Hierarchical optimisation for planning and dispatching of regional energy systems integrated with power-to-methanol

Kai Xue, Jinshi Wang, Quanbin Zhao, Chun Li, Daotong Chong and Junjie Yan

Renewable and Sustainable Energy Reviews, 2025, vol. 210, issue C

Abstract: Regional energy systems focus on multi-energy collaboration and localised utilisation, which are promising options for carbon neutrality. Power-to-methanol based on renewable energy helps reduce greenhouse gas emissions in line with the requirements of sustainable development. To address the planning and dispatching of regional energy systems with power-to-methanol, a hierarchical computational framework integrating a Gaussian mixture model, multi-objective evolution, and decision-making alongside an optimisation solver was devised. The upper-layer model is used to promote the optimal configuration of devices, whereas the lower layer contributes to further refinement of the hourly operation. Considering temporal continuity, a Gaussian mixture model was employed to reduce the annual calculation complexity through scenario clustering, and its validity was verified through a case study. Following a comparative analysis of six distinct dispatching schemes, the proposed system demonstrated superior performance, with carbon emissions, annualised cost, and primary energy consumption decreasing by 56.76 %, 57.79 %, and 34.17 %, respectively, compared with the conventional energy supply. The annual methanol production amounts to 1504.67 tons. Owing to the effectiveness of renewable energy and carbon capture, carbon allowance purchase can be reduced by 64.64 % and 14497 green certificates are harvested. Additionally, the impact of key operational and economic parameters on system performance was explored. This study contributes to the transition to a net-zero society and may provide a fresh perspective for regional energy systems towards profitable, clean, and sustainable potential.

Keywords: Regional energy system; Hierarchical optimisation; Gaussian mixture model; Methanol synthesis; Carbon reduction; Performance analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.rser.2024.115261

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