Lévy-flight moth-flame optimisation algorithm-based micro-grid equipment sizing: An integrated investment and operational planning approach

Bridging the gap between simulation and reality for successful micro-grid (MG) implementation requires accurate mathematical modelling of underlying energy infrastructure extensive optimisation design space defined by all possible combinations size equipment. While exact approaches to MG capacity planning are highly computationally efficient, they often fail preserve associated problem nonlinearities non-convexities. This translates into fact that available sizing tools potentially return a sub-optimal (inferior) design. brings light importance nature-inspired, swarm-based meta-heuristic algorithms able effectively handle nonlinear non-convex nature – better approximate globally optimum solution though at expense increased computational complexity. Accordingly, this paper introduces robust framework based on state-of-the-art meta-heuristic, namely Lévy-flight moth-flame algorithm (MFOA). An intelligent linear programming-based day-ahead scheduling is, additionally, integrated proposed model. A case study is presented real grid-tied community in rural New Zealand. comparison results with those most popular tool literature industry, HOMER Pro, verifies superiority meta-heuristic-based Additionally, efficiency MFOA compared nine well-established meta-heuristics literature. The comparative analyses have revealed statistically significant outperformance examined meta-heuristics. Notably, its original MFOA, hybrid genetic algorithm-particle swarm optimisation, ant colony optimiser, least ~6.5%, ~8.4%, ~12.8%, demonstrated. Moreover, comprehensive capital budgeting confirmed financial viability test-case system optimised