IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v350y2023ics0306261923010450.html
   My bibliography  Save this article

Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy

Author

Listed:
  • Meng, Yan
  • Fan, Shuai
  • Shen, Yu
  • Xiao, Jucheng
  • He, Guangyu
  • Li, Zuyi

Abstract

Large-scale demand response (DR) is a promising solution to mitigate the problem of renewable energy (RE) curtailment with the rising proliferation of RE in both the transmission system (TS) and distribution system (DS). However, the current DR schemes face significant limitations when applied at a large scale, and the lack of consideration of TS and DS networks stymies the potential of large-scale DR in facilitating RE accommodation. To address these gaps, this paper proposes a novel hierarchical DR scheme based on locational customer directrix load (LCDL) that takes into account both TS and DS, involving interactions among tri-layer entities: the transmission system operator, distribution system operators, and customers. Firstly, the concept of LCDLs is proposed to characterize the desired load profiles at various locations, considering the constraints of both TS and DS networks. Subsequently, transmission-level and distribution-level LCDLs are formulated respectively and leveraged to induce the load reshaping of flexible resources at pertinent locations, thereby unlocking the deliverable flexibilities over TS and DS. Furthermore, the collaborative interaction among the three layers is depicted by a two-loop Stackelberg game, and a distributed algorithm is presented to achieve an equilibrium solution without compromising the privacy of the respective entities. Case studies testify that the proposed DR scheme significantly enhances the capacity to accommodate RE in both TS and DS, maintains economic balance in the trading process of DR services, and benefits all involved entities. Conducted on a practical city power grid with a substantial share of RE and DR customers, the simulation test validates the scalability of the proposed DR scheme and results show that with a 10% increment of DR customers in one DS, the RE curtailment rate could be reduced by 3% and 15% for TS and DS, respectively.

