Two-stage multi-timescale optimal scheduling for electricity-hydrogen coupling systems based on scenario approach and deep reinforcement learning
In electricity-hydrogen coupling systems， fluctuations in wind and solar power output， as well as the different timescales for electricity and hydrogen energy dispatch， pose significant challenges for economic and efficient system scheduling. To address these challenges， this paper， using scenario a...
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zhejiang electric power
2025-01-01
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| Series: | Zhejiang dianli |
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| Online Access: | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=f69837de-9ae8-4c4f-8bb3-61e3ffea2a34 |
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| _version_ | 1823857064075591680 |
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| author | CHEN Zhe WEI Meijia LIN Da LI Zhihao CHEN Jian |
| author_facet | CHEN Zhe WEI Meijia LIN Da LI Zhihao CHEN Jian |
| author_sort | CHEN Zhe |
| collection | DOAJ |
| description | In electricity-hydrogen coupling systems， fluctuations in wind and solar power output， as well as the different timescales for electricity and hydrogen energy dispatch， pose significant challenges for economic and efficient system scheduling. To address these challenges， this paper， using scenario approach and deep reinforcement learning （DRL）， proposes a two-stage multi-timescale optimal scheduling method for electricity-hydrogen coupling systems considering uncertainties of wind and solar power generation. First， the operational characteristics of energy storage devices， including electrical and hydrogen storage devices， are analyzed， and a two-stage optimal scheduling framework for electricity-hydrogen coupling systems is designed. Next， with the uncertainties of wind and solar power generation considered， long-term and short-term timescale optimal models are developed. The long-term timescale model aims to maximize the systems’ energy self-balance by generating typical wind and solar output scenarios using Latin hypercube sampling （LHS） for scenario generation and reduction， followed by optimization. The short-term model focuses on minimizing the systems’ operational costs and is solved using the deep deterministic policy gradient （DDPG） algorithm. Finally， case study simulations demonstrate that the proposed method effectively facilitates hydrogen energy shifting， smooths fluctuations in wind and solar output， verifying the method’s effectiveness. |
| format | Article |
| id | doaj-art-6adad638eaff4022bd66e18130c0cf12 |
| institution | Kabale University |
| issn | 1007-1881 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | zhejiang electric power |
| record_format | Article |
| series | Zhejiang dianli |
| spelling | doaj-art-6adad638eaff4022bd66e18130c0cf122025-02-12T00:54:58Zzhozhejiang electric powerZhejiang dianli1007-18812025-01-01441546710.19585/j.zjdl.2025010061007-1881（2025）01-0054-14Two-stage multi-timescale optimal scheduling for electricity-hydrogen coupling systems based on scenario approach and deep reinforcement learningCHEN Zhe0WEI Meijia1LIN Da2LI Zhihao3CHEN Jian4State Grid Zhejiang Electric Power Co.， Ltd. Research Institute， Hangzhou 310014， ChinaKey Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education （Shandong University）， Jinan 250061， ChinaState Grid Zhejiang Electric Power Co.， Ltd. Research Institute， Hangzhou 310014， ChinaState Grid Zhejiang Electric Power Co.， Ltd. Research Institute， Hangzhou 310014， ChinaKey Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education （Shandong University）， Jinan 250061， ChinaIn electricity-hydrogen coupling systems， fluctuations in wind and solar power output， as well as the different timescales for electricity and hydrogen energy dispatch， pose significant challenges for economic and efficient system scheduling. To address these challenges， this paper， using scenario approach and deep reinforcement learning （DRL）， proposes a two-stage multi-timescale optimal scheduling method for electricity-hydrogen coupling systems considering uncertainties of wind and solar power generation. First， the operational characteristics of energy storage devices， including electrical and hydrogen storage devices， are analyzed， and a two-stage optimal scheduling framework for electricity-hydrogen coupling systems is designed. Next， with the uncertainties of wind and solar power generation considered， long-term and short-term timescale optimal models are developed. The long-term timescale model aims to maximize the systems’ energy self-balance by generating typical wind and solar output scenarios using Latin hypercube sampling （LHS） for scenario generation and reduction， followed by optimization. The short-term model focuses on minimizing the systems’ operational costs and is solved using the deep deterministic policy gradient （DDPG） algorithm. Finally， case study simulations demonstrate that the proposed method effectively facilitates hydrogen energy shifting， smooths fluctuations in wind and solar output， verifying the method’s effectiveness.https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=f69837de-9ae8-4c4f-8bb3-61e3ffea2a34electricity-hydrogen coupling systemmulti-timescale optimal schedulingscenario generation and reductiondrluncertainties of wind and solar power generation |
| spellingShingle | CHEN Zhe WEI Meijia LIN Da LI Zhihao CHEN Jian Two-stage multi-timescale optimal scheduling for electricity-hydrogen coupling systems based on scenario approach and deep reinforcement learning Zhejiang dianli electricity-hydrogen coupling system multi-timescale optimal scheduling scenario generation and reduction drl uncertainties of wind and solar power generation |
| title | Two-stage multi-timescale optimal scheduling for electricity-hydrogen coupling systems based on scenario approach and deep reinforcement learning |
| title_full | Two-stage multi-timescale optimal scheduling for electricity-hydrogen coupling systems based on scenario approach and deep reinforcement learning |
| title_fullStr | Two-stage multi-timescale optimal scheduling for electricity-hydrogen coupling systems based on scenario approach and deep reinforcement learning |
| title_full_unstemmed | Two-stage multi-timescale optimal scheduling for electricity-hydrogen coupling systems based on scenario approach and deep reinforcement learning |
| title_short | Two-stage multi-timescale optimal scheduling for electricity-hydrogen coupling systems based on scenario approach and deep reinforcement learning |
| title_sort | two stage multi timescale optimal scheduling for electricity hydrogen coupling systems based on scenario approach and deep reinforcement learning |
| topic | electricity-hydrogen coupling system multi-timescale optimal scheduling scenario generation and reduction drl uncertainties of wind and solar power generation |
| url | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=f69837de-9ae8-4c4f-8bb3-61e3ffea2a34 |
| work_keys_str_mv | AT chenzhe twostagemultitimescaleoptimalschedulingforelectricityhydrogencouplingsystemsbasedonscenarioapproachanddeepreinforcementlearning AT weimeijia twostagemultitimescaleoptimalschedulingforelectricityhydrogencouplingsystemsbasedonscenarioapproachanddeepreinforcementlearning AT linda twostagemultitimescaleoptimalschedulingforelectricityhydrogencouplingsystemsbasedonscenarioapproachanddeepreinforcementlearning AT lizhihao twostagemultitimescaleoptimalschedulingforelectricityhydrogencouplingsystemsbasedonscenarioapproachanddeepreinforcementlearning AT chenjian twostagemultitimescaleoptimalschedulingforelectricityhydrogencouplingsystemsbasedonscenarioapproachanddeepreinforcementlearning |