Recent demand response (DR) research efforts have focused on reducing the peak demand, and thereby electricity prices. Load reductions from DR programs can be viewed as equivalent electricity generation by conventional means. Thus, utility companies must pay incentives to customers who reduce their demand accordingly. However, many key variables intrinsic to residential customers are significantly more complicated compared to those of commercial and industrial customers. Thus, residential DR programs are economically difficult to operate, especially because excess incentive settlements can result in free riders, who get incentives without reducing their loads. Improving baseline estimation accuracy is insufficient to solve this problem. To alleviate the free rider problem, we proposed an improved two-step method—estimating the baseline load using regression and implementing a minimum-threshold payment rule. We applied the proposed method to data from residential customers participating in a peak-time rebate program in Korea. It initially suffered from numerous free riders caused by inaccurate baseline estimation. The proposed method mitigated the issue by reducing the number of free riders. The results indicate the possibility of lowering the existing incentive payment. The findings indicate that it is possible to run more stable residential DR programs by mitigating the uncertainty associated with customer electricity consumption.
Data-Driven Baseline Estimation of Residential Buildings for Demand Response
