No abstract

The integrated energy system (IES) is recognized as a promising energy utilization approach enabled to both improve the energy efficiency and reduce pollutant emissions. Although the economic-environmental dispatch (EED) problem of the IES has been widely studied, the fact that the IES is operated under off-design conditions, having a significant influence on the efficiency of energy devices, is neglected usually, resulting in the scheduled operations, for the IES could not be accurate enough in many actual situations. This study investigates the EED problem of the IES under off-design conditions. Technically, by integrating an efficiency correction process into the traditional energy hub (EH) model, a dynamic energy hub (DEH) model is first formulated for adapting itself to variable energy conversion efficiencies. Then, a deep neural network (DNN)-based efficiency correction method is proposed to predict and correct the time-varying efficiency of energy devices based on three main off-design conditions including the load rate, air temperature, and pressure. A multi-objective EED model is finally formulated for the IES, with the framework of the DEH model, aiming at establishing a trade-off between operational cost and emitted pollutants. Case studies show that the proposed approach helps in enhancing the accuracy of IES dispatch while having a good performance in both the operational cost and pollutant emission reduction.