价格永远是最为关键所在

正业内人士指出,价格的高低直接关系到企业的效益和生死存亡,因此是买卖双方的核心利益。确保价格由市场形成,并反映市场的供求状况不仅是改革的目的,也是电力改革是否取得成功的标志。要核定独立的输配电价价格形成机制改革被认为是电改的核心和主要任务。那么目前我国的电价是什么情况呢?据国家发展改革委价格司相关负责     

边际电价预测的三时点模型

从供求决定价格、价格引导供求两个角度出发，分析了电力市场系统供给、需求与电价的关系，建立了边际电价的三时点预到模型。运用该模型对西部某电网次日96点现货电价进行预到，取得了较好的预测效果。     

基于改进引力搜索算法的电力市场环境下可再生能源竞标消纳优化

为解决新能源参与电力市场竞标、促进新能源消纳的问题,文章提出了基于改进引力搜索算法的电力市场环境下可再生能源竞标消纳优化模型。建立了风电、光伏机组出力模型,确定了风电光伏不确定出力场景削减。针对可再生能源竞标消纳模型建立了出清电价模型,确定了交易双方利润模型,并针对交易过程中的不确定概率进行了分析。提出了采用改进引力搜索算法联合模式搜索算法对出清电价模型进行求解。最后通过仿真分析,说明了所提方法的有效性。     

零售竞争对电力合约市场及电力批发价格的影响

针对我国电力市场零售竞争对电力合约市场及批发电价的影响问题,结合电力零售模型和批发市场竞争模型,评估了零售竞争对合约市场及由此引起的电力批发价格的影响程度。结果显示,若短期市场价格波动剧烈且远期合约市场存在竞争,则零售竞争将减少市场中的远期合约交易量,并促使电力批发价格升高。     

世界主要地区煤电市场情况

美国煤电比例：42%电力市场结构：三个主要电网以及一批区域电网构成了电力批发市场。各州的电力系统政策有所不同。电价构成：在受监管州,价格由全部成本和原料成本决定,在电力重组的州,价格取市场上的最低价。煤炭供应：所需煤炭全部自给。电价对煤炭的影响：煤炭价格数十年保持稳定,与电价之间的关联性很弱。在部分电力市场重组的州,由于燃气发电装机容量的增加,电价降低,拉低了煤炭价格。     

PJM电力市场交易价格分布特征及波动风险度量

根据PJM电力市场披露的逐时电力交易价格信息,采用假设检验方法,对日前市场、实时市场及辅助服务市场部分交易品种的电价分布特征进行了统计建模,并针对电价分布的尖峰厚尾特性,结合VaR、CVaR尾部风险度量指标,对各市场中的电价波动风险进行精确度量。研究结果表明：PJM电力市场负荷与日前市场电价、实时市场电价具有强相关性,日前市场及实时市场电价服从对数正态分布,辅助服务价格差异较大,分布拖尾特征显著;在给定置信水平下,负荷、日前市场电价和实时市场电价波动风险逐渐增大,但显著小于辅助服务价格波动风险。     

美国加州电力市场电价的分形特征

以美国加州电力市场长期电价序列为研究对象，并由此引入了分数布朗运动模型和R／S分析法，计算出了不同时间区间内电价的赫斯特指数，发现加州电力市场符合分形市场假设，电价遵循有偏的随机游走．具有长期相关性和统计自相关性等性质。这些发现为描述市场环境下的电价分布提供了一种新模式。     

考虑调峰附加成本的新能源送出型电网电价动态优化模型

针对现货市场交易机制下新能源送出型电网风电不确定性对电网调峰附加成本影响,提出考虑调峰附加成本的电网电价动态优化模型,为火电、储能和电热联合需求响应等调峰资源参与电网调峰提供灵活的电价信号,保障现货交易市场出清价格的准确性,同时提高各电力主体对新能源的消纳能力和系统碳减排能力。首先,以新能源送出型电网为基础,研究考虑风电不确定性的电网调峰需求量化方法,并建立火电、储能和电热联合需求响应等调峰资源的调峰附加成本模型;其次,研究现货市场中各电力主体的竞价策略,考虑调峰附加成本交易机制,建立以最小化运行成本为目标的现货市场出清模型,并采用合作博弈算法对模型进行求解,计算新能源送出型电网各主体出清价格;最后,以东北某新能源送出型电网实际运行数据为例进行仿真验证,结果表明所提出的考虑调峰附加成本的电网价格动态优化模型能有效提升电网对新能源的消纳能力,保障电力现货市场的高效稳定运行。     

计及负荷分类及其需求响应的电力现货市场研究

在逐渐深入推进电力现货市场的环境下,提出一种考虑负荷分类及其需求响应的电力现货市场优化调度模型,该模型融入负荷需求响应并以节点电价作为价格传导机制来优化次日各节点负荷曲线,提升电力系统运行效益.并将社会生产机会成本的概念引入负荷侧,综合优化各节点的购电成本与社会生产机会成本.以IEEE39节点系统为算例进行仿真,验证了...     

