Residential Peak Load Reduction via Direct Load Control under Limited Information

TL;DR

This paper tackles residential peak shaving in low-voltage networks by coordinating thermostatically controlled loads (HP and EWH) and electric vehicles (EV) through centralized direct load control (DLC) under information constraints. It develops two optimization-based DLC formulations (L1 and L2) that operate on a rolling 24-hour horizon, comparing an ideal, fully informed controller to practical implementations that rely on limited information and predictions of inflexible load. The contributions include the derivation of L1 and L2 with explicit HP, EWH, and EV constraints, their validation in a real-world pilot (OrtsNetz) and extensive simulations, and a quantified comparison to an ideal controller showing that limited-information DLC can achieve roughly 48% (summer) and 41.8% (winter) of the ideal peak reductions. The work demonstrates technical feasibility, preserves customer comfort, and highlights practical considerations such as load forecasting accuracy and device-modeling refinements, underpinning the potential for utility-scale demand response with modest infrastructure changes.

Abstract

Thermostatically controlled loads and electric vehicles offer flexibility to reduce power peaks in low-voltage distribution networks. This flexibility can be maximized if the devices are coordinated centrally, given some level of information about the controlled devices. In this paper, we propose novel optimization-based control schemes with prediction capabilities that utilize limited information from heat pumps, electric water heaters, and electric vehicles. The objective is to flatten the total load curve seen by the distribution transformer by restricting the times at which the available flexible loads are allowed to operate, subject to the flexibility constraints of the loads to preserve customers' comfort. The original scheme was tested in a real-world setup, considering both winter and summer days. The pilot results confirmed the technical feasibility but also informed the design of an improved version of the controller. Computer simulations using the adjusted controller show that, compared to the original formulation, the improved scheme achieves greater peak reductions in summer. Additionally, comparisons were made with an ideal controller, which assumes perfect knowledge of the inflexible load profile, the models of the controlled devices, the hot water and space heating demand, and future electric vehicle charging sessions. The proposed scheme with limited information achieves almost half of the potential average daily peak reduction that the ideal controller with perfect knowledge would achieve.

Figures (7)

• Figure 1: Estimated variation of the HP' power demand with ambient temperature.
• Figure 2: Pilot results for summer (left) and winter (right): Distribution of daily peaks (top), mean daily profiles and daily peak values using the MSD based on weather data (middle), and TS load profiles and device blockings for a single day using the MSD based on weather data (bottom).
• Figure 3: Measured and simulated load for two summer days (top) and two winter days (bottom).
• Figure 4: Duration curves for the simulations in summer (left) and winter (right).
• Figure 5: Relative MIP gap per iteration during the simulated periods.