DG) sources has increased recently, resulting in a rise in the quantity of generators linked to distribution network. DG provides atmospheric benefits, enhance reliability, reduces losses, load managements, and provides economic benefits. Notwithstanding, the integration of distributed generation (DG) power stations into distribution networks (DNs) raises a number of concerns such as system steadiness, devices protection, serious network problems risk increment, and switching frequency. As a result, the need to develop appropriate control methods to enable power delivery to clients while adhering to power reliability and quality standards has grown into a significant concern in current years This effect has been widely studied in [Vito C. and et al ,2014]. One of the key traits about loads in electrical networks is that require continuous suitable control in their ability to change. This article suggests an optimised fuzzy strategy for managing the various load demands on the basis of available power sources and chooses the best possible power source relying on two primary variables: the existing power capacity and the distance of the power source to the load centre. Moreover, HOMER software is utilized to investigate the most appropriate renewable DGs which may be utilised on the basis of the ecology of the area and the abundant supply of renewable energy. Because fuzzy control, unlike classical control, does not require complicated mathematical expressions, the number and variety of applications of fuzzy logic have recently increased dramatically. Furthermore, with the necessary knowledge and experience of a skilled operator, it’s simple to plan a fuzzy control for nonlinear intricate systems. In [Ashabani M., Abdel-rady Y. and Mohamed I.,2014] an original family of widespread control and management tactics for micro grids in smart distribution grids is presented. In addition, the paper offers an overall framework for modelling and analysing power management strategies in a micro grid with several DG sources. The paper [Azzouz M., Farag H. and El-Saadany E.,2013] unveils a fuzzy algorithm to offer an intuitive reference of the on-load tape selector controller to alleviate the impact of the DG units' high permeation. The article [Wang A.2011] describes a fuzzy approach that is built on a genetic algorithm for distributing power requirements between many currently offered power sources. 2. System Overview Distributed generation networks use various energy sources, particularly RES such as solar, wind, and geothermal energy. This system provides a lot of benefits over traditional networks. "Distributed generation assurances several potential advantages, such as peak shaving, price currency swaps, energy recovery, enhanced power quality and reliability, improved productivity, and better environmental performance," according to the report. For all these causes, DG is expected to play a larger role in future electric power systems [Ramalakshmi S., 2010]. The proposed technique for DG power networks in this article can be described in the Fig. 1 . As distributed generation, three distinct power sources are employed in this paper: wind, solar, and micro-turbine. In conversely, there is no restriction on the number of DGs. Initially, metering centres gather data from loads and sources and submit it to FLC. The FLC will then make a comparison the available and required power and send the prompt to the switching centre to allocate the optimal one. 2.1. DG Power System Components Modelling Because all installation sites are not appropriate for the setting up of an effective wind turbine, HOMER software is a useful tool for determining the feasibility of renewable energy sources. The annual average wind speed can be a suitable indicator for installation of a wind turbine at a specific site, and wind speed values above 5m/s are generally considered sufficient for satisfactory results [Dubey C. and Tiwari Y.,2012]. To achieve a reliable source of power from solar panels, the average radiation should have a consistent trend and the annual radiation should be greater than 4kwh/sqm/day. Furthermore, micro turbines are minor gas turbines in which gaseous or liquid fuels are burnt to generate a high energy gas flow which drives the electrical generator. Micro-turbines can start rapidly and are particularly useful in peak electrical supply for utility grid so they are more popular in recent years. 