The large-scale heat pump access leads to the need for large-scale expansion and transformation of local distribution network. Since the peak-valley difference of daily heating load is big

the utilization rate of the equipment is low. In order to reduce the cost of distribution network expansion and improve the utilization rate of equipment

the strategy for orderly power consumption needs to be studied. As an important part of the strategy for orderly power consumption

the decision-making of day-ahead heat pump scheduling is facing a strong uncertainty of source-load forecasting. This paper proposes a chance constrained model for the day-ahead heat pump scheduling

which can effectively handle those source-load uncertainties. Furthermore

the chance constrained model is transformed into a deterministic mixed-integer second-order cone programming model

so it can be efficiently solved. The example system test shows that the optimal scheduling strategy can reduce the peak-valley difference of distribution network and related courts under the premise of ensuring indoor temperature comfort.