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A two-stage stochastic Stackelberg model for microgrid operation with chance constraints for renewable energy generation uncertainty

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  • Matamala, Yolanda
  • Feijoo, Felipe

Abstract

In order to reduce greenhouse gas emissions, countries worldwide are transforming their energy systems with higher shares of renewable energy and smart technologies for demand response. Microgrids play an essential role in the transformation of electric grids to smart grids. However, renewable sources present new challenges, particularly those of high variability, which creates uncertainties in the supply side that can affect the security of electricity access at affordable prices. This paper proposes a novel Stackelberg stochastic model to account for different sources of uncertainty. The Stackelberg model considers microgrids as leaders (upper-level problem) with uncertainty regarding the availability of wind and solar sources and electricity prices. Availability of renewable sources is modeled via chance constraints, which allows assessing the risk of microgrids over-committing supply levels. Uncertainty in electricity prices is modeled via a set of demand scenarios with a given probability distribution. The lower-level problem of the Stackelberg problem considers an electricity dispatch problem for each demand scenario. The proposed model allows measuring the strategic actions of microgrids when facing different types of uncertainties and how the smart grid should adapt to guarantee that demand levels are supplied. The results show the effectiveness of the proposed method. We find that microgrids risk levels above 30% do not correlate with further benefits, such as reduced electricity prices. We also identified that in average, depending on the social cost of carbon and demand level, microgrids can cover their own demand and supply 15% of the electricity demand in the grid.

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  • Matamala, Yolanda & Feijoo, Felipe, 2021. "A two-stage stochastic Stackelberg model for microgrid operation with chance constraints for renewable energy generation uncertainty," Applied Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:appene:v:303:y:2021:i:c:s0306261921009788
    DOI: 10.1016/j.apenergy.2021.117608
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