基于两阶段随机优化的电能量与深度调峰融合市场出清模型及定价方法

An efficient power market mechanism is an important support for the construction of a new power system. The deep peak shaving regulation market, as a characteristic power market proposed first in our country, has achieved fruitful results in two aspects: One is it improves the transformation of the thermal units and increases the deep peak regulation capacity of the whole system, and the other is it promotes the new energy consumption through transactions. In recent years, some provinces have gradually introduced the spot electricity markets. How the deep peak shaving regulation market should adapt to the spot market has become a problem that needs to be solved. Based on a two-stage stochastic optimization method, this paper establishes a market-clearing and primal-dual non-convex pricing model considering the deep peak shaving transactions. In the first stage of the market, the day-ahead electricity energy market clearing is carried out, while in the second stage, considering the randomness of new energy, the real-time market electricity energy adjustment and the deep peak shaving regulation transactions are conducted. In addition, in view of the economic loss of the thermal units caused by the traditional method, the cost recovery constraints of the unit are introduced to ensure the income of the units based on the primal-dual pricing model, realizing the healthy and orderly development of the market. Finally, this paper uses the ROTS system to verify the applicability of the proposed method and model.