Multi-objective Distributionally Robust Optimization Scheduling for Integrated Energy System Considering Wind Power Uncertainty
The uncertainty of wind power output presents certain risks to the stable operation of integrated energy systems.A multi-objective distributionally robust optimization approach that incorporates chance constraint is introduced.Initially,to balance low-carbon economy and robustness during system operation,to minimize both the comprehensive operational costs and carbon emissions,a multi-objective distributionally robust chance-constrained optimization model is constructed.Subsequently,a distributionally robust bound is determined to address the uncertainty of wind power,transforming the multi-objective distributionally robust chance-constrained model into a multi-objective deterministic optimization model.To achieve a well-distributed Pareto frontier,the model is solved by using the Normalized Normal Constraint(NNC)method.Finally,through comparative case study analysis,it is shown that the proposed multi-objective optimization model can effectively balance low-carbon economic considerations and robustness in system decision-making,offer a new approach to solve the wind power uncertainty in integrated energy systems.
multi-objectivedistrbutionally robust optimizationchance constraintsnormalized normal constrain
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