IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v189y2017icp157-176.html
   My bibliography  Save this article

Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems

Author

Listed:
  • Zhang, Xiaoshun
  • Chen, Yixuan
  • Yu, Tao
  • Yang, Bo
  • Qu, Kaiping
  • Mao, Senmao

Abstract

This paper proposes a novel equilibrium-inspired multiagent optimizer (EMO) with extreme transfer learning for decentralized optimal carbon-energy combined-flow (OCECF) of large-scale power systems. The original large-scale power system is firstly divided into several small-scale subsystems, in which each subsystem is regarded as an agent, such that a decentralized OCECF can be achieved via a Nash game among all the agents. Then, a knowledge matrix associated with a state-action chain is presented for knowledge storing of the previous optimization tasks, which can be updated by a continuous interaction with the external environment. Furthermore, an extreme learning machine is adopted for an efficient transfer learning, such that the convergence rate of a new task can be dramatically accelerated by properly exploiting the prior knowledge of the source tasks. EMO has been thoroughly evaluated for the decentralized OCECF on IEEE 57-bus system, IEEE 300-bus system, and a practical Shenzhen power grid of southern China. Case studies and engineering application verify that EMO can effectively handle the decentralized OCECF of large-scale power systems.

Suggested Citation

  • Zhang, Xiaoshun & Chen, Yixuan & Yu, Tao & Yang, Bo & Qu, Kaiping & Mao, Senmao, 2017. "Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems," Applied Energy, Elsevier, vol. 189(C), pages 157-176.
    • Handle: RePEc:eee:appene:v:189:y:2017:i:c:p:157-176
    DOI: 10.1016/j.apenergy.2016.12.080
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916318475
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.12.080?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. Abdul Manaf, Norhuda & Qadir, Abdul & Abbas, Ali, 2016. "Temporal multiscalar decision support framework for flexible operation of carbon capture plants targeting low-carbon management of power plant emissions," Applied Energy, Elsevier, vol. 169(C), pages 912-926.
    2. Abhijit Gosavi, 2009. "Reinforcement Learning: A Tutorial Survey and Recent Advances," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 178-192, May.
    3. Razeghi, Ghazal & Brouwer, Jack & Samuelsen, Scott, 2016. "A spatially and temporally resolved model of the electricity grid – Economic vs environmental dispatch," Applied Energy, Elsevier, vol. 178(C), pages 540-556.
    4. Lin, Yashen & Johnson, Jeremiah X. & Mathieu, Johanna L., 2016. "Emissions impacts of using energy storage for power system reserves," Applied Energy, Elsevier, vol. 168(C), pages 444-456.
    5. Ali, Ehab S., 2015. "Speed control of induction motor supplied by wind turbine via Imperialist Competitive Algorithm," Energy, Elsevier, vol. 89(C), pages 593-600.
    6. Yu, Shiwei & Wei, Yi-Ming & Guo, Haixiang & Ding, Liping, 2014. "Carbon emission coefficient measurement of the coal-to-power energy chain in China," Applied Energy, Elsevier, vol. 114(C), pages 290-300.
    7. Kumar, Pawan & Kim, Ki-Hyun, 2016. "Recent progress and innovation in carbon capture and storage using bioinspired materials," Applied Energy, Elsevier, vol. 172(C), pages 383-397.
    8. Han, Xiaojuan & Zhang, Hua & Yu, Xiaoling & Wang, Lina, 2016. "Economic evaluation of grid-connected micro-grid system with photovoltaic and energy storage under different investment and financing models," Applied Energy, Elsevier, vol. 184(C), pages 103-118.
    9. Lenzen, Manfred & Murray, Joy & Sack, Fabian & Wiedmann, Thomas, 2007. "Shared producer and consumer responsibility -- Theory and practice," Ecological Economics, Elsevier, vol. 61(1), pages 27-42, February.
    10. Liu, Liwei & Sun, Xiaoru & Chen, Chuxiang & Zhao, Erdong, 2016. "How will auctioning impact on the carbon emission abatement cost of electric power generation sector in China?," Applied Energy, Elsevier, vol. 168(C), pages 594-609.
