The Model Of Cost Allocation Mechanism Of Ancillary Services For Wind Integration

Global climate change, resulting mainly from rapid increase of CO2emitted by human activities, is a considerable challenge facing the whole world and exerts increasingly significant impacts on environment. China is the world's biggest CO2emitter, taking up20%of total amount. It is estimated that the proportion will continue increasing. China has an increasingly heavy responsibility to CO2mitigation in the world. In China, power sector is the biggest CO2emitter, accounting for about40%of the total emission in China. In order to realize the2020target for CO2emission in China, reduced CO2intensity in power sector is crucial.Large-scale wind power development plays an active role in CO2reduction in power sector. For this purpose, China's wind power has witnessed rapid development. However, wind power is intermittent, uncertain, and cannot be moved down. In order to meet the requirements of dispatching, ancillary services such as peak-shaving and operational reserve from conventional power are required for integration. With the large-scale wind power development, demand for ancillary services provided by conventional power is increasing. The capacity of conventional power's provision of ancillary services is crucial to wind power development. Former SERC statistics show that in the period between January and June in2010,2.776billion kWh of electricity was lost due to wind curtailment, taking up12.47%of the total integrated electricity in the same period. The major reason for the loss is the lack of ancillary services. Therefore, increased capacity of ancillary service provision in power system has very important significance towards wind power integration.Improved capacity of ancillary service provision in power system requires huge investment, which can hardly be afforded by power system itself. Also, it would be unfair to have the power system afford the cost. Hence, in order to incentivize ancillary service provision for wind power integration, further research is necessary on distribution and compensation mechanisms for the ancillary service cost. Only in this way can capacity of ancillary service provision in power system be enhanced, and wind power integration be guaranteed.Therefore, in order to study the above problems, this paper will conduct the following analysis. Firstly, the author will examine the ancillary services costs resulting from wind power integration to summarize its characteris and main influencing factors. Then the author will adopt static cooperation game theory to allocate the wind power incurred intergration ancillary service cost, with thorough comparison and disucssion on the anlaysis results. Base on the anlysis resutls from the staic cooperation game theory, the author will further use a dynamic cooperation game theory to allocate the ancillary service cost between wind farms in a dynamic modelling. Lastly, by combining the analysis results from both static and dynamic cooperation game theories, the thesis will come up with a wind power integration incurred ancillary services cost distribution mechanism that fits China's context.In China, the scope of ancillary services covers primarily the peak-shaving, operational reserve and reactive services. Most of them share identical concepts with international ones. Along with the development of wind power generation technoglogy, particularly the invention of double-fed induction asynchronous wind turbines which can suffice its own reactive service, the power system requires little reactive ancillary service. Thus this paper will focus on the wind power integration's impact on peak-shaving and operational reserve services.The main analyzing tool to the wind power integration's degree of impact on the ancillary services is called "With&Without Antitheses", that is by comparing the difference of the power grid between wind power connected and disconnected situations to determine the wind power intergration impact. Furthermore, different types of ancilary services have different impacts on wind power integration incurred ancilary services cost. Peak-shaving ancillary service was mainly due to the volatility of wind power output, so that the peak-shaving ancillary cost brought by wind power integration should be the difference of the ancillary service cost between non-wind power load duration curve and wind power integrated load duration curve (load and wind power integrated). Operational reserve ancillary service was mainly caused by wind power output prediction error, so that the extra ancillary service cost brought by wind power integration should be the difference between operational reserve ancillary service cost of the non-wind power output prediction error and the wind power integrated output prediction error.Meanwhile, the paper found that as long as the correlation coefficient of two wind farms' power output is not1, the total peak-shaving and operational reserve ancillary service costs of two wind farms'joint intergration is less than respective integration, which means the combined integration of wind power would have a "smoothing effect" on peak-shaving&operational reserve ancillary services. Therefore, it is necessary to further analyze the allocation of combined wind intergration ancillery service cost among wind farms. The paper further build wind