###### Abstract  
With the development of smart grid and low-carbon electricity, a high proportion of renewable energy is connected to the grid. In addition, the peak-valley difference of system load increases, which makes the traditional grid scheduling method no longer suitable. Therefore, this paper proposes a two-stage low-carbon economic scheduling model considering the characteristics of wind, light, thermal power units, and demand response at different time scales. This model not only concerns the deep peak state of thermal power units under the condition of large-scale renewable energy, but also sets the uncertain models of PDR (Price-based Demand Response) virtual units and IDR (Incentive Demand Response) virtual units. Taking the system operation cost and carbon treatment cost as the target, the improved bat algorithm and 2 PM (Two-point Estimation Method) are used to solve the problem. The introduction of climbing costs and low load operating costs can more truly reflect the increased cost of thermal power units. Meanwhile, the source-load interaction can weigh renewable energy limited costs and the increased costs of balancing volatility. The proposed method can be applied to optimal dispatch and safe operation analysis of the power grid with a high proportion of renewable energy. Compared with traditional methods, the total scheduling cost of the system can be reduced, and the rights and obligations of contributors to system operation can be guaranteed to the greatest extent. Keywords:price-based demand response; incentive demand response; uncertainty; deep peak-shaving; two-stage scheduling +
Footnote †: journal: Applied Softnetworks\", smart grid has a high penetration rate of new energy, so it is urgent to make full use of the generation-load-storage data to solve the low-carbon economic dispatch problem of wind power, PV (Photovoltaic) power generation, thermal power, energy storage, and other kinds of energy in multiple time scales. On the one hand, this problem can be solved by the power generation side. The multi-energy complementary characteristics of fire, wind, light and energy storage power stations can be rationally used to mitigate the impact of new energy on the power grid. Replacing conventional power plants with renewable energy plants can significantly reduce the carbon emissions of the whole power system [1]. The charge-discharge characteristics of energy storage can solve the uncertainty related to wind and light power generation, and it also can alleviate line congestion to a certain extent [2]. The rapid development of large-scale renewable energy power stations has led to an explosive growth of dispatching data, and it is a challenge for power grid operation to establish scheduling plan by analyzing the characteristics and correlation of water, sunlight, wind and other new energy sources [3]. The BESS (Battery Energy Storage Station) has good peak regulation characteristics, which is used to reduce the influence of prediction error on wind power generation output in [4]. Meanwhile, an optimal scheduling model of thermal, wind, and storage system is proved to be superior in reducing the system operating cost. In [5], low load operation cost and slope climbing cost of thermal power units are concerned, and a day-ahead optimal scheduling model is established for wind power, photovoltaic, thermal power, pumped storage and energy storage power stations, and the dynamic inertia weight particle swarm optimization method is used to solve this problem. The uncertainty of generator failure, wind power, photovoltaic power generation, and load prediction is considered in [6], and a day-ahead dispatching optimization model is set with the system operating cost, reliability, and emission cost as the objective function.