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Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources

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  • Das, Saborni
  • Basu, Mousumi

Abstract

In restructured electricity markets, microgrids are becoming smarter, more reliable and more economic electricity providers with respect to the incorporation of advanced smart grid technologies, distributed energy resources, efficient energy storage systems, and demand response programs (DRPs). Moreover, better bidding strategies, prepared by MG operators, boost the profits of MG market players. But, highly intermittent nature of renewable energy resources and their higher rate of outages make bidding strategies inefficient. To solve these issues, this study suggests an optimal bidding strategy considering uncertainty of renewable energy resources and DRP based on their outage probabilities. Tent chaos mapping is used to generate load scenarios and all possible renewable power output scenarios within the confidence intervals in non-repetitive and adaptive manner. Reserve and penalty costs for incorrect estimation of renewable energies are invoked to design more robust bidding. Moreover, the risk of participation in the competitive energy market is assessed using CVaR criteria. The proposed bidding model is optimized using mixed integer nonlinear programming. ‘Value of stochastic solution’ is used to investigate the efficiency of the stochastic programming in uncertainty integration into the bidding problem.

Suggested Citation

  • Das, Saborni & Basu, Mousumi, 2020. "Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources," Energy, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:energy:v:190:y:2020:i:c:s036054421932136x
    DOI: 10.1016/j.energy.2019.116441
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    1. Kashefi Kaviani, A. & Riahy, G.H. & Kouhsari, SH.M., 2009. "Optimal design of a reliable hydrogen-based stand-alone wind/PV generating system, considering component outages," Renewable Energy, Elsevier, vol. 34(11), pages 2380-2390.
    2. Salkuti, Surender Reddy, 2019. "Day-ahead thermal and renewable power generation scheduling considering uncertainty," Renewable Energy, Elsevier, vol. 131(C), pages 956-965.
    3. Ferruzzi, Gabriella & Cervone, Guido & Delle Monache, Luca & Graditi, Giorgio & Jacobone, Francesca, 2016. "Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production," Energy, Elsevier, vol. 106(C), pages 194-202.
    4. Wang, Yong & Li, Lin, 2013. "Time-of-use based electricity demand response for sustainable manufacturing systems," Energy, Elsevier, vol. 63(C), pages 233-244.
    5. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Kendall, Alissa & Træholt, Chresten, 2018. "Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty," Applied Energy, Elsevier, vol. 230(C), pages 836-844.
    6. Fazlalipour, Pary & Ehsan, Mehdi & Mohammadi-Ivatloo, Behnam, 2019. "Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets," Energy, Elsevier, vol. 171(C), pages 689-700.
    7. Wang, Jianxiao & Zhong, Haiwang & Tang, Wenyuan & Rajagopal, Ram & Xia, Qing & Kang, Chongqing & Wang, Yi, 2017. "Optimal bidding strategy for microgrids in joint energy and ancillary service markets considering flexible ramping products," Applied Energy, Elsevier, vol. 205(C), pages 294-303.
    8. Javed, Fahad & Arshad, Naveed & Wallin, Fredrik & Vassileva, Iana & Dahlquist, Erik, 2012. "Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting," Applied Energy, Elsevier, vol. 96(C), pages 150-160.
    9. 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.
    10. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
    11. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
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