Flexible operation of a CHP-VPP considering the coordination of supply and demand based on a strengthened distributionally robust optimization

Due to the generation variability, the growing capacity of renewable energy has posed unprecedented challenges to ensure the security of power system operation. Here, a two-stage strengthened distributionally robust optimization (DRO) scheme is proposed for theself-scheduling of a combined heat and power virtual power plant (CHP-VPP) over a coupled electric power network (EPN) and district heating network (DHN). The CHP-VPP operator maximizes its profits in the day-ahead market and minimizes its cost in the real-time market under the worst-case realization of the uncertainties. Instead of assuming that the uncertainties follow known probability distributions or confidence bounds, a strengthened ambiguity set based on moment information and Wasserstein metric is built to provide more accurate characterizations of the true probability distribution of uncertainties. In addition, in order to enhance the flexibility of the system, a HOMIE model considering indoor activities and outside temperatures of each building is built to satisfy the comfortable indoor temperature. To make the whole problem tractable, linearisation and duality theory are adopted, and then a tailored column-and-constraint generation algorithm is developed to solve the problem. The validity and applicability of the strengthened DRO scheme are verified by an IEEE 33-bus EPN and 14-node DHN.