In this paper, a novel dynamic programming technique is presented for optimal operation of a typical renewable microgrid including battery energy storage. The main idea is to use the scenarios analysis technique to proceed the uncertainties related to the available output power of wind and photovoltaic units and dynamic programming technique to obtain the optimal control strategy for a renewable microgrid system in a finite time period. First, to properly model the system, a mathematical model including power losses of the renewable microgrid is established, where the uncertainties due to the fluctuating generation from renewable energy sources are considered. Next, considering the dynamic power constraints of the battery, a new performance index function is established, where the Lagrange multipliers and interior point method will be presented for the equality and inequality operation constraints. Then, a feedback control scheme based on the dynamic programming is proposed to solve the model and obtain the optimal solution. Finally, simulation and comparison results are given to illustrate the performance of the presented method.
This study presents a multiobjective energy management to optimize the renewable microgrid operation while satisfying a demand response and various operation constraints. With regard to energy cost minimization, pollutant emission reduction for better utilization of the renewable energy resources, such as wind and solar, as a competitive objective is proposed. Moreover, for maximizing the renewable microgrid operators, demand response benefits satisfying the load demand constraints amongst other operation constraints are incorporated into the operations of the renewable microgrid. The overall problem is formulated as a mixed-integer nonlinear constraint multiobjective optimization problem with different equality and inequality constraints. In this paper, an improved decomposition based multiobjective evolutionary algorithm is presented for optimal operation of the renewable microgrid with renewable energy sources and various devices such as diesel generators, micro-turbines, fuel cells, and battery energy storage. To improve the optimization process, differential evolution (DE) and the niche guided mating selection strategy are incorporated. Meanwhile, decomposition-based multiobjective evolutionary algorithm-DE is extended to tackle the constrained optimization problem. Finally, the proposed algorithm is applied in a renewable microgrid, and its superior performance is compared with the conventional evolutionary algorithms such as the multiobjective genetic algorithm and the original decomposition based multiobjective evolutionary algorithm.
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