IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v278y2023ipbs0360544223013099.html
   My bibliography  Save this article

A spatio-temporality-enabled parallel multi-agent-based real-time dynamic dispatch for hydro-PV-PHS integrated power system

Author

Listed:
  • Yang, Jingxian
  • Liu, Junyong
  • Qiu, Gao
  • Liu, Jichun
  • Jawad, Shafqat
  • Zhang, Shuai

Abstract

Integrated power system has emerged as a powerful alternative to penetrate renewables, due to its ability to reconcile energy discrepancy. However, due to limited mainstreams and complex mountainous meteorology, the dispatch of hydro-photovoltaic-pumped hydro storage (Hydro-PV-PHS) integrated power system (IPS) which are predominantly composed of cascaded daily-regulation and uncontrollable runoff hydropower stations and PVs still miss the expected clean energy utilization rates. To conquer the issue, a novel spatio-temporality-enabled parallel multi-agent-based dynamic dispatch method is proposed. At the outset, a temporal dispatch model fed by dynamic measurements of available PV generation and inflow is presented. To master the uncertainties, such model must be solved in real-time upon the incoming measurements. Whereupon the presented model is recast as Markov decision process for learning dispatch policies, parameterized by neural network agents. To manage the enormous spatial-temporal operating space of the hydro-PV-PHS IPS and to prevent conflict policies, a long short-term memory auto-encoder (LSTM-AE) combined unsupervised learning scheme is used to alleviate divergence and decrease stochasticity of renewables to pre-uncouple policies, which are then distributed to multiple parallel agents. Finally, distributed proximal policy optimization is conducted to produce dispatch policies in an offline parallel manner, with each agent responsible for dispatching the hydro-PV-PHS IPS within the respective operating subspace. The numerical studies in a real-world case demonstrate that the proposed scheme enables real-time and near-optimal dynamic dispatch for the concern IPS, and outperforms other rivals in terms of adaptability, robustness, and efficiency.

