Stochastic price modeling of high volatility, mean-reverting, spike-prone commodities: The Australian wholesale spot electricity market

It is commonly known that wholesale spot electricity markets exhibit high price volatility, strong mean-reversion and frequent extreme price spikes. This paper employs a basic stochastic model, a mean-reverting model and a regime-switching model to capture these features in the Australian national electricity market (NEM), comprising the interconnected markets of New South Wales, Queensland, South Australia and Victoria. Daily spot prices from 1 January 1999 to 31 December 2004 are employed. The results show that the regime-switching model outperforms the basic stochastic and mean-reverting models. Electricity prices are also found to exhibit stronger mean-reversion after a price spike than in the normal period, and price volatility is more than fourteen times higher in spike periods than in normal periods. The probability of a spike on any given day ranges between 5.16% in NSW and 9.44% in Victoria.     

Capacity commitment and price volatility in a competitive electricity market

The long lead time required to add new capacity in the electricity generation industry means that daily demands are necessarily served by capacity already installed. However, in a competitive market, even if the installed capacity was designed to serve the projected demands, frequent surpluses and occasional full utilization inevitably lead to price volatility. This paper develops a two-stage model of the generation market in which capacity construction occurs in stage 1, before demand realization, and price determination occurs in stage 2, when the equilibrium price ensures that the realized demand does not exceed the installed capacity. We show that price volatility and price spikes are inevitable, and that while price capping can mitigate high and volatile prices, it causes unmet demands and reduction in system reliability. This paper accentuates the interdependence among generating capacity, price volatility and service reliability, a primary cause of concern in the debate on electricity market reform.     

Incorporating the value of changes in price volatility into cost-benefit analysis—an application to oil prices in the transport sector

This paper contains a tentative suggestion of how to take into account the value of changes in price volatility in real world cost-benefit analyses. Price volatility is an important aspect of security of supply which first of all concerns physical availability, but assuming that consumers are risk averse, security of supply can also be viewed as a matter of avoiding oscillations in consumption originating from volatile prices of for instance oil. When the government makes transport-related choices on behalf of the consumers, the effect on oscillations in general consumption should be included in the policy assessment taking into account the most significant correlations between prices of alternative fuels and between fuel prices and consumption in general. In the present paper, a method of valuing changes in price volatility based on portfolio theory is applied to some very simple transport-related examples. They indicate that including the value of changes in price volatility often makes very little difference to the results of cost-benefit analyses, but more work has to be done on quantifying, among other things, consumers’ risk aversion and the background standard deviation in total consumption before firm conclusions can be drawn.     

Electricity market design for facilitating the integration of wind energy: Experience and prospects with the Australian National Electricity Market

Australia has been an early and enthusiastic adopter of both electricity industry restructuring and market-based environmental regulation. The Australian National Electricity Market (NEM) was established in 1999 and Australia also implemented one of the world's first renewable energy target schemes in 2001. With significant recent growth in wind generation, Australia provides an interesting case for assessing different approaches to facilitating wind integration into the electricity industry. Wind project developers in Australia must assess both potential energy market and Tradeable Green Certificate income streams when making investments. Wind-farm energy income depends on the match of its uncertain time varying output with the regional half hourly market price; a price that exhibits daily, weekly and seasonal patterns and considerable uncertainty. Such price signals assist in driving investments that maximize project value to the electricity industry as a whole, including integration costs and benefits for other participants. Recent NEM rule changes will formally integrate wind generation in the market's scheduling processes while a centralized wind forecasting system has also been introduced. This paper outlines experience to date with wind integration in the NEM, describes the evolution of market rules in response and assesses their possible implications for facilitating high future wind penetrations.     

Electricity markets volatility: estimates,regularities and risk management applications

The recent deregulation of the market for electric power in many parts of the US and Canada has expanded the set of potential tools for managing the types of risks faced by both generators and consumers of electric power. In particular manufacturing and other firms whose operations are powered by electricity now face, on a continuing basis, the engineering management decisions concerning whether they should buy or produce electricity, and if they are to buy or sell electricity, what types of contracts are optimum. These types of risk management decisions typically involve futures, forwards, options and other financial derivatives. The price and volatility of electric power are known to play an essential role in determining which of these instruments should be used. However, electricity as a commodity possesses certain special features not shared by other commodities and hence its risk properties are not yet well understood. In this paper we consider and test certain hypotheses about the properties of electricity price using recent market data. We find that electricity prices possess certain volatility and other systematic properties that can be characterized by the type and method of delivery of electricity. These properties can be used by firms in formulating their optimal demand and supply schedules of electric power.     

