Multi-Objective Optimization for Peak Shaving with Demand Response under Renewable Generation Uncertainty

Abstract

With high penetration of renewable energy sources (RESs), advanced microgrid distribution networks are considered to be promising for covering uncertainties from the generation side with demand response (DR). This paper analyzes the effectiveness of multi-objective optimization in the optimal resource scheduling with consumer fairness under renewable generation uncertainty. The concept of consumer fairness is considered to provide optimal conditions for power gaps and time gaps. At the same time, it is used to mitigate system peak conditions and prevent creating new peaks with the optimal solution. Multi-objective gray wolf optimization (MOGWO) is applied to solve the complexity of three objective functions. Moreover, the best compromise solution (BCS) approach is used to determine the best solution from the Pareto-optimal front. The simulation results show the effectiveness of renewable power uncertainty on the aggregate load profile and operation cost minimization. The results also provide the performance of the proposed optimal scheduling with a DR program in reducing the uncertainty effect of renewable generation and preventing new peaks due to over-demand response. The proposed DR is meant to adjust the peak-to-average ratio (PAR) and generation costs without compromising the end-user’s comfort.

IoT and machine learning for enabling sustainable development goals

“…Another challenge resides in the complexity of the peak shaving problem in view of the underlying multi-objective optimisation and inter-dependencies between the multi-agents involved (Su et al, 2022) (Wynn et al, 2022). It follows that solving optimisation problems related to peak shaving is computationally expensive and requires lengthy processes.…”