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Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs

Author

Listed:
  • Pouria Sheikhahmadi

    (Department of Electrical and Computer Engineering, University of Kurdistan, Sanandaj 66131, Iran)

  • Ramyar Mafakheri

    (Department of Electrical Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj 66131, Iran)

  • Salah Bahramara

    (Department of Electrical Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj 66131, Iran)

  • Maziar Yazdani Damavandi

    (C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal)

  • João P. S. Catalão

    (C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal
    INESC TEC and the Faculty of Engineering of the University of Porto, 4200-465 Porto, Portugal
    INESC-ID, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal)

Abstract

The operation problem of a micro-grid (MG) in grid-connected mode is an optimization one in which the main objective of the MG operator (MGO) is to minimize the operation cost with optimal scheduling of resources and optimal trading energy with the main grid. The MGO can use incentive-based demand response programs (DRPs) to pay an incentive to the consumers to change their demands in the peak hours. Moreover, the MGO forecasts the output power of renewable energy resources (RERs) and models their uncertainties in its problem. In this paper, the operation problem of an MGO is modeled as a risk-based two-stage stochastic optimization problem. To model the uncertainties of RERs, two-stage stochastic programming is considered and conditional value at risk (CVaR) index is used to manage the MGO’s risk-level. Moreover, the non-linear economic models of incentive-based DRPs are used by the MGO to change the peak load. The numerical studies are done to investigate the effect of incentive-based DRPs on the operation problem of the MGO. Moreover, to show the effect of the risk-averse parameter on MGO decisions, a sensitivity analysis is carried out.

Suggested Citation

  • Pouria Sheikhahmadi & Ramyar Mafakheri & Salah Bahramara & Maziar Yazdani Damavandi & João P. S. Catalão, 2018. "Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs," Energies, MDPI, vol. 11(3), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:610-:d:135557
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    References listed on IDEAS

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    7. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    8. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
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