Capacity planning for hydro-wind-photovoltaic-storage systems considering high-dimensional uncertainties
Abstract The rapid development of renewable energy has made hydropower’s role as a flexible resource increasingly important in power systems. However, hydropower generation capability highly depends on water inflows, particularly during dry seasons, making it difficult to independently meet growing...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-024-00462-9 |
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| author | Xiongwei Li Jintao Song Yuquan Ma Ziqi Zhu Hongxu Liu Chuxi Wei |
| author_facet | Xiongwei Li Jintao Song Yuquan Ma Ziqi Zhu Hongxu Liu Chuxi Wei |
| author_sort | Xiongwei Li |
| collection | DOAJ |
| description | Abstract The rapid development of renewable energy has made hydropower’s role as a flexible resource increasingly important in power systems. However, hydropower generation capability highly depends on water inflows, particularly during dry seasons, making it difficult to independently meet growing load demands. The application of hydro-wind-photovoltaic-storage systems offers a promising solution, yet faces challenges from the high-dimensional uncertainties in natural conditions. This paper proposes a capacity planning method that considers high-dimensional uncertainties characterized by spatiotemporal correlations of natural factors. Firstly, a scenario generation method based on the transition probability matrix and C-Vine Copula model is developed. The constructed scenario sets capture the temporal correlations of natural conditions and spatial correlations between different parameters. Secondly, a bi-level optimization model for capacity planning is established. The upper level minimizes the deviation of operational cost and grid supply revenue to determine optimal capacity allocation, while the lower level optimizes both economic and safe objectives for operational dispatch. The normal boundary intersection method is employed to obtain Pareto front solutions that balance economy and safety. Different case studies are conducted to validate the effectiveness of the proposed method. Compared with the fixed ratio and variable ratio capacity allocation strategies without uncertainty, the optimal total system cost is reduced by 2.90% and 3.88%, respectively. |
| format | Article |
| id | doaj-art-66212487c869478a999bdb2058aa8616 |
| institution | Kabale University |
| issn | 2520-8942 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-66212487c869478a999bdb2058aa86162025-01-12T12:41:43ZengSpringerOpenEnergy Informatics2520-89422025-01-018111810.1186/s42162-024-00462-9Capacity planning for hydro-wind-photovoltaic-storage systems considering high-dimensional uncertaintiesXiongwei Li0Jintao Song1Yuquan Ma2Ziqi Zhu3Hongxu Liu4Chuxi Wei5China Energy Investment Co., Ltd.China Energy Investment Co., Ltd.National Energy Group Qinghai Electric Power Co., Ltd.National Energy Group Qinghai Electric Power Co., Ltd.National Energy Group Qinghai Electric Power Co., Ltd.National Energy Group Qinghai Electric Power Co., Ltd.Abstract The rapid development of renewable energy has made hydropower’s role as a flexible resource increasingly important in power systems. However, hydropower generation capability highly depends on water inflows, particularly during dry seasons, making it difficult to independently meet growing load demands. The application of hydro-wind-photovoltaic-storage systems offers a promising solution, yet faces challenges from the high-dimensional uncertainties in natural conditions. This paper proposes a capacity planning method that considers high-dimensional uncertainties characterized by spatiotemporal correlations of natural factors. Firstly, a scenario generation method based on the transition probability matrix and C-Vine Copula model is developed. The constructed scenario sets capture the temporal correlations of natural conditions and spatial correlations between different parameters. Secondly, a bi-level optimization model for capacity planning is established. The upper level minimizes the deviation of operational cost and grid supply revenue to determine optimal capacity allocation, while the lower level optimizes both economic and safe objectives for operational dispatch. The normal boundary intersection method is employed to obtain Pareto front solutions that balance economy and safety. Different case studies are conducted to validate the effectiveness of the proposed method. Compared with the fixed ratio and variable ratio capacity allocation strategies without uncertainty, the optimal total system cost is reduced by 2.90% and 3.88%, respectively.https://doi.org/10.1186/s42162-024-00462-9Capacity planningHigh-dimensional uncertaintiesHydro-wind-photovoltaic-storage systems |
| spellingShingle | Xiongwei Li Jintao Song Yuquan Ma Ziqi Zhu Hongxu Liu Chuxi Wei Capacity planning for hydro-wind-photovoltaic-storage systems considering high-dimensional uncertainties Energy Informatics Capacity planning High-dimensional uncertainties Hydro-wind-photovoltaic-storage systems |
| title | Capacity planning for hydro-wind-photovoltaic-storage systems considering high-dimensional uncertainties |
| title_full | Capacity planning for hydro-wind-photovoltaic-storage systems considering high-dimensional uncertainties |
| title_fullStr | Capacity planning for hydro-wind-photovoltaic-storage systems considering high-dimensional uncertainties |
| title_full_unstemmed | Capacity planning for hydro-wind-photovoltaic-storage systems considering high-dimensional uncertainties |
| title_short | Capacity planning for hydro-wind-photovoltaic-storage systems considering high-dimensional uncertainties |
| title_sort | capacity planning for hydro wind photovoltaic storage systems considering high dimensional uncertainties |
| topic | Capacity planning High-dimensional uncertainties Hydro-wind-photovoltaic-storage systems |
| url | https://doi.org/10.1186/s42162-024-00462-9 |
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