Estimation of Residential Heat Pump Consumption for Flexibility Market Applications

Recent technological advancements have facilitated the evolution of traditional distribution grids to smart grids. In a smart grid scenario, flexible devices are expected to aid the system in balancing the electric power in a technically and economically efficient way. To achieve this, the flexible devices' consumption data are theoretically recorded, elaborated, and their upcoming flexibility is bid to flexibility markets. However, there are many cases where explicit flexible device consumption data are absent. This paper presents a way to circumvent this problem and extract the potentially flexible load of a flexible device, namely a heat pump (HP), out of the aggregated energy consumption of a house. The main idea for accomplishing this is a comparison of the flexible consumer with electrically similar nonflexible consumers. The methodology is based on machine-learning techniques, probability theory, and statistics. After presenting this methodology, the general trend of the HP consumption is estimated and an hour-ahead forecast is conducted by employing seasonal autoregressive integrated moving average modeling. In this manner, the flexible consumption is predicted, establishing the basis for bidding flexibility in intraday markets, even in the absence of explicit device measurements.