Distributional Robust Optimal Dispatching of Microgrid Considering Risk and Carbon Trading Mechanism
Improving the consumption capacity of microgrid wind power is an important means to achieve the carbon peak and carbon neutrality goal.We constructeda distributional robust optimization model for low carbon of microgrid considering the extreme scenarios and the risk value of payload conditions.Firstly,we construct a fuzzy set of probability distribution based on Wasserstein distance for source-load uncertainty,modify the fuzzy set by considering the extreme scenarios to improve the robustness of the fuzzy set,and improve the solution efficiency by reducing the number of sce-narios.Secondly,a distributional robust optimization scheduling model is established with the objective of minimizing the daily operation cost,including energy supply cost,stepped carbon transaction cost and the risk value cost,and the model is converted to a linear programming problem solved by using segmented linear approximation and duality theory.Thirdly,the proposed model is validated by simulations.The results show that the distributional robust optimization method con-sidering extreme scenarios has strong robustness,the introduction of stepped carbon trading cost can effectively reduce carbon emissions from microgrids and improve the absorption capacity of wind power,and payload condition value at risk can effectively balance the economy and risk of micro grid operation.
extreme scenariosrisk value of payload conditionsstepped carbon tradingdistributionally robust optimi-zationWasserstein distanceuncertainty
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维普
