GPNBI-inspired MOSFA for Pareto operation optimization of integrated energy system

Abstract

With the rapid growth of penetration of distributed generations and the strong interdependencies of multiple energy sources, the optimal operation of integrated energy systems (IESs) is becoming more important than ever before. However, current optimal operation strategies for IESs may be impractical because their potential negative impacts on utility grids are generally not considered. Therefore, in this study, an original multiobjective optimization model for IES operation is formulated to minimize the operational cost, primary energy consumption, and carbon dioxide emission of IESs and to optimally reduce the power loss and voltage magnitude deviation of the utility grid. Then, a novel Pareto optimization algorithm, called multiobjective strength firefly algorithm (MOSFA), is proposed to solve the multiobjective operation problem of IES. The strength firefly algorithm (SFA) uses a Boltzmann distribution and a chaotic-sequence-based population selection process to facilitate local exploitation and global exploration. In addition, a generalized piecewise normal boundary intersection (GPNBI) method is developed to transform the multiobjective operation problem into a series of highly constrained single-objective optimization sub-problems that can be effectively solved by the SFA. Finally, a hyper-plane-based decision making strategy is introduced to identify the best compromise solution for the obtained Pareto frontiers. The GPNBI-inspired MOSFA was comprehensively evaluated on a novel IES powered by natural gas, wind and photovoltaic generators. The standard IEEE 39-bus system is considered as the utility grid. The numerical results demonstrated that the proposed MOSFA exhibits competitive performance when compared to the algorithms of the state, which means that the optimized operation strategy provides a better trade-off between all objectives considered in this study.