Building cluster demand flexibility: An innovative characterization framework and applications at the planning and operational levels

•A framework is created for building cluster demand flexibility characterization.•Holistic demand flexibility characterization is required at the operational level.•A novel, scalable virtual storage model is proposed.•Changes in temperature setpoints and dead-bands are considered. Flexible loads in building clusters are a sizable source of demand flexibility that can be tapped to provide grid services. Even with extensive research on demand flexibility characterization, it is not well studied how characterizing demand flexibility can facilitate energy aggregators bidding into wholesale electricity markets. In this paper, we propose an innovative framework that characterizes demand flexibility from a building cluster in a bottom-up manner and generates detailed information that can be used for bidding into the ancillary services market. Our framework allows system operators to perform a comprehensive evaluation of grid service bids submitted by energy aggregators. Different characterization parameters have been suggested depending on temperature setpoint and dead-band schedules. As a computationally efficient representation of a building cluster, we introduce a novel, scalable virtual storage model capable of accounting for temporal changes in temperature setpoints and/or dead-bands. Unlike its existing counterparts, our model is applicable to any building cluster regardless of the level of heterogeneity in building properties. The proposed framework is tested on a cluster consisting of 1000 single-family houses, each equipped with a heat pump as the heating system, controlled by a three-layer hierarchical controller. The results demonstrate that detailed flexibility characterization allows system operators to procure demand-side grid services without thermal comfort being jeopardized or power systems being adversely affected. For the introduced bidding scheme to be viable, the system operator needs to have an accurate estimate of the baseline power trajectory of each aggregator. This is important for compensating aggregators and preventing a malicious one from submitting fraudulent bids.