2.2. Grid-connected Vs stand-alone DG As illustrated in Fig. 2 , DG can operate in twin modes: stand-alone and grid-connected. Stand-alone distributed system provides power to local loads straightforwardly through a low voltage bus. They are usually equipped of battery storage. However, as in the instance of directly coupled stand-alone systems, battery storage may not be always present. Grid-connected distributed systems are integrated to regular distribution lines either through Medium Voltage (MV) or Low Voltage (LV) Networks, on the basis of the power ratings and voltage ratings accessible for the systems; to provide the enhanced power needed by the loads. Grid-connected distributed systems could also be utilized to offer reactive power and voltage assistance to the utility grid. So, the DG systems might extent around the distributed system that is linked with grid. Grid-connected systems may also be fortified with or without a power backup choice [Wang C.,2006]. When a system is linked to a utility grid, even so, main operation and performance necessities, such as voltage, frequency and harmonic regulations, are levied. 3. Controller for Fuzzy Logic This paper elaborates on a fuzzy logic controller based on the Mamdani method, which has huge advantages and motives to be used. For example, fuzzy logic is engaged to govern intricate and nonlinear systems without the need for analysis. Second, it is adaptable to any provided system, so that if changes occur in the system, we do not have to restart from the beginning. Fuzzy logic can also be combined with traditional techniques to streamline their operation [Saravanan K.,2012]. $${\text{R}}^{\left(\text{k}\right)}:\text{I}\text{F} {\text{x}}_{1} \text{a}\text{n}\text{d} {\text{x}}_{2} \text{i}\text{s} {\text{F}}^{\text{k}} ,\text{t}\text{h}\text{e}\text{n} {\text{y}}_{1} \text{i}\text{s} {\text{G}}^{\text{k}}, \text{f}\text{o}\text{r} \text{k}=\text{1,2},\dots \dots .,\text{n}$$ 1 Where x 1 - power capacity of altered DGs per unit (p.u.), x 2 - distance in kilometers (Km) between the altered DG sources and the loads, y 1 - priority level of assigning the optimal source to the appropriate load. Where x 1 , x 2 …. x n ε u, and y1 ε R are the i/p & o/p of fuzzy sets in U1, U2 and R representing the kth precursor pairs and decisions pair resp. and n is the rules numbers. Our system's I/P and O/P are an amalgamation of two types member functions. The first in triangular form as termed in Eq. (2) and the other in trapezoidal form as termed in Eq. (3). The I/P and O/P member functions are depicted in Fig. 3 . Defuzzification, on the other hand, employs a number of algorithms. [Saravanan K.2012, Mellit, A.,2007] lists the most commonly used algorithms. trimf (x;a,b,c) = max⁡(min((x-a)/(b-a),(c-x)/(c-b)),0) trimf (x;a,b,c) = max⁡(min((x-a)/(b-a),(c-x)/(c-b)),0) $$\text{t}\text{r}\text{i}\text{m}\text{f} (\text{x};\text{a},\text{b},\text{c})=\text{max}\left(\text{m}\text{i}\text{n}\left(\frac{\text{x}-\text{a}}{\text{b}-\text{a}},\frac{\text{c}-\text{x}}{\text{c}-\text{b}}\right),0\right)$$ (2) $$\text{t}\text{r}\text{a}\text{p}\text{m}\text{f} (\text{x};\text{a},\text{b},\text{c})=\text{max}\left(\text{m}\text{i}\text{n}\left(\frac{\text{x}-\text{a}}{\text{b}-\text{a}},1,\frac{\text{d}-\text{x}}{\text{d}-\text{c}}\right),0\right)$$ (3) 4. Algorithm Proposed and Simulation Results The proposed fuzzy algorithm for distributed generation power networks in this article can be described in Fig. 4 . The flowchart begins by interpretation of the power capacity levels of all accessible DGs as well as the different distances between DGs and loads using smart metering. As previously stated, the brain of FLC is the creation of rule bases. The chief three rule bases will be as shown in equations (4), (5), and (6). Furthermore, Table (1) depicts entirely of our FLC's rule bases, with the first and second columns representing inputs and the third column representing output. If Wind power capacity is High and Distance is Low, then Assign Wind DG (4) If Solar power capacity is High and Distance is Low, then Assign PV DG (5) If Fossil power capacity is High and Distance is Low, then Assign Fossil DG (6) Table (1) shows that two or more DGs may have the similar power capacity and distance. As a result, the proposed technique will face another challenge which requires a suitable solution. In this state, we can improve the fuzzy controller by addition of another comparison input, such as the cost of generated power, where low cost power has a high priority. Table 1 Rule bases for proposed fuzzy controller Power Capability Distance DG Priority Allocation Less Less Less Less Moderate Less Less More Less Moderate Less Moderate Moderate Moderate Less Moderate More Less More Less More More Moderate Moderate More More Less In this case, the DG source with the more power capability, less distance, and least cost will be allocated to the load first. 