Demand charge determination for managing peak demand in energy-aware production scheduling

The increasing electrification of industrial systems has intensified concerns over peak electricity demand, highlighting the need for effective peak shaving strategies to mitigate operational and infrastructure challenges. To manage peak demand, electricity utilities commonly employ two pricing components: time-varying energy prices (kWh-based) and demand charges based on peak power consumption (kW). Under such pricing structures, industrial consumers adopt energy-aware production scheduling to minimize total electricity costs consisting of both energy and demand charge components. However, determining an appropriate demand charge rate that induces peak shaving toward a desired target level under given time-varying prices remains a significant challenge. This study proposes an optimization-based pricing design framework that enables pricing-enabled peak shaving by estimating demand charge rates through the generation of a peak–energy curve, which characterizes the interrelation between peak demand and energy cost induced by production scheduling decisions. By leveraging the structure of cost-minimizing schedules, the proposed method efficiently derives demand charge rates that guide consumer behavior toward a targeted peak demand level without enforcing explicit peak demand limits or direct operational control. Numerical experiments under various time-varying pricing schemes demonstrate that the estimated demand charge rates achieve the targeted peak demand with an average absolute deviation of 8.38% while achieving up to a 98.7% reduction in computational time compared to baseline methods, demonstrating its high suitability for real-time industrial applications. Overall, the proposed framework offers a practical decision-support tool for peak shaving, aligning consumer scheduling behavior with system-level grid objectives.