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Efficient demand response location targeting for price spike mitigation by exploiting price-demand relationship

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  • Zhang, Yufan
  • Wen, Honglin
  • Feng, Tao
  • Chen, Yize

Abstract

Demand response (DR) leverages demand-side flexibility, offering a promising approach to enhance market conditions like mitigating wholesale price spikes. However, poorly chosen DR locations can inadvertently increase electricity prices. For that, we introduce a method to rigorously select DR locations and corresponding demand reductions. We formulate a bilevel program where the upper level determines the DR locations and demand reductions while ensuring the average nodal prices meet a predetermined target. The lower level tackles an economic dispatch (ED) problem and feeds the resulting nodal prices back to the upper level based on post-DR demands. This bilevel formulation presents challenges due to the lower-level non-convexity affecting the upper-level constraints on average nodal prices. To address this, we propose to replace the lower level with a piecewise linear function representing the price-demand relationship, solving iteratively for each linear segment. This results in a tractable mixed-integer linear program. An acceleration strategy is proposed to further reduce the computation time. Numerical studies demonstrate the ability of the proposed approach to reduce prices to a desired level. Besides, we empirically show that the proposed approach is robust against inaccurate system parameters and can reduce computation time by over 50%.

Suggested Citation

  • Zhang, Yufan & Wen, Honglin & Feng, Tao & Chen, Yize, 2024. "Efficient demand response location targeting for price spike mitigation by exploiting price-demand relationship," Applied Energy, Elsevier, vol. 376(PA).
    • Handle: RePEc:eee:appene:v:376:y:2024:i:pa:s0306261924015241
    DOI: 10.1016/j.apenergy.2024.124141
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    References listed on IDEAS

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    1. Doering, Kenji & Sendelbach, Luke & Steinschneider, Scott & Lindsay Anderson, C., 2021. "The effects of wind generation and other market determinants on price spikes," Applied Energy, Elsevier, vol. 300(C).
    2. de Souza Dutra, Michael David & Alguacil, Natalia, 2020. "Optimal residential users coordination via demand response: An exact distributed framework," Applied Energy, Elsevier, vol. 279(C).
    3. Vivek Dua & Efstratios Pistikopoulos, 2000. "An Algorithm for the Solution of Multiparametric Mixed Integer Linear Programming Problems," Annals of Operations Research, Springer, vol. 99(1), pages 123-139, December.
    4. Su, Yufei & Kern, Jordan D. & Reed, Patrick M. & Characklis, Gregory W., 2020. "Compound hydrometeorological extremes across multiple timescales drive volatility in California electricity market prices and emissions," Applied Energy, Elsevier, vol. 276(C).
    5. Mari, Carlo, 2014. "Hedging electricity price volatility using nuclear power," Applied Energy, Elsevier, vol. 113(C), pages 615-621.
    6. Zareipour, Hamidreza & Bhattacharya, Kankar & Canizares, Claudio A., 2007. "Electricity market price volatility: The case of Ontario," Energy Policy, Elsevier, vol. 35(9), pages 4739-4748, September.
    7. Davarzani, Sima & Granell, Ramon & Taylor, Gareth A. & Pisica, Ioana, 2019. "Implementation of a novel multi-agent system for demand response management in low-voltage distribution networks," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    8. Mulhall, Rachel Ann & Bryson, John R., 2014. "Energy price risk and the sustainability of demand side supply chains," Applied Energy, Elsevier, vol. 123(C), pages 327-334.
    9. Han, Rushuai & Hu, Qinran & Cui, Hantao & Chen, Tao & Quan, Xiangjun & Wu, Zaijun, 2022. "An optimal bidding and scheduling method for load service entities considering demand response uncertainty," Applied Energy, Elsevier, vol. 328(C).
    10. Sirin, Selahattin Murat & Camadan, Ercument & Erten, Ibrahim Etem & Zhang, Alex Hongliang, 2023. "Market failure or politics? Understanding the motives behind regulatory actions to address surging electricity prices," Energy Policy, Elsevier, vol. 180(C).
    11. Stawska, Anna & Romero, Natalia & de Weerdt, Mathijs & Verzijlbergh, Remco, 2021. "Demand response: For congestion management or for grid balancing?," Energy Policy, Elsevier, vol. 148(PA).
    12. Nizami, M.S.H. & Hossain, M.J. & Amin, B.M. Ruhul & Fernandez, Edstan, 2020. "A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading," Applied Energy, Elsevier, vol. 261(C).
    13. Nolan, Sheila & O’Malley, Mark, 2015. "Challenges and barriers to demand response deployment and evaluation," Applied Energy, Elsevier, vol. 152(C), pages 1-10.
    14. Lin, Jianing & Bao, Minglei & Liang, Ziyang & Sang, Maosheng & Ding, Yi, 2022. "Spatio-temporal evaluation of electricity price risk considering multiple uncertainties under extreme cold weather," Applied Energy, Elsevier, vol. 328(C).
    Full references (including those not matched with items on IDEAS)

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