5. Benefits and savings estimation Global energy consumption is expected to increase by 57% over the next two decades [International Energy Outlook 2005]. A significant increase in installed generating capacity is required to encounter forthcoming universal electricity demand. The global electricity generation capacity of is estimated to rise up to 5495 GW by 2025 [System for the Analysis of Global Energy Markets 2005 ]. To cover the extent of deficiency and overawed the shortcomings of orthodox electricity generation techniques, the permeation of renewable DG technologies into recent power grids improved radically, with many benefits and motives to use them. The modest competitive DG technologies are put forth in Table (2). Table 2 Modest DGs technologies Comparison. Technology Wind Turbine Solar Arrays Fuel Cells Micro-Turbine Size (Output Produced) 0.00030 to 5.0 MW 0.00030 to 2.0 MW 0.0010 to 10.0 MW 0.025 to 0.50 MW Installation Cost (₹/KW) 80000-4,20,000 4,70,000–80,50,000 75,000–4,50,000 90,000–1,50,000 Operation & Maintenance Cost (₹/Kwh) 4-12.8 12–16 4.5 16.5 Electrical Efficiency 26–36% 36–66% 26–46% 06–21% Overall Efficiency 76–81% 81–91% 26–46% 06–21% Type of fuel Wind Sunlight Natural gas, Hydrogen and Hydrogen based fuels Natural gas, liquid fuels HOMER software is employed to analyse wind and solar energy data as shown in Figs. 5 a and 5 b, to demonstrate the competence of proposed algorithm. As shown in Fig. 6 , our proposed system is built in HOMER. After simulating our system with HOMER, we generated a list of distinct arrangements, sorted by the maximum operative total net present cost (NPC). The proposed example formed thirty-six different configurations organized by the most active total NPC. Conversaly, as shown in Fig. 7 , the programme presented the best four configurations. We will compare the first and second configurations to study the efficiency of these configurations. It was discovered that the total NPC of the first simulated system is 1,16,97,950/- while the total NPC of the second simulated system is ₹1,11,37,778. The comparison between electrical production power and sustainability of configurations A and B is shown in Table 3 . Table 3 Proposed DGs configuration A & B comparison Phase Configuration A Configuration B Used DGs Wind + Solar + MT (Diesel Gen.) + Batteries Wind + MT (Diesel Gen.) + Batteries Total NPC (₹) 1,16,97,950 1,11,37,778 Wind (% ) 70.0 74.0 Solar (%) 6.0 0 Diesel (%) 24.0 263.0 COE/KWh(₹) 33.0 31.0 Excess Elec. (%) 30.0 28.0 CO 2 emission (Kg/yr) @18,200 @ 20,300 6. Conclusion This paper describes an optimum fuzzy approach for DG in electrical power distribution systems that incorporates wind and solar resources into traditional electric networks. The fuzzy controller is intended to select the appropriate DG source and assign it to the load. As previously stated, DG technologies present very different research challenges as traditional centralized power sources. Lower cost, higher efficiency, and longer life-time DGs are among these research issues. Finally, HOMER software is employed to determine the best DG configurations based on total NPC. Declarations Conflicts of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution N.T. wrote the main manuscriptK.D. provided his guidence References Ashabani M, Abdel-rady Y, Mohamed I. New Family of Micro-grid Control and Management Strategies in Smart Distribution Grids-Analysis, Comparison and Testing. Power Syst IEEE Trans, Vol. PP, 2014. Azzouz M, Farag H, El-Saadany E. Fuzzy-Based Control of On-Load Tap Changers under High Penetration of Distributed Generators, 3rd International Conference on Electric Power and Energy Conversion Systems in: October 2–4,2013, Yildiz Teclmical University, Istanbul, Turkey, 2013. Dubey C, Tiwari Y. To design solar (photovoltaic) - Wind hybrid power generation system, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) In: November – December 2012, Vol. 1, 2012. International Energy Outlook 2005, Energy Information Administration (EIA), http://www.eia.doe.gov/iea . Mellit A, Benghanem M, Kalogirou SA. Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: proposition for a new sizing procedure. Renewable Energy, 32, 2007. Ramalakshmi S. Optimal Siting and Sizing of Distributed Generation Using Fuzzy-EP, Department of EEE Sethu Institute of Technology Kariapattii-626115, India,2010. System for the Analysis of Global Energy Markets. 2005, EIA, http://www.eia.doe.gov/iea . Saravanan K. Fuzzy controller design of lighting control system by using VI package, International Journal of Artificial Intelligence (IJ-AI) 2012; 5: 73–78, 2012. Vito C. and Optimal Decentralized Voltage Control for Distribution Systems with Inverter-Based Distributed Generators. Power Syst IEEE Trans, 29, 2014. Wang A. Design of power control system for distributed generation system using fuzzy control, Intelligent Computation Technology and Automation (ICICTA) In: 2011, International Conference, Vol. 1, 2011. Wang C. Modeling and Control of Hybrid Wind/Photovoltaic/Fuel Cell Distributed Generation Systems, PhD. Thesis, Montana State University, Bozeman, Montana, 2006. