{"title":"多利益相关者利益协调下的电氢一体化能源系统配置选择随机多准则决策","authors":"Zhiying Zhang , Yingying Xu , Yaoyao Yu , Xiaoyuan Chen , Huchang Liao","doi":"10.1016/j.engappai.2025.111323","DOIUrl":null,"url":null,"abstract":"<div><div>The development of electricity-hydrogen integrated energy systems (IESs) is an important solution for promoting the transition to clean energy. Determining the optimal system configuration is challenging due to the involvement of multiple criteria and stakeholders. This paper introduces a robust evaluation framework for the configuration selection of park-level electricity-hydrogen IESs considering multiple stakeholder interests. Firstly, eight schemes are designed for an industrial park, and configuration optimization models are constructed to obtain the optimal capacities of the components. Then, a group decision-making model based on stochastic multiple criteria acceptability analysis and multi-objective optimization on the basis of ratio analysis plus full multiplicative form (MULTIMOORA) method is developed to analyze and rank schemes, which connects the optimization with decision-making under multiple factors. The proposed framework accommodates the imprecise preferences of multiple stakeholders and coordinates their interests from technological, economic, environmental, and social aspects. Additionally, compensatory effects between multiple criteria are modeled, enhancing the robustness of decision results. Results indicate that the combination of natural gas, photovoltaic panel, hydrogen storage, and gas boiler is the winning scheme, with an energy utilization ratio of 54 % and a 76 % reduction in carbon emissions. Economically, the scheme results in a levelized cost of energy of 0.201$/kWh and creates 28 jobs annually. Scenario analysis indicates that, under current high configuration costs, a single policy aimed at reducing hydrogen storage costs is insufficient to facilitate the widespread deployment of near-zero carbon emission electricity-hydrogen IESs. It is recommended that power restriction measures be implemented simultaneously.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"157 ","pages":"Article 111323"},"PeriodicalIF":8.0000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Stochastic multiple criteria decision-making for configuration selection of electricity-hydrogen integrated energy systems with multiple stakeholder interest coordination\",\"authors\":\"Zhiying Zhang , Yingying Xu , Yaoyao Yu , Xiaoyuan Chen , Huchang Liao\",\"doi\":\"10.1016/j.engappai.2025.111323\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The development of electricity-hydrogen integrated energy systems (IESs) is an important solution for promoting the transition to clean energy. Determining the optimal system configuration is challenging due to the involvement of multiple criteria and stakeholders. This paper introduces a robust evaluation framework for the configuration selection of park-level electricity-hydrogen IESs considering multiple stakeholder interests. Firstly, eight schemes are designed for an industrial park, and configuration optimization models are constructed to obtain the optimal capacities of the components. Then, a group decision-making model based on stochastic multiple criteria acceptability analysis and multi-objective optimization on the basis of ratio analysis plus full multiplicative form (MULTIMOORA) method is developed to analyze and rank schemes, which connects the optimization with decision-making under multiple factors. The proposed framework accommodates the imprecise preferences of multiple stakeholders and coordinates their interests from technological, economic, environmental, and social aspects. Additionally, compensatory effects between multiple criteria are modeled, enhancing the robustness of decision results. Results indicate that the combination of natural gas, photovoltaic panel, hydrogen storage, and gas boiler is the winning scheme, with an energy utilization ratio of 54 % and a 76 % reduction in carbon emissions. Economically, the scheme results in a levelized cost of energy of 0.201$/kWh and creates 28 jobs annually. Scenario analysis indicates that, under current high configuration costs, a single policy aimed at reducing hydrogen storage costs is insufficient to facilitate the widespread deployment of near-zero carbon emission electricity-hydrogen IESs. It is recommended that power restriction measures be implemented simultaneously.</div></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":\"157 \",\"pages\":\"Article 111323\"},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2025-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0952197625013259\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197625013259","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Stochastic multiple criteria decision-making for configuration selection of electricity-hydrogen integrated energy systems with multiple stakeholder interest coordination
The development of electricity-hydrogen integrated energy systems (IESs) is an important solution for promoting the transition to clean energy. Determining the optimal system configuration is challenging due to the involvement of multiple criteria and stakeholders. This paper introduces a robust evaluation framework for the configuration selection of park-level electricity-hydrogen IESs considering multiple stakeholder interests. Firstly, eight schemes are designed for an industrial park, and configuration optimization models are constructed to obtain the optimal capacities of the components. Then, a group decision-making model based on stochastic multiple criteria acceptability analysis and multi-objective optimization on the basis of ratio analysis plus full multiplicative form (MULTIMOORA) method is developed to analyze and rank schemes, which connects the optimization with decision-making under multiple factors. The proposed framework accommodates the imprecise preferences of multiple stakeholders and coordinates their interests from technological, economic, environmental, and social aspects. Additionally, compensatory effects between multiple criteria are modeled, enhancing the robustness of decision results. Results indicate that the combination of natural gas, photovoltaic panel, hydrogen storage, and gas boiler is the winning scheme, with an energy utilization ratio of 54 % and a 76 % reduction in carbon emissions. Economically, the scheme results in a levelized cost of energy of 0.201$/kWh and creates 28 jobs annually. Scenario analysis indicates that, under current high configuration costs, a single policy aimed at reducing hydrogen storage costs is insufficient to facilitate the widespread deployment of near-zero carbon emission electricity-hydrogen IESs. It is recommended that power restriction measures be implemented simultaneously.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.