Operation Optimization Of Virtual Power Plant With Multiple Distributed Energy In Electricity Markets

The efficient use of clean energy can effectively alleviate the contradiction between energy and the environment in China.It is of great significance to the lowcarbon transformation of the energy structure.The 14th Five-Year Plan for a modern energy system proposed to comprehensively promote the large-scale and high-quality development of wind and photovoltaic power.Priority should be given to local and nearby development and utilization.The construction of decentralized wind power and distributed photovoltaic in load centers and surrounding areas should be accelerated.However,distributed renewable energy resources have the characteristics of small installed capacity and random power output.Its large-scale and high proportion of connection to the grid.brings risks to the stable operation of the power system.On the other hand,the new round of power system reform continues to progress.The electricity market is gradually showing its unique advantages in optimizing the allocation of resources and improving the effi ciency of energy use.The characteristics of distributed energy resources make it difficult to guarantee their competitiveness when participatin g in the market alone.As an important way to optimizing their aggregation and coordination,the virtual power plant can effectively improve the competitiveness of all types of distributed energy resources,thus promoting the efficient consumption of renewable energy resources while ensuring the economy of system operations.The concept of virtual power plant offers a new and viable solution for energy management on both the supply and demand side.In conjunction with China’s electricity market reform path,trading optimization of virtual power plant participation in the medium and longterm market,spot market and ancillary services market is carried out.At the same time,the rationality of the internal multi-subject benefit allocation scheme is analyzed in order to guarantee the stability of the virtual power plant.The operation optimization of the whole process of "aggregation-transaction-settlement" of virtual power plant is realized.The main contents of this paper are as follow:(1)A distributed energy aggregation optimization model for virtual power.plant is proposed.Firstly,according to the aggregation characteristics of the virtual power plant,the choice is made to aggregate from distributed energy resources such as gas turbines,wind turbines,photovoltaic units,energy storage systems and demand response.The operation of various types of distributed energy resources is analyzed and modelled.Then,considering the strategic goal of "carbon emission reduction",the penalty costs of pollutant emission and the generation abandonment costs of wind and photovoltaic power during the operation of the virtual power plant are further included based on the economic costs.This clarifies the integrated benefits when various types of distributed energy are aggregated to the virtual power plant.Finally,the uncertainties of wind power,photovoltaic power and load demand are dealt with using interval optimization methods.With the objective of minimizing the total economy-environment cost,the optimal aggregation strategy of virtual power plant is determined.(2)An optimization model is proposed for the participation of virtual power plant in medium and long-term contract trading.Firstly,the trading mechanism of the medium and long-term market is described and bilateral contract is selected as the type of trading.Secondly,the relationship between contracted quantity and price is analyzed.The traditional stepwise price contract is transformed into a liner one,thus providing more flexible contract choice for both parties.In operation,the contracted quantity is gradually decomposed from annual to hourly for execution,which can also help the virtual power plant to clarify its trading capacity in the spot market.Then,considering the randomness of wind and photovoltaic power output in the virtual power plant,the central limit theorem is introduced into Monte Carlo simulation to predict their output.This can reduce the impact of renewable energy output uncertainty on the transaction of virtual power plant,thereby reducing the contract execution deviation.Finally,the life cycle cost of energy storage system is discounted to an annualized cost based on microeconomic theory.The medium and long-term contract trading strategy for virtual power plant is optimized from four perspectives:different contract price mechanisms,different quantity decomposition methods,different deviation penalty mechanisms and different energy storage sizes.(3)A two-stage trading optimization model for virtual power plant participation in the spot market is proposed.Firstly,the trading mechanism of the spot market is described.In view of the different trading stages in the spot market,the total profit of the medium and long-term market and the spot market is maximized in the day-ahead stage,and the total cost of deviation penalty and energy storage regulation is minimized in the real-time stage.Secondly,the scenario sets are used to determine the output of wind and photovoltaic power.The comprehensive forecasting error model of renewable energy output is proposed based on the convolution formula to cope with the short-term output deviation.Then,the energy storage systems can play two roles simultaneously in the spot market:regulating deviations in renewable energy output and arbitraging based on electricity prices.Its capacity is divided into reserve capacity and arbitrage capacity based on the comprehensive forecasting error distribution,thereby improving the efficiency of utilization.Finally,the conditional value-at-risk is used to measure the risk of different trading strategies.The trading strategies of virtual power plant participation in the medium and long-term market and the spot market are optimized under different risk levels.(4)A multi-objective optimization model is proposed for the participation of virtual power plant in peak regulation ancillary services market.Firstly,the trading mechanism of the ancillary services market is described and peak regulation is selected as the type of trading.Secondly,a dynamic response price mechanism is proposed based on the characteristics of the wind and photovoltaic power output.The price-based and incentive-based demand response models are used to regulate flexible loads.Then,considering that demand response affects consumers’experience,a comprehensive satisfaction model is proposed in terms of comfort and economy.Finally,with the objective of maximizing profitability and customer satisfaction,the trading strategy for virtual power plant participation in the energyancillary services market is optimized under three scenarios:fixed price,peak-tovalley price and dynamic price.(5)The benefit allocation and reasonableness evaluation model for multisubject in virtual power plant is proposed.Firstly,the rationale for the benefit allocation of cooperative games is described.Typical benefit allocation methods are analyzed:the core method,the nucleolus method,the Shapley value method,the simplified MCRS method,the proportional method and the non-separable profit method.Secondly,a qualitative analysis of the advantages and disadvantages of the above methods is presented.According to their solving principle,a comprehensive evaluation index system is constructed from four dimensions:stability,fairness,tendency and operability to evaluate the reasonableness of the benefit allocation schemes.Then,the subjective and objective weights are obtained based on the analytic hierarchy process method and the entropy weight method respectively.The final weights are combined by using the level difference maximization method.Finally,the VIKOR-Grey relation analysis method is used to quantitatively measure the rationality of the benefit allocation schemes from multiple perspectives and levels.