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 03 Jul, 2024 Submission checks completed at journal 03 Jul, 2024 First submitted to journal 25 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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method\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4634514/v1/015255bae9c741005be1cf75.png"},{"id":61098110,"identity":"af512c32-88f3-4666-931c-cb98271eff17","added_by":"auto","created_at":"2024-07-25 14:30:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":106825,"visible":true,"origin":"","legend":"\u003cp\u003ea Data of daily solar radiation (one year)\u003c/p\u003e\n\u003cp\u003eb Data of Wind speed (One Year)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4634514/v1/8118f4453e271efb3ae04872.png"},{"id":61097569,"identity":"e563691b-8dea-4a18-9976-f1d41c788a1a","added_by":"auto","created_at":"2024-07-25 14:22:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":330111,"visible":true,"origin":"","legend":"\u003cp\u003eDG construction using HOMER\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4634514/v1/0e1e82bdf40c58a1e80a19ee.png"},{"id":61097571,"identity":"8780050a-e040-4fde-bec4-b2103123f22c","added_by":"auto","created_at":"2024-07-25 14:22:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":49652,"visible":true,"origin":"","legend":"\u003cp\u003eProposed DGs four optimum configuration\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4634514/v1/f08c32e1050ed0cfb70cfb63.png"},{"id":61098637,"identity":"079b424e-6b98-403e-8218-ce1ee23bdbdf","added_by":"auto","created_at":"2024-07-25 14:38:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1222149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4634514/v1/b367577d-1b38-4e32-bb7e-f7d04c8cf35d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fuzzy Logic and HOMER: Innovations in Distributed Generation for System Stability and Performance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRenewable energy sources (RES) like wind and solar energy are pure, budget - friendly, household and limitless. These are non-polluting energy sources and safer sources of energy for everyone. These benefits prompted inoculating RESs into electrical networks to address the ongoing energy needs and to incorporate alternative solutions to the global energy crunch. In addition to these reasons, interest in distributed generation (DG) sources has increased recently, resulting in a rise in the quantity of generators linked to distribution network.\u003c/p\u003e \u003cp\u003eDG provides atmospheric benefits, enhance reliability, reduces losses, load managements, and provides economic benefits. Notwithstanding, the integration of distributed generation (DG) power stations into distribution networks (DNs) raises a number of concerns such as system steadiness, devices protection, serious network problems risk increment, and switching frequency.\u003c/p\u003e \u003cp\u003eAs a result, the need to develop appropriate control methods to enable power delivery to clients while adhering to power reliability and quality standards has grown into a significant concern in current years This effect has been widely studied in [Vito C. and et al ,2014].\u003c/p\u003e \u003cp\u003eOne of the key traits about loads in electrical networks is that require continuous suitable control in their ability to change. This article suggests an optimised fuzzy strategy for managing the various load demands on the basis of available power sources and chooses the best possible power source relying on two primary variables: the existing power capacity and the distance of the power source to the load centre. Moreover, HOMER software is utilized to investigate the most appropriate renewable DGs which may be utilised on the basis of the ecology of the area and the abundant supply of renewable energy. Because fuzzy control, unlike classical control, does not require complicated mathematical expressions, the number and variety of applications of fuzzy logic have recently increased dramatically. Furthermore, with the necessary knowledge and experience of a skilled operator, it\u0026rsquo;s simple to plan a fuzzy control for nonlinear intricate systems. In [Ashabani M., Abdel-rady Y. and Mohamed I.,2014] an original family of widespread control and management tactics for micro grids in smart distribution grids is presented. In addition, the paper offers an overall framework for modelling and analysing power management strategies in a micro grid with several DG sources. The paper [Azzouz M., Farag H. and El-Saadany E.,2013] unveils a fuzzy algorithm to offer an intuitive reference of the on-load tape selector controller to alleviate the impact of the DG units' high permeation. The article [Wang A.2011] describes a fuzzy approach that is built on a genetic algorithm for distributing power requirements between many currently offered power sources.