导　　师： 文福拴

学科专业： 080802

授予学位： 博士

作　　者： ;

机构地区： 华南理工大学

摘　　要： 气候变化已成为人们广泛关注的一个重要问题。如何缓解气候变化呢?实行排污权交易就是减轻温室气体排放,缓解气候变化的一个有效途径。排污权交易已经在欧洲实行,也将成为其它国家减少温室气体排放的重要手段,如澳大利亚。由于在大多数国家,电力工业都是排放温室气体最多的行业,要有效的改善污染状况就必须改善电力行业的污染物排放现状。排污权交易的实行将改变电力工业的排污现状,因此,排污权交易的实行必然对电力工业和电力市场产生影响。如:排污权交易计划的设计、排污权市场的交易品种、排污权许可证的分配方式等都会对电力工业和电力市场产生影响。 本文的研究目的是从发电公司的角度探讨排污权交易对不同类型的发电公司的影响,其中包括了燃煤,燃气等有污染物排放的发电公司,也包括了核电,风电等无污染物排放的发电公司。研究的内容包括排污权交易实行后对发电公司行为的短期影响,表现为实行排污权交易后的发电公司策略和同时参与各类电力市场和排污权交易市场的竞争者之间的博弈均衡;以及排污权交易实行后对各类发电公司的长期影响,表现为发电投资者如何选择投资于不同类型的发电机组,也就是投资策略问题。 本文的主要工作如下: 1.由于电力市场本身是一个寡头垄断市场,针对存在排污权交易品种仅为二氧化碳这单一品种的排污权交易市场,发电公司需要同时考虑排污权市场中排污权价格波动和电力市场中电价变化以及其他发电公司的行动对本公司利润的影响。为此,建立了二氧化碳排污权价格估算模型,并导出了发电公司供给函数均衡模型来分析多个发电公司同时参与排污权市场和电力市场竞争时的策略行为。仿真结果表明了排污权交易能够促进节能减排,但是,排污权交易在短期内也会造成电价升高,而且使得发电公司能够获取暴利。 2.对于一个能够同时交易二氧化碳、二氧化硫和氧氮化物等交易品种的排污权交易市场,建立了推测变量模型和各类排污权价格的估算模型来分析同时参与排污权交易市场和电力市场竞争的发电公司的策略行为。分析了各发电公司间分别进行古诺博弈、合谋均衡、伯川德博弈和斯塔克博格博弈时的报价和出力策略。结果表明,排污权交易能够升高电力市场出清电价,抑制高排放发电公司的排放,增加低排放发电公司的出力和利润,减少某个地区所有发电公司总的污染物排放量。 3.在双边合同市场中,发电公司能够直接和用户进行交易。采用互补方法建立了有电力用户、火电公司、风电公司、输电权拥有者参与博弈的日前电力市场均衡模型。模型还同时考虑了火电公司和用户的远期双边合同,备用市场、输电权交易市场以及排污权价格波动性和风电公司出力不稳定等特点。所编制的GAMS仿真程序的仿真结果表明:排污权交易能够提高风电公司的发电份额,减少火电公司的温室气体的排放量;发电公司和电力用户的远期双边合同能够有效的抑制发电公司行使市场势力提高电价的行为,降低电价;随着排污权配额的减少,排污权的价格显著增长,各类发电公司间的调度序位改变,排污量小的CCGT发电公司能够获得更多的发电机会,但是由于风力发电成本过高,其发电份额并未显著提高,因此,风电公司要获得更多出力份额关键在于降低发电成本,或者获得政府补贴。 4.在联营体市场和双边合同市场共存的市场环境中,研究了存在排污权交易情况下的多个市场竞争者同时参与跨期能量市场、备用市场、输电权交易市场和排污权市场竞争的博弈均衡问题,建立了描述为混合线性互补问题的综合互补模型。考虑的市场参与者包括发电公司、输电权拥有者、套利者和用户。并针对所建立的模型和算例,编制了相应的GAMS应用程序进行仿真。最后,得到的仿真结果说明了所提出的模型与方法的基本特征。对研究多个博弈方在多市场内的博弈均衡问题进行了有益的探索。 5.排污权交易实行后,增加了发电投资的不确定性,传统的净现值分析法已经无法满足投资决策的需要。根据实物期权理论,将多种不确定性因素综合,建立了实物期权模型,分析了发电投资者在面临电价、燃料价格、排污权价格、政策变动、碳捕集与封存技术发展等不确定性时,选择投资于传统的燃煤发电、CCGT机组还是风电、核电等新能源机组。首先,假设各种不确定因素的变化符合几何布朗运动。接着,使用EViews 6.0软件对各种不确定因素的历史时间序列进行分析,使用一阶移动平均自回归条件异方差GARCH/(1,1/)模型来获得预测参数。然后,建立实物期权模型。最后,采用水晶球软件进行了蒙特卡洛模拟仿真,确定了各种项目选择的投资价值和投资时间,验证了模型的有效性。 Climate change has become an important and widespread concern of the public around the globe. How to mitigate the ever-worsen climate change problem? Introducing the emissions trading /(ET/) is an effective way to curtail the emissions of greenhouse gases. ET has been implemented in the European Union, and has been proposed in a number of other countries including Australia and the United States. The power industry is the largest emitter of greenhouse gases in many countries of the world, and should implement the ET into it to improve the status of power sector emissions. However, the emissions trading will inevitably have some impacts on power industries and electricity markets. These potential impacts include some key issues associated with the Emissions Trading Scheme /(ETS/) design, the types of emission permits trading, as well as the methods of allowance allocations. The objective of this dissertation is to examine the interactions between ET and generation companies /(GenCos/) involved with emissions, such as coal-fired and gas-fired companies, and those not involved with emissions, such as wind and nuclear power generation companies. The short-term and long-term impacts of emissions trading on the behavior of GenCos are studied. The short-term impacts include the bidding strategies of GenCos and the markets equilibrium of gaming among GenCos participating in both the ET market and electricity market. The long-term impact is mainly concerned with the changes of investment strategies after the implementation of the ET. Specifically, this dissertation is comprised of five chapters. 1. The emerging electricity market is more akin to an oligopoly market. A GenCo should analyze the price fluctuation of emission rights as well as that of electricity, and the response of its rivals so as to maximize its own profit. In the first chapter, a model used to forecast the price of emission permits of carbon dioxide is established and a Supply Function Equilibrium Model is derived to analyze the action of GenCos who gamed in both the emission trading market and electricity market. Simulation results show the implementation of emission trading could limit the emission but lead to the electricity price increase and the―windfall profit‖of some GenCos. 