The energy resource management (ERM) has been considered one of the most complicated optimization problems in power systems due to its combinatorial nature, nonlinearities and a large number of energy resources which leads to high dimensionality and highly constrained problems (Soares et al. 2016; Soares et al. 2018). Therefore, adequate optimization frameworks and extensive analysis of case studies are a crucial part of the new paradigm brought the by the smart grid operation.
One of the main challenges in the new power system paradigm is that advanced (and occasionally expensive) infrastructure is required to study, validate, and provide accurate projections about how efficient a new control algorithm can be in real implementations. Besides, when a new approach to solve the ERM problem is proposed, it is desired to include a broad range of aspects that can impact its performance (i.e., scalability, uncertainty, memory requirements, operational costs) before real implementation (Di Somma et al. 2018). In this situation, ERM simulation platforms can provide a more accurate perception of the impact of different aspects of a new solution before the actual execution takes place (Yan et al. 2013; Cheng et al. 2017; Lezama et al. 2018a).
In this paper, we present “Meta-ERM”, a MATLAB© platform for solving energy resource management problems using stochastic optimization techniques. The platform allows the optimization of large-scale centralized day-ahead scheduling problems with the use of modern metaheuristic algorithms (e.g., PSO, DE, GA, SA, ABC, and others) (Talbi 2009; Faia et al. 2017). The simulation tool has been designed to easy test and assess the performance of any existing metaheuristic.