\u003c/p\u003e"},{"header":"2. System Overview","content":"\u003cp\u003eDistributed generation networks use various energy sources, particularly RES such as solar, wind, and geothermal energy. This system provides a lot of benefits over traditional networks. \"Distributed generation assurances several potential advantages, such as peak shaving, price currency swaps, energy recovery, enhanced power quality and reliability, improved productivity, and better environmental performance,\" according to the report. For all these causes, DG is expected to play a larger role in future electric power systems [Ramalakshmi S., 2010]. The proposed technique for DG power networks in this article can be described in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As distributed generation, three distinct power sources are employed in this paper: wind, solar, and micro-turbine. In conversely, there is no restriction on the number of DGs. Initially, metering centres gather data from loads and sources and submit it to FLC. The FLC will then make a comparison the available and required power and send the prompt to the switching centre to allocate the optimal one.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. DG Power System Components Modelling\u003c/h2\u003e \u003cp\u003eBecause all installation sites are not appropriate for the setting up of an effective wind turbine, HOMER software is a useful tool for determining the feasibility of renewable energy sources. The annual average wind speed can be a suitable indicator for installation of a wind turbine at a specific site, and wind speed values above 5m/s are generally considered sufficient for satisfactory results [Dubey C. and Tiwari Y.,2012]. To achieve a reliable source of power from solar panels, the average radiation should have a consistent trend and the annual radiation should be greater than 4kwh/sqm/day. Furthermore, micro turbines are minor gas turbines in which gaseous or liquid fuels are burnt to generate a high energy gas flow which drives the electrical generator. Micro-turbines can start rapidly and are particularly useful in peak electrical supply for utility grid so they are more popular in recent years.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Grid-connected Vs stand-alone DG\u003c/h2\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, DG can operate in twin modes: stand-alone and grid-connected. Stand-alone distributed system provides power to local loads straightforwardly through a low voltage bus. They are usually equipped of battery storage. However, as in the instance of directly coupled stand-alone systems, battery storage may not be always present.\u003c/p\u003e \u003cp\u003eGrid-connected distributed systems are integrated to regular distribution lines either through Medium Voltage (MV) or Low Voltage (LV) Networks, on the basis of the power ratings and voltage ratings accessible for the systems; to provide the enhanced power needed by the loads. Grid-connected distributed systems could also be utilized to offer reactive power and voltage assistance to the utility grid. So, the DG systems might extent around the distributed system that is linked with grid. Grid-connected systems may also be fortified with or without a power backup choice [Wang C.,2006]. When a system is linked to a utility grid, even so, main operation and performance necessities, such as voltage, frequency and harmonic regulations, are levied.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Controller for Fuzzy Logic","content":"\u003cp\u003eThis paper elaborates on a fuzzy logic controller based on the Mamdani method, which has huge advantages and motives to be used. For example, fuzzy logic is engaged to govern intricate and nonlinear systems without the need for analysis. Second, it is adaptable to any provided system, so that if changes occur in the system, we do not have to restart from the beginning.\u003c/p\u003e \u003cp\u003eFuzzy logic can also be combined with traditional techniques to streamline their operation [Saravanan K.,2012].\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\text{R}}^{\\left(\\text{k}\\right)}:\\text{I}\\text{F} {\\text{x}}_{1} \\text{a}\\text{n}\\text{d} {\\text{x}}_{2} \\text{i}\\text{s} {\\text{F}}^{\\text{k}} ,\\text{t}\\text{h}\\text{e}\\text{n} {\\text{y}}_{1} \\text{i}\\text{s} {\\text{G}}^{\\text{k}}, \\text{f}\\text{o}\\text{r} \\text{k}=\\text{1,2},\\dots \\dots .,\\text{n}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere\u003c/p\u003e \u003cp\u003ex\u003csub\u003e1\u003c/sub\u003e- power capacity of altered DGs per unit (p.u.),\u003c/p\u003e \u003cp\u003ex\u003csub\u003e2\u003c/sub\u003e- distance in kilometers (Km) between the altered DG sources and the loads,\u003c/p\u003e \u003cp\u003ey\u003csub\u003e1\u003c/sub\u003e- priority level of assigning the optimal source to the appropriate load.\u003c/p\u003e \u003cp\u003eWhere x\u003csub\u003e1\u003c/sub\u003e, x\u003csub\u003e2\u003c/sub\u003e\u0026hellip;. x\u003csub\u003en\u003c/sub\u003e ε u, and \u003csub\u003ey1\u003c/sub\u003e ε R are the i/p \u0026amp; o/p of fuzzy sets in U1, U2 and R representing the kth precursor pairs and decisions pair resp. and n is the rules numbers.\u003c/p\u003e \u003cp\u003eOur system's I/P and O/P are an amalgamation of two types member functions. The first in triangular form as termed in Eq.\u0026nbsp;(2) and the other in trapezoidal form as termed in Eq.\u0026nbsp;(3). The I/P and O/P member functions are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Defuzzification, on the other hand, employs a number of algorithms. [Saravanan K.2012, Mellit, A.,2007] lists the most commonly used algorithms.