Distributed Operation for Integrated Electricity and Heat System With Hybrid Stochastic/Robust Optimization

With the wide application of combined heat and power (CHP) and power to heat (P2H) technology, the integrated electricity and heat system (IEHS) has been attracting great attention. The hybrid stochastic/robust optimization is combined to handle the uncertainties of IEHS, in which the stochastic optimization is concentrated on the uncertainties and spatial-temporal correlativity of load and wind power, while the robust optimization is used to deal with the market electricity price uncertainty. Considering the multi-entities characteristics of IEHS, the MINLP model of the original IEHS is decoupled to the one MILP power network and one NLP heat network based on the Bregman alternating direction method of multipliers (BADMM) and the improved quantity regulation. The simulation results show that the constructed model can effectively increase the flexibility of IEHS, and the uncertainty and spatial-temporal correlativity of IEHS can affect the system state. Furthermore, the proposed distributed model based on BADMM can not only improve the convergence effectively compared with traditional ADMM, but also realize the distributed cooperative operation of IEHS.

Automated summary

This paper investigates the uncertainty handling methods of integrated electricity and heat systems (IEHS) using a hybrid stochastic/robust optimization model, and utilizes BADMM to decouple the original IEHS model, thereby enhancing system flexibility. Additionally, the simulation results show the impact of uncertainty and spatial-temporal correlativity on IEHS system state, and the distributed model proposed based on BADMM can improve convergence and realize cooperative operation of IEHS.