2. The second chapter extends the type of emissions permits. Not only the permits of carbon dioxide but also those of sulfur dioxide and nitrogen oxide are traded in the emissions trading market. By using the well-established conjectural variation method, a mathematical model is developed for investigating the strategic behavior of GenCos participating in both the ET market and the electricity market. Four scenarios with GenCos playing games of Cournot, Cartel, Bertrand and Stackelberg are demonstrated respectively. The strategic behavior in each scenario is examined. As expected, the results show that the implementation of the emissions trading will increase the electricity market clearing price /(MCP/), limit the emissions form high-emission GenCos, lead to the outputs from those GenCos with low-emission increased, and then curtail the overall emissions from GenCos. 3. In a bilateral contract market, the GenCos could trade with customers directly. In the third chapter, the complementarity method is used to simulate the equilibrium among customers, fossil-fueled generation units, wind power units and the grid company, which participate in the emissions trading market and the day-ahead electricity market. Forward contracts, the operating reserve market, the fluctuation of generation outputs from wind GenCos and the price of emissions allowances /(EA/) are considered in this model. A program of General Algebraic Modeling System /(GAMS/) is developed for simulations. Simulation results show that the ET could increase the share of the generation output of wind GenCos, and decrease the emissions from fuel-fossil GenCos; the bilateral contracts between GenCos and users could limit the ability of GenCos’exercising market power by driving up electricity price; when the emissions allowances allocated decrease, the price of EA will increase, and hence the dispatch orders of different generation units will changed. Because of the cost of wind GenCos is still very high, it will be more realistic to increase the market share of wind GenCos by reducing cost rather than by relying on the ET implementation. 4. In the fourth chapter, a mathematical model is developed for investigating the equilibrium state of an interperiod multi-market with different kinds of market participants playing in energy, reserve, point-to-point financial transmission right /(FTR/) as well as emissions trading markets. This complicated problem is formulated as a well-established mixed linear complementarity problem /(MLCP/). The market participants considered include generation companies, FTR owners, arbitrageurs and electricity consumers. A GAMS program is coded to simulate the MLCP. Finally, the results are served for demonstrating the essential characteristics of the developed model and method. This is a useful exploration to analyze the Nash equilibrium state of multiple players participating in multiple markets. 5. Uncertainties of generation investment increase after the implementation of the emissions trading, and the traditional net present value analysis is no longer able to meet the needs of power generation investment decision-making. In the fifth chapter, in the framework of the real options theory, some uncertain factors are integrated into a model to help the investors to make decision, including fluctuations of the electricity price, fuel price and emission rights price, policy change, carbon capture and storage technology development. Investors could choose to invest in conventional coal-fired power, CCGT units, nuclear power, wind power and other renewable energy. First, we assumed that the variation of the uncertainties follow Geometric Brownian Motion /(GBM/). Secondly, the software of EViews 6.0 is used to develop a statistical relation from the history of time series, such as the price of electricity and permits of emissions. An Generalized Autoregressive Conditional Heteroskedasticity /(GARCH/) model, GARCH/(1,1/), is applied to obtain the parameters for prediction of each uncertainty. Finally, a real option model is established to evaluate each investment project, and the software named Crystal Ball is utilized for the Monte Carlo simulation in order to help the investor make decision in choosing the best project and the best time of investment. The effectiveness of this model is verified by simulations.

关 键 词： 发电公司 电力市场 排污权交易 供给函数均衡 推测变量法 线性互补问题 实物期权 蒙特卡洛模拟 发电投资

分 类 号： [X196 F426.61]

领　　域： [环境科学与工程] [经济管理]