Abstract
Scheduling of community microgrids (CMGs) is an important and challenging optimization problem. Generally, the optimization is performed to schedule resources of CMGs at minimum cost. In recent years, a number of algorithms have been proposed to solve such problems. However, the performance of these algorithms is far from ideal due to the presence of different complex equality and inequality constraints in CMGs. Furthermore, most of the current works ignore energy storage (ES) degradation costs in the optimization model, which has a significant impact on the life of ES. This paper develops both single and bi-objective optimization models by considering the life of ES along with the operating cost for scheduling a CMG. An efficient heuristic-enhanced Differential Evolution (DE) approach is proposed to solve these models; by exploiting the structure of equality constraints, the proposed heuristic is able to generate feasible solutions quickly. The significance of the proposed heuristic is that it can generate a high-quality solution with a considerably lower computational effort. Numerical simulations were performed to evaluate the performance of the proposed method, and obtained results were compared with the state-of-the-art algorithm. The simulation results corroborate the efficacy of the proposed method.
Highlights
The conventional electrical grid is a vast electric network comprising of energy consumers, long transmission and distribution systems, and large-scale fossil fuel-based power plants
The proposed heuristic-based Differential Evolution (DE) is applied for scheduling a community microgrid
On the contrary, when degradation cost was taken into consideration, energy storage systems (ESSs) degradation cost dropped by 71.31% and ESS life increased about threefold
Summary
The conventional electrical grid is a vast electric network comprising of energy consumers, long transmission and distribution systems, and large-scale fossil fuel-based power plants. Such optimization problems consider the reduction of operating cost, and environmental pollution, or maximization of profit as the objective function These objectives of the CMG energy management problem can be obtained by optimal scheduling of different DGs, energy exchange with utility grid and ESSs while satisfying associated constraints [7]. In [13], MILP is used for day-ahead scheduling of the combined heat and power (CHP) unit in a residential MG These conventional techniques have been found to be effective in small-scale optimization problems. The later considers two different objective functions: one is to minimize the operating cost and second is to maximize the ESS life To solve both optimization problems, heuristic enhanced differential evolution (DE) is introduced.
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