###### Abstract
Considering wind power and PV volatility, a day-ahead, and real-time scheduling model is set for wind power, PV station with energy storage, and thermal power joint dispatching under the background of the power market in [7], and two-point estimation and genetic algorithm are used to solve them. From the user side, the load curve can be optimized by actively guiding the user to participate in the demand response. The demand-side resources have a lot of influence on load peak reduction amount, among which electricity price-based demand response (PDR) accounts for 8%, and incentive-based demand response (IDR) accounts for the majority [8; 9]. [10] points out that demand response and wind power generation have different variation characteristics, and the impact of wind power generation volatility on the power system can be alleviated by combining them. In [11], demand response and energy storage are introduced into the load side and the power generation side respectively, and the two-stage optimal scheduling model of wind and storage system is set based on the uncertainty of wind power output forecasts in the day-ahead and ultra-short period, and this model can be solved by chaos binary particle swarm optimization algorithm. Considering the deviation of wind power output prediction and user response in multiple time scales, an interactive decision-making model between the day-ahead and the intraday stage is set for wind power, demand response, and emergency peak regulation resources in [12]. Based on the uncertain model of market price, load, wind speed, temperature, and radiation, the compressed air storage and PDR are introduced to solve the random self-regulation problem of renewable energy in [13], and the proposed method can reduce the system operating cost by simulation. Considering the uncertainty of high load and wind power generation, a scheduling model is established to minimize the total cost of system operating and sewage discharge in [14]. Similarly, a joint optimal schedule method is proposed for wind-solar-water complementary power generation system and high energy consumption data center in [15]. In [16], considering wind, thermal, storage, and demand response on both sides of generation-load, a two-stage optimal scheduling model is established based on the interval method to simulate wind power scenarios. The above literature mainly focuses on the day-ahead dispatching of various renewable energy, and the volatility of large-scale new energy generation cannot be ignored. On the user side, the demand response scheduling strategy with multiple time scales attracts the most scholars, and the joint scheduling with multiple energy sources attracts less attention. Meanwhile, with the gradual deepening of building a strong smart grid, the permeability of renewable energy continues to increase, and its intermittently and volatility bring great difficulties to the peak regulation of the grid. Thermal power units will often be in the state of severe peak load regulation and frequent climbing [17], and the impact on the operating cost of the system cannot be ignored. The scheduling mode combining day-ahead dispatching and real-time automatic generation control is widely used in China, but its scheduling stage span is too large. With the grid connection of large-scale new energy generation, the capacity requirements of real-time AGC (Automatic Generation Control) units are becoming higher and higher [18; 19]. As the uncertainty of source-charge decreases with time, intraday rolling scheduling can be added into the scheduling mode to complete load distribution in a shorter time scale based on the day-ahead scheduling plan and update the day-ahead generation plan in a rolling manner. Based on this, this paper comprehensively considers the characteristics of wind, solar, thermal units and demand response in different time scales, and proposes a day-ahead and intraday scheduling model to solve the low-carbon economic dispatch problem of multiple energy.