Multi‐stage coordinated planning of energy stations and networks in park‐level integrated energy systems
Abstract With the development of distributed energy resources and intelligent energy management technologies, park‐level integrated energy systems (PIESs) are essential for multi‐energy flow conversion and utilization. However, coordinating the upstream energy sources, internal energy stations, dist...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13166 |
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| author | Yue Qiu Yunting Yao Suyang Zhou Hanlin Zhang Yuqing Bao |
| author_facet | Yue Qiu Yunting Yao Suyang Zhou Hanlin Zhang Yuqing Bao |
| author_sort | Yue Qiu |
| collection | DOAJ |
| description | Abstract With the development of distributed energy resources and intelligent energy management technologies, park‐level integrated energy systems (PIESs) are essential for multi‐energy flow conversion and utilization. However, coordinating the upstream energy sources, internal energy stations, distribution networks, and downstream loads within the PIES framework remains a challenge. Developing a station‐network coordinated planning scheme for PIES confronts challenges, such as overly complex models that are difficult to solve and the difficulty of implementing standard methods for medium and long‐term planning. This paper proposes a multi‐stage coordinated planning approach for PIES, containing energy stations, multi‐energy networks, and load aggregation nodes. The energy equipment and energy networks are precisely modelled to enhance the reliability of the planning scheme. The chance‐constrained programming (CCP) method is adopted to address uncertainties arising from renewable energy generation. Additionally, the improved big‐M method and second‐order cone (SOC) relaxation technique are utilized to manage non‐linear elements, transforming the model into a mixed‐integer second‐order cone programming (MISOCP) form to enhance solution efficiency. A case study illustrates that the proposed method enables effective allocation of equipment and networks, outperforming single‐stage planning by reducing investment costs, enhancing facility utilization rates, and facilitating renewable energy integration. |
| format | Article |
| id | doaj-art-e433ec2d3a4c40a5bd7df735a84723e9 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-e433ec2d3a4c40a5bd7df735a84723e92024-12-23T18:44:18ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118S14543456410.1049/rpg2.13166Multi‐stage coordinated planning of energy stations and networks in park‐level integrated energy systemsYue Qiu0Yunting Yao1Suyang Zhou2Hanlin Zhang3Yuqing Bao4School of Electrical and Automation Engineering Nanjing Normal University Nanjing ChinaSchool of Electrical and Automation Engineering Nanjing Normal University Nanjing ChinaSchool of Electrical Engineering Southeast University Nanjing ChinaSchool of Electrical Engineering Southeast University Nanjing ChinaSchool of Electrical and Automation Engineering Nanjing Normal University Nanjing ChinaAbstract With the development of distributed energy resources and intelligent energy management technologies, park‐level integrated energy systems (PIESs) are essential for multi‐energy flow conversion and utilization. However, coordinating the upstream energy sources, internal energy stations, distribution networks, and downstream loads within the PIES framework remains a challenge. Developing a station‐network coordinated planning scheme for PIES confronts challenges, such as overly complex models that are difficult to solve and the difficulty of implementing standard methods for medium and long‐term planning. This paper proposes a multi‐stage coordinated planning approach for PIES, containing energy stations, multi‐energy networks, and load aggregation nodes. The energy equipment and energy networks are precisely modelled to enhance the reliability of the planning scheme. The chance‐constrained programming (CCP) method is adopted to address uncertainties arising from renewable energy generation. Additionally, the improved big‐M method and second‐order cone (SOC) relaxation technique are utilized to manage non‐linear elements, transforming the model into a mixed‐integer second‐order cone programming (MISOCP) form to enhance solution efficiency. A case study illustrates that the proposed method enables effective allocation of equipment and networks, outperforming single‐stage planning by reducing investment costs, enhancing facility utilization rates, and facilitating renewable energy integration.https://doi.org/10.1049/rpg2.13166energy management systemsoptimisationpower system planningrenewable energy sourcessmart power grids |
| spellingShingle | Yue Qiu Yunting Yao Suyang Zhou Hanlin Zhang Yuqing Bao Multi‐stage coordinated planning of energy stations and networks in park‐level integrated energy systems IET Renewable Power Generation energy management systems optimisation power system planning renewable energy sources smart power grids |
| title | Multi‐stage coordinated planning of energy stations and networks in park‐level integrated energy systems |
| title_full | Multi‐stage coordinated planning of energy stations and networks in park‐level integrated energy systems |
| title_fullStr | Multi‐stage coordinated planning of energy stations and networks in park‐level integrated energy systems |
| title_full_unstemmed | Multi‐stage coordinated planning of energy stations and networks in park‐level integrated energy systems |
| title_short | Multi‐stage coordinated planning of energy stations and networks in park‐level integrated energy systems |
| title_sort | multi stage coordinated planning of energy stations and networks in park level integrated energy systems |
| topic | energy management systems optimisation power system planning renewable energy sources smart power grids |
| url | https://doi.org/10.1049/rpg2.13166 |
| work_keys_str_mv | AT yueqiu multistagecoordinatedplanningofenergystationsandnetworksinparklevelintegratedenergysystems AT yuntingyao multistagecoordinatedplanningofenergystationsandnetworksinparklevelintegratedenergysystems AT suyangzhou multistagecoordinatedplanningofenergystationsandnetworksinparklevelintegratedenergysystems AT hanlinzhang multistagecoordinatedplanningofenergystationsandnetworksinparklevelintegratedenergysystems AT yuqingbao multistagecoordinatedplanningofenergystationsandnetworksinparklevelintegratedenergysystems |