Chance-constrained programming-based distributed energy management strategy for active distribution network integrating intelligent buildings and soft open points

Abstract To achieve the flexible operation of the active distribution network (ADN), a distributed energy management strategy for ADN integrating intelligent buildings (IBs) and soft open points (SOPs) is proposed. Firstly, based on the thermal storage capacity of the buildings and the power flow regulation ability of SOPs, a mathematical model of IBs with air conditioners (ACs) access and a mathematical model of SOPs are constructed. Secondly, an ADN operation model integrating IBs and SOPs is established. Then, Chance Constrained Programming (CCP) is introduced to formulate the stochastic indoor temperature constraints in this work, so as to balance constraint reliability and scheduling optimality. Finally, the alternating direction method of multipliers (ADMM) is used to decompose the original optimization problem into a network-side Mixed Integer second-order cone programming (MISOCP) sub-problem and a building-side mixed-integer linear programming (MILP) sub-problem. This paper proposes a distributed energy management strategy for active distribution networks that tightly couples two flexible resources for joint scheduling: the thermal inertia of load-side intelligent buildings and the bidirectional power regulation capability of network-side soft open points. A customized deterministic transformation scheme for chance constraints, tailored to multi-zone building thermal dynamics, is developed to address photovoltaic and ambient temperature uncertainties. A prediction-correction accelerated ADMM algorithm, specially designed for the resultant heat-power coupled mixed-integer model, achieves privacy-preserving distributed optimization.