power integration incurred ancillary service cost distribution in a static cooperative game model and a dynamic cooperative game model. Static cooperative game model consists of static methods in accordance with EANS allocation model, Shapley value allocation model, Owen value apportion model and nucleolus apportion model. While dynamic cooperative game model includes open-loop dynamic cooperative Nash equilibrium allocation model, dynamic cooperative feedback Nash equilibrium allocation model and Shapley value cooperation dynamic allocation model. The comparative analysis of the model results show that wind power integration peak-shaving inccured ancillary services cost, in the case of insufficient data, distribution in accordance with the electricity output is a more viable option; while in the case of improving data, gradual transition to the nucleolus method or Shapley value allocation method is feasible. On the other hand, for wind integration incurred operational reserve ancillary service cost, distribution in accordance with the electricity output is not feasible. It is suggested that at the initial stage, cost can be allocated in accordance with their power proportion integrated, and then gradually transfer to nucleolus allocation methods or Shapley value method.The current compensation distribution mechanism for ancillary service which in accordance with on-grid electricity output (or electricity tariff) proportion isn't suitable for wind power integration inccured ancillary service cost distribution. It is unfair for the wind farm and thermal power plants to bear the cost following current distribution method. Therefore, the ancillary service compensation distribution mechanism needs to be adjusted and improved to fit the wind energy.To calculate the wind power peak-shaving ancillary services cost, it requires a costly large data monitroing and accumulaion which includes:every minute fluctuation of the power load, wind power output per minute and the wind power output per minute smoothing effect. Thus it is difficult to calculate the wind integration incurred peak-shaving ancillary service cost without sufficient data. However, wind power output fluctuations are mainly determined by the characteristic of wind energy resource that can not be overcomed by the wind farm. Therefore, at early stage, the wind power integration inccured peak-shaving ancillary cost is suggested to be borne by the consumer instead of the wind farms. After establishing a sufficient data accumulation and data monitoring capacity, the peak-shaving cost can be transferred back to the wind farm respectively. The distribution methods can start from distributing in accordance with the electricity output, and gradually transfer to a more rational nucleolus method or Shapley value apportion approach. Considering the difficultis at different stages of reform, this paper suggests to start the reform from the easier sector, and to achieve a rational distribution and compensation mechanism for the wind power integration incurred ancillary service cost. At the first stage, while keeping the current ancillary service assessment and compensation measures, it is suggested to add a wind power prediction accuracy assessment, based on which compensating wind farm's operational reserve ancillary service cost, whereas the wind power inccured peak-shaving ancillary service cost can be compensated by raising the feed-in tariff that transmitted to the sales tariff. At the second stage, it is suggested to change the internal loop of fund rasing and compensating for the ancillaery service assesment and compensation within the power generation sector. And the grid companies will bear the shortfall of the compensation after assessment which can be reimbursed in the sales tariff, instad of feed-in tariff. And wind farm bears its self-inccured spare ancillary cost. However, the assessmet determined ancillery service cost is not economical, hence it won't reach the lowest cost. At the third stage, with the establishment of electricity spot trade market, the peak-shaving ancillery cost will be determined by power generation companies offer. With sufficient data and knowledge of wind integration incurred peak-shaving ancillary service cost, the wind power compensation and distirubtion will be based on wind electricity output proportion. On the other hand, while building a power operational reserve ancillary service market, the generation company's offer can determine the ancillary service cost which the consumer and the wind power will compensate their inccured ancillary costs respectivly.Further study on the compensation and distribution of wind power integration inccured ancillary service cost is required, including:1. The compensation to wind power inccured reacive and other ancillary service cost;2, Technologies to optimize ancillary service provision;3, The compensation to wind power integration inccured ancillary service cost among provinces and even among regions;4, The distribution of wind power integration inccured ancillary cost considering wind power's positive externalities;5, grid companies invovlement in ancillary service cost compensation;6, The design of the electricity market trading mechanism to avoid the impact of market power;7, The design of a reasonable ancillary service mechanism that promote wind power's appropriate participation.