A regional electricity-heating integrated energy system (REH-IES) can make extensive use of renewable energy sources

realize complementary and coordinated operation of multiple energy sources

reduce carbon emissions and promote the development of zero/low carbon systems. This paper proposes a risk-averse stochastic optimal scheduling model for REH-IES. An energy flow framework for the REH-IES is proposed considering energy interaction between the electric-heating microgrids (EHMs) and electricity distribution network and the heating network. Then

considering the uncertainties of power output of renewable energy sources

dynamic characteristics of pipelines in the heating network

and thermal inertia of smart buildings

a stochastic optimal scheduling model for the REH-IES is established. Uncertainties of renewable energy sources bring financial risks to optimal scheduling of the REH-IES. Therefore

conditional value-at-risk (CVaR) theory is adopted to measure the risk and to limit the risk within an acceptable range

to achieve minimum expected scheduling cost of the REH-IES. The stochastic programming-based problem is transformed into a second-order cone programming (SOCP) model through second-order cone relaxation method. Case studies verify the stochastic optimal scheduling model can reduce expected scheduling cost of the REH-IES

promote consumption of renewable energy sources and reduce carbon emissions.