| Abstrakt: |
• Optimal planning of dispatchable distributed generators in distribution systems. • Distributed generators are designed to lower annual economic costs, improve voltage profiles, and reduce system energy loss. • Probability density functions are used to address the uncertainty in solar radiation and wind speed. • The multi-objective velocity-based butterfly optimization algorithm is implemented. • The most compromised solution from the optimal front is found using the TOPSIS approach. Nowadays, because of the enormous increase in load demand, the electrical distribution system faces problems like poor system efficiency due to high I2R losses and poor voltage profile. Therefore, distribution system operators are looking for various alternatives for enhancing system efficiency & voltage profile. Distributed generation (DGs) technology has recently been the focus of several researchers due to its enormous technological advantages in mitigating the above problems. In this article, an approach is presented for the optimal integration of dispatchable distributed generations (DDG): PV-BESS (Photovoltaic System-Battery energy storage system), WT-Biomass (Wind Turbine) units in the distribution system in the presence of optimal network reconfiguration. Distribution generations like PV & WT are non-dispatchable in nature due to the intermittency nature of solar radiance and wind speed. The PV unit is supported by BESS, while the WT unit is supported by Biomass to make the PV and WT units dispatchable. Therefore, the paper's main intent is to determine the best locations & best sizes of PV-BES, Wind -Biomass units in the distribution system in the presence of network reconfiguration considering the time-varying 24-hour load pattern, probabilistic nature of solar irradiance & wind speed. To reduce system energy loss, voltage deviation index, and annual economic loss, a multi-objective pareto-based velocity butterfly optimization algorithm (MOVBOA) is used. IEEE 33,69 & 118 bus test systems are being used to implement the proposed approach. The MOVBOA algorithm gives better results for solving the problem than the multi-objective Butterfly optimization algorithm (MOBOA) & Non-dominated sorting genetic algorithm (NSGA-II). [ABSTRACT FROM AUTHOR] |
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