Optimal selection for wind power coupled hydrogen energy storage from a risk perspective, considering the participation of multi-stakeholder

Abstract

Wind power coupled hydrogen energy storage (WPCHES) has recently emerged as a key to achieving the goal of peaking carbon dioxide emissions as well as carbon neutrality. However, WPCHES industry develops sluggishly with numerous uncertainties due to the complex interest environment caused by plant and power grid separation. To select the optimal site of WPCHES with enormous difficulties lying in different and intersecting interest demands of stakeholders and different risk appetites of decision-makers, this paper establishes an overarching risk analysis framework of WPCHES considering the participation of multi-stakeholder. First, a synthetic risk factor system for WPCHES is constructed based on the interest claim of stakeholders, containing 5 categories of risk and 13 factors concerned by 3 stakeholders. Subsequently, Decision Making Trial and Evaluation Laboratory and Entropy are combined under the proportional hesitant fuzzy environment to ensure the complementarity of approaches and the uncertainty of evaluation information. Finally, novel usage of TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) is proposed to investigate potential trade-offs between conflicting goals among stakeholders. The case result demonstrates that Guyuan county of Shijiazhuang city in Hebei province is the optimal choice with the biggest advantage and the least stakeholders’ risk appetite sensitivity. A further finding is that the overall results are more sensitive to Government and Investor, rather than to Power grid. Interestingly, various solutions have different sensitivity to each stakeholder. Besides, the bidirectional preferences between stakeholders and alternatives are also obtained. At last, the sensitivity analysis and the comparative analysis with popular algorithms confirmed that the model proposed by this paper is a reliable and practical choice for complex multi-criteria decision-making (MCDM) problems.