Short-term electric power and energy balance optimization scheduling based on low-carbon bilateral demand response mechanism from multiple perspectives

Carbon emissions limit the output of traditional fuel-fired generating units, significantly affecting the new power system scheduling mechanism. This paper proposes a short-term electric power and energy balance optimization scheduling method with low-carbon bilateral demand response (LCBDR). The LCBDR mechanism framework is constructed by combining the analysis of short-term electric power and energy balance of the system under a dual perspective, along with the electric-carbon coupling mechanism of the dynamic scheduling on the source-load side. Based on the carbon emission flow (CEF) theory, the carbon emission index information of load-side users is obtained. An optimal scheduling model of LCBDR is established. The enhanced decision tree classifier (EDTC) algorithm is used to predict the electricity consumption behavior of transferable load (TL) users, and an improved particle swarm optimization (PSO) algorithm with "epsilon-greedy" strategy is proposed to solve this model. Comprehensive case studies from three different perspectives verify that this method can effectively realize the low-carbon economic operation of the system, with the peak net load reduced by 24.02% and valley net load increased by 20.43%. Compared with a single perspective, the total operational costs can be reduced by 5.27%, and the carbon emissions of users can be reduced by 5.70%.