Suggested Citation

  • Yang, Jingxian & Liu, Junyong & Qiu, Gao & Liu, Jichun & Jawad, Shafqat & Zhang, Shuai, 2023. "A spatio-temporality-enabled parallel multi-agent-based real-time dynamic dispatch for hydro-PV-PHS integrated power system," Energy, Elsevier, vol. 278(PB).
  • Handle: RePEc:eee:energy:v:278:y:2023:i:pb:s0360544223013099
    DOI: 10.1016/j.energy.2023.127915
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223013099
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.127915?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    2. Wen, Xin & Sun, Yuanliang & Tan, Qiaofeng & Tang, Zhengyang & Wang, Zhenni & Liu, Zhehua & Ding, Ziyu, 2022. "Optimizing the sizes of wind and photovoltaic plants complementarily operating with cascade hydropower stations: Balancing risk and benefit," Applied Energy, Elsevier, vol. 306(PA).
    3. Jiao, Feixiang & Zou, Yuan & Zhang, Xudong & Zhang, Bin, 2022. "Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station," Energy, Elsevier, vol. 247(C).
    4. Makhdoomi, Sina & Askarzadeh, Alireza, 2020. "Daily performance optimization of a grid-connected hybrid system composed of photovoltaic and pumped hydro storage (PV/PHS)," Renewable Energy, Elsevier, vol. 159(C), pages 272-285.
    5. Guo, Chenyu & Wang, Xin & Zheng, Yihui & Zhang, Feng, 2022. "Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    6. Stoppato, Anna & Cavazzini, Giovanna & Ardizzon, Guido & Rossetti, Antonio, 2014. "A PSO (particle swarm optimization)-based model for the optimal management of a small PV(Photovoltaic)-pump hydro energy storage in a rural dry area," Energy, Elsevier, vol. 76(C), pages 168-174.
    7. Zhang, Yusheng & Ma, Chao & Yang, Yang & Pang, Xiulan & Liu, Lu & Lian, Jijian, 2021. "Study on short-term optimal operation of cascade hydro-photovoltaic hybrid systems," Applied Energy, Elsevier, vol. 291(C).
    8. Wei, Hu & Hongxuan, Zhang & Yu, Dong & Yiting, Wang & Ling, Dong & Ming, Xiao, 2019. "Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks," Applied Energy, Elsevier, vol. 250(C), pages 389-403.
    9. Gomes, I.L.R. & Melicio, R. & Mendes, V.M.F., 2021. "A novel microgrid support management system based on stochastic mixed-integer linear programming," Energy, Elsevier, vol. 223(C).
    10. Zhang, Yan & Meng, Fanlin & Wang, Rui & Kazemtabrizi, Behzad & Shi, Jianmai, 2019. "Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid," Energy, Elsevier, vol. 179(C), pages 1265-1278.
    11. Yin, Yue & Liu, Tianqi & He, Chuan, 2019. "Day-ahead stochastic coordinated scheduling for thermal-hydro-wind-photovoltaic systems," Energy, Elsevier, vol. 187(C).
    12. Beluco, Alexandre & Kroeff de Souza, Paulo & Krenzinger, Arno, 2012. "A method to evaluate the effect of complementarity in time between hydro and solar energy on the performance of hybrid hydro PV generating plants," Renewable Energy, Elsevier, vol. 45(C), pages 24-30.
    13. Lei, Kaixuan & Chang, Jianxia & Long, Ruihao & Wang, Yimin & Zhang, Hongxue, 2022. "Cascade hydropower station risk operation under the condition of inflow uncertainty," Energy, Elsevier, vol. 244(PA).
    14. Yang, Zhikai & Liu, Pan & Cheng, Lei & Liu, Deli & Ming, Bo & Li, He & Xia, Qian, 2021. "Sizing utility-scale photovoltaic power generation for integration into a hydropower plant considering the effects of climate change: A case study in the Longyangxia of China," Energy, Elsevier, vol. 236(C).
    15. François, B. & Zoccatelli, D. & Borga, M., 2017. "Assessing small hydro/solar power complementarity in ungauged mountainous areas: A crash test study for hydrological prediction methods," Energy, Elsevier, vol. 127(C), pages 716-729.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wilson Pavon & Esteban Inga & Silvio Simani & Maddalena Nonato, 2021. "A Review on Optimal Control for the Smart Grid Electrical Substation Enhancing Transition Stability," Energies, MDPI, vol. 14(24), pages 1-15, December.
    2. Cheng, Qian & Liu, Pan & Xia, Qian & Cheng, Lei & Ming, Bo & Zhang, Wei & Xu, Weifeng & Zheng, Yalian & Han, Dongyang & Xia, Jun, 2023. "An analytical method to evaluate curtailment of hydro–photovoltaic hybrid energy systems and its implication under climate change," Energy, Elsevier, vol. 278(C).
