Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets
Past research has predominantly focused on utilizing meta-heuristic algorithms to optimize neural network structures, while the exploration of deep learning in optimization has remained relatively limited. The proposed hybrid approach seeks to enhance wind power bidding strategies, improving profita...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447925000267 |
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| author | Viet Anh Truong Ngoc Sang Dinh Thanh Long Duong Ngoc Thien Le Cong Dinh Truong Linh Tung Nguyen |
| author_facet | Viet Anh Truong Ngoc Sang Dinh Thanh Long Duong Ngoc Thien Le Cong Dinh Truong Linh Tung Nguyen |
| author_sort | Viet Anh Truong |
| collection | DOAJ |
| description | Past research has predominantly focused on utilizing meta-heuristic algorithms to optimize neural network structures, while the exploration of deep learning in optimization has remained relatively limited. The proposed hybrid approach seeks to enhance wind power bidding strategies, improving profitability by predicting optimal output power for day-ahead electricity markets. This method integrates Long Short-Term Memory (LSTM) with Particle Swarm Optimization (PSO), leveraging LSTM’s ability to predict the active movement tendencies of particles for more efficient and faster optimization. Experiments conducted on the IEEE 30-bus power system show that the LSTM-PSO hybrid outperforms mathematical models and standalone PSO algorithms. It also delivers an optimal wind power bidding strategy, yielding peak annual revenue, while recommending a 16 % reduction in bidding output power variance in models that integrate wind power with thermal power and energy storage systems (ESS). Ultimately, this approach fosters confidence in wind energy investment, contributing to sustainable development. |
| format | Article |
| id | doaj-art-ec36c4d4e09a49c0a10afd725f4318d2 |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-ec36c4d4e09a49c0a10afd725f4318d22025-02-04T04:10:23ZengElsevierAin Shams Engineering Journal2090-44792025-02-01162103285Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity marketsViet Anh Truong0Ngoc Sang Dinh1Thanh Long Duong2Ngoc Thien Le3Cong Dinh Truong4Linh Tung Nguyen5Hochiminh City University of Technology and Education, Ho Chi Minh City 71300, Viet NamHochiminh City University of Technology and Education, Ho Chi Minh City 71300, Viet Nam; University of Architecture Hochiminh City, Ho Chi Minh City 72400, Viet Nam; Corresponding author.Industrial University of Hochiminh City, Ho Chi Minh City 71400, Viet NamUniversity of Architecture Hochiminh City, Ho Chi Minh City 72400, Viet NamUniversity of Architecture Hochiminh City, Ho Chi Minh City 72400, Viet NamElectric Power University, Ha Noi City 11900, Viet NamPast research has predominantly focused on utilizing meta-heuristic algorithms to optimize neural network structures, while the exploration of deep learning in optimization has remained relatively limited. The proposed hybrid approach seeks to enhance wind power bidding strategies, improving profitability by predicting optimal output power for day-ahead electricity markets. This method integrates Long Short-Term Memory (LSTM) with Particle Swarm Optimization (PSO), leveraging LSTM’s ability to predict the active movement tendencies of particles for more efficient and faster optimization. Experiments conducted on the IEEE 30-bus power system show that the LSTM-PSO hybrid outperforms mathematical models and standalone PSO algorithms. It also delivers an optimal wind power bidding strategy, yielding peak annual revenue, while recommending a 16 % reduction in bidding output power variance in models that integrate wind power with thermal power and energy storage systems (ESS). Ultimately, this approach fosters confidence in wind energy investment, contributing to sustainable development.http://www.sciencedirect.com/science/article/pii/S2090447925000267Optimal algorithmLong short-term memoryParticle swarm optimizationWind farmElectricity market |
| spellingShingle | Viet Anh Truong Ngoc Sang Dinh Thanh Long Duong Ngoc Thien Le Cong Dinh Truong Linh Tung Nguyen Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets Ain Shams Engineering Journal Optimal algorithm Long short-term memory Particle swarm optimization Wind farm Electricity market |
| title | Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets |
| title_full | Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets |
| title_fullStr | Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets |
| title_full_unstemmed | Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets |
| title_short | Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets |
| title_sort | hybrid lstm pso optimization techniques for enhancing wind power bidding efficiency in electricity markets |
| topic | Optimal algorithm Long short-term memory Particle swarm optimization Wind farm Electricity market |
| url | http://www.sciencedirect.com/science/article/pii/S2090447925000267 |
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