A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid
Optimal project portfolio planning is a typical nonconvex, multiobjective, highly constrained, multitemporal coupling, and combinatorial optimization problem. This paper proposes a novel multistep iterative ranking learning method (MIRL) to solve this complex combinatorial optimization problem from...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2023/1358099 |
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| _version_ | 1850214547509477376 |
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| author | Cong Liu Xianghua Li Jian Liang Kun Sheng Lingzhao Kong Xiaoyan Peng Wenxin Zhao |
| author_facet | Cong Liu Xianghua Li Jian Liang Kun Sheng Lingzhao Kong Xiaoyan Peng Wenxin Zhao |
| author_sort | Cong Liu |
| collection | DOAJ |
| description | Optimal project portfolio planning is a typical nonconvex, multiobjective, highly constrained, multitemporal coupling, and combinatorial optimization problem. This paper proposes a novel multistep iterative ranking learning method (MIRL) to solve this complex combinatorial optimization problem from massive infrastructure projects of smart grid. The optimal project portfolio planning problem of power grid is formulated as the optimization process of massive project priority sorting with an improved knapsack model. The proposed method dynamically optimizes the best infrastructure project combination for each round to maximize the economic, social, and security benefits without exceeding the annual investment limit. A pairwise-based ranking learning algorithm is used to mine the priority sorting law from massive historical combination data of power grid to initialize candidate project portfolio. In order to approach the optimal portfolio planning solution with the constraint satisfactions of project construction duration and electric load supplies, a heuristic greedy strategy is designed to search the solution dynamically for selecting the project having highest construction benefits iteratively. The effectiveness of the proposed method is proved by experiments with real-world project data of Hunan power grid in China, and experimental results show that the proposed MIRL can outperform other methods on investment efficiency, calculation time, and rationality of project construction period schedule. |
| format | Article |
| id | doaj-art-81568568499a4799a10ccf2b491cb821 |
| institution | OA Journals |
| issn | 2050-7038 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-81568568499a4799a10ccf2b491cb8212025-08-20T02:08:50ZengWileyInternational Transactions on Electrical Energy Systems2050-70382023-01-01202310.1155/2023/1358099A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart GridCong Liu0Xianghua Li1Jian Liang2Kun Sheng3Lingzhao Kong4Xiaoyan Peng5Wenxin Zhao6State Grid Hunan Electric Power Company Limited Economic & Technical Research InstituteState Grid Hunan Electric Power Company Limited Economic & Technical Research InstituteState Grid Hunan Electric Power Company LimitedState Grid Hunan Electric Power Company LimitedCollege of Computer Science and Electronic EngineeringCollege of Computer Science and Electronic EngineeringCollege of Electrical and Information EngineeringOptimal project portfolio planning is a typical nonconvex, multiobjective, highly constrained, multitemporal coupling, and combinatorial optimization problem. This paper proposes a novel multistep iterative ranking learning method (MIRL) to solve this complex combinatorial optimization problem from massive infrastructure projects of smart grid. The optimal project portfolio planning problem of power grid is formulated as the optimization process of massive project priority sorting with an improved knapsack model. The proposed method dynamically optimizes the best infrastructure project combination for each round to maximize the economic, social, and security benefits without exceeding the annual investment limit. A pairwise-based ranking learning algorithm is used to mine the priority sorting law from massive historical combination data of power grid to initialize candidate project portfolio. In order to approach the optimal portfolio planning solution with the constraint satisfactions of project construction duration and electric load supplies, a heuristic greedy strategy is designed to search the solution dynamically for selecting the project having highest construction benefits iteratively. The effectiveness of the proposed method is proved by experiments with real-world project data of Hunan power grid in China, and experimental results show that the proposed MIRL can outperform other methods on investment efficiency, calculation time, and rationality of project construction period schedule.http://dx.doi.org/10.1155/2023/1358099 |
| spellingShingle | Cong Liu Xianghua Li Jian Liang Kun Sheng Lingzhao Kong Xiaoyan Peng Wenxin Zhao A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid International Transactions on Electrical Energy Systems |
| title | A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid |
| title_full | A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid |
| title_fullStr | A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid |
| title_full_unstemmed | A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid |
| title_short | A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid |
| title_sort | multistep iterative ranking learning method for optimal project portfolio planning of smart grid |
| url | http://dx.doi.org/10.1155/2023/1358099 |
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