Inertia-weight local-search-based TLBO algorithm for energy management in isolated micro-grids with renewable resources

•Introducing an inertia-weight local-search based TLBO (IWLS-TLBO), as an exploration-exploitation balanced TLBO.•Proposing an adaptive inertia weight factor in both teacher and learner phases of the TLBO.•Proposing a neighborhood structure comprising local search operators in the teacher phase of the TLBO.•Applying the proposed IWLS-TLBO algorithm to solve EMS problem in an isolated microgrid with renewable energy resources. Integration of distributed generators and loads in the form of a microgrid enhances the supply reliability and reduces the generation costs and greenhouse gas emissions. Microgrids should be properly managed via an energy management system (EMS) to minimize the overall costs of the system. In this paper, an Inertia-Weight Local-Search based Teaching-Learning-Based Optimization (named IWLS-TLBO), is proposed as an exploration-exploitation balanced metaheuristic algorithm to solve the EMS problem. The proposed IWLS-TLBO algorithm is inspired by the principles of human learning based on self-perceptual and self-regulatory abilities of people in facing various problems and adopting the best decision based on the previous experiences. By emphasizing of the IWLS-TLBO algorithm on both exploration (ability to search for new solutions) and exploitation (ability to exploit from existing solutions), the search space can be more effectively investigated than the original TLBO algorithm. In order to justify the performance of the proposed IWLS-TLBO algorithm, obtained results on different benchmark functions are compared with those of achieved by the original TLBO and other metaheuristics. Then, the IWLS-TLBO is applied to optimize the EMS problem which is formulated based on a unit commitment problem in an isolated microgrid with renewable energy resources. Simulation results demonstrate the superiority of the proposed IWLS-TLBO algorithm against the existing metaheuristic algorithms, in terms of solution quality and convergence speed.