Distributed robust synergistic scheduling of electricity, natural gas, heating and cooling systems via alternating direction method of multipliers

Due to the development of multi-energy conversion technologies and the higher public awareness of the sustainability, the energy system has begun a transition toward the energy hub (EH) based integrated energy system (IES). This paper proposes a distributed robust synergistic scheduling method for multi-EH-based IES coupled with electricity, natural gas, heating and cooling, to minimize the operating cost of the IES while considering the uncertainties of renewable generations (RGs) and multi-energy load demands. The nonconvexity of energy network constraints, uncertainties in multiple EHs as well as limitation of information exchange lead to significant challenges for IES scheduling. To deal these issues, the original robust scheduling model of IES is first reformulated as a mixed integer second-order cone programming (MISOCP) by convex relaxation method. Furthermore, to improve the economy and flexibility of robust scheduling solution, a light robust model is developed to address the multiple uncertainties from EHs. Finally, a consensus-based alternating direction method of multipliers (ADMM) approach is developed to tackle the robust scheduling model in MISOCP form, where EH operators parallelly solve their respective EH subproblems with limited information exchange, and subsequently the EH subproblem in MISOCP form is solved by a nonconvex ADMM (NC-ADMM) approach to guarantee the convergence of the consensus-based ADMM. Simulation results in a three-EH IES are presented to illustrate the effectiveness of proposed method for optimal synergy of multiple EHs with uncertainties.

Automated summary

This paper proposes a distributed robust synergistic scheduling method for multi-energy hub-based integrated energy systems, aiming to minimize operating costs while taking into account uncertainties in renewable energy generation and multi-energy load demands. The method reformulates the robust scheduling model into a MISOCP form, develops a light robust model to address uncertainties from energy hubs, and utilizes a consensus-based ADMM approach to tackle the scheduling model with limited information exchange. Simulation results demonstrate the effectiveness of the proposed method for optimal synergy of multiple energy hubs with uncertainties.