含双边储备市场及绿证交易的现货市场分析

构建适宜于清洁能源消纳的现货市场是当前我国电力市场的建设重点,提出一种含双边储备市场及绿证交易的现货市场交易机制。首先,引入促进风电并网交易的绿色证书交易机制。结合当前国内清洁能源市场形势,提出绿证强制交易配额制度,确定绿色证书交易数额及定价模型。其次,为减少风电因投标偏差产生的高额考核费用,引入风电商参与购售电交易的双边储备市场。在该市场机制下,火电商作为交易电量的提供者与风电商进行购售博弈。采用古诺模型和鲁宾斯坦模型相结合后的变形模型,对双边储备市场的交易电量和交易电价进行求解。然后,通过蒙特卡洛模拟和场景缩减技术模拟风电的实际出力,代入函数模型对发电商的利润进行求解。仿真结果表明,同时参与日前市场与双边储备市场进行交易的风电商可以合理规避偏差惩罚,同时各个发电商的购售利润均有提升,验证了所提模型的合理性和有效性。

Spot market analysis including bilateral reserve market and green certificate trading

The construction of a spot market which is suitable for clean energy consumption is a focus of China''s current power market. A spot market trading mechanism with bilateral reserve markets and green credentials transactions is proposed. First, a green certificate trading mechanism is introduced to promote wind power grid-connected transactions. Combined with the current domestic clean energy market situation, this paper presents a green certificate mandatory trading quota system and determines the green certificate transaction amount and pricing model; second, to reduce the high assessment fee of wind power due to bid bias, a bilateral reserve market is introduced in which wind power suppliers participate in the purchase and sale of electricity. Under this market mechanism, the thermal power supplier is regarded as a supplier of trading power to play with the wind power supplier in the purchase and sale game. The post-deformation model which is based on the Cournot model and the Rubinstein model are employed to determine the transaction volume and transaction price of the bilateral reserve market. Then, a function model is used to determine the profit of the generator by simulating the actual output of wind power through Monte Carlo simulation and scene reduction technology. The simulation results show that the wind power suppliers who participate in the trading between the market and the bilateral reserve market can reasonably avoid the deviation penalty, and the profit of each generator is improved. This verifies both the logic and effectiveness of the proposed model at the same time. This work is supported by National Natural Science Foundation of China (No. 51807125).