    11. Lau, E.T. & Yang, Q. & Stokes, L. & Taylor, G.A. & Forbes, A.B. & Clarkson, P. & Wright, P.S. & Livina, V.N., 2015. "Carbon savings in the UK demand side response programmes," Applied Energy, Elsevier, vol. 159(C), pages 478-489.
    12. Tan, Sieting & Yang, Jin & Yan, Jinyue & Lee, Chewtin & Hashim, Haslenda & Chen, Bin, 2017. "A holistic low carbon city indicator framework for sustainable development," Applied Energy, Elsevier, vol. 185(P2), pages 1919-1930.
    13. Wei, Wei & Liu, Feng & Wang, Jianhui & Chen, Laijun & Mei, Shengwei & Yuan, Tiejiang, 2016. "Robust environmental-economic dispatch incorporating wind power generation and carbon capture plants," Applied Energy, Elsevier, vol. 183(C), pages 674-684.
    14. Koltsaklis, Nikolaos E. & Georgiadis, Michael C., 2015. "A multi-period, multi-regional generation expansion planning model incorporating unit commitment constraints," Applied Energy, Elsevier, vol. 158(C), pages 310-331.
    15. Xi, Lei & Yu, Tao & Yang, Bo & Zhang, Xiaoshun & Qiu, Xuanyu, 2016. "A wolf pack hunting strategy based virtual tribes control for automatic generation control of smart grid," Applied Energy, Elsevier, vol. 178(C), pages 198-211.
    16. Zhang, Xiaoshun & Yu, Tao & Yang, Bo & Li, Li, 2016. "Virtual generation tribe based robust collaborative consensus algorithm for dynamic generation command dispatch optimization of smart grid," Energy, Elsevier, vol. 101(C), pages 34-51.
    17. Blanca Gallego & Manfred Lenzen, 2005. "A consistent input-output formulation of shared producer and consumer responsibility," Economic Systems Research, Taylor & Francis Journals, vol. 17(4), pages 365-391.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Roslan, M.F. & Hannan, M.A. & Jern Ker, Pin & Begum, R.A. & Indra Mahlia, TM & Dong, Z.Y., 2021. "Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction," Applied Energy, Elsevier, vol. 292(C).
    2. Yin, Linfei & Wang, Tao & Wang, Senlin & Zheng, Baomin, 2019. "Interchange objective value method for distributed multi-objective optimization: Theory, application, implementation," Applied Energy, Elsevier, vol. 239(C), pages 1066-1076.
    3. Hu, Chenxi & Zhang, Jun & Yuan, Hongxia & Gao, Tianlu & Jiang, Huaiguang & Yan, Jing & Wenzhong Gao, David & Wang, Fei-Yue, 2022. "Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19," Applied Energy, Elsevier, vol. 309(C).
    4. Wang, Tianjing & Tang, Yong, 2022. "Transfer-Reinforcement-Learning-Based rescheduling of differential power grids considering security constraints," Applied Energy, Elsevier, vol. 306(PB).
    5. Qian, Fanyue & Gao, Weijun & Yang, Yongwen & Yu, Dan, 2020. "Potential analysis of the transfer learning model in short and medium-term forecasting of building HVAC energy consumption," Energy, Elsevier, vol. 193(C).
    6. Xiaoming Zhou & Maosheng Sang & Minglei Bao & Yi Ding, 2022. "Tracing and Evaluating Life-Cycle Carbon Emissions of Urban Multi-Energy Systems," Energies, MDPI, vol. 15(8), pages 1-19, April.
    7. Yin, Linfei & Wang, Tao & Zheng, Baomin, 2021. "Analytical adaptive distributed multi-objective optimization algorithm for optimal power flow problems," Energy, Elsevier, vol. 216(C).
    8. Chuanjia Han & Bo Yang & Tao Bao & Tao Yu & Xiaoshun Zhang, 2017. "Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer," Energies, MDPI, vol. 10(5), pages 1-24, May.
    9. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun & Li, Fusheng & Lin, Dan & Zhu, Hanxin, 2021. "Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system," Applied Energy, Elsevier, vol. 285(C).
    10. Yin, Linfei & Li, Yu, 2022. "Hybrid multi-agent emotional deep Q network for generation control of multi-area integrated energy systems," Applied Energy, Elsevier, vol. 324(C).
    11. Fan Li & Jingxi Su & Bo Sun, 2023. "An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm," Energies, MDPI, vol. 16(9), pages 1-22, April.
    12. Dong, Lei & Lin, Hao & Qiao, Ji & Zhang, Tao & Zhang, Shiming & Pu, Tianjiao, 2024. "A coordinated active and reactive power optimization approach for multi-microgrids connected to distribution networks with multi-actor-attention-critic deep reinforcement learning," Applied Energy, Elsevier, vol. 373(C).

