Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: an improved soft actor–critic approach

Due to uncertainties in renewable energy generation and load demands, traditional energy dispatch schemes for an integrated electricity–gas system (IEGS) considerably depend on explicit forecast mathematical models. In this study, a novel data-driven deep reinforcement learning method is applied to...

Full description

Saved in:
Bibliographic Details
Main Authors: Zhang, Bin, Wu, Xuewei, Ghias, Amer M. Y. M., Chen, Zhe
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2023
Subjects:
Online Access:https://hdl.handle.net/10356/172498
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-172498
record_format dspace
spelling sg-ntu-dr.10356-1724982023-12-12T02:06:46Z Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: an improved soft actor–critic approach Zhang, Bin Wu, Xuewei Ghias, Amer M. Y. M. Chen, Zhe School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Electricity-Gas Coupled System Prioritized Experience Replay Due to uncertainties in renewable energy generation and load demands, traditional energy dispatch schemes for an integrated electricity–gas system (IEGS) considerably depend on explicit forecast mathematical models. In this study, a novel data-driven deep reinforcement learning method is applied to solve the IEGS dynamic dispatch problem with the targets of minimizing carbon emission and operating cost. Moreover, a flexible operation of carbon capture system and power-to-gas facility is proposed to attain low operating costs. The IEGS dynamic dispatch problem is formulated as a Markov game, and a soft actor–critic (SAC) algorithm is applied to learn the optimal dispatch solution. To improve training efficiency and convergence, prioritized experience replay (PER) is employed. In the simulation, the proposed PER–SAC algorithm compared with deep Q-network and SAC has fast and stable learning performance. In contrast to a modified sequential quadratic programming based on uncertainty prediction, the proposed method can reduce the target cost by 11.62% when the prediction error exceeds 10%. The computational time of scenario analysis solution on the same hardware platform is 4.58 times than that of training the PER–SAC method. Finally, the simulation results under different scenarios demonstrate that the PER–SAC-based dispatch strategy has satisfactory generalization and adaptability. 2023-12-12T02:06:45Z 2023-12-12T02:06:45Z 2023 Journal Article Zhang, B., Wu, X., Ghias, A. M. Y. M. & Chen, Z. (2023). Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: an improved soft actor–critic approach. Energy, 271, 126965-. https://dx.doi.org/10.1016/j.energy.2023.126965 0360-5442 https://hdl.handle.net/10356/172498 10.1016/j.energy.2023.126965 2-s2.0-85149268435 271 126965 en Energy © 2023 Published by Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Electricity-Gas Coupled System
Prioritized Experience Replay
spellingShingle Engineering::Electrical and electronic engineering
Electricity-Gas Coupled System
Prioritized Experience Replay
Zhang, Bin
Wu, Xuewei
Ghias, Amer M. Y. M.
Chen, Zhe
Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: an improved soft actor–critic approach
description Due to uncertainties in renewable energy generation and load demands, traditional energy dispatch schemes for an integrated electricity–gas system (IEGS) considerably depend on explicit forecast mathematical models. In this study, a novel data-driven deep reinforcement learning method is applied to solve the IEGS dynamic dispatch problem with the targets of minimizing carbon emission and operating cost. Moreover, a flexible operation of carbon capture system and power-to-gas facility is proposed to attain low operating costs. The IEGS dynamic dispatch problem is formulated as a Markov game, and a soft actor–critic (SAC) algorithm is applied to learn the optimal dispatch solution. To improve training efficiency and convergence, prioritized experience replay (PER) is employed. In the simulation, the proposed PER–SAC algorithm compared with deep Q-network and SAC has fast and stable learning performance. In contrast to a modified sequential quadratic programming based on uncertainty prediction, the proposed method can reduce the target cost by 11.62% when the prediction error exceeds 10%. The computational time of scenario analysis solution on the same hardware platform is 4.58 times than that of training the PER–SAC method. Finally, the simulation results under different scenarios demonstrate that the PER–SAC-based dispatch strategy has satisfactory generalization and adaptability.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Zhang, Bin
Wu, Xuewei
Ghias, Amer M. Y. M.
Chen, Zhe
format Article
author Zhang, Bin
Wu, Xuewei
Ghias, Amer M. Y. M.
Chen, Zhe
author_sort Zhang, Bin
title Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: an improved soft actor–critic approach
title_short Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: an improved soft actor–critic approach
title_full Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: an improved soft actor–critic approach
title_fullStr Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: an improved soft actor–critic approach
title_full_unstemmed Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: an improved soft actor–critic approach
title_sort coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: an improved soft actor–critic approach
publishDate 2023
url https://hdl.handle.net/10356/172498
_version_ 1787136687026995200