Research On Market Risk Management Model And Decision Simulation Of Renewable Energy Accommodation

In 2019,the cumulative installed capacity of wind power and photovoltaic in China exceeded 410 million kilowatts,and the total power generation of the two accounted for 8.5%.The renewable energy represented by wind and solar is changing from a supplementary power source to a main power source.However,renewable energy accommodation still faces challenges.After five years of new power system reform,China's power market construction has achieved initial results,a complete power market is about to be formed,and green power certificate system and quota system are coming.However,the roles and interests of the participants in the market are still being adjusted,and the corresponding mechanisms and policies are still being improved.As the renewable energy policy shifts from declining subsidies to grid parity,supplement measure of technology and market is the key to further promoting renewable energy accommodation.In addition,the rapid development of artificial intelligence and other technologies in recent years has changed the data production form and transformed the data processingcapabilities of the power system.In this context,this paper proposes to study the market risk management and decision simulation of renewable energy accommodation based on advanced information technology.The main research contents are as follows:(1)Build market risk dynamics models and conduct market risk causation analysis.This article firstly constructs the macro-micro market risk dynamics model of renewable energy accommodation and clarifies the logical process of market promoting renewable energy accommodation.Then,based on the dynamic model,the market risk causation analysis was carried out,and the influence relationship between key risks and risk factors was preliminarily discussed,and the analysis framework of causal risks influencing resultive risks and influencing renewable energy consumption in turn is formed.(2)Build a two-sided random risk management model to achieve effective prediction of key risk factors.Based on the different characteristics and forecasting needs of the time series of key risk factors,this paper proposes a variety of different forecasts and corrections:for the REC price,the AdaBoost-ELM regression model is designed,the ARIMA model is used for forecasting,and then the integrated learning method is used for forecasting Error correction.For the output of photovoltaic power generation,a prediction based on LSTM is proposed.Aiming at load and line loss,a deep network LSMNet with convolution,loop,loop skip and autoregressive components is designed for prediction.(3)Build a market effectiveness risk analysis model to achieve quantitative analysis of the relationship between key risk factors.This article takes the international advanced power market as an example to study its price formation mechanism.First,for the German power spot market,based on the time series model,the relationship between electricity price and the two-sided randomness and prediction error is studied.Then,based on the symbolic dynamics and complex network theory,the joint evolution law between LMP and renewable energy power generation is analyzed based on the symbolic dynamics and complex network theory.(4)Construct a resultive risk decision-making model to realize the risk optimization of market participants.This paper takes renewable energy power generation and load forecasting as decision variables,and results-based risk as decision goals,and constructs corresponding multi-objective decision-making problems.Then,based on the Spanish electricity market data,the distribution of key risk factors in the electricity market is simulated to obtain model parameters.Finally,the MNSGA algorithm is designed to solve the decision.Compared with the existing EMO algorithm,MNSGA can achieve better optimization results while maintaining the advantage in efficiency.(5)Design a market risk management and decision simulation system to realize the transformation and application of the theoretical results in this paper.Considering the subject of quota obligations,the enterprise cloud application and lightweight distributed application integration scheme are designed;for enterprise cloud applications,the architecture of the risk management module are designed and the application of cloud service models are explored;for lightweight distributed applications,the Tangle distributed ledger is adopted to implement P2P power transaction simulation and verify the application value of blockchain technology in the power market.This paper focuses on the regulation of electricity market to promoting renewable energy renewable energy accommodation.It has conducted in-depth market risk management and decision simulation from the perspective of two-sided random risk control,market effectiveness risk analysis,and result-based risk multi-objective decision-making.Innovative algorithms such as M-HITS and MNSGA are designed,time series multivariate analysis method system is proposed,various related models of market risk management are constructed,and empirical research on domestic and foreign power markets are conducted.A certain amount of innovation has been achieved both in theory and practice,which has important reference significance for renewable energy accommodation and power market risk management.