Robust optimal scheduling of integrated energy systems considering multiple uncertainties

Multiple uncertainties such as renewable energy output, energy purchase prices and integrated demand responses have brought about severe challenges to the safe and economic operation of integrated energy system (IES). To meet this challenge, this study takes the combined cooling heating and power as an example of IES and proposes an optimization model considering multiple uncertainties. First, the structure of IES is given, and these mathematical models and constraints are listed according to the energy supply characteristics. Second, considering uncertain factors such as the wind and solar output, comprehensive demand response and energy purchase price, the different characterization methods are selected to model the uncertainty sources by identifying the characteristics of multiple uncertainty sources. Then, combining the stochastic scenario method and robust optimization method, a day-ahead optimal scheduling model of IES with multiple uncertainties is established, and the whale optimization algorithm is improved to obtain the optimal solution of the model. Finally, the actual data of an IES is selected to verify the rationality and effectiveness of the model. Meanwhile, the influence of uncertain factors on scheduling is introduced to verify the perfect coordination of economy and robustness of the model at runtime.

Automated summary

This study focuses on the safe and economic operation of integrated energy system (IES) in the presence of multiple uncertainties. A optimization model considering uncertainties in renewable energy output, energy purchase prices and integrated demand responses is proposed, using combined cooling heating and power as an example. Mathematical models and constraints are provided, and different methods are used to model uncertainty sources. A day-ahead optimal scheduling model for IES with multiple uncertainties is established using the stochastic scenario method and robust optimization method, and the whale optimization algorithm is improved to obtain the optimal solution.