Review of bio-inspired optimization applications in renewable-powered smart grids: Emerging population-based metaheuristics

The management of renewable-powered smart grids deals with nonlinear optimization problems featuring a variety of linear or nonlinear constraints, discrete or continuous optimization variables, involving high dimensionality of the solution space, and strict time requirements to identify the optimal or near-optimal solution. One promising approach for addressing such optimization problems is to apply bio-inspired population-based optimization algorithms, many such metaheuristics emerging lately. In this paper, we have identified the metaheuristics with the highest impact published recently and reviewed their applications in the management of renewable-powered smart energy grids using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology and the Web of Science Core Collection as the reference database. Four main smart grid application domains we been analyzed: (i) energy prediction models' optimization to reduce uncertainty (ii) energy resources coordination to handle the stochastic nature of renewables, (iii) demand response using controllable loads and flexibility while considering the consumers' needs and constraints and (iv) optimization of grid energy efficiency and costs. The results showed the advantages of such metaheuristics for decentralized optimization problems with low computational time and resource overhead. At the same time, several issues need to be addressed to increase their adoption in the smart grid management scenarios: the lack of standard testing methodologies and benchmarks, efficient management of exploration and exploitation of the optimization search space, guidelines for metaheuristics application with clear links to the type of optimization problems, etc.