Capacity configuration optimisation for stand‐alone micro‐grid based on an improved binary bat algorithm

Capacity configuration optimisation (CCO) for stand‐alone micro‐grid has become an urgent issue that needs to be addressed effectively, which is of great significance for saving operational costs, raising renewable energy source utilisation ratio and improving reliability of power supply in island. This study proposes a CCO model for stand‐alone wind–PV–diesel–battery micro‐grid based on an improved binary bat algorithm (BBA). CCO is formulated as a multi‐objective integer programming problem with three conflictive objective functions (i.e. economic, reliability, and environmental criteria) and various non‐linear constraints. Aiming at the local optimal problem caused by directly applying continuous optimisation algorithm into integer programming model, an improved BBA (IBBA) is proposed to solve this problem, which adopts mutation, crossover, and selection operations from differential evolution to improve the global searching ability of standard BBA. Finally, a simulation case is conducted to verify feasibility and effectiveness of the proposed method, in which IBBA is compared with genetic algorithm and particle swarm optimisation. The simulation results indicate that IBBA shows better performance in terms of both the solution quality and convergence speed.