Suggested Citation

  • Meng, Yan & Fan, Shuai & Shen, Yu & Xiao, Jucheng & He, Guangyu & Li, Zuyi, 2023. "Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy," Applied Energy, Elsevier, vol. 350(C).
    • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923010450
    DOI: 10.1016/j.apenergy.2023.121681
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923010450
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121681?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. Li, Kangping & Wang, Fei & Mi, Zengqiang & Fotuhi-Firuzabad, Mahmoud & Duić, Neven & Wang, Tieqiang, 2019. "Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    3. Good, Nicholas, 2019. "Using behavioural economic theory in modelling of demand response," Applied Energy, Elsevier, vol. 239(C), pages 107-116.
    4. Ming, Hao & Meng, Jing & Gao, Ciwei & Song, Meng & Chen, Tao & Choi, Dae-Hyun, 2023. "Efficiency improvement of decentralized incentive-based demand response: Social welfare analysis and market mechanism design," Applied Energy, Elsevier, vol. 331(C).
    5. Liu, Shuo & Yang, Zhifang & Xia, Qing & Lin, Wei & Shi, Lianjun & Zeng, Dan, 2020. "Power trading region considering long-term contract for interconnected power networks," Applied Energy, Elsevier, vol. 261(C).
    6. Misconel, Steffi & Zöphel, Christoph & Möst, Dominik, 2021. "Assessing the value of demand response in a decarbonized energy system – A large-scale model application," Applied Energy, Elsevier, vol. 299(C).
    7. Ziras, Charalampos & Heinrich, Carsten & Bindner, Henrik W., 2021. "Why baselines are not suited for local flexibility markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Wei, F. & Jing, Z.X. & Wu, Peter Z. & Wu, Q.H., 2017. "A Stackelberg game approach for multiple energies trading in integrated energy systems," Applied Energy, Elsevier, vol. 200(C), pages 315-329.
    9. 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.
    10. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2022. "Peer-to-Peer trading with Demand Response using proposed smart bidding strategy," Applied Energy, Elsevier, vol. 327(C).
    11. Wang, Bo & Yang, Zihan & Le Hoa Pham, Thi & Deng, Nana & Du, Heran, 2023. "Can social impacts promote residents’ pro-environmental intentions and behaviour: Evidence from large-scale demand response experiment in China," Applied Energy, Elsevier, vol. 340(C).
    12. Thomas Morstyn & Niall Farrell & Sarah J. Darby & Malcolm D. McCulloch, 2018. "Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants," Nature Energy, Nature, vol. 3(2), pages 94-101, February.
    13. Yan, Lei & Tian, Wei & Wang, Hong & Hao, Xing & Li, Zuyi, 2023. "Robust event detection for residential load disaggregation," Applied Energy, Elsevier, vol. 331(C).
    14. Wang, Yong & Li, Lin, 2016. "Critical peak electricity pricing for sustainable manufacturing: Modeling and case studies," Applied Energy, Elsevier, vol. 175(C), pages 40-53.
    15. Pearson, Simon & Wellnitz, Sonja & Crespo del Granado, Pedro & Hashemipour, Naser, 2022. "The value of TSO-DSO coordination in re-dispatch with flexible decentralized energy sources: Insights for Germany in 2030," Applied Energy, Elsevier, vol. 326(C).
    16. Wu, Z. & Guo, F. & Polak, J. & Strbac, G., 2019. "Evaluating grid-interactive electric bus operation and demand response with load management tariff," Applied Energy, Elsevier, vol. 255(C).
    17. Zhenyu Zhuo & Ershun Du & Ning Zhang & Chris P. Nielsen & Xi Lu & Jinyu Xiao & Jiawei Wu & Chongqing Kang, 2022. "Cost increase in the electricity supply to achieve carbon neutrality in China," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    18. Dong, Lianxin & Wu, Qing & Hong, Juhua & Wang, Zhihua & Fan, Shuai & He, Guangyu, 2023. "An adaptive decentralized regulation strategy for the cluster with massive inverter air conditionings," Applied Energy, Elsevier, vol. 330(PA).
    19. Lee, Eunjung & Lee, Kyungeun & Lee, Hyoseop & Kim, Euncheol & Rhee, Wonjong, 2019. "Defining virtual control group to improve customer baseline load calculation of residential demand response," Applied Energy, Elsevier, vol. 250(C), pages 946-958.
    20. Lin, Boqiang & Zhu, Penghu, 2021. "Measurement of the direct rebound effect of residential electricity consumption: An empirical study based on the China family panel studies," Applied Energy, Elsevier, vol. 301(C).
    21. Harrison Fell & Jeremiah X. Johnson, 2021. "Regional disparities in emissions reduction and net trade from renewables," Nature Sustainability, Nature, vol. 4(4), pages 358-365, April.
    22. Duan, Jiandong & Liu, Fan & Yang, Yao, 2022. "Optimal operation for integrated electricity and natural gas systems considering demand response uncertainties," Applied Energy, Elsevier, vol. 323(C).
    23. Fan, Shuai & Liu, Jiang & Wu, Qing & Cui, Mingjian & Zhou, Huan & He, Guangyu, 2020. "Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm," Applied Energy, Elsevier, vol. 277(C).
