A platform for testing the performance of metaheuristics solving the energy resource management problem in smart grids

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.

Meta-ERM platform solves the energy resource management problem faced by an aggregator (see Fig. 1) aiming at procuring energy needs from distributed resources and the electricity market. The aggregator looks for the minimization of operational costs while making revenues from selling energy in available electricity markets. Moreover, it may use its assets, e.g., energy storage systems (ESS), to supply the load demand. The main idea is that the optimization software performs the energy resource scheduling of the dedicated resources for the 24 h of the following day (i.e., day-ahead scheduling).

Even with state-of-the-art solvers, it is somehow difficult to provide efficient solutions to some of the scenarios that can be faced in the context of large-scale ERM problems. Therefore, the final purpose of the Meta-ERM platform is to be used for the assessment and comparison of stochastic optimization algorithms (e.g., PSO or DE) to mitigate the exponential execution time and large memory requirements using traditional mathematical tools. Moreover, due to the stochastic nature of these optimizers, an assessment tool is of particular interest for the scientific community working in the development of such approaches.

The simulation platform is already configured to allow users choose from a wide variate of case studies (e.g., buildings or microgrids with resources from renewables to EVs) and a comprehensive set of evolutionary algorithms including PSO, DE (in different variants), QPSO, ABC, GA, and others.

Since the performance of stochastic optimizers typically requires a statistical assessment of the provided results, “Meta-ERM” has the capability of configuring many runs and provide statistical indicators (e.g., mean fitness value, execution time, convergence rate) to determine the statistical relevance of the results. This data is provided in the form of output Tables (not shown in the paper for space limitation).

Additionally, the platform is also able to provide a set of different plots for analysis of the solutions. The user can easily configure the type of plots they want to recover from a simulation. The plots are not only limited to the analysis of the quality of the solution provided by an algorithm regarding the scheduling of resources (see, for instance, Fig. 3, but also to the insights concerning the performance of an algorithm (or a group of algorithms) in terms of fitness quality, convergence, robustness or other indicators (see, for instance, Figs. 4 and 5).

“Meta-ERM” platform is a tool designed to test and validate the performance of metaheuristics when solving the ERM problem in smart grids. Different functionalities are provided by the platform, for instance, different case studies can be selected, a set of popular metaheuristics is available for comparison and performance validation, capabilities for statistical analysis and plot functionalities, among other options. Moreover, because of the building-block structure, new approaches can be implemented and integrated into the platform easily, allowing to expand the state of the art regarding metaheuristic optimization on power systems.