简介大功率柴油机发电厂总体设计要点

随着京九铁路的开通运行和香港回归的迫近,江西省将逐渐成为外商投资的热点,因此,营造一个良好的投资环境,是江西省经济主管部门迫切需要解决的一个问题,就电力而言,江西短缺严重,价格偏低,政府以补贴形式扶持电厂生产,以目前电价供应外商投资企业,无疑会造成国有资产的流失,因此,以市场电价供应外商投资企业,是大势     

Short-term electricity price forecast based on the improved hybrid model

Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.     

偏差电量考核下风光储混合发电商的市场均衡博弈研究

为了更直观地分析风光储混合发电商对市场均衡博弈结果和市场力的影响,在偏差电量考核衡量风光储混合发电出力随机波动性的基础上,综合考虑各发电商的出力限制和储能系统的运行约束,建立以风光储混合发电商和传统发电商收益最大化为目标的市场均衡博弈模型,调用CPLEX软件求解利用非线性互补函数处理得到的混合整数规划模型,并对比分析了风光储混合发电商作为价格接受者和影响者参与电力市场竞争对均衡博弈电价和投标出力的影响。结果表明,风光储混合发电商参与电力市场竞争可抑制传统发电商的市场力,对电价有显著的"削峰填谷"作用;当风光储混合发电商作为价格影响者时,对电价的影响会减弱,但其收益有所增加。     

基于电价波动率的现货电价风险度量研究

风险度量因子的选取是风险度量的核心工作之一,统计性质好的指标通常能降低建模复杂度,提高精度。根据电价序列的特征及电价风险度量模型的特点,提出以电价波动率替代电价作为风险度量因子,避开电价序列的非平稳性,建立GARCH-VaR模型用于现货电价风险度量,以北欧电力市场的电价风险度量为例,对模型的可行性和有效性进行检验,并将所提出的电价风险度量方法与电价波动率正态分布法、电价ARMA-GARCH模型度量的电价风险进行比较。结果表明,所提方法不仅能有效降低电价风险度量的模型复杂度,还可提高风险度量的准确性。     

Advanced mechanisms for the promotion of renewable energy—Models for the future evolution of the German Renewable Energy Act

The German Renewable Energy Act (EEG) has been very successful in promoting the deployment of renewable electricity technologies in Germany. The increasing share of EEG power in the generation portfolio, increasing amounts of fluctuating power generation, and the growing European integration of power markets governed by competition calls for a re-design of the EEG. In particular, a more efficient system integration and commercial integration of the EEG power is needed to, e.g. better matching feed-in to demand and avoiding stress on electricity grids. This article describes three different options to improve the EEG by providing appropriate incentives and more flexibility to the promotion mechanism and the quantitative compensation scheme without jeopardising the fast deployment of renewable energy technologies. In the “Retailer Model”, it becomes the responsibility of the end-use retailers to adapt the EEG power to the actual demand of their respective customers. The “Market Mediator Model” establishes an independent market mediator responsible to market the renewable electricity. This model is the primary choice when new market entrants are regarded as crucial for the better integration of renewable energy and enhanced competition. The “Optional Bonus Model” relies more on functioning markets since power plant operators can alternatively choose to market the generated electricity themselves with a premium on top of the market price instead of a fixed price.     

Financial optimization in the Turkish electricity market: Markowitz's mean-variance approach

Electricity constitutes the input into many products that produced by industry and used by people. Hence, it can be considered as a product or service that has vital importance in human life and economy. Since it has such special properties of instantaneous production and consumption obligation and unfeasible storage, electricity market is not like other markets. In a competitive electricity market, generation company faces price risks and delivery risks. So that risk management is an important part of a generation company and can deeply effect companies’ profitability. This paper focuses on electricity generation asset allocation between bilateral contracts, such as forward contracts, and daily spot market, considering constraints of generating units and spot price risks. The problem is to find the optimal portfolio based on known electricity generation total costs, bilateral contract prices, it employed Turkish historical balanced market hourly system marginal and day-ahead hourly market prices between of 2006 and 2011. There are limited studies about portfolio optimization in electricity markets in literature and this paper should be considered frontier study taking spot market's hourly prices separately as risky assets. Markowitz mean-variance optimization which is claimed to be the beginning of modern portfolio theory in financial sector is used to demonstrate this approach. Mean-variance optimization has been successfully applied to all cases that modeled for electricity market. Some suggestions for future work are also listed in this paper.     

