Joint optimization of maintenance and demand response investments in distribution networks using a hybrid GA-SQP approach - Roshan Alirezaei; Reza Dashti; Mojtaba Mirhosseini (Sustainable Futures)
This study proposes a novel integrated optimization framework that simultaneously allocates capital to preventive maintenance and demand response (DR) programs in power distribution networks under budget constraints. To address the lack of coordination between maintenance and DR investment in existing literature, the model incorporates asset reliability through an asset value index considering equipment aging and failure risk. Demand response is modeled using a price elasticity-based load reduction mechanism. A hybrid optimization algorithm combining Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) is employed to solve the resulting nonlinear, mixed-integer problem. The model is applied to the IEEE 33-bus distribution system, demonstrating that joint optimization significantly reduces imposed costs compared to maintenance-only or DR-only strategies. Specifically, allocating 80 % of the budget to maintenance and 20 % to DR minimizes total costs, reducing them by 2.05 % and 13.03 % compared to maintenance-only and DR-only strategies, respectively. Sensitivity analyses on budget size, inflation, and elasticity confirm the robustness of the proposed approach. The results highlight the economic and operational benefits of coordinated planning, offering a valuable tool for distribution companies facing investment trade-offs between reliability enhancement and load flexibility.