Two-Stage Carbon-Oriented Scheduling of an Active Distribution Network With Thermostatically Controlled Load Aggregators

Thermostatically controlled loads (TCLs) are deemed as essential flexible resources on the demand side that can facilitate the cost-effective transition to a low-carbon power system. However, integrating massive TCLs into the optimal operation of active distribution network (ADN) is challenging due to uncertainty and computation complexity. A reliable and efficient TCLs aggregation technique is the key enabler. Firstly, this paper proposes an improved aggregation approach that introduces the minimal representation and reconstruction operations of polytopes to boost the aggregation efficiency. In addition, the uncertainties of outdoor temperature and indoor heat loads are comprehensively considered via the derived distributionally robust joint chance constraints with optimized violation probability. Then, in the coupled electricity-carbon market environment, a two-stage ADN scheduling model is established, which facilitates the realization of economic operation and environmental friendliness of the system using the distribution locational marginal price and nodal carbon intensity signals. To solve the developed scheduling model, a customized bisection-based iterative method is designed to avoid possible oscillations. Case studies illustrate that the proposed approach leads to higher profits and lower carbon emissions for TCLs aggregators than traditional methods while maintaining users’ thermal comfort.