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A linear programming model for power distribution with demand response and variable renewable energy

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  • Babonneau, Frédéric
  • Caramanis, Michael
  • Haurie, Alain

Abstract

A linear programming framework is proposed to model distribution network characteristics, and market clearing processes for flexible load and distributed energy resources providing reserve and reactive power compensation. One shows that the Nash equilibrium solution representing the interaction between utility and customers for demand response and distributed reserve transactions can be approximated by a linear program when the players (i.e. the customers) are numerous and tend to become infinitesimal. Then a linear program is shown to reveal the market prices, corresponding to the marginal cost for the utility. The goal in developing this model is to provide a new module for a regional long term model of development of smart energy systems. This module will then introduce in the modeling of energy transition, the new options and constraints that are provided by a penetration of renewables with the possibility of implementing distributed markets for demand response and system services permitted by the development of the cyber-physical layer. A case study of a potential smart urban distribution network in Europe is carried out and provides numerical results that illustrate the proposed framework.

Suggested Citation

  • Babonneau, Frédéric & Caramanis, Michael & Haurie, Alain, 2016. "A linear programming model for power distribution with demand response and variable renewable energy," Applied Energy, Elsevier, vol. 181(C), pages 83-95.
    • Handle: RePEc:eee:appene:v:181:y:2016:i:c:p:83-95
    DOI: 10.1016/j.apenergy.2016.08.028
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    12. 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.
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    15. Chenhao Sun & Xiwei Jiang & Zhiwei Jia & Kun Yu & Sheng Xiang & Jianhong Su, 2023. "An Optimization Ensemble for Integrated Energy System Configuration Strategy Incorporating Demand–Supply Coordination," Sustainability, MDPI, vol. 15(21), pages 1-17, October.
    16. Liu, Jia & Cheng, Haozhong & Zeng, Pingliang & Yao, Liangzhong & Shang, Ce & Tian, Yuan, 2018. "Decentralized stochastic optimization based planning of integrated transmission and distribution networks with distributed generation penetration," Applied Energy, Elsevier, vol. 220(C), pages 800-813.
    17. F. Babonneau & R. T. Foguen & A. Haurie & R. Malhamé, 2021. "Coupling a Power Dispatch Model with a Wardrop or Mean-Field-Game Equilibrium Model," Dynamic Games and Applications, Springer, vol. 11(2), pages 217-241, June.
    18. Ajagekar, Akshay & Decardi-Nelson, Benjamin & You, Fengqi, 2024. "Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 355(C).
    19. Máximo A. Domínguez-Garabitos & Víctor S. Ocaña-Guevara & Félix Santos-García & Adriana Arango-Manrique & Miguel Aybar-Mejía, 2022. "A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market," Energies, MDPI, vol. 15(4), pages 1-28, February.
    20. Lund, Henrik & Østergaard, Poul Alberg & Connolly, David & Mathiesen, Brian Vad, 2017. "Smart energy and smart energy systems," Energy, Elsevier, vol. 137(C), pages 556-565.
    21. Zheng, Zhuang & Shafique, Muhammad & Luo, Xiaowei & Wang, Shengwei, 2024. "A systematic review towards integrative energy management of smart grids and urban energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

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