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

Abstract

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