Metaheuristic-based isolated microgrid sizing and uncertainty quantification considering EVs as shiftable loads. (November 2022)

• Record Type: Journal Article
• Authors: Mohseni, Soheil; Khalid, Roomana; Brent, Alan C.
• Is Part Of: Energy reports. Volume 8(2022)
• Page Start: 11288
• Page End: 11308
• Publication Date: 2022-11
• Subjects: Microgrids -- Off-grid -- Optimal sizing -- Optimal planning -- Metaheuristics -- Optimisation; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.08.254
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Isolated microgrids are increasingly recognised as an effective platform for the optimal coordination of integrated distributed energy resources – including renewable energy generation and storage technologies – for remote, island, and peripheral communities. However, they require advanced off-grid microgrid capacity planning optimisation models to achieve the globally optimum sizing results. In this context, while considerable attention has been devoted to a range of off-grid microgrid sizing methods, leveraging the potential of metaheuristic optimisation algorithms is less well-explored. Furthermore, there is a general lack of electrified transportation interventions considered during long-term grid-independent microgrid planning phases. In response, this paper introduces a novel metaheuristic-based strategic off-grid microgrid capacity planning optimisation model that is applicable to associated integrated energy and e-mobility resource planning problems. To test the effectiveness of the proposed model, three independent microgrid development projects have been considered for three communities residing on Aotea–Great Barrier Island in Aotearoa–New Zealand. The sites of interest have different demand profiles and renewable energy potentials – with consequent changes in the technologies considered in the associate candidate pools. Moreover, to generate key insights into the impact of the forecast uncertainty of different variables on the costing and configuration of the associated microgrids, a number of scenario analyses have been performed. In addition, the numeric simulation results and the associated capital budgeting analyses have demonstrated the economic viability of the project proposals formulated based on the proposed off-grid microgrid planning method, especially with controlling the charging of electric vehicles. Particularly, the potential of the artificial hummingbird algorithm in reducing the total discounted costs of off-grid microgrids by at least ∼ 6% has been shown. Also, the importance of electric vehicle charging coordination in reducing the off-grid microgrid costs (by at least ∼ 9%) has been substantiated. Highlights: The artificial hummingbird algorithm outperforms the mainstream metaheuristics in off-grid microgrid sizing applications. EV-load scheduling is able to significantly reduce the cost of stand-alone microgrids. Off-grid microgrids' cost estimate is more sensitive to changes in load demand compared to weather data. Effective utilisation of battery storage can substantially reduce the risks of overbuilt and excessive curtailment.