Risk-Averse Multiobjective Optimization for Integrated Electricity and Heating System: An Augment Epsilon-Constraint Approach

Uncertainties including demand and environmental factors would increase the operational risk of the energy supply networks. It significantly affects the technical and financial aspects of the integrated electricity and heating system (IEHS). This article presents a risk-averse multiobjective optimization strategy for short-term decision-making. The objective is to optimize the total cost and the technical dissatisfaction in a way that the economy of system operation and energy supply quality for the risk-averse scheme is guaranteed simultaneously. Based on the trapezoidal fuzzy membership function, the fuzzy constraint is constructed to quantify the technical dissatisfaction of the nodal voltage and supply temperature. Then, a multiobjective min–max–min problem is formulated to hedge the IEHS against risk imposed by the uncertain variables. Since the formulated two objectives compete with each other, a risk-averse multiobjective solution procedure based on the augmented epsilon-constraint algorithm is developed to recast the multiobjective problem into its equivalent single objective problem. And the accurate Pareto fronts under different risk levels are obtained with several iterations. Numerical case studies are conducted to evaluate the effectiveness and applicability of the proposed method. It provides benefits in the aspect of balancing the economy and energy supply quality under network constraints and uncertainties.