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Optimal procurement of flexibility services within electricity distribution networks

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  • Laur, Arnaud
  • Nieto-Martin, Jesus
  • Bunn, Derek W.
  • Vicente-Pastor, Alejandro

Abstract

The increased injection of volatile renewable energy at local levels into the electricity grid is forcing the distribution network operators to seek participation in emerging service markets in order to obtain the flexibility required to balance their systems. However, the distribution companies lack experience in designing new market arrangements. We consider a market framework wherein a proactive distribution company is willing to purchase reserve capacity for overload management, using a two-part tariff. The problem is modelled as a three-stage stochastic market including Day-Ahead, Intra-Day and Real-Time, with uncertainty on both demand and generation. After assessing our formulation, we discuss the impact of risk-aversion at each stage with an objective function based on CVaR. Finally, different Intra-Day clearing horizons and delivery settings are evaluated. The results show that risk-aversion close to Real-Time becomes the main driver for decision makers and that early hedging strategies lead to sub-optimal solutions.

Suggested Citation

  • Laur, Arnaud & Nieto-Martin, Jesus & Bunn, Derek W. & Vicente-Pastor, Alejandro, 2020. "Optimal procurement of flexibility services within electricity distribution networks," European Journal of Operational Research, Elsevier, vol. 285(1), pages 34-47.
    • Handle: RePEc:eee:ejores:v:285:y:2020:i:1:p:34-47
    DOI: 10.1016/j.ejor.2018.11.031
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    1. Homem-de-Mello, Tito & Pagnoncelli, Bernardo K., 2016. "Risk aversion in multistage stochastic programming: A modeling and algorithmic perspective," European Journal of Operational Research, Elsevier, vol. 249(1), pages 188-199.
    2. Bjørndal, Endre & Bjørndal, Mette & Cai, Hong & Panos, Evangelos, 2018. "Hybrid pricing in a coupled European power market with more wind power," European Journal of Operational Research, Elsevier, vol. 264(3), pages 919-931.
    3. Chaves-Ávila, J.P. & Fernandes, C., 2015. "The Spanish intraday market design: A successful solution to balance renewable generation?," Renewable Energy, Elsevier, vol. 74(C), pages 422-432.
    4. Olivella-Rosell, Pol & Bullich-Massagué, Eduard & Aragüés-Peñalba, Mònica & Sumper, Andreas & Ottesen, Stig Ødegaard & Vidal-Clos, Josep-Andreu & Villafáfila-Robles, Roberto, 2018. "Optimization problem for meeting distribution system operator requests in local flexibility markets with distributed energy resources," Applied Energy, Elsevier, vol. 210(C), pages 881-895.
    5. Scharff, Richard & Amelin, Mikael, 2016. "Trading behaviour on the continuous intraday market Elbas," Energy Policy, Elsevier, vol. 88(C), pages 544-557.
    6. Najafi, Amir Abbas & Mushakhian, Siamak, 2015. "Multi-stage stochastic mean–semivariance–CVaR portfolio optimization under transaction costs," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 445-458.
    7. Devine, Mel T. & Bertsch, Valentin, 2018. "Examining the benefits of load shedding strategies using a rolling-horizon stochastic mixed complementarity equilibrium model," European Journal of Operational Research, Elsevier, vol. 267(2), pages 643-658.
    8. Ruester, Sophia & Schwenen, Sebastian & Batlle, Carlos & Pérez-Arriaga, Ignacio, 2014. "From distribution networks to smart distribution systems: Rethinking the regulation of European electricity DSOs," Utilities Policy, Elsevier, vol. 31(C), pages 229-237.
    9. Victor M. Zavala & Kibaek Kim & Mihai Anitescu & John Birge, 2017. "A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties," Operations Research, INFORMS, vol. 65(3), pages 557-576, June.
    10. Laureano Escudero & Araceli Garín & María Merino & Gloria Pérez, 2007. "The value of the stochastic solution in multistage problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 48-64, July.
    11. Benjamin, Richard, 2013. "A two-part tariff for financing transmission expansion," Utilities Policy, Elsevier, vol. 27(C), pages 98-107.
    12. Kai Detlefsen & Giacomo Scandolo, 2005. "Conditional and Dynamic Convex Risk Measures," SFB 649 Discussion Papers SFB649DP2005-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    13. Torbat, Sheida & Khashei, Mehdi & Bijari, Mehdi, 2018. "A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 22-31.
    14. Hu, Jing & Harmsen, Robert & Crijns-Graus, Wina & Worrell, Ernst & van den Broek, Machteld, 2018. "Identifying barriers to large-scale integration of variable renewable electricity into the electricity market: A literature review of market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2181-2195.
    15. Antweiler, Werner, 2017. "A two-part feed-in-tariff for intermittent electricity generation," Energy Economics, Elsevier, vol. 65(C), pages 458-470.
    16. Sovacool, Benjamin K. & Kivimaa, Paula & Hielscher, Sabine & Jenkins, Kirsten, 2017. "Vulnerability and resistance in the United Kingdom's smart meter transition," Energy Policy, Elsevier, vol. 109(C), pages 767-781.
    17. Mohan, Vivek & Singh, Jai Govind & Ongsakul, Weerakorn, 2015. "An efficient two stage stochastic optimal energy and reserve management in a microgrid," Applied Energy, Elsevier, vol. 160(C), pages 28-38.
    18. Morales, Juan M. & Zugno, Marco & Pineda, Salvador & Pinson, Pierre, 2014. "Electricity market clearing with improved scheduling of stochastic production," European Journal of Operational Research, Elsevier, vol. 235(3), pages 765-774.
    19. Kai Detlefsen & Giacomo Scandolo, 2005. "Conditional and dynamic convex risk measures," Finance and Stochastics, Springer, vol. 9(4), pages 539-561, October.
    20. Chen, J.J. & Wu, Q.H. & Zhang, L.L. & Wu, P.Z., 2017. "Multi-objective mean–variance–skewness model for nonconvex and stochastic optimal power flow considering wind power and load uncertainties," European Journal of Operational Research, Elsevier, vol. 263(2), pages 719-732.
    21. de Joode, J. & Jansen, J.C. & van der Welle, A.J. & Scheepers, M.J.J., 2009. "Increasing penetration of renewable and distributed electricity generation and the need for different network regulation," Energy Policy, Elsevier, vol. 37(8), pages 2907-2915, August.
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    2. Hermann, Alexander & Jensen, Tue Vissing & Østergaard, Jacob & Kazempour, Jalal, 2022. "A complementarity model for electric power transmission-distribution coordination under uncertainty," European Journal of Operational Research, Elsevier, vol. 299(1), pages 313-329.
    3. Skolfield, J. Kyle & Escobedo, Adolfo R., 2022. "Operations research in optimal power flow: A guide to recent and emerging methodologies and applications," European Journal of Operational Research, Elsevier, vol. 300(2), pages 387-404.
    4. Varawala, Lamia & Dán, György & Hesamzadeh, Mohammad Reza & Baldick, Ross, 2023. "A generalised approach for efficient computation of look ahead security constrained optimal power flow," European Journal of Operational Research, Elsevier, vol. 310(2), pages 477-494.
    5. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    6. Juan Sebastian Roncancio & José Vuelvas & Diego Patino & Carlos Adrián Correa-Flórez, 2022. "Flower Greenhouse Energy Management to Offer Local Flexibility Markets," Energies, MDPI, vol. 15(13), pages 1-20, June.

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