Economic viability assessment of sustainable hydrogen production, storage, and utilisation technologies integrated into on- and off-grid micro-grids: A performance comparison of different meta-heuristics. (4th December 2020)

• Record Type: Journal Article
• Authors: Mohseni, Soheil; Brent, Alan C.
• Is Part Of: International journal of hydrogen energy. Volume 45:Number 59(2020)
• Page Start: 34412
• Page End: 34436
• Publication Date: 2020-12-04
• Subjects: Optimal planning -- Micro-grids -- Meta-heuristics -- Hydrogen technologies -- Hydrogen economy -- New Zealand; ABC Artificial Bee Colony -- ACO Ant Colony Optimization -- AI Artificial Intelligence -- ALO Ant Lion Optimizer -- BA Bat Algorithm -- BB-BC Big Bang-Big Crunch -- BBA Binary Bat Algorithm -- CSA Cuckoo Search Algorithm -- DA Dragonfly Algorithm -- DG Distributed Generation -- DPP Discounted Payback Period -- FA Firefly Algorithm -- GA Genetic Algorithm -- GOA Grasshopper Optimization Algorithm -- GSA Gravitational Search Algorithm -- GWO Grey Wolf Optimizer -- H2 Hydrogen -- HABC-ACO Hybrid ABC-ACO -- HGA-PSO Hybrid GA-PSO -- HSA Harmony Search Algorithm -- IHSA Improved HSA -- IRR Internal Rate of Return -- LCOE Levelized Cost of Electricity -- LCOH Levelized Cost of Hydrogen -- LPSP Loss of Power Supply Probability -- MFOA Moth-Flame Optimization Algorithm -- MG Micro-Grid -- MHP Micro-Hydro Power -- MPPT Maximum Power Point Tracking -- MSW Municipal Solid Waste -- MVO Multi-Verse Optimizer -- NFL No-Free-Lunch -- NIWA National Institute of Water and Atmospheric Research -- NP-hard Non-deterministic Polynomial time-hard -- NPC Net Present Cost -- PEM Polymer Electrolyte Membrane -- PI Profitability Index -- PSO Particle Swarm Optimization -- PV Photovoltaic -- RESs Renewable Energy Sources -- SA Simulated Annealing -- SCA Sine-Cosine Algorithm -- SC Super-Capacitor -- SSA Salp Swarm Algorithm -- TNPC Total NPC -- WEO Water Evaporation Optimization -- WT Wind Turbine -- WtE Waste-to-Energy; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2019.11.079
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: In recent years, there has been considerable interest in the development of zero-emissions, sustainable energy systems utilising the potential of hydrogen energy technologies. However, the improper long-term economic assessment of costs and consequences of such hydrogen-based renewable energy systems has hindered the transition to the so-called hydrogen economy in many cases. One of the main reasons for this is the inefficiency of the optimization techniques employed to estimate the whole-life costs of such systems. Owing to the highly nonlinear and non-convex nature of the life-cycle cost optimization problems of sustainable energy systems using hydrogen as an energy carrier, meta-heuristic optimization techniques must be utilised to solve them. To this end, using a specifically developed artificial intelligence-based micro-grid capacity planning method, this paper examines the performances of twenty meta-heuristics in solving the optimal design problems of three conceptualised hydrogen-based micro-grids, as test-case systems. Accordingly, the obtained numeric simulation results using MATLAB indicate that some of the newly introduced meta-heuristics can play a key role in facilitating the successful, cost-effective development and implementation of hydrogen supply chain models. Notably, the moth-flame optimization algorithm is found capable of reducing the life-cycle costs of micro-grids by up to 6.5% as compared to the dragonfly algorithm. Graphical abstract: Image 1 Highlights: A metaheuristic-driven model is proposed to optimally design H2 -based microgrids. An energy management strategy is used to facilitate the uptake of fuel cell vehicles. The performances of twenty metaheuristics are studied in terms of solution quality. The moth-flame optimizer outperforms the well-respected algorithms in this area. Levelized costs of electricity and H2 are found to be well below the present rates.