\u003c/p\u003e \u003cp\u003etrimf (x;a,b,c)\u0026thinsp;=\u0026thinsp;max⁡(min((x-a)/(b-a),(c-x)/(c-b)),0)\u003c/p\u003e \u003cp\u003etrimf (x;a,b,c)\u0026thinsp;=\u0026thinsp;max⁡(min((x-a)/(b-a),(c-x)/(c-b)),0)\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{t}\\text{r}\\text{i}\\text{m}\\text{f} (\\text{x};\\text{a},\\text{b},\\text{c})=\\text{max}\\left(\\text{m}\\text{i}\\text{n}\\left(\\frac{\\text{x}-\\text{a}}{\\text{b}-\\text{a}},\\frac{\\text{c}-\\text{x}}{\\text{c}-\\text{b}}\\right),0\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e(2)\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\text{t}\\text{r}\\text{a}\\text{p}\\text{m}\\text{f} (\\text{x};\\text{a},\\text{b},\\text{c})=\\text{max}\\left(\\text{m}\\text{i}\\text{n}\\left(\\frac{\\text{x}-\\text{a}}{\\text{b}-\\text{a}},1,\\frac{\\text{d}-\\text{x}}{\\text{d}-\\text{c}}\\right),0\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Algorithm Proposed and Simulation Results","content":"\u003cp\u003eThe proposed fuzzy algorithm for distributed generation power networks in this article can be described in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The flowchart begins by interpretation of the power capacity levels of all accessible DGs as well as the different distances between DGs and loads using smart metering.\u003c/p\u003e \u003cp\u003eAs previously stated, the brain of FLC is the creation of rule bases. The chief three rule bases will be as shown in equations (4), (5), and (6). Furthermore, Table\u0026nbsp;(1) depicts entirely of our FLC's rule bases, with the first and second columns representing inputs and the third column representing output.\u003c/p\u003e \u003cp\u003eIf Wind power capacity is High and Distance is Low, then Assign Wind DG (4)\u003c/p\u003e \u003cp\u003eIf Solar power capacity is High and Distance is Low, then Assign PV DG (5)\u003c/p\u003e \u003cp\u003eIf Fossil power capacity is High and Distance is Low, then Assign Fossil DG (6)\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(1) shows that two or more DGs may have the similar power capacity and distance. As a result, the proposed technique will face another challenge which requires a suitable solution. In this state, we can improve the fuzzy controller by addition of another comparison input, such as the cost of generated power, where low cost power has a high priority.\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\u003eRule bases for proposed fuzzy controller\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePower Capability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDG Priority Allocation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMore\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this case, the DG source with the more power capability, less distance, and least cost will be allocated to the load first.\u003c/p\u003e"},{"header":"5. Benefits and savings estimation","content":"\u003cp\u003eGlobal energy consumption is expected to increase by 57% over the next two decades [International Energy Outlook 2005]. A significant increase in installed generating capacity is required to encounter forthcoming universal electricity demand. The global electricity generation capacity of is estimated to rise up to 5495 GW by 2025 [System for the Analysis of Global Energy Markets \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo cover the extent of deficiency and overawed the shortcomings of orthodox electricity generation techniques, the permeation of renewable DG technologies into recent power grids improved radically, with many benefits and motives to use them. The modest competitive DG technologies are put forth in Table\u0026nbsp;(2).\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\u003eModest DGs technologies Comparison.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eWind Turbine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolar Arrays\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFuel Cells\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMicro-Turbine\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003cp\u003e(Output Produced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00030 to 5.0 MW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00030 to 2.0 MW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0010 to 10.0 MW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025 to 0.50 MW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstallation Cost\u003c/p\u003e \u003cp\u003e(₹/KW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80000-4,20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,70,000\u0026ndash;80,50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75,000\u0026ndash;4,50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90,000\u0026ndash;1,50,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation \u0026amp; Maintenance