电力市场三种寡头竞争模型的市场力分析比较

市场在经济学中表现为市场参与者能够抬高市场价格的能力，在寡头竞争电力市场中则表征市场内电力公司影响市场结清电价的能力。应用博弈论分析研究了寡头竞争电力市场的市场力。以完全竞争市场下的均衡发电量和均衡电价为基准，分别比较了按Cournot模型、Stackelberg模型(领导者—跟随者模型)和Forchheimer模型(领导者—价格接受者模型)模拟寡头竞争电力市场情况下的电力公司所拥有的市场力，并且分析生产成本、市场中的电力需求弹性、市场中作为领导者的电力公司数量以及容量限制对市场力的影响。研究表明，在3种模型市场中，Cournot模型市场拥有的市场力为最大，Stackelberg模型市场次之，Forehheimer模型市场为最小。     

Cournot Prices Considering Generator Availability and Demand Uncertainty

When electricity becomes deregulated, the price of electricity is not determined by a regulatory authority but by market demand, supply conditions, and strategic behavior. Several oligopoly models have been proposed for representing strategic behavior in electricity markets, notable among which is the Cournot model. The literature on this model has for the most part provided a deterministic treatment of the demand and supply side quantities. The market, however, is subject to both supply and demand side uncertainties. This paper provides analytical models and procedures based on generation system and load data for probabilistically representing two different market competition models-one is based on perfect competition and the other based on asymmetric oligopoly (Cournot). We account for the availability of generating units and show its effects on market prices. We also study the effects on a firm's expected profit when generator availabilities are neglected in the offers decisions. A numerical example is given for an electricity market for which the expected duration curve of the Nash-equilibrium electricity price as well as the mean of the individual firms' profits are calculated. The effects of unit commitment, transmission congestion, and transmission outages are not considered     

基于仿射跳跃-扩散过程的电力市场电价随机模型

基于金融工程理论的电价随机模型对于竞争性电力市场中的电力衍生产品定价、风险管理、资产定价及不确定条件下的投资决策等具有基础意义。文中提出了基于仿射跳跃-扩散过程的、具有2个和3个跳跃分量的电价随机模型以及一种新的参数标定方法。所提出的近似参数标定方法可利用历史电价数据快速求解模型参数,且计算量与电价样本点数量无关。由于直接根据历史电价的数字特征求解,不存在误差累积的问题。根据法国Powernext、德国EEX和荷兰APX等3个主要欧洲电力市场的历史日前小时电价数据标定了模型参数并采用Monte Carlo方法分析了模型的模拟效果。计算表明,文中提出的电价随机模型能够较为准确地描述电价的整体概率分布,满足进一步研究的需要。     

考虑绿色证书交易的寡头电力市场均衡分析

可再生能源配额和可交易绿色证书制度作为一种基于市场机制的新能源扶持政策,得到越来越多的关注。该政策的引入会对电力批发市场的竞争产生重要影响,特别是绿色证书交易市场与电力批发交易市场之间存在复杂的相互影响关系。为了定量研究该影响关系,采用寡头竞争均衡理论,建立了一个考虑绿色证书交易市场和电力批发交易市场的两阶段联合均衡模型。在第1阶段,可再生能源发电商以供应函数模式参与证书市场竞争;在第2阶段,所有发电商以古诺模式参与批发市场竞争。该模型描述的均衡问题可以表示为一个具有均衡约束的均衡问题,可采用非线性互补方法来求解。最后,算例仿真重点研究不同配额要求下发电商在两个市场中的策略性行为及相互影响关系,表明在不同的配额要求下,可再生能源发电商会选择在不同市场中行使市场力,并导致电力批发市场价格与绿色证书市场价格上升。     

A Probability Model for the Electricity Price Duration Curve Under an Oligopoly Market

In this paper, we propose a new formulation for the “price duration curve” (PDC) using probability considerations and provide a procedure for constructing it. This curve is expected to be useful in the long-term prediction of market prices and is similar in spirit to the load duration curve. It shows the proportion of time over a given time horizon during which the real-time market price of electricity is expected to exceed specified dollar amounts. The price over a long term is a stochastic quantity that depends on physical factors such as production cost, load, generation availability, unit commitment, and transmission constraints. It also depends on economic factors such as strategic bidding and load elasticity. We illustrate a procedure for constructing a stochastic system-based model for the PDC, taking into account the randomness associated with load and generator outages. The effects of unit commitment, transmission congestion, and transmission outages are not considered. We use two economic models. One is due to Bertrand and represents perfect competition. The other model is due to Rudkevich who have given a closed-form expression for the market- clearing price in an oligopoly consisting of several identical firms.     

