Energy, Vol.144, 404-419, 2018
GPNBI inspired MOSDE for electric power dispatch considering wind energy penetration
This paper aims to solve wind energy integrated electric power dispatch (EPD) problem from the perspectives of stochastic modelling and multiobjective optimization. At first, the competing objectives in modern electric power and energy system is analyzed and a new interval optimization model for each objective is proposed based on the uncertainties with respect to wind power. Then, a novel strength differential evolution (SDE) algorithm is developed to address the interval optimization model. The SDE adopts a population selection process based on chaotic sequence and Boltzmann distribution to balance the tradeoff between local exploitation and global exploration. Afterwards, a multiobjective EPD model is established and a novel generalized piecewise normal boundary intersection (GPNBI) method is mooted to transform multiobjective EPD into a series of single-objective sub-problems which can be effectively solved by SDE. In order to deal with the highly constrains in GPNBI, a new heuristic constraint handling strategy is proposed accordingly to accelerate the convergence speed. At last, a hyper-plane based decision-making strategy is originally developed to identify the best compromise solution in the obtained Pareto frontiers (PFs). The feasibility and effectiveness of the interval optimization model and GPNBI inspired multiobjective SDE (MOSDE) have been comprehensively evaluated on a modified IEEE 30-bus system and a 118-bus system. The statistical results confirm that the proposed interval optimization model can approximately quantify the potential uncertainty in each objective and also demonstrate that the proposed MOSDE exhibits better performance than the algorithms of the state. Therefore, it is convinced that the proposed optimization model and method have high potentials to address the practical implementation problems in electric power and energy systems with wind energy penetration. (C) 2017 Elsevier Ltd. All rights reserved.