An Improved Bare Bone Multi-Objective Particle Swarm Optimization Algorithm for Solving Stochastic Wind-Solar-Small Hydro Power Dispatch Problems

Qun Niu, Lipeng Tang, Zhile Yang

Abstract: Substantive consumptions of fossil fuels in thermal power generation have generated astronomical amount of greenhouse gas emission in the past centuries, leading to the climate change crisis that the mankind is currently facing. From the environmental sustainability perspective, it is critical to reduce the green-house gas emissions, utilizing as much renewable energies as possible. The economic emission dispatch (EED) of power systems with significant penetration of renewable generation has attracted considerable interests in recent years. In this paper, an improved multi-objective bare-bones particle swarm optimization (IBBMOPSO) is proposed to solve the multi-objective economic and environmental dispatching (EED) problem of power systems with random wind energy, solar energy, and small hydropower while considering the network security constraints. This algorithm uses a parameter-free Gauss sampling formula instead of traditional speed and position updates. A Levy-Cauchy hybrid mutation operation is introduced to improve the global search ability of the algorithm. To overcome the shortcomings of the endpoint crossover processing mechanism in the IBBMOPSO algorithm, a random disturbance crossover processing mechanism is proposed. IBBMOPSO selects the global optimum solution according to the particle crowding distance. A constraint handling method namely the superiority of feasible solutions (SF) is introduced. The proposed IBBMOPSO is used to optimize the multi-objective economic emission dispatching problems for power systems with stochastic wind energy, solar energy, and small hydropower, and simulation results show that the proposed method outperforms popular MOPSO, NSGAII, and SAMOCE methods proposed in the literature.