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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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    8. 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.
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    10. 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.
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    13. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    14. Aliakbari Sani, Sajad & Bahn, Olivier & Delage, Erick, 2022. "Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning," European Journal of Operational Research, Elsevier, vol. 303(1), pages 438-455.
    15. Babonneau, Frédéric & Benlahrech, Maroua & Haurie, Alain, 2022. "Transition to zero-net emissions for Qatar: A policy based on Hydrogen and CO2 capture & storage development," Energy Policy, Elsevier, vol. 170(C).
    16. Emanuel Canelas & Tânia Pinto-Varela & Bartosz Sawik, 2020. "Electricity Portfolio Optimization for Large Consumers: Iberian Electricity Market Case Study," Energies, MDPI, vol. 13(9), pages 1-21, May.
    17. Magdalena Krystyna Wyrwicka & Ewa Więcek-Janka & Łukasz Brzeziński, 2023. "Transition to Sustainable Energy System for Smart Cities—Literature Review," Energies, MDPI, vol. 16(21), pages 1-26, October.
    18. 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.
    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.

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