    3. Guo, Chenyu & Wang, Xin & Zheng, Yihui & Zhang, Feng, 2022. "Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    4. Huang, Kangdi & Liu, Pan & Ming, Bo & Kim, Jong-Suk & Gong, Yu, 2021. "Economic operation of a wind-solar-hydro complementary system considering risks of output shortage, power curtailment and spilled water," Applied Energy, Elsevier, vol. 290(C).
    5. Jurasz, Jakub & Kies, Alexander & Zajac, Pawel, 2020. "Synergetic operation of photovoltaic and hydro power stations on a day-ahead energy market," Energy, Elsevier, vol. 212(C).
    6. Guo, Yi & Ming, Bo & Huang, Qiang & Liu, Pan & Wang, Yimin & Fang, Wei & Zhang, Wei, 2022. "Evaluating effects of battery storage on day-ahead generation scheduling of large hydro–wind–photovoltaic complementary systems," Applied Energy, Elsevier, vol. 324(C).
    7. Ma, Chao & Liu, Lu, 2022. "Optimal capacity configuration of hydro-wind-PV hybrid system and its coordinative operation rules considering the UHV transmission and reservoir operation requirements," Renewable Energy, Elsevier, vol. 198(C), pages 637-653.
    8. Lu, Na & Wang, Guangyan & Su, Chengguo & Ren, Zaimin & Peng, Xiaoyue & Sui, Quan, 2024. "Medium- and long-term interval optimal scheduling of cascade hydropower-photovoltaic complementary systems considering multiple uncertainties," Applied Energy, Elsevier, vol. 353(PA).
    9. Chaoyang Chen & Hualing Liu & Yong Xiao & Fagen Zhu & Li Ding & Fuwen Yang, 2022. "Power Generation Scheduling for a Hydro-Wind-Solar Hybrid System: A Systematic Survey and Prospect," Energies, MDPI, vol. 15(22), pages 1-31, November.
    10. Ming, Bo & Liu, Pan & Guo, Shenglian & Cheng, Lei & Zhou, Yanlai & Gao, Shida & Li, He, 2018. "Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1341-1352.
    11. Bio Gassi, Karim & Baysal, Mustafa, 2023. "Improving real-time energy decision-making model with an actor-critic agent in modern microgrids with energy storage devices," Energy, Elsevier, vol. 263(PE).
    12. Huang, Kangdi & Liu, Pan & Kim, Jong-Suk & Xu, Weifeng & Gong, Yu & Cheng, Qian & Zhou, Yong, 2023. "A model coupling current non-adjustable, coming adjustable and remaining stages for daily generation scheduling of a wind-solar-hydro complementary system," Energy, Elsevier, vol. 263(PB).
    13. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Yan, Zhiyu, 2022. "A Wasserstein metric-based distributionally robust optimization approach for reliable-economic equilibrium operation of hydro-wind-solar energy systems," Renewable Energy, Elsevier, vol. 196(C), pages 204-219.
    14. Zhang, Yusheng & Zhao, Xuehua & Wang, Xin & Li, Aiyun & Wu, Xinhao, 2023. "Multi-objective optimization design of a grid-connected hybrid hydro-photovoltaic system considering power transmission capacity," Energy, Elsevier, vol. 284(C).
    15. Wang, Yijian & Cui, Yang & Li, Yang & Xu, Yang, 2023. "Collaborative optimization of multi-microgrids system with shared energy storage based on multi-agent stochastic game and reinforcement learning," Energy, Elsevier, vol. 280(C).
    16. Zhou, Siyu & Han, Yang & Zalhaf, Amr S. & Chen, Shuheng & Zhou, Te & Yang, Ping & Elboshy, Bahaa, 2023. "A novel multi-objective scheduling model for grid-connected hydro-wind-PV-battery complementary system under extreme weather: A case study of Sichuan, China," Renewable Energy, Elsevier, vol. 212(C), pages 818-833.
    17. Siqin, Zhuoya & Niu, DongXiao & Wang, Xuejie & Zhen, Hao & Li, MingYu & Wang, Jingbo, 2022. "A two-stage distributionally robust optimization model for P2G-CCHP microgrid considering uncertainty and carbon emission," Energy, Elsevier, vol. 260(C).
    18. Gong, Yu & Liu, Pan & Ming, Bo & Feng, Maoyuan & Huang, Kangdi & Wang, Yibo, 2022. "Identifying the functional form of operating rules for hydro–photovoltaic hybrid power systems," Energy, Elsevier, vol. 243(C).
    19. Jiang, Jianhua & Ming, Bo & Huang, Qiang & Guo, Yi & Shang, Jia’nan & Jurasz, Jakub & Liu, Pan, 2023. "A holistic techno-economic evaluation framework for sizing renewable power plant in a hydro-based hybrid generation system," Applied Energy, Elsevier, vol. 348(C).
    20. Canales, Fausto A. & Jurasz, Jakub & Beluco, Alexandre & Kies, Alexander, 2020. "Assessing temporal complementarity between three variable energy sources through correlation and compromise programming," Energy, Elsevier, vol. 192(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:278:y:2023:i:pb:s0360544223013099. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.