Dynamic Environmental/Economic Scheduling for Microgrid Using Improved MOEA/D-M2M

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.

A Hybrid Constraints Handling Strategy for Multiconstrained Multiobjective Optimization Problem of Microgrid Economical/Environmental Dispatch

“…The structure of the typical MG used in this paper is shown in Figure 1. It can be seen that two kinds of uncontrollable DGs, namely, photovoltaic cells (PVs) and wind turbines (WTs), are considered, of which the mathematical models and related parameters are described in [22,24]. Two microturbines (MTs) and two fuel cells (FCs) with different parameter settings are considered as the controllable DGs to supply electricity power.…”

“…In [21], the authors introduced a method to handle the constraints in multiobjective problems taking account of both feasibility and domination, which is called "Deb's constraints handling criteria". It was used in some of the studies [13,[22][23][24], and the results showed that by using this method considering both feasibility and domination, the proportion of the infeasible solutions could be reduced evidently [13,23]. However, this method needed to add the overall constraints which were actually of different types and could not be easily quantified.…”

Dynamic Operation Management of a Renewable Microgrid including Battery Energy Storage

“…In other words, they are static optimal operation scheduling which do not consider the relationships among different time periods. Dynamic optimal operation scheduling problem is another fundamental part of the renewable microgrid operation to maintain the power balance [19][20][21][22][23][24]. Among these methods, Cheng et al [23] have used an enhanced quorum sensing based particle swarm optimization to deal with the dynamic operation optimization problem.…”

“…And a challenging aspect of this method is determining which of the inequality constraints are binding at the solution. Another relevant aspect in renewable microgrid operation management should be coping with uncertainty in the renewable energy sources, load demand, and market prices [24][25][26]. Similarly, power line losses should also be considered in the renewable microgrid system.…”

Research on Improved NSGA-II Algorithm and Its Application in Emergency Management

“…Thus the emergency facility location is a multiobjective optimization problem which has to take quantities of complex factors into consideration, while the evolutionary algorithms such as GA [9], PSO [10], NSGA [11], and MOEA/D [12] are usually applied to solve such kind of problems. Gadhvi et al [13] presented a constrained multiobjective optimization problem which is solved by NSGA-II, SPEA2, and PESA-II; the results show that NSGA-II is able to yield a better Pareto front.…”