Multiobjective Framework for Optimal Integration of Solar Energy Source in Three-Phase Unbalanced Distribution Network

Recently, there is a global consent regarding the high penetration of solar energy (SE) resources to mitigate technical and environmental concerns. SE is intermittent in nature and if its incorporation is not properly planned, a multitude of serious technical issues will arise in the network. In this perspective, considering the energy industry's practical aspects, a comprehensive robust practical planning model is presented in this article to optimally integrate intermittent SE source with battery and DSTATCOM to maximize reliability and financial, environmental, and technical benefit of the system. Voltage unbalance in the system creates varieties of problems, such as poor power quality, higher power loss, and lesser system efficiency. However, owing to the lack of proper industrial real data, researchers were unable to judge and incorporate the voltage unbalance issues, which are significant for the planning engineers to provide efficient solutions. Keeping this into view, the proposed method is tested on the real three-phase unbalanced 240-node network of the USA where the customers are equipped with smart meters to provide real power data for realistic analysis. K-means clustering algorithm is utilized to generate the appropriate number of scenarios that can accurately include the intermittency of solar power generation and load. A multiobjective algorithm, namely the extended version of NSGA II (E_NSGA II), is utilized for the optimal allocation of the devices. Compare to other algorithms, results reveal the competence and robustness of the proposed planning methodology for the optimal allocation of SE with battery storage and DSTATCOM in order to maximize the overall benefits of the three-phase network.