Stochastic operation of Volt-VAr optimization and network reconfiguration-based energy management under different loading patterns

This article proposes a conservation voltage reduction (CVR), Var optimization (VO), and network reconfiguration (NR)-based energy management technique. The main focus of this article is to maximize substation demand reduction, annual cost savings, voltage improvement, and node voltage optimization under different loading patterns. The proposed energy management method has been solved using 2 different optimization methods. Optimal tap setting of on-load tap changer (OLTC) while applying CVR and VO has been performed using crayfish optimization algorithm (COA), whereas NR has been carried out using binary particle swarm optimization (BPSO). The proposed scheme has been tested on the IEEE-69 node system, assessing various cases, including no control, with CVR alone, with CVR and VO, with CVR and NR, and with CVR, VO, and NR. Simulation results show that the combined operation of CVR, VO, and NR leads to improved outcomes in reducing substation demand and achieving cost savings. Furthermore, the proposed scheme has been tested for different network loading conditions, and improved results are obtained compared to other cases. Additionally, the efficacy of the COA has been compared to existing optimization algorithms under higher loading patterns. The COA algorithm converges to a minimal value of 0.2160 promptly in 22.47 s.