Energy management for active distribution network incorporating office buildings based on chance-constrained programming

Abstract Aiming to the more flexible operation of the active distribution network (ADN), an energy management method for ADN incorporating office buildings is proposed based on chance-constrained programming. First, based on the thermal dynamics of buildings, an energy consumption prediction model of office buildings with integrated thermostatically controlled loads (TCLs) is developed. Then, an optimal energy management strategy for the ADN is proposed through the branch flow model (BFM) and the second-order cone relaxation (SOCR), considering the constraints of the grid and office buildings. The chance-constrained programming is exploited to consider further the uncertainties of photovoltaic (PV) power and ambient temperature, and the optimization model of the ADN incorporating office buildings is reformulated as a mixed-integer second-order cone programming (MISOCP) problem, using the deterministic transformation of chance constraints. Finally, the impact of the office buildings with TCLs on the economic operation of the ADN is analyzed under different confidence levels in the winter heating scenario. Numerical studies justify that the lower confidence level capitalizes on the thermal storage characteristics of office buildings retaining the temperature comfort of office workers to attain the flexible operation of the ADN additionally.