Potential Assessment and Interaction Framework of Thermostatically Controlled Air-Conditioning Load Cluster Participating in Photovoltaic Consumption

Abstract

As one of the most potential demand side response resources, thermostatically controlled air-conditioning load cluster will play an important role in distributed photovoltaic consumption. An adjustable potential assessment and interaction framework of air conditioning thermostatically controlled load cluster participating in distributed photovoltaic consumption based on data-driven and Deep Belief Nets (DBN) is proposed in the power market environment. Firstly, a data-driven adjustable potential assessment model based on Deep Belief Nets is constructed to output the adjustable potential of thermostatically controlled load cluster in real time. Secondly, within the certain range of power adjustment, a demand interaction framework based on Deep Belief Nets is also constructed to regulate the real-time temperature setting of thermostatically controlled load cluster. Finally, taking a 10KV feeder in Northern Hebei as an example, the results show that the proposed framework can make full use of the adjustable potential of the thermostatically controlled air-conditioning load cluster, quickly and accurately participate in the consumption of distributed photovoltaic, and have high engineering application value.