Heuristic Enhanced Evolutionary Algorithm for Community Microgrid Scheduling

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.

Review of Recent Developments in Microgrid Energy Management Strategies

“…The results show that this technique provided a better solution than the genetic algorithm in terms of the global optimum solution and the time of computation. Apart from these two well-known solution approaches, the genetic algorithm and PSO methods of the EMS, there are other approaches, such as differential evolution [134], gray wolf optimization (GWO) [135], ant colony optimization (ACO) [136], etc.…”

A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion

“…For green scheduling as the high-dimensional multi-objective optimization problems under the strong restriction in the real complex super-system, metaheuristics algorithms like genetic algorithms and artificial immune algorithms, have been used [2] [3] . Although with many achievements in homogeneous scheduling, metaheuristics algorithms are underperforming in the nonlinear heterogeneous green scheduling, with the balance conflict between convergence and distribution [4][5][6] .…”

Evolutionary Algorithms for Energy Scheduling under uncertainty considering Multiple Aggregators

“…They are defined as population-based metaheuristics. Since electrical energy systems are large systems with many variables and constraints, evolutionary computational (EC) based algorithms are adequate because they can generate feasible solutions with low computational effort [10].…”