Data-driven distributionally robust economic dispatch for park integrated energy systems with coordination of carbon capture and storage devices and combined heat and power plants

Park integrated energy system (PIES) can utilize multiple energy resources complementarily and promote comprehensive energy efficiency. However, the uncertainty of renewable energy generation poses significant challenges to the optimal operation of PIES. This paper proposes a data-driven distributionally robust optimization (DDRO) model for the day-ahead scheduling of PIESs with coordination of carbon capture and storage devices (CCS) and combined heat and power plants (CHP). First, a deterministic economic dispatch model of PIES was presented with the aim at minimizing the total operating costs of PIES and promoting the photovoltaic (PV) power accommodation. Then, a DDRO model was developed based on the historical data to yield the optimal solution in the worst PV output scenario, where the confidence set is established with comprehensive consideration of norm-1 and norm-inf constraints. Furthermore, an efficient solving framework was proposed for the DDRO based on the combination of the column-and-constraint generation (C&CG) algorithm and a duality-free decomposition method. Finally, case studies are carried out to demonstrate the effectiveness of the proposed model.