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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Main Authors: CHEN Zhe, WEI Meijia, LIN Da, LI Zhihao, CHEN Jian
Format: Article
Language:zho
Published: zhejiang electric power 2025-01-01
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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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
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AT weimeijia twostagemultitimescaleoptimalschedulingforelectricityhydrogencouplingsystemsbasedonscenarioapproachanddeepreinforcementlearning
AT linda twostagemultitimescaleoptimalschedulingforelectricityhydrogencouplingsystemsbasedonscenarioapproachanddeepreinforcementlearning
AT lizhihao twostagemultitimescaleoptimalschedulingforelectricityhydrogencouplingsystemsbasedonscenarioapproachanddeepreinforcementlearning
AT chenjian twostagemultitimescaleoptimalschedulingforelectricityhydrogencouplingsystemsbasedonscenarioapproachanddeepreinforcementlearning