Multi Objective Energy Management for Power Systems under Heatwave Induced Constraints

Abstract

Heatwaves triggered by climate change impose severe, coupled constraints on power systems by simultaneously surging electricity demand through widespread air-conditioning usage and degrading the operational capacity of generation, transmission, and distribution assets. This research paper develops a comprehensive multi-objective energy management framework for power systems under heatwave-induced constraints. It formulates mixed-integer linear or nonlinear programs based on security-constrained unit commitment (SCUC) and optimal power flow (OPF) with explicit temperature-dependent parameters, including dynamic line ratings (DLR), derated generation limits, temperature-sensitive loads, and increased losses. Pareto optimal solutions are explored using weighted-sum, ε-constraint, or evolutionary algorithms (e.g., NSGA-II), balancing four key objectives: operational cost, expected unserved energy or CVaR of reliability risk, greenhouse gas emissions, and equity-weighted load curtailment (using social vulnerability indices or grid Gini coefficients). The framework integrates flexibility resources demand response with comfort constraints, energy storage, virtual power plants, and interregional coordination while employing robust or distributionally robust optimization to hedge against temperature forecast uncertainty.