    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. Shin, Hansol & Kim, Tae Hyun & Kim, Hyoungtae & Lee, Sungwoo & Kim, Wook, 2019. "Environmental shutdown of coal-fired generators for greenhouse gas reduction: A case study of South Korea," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. Airebule, Palizha & Cheng, Haitao & Ishikawa, Jota, 2023. "Assessing carbon emissions embodied in international trade based on shared responsibility," Journal of the Japanese and International Economies, Elsevier, vol. 68(C).
    3. Ivanova, Diana & Wieland, Hanspeter, 2023. "Tracing carbon footprints to intermediate industries in the United Kingdom," Ecological Economics, Elsevier, vol. 214(C).
    4. Chen, G.Q. & Chen, Z.M., 2011. "Greenhouse gas emissions and natural resources use by the world economy: Ecological input–output modeling," Ecological Modelling, Elsevier, vol. 222(14), pages 2362-2376.
    5. Rui Xie & Chao Gao & Guomei Zhao & Yu Liu & Shengcheng Xu, 2017. "Empirical Study of China’s Provincial Carbon Responsibility Sharing: Provincial Value Chain Perspective," Sustainability, MDPI, vol. 9(4), pages 1-16, April.
    6. Goudarzi, Arman & Swanson, Andrew G. & Van Coller, John & Siano, Pierluigi, 2017. "Smart real-time scheduling of generating units in an electricity market considering environmental aspects and physical constraints of generators," Applied Energy, Elsevier, vol. 189(C), pages 667-696.
    7. Springmann, Marco & Zhang, Da & Xiliang, Zhang & Karplus, Valerie J., 2013. "Incorporating consumption-based emissions accounting into climate policy in China: Provincial target setting and ETS baseline allocations," Conference papers 332341, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    8. Serrano, Mònica & Dietzenbacher, Erik, 2010. "Responsibility and trade emission balances: An evaluation of approaches," Ecological Economics, Elsevier, vol. 69(11), pages 2224-2232, September.
    9. M. Cordier & T. Poitelon & W. Hecq, 2019. "The shared environmental responsibility principle: new developments applied to the case of marine ecosystems," Economic Systems Research, Taylor & Francis Journals, vol. 31(2), pages 228-247, April.
    10. Arto, Iñaki & Roca, Jordi & Serrano, Mònica, 2014. "Measuring emissions avoided by international trade: Accounting for price differences," Ecological Economics, Elsevier, vol. 97(C), pages 93-100.
    11. Xu, Xueliu & Wang, Qian & Ran, Chenyang & Mu, Mingjie, 2021. "Is burden responsibility more effective? A value-added method for tracing worldwide carbon emissions," Ecological Economics, Elsevier, vol. 181(C).
    12. Kang, Charles A. & Brandt, Adam R. & Durlofsky, Louis J. & Jayaweera, Indira, 2016. "Assessment of advanced solvent-based post-combustion CO2 capture processes using a bi-objective optimization technique," Applied Energy, Elsevier, vol. 179(C), pages 1209-1219.
    13. Hanak, Dawid P. & Kolios, Athanasios J. & Manovic, Vasilije, 2016. "Comparison of probabilistic performance of calcium looping and chemical solvent scrubbing retrofits for CO2 capture from coal-fired power plant," Applied Energy, Elsevier, vol. 172(C), pages 323-336.
    14. Wencheng Zhang & Shuijun Peng, 2016. "Analysis on CO 2 Emissions Transferred from Developed Economies to China through Trade," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 24(2), pages 68-89, March.
    15. Li, Meng & Meng, Bo & Gao, Yuning & Wang, Zhi & Zhang, Yaxiong & Sun, Yongping, 2022. "Tracing CO₂ emissions in global value chains: Multinationals vs. domestically-owned firms," Sustainable Global Supply Chains Discussion Papers 2, Research Network Sustainable Global Supply Chains.
    16. Shin, Hansol & Kim, Wook, 2023. "Comparison of the centralized and decentralized environmentally constrained economic dispatch methods of coal-fired generators: A case study for South Korea," Energy, Elsevier, vol. 275(C).
    17. Boya Zhang & Shukuan Bai & Yadong Ning & Tao Ding & Yan Zhang, 2020. "Emission Embodied in International Trade and Its Responsibility from the Perspective of Global Value Chain: Progress, Trends, and Challenges," Sustainability, MDPI, vol. 12(8), pages 1-26, April.
    18. Xiang Gao & Sandy Dall'erba & Brenna Ellison & Andre F. T. Avelino & Cuihong Yang, 2022. "When one cannot bypass the byproducts: Plastic packaging waste embedded in production and export," Journal of Industrial Ecology, Yale University, vol. 26(4), pages 1460-1474, August.
    19. Franco Solís, Alberto & F.T. Avelino, André & Carrascal-Incera, André, 2020. "The evolution of household-induced value chains and their environmental implications," Ecological Economics, Elsevier, vol. 174(C).
    20. Hana Nielsen & Astrid Kander, 2020. "Trade in the Carbon-Constrained Future: Exploiting the Comparative Carbon Advantage of Swedish Trade," Energies, MDPI, vol. 13(14), pages 1-25, July.

    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:appene:v:189:y:2017:i:c:p:157-176. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.