Cost\u003c/p\u003e \u003cp\u003e(₹/Kwh)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4-12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u0026ndash;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectrical Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026ndash;36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u0026ndash;66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u0026ndash;46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e06\u0026ndash;21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76\u0026ndash;81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u0026ndash;91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u0026ndash;46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e06\u0026ndash;21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of fuel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSunlight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNatural gas, Hydrogen and Hydrogen based fuels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNatural gas,\u003c/p\u003e \u003cp\u003eliquid fuels\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHOMER software is employed to analyse wind and solar energy data as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, to demonstrate the competence of proposed algorithm. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003e, our proposed system is built in HOMER.\u003c/p\u003e \u003cp\u003eAfter simulating our system with HOMER, we generated a list of distinct arrangements, sorted by the maximum operative total net present cost (NPC). The proposed example formed thirty-six different configurations organized by the most active total NPC. Conversaly, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the programme presented the best four configurations.\u003c/p\u003e \u003cp\u003eWe will compare the first and second configurations to study the efficiency of these configurations. It was discovered that the total NPC of the first simulated system is 1,16,97,950/- while the total NPC of the second simulated system is ₹1,11,37,778. The comparison between electrical production power and sustainability of configurations A and B is shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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\u003eProposed DGs configuration A \u0026amp; B comparison\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConfiguration A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConfiguration B\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsed DGs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWind\u0026thinsp;+\u0026thinsp;Solar\u0026thinsp;+\u0026thinsp;MT (Diesel Gen.)\u0026thinsp;+\u0026thinsp;Batteries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWind\u0026thinsp;+\u0026thinsp;MT (Diesel Gen.)\u0026thinsp;+\u0026thinsp;Batteries\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal NPC (₹)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,16,97,950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,11,37,778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind (% )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolar (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiesel (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOE/KWh(₹)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcess Elec. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e emission (Kg/yr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e@18,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e@ 20,300\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":"6. Conclusion","content":"\u003cp\u003eThis paper describes an optimum fuzzy approach for DG in electrical power distribution systems that incorporates wind and solar resources into traditional electric networks. The fuzzy controller is intended to select the appropriate DG source and assign it to the load. As previously stated, DG technologies present very different research challenges as traditional centralized power sources. Lower cost, higher efficiency, and longer life-time DGs are among these research issues. Finally, HOMER software is employed to determine the best DG configurations based on total NPC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.T. wrote the main manuscriptK.D. provided his guidence\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAshabani M, Abdel-rady Y, Mohamed I. New Family of Micro-grid Control and Management Strategies in Smart Distribution Grids-Analysis, Comparison and Testing. Power Syst IEEE Trans, Vol. PP, 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzzouz M, Farag H, El-Saadany E. Fuzzy-Based Control of On-Load Tap Changers under High Penetration of Distributed Generators, 3rd International Conference on Electric Power and Energy Conversion Systems in: October 2\u0026ndash;4,2013, Yildiz Teclmical University, Istanbul, Turkey, 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDubey C, Tiwari Y. To design solar (photovoltaic) - Wind hybrid power generation system, International Journal of Emerging Trends \u0026amp; Technology in Computer Science (IJETTCS) In: November \u0026ndash; December 2012, Vol. 1, 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Energy Outlook 2005, Energy Information Administration (EIA), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.eia.doe.gov/iea\u003c/span\u003e\u003cspan address=\"http://www.eia.doe.gov/iea\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMellit A, Benghanem M, Kalogirou SA. Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: proposition for a new sizing procedure. Renewable Energy, 32, 2007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamalakshmi S. Optimal Siting and Sizing of Distributed Generation Using Fuzzy-EP, Department of EEE Sethu Institute of Technology Kariapattii-626115, India,2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSystem for the Analysis of Global Energy Markets. 2005, EIA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.eia.doe.gov/iea\u003c/span\u003e\u003cspan address=\"http://www.eia.doe.gov/iea\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaravanan K. Fuzzy controller design of lighting control system by using VI package, International Journal of Artificial Intelligence (IJ-AI) 2012; 5: 73\u0026ndash;78, 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVito C. and Optimal Decentralized Voltage Control for Distribution Systems with Inverter-Based Distributed Generators. Power Syst IEEE Trans, 29, 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang A. Design of power control system for distributed generation system using fuzzy control, Intelligent Computation Technology and Automation (ICICTA) In: 2011, International Conference, Vol. 1, 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C. Modeling and Control of Hybrid Wind/Photovoltaic/Fuel Cell Distributed Generation Systems, PhD. Thesis, Montana State University, Bozeman, Montana, 2006.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-energy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dien","sideBox":"Learn more about [Discover Energy](https://www.springer.com/43937)","snPcode":"","submissionUrl":"","title":"Discover Energy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Distributed generation, Fuzzy, HOMER, Wind, Solar Energy","lastPublishedDoi":"10.21203/rs.3.rs-4634514/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4634514/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe HOMER software is described in this article to provide the efficient distributed generation configurations. This is based on nature of the area and its renewable energy availability. This software is used to develop and assess alternatives for stand-alone and grid-connected power distributed generation applications, both technically and financially. It enables the consideration of a huge range of technological alternatives. It\u0026rsquo;s a powerful tool for simulating both the traditional and sustainable energy techniques. Furthermore, in distributed generation electric networks, a fuzzy-based strategy is suggested to choose the suitable power source and ascribe it to the appropriate load. Fuzzy Logic Control is an effective approach to resolve complex problems using algebraic calculations Moreover, sustainable energy distributed generation has grown rapidly as large central power plants have become financially unviable because of substantial decreases in fossil fuels, rising fuel costs, global energy crunch and the emergence of environment degradation problems. Nevertheless, the integration of distributed power generation into conventional electric power networks has influence on their consistency, safeness, and power feature. As a result, inoculation of distributed power generation necessitates the use of adequate controllers to preserve system stability and performance.\u003c/p\u003e","manuscriptTitle":"Fuzzy Logic and HOMER: Innovations in Distributed Generation for System Stability and Performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-25 14:22:02","doi":"10.21203/rs.3.rs-4634514/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-07-03T08:00:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-03T08:00:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Energy","date":"2024-06-25T07:45:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-energy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dien","sideBox":"Learn more about [Discover Energy](https://www.springer.com/43937)","snPcode":"","submissionUrl":"","title":"Discover Energy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e50471d-02c8-45e8-9158-107281cb1bb3","owner":[],"postedDate":"July 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-25T14:22:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-25 14:22:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4634514","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4634514","identity":"rs-4634514","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}