China's large-scale clean energy base has formed a regional power-heat combined system with multiple randomness

such as wind power

photovoltaic power generation and power/heat load

which highly depends on flexible and adjustable resources. Its low-carbon and economic operation is also challenging. In order to fully integrate the resources from source and load sides and maximize the consumption of new energy power

a dayahead optimal dispatching method of the regional integrated electric-heating operation system considering demand response of electric

heating loads and prediction error scenario is proposed. Firstly

sliding time window multivariate Gaussian mixture distribution and Monte Carlo are used to generate and correct the random sourcecharge day-ahead prediction error to further optimize the forward scheduling results. Secondly

the demand response models of electric and heating loads are analogically defined. A regional electric-thermal system model considering the source-charge interaction is established. Then

considering multiple costs

a low-carbon and economic pre-ahead scheduling expectation model of the joint system is established in all typical scenarios. Finally

by taking the winter energy supply in a region in northern China as an example

the multi-scenario optimization operation results of electricity with and without electricity and heat load demand response are gradually compared. The results show that

fully introducing the electric and heating load demand responses and molten-salt thermal energy storage boiler can promote the consumption of large-scale wind and photovoltaic power

and significantly enhance the low-carbon and economic operation capability of the regional integrated electric-heating system.