An adaptive inertia weight teaching–learning-based optimization for optimal energy balance in microgrid considering islanded conditions

The energy balance in islanded microgrids is a complex task due to various operational constraints. This paper proposes a new approach to multi-objective optimization for achieving energy balance in a Microgrid (MG) in both islanded and normal modes. Optimal load control (OLC) is a challenge, due to a lack of capacity to generate the global optimum after each run. The latest variant of Teaching Learning Based Optimization (TLBO), known as Adaptive-TLBO, includes both modifications during exploitation and exploration stages (ATLBO). The results achieved with the proposed method are exceptional on a modified IEEE 33-bus system. In addition to the improvement of the voltage profile and the decrease of the distribution losses, the energy balance improves with the method. The proposed ATLBO algorithm overrides any proposed other algorithm, as shown by comparison with PSO, base TLBO, Backtracking search algorithm (BSA) and cuckoo search algorithms, etc. (CSA).