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Optimal dispatch for a microgrid incorporating renewables and demand response

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  • Nwulu, Nnamdi I.
  • Xia, Xiaohua

Abstract

This paper proposes an optimal economic dispatch of a grid connected microgrid. The microgrid consists of solar photovoltaic, diesel and wind power sources. An Incentive Based Demand Response Program is incorporated into the operations of the grid connected microgrid. The optimal dispatch strategy is obtained by minimizing the conventional generators fuel cost, the transaction costs of the transferable power and maximizing the microgrid operator's demand response benefit whilst simultaneously satisfying the load demand constraints amongst other constraints. The developed mathematical model is tested on two practical case studies and sensitivity analysis of the model to key parameters was also performed. Case study 1 consists of three conventional generator units, one wind generator, one solar generator and three rural customers. Case study 2 is a much larger microgrid and was chosen to test the applicability of our model to larger microgrids and also to verify the scalability of our algorithm. Results show that the demand response program curtails significant grid relieving amounts of energy in the two case studies considered and integration of an incentive based demand response programs into the microgrid energy management problem introduces optimality at both the supply and demand spectrum of the grid.

Suggested Citation

  • Nwulu, Nnamdi I. & Xia, Xiaohua, 2017. "Optimal dispatch for a microgrid incorporating renewables and demand response," Renewable Energy, Elsevier, vol. 101(C), pages 16-28.
    • Handle: RePEc:eee:renene:v:101:y:2017:i:c:p:16-28
    DOI: 10.1016/j.renene.2016.08.026
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    References listed on IDEAS

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    1. Nwulu, Nnamdi I. & Xia, Xiaohua, 2015. "Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs," Energy, Elsevier, vol. 91(C), pages 404-419.
    2. Neves, Diana & Brito, Miguel C. & Silva, Carlos A., 2016. "Impact of solar and wind forecast uncertainties on demand response of isolated microgrids," Renewable Energy, Elsevier, vol. 87(P2), pages 1003-1015.
    3. Soshinskaya, Mariya & Crijns-Graus, Wina H.J. & Guerrero, Josep M. & Vasquez, Juan C., 2014. "Microgrids: Experiences, barriers and success factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 659-672.
    4. Velik, Rosemarie & Nicolay, Pascal, 2014. "Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer," Applied Energy, Elsevier, vol. 130(C), pages 384-395.
    5. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.
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