Feed-in tariff and market electricity price comparison: The case of cogeneration units in Croatia

In August 2007, the Government of the Republic of Croatia instituted a feed-in tariff system, requiring the Croatian Electricity Market Operator (HROTE) to off-take the electricity produced from renewable energy sources or cogeneration units fueled by natural gas. Analysis of the off-take electricity price range, which depends on the net electrical output and electricity market trends, indicates that it is more cost effective for cogeneration units greater than 1 MW to sell their electricity on the exchange market. This was confirmed by developing a mathematical model to calculate the cost-effectiveness ratio of a cogeneration unit. This ratio represents the relation between the profit spread, i.e. the difference between the profit generated from selling the electricity on the exchange market and the profit made from dispatching the electricity to HROTE, as well as the total investment costs. The model can be applied for changes in certain parameters, such as the net electrical output, volatility and spot electricity price. The Monte Carlo method is used to obtain the most probable cost-effectiveness ratio and average future electricity price. Together with these two economic parameters and market price analysis, it is possible to calculate and calibrate an acceptable off-take electricity price.     

Investigating price clustering in the oil futures market

Price clustering can be a source of market inefficiency. It follows that searching for price clustering in markets have gone beyond share prices into real estate, interest rate, and exchange rate markets. In this paper, we extend this line of research to oil futures markets. In particular, we consider five different forms of oil futures contracts and test for evidence of price clustering. Our results reveal strong presence of price clustering in the oil futures market. This finding implies that price clustering can potentially be a source of oil market inefficiency, which can influence trading strategies.     

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。     

厂网分开、竞价上网后电力投资公司的对策

电力体制改革将使电力投资公司面临的既有冲击与挑战,又有希望和机遇。本文就电力投资公司如何在我国正式加入WTO,开始与世界经济实现全面对接的大背景下,如何继续保持可持续发展提出了对策。     

Loss aversion and price volatility as determinants of attitude towards and preference for variable price in the Swedish electricity market

The results of a survey of a random sample of 488 Swedish residents showed that a positive attitude towards and preference for a variable price agreement with the incumbent electricity supplier was negatively affected by loss aversion, and a positive attitude also negatively affected by beliefs about price volatility. Although correlated with attitude and preference, age, education, and current choice of a variable price agreement had no independent effects. Income and current electricity costs had no effects.     

电价预测技术综述

电力工业从垄断走向市场,使得电价不再由政府确定,而是在市场机制下产生。电价波动会影响市场参与者的经济利益。对电力市场参与者而言,准确地预测电价具有非常重要的意义。该论文以电力系统短期边际价格为主要研究对象。首先分析了电价的变化特点、影响电价的主要因素,明确电价变化的规律性。然后介绍了一些现有因素分析的方法。并对当前电价预测方法按其工作原理进行分类总结,最后根据各类电价预测模型的特点尤其利用神经网络方法建立的预测模型进行了深入分析和总结。     

Regime-switching stochastic volatility: Evidence from the crude oil market

This paper incorporates regime-switching into the stochastic volatility (SV) framework in an attempt to explain the behavior of crude oil prices in order to forecast their volatility. More specifically, it models the volatility of oil return as a stochastic volatility process whose mean is subject to shifts in regime. The shift is governed by a two-state first-order Markov process. The Bayesian Markov Chain Monte Carlo method is used to estimate the models. The main findings are: first, there is clear evidence of regime-switching in the oil market. Ignoring it will lead to a false impression that the volatility is highly persistent and therefore highly predictable. Second, incorporating regime-switching into the SV framework significantly enhances the forecasting power of the SV model. Third, the regime-switching stochastic volatility model does a good job in capturing major events affecting the oil market.     