Optimizing demand-side bids in day-ahead electricity markets

We present a model of a purchaser of electricity in Norway, bidding into a wholesale electricity pool market that operates a day ahead of dispatch. The purchaser must arrange purchase for an uncertain demand that occurs the following day. Deviations from the day-ahead purchase are bought in a secondary market at a price that differs from the day-ahead price by virtue of regulating offers submitted by generators. Under an assumption that arbitrageurs are absent in these markets, we study conditions under which the purchaser should bid their expected demand and examine the two-period game played between a single generator and purchaser in the presence of a competitive fringe. In all our models, it is found that purchasers have an incentive to underbid their expected demand, and so the day-ahead prices will be below expected real-time prices. We also derive conditions on the optimal demand curve that purchasers should bid if the behavior of the other participants is unknown but can be modeled by a market distribution function.     

An oligopolistic power market model with tradable NO/sub x/ permits

Models formulated as complementarity problems have been applied previously to assess the potential for market power in transmission-constrained electricity markets. Here, we use the complementarity approach to simulate the interaction of pollution permit markets with electricity markets, considering forward contracts and the operating reserve market. Because some power producers are relatively large consumers of permits, there could be interaction between market power in the permits and energy markets. Market power in the energy market is modeled using a Cournot game, while a conjectured price response model is used in the permits market. An illustrative application is made to Pennsylvania-New Jersey-Maryland Interconnection (PJM), which we represent by a 14-node dc load-flow model, and the USEPA Ozone Transport Commission NO/sub x/ Budget Program. The results show that forward contracts effectively mitigate market power in PJM energy market and both simulated solutions of perfect and Cournot (oligopoly) competition are a good approximation to actual prices in 2000, except that the Cournot model yielded higher peak prices. The NO/sub x/ market influences the Cournot energy market in several ways. One is that Cournot competition lowers the price of NO/sub x/ permits, which in turn affects on low- and high-emission producers differently. In general, because pollution permits are an important cost, high concentration in the market for such permits can exacerbate the effects of market power in energy markets.     

非寿险精算在零售价差风险管理中的运用

价差风险对零售市场的稳定运行和企业的稳健经营影响很大。零售商必须构建价差风险管理体系才能对风险进行系统、有效的管理。在从概率论角度准确定义价差风险的基础上,运用非寿险精算原理,进一步定义了零售平均价格的精算值。精算值把零售平均价格分为精算值和风险溢价。根据零售企业风险偏好,价差风险溢价又可分为价差风险管理所需要的风险资本和价差风险报酬。价差风险资本的份额大小由安全附加系数测量。以风险资本与价差风险水平的对应关系为核心,以电力市场衍生合同和企业自身的价差风险准备金为风险控制手段,建立价差风险管理体系的初步框架。模拟结果显示,安全附加系数的增加明显使价差风险降低,降低的速度是先快后慢;电力终端需求量与批发市场电价的相关性越大,安全附加系数对价差风险管理效率越高。     

基于自回归条件异方差-反向传播网络模型的日前边际电价预测

针对日前电力市场提出了一种基于自回归条件异方差分析的改进神经网络模型。首先利用自回归条件异方差分析得到边际电价序列的条件方差,然后以条件方差作为电价波动风险指标,建立基于历史电价、历史负荷和历史电价条件方差等输入量的自回归条件异方差-反向传播网络模型,并利用该模型对美国PJM电力市场的日前边际电价进行了预测。结果表明,引入自回归条件异方差分析可以有效提高传统反向传播网络的预测精度。     