    24. Hou, Qingchun & Zhang, Ning & Du, Ershun & Miao, Miao & Peng, Fei & Kang, Chongqing, 2019. "Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China," Applied Energy, Elsevier, vol. 242(C), pages 205-215.
    25. Xiao, Jucheng & He, Guangyu & Fan, Shuai & Li, Zuyi, 2022. "Substitute energy price market mechanism for renewable energy power system with generalized energy storage," Applied Energy, Elsevier, vol. 328(C).
    26. Liu, Jizhe & Zhang, Yuchen & Meng, Ke & Dong, Zhao Yang & Xu, Yan & Han, Siming, 2022. "Real-time emergency load shedding for power system transient stability control: A risk-averse deep learning method," Applied Energy, Elsevier, vol. 307(C).
    27. Xiao, Jucheng & He, Guangyu & Fan, Shuai & Zhang, Siyuan & Wu, Qing & Li, Zuyi, 2020. "Decentralized transfer of contingency reserve: Framework and methodology," Applied Energy, Elsevier, vol. 278(C).
    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. Ziras, Charalampos & Heinrich, Carsten & Bindner, Henrik W., 2021. "Why baselines are not suited for local flexibility markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Bhatti, Bilal Ahmad & Broadwater, Robert, 2019. "Energy trading in the distribution system using a non-model based game theoretic approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Wang, Lu & Gu, Wei & Wu, Zhi & Qiu, Haifeng & Pan, Guangsheng, 2020. "Non-cooperative game-based multilateral contract transactions in power-heating integrated systems," Applied Energy, Elsevier, vol. 268(C).
    4. Lin, Jin & Dong, Jun & Liu, Dongran & Zhang, Yaoyu & Ma, Tongtao, 2022. "From peak shedding to low-carbon transitions: Customer psychological factors in demand response," Energy, Elsevier, vol. 238(PA).
    5. Dong, Lianxin & Fan, Shuai & Wang, Zhihua & Xiao, Jucheng & Zhou, Huan & Li, Zuyi & He, Guangyu, 2021. "An adaptive decentralized economic dispatch method for virtual power plant," Applied Energy, Elsevier, vol. 300(C).
    6. Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
    7. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    8. Zhang, Xiaoyan & Zhu, Shanying & He, Jianping & Yang, Bo & Guan, Xinping, 2019. "Credit rating based real-time energy trading in microgrids," Applied Energy, Elsevier, vol. 236(C), pages 985-996.
    9. Todd, Annika & Cappers, Peter & Spurlock, C. Anna & Jin, Ling, 2019. "Spillover as a cause of bias in baseline evaluation methods for demand response programs," Applied Energy, Elsevier, vol. 250(C), pages 344-357.
    10. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    11. Ziras, Charalampos & Heinrich, Carsten & Pertl, Michael & Bindner, Henrik W., 2019. "Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data," Applied Energy, Elsevier, vol. 242(C), pages 1407-1421.
    12. Li, Jiamei & Ai, Qian & Yin, Shuangrui & Hao, Ran, 2022. "An aggregator-oriented hierarchical market mechanism for multi-type ancillary service provision based on the two-loop Stackelberg game," Applied Energy, Elsevier, vol. 323(C).
    13. Bhatti, Bilal Ahmad & Broadwater, Robert, 2020. "Distributed Nash Equilibrium Seeking for a Dynamic Micro-grid Energy Trading Game with Non-quadratic Payoffs," Energy, Elsevier, vol. 202(C).
    14. Pan, Chongchao & Jin, Tai & Li, Na & Wang, Guanxiong & Hou, Xiaowang & Gu, Yueqing, 2023. "Multi-objective and two-stage optimization study of integrated energy systems considering P2G and integrated demand responses," Energy, Elsevier, vol. 270(C).
    15. Zhang, Yijie & Ma, Tao & Yang, Hongxing, 2022. "Grid-connected photovoltaic battery systems: A comprehensive review and perspectives," Applied Energy, Elsevier, vol. 328(C).
    16. Kaijun Lin & Junyong Wu & Di Liu & Dezhi Li & Taorong Gong, 2018. "Energy Management of Combined Cooling, Heating and Power Micro Energy Grid Based on Leader-Follower Game Theory," Energies, MDPI, vol. 11(3), pages 1-21, March.
    17. Kobashi, Takuro & Choi, Younghun & Hirano, Yujiro & Yamagata, Yoshiki & Say, Kelvin, 2022. "Rapid rise of decarbonization potentials of photovoltaics plus electric vehicles in residential houses over commercial districts," Applied Energy, Elsevier, vol. 306(PB).
    18. Guixing Yang & Haoran Liu & Weiqing Wang & Junru Chen & Shunbo Lei, 2023. "Distributed Optimal Coordination of a Virtual Power Plant with Residential Regenerative Electric Heating Systems," Energies, MDPI, vol. 16(11), pages 1-15, May.
    19. Lin, Jin & Dong, Jun & Dou, Xihao & Liu, Yao & Yang, Peiwen & Ma, Tongtao, 2022. "Psychological insights for incentive-based demand response incorporating battery energy storage systems: A two-loop Stackelberg game approach," Energy, Elsevier, vol. 239(PC).
    20. Keda Pan & Changhong Xie & Chun Sing Lai & Dongxiao Wang & Loi Lei Lai, 2020. "Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems," Forecasting, MDPI, vol. 2(4), pages 1-18, November.

    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:350:y:2023:i:c:s0306261923010450. 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.