Study On The Optimal Clearing Model And Method Of Day-Ahead Market To Improve Accommodation Of Hydro-Wind-Photovoltaic Clean Energy

The electric power market construction in China has made stage achievements in introducing the market competition mechanism and restoring the attributes of electric power commodities,since the enablement of the new round of electric power system reform.At the same time,China’s clean energy sector is developing rapidly and its installed capacity continues to grow in order to alleviate the increasingly severe ecological and environmental situation and achieve the goal of peak carbon dioxide emissions before 2030 and carbon neutrality before 2060.Under the dual background of the steady advance of the spot market reform and the penetration rate acceleration of clean energy,the design of the market trading mechanism has important research significance for ensuring the safe and economic operation of the power system,giving play to the role of price-guided resource allocation and improving the accommodation capacity of clean energy.Therefore,on the basis of predicting the day-ahead market clearing price,combined with the characteristics of hydro-dominated power market and the uncertainty characteristics of renewable energy output,this thesis studies the day-ahead market optimization clearing model.The specific research contents of this thesis are as follows:(1)Proposed a prediction model of day-ahead market clearing price based on support vector machine(SVM).Based on the brief introduction of the clearing process of the dayahead market and the clarification and analysis of the influencing factors of the clearing price,the dynamic time warping algorithm is used to measure the similarity of the market historical transaction data.Finally,similar daily price of historical load,similar sequence of historical price and forecast load of operation day are selected as input parameters of the model.Through the analysis of the results,it is shown that the certainty factor of this prediction model is 0.82,and the other prediction error analysis indicators are better than the comparison model,which effectively improves the prediction accuracy of the clearing price.(2)Combined with the characteristics of the power market in the hydro-dominated region and considering the complex coupling relationship of water balance and hysteresis and other factors in the cascaded hydropower stations,a day-ahead optimal clearing model hydro-dominated power market is proposed.This model innovatively divides the output of hydropower units into two parts: active bid-winning output and passive accepting output.The improved NSGA-II algorithm based on step-by-step optimization strategy and mixedinteger linear programming(MILP)were applied to find an optimal solution.The clearing results showed that the difference between the two solutions in the trend of clearing electricity price changes and unit revenue were not obvious,but the MILP showed better solution performance,it increased the total market revenue by 20.91 ten thousand yuan and reduced the waste water power of the system by 80.3 MWh.And explored the influence of the penalty coefficient of surplus water and the pricing strategy of hydropower units on the day-ahead clearing price.(3)Studied and analyzed the new energy output characteristics.Latin hypercube sampling(LHS)and K-means clustering algorithm were used to generate probabilistic deterministic typical scenarios to describe the uncertainty of renewable energy output.Based on the analysis of uncertain scenarios,integrating the purchase cost,reserve capacity cost and real-time balance cost of day-ahead market,an optimized day-ahead market clearing model which aims at minimizing the total transaction cost of the market is proposed.The improved IEEE-30 bus system was used to compare several day-ahead clearing models and clearing schemes,where the results show that the model not only reduces the total transaction cost of the market,but also effectively controls the system’s load shedding and energy abandoning quantity by rationally allocating the system’s reserve capacity,taking into account the security and economic requirements of the system operation to achieve the purpose of promoting accommodation of clean energy.