| Energy is the material basis of social progress and human survival.With the increasing constraints of energy resources,green and low-carbon development has become a major strategy for China's economic and social development,as well as an important approach to the construction of ecological civilization.China urgently needs to accelerate the construction of a clean,low-carbon,safe and efficient energy system dominated by renewable energy to achieve the " 30·60 " dual carbon goals.Electric power industry is at the core of modern energy system and plays an important role in reducing greenhouse gas emissions.Efforts should be made to develop green electricity represented by wind and solar power.However,due to the reverse distribution of wind and solar enrichment and demand areas in China,the role of the market in optimizing resource allocation is insufficient.It is urgent to promote the construction of electricity market mechanism,improve the electricity spot market trading mechanism with new energy participation,and construct the decision support methods for electricity spot market trading with high proportion of new energy penetration,so as to realize the effective allocation of resources,promote the consumption of new energy.In view of the above considerations,this paper analyzes the impact of new energy participation on the electricity spot market,the prediction of new energy generation power and electricity pot market price,the connection and clearing mechanism between medium and long-term contracts and spot day-ahead market,the coordination and clearing mechanism at various stages of the electricity spot market,and the coupling mechanism between carbon trading rights and electricity spot market.The main achievements and innovations are as follows.(1)The influence of new energy participation on electricity spot market is studied.Based on the complexity of price signals,an analysis model of the impact of new energy on electricity spot market is constructed,which is composed of three modules,including correlation analysis based on data statistics,all features analysis based on wavelet transform and fractal theory,and correlation analysis based on critical factors extraction.Based on the historical data of electricity spot markets in Denmark,it is confirmed that the impact of new energy generation on electricity price is higher than that of conventional historical data.Based on wavelet transform analysis and fractal theory,all the eigenvalues are obtained,and the classification accuracy of DK1 and DK2 is 80.35%and 82.30%respectively.The classification results show that the classification error rate of load,new energy power generation and the proportion of new energy power generation is higher.According to the correlation analysis results of critical features extraction,the top three important factors are related to new energy power generation.Therefore,only considering the conventional factors such as load is not enough to support the study of electricity spot market price forecasting,transaction matching and clearing problems in the electricity spot market.(2)The the prediction of new energy generation power and price in the electricity spot market is studied.A hybrid new energy power generation forecasting model based on the data preprocessing strategy of CEEMD and SE,and the KELM optimized by HS algorithm,and a hybrid electricity spot price forecasting model based on similar day screening and LSTM are constructed.Aiming at the nonlinear and unstable short-term power generation prediction,this paper first filters the input data of the model by pearson correlation coefficient select the input data and reduce data redundancy.Secondly,the combined data preprocessing strategy of CEEMD-SE is used to decompose and reconstruct the time sequence of power generation,eliminate data noise and reduce model calculation.Thirdly,the HS-KELM model is used to forecast the reconstructed multiple subsequences,and the final new energy power forecasting result is obtained after integrated processing.The forecasting model of new energy power generation based on CEEMD-SE-HS-KELM has higher forecasting accuracy.Aiming at the electricity spot market price forecasting,the new energy output index is incorporated into the electricity price forecasting.Firstly,the CEEMD-SE combined data preprocessing strategy is adopted to decompose and reconstruct the electricity spot market price sequence.Secondly,the quantitative model of new energy output impact is constructed based on RF-IAGIV is constructed,and historical similar days with high correlation w are selected as the input sets.Thirdly,LSTM model is used to predict the reconstructed multiple subsequences respectively.The electricity spot market price forecasting model based on CEEMD-SE-RF-IAGIV-LSTM has a good fitting effect on electricity price sequences,which can provide support for the participants in the electricity spot market to formulate trading strategies,match the spot market clearing,and reduce the risks in the electricity spot market.(3)The day-ahead electricity market clearing model which takes into account the impact of new energy and medium and long-term electricity market is studied.In this paper,an optimization model of day ahead electricity market transaction is proposed,which takes into account the decomposition of medium and long-term contract electricity and the participation of new energy.Firstly,a medium and long-term contract electricity decomposition model considering the completion schedule deviation of thermal power plant contract electricity is introduced into the day-ahead market clearing optimization model as a constraint to ensure the physical execution of medium and long-term contract electricity.Secondly,the new energy penetration ratio is added into the price simulation of the spot market to more accurately describe the impact of energy participation on the spot market.The multi-objective clearing optimization model of day-ahead market with new energy participation is constructed,and the fuzzy optimization method is used to transform the multi-objective to better balance the economy and energy conservation and emission reduction objectives.Finally,the combined optimization model based on GA-PSO is used to solve the model.The results show that the multi-objective optimization function constructed in this paper can maximize the environmental benefits and achieve the effect of energy saving and emission reduction on the basis of ensuring the economic benefits of the system operation.At the same time,with the increase of new energy penetration,the system uncertainty increases,and the transaction power of conventional units decreases.(4)This paper analyzes the connection mechanism between day-ahead market and intraday market,and constructs the corresponding clearing optimization model.The intraday market should be added between the day-ahead market and the real-time market to reduce the cost of system auxiliary services,reduce the capacity of fossil fuels used to balance intermittent and volatile new energy,flexibly allocate resources,improve the efficiency of the spot market,and better play the role of the market in optimizing the allocation of resources.The uncertainty description model based on the combination of model forecasting results and error distribution function is adopted,and then the scene set generation method based on Latin hypercube sampling and the scene set reduction strategy based on improved spectral clustering analysis are constructed,which can select the most representative scene set.Based on the clearing process of electricity spot market,the system with more new energy will be introduced into the intraday market to reduce the power deviation in the real-time market and improve the economy and stability of the system operation.The new energy power output and electricity load are predicted by using the forecasting model proposed in this paper,and the system uncertainty is described by combining the forecasting error distribution function.The combination clearing optimization model of day-ahead and intraday market is constructed.Considering the uncertainty of new energy deviation power and electricity price in the real-time market,the joint optimization model of intraday market and simulated real-time market is established.(5)Considering China's "30·60" dual carbon target and macro development plan,the carbon emissions forecasting model based on analysis of carbon emissions influencing factors of the STIRPAT model,and GRNN prediction model optimized by IFWA is constructed.Based on different social and policy environment,the influencing factors of carbon emissions are simulated and set.The prediction results show that China's total carbon emissions will reach the peak in 2031.On this basis,the pressure of carbon emissions reduction in China under the current policy,and the necessity of constructing a national unified carbon trading rights market is analyzed.Then,based on the construction status of electricity and carbon trading rights market,the impact of carbon trading on the electricity spot market is analyzed by using system dynamics(SD)model,and the results of SD model analysis confirm that electricity market and carbon trading price are positively correlated.Finally,based on the analysis of the mechanism of carbon trading on the electricity market,the suggestions on the collaborative construction of carbon trading and electricity spot market mechanism are put forward. |
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