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Scenario-based stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs

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  • Samimi, Abouzar
  • Nikzad, Mehdi
  • Siano, Pierluigi

Abstract

In this paper, an efficient stochastic framework is proposed to develop a coupled active and reactive market in smart distribution systems. Distributed Energy Resources (DERs) can offer active powers to the market and also offer their reactive powers via a multi-component bidding framework constructed based on their reactive power capability diagrams. Distribution Company (Disco) buys active and reactive powers from a wholesale market and sells them via this market. Aggregators on behalf of responsive loads can participate in the market using a demand buyback program (DBP). The uncertainties of forecasted loads and wind power generation are considered in the proposed framework. To model the stochastic variables, the scenario tree is created using the Weibull and the Gaussian probability density functions (PDFs). The cost objective function of the stochastic coupled market clearing consists of the expected costs of energy and reactive power purchased from the DERs and Disco, the expected penalty cost of CO2 emissions of DERs and the main grid as well as the expected cost of running DBP. The proposed market is cleared through a mixed-integer nonlinear optimization problem solved in GAMS software. The effectiveness of the proposed method is investigated based on a 22-bus 20-kV radial distribution test system.

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  • Samimi, Abouzar & Nikzad, Mehdi & Siano, Pierluigi, 2017. "Scenario-based stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs," Renewable Energy, Elsevier, vol. 109(C), pages 22-40.
    • Handle: RePEc:eee:renene:v:109:y:2017:i:c:p:22-40
    DOI: 10.1016/j.renene.2017.03.010
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    7. Mimica, Marko & Dominković, Dominik Franjo & Capuder, Tomislav & Krajačić, Goran, 2021. "On the value and potential of demand response in the smart island archipelago," Renewable Energy, Elsevier, vol. 176(C), pages 153-168.
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    11. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Træholt, Chresten, 2018. "Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage," Renewable Energy, Elsevier, vol. 123(C), pages 204-217.
    12. Gonzalez-Reina, Antonio Enrique & Garcia-Torres, Felix & Girona-Garcia, Victor & Sanchez-Sanchez-de-Puerta, Alvaro & Jimenez-Romero, F.J. & Jimenez-Hornero, Jorge E., 2024. "Cooperative model predictive control for avoiding critical instants of energy resilience in networked microgrids," Applied Energy, Elsevier, vol. 369(C).
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