A combined modeling approach for wind power feed-in and electricity spot prices

Wind power generation and its impacts on electricity prices has strongly increased in the EU. Therefore, appropriate mark-to-market evaluation of new investments in wind power and energy storage plants should consider the fluctuant generation of wind power and uncertain electricity prices, which are affected by wind power feed-in (WPF). To gain the input data for WPF and electricity prices, simulation models, such as econometric models, can serve as a data basis.This paper describes a combined modeling approach for the simulation of WPF series and electricity prices considering the impacts of WPF on prices based on an autoregressive approach. Thereby WPF series are firstly simulated for each hour of the year and integrated in the electricity price model to generate an hourly resolved price series for a year. The model results demonstrate that the WPF model delivers satisfying WPF series and that the extended electricity price model considering WPF leads to a significant improvement of the electricity price simulation compared to a model version without WPF effects. As the simulated series of WPF and electricity prices also contain the correlation between both series, market evaluation of wind power technologies can be accurately done based on these series.     

基于VAR模型的远期合同定金的定价方法

为规避电力市场改革给市场带来的风险、提高市场的运作效率、增大电力商品的交易量，提出了发电商与电网公司之间一种带定金的远期合同模型。介绍了VAR风险评估方法及其分析法——Delta类模型在电力市场金融风险评估中的应用；运用VAR风险评估方法计算出该模型中发电商存在的风险，进而确定合同定金。应用该模型对不同的合同电价和置信水平分别计算出相应的定金，结果表明，合同定金随合同电价的减小而减小，随置信水平的增大而增大，说明了该模型能较真实地反映发电商所面临市场风险的本质特征。     

Dynamic model for market-based capacity investment decision considering stochastic characteristic of wind power

This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market with significant wind power generation. In such an environment, investment and construction decisions are based on price signal feedbacks and imperfect foresight of future conditions in electricity market. System dynamics concepts are used to model structural characteristics of power market such as, long-term firms’ behavior and relationships between variables, feedbacks and time delays. For conventional generation units, short-term price feedback for generation dispatching of forward market is implemented as well as long-term price expectation for profitability assessment in capacity investment. For wind power generation, a special framework is proposed in which generation firms are committed depending on the statistical nature of wind power. The method is based on the time series stochastic simulation process for prediction of wind speed using historical and probabilistic data. The auto-correlation nature of wind speed and the correlation with demand fluctuations are modeled appropriately. The Monte Carlo simulation technique is employed to assess the effect of demand growth rate and wind power uncertainties. Such a decision model enables the companies to find out the possible consequences of their different investment decisions. Different regulatory policies and market conditions can also be assessed by ISOs and regulators to check the performance of market rules. A case study is presented exhibiting the effectiveness of the proposed model for capacity expansion of electricity markets in which the market prices and the generation capacities are fluctuating due to uncertainty of wind power generation.     

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square technique

This paper proposes the day-ahead electricity price forecasting using the artificial neural networks (ANN) and weighted least square (WLS) technique in the restructured electricity markets. Price forecasting is very important for online trading, e-commerce and power system operation. Forecasting the hourly locational marginal prices (LMP) in the electricity markets is a very important basis for the decision making in order to maximize the profits/benefits. The novel approach proposed in this paper for forecasting the electricity prices uses WLS technique and compares the results with the results obtained by using ANNs. To perform this price forecasting, the market knowledge is utilized to optimize the selection of input data for the electricity price forecasting tool. In this paper, price forecasting for Pennsylvania-New Jersey-Maryland (PJM) interconnection is demonstrated using the ANNs and the proposed WLS technique. The data used for this price forecasting is obtained from the PJM website. The forecasting results obtained by both methods are compared, which shows the effectiveness of the proposed forecasting approach. From the simulation results, it can be observed that the accuracy of prediction has increased in both seasons using the proposed WLS technique. Another important advantage of the proposed WLS technique is that it is not an iterative method.