考虑多重周期性的短期电价预测

考虑到电价各时段变化以及周末与工作日变化的差异,提出了区分周末的分时段短期电价预测模型。该模型首先将各日中同一时段的电价形成该时段的电价序列,再将各时段电价序列分为工作日电价序列和周末电价序列。这样形成了多个消除了日周期性和星期周期性的子电价序列,分别对各子电价序列进行预测以得到预测日电价。采用基于小波分析的广义回归神经网络对这些子电价序列分别进行提前一天的预测,各子电价序列的预测电价就形成了下一天的预测电价。采用该方法对西班牙电力市场电价进行了长时间的连续预测,并与已有的预测方法进行了详细的比较分析,研究表明该方法能够提供更准确的预测电价。     

Electricity price forecasting using artificial neural networks

Electricity price forecasting in deregulated open power markets using neural networks is presented. Forecasting electricity price is a challenging task for on-line trading and e-commerce. Bidding competition is one of the main transaction approaches after deregulation. Forecasting the hourly market-clearing prices (MCP) in daily power markets is the most essential task and basis for any decision making in order to maximize the benefits. Artificial neural networks are found to be most suitable tool as they can map the complex interdependencies between electricity price, historical load and other factors. The neural network approach is used to predict the market behaviors based on the historical prices, quantities and other information to forecast the future prices and quantities. The basic idea is to use history and other estimated factors in the future to “fit” and “extrapolate” the prices and quantities. A neural network method to forecast the market-clearing prices (MCPs) for day-ahead energy markets is developed. The structure of the neural network is a three-layer back propagation (BP) network. The price forecasting results using the neural network model shows that the electricity price in the deregulated markets is dependent strongly on the trend in load demand and clearing price.     

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes, which may be tens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far, most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply–demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour, electricity demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results.     

Equilibria in futures and spot electricity markets

We describe a model to analyze the equilibrium encompassing an electricity futures market and a number of electricity spot markets sequentially arranged along the time horizon spanned by the futures market. Profit-maximizing strategic electricity producers react to both prices and rival production changes, in both the spot and the futures markets. At each time period, the total demand is considered to depend linearly on the spot price of the considered time period, and the futures market price is assumed to equal the average spot price over the time horizon. Equilibrium conditions at each spot market are described as a function of the futures market decision variables, which in turn allows describing the equilibrium in the futures market implicitly enforcing equilibrium in each spot market. The proposed model allows deriving analytical expressions that characterize such multi-market equilibrium and that can be recast as a mixed linear complementarity problem. This model is useful to gain insight on the outcomes and characteristics of the considered multi-market equilibrium. Such insight may allow the regulator to better design the futures and spot trading floors, their rules and sequential timing. It may also allow producers to increase the effectiveness of their respective offering strategies.     

Oligopolistic competition in power networks: a conjectured supply function approach

Conjectured supply function (CSF) models of competition among power generators on a linearized DC network are presented. As a detailed survey of the power market modeling literature shows, CSF models differ from previous approaches in that they represent each GenCo's conjectures regarding how rival firms will adjust sales in response to price changes. The CSF approach is a more realistic and flexible framework for modeling imperfect competition than other models for three reasons. First, the models include as a special case the Cournot conjecture that rivals will not change production if prices change; thus, the CSF framework is more general. Second, Cournot models cannot be used when price elasticity of demand is zero, but the proposed models can. Third, unlike supply function equilibrium models, CSF equilibria can be calculated for large transmission networks. Existence and uniqueness properties for prices and profits are reported. An application shows how transmission limits and strategic interactions affect equilibrium prices under forced divestment of generation.     

基于实时电价的产消者综合响应模型

产消者作为新兴的特殊电力消费者,拥有电力供给和消费的双重角色,具有较强参与实时市场的响应潜力。为提高分布式发电利用率和用户供用电收益,提出基于实时电价的产消者的多阶段综合响应模型。前期优化阶段,综合考虑用户用电经济性、舒适度和电价激励作用,提出用户最优小时需求出价模型;以用户供电收益最大为目标,考虑储能容量和功率约束、储能闭锁和动作死区等因素,提出用户最优小时供给报价模型。出清响应阶段,基于实时电价,结合用户最优小时供需出报价信息,提出用户可控负荷需求响应和分布式发电—储能系统供给响应交替的综合响应模型。算例分析表明,所提综合响应方法能及时响应实时电价的波动,不仅灵活削减和转移了用户在电价高峰区的负荷,而且实现了分布式发电供给从低电价区域向高电价区域的转移,提高产消者供用电的经济性。     

