Dynamic optimal network reconfiguration under photovoltaic generation and electric vehicle fleet load variability using self-adaptive butterfly optimization algorithm
butterfly optimization algorithm; dynamic reconfiguration; electric vehicle fleet; electrical distribution network; power loss; renewable energy
Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it's improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values. 2021 Walter de Gruyter GmbH, Berlin/Boston 2021.
International Journal of Emerging Electric Power Systems, Vol-22, No. 4, pp. 423-437.
https://doi.org/10.1515/ijeeps-2021-0009 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107230791&doi=10.1515%2fijeeps-2021-0009&partnerID=40&md5=3b3a492733af519b75a5c6a5eb7b4399
Pandraju T.K.S., Department of Electrical and Electronics Engineering, Dhanekula Institute of Engineering and Technology, Andhra Pradesh, Vijayawada, 521 139, India; Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560 074, India