Optimal stochastic power flow using enhanced multi-objective mayfly algorithm
Jianjun Zhu,
Yongquan Zhou,
Yuanfei Wei,
Qifang Luo,
Huajuan Huang
Affiliations
Jianjun Zhu
College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China
Yongquan Zhou
College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China; Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia; Xiangsihu College of Gunagxi University for Nationalities, Nanning, Guangxi, 532100, China; Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, 530006, China; Corresponding author. College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China.
Yuanfei Wei
Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia; Xiangsihu College of Gunagxi University for Nationalities, Nanning, Guangxi, 532100, China; Corresponding author. Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia.
Qifang Luo
College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China; Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, 530006, China
Huajuan Huang
College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China; Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, 530006, China
For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the thermal generators in the system with wind turbines and solar photovoltaics (PV) generators. The optimization objectives of MOOPF with renewable energy sources vary with the study case. They are mainly a combination of 2–4 objectives from fuel cost, emissions, power loss and voltage deviation (VD). In addition, reasonable prediction of renewable power is a major difficulty due to the discontinuous, disordered and unstable nature of renewable energy. In this paper, the Weibull probability distribution function (PDF) and lognormal PDF are applied to evaluate the available wind and available solar power, respectively. In this paper, an enhanced multi-objective mayfly algorithm (NSMA-SF) based on non-dominated sorting and the superiority of feasible solutions is implemented to tackle the MOOPF problem with wind and solar energy. The algorithm NSMA-SF is applied to the modified IEEE-30 and standard IEEE-57 bus test systems. The simulation results are analyzed and compared with the recently reported MOOPF results.