基于价格配额曲线的售电商投标策略优化

近年来随着中国电力体制的改革,大量售电商参与到电力市场之中.对于部分售电商而言,其可能存在一定的市场影响力,投标行为会对市场出清电价产生影响,进而影响到自身的投标策略.考虑到售电商自身的投标策略对市场出清电价的影响以及出清电价本身的不确定性,文中将市场出清电价分布与价格配额曲线(PQC)相结合,建立了基于PQC的市场出清电价的分布模型,进而建立了售电商在电力市场的投标行为优化模型.同时,研究了不同市场供求关系下对应的出清电价分布情况对售电商投标策略的影响.算例表明,对比不同情况下售电商的最优投标策略,基于PQC的方法可以使售电商更好地适应市场并合理地调整投标策略.     

基于动态计量经济学模型的短期电价预测

电力市场中的电价受众多因素影响,单变量时间序列法已很难提高短期电价的预测精度。针对该问题,文中运用时间序列模型的动态计量方法来预测短期电价。首先建立电价和电量的一般自回归分布滞后模型;然后对电价和电量的时间序列数据进行预处理;在通过平稳性和协整性检验后,建立误差修正模型,最终由Eviews 5.0估计出模型的参数。利用此模型对澳大利亚新南威尔士州电力市场的短期电价进行预测,结果表明此模型具有较高的预测精度。     

Price forecasting and validation in the Spanish electricity market using forecasts as input data

The electricity sector has been subjected to major changes in the last few years. Previously, there existed a regulated system where electric companies could know beforehand the amount of energy each generator would produce, hence basing their largely operational strategy on cost minimization in order to increase their profits. In Spain, from 1988 till 1997, electricity prices were established by the ‘Marco Legal Estable’ – Stable Legal Framework –, where the Ministry of Industry and Energy acknowledged the existence of certain generation costs related to each type of technology. It was an industrial sector with no actual competition and therefore, with very few controllable risks. In the aftermath of the electricity market liberalization competition and uncertainty arose. Electricity spot prices became highly volatile due to the specific characteristics of electricity as a commodity. Long-term contracts allowed for hedge funds to act against price fluctuation in the electricity market. As a consequence, developing an accurate electricity price forecasting model is an extremely difficult task for electricity market agents. This work aims to propose a methodology to improve the limitations of those methodologies just using historical data to forecast electricity prices. In this manner, and in order to gain access to more recent data, instead of using natural gas prices and electricity load historical data, a regression model to forecast the evolution of natural gas prices, and a model based on artificial neural networks (ANN) to forecast electricity loads, are proposed. The results of these models are used as input for an electricity price forecast model. Finally, and to demonstrate the effectiveness of the proposed methodology, several study cases applied to the Spanish market, using real price data, are presented.     

含误差预测校正的ARIMA电价预测新方法

在电力市场中，准确的电价预测是各市场参与方共同关心的重要问题。已经提出多种理论和方法尝试提高电价预测精度，然而由于影响电价的因素十分复杂，实践证明靠建立单一的电价预测模型来提高预测精度是非常困难的。该文在分析电价波动特性和现有预测方法的基础上，首次提出结合误差预测校正电价预测来提高预测精度的新思路。在建立常规电价预测模型的基础上，对预测后的残差形成的随机序列也迭代地建立预测模型，并用预测的误差修正电价预测结果。该文采用ARIMA方法建立电价预测和误差预测模型，并用加州电力市场的历史数据建立基于ARIMA的日平均电价预测模型，预测结果表明所提方法能明显改善预测精度，而且方法简捷明了，能够推广到小时电价预测、负荷预测和其它预测领域。     

电力市场古诺模型的均衡分析

完全竞争的市场可以使商品的价格达到最低，并使资源得到有效配置，然而实际的电力市场是一个接近寡头垄断的市场，各发电商可以通过估计对手的策略，采用最优投标来获得较大的收益，此时市场处于纳什(Nash)均衡状态，市场电价高于完全竞争的市场电价。作者根据电力市场的特点，利用古诺模型来模拟发电商的策略行为，给出了在信息完全与信息不完全情况下该模型的均衡解析解，并通过简单算例的结果分析了不完全的成本信息对市场均衡状态的影响。