IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i6p1024-d214354.html
   My bibliography  Save this article

Aggregating Large-Scale Generalized Energy Storages to Participate in the Energy and Regulation Market

Author

Listed:
  • Yao Yao

    (Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Peichao Zhang

    (Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Sijie Chen

    (Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

This paper proposes a concept of generalized energy storage (GES) to facilitate the integration of large-scale heterogeneous flexible resources with electric/thermal energy storage capacity, in order to participate in multiple markets. First, a general state variable, referred to as the degree of satisfaction (DoS), is defined, and dynamic models with a unified form are derived for different types of GESs. Then, a real-time market-based coordination framework is proposed to facilitate control, as well as to ensure user privacy and device security. Demand curves of different GESs are then developed, based on DoS, to express their demand urgencies as well as flexibilities. Furthermore, a low-dimensional aggregate dynamic model of a GES cluster is derived, thanks to the DoS-equality control feature provided by the design of the demand curve. Finally, an optimization model for large-scale GESs to participate in both the energy market and regulation market is established, based on the aggregate model. Simulation results demonstrate that the optimization algorithm could effectively reduce the total cost of an aggregator. Additionally, the proposed coordination method has a high tracking accuracy and could well satisfy a diversified power demand.

Suggested Citation

  • Yao Yao & Peichao Zhang & Sijie Chen, 2019. "Aggregating Large-Scale Generalized Energy Storages to Participate in the Energy and Regulation Market," Energies, MDPI, vol. 12(6), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1024-:d:214354
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/6/1024/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/6/1024/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yebai Qi & Dan Wang & Xuyang Wang & Hongjie Jia & Tianjiao Pu & Naishi Chen & Kaixin Liu, 2017. "Frequency Control Ancillary Service Provided by Efficient Power Plants Integrated in Queuing-Controlled Domestic Water Heaters," Energies, MDPI, vol. 10(4), pages 1-21, April.
    2. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    3. Kai Ma & Chenliang Yuan & Jie Yang & Zhixin Liu & Xinping Guan, 2017. "Switched Control Strategies of Aggregated Commercial HVAC Systems for Demand Response in Smart Grids," Energies, MDPI, vol. 10(7), pages 1-18, July.
    4. Weiliang Wang & Dan Wang & Hongjie Jia & Guixiong He & Qing’e Hu & Pang-Chieh Sui & Menghua Fan, 2017. "Performance Evaluation of a Hydrogen-Based Clean Energy Hub with Electrolyzers as a Self-Regulating Demand Response Management Mechanism," Energies, MDPI, vol. 10(8), pages 1-23, August.
    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. Qitian Mu & Yajing Gao & Yongchun Yang & Haifeng Liang, 2019. "Design of Power Supply Package for Electricity Sales Companies Considering User Side Energy Storage Configuration," Energies, MDPI, vol. 12(17), pages 1-16, August.
    2. Ricardo Faia & Pedro Faria & Zita Vale & João Spinola, 2019. "Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House," Energies, MDPI, vol. 12(9), pages 1-18, April.
    3. Yizhi Cheng & Peichao Zhang & Xuezhi Liu, 2019. "Collaborative Autonomous Optimization of Interconnected Multi-Energy Systems with Two-Stage Transactive Control Framework," Energies, MDPI, vol. 13(1), pages 1-21, December.
    4. Fernando Lezama & Ricardo Faia & Pedro Faria & Zita Vale, 2020. "Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms," Energies, MDPI, vol. 13(10), pages 1-18, May.

    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. Rahmat Khezri & Arman Oshnoei & Mehrdad Tarafdar Hagh & SM Muyeen, 2018. "Coordination of Heat Pumps, Electric Vehicles and AGC for Efficient LFC in a Smart Hybrid Power System via SCA-Based Optimized FOPID Controllers," Energies, MDPI, vol. 11(2), pages 1-21, February.
    2. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    3. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    4. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Biskas, Pandelis N. & Keranidis, Stratos D. & Symeonidis, Polychronis A. & Voulgarakis, Dimitrios K., 2022. "A novel system for providing explicit demand response from domestic natural gas boilers," Applied Energy, Elsevier, vol. 317(C).
    5. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    6. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    7. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    8. Tiejiang Yuan & Qingxi Duan & Xiangping Chen & Xufeng Yuan & Wenping Cao & Juan Hu & Quanmin Zhu, 2017. "Coordinated Control of a Wind-Methanol-Fuel Cell System with Hydrogen Storage," Energies, MDPI, vol. 10(12), pages 1-21, December.
    9. Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2018. "Modelling urban energy requirements using open source data and models," Applied Energy, Elsevier, vol. 231(C), pages 1100-1108.
    10. Xiaojin Xie & Kangyang Luo & Zhixiang Yin & Guoqiang Wang, 2021. "Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    11. Wang, Deyun & Yue, Chenqiang & ElAmraoui, Adnen, 2021. "Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    12. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    13. Qiangqiang Cheng & Yiqi Yan & Shichao Liu & Chunsheng Yang & Hicham Chaoui & Mohamad Alzayed, 2020. "Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling," Energies, MDPI, vol. 13(24), pages 1-15, December.
    14. Ana Fernández-Guillamón & Guillermo Martínez-Lucas & Ángel Molina-García & Jose Ignacio Sarasua, 2020. "An Adaptive Control Scheme for Variable Speed Wind Turbines Providing Frequency Regulation in Isolated Power Systems with Thermal Generation," Energies, MDPI, vol. 13(13), pages 1-19, July.
    15. Yang, Qing & Zhang, Lei & Zou, Shaohui & Zhang, Jinsuo, 2020. "Intertemporal optimization of the coal production capacity in China in terms of uncertain demand, economy, environment, and energy security," Energy Policy, Elsevier, vol. 139(C).
    16. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
    17. Jieyi Kang & David Reiner, 2021. "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Working Papers EPRG2113, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    18. Zhaojing Yin & Yanbo Che & Dezhi Li & Huanan Liu & Dongmin Yu, 2017. "Optimal Scheduling Strategy for Domestic Electric Water Heaters Based on the Temperature State Priority List," Energies, MDPI, vol. 10(9), pages 1-15, September.
    19. Jian Yang & Xin Zhao & Haikun Wei & Kanjian Zhang, 2019. "Sample Selection Based on Active Learning for Short-Term Wind Speed Prediction," Energies, MDPI, vol. 12(3), pages 1-12, January.
    20. Ahmad, Tanveer & Huanxin, Chen & Zhang, Dongdong & Zhang, Hongcai, 2020. "Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions," Energy, Elsevier, vol. 198(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:gam:jeners:v:12:y:2019:i:6:p:1024-:d:214354. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.