A CCP-Based Decentralized Optimization Approach for Electricity–Heat Integrated Energy Systems with Buildings

With the widespread application of combined heat and power (CHP) units, the coupling between electricity and heat systems has become increasingly close. In response to the problem of low operational efficiency of electricity–heat integrated energy systems (EH-IESs) with buildings in uncertain environments, this paper proposes a chance-constrained programming (CCP)-based decentralized optimization method for EH-IESs with buildings. First, based on the thermal storage capacity of building envelopes and considering the operational constraints of an electrical system (ES) and thermal system (TS), a mathematical model of EH-IESs, accounting for building thermal inertia, was constructed. Considering the uncertainty of sunlight intensity and outdoor temperature, a CCP-based optimal scheduling strategy for EH-IESs is proposed to achieve a moderate trade-off between the optimal objective function and constraints. To address the disadvantages of high computational complexity and poor information privacy in centralized optimization, an accelerated asynchronous decentralized alternating direction method of multipliers (A-AD-ADMM) algorithm is proposed, which decomposes the original optimization problem into sub-problems of ES and TS for distributed solving, significantly improving solution efficiency. Finally, numerical simulations prove that the proposed strategy can fully utilize the thermal storage characteristics of building envelopes, improve the operational economics of the EH-IES under uncertain environments, and ensure both user temperature comfort and the information privacy of each subject.