Abstract
The environmental/economic dynamic scheduling for microgrids (MGs) is a complex multiobjective optimization problem, which usually has dynamic system parameters and constraints. In this paper, a biobjective optimization model of MG scheduling is established. And various types of microsources (like the conventional sources, various types of renewable sources, etc.), electricity markets, and dynamic constraints are considered. A recently proposed MOEA/D-M2M framework is improved (I-MOEA/D-M2M) to solve the real-world MG scheduling problems. In order to deal with the constraints, the processes of solutions sorting and selecting in the original MOEA/D-M2M are revised. In addition, a self-adaptive decomposition strategy and a modified allocation method of individuals are introduced to enhance the capability of dealing with uncertainties, as well as reduce unnecessary computational work in practice and meet the time requirements for the dynamic optimization tasks. Thereafter, the proposed I-MOEA/D-M2M is applied to the independent MG scheduling problems, taking into account the load demand variation and the electricity price changes. The simulation results by MATLAB show that the proposed method can achieve better distributed fronts in much less running time than the typical multiobjective evolutionary algorithms (MOEAs) like the improved strength Pareto evolutionary algorithm (SPEA2) and the nondominated sorting genetic algorithm II (NSGAII). Finally, I-MOEA/D-M2M is used to solve a 24-hour MG dynamic operation scheduling problem and obtains satisfactory results.
Highlights
The energy shortages and environmental pollution have given an impetus to the development of the microgrids (MGs), which are more flexible, energy saving, and environmentally friendly [1,2,3,4]
It is evident that I-multiobjective evolutionary algorithms (MOEAs)/D-M2M performs well in all the three cases, the solutions obtained are distributed uniformly in the objective space, and it can be seen from Figures 5(a), 5(d), and 5(g) that the fronts are quite different in the three multiobjective optimization problems (MOPs)
An improved MOEA/D-M2M algorithm is proposed for solving the dynamic MG environmental/economic scheduling problem
Summary
The energy shortages and environmental pollution have given an impetus to the development of the microgrids (MGs), which are more flexible, energy saving, and environmentally friendly [1,2,3,4]. Several studies have been made on the improvement of the nonlinear optimization methods, such as dual and quadratic programming [7], and their application on MOPs of scheduling for all kinds of power systems These methods cannot handle nonconvex objective functions [8]. (2) In a real-world MG dynamic scheduling problem, there are always dynamic changes in system parameters (like the electricity prices) and constraints (such as the load demand), as a result of which the search space varies with time and the optimization algorithm may not always work well. Considering the above challenges, the optimization approaches in handling the MOPs of MG dynamic scheduling should obtain the set of solutions as close to the true Pareto front as possible every time, which means the robustness of the algorithm performance is highly required under uncertainties.
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