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

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, metaheuristic 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 metaheuristics 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.

A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm

“…Consequently, a decrease in the objective function is vital because it leads to having extra information about the optimal values of the design variables. A thorough examination of the convergence curve indicates that REMS-GOA yields a better result as it offers the minimum objective function of $0.3656/kW h, followed by REMS-CSA with $0.3662/kW h and REMS-PSO with $0.3674/kW h. Nevertheless, the REMS-GOA convergence period in the minimum condition is 17 s and better than the REMS-CSA, which is 25 s. The REMS-PSO converges to the minimum condition at 7 s because PSO is prone to premature convergences [11] . Full details of the results obtained for the considered microgrid in the case of = 0% are presented in Table 8 .…”

“…Moreover, the output power of the WT generator is influenced by the cut-in, cut-out, and rated speed specified by the manufacturer. Thus, it is recommended to hybridize various RESs in conjunction with an energy storage system (ESS) to form a RE-based microgrid [11] . The integration of RES and ESS requires a strategic and harmonized energy management scheme (EMS) to avoid unnecessary costs on the microgrid.…”

“…The optimization problem of microgrids is non-convex, nonlinear, multi-objective in nature, with discrete/integer variables and nonlinear/linear constraints [6] . The modality of the optimization problem is multimodal with many local optimums and a global optimum solution [10] , [11] . This has precluded the application of exact mathematical (deterministic) optimizations to compute for the problem.…”

“…At the moment, research in artificial intelligence (AI) has resulted in a wide variety of procedures that can either be applied directly or adapted to solve complex problems in the energy sector (for example, in the energy system planning, design, operation, investment, and dynamic control and stability), which were thought to be unsolvable without several simplified assumptions [12] . Metaheuristic or heuristic optimization is a sub-division of AI that computes for global optimum solutions of non-deterministic polynomial time-hard (NP-hard) problems, which are not cooperative with exact mathematical optimization techniques, by using suitable computational variations of the natural systems [11] .…”

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].…”