Incomplete information oriented optimal scheduling of multi-energy hub systems with thermal energy storage

The research on optimal scheduling of multi-energy hub(EH) systems based on EH is the direction of advance of optimizing energy structure. However, the restriction relationship and incomplete information among the multiEHs have brought challenges to the optimal scheduling of the multi-EH system. Therefore, considering the uncertainty of renewable energy input, combined heat and power unit(CHP) thermoelectric coupling limit and electric-cooling-heat demand response(DR) in EH, this paper introduces thermal energy storage (TES) and power trading among multi-EHs, establishes economic optimization scheduling model of multi-EH system, and analyzes the constraints among multiple EHs under dynamic pricing mechanism, the incomplete information game decision method is used to optimize the scheduling of multi-EH systems. Based on considering the impact of user behavior characteristics on user participation in DR, interruptible load types are modelled as discrete random variables by introducing users' load interruption willingness factor. On this basis, an optimal scheduling model of multi-EH systems is formulated, in which the information of DR between EHs is incomplete, and the BayesianNash equilibrium solving algorithm description of this model is given, the optimal scheduling scheme of the model is obtained. Finally, case studies are conducted to verify the effectiveness of the proposed model and approach. The results show that the proposed multi-EH system optimization model can effectively decrease the system total cost and improve the dependability of system operation under the condition of incomplete information. Compared with the dispatching scheme without DR, TES and power trading, the cost of multi-EH systems is reduced by 1.6% on average.