Multi‐objective Optimization of Optimal Placement and Sizing of Distributed Generators in Distribution Networks

Abstract

Due to the growing attention for the environmental impacts and power loss minimization, distributed generators (DGs) have been introduced widely into the electric power system. One of the challenges of integrating with the power system is to determine their optimal placement and sizing, which, when not respected, adversely affects the performance of the electrical network. In this paper, three multi‐objective algorithms of particle swarm optimization (PSO), variable constants (VCPSO) and genetic algorithm (GA) are adopted and implemented. The main objectives are to detect the optimum size and location of multiple DGs aiming to reduce the active power loss and improve bus voltage deviations in the distribution networks. The paper conducts a comprehensive review of the optimal size and location of the DG via systematic procedures, including definition, classifications, technologies of DGs. Then, the performances evaluation of the three optimization methods are presented and compared with other methods. The presented optimization methods are tested on the IEEE‐33 bus, 32‐line radial distribution network. Four different scenario‐based studies including base case and different number of DGs are performed to examine the accuracy of the presented algorithms. The obtained results prove that all the three algorithms are suitable for this multi‐objective optimization and VCPSO offers the best solution in terms of convergence and, and it has lowest average computation time. The performance and accuracy of the presented approaches and their improvements in the power loss and bus voltage profile are discussed and presented in detail. The obtained results show that more than 65% of active power loss reduction has been attained with the proper sizing and placement of DG systems. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO

“…Constraints ( 14) and ( 15) are similar to (12) and ( 13) but from the perspective of discharging action. Constraints (17) and (18) prevent the simultaneous charging and discharging event at time t by introducing a binary decision variable, u t . Lastly, the model assumes that the BESS can only be charged from either the solar PV or the wind, as given in Constraint (19…”

Strategic Sizing and Placement of Distributed Generation in Radial Distributed Networks Using Multiobjective PSO

“…The intermittent nature of renewable DGs, such as wind and solar, can introduce uncertainty and variability into the power system [18] (i) Three optimization techniques, PSO, variable constraint PSO (VCPSO), and GA algorithms, are applied to find the optimal size and placement of multiple DGs integrated into electrical power network. VCPSO was offered an improved solution for the optimal placement and size of DGs in terms of the accuracy of the global optimality (ii) Draw back: did not address the application of the hybrid PSO algorithm in real-world distribution networks [19] (i) Cost-based analysis was used on distributed generators (DGs), to determine installation costs, operational costs, and maintenance costs. The objective of the analysis was to minimize losses and maximize the loading capability of the system while ensuring that voltage stability is not compromised (ii) Research gaps: assumed a constant factor of 0.95 as power factor for DG operation which is not the case in real-life situations; the power factor of DGs may vary depending on various factors such as load conditions, system requirements, and control strategies [20] (i) Crisscross optimization algorithm and Monte Carlo simulation method (CSO MCS), used to address the optimal distributed generation allocation (ODGA) problem.…”

“…Some literature focus on technical aspects [8][9][10][11][12][13][14], economic impacts [15][16][17], and environmental considerations [18]. Some literature considers a combination of both [16,19,20]. Various methods exist in the literature for solving the DG siting and sizing problem, each with its own advantages and disadvantages.…”

Optimal Placement of Distributed Energy Resources to Reduce Losses, Improve Voltage Profile, and Convert it into a Self-healing Smart Grid

“…This is realized by minimizing the loss and improving the voltage stability using probabilistic nonlinear optimization. The study [13] employs three multi-objective algorithms to determine the optimal placement and quantity of DGs in power systems. The effectiveness of this approach is evaluated Journal Green Energy Research and Innovation 1(1) (2024) 16-33 using the standard IEEE 33-bus distribution network, and the results demonstrate a notable decrease in active power losses by strategically placing DGs, thereby enhancing system performance.…”