Optimal integration of distributed generation resources in active distribution networks for techno-economic benefits

Abstract In recent years, modern electricity utilities face great challenges regarding the deregulated energy markets, transition toward sustainable smart grids and the increased load demand. These challenges are a part of the reasons that have paved the way toward the rapid progress of spreading the different Distributed Generation (DG) technologies into the modern Distribution Networks (DNs). DGs integration into DNs can be employed as a key solution for tackling the problems, facing the distribution systems, and verifying more technical and economic benefits while considering the systems’ uncertainties and the operational policies of the distribution utilities. This paper introduces the application of Modified Sine Cosine Algorithm (MSCA) for enhancing the DNs performance through the integration of multiple DG technologies in order to optimize the active power losses, the fast voltage stability index and the total costs, considering the DGs penetration level as well as the DG units’ operating power factor constraints. The proposed algorithm has been implemented using MATLAB software and applied on three-benchmark IEEE test systems (30-bus, 33-bus and 300-bus) as different models of electric power networks. The attained results show that the suggested optimization platform especially using MSCA, is more effective and successful in determining and finding better results than existing results.

[1]  Hassan M. H. Farh,et al.  A Novel Crow Search Algorithm Auto-Drive PSO for Optimal Allocation and Sizing of Renewable Distributed Generation , 2020, IEEE Access.

[2]  Pierluigi Siano,et al.  A Two-Loop Hybrid Method for Optimal Placement and Scheduling of Switched Capacitors in Distribution Networks , 2020, IEEE Access.

[3]  Vivek Shrivastava,et al.  Artificial immune system based approach for size and location optimization of distributed generation in distribution system , 2019, Int. J. Syst. Assur. Eng. Manag..

[4]  Hadi Saadat,et al.  Power System Analysis , 1998 .

[5]  Roman Kuiava,et al.  Loading Margin Sensitivity in Relation to the Wind Farm Generation Power Factor for Voltage Preventive Control , 2019, Journal of Control, Automation and Electrical Systems.

[6]  Salah Kamel,et al.  Performance Assessment of a Realistic Egyptian Distribution Network Including PV Penetration with DSTATCOM , 2019, 2019 International Conference on Innovative Trends in Computer Engineering (ITCE).

[7]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[8]  Ismail Musirin,et al.  Pareto optimal approach in Multi-Objective Chaotic Mutation Immune Evolutionary Programming (MOCMIEP) for optimal Distributed Generation Photovoltaic (DGPV) integration in power system , 2019 .

[9]  Nadarajah Mithulananthan,et al.  AN ANALYTICAL APPROACH FOR DG ALLOCATION IN PRIMARY DISTRIBUTION NETWORK , 2006 .

[10]  Shelly Vadhera,et al.  Probabilistic Approach to Determine Penetration of Hybrid Renewable DGs in Distribution Network Based on Voltage Stability Index , 2020, Arabian Journal for Science and Engineering.

[11]  Almoataz Y. Abdelaziz,et al.  Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations , 2017 .

[12]  E. A. Mohamed,et al.  Ant-lion Optimizer Based Optimal Allocation of Distributed Generators in Radial Distribution Networks , 2017 .

[13]  Ravi Shankar Pandey,et al.  A Multi-objective Hybrid Algorithm for Optimal Planning of Distributed Generation , 2020 .

[14]  Temitope Raphael Ayodele,et al.  Impact of distributed generators on the power loss and voltage profile of sub-transmission network , 2016 .

[15]  Sudipta De,et al.  Optimum combination of renewable resources to meet local power demand in distributed generation: A case study for a remote place of India , 2020 .

[16]  Nand K. Meena,et al.  Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks , 2020, Applied Energy.

[17]  Salah Kamel,et al.  Optimal allocation of distribution static compensators using a developed multi-objective sine cosine approach , 2020, Comput. Electr. Eng..

[18]  Adel Khedher,et al.  Integration of distributed generation in electrical grid: Optimal placement and sizing under different load conditions , 2019, Comput. Electr. Eng..

[19]  Isaac Kofi Otchere,et al.  An investigative study on penetration limits of distributed generation on distribution networks , 2017, 2017 IEEE PES PowerAfrica.

[20]  T. Ananthapadmanabha,et al.  Optimal Allocation of Combined DG and Capacitor Units for Voltage Stability Enhancement , 2015 .

[21]  Ashwani Kumar,et al.  Comparison of optimal DG allocation methods in radial distribution systems based on sensitivity approaches , 2013 .

[22]  V. C. Veera Reddy,et al.  Optimal renewable resources placement in distribution networks by combined power loss index and whale optimization algorithms , 2018 .

[23]  B. Kroposki,et al.  Steady-State Analysis of Maximum Photovoltaic Penetration Levels on Typical Distribution Feeders , 2013, IEEE Transactions on Sustainable Energy.

[24]  Hany S. E. Mansour,et al.  Optimal Allocation and Hourly Scheduling of Capacitor Banks Using Sine Cosine Algorithm for Maximizing Technical and Economic Benefits , 2019, Electric Power Components and Systems.

[25]  Abdelazeem A. Abdelsalam,et al.  Optimal Distributed Energy Resources Allocation for Enriching Reliability and Economic Benefits Using Sine-Cosine Algorithm , 2020, Technology and Economics of Smart Grids and Sustainable Energy.

[26]  Akbar Bayat,et al.  Optimal active and reactive power allocation in distribution networks using a novel heuristic approach , 2019, Applied Energy.

[27]  Hossein Shayeghi,et al.  Simultaneous placement of renewable DGs and protective devices for improving the loss, reliability and economic indices of distribution system with nonlinear load model , 2018, International Journal of Ambient Energy.

[28]  A. Halog,et al.  Estimating the impacts of financing support policies towards photovoltaic market in Indonesia: A social-energy-economy-environment model simulation. , 2019, Journal of environmental management.

[29]  S. Tamilselvi,et al.  Optimum placement of multi type DG units for loss reduction in a radial distribution system considering the distributed generation , 2018 .

[30]  M. S. Sujatha,et al.  Multiple DG Placement and Sizing in Radial Distribution System Using Genetic Algorithm and Particle Swarm Optimization , 2018, Computational Intelligence and Big Data Analytics.

[31]  K. Ravi,et al.  Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using Bacterial Foraging Optimization Algorithm , 2016 .

[32]  Usharani Raut,et al.  A new Pareto multi-objective sine cosine algorithm for performance enhancement of radial distribution network by optimal allocation of distributed generators , 2020, Evol. Intell..

[33]  Chee Wei Tan,et al.  Optimal sizing of an autonomous photovoltaic/wind/battery/diesel generator microgrid using grasshopper optimization algorithm , 2019, Solar Energy.

[34]  Salem Arif,et al.  Optimal Location and Size of Wind Source in Large Power System for Losses Minimization , 2019 .

[35]  Kusum Deep,et al.  A modified Sine Cosine Algorithm with novel transition parameter and mutation operator for global optimization , 2020, Expert Syst. Appl..

[36]  Bruno Henriques Dias,et al.  Optimal distributed generation allocation in unbalanced radial distribution networks via empirical discrete metaheuristic and steepest descent method , 2020 .

[37]  Mehmet Emin Meral,et al.  Current control based power management strategy for distributed power generation system , 2019, Control Engineering Practice.

[38]  Yi Tan,et al.  Linearizing Power Flow Model: A Hybrid Physical Model-Driven and Data-Driven Approach , 2020, IEEE Transactions on Power Systems.

[39]  P. Sujatha,et al.  Cost–benefit analysis for optimal DG placement in distribution systems by using elephant herding optimization algorithm , 2019 .