A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid

Abstract

A metaheuristic-based model is proposed to optimally size a hydrogenbased microgrid.• The microgrid system is equipped with an innovative hydrogen refuelling station.• The performances of eight metaheuristics are studied in terms of accuracy and speed.

Strategic design optimisation of multi-energy-storage-technology micro-grids considering a two-stage game-theoretic market for demand response aggregation

“…The MG capacity-optimisation model consists of three key elements: (1) the net present cost (NPC) and net present value (NPV) methods utilised to formulate the total discounted system cost function, (2) the loss of power supply probability (LPSP) technique to quantify the reliability of the system in servicing the electrical and hydrogen load demands, and (3) the moth-flame optimisation algorithm (MFOA) [100] as a single-objective meta-heuristic optimisation algorithm to find the globally optimum solution to the problem by minimising the life-cycle cost of the MG, whilst adhering to the technical, reliability, and systemic constraints (see Supplementary Material (Additional File 2: Techniques used in the micro-grid capacity-optimisation model) for details). The superiority of the single-objective MFOA to the well-established meta-heuristics in the MG investment planning literature-for instance, the genetic algorithm (GA) [101] and the PSO [102]-as well as to a wide variety of state-of-the-art meta-heuristics in terms of nearing the globally optimum solution is supported in previous studies [41,84,103,104,105].…”

The Hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm: A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids

“…The list is quite long but it is worth mentioning the names of all the algorithms and the associated references dedicated to sizing optimization. These are the Harris hawks optimization algorithm, the firefly algorithm [8], the moth-flame optimization algorithm, the genetic algorithm, the grey wolf optimizer, the particle swarm optimization, the ant colony optimization, the salp swarm algorithm, and the dragonfly algorithm [9], the grasshopper optimization algorithm [10], the cuckoo search, the simulated annealing, the harmony search, the jaya algorithm, the flower pollination algorithm, the brainstorm optimization in objective space, and the simplified squirrel search algorithm [11], the antlion optimizer [12], the evolutionary particle swarm optimization [13], the water cycle algorithm [14], the smell agent optimization [15], the artificial ecosystem optimization [16], the accelerated particle swarm optimization algorithm, the generalized evolutionary walk algorithm and the bat algorithm [17], the krill herd algorithm [18], the equilibrium optimizer, the artificial electric field algorithm, the sooty tern optimization algorithm [19],…”

Community Resilience-Oriented Optimal Micro-Grid Capacity Expansion Planning: The Case of Totarabank Eco-Village, New Zealand

“…2 WT costs include the cost of the required guyed tower. 3 The replacement and O&M costs were adjusted in accordance with the capital-to-replacement and capital-to-O&M cost ratios used in [44][45][46]. 4 The approximate average operational service life of small and micro WTs is 120,000 h. However, since they do not operate at wind speeds below cut-in, their service life is typically considered as 20 years [47].…”