市场经济条件下电力行业应用CIMS概述

本文从CIMS的概念出发,讲述了电力行业采用CIMS的必要性和特点,概述了电力市场条件下在电力行业中CIMS的实施。     

The market for wireless electricity: The case of India

A wireless revolution has transformed telecoms in India and in other emerging markets. The electricity market, on the other hand, remains underdeveloped. We define Wireless Electricity as renewable energy produced within a few hundred meters of the point of consumption. A wireless revolution in electricity would solve the problem of electricity deficit, empower people at the bottom of the pyramid and mitigate the environmental impact of bringing hundreds of millions out of poverty as the Indian economy grows. Renewables are technically proven and economically viable in certain situations, but their use remains peripheral. The stark difference in the diffusion patterns in telecoms and electricity has been ignored by leaders in government, business and academics. We present common frameworks to explain the different directions of reform in telecoms and electricity. We explain some of the dynamics which prevent the diffusion of Wireless Electricity. We use a causal loop diagram to explain the status quo in the off-grid electricity market and propose changes which will lead to the formation of a market for Wireless Electricity. India has the entrepreneurial talent to develop this market—and the largest number of potential customers. The world will benefit as a result.     

Carbon price volatility: Evidence from EU ETS

This paper examines carbon price volatility using data from the European Union Emission Trading Scheme from a nonlinear dynamics point of view. First, we use a random walk model, including serial correlation and variance ratio tests, to determine whether carbon price history information is fully reflected in current carbon price. The empirical research results show that carbon price is not a random walk: the price history information is not fully reflected in current carbon price. Second, use R/S, modified R/S and ARFIMA to analyse the memory of carbon price history. For the period April 2005–December 2008, the modified Hurst index of the carbon price is 0.4859 and the d value of ARFIMA is −0.1191, indicating short-term memory of the carbon price. Third, we use chaos theory to analyse the influence of the carbon market internal mechanism on carbon price, i.e., the market’s positive and negative feedback mechanism and the heterogeneous environment. Chaos theory proves that the correlation dimension of carbon price increases. The maximal Lyapunov exponent is positive and large. There is no obvious complex endogenous phenomenon of nonlinear dynamics the carbon price fluctuation. The carbon market is mildly chaotic, showing both market and fractal market characteristics. Price fluctuation is not only influenced by the internal market mechanism, but is also impacted by the heterogeneous environment. Finally, we provide suggestions for regulation and development of carbon market.     

Stochastic reactive power market with volatility of wind power considering voltage security

While wind power generation is growing rapidly around the globe; its stochastic nature affects the system operation in many different aspects. In this paper, the impact of wind power volatility on the reactive power market is taken into account. The paper presents a novel stochastic method for optimal reactive power market clearing considering voltage security and volatile nature of the wind. The proposed optimization algorithm uses a multiobjective nonlinear programming technique to minimize market payment and simultaneously maximize voltage security margin. Considering a set of probable wind speeds, in the first stage, the proposed algorithm seeks to minimize expected system payment which is summation of reactive power payment and transmission loss cost. The object of the second stage is maximization of expected voltage security margin to increase the system loadability and security. Finally, in the last stage, a multiobjective function is presented to schedule the stochastic reactive power market using results of two previous stages. The proposed algorithm is applied to IEEE 14-bus test system. As a benchmark, Monte Carlo Simulation method is utilized to simulate the actual market of given period of time to evaluate results of the proposed algorithm, and satisfactory results are achieved.     

电力体制改革的投资机会分析

电力体制改革对电力行业上市公司会产生一定的影响,也会使发电领域中的竞争态势发生变化。对于电源类公司而言,应努力降低本身的发电成本,求得更大市场竞争力。水电公司发电成本主要包括机组折旧与人力成本等方面,降低成本的空间比较小;而火电类公司的发电成本中由于原料成本占到了其总成本的70％以上,所以,降低的空间非常大。     

抽水蓄能电站"三部制"电价结构的构想

讨论了国内已有电价制度在反映PS电站特性时结构方面存在的不足，同时通过对国内外PS电站电价实践的分析，阐明了PS电站改革电价制度的必要性，并提出关于PS电站“三部制”电价结构的构想。     

生物质能发电电价的敏感因素分析

通过设定电价敏感度和电价下降系数两个分析参数，对加工废弃物发电、秸秆发电、沼气发电和垃圾发电等4大类生物质能发电技术的电价敏感性因素进行了定量分析。分析结果显示．发电时间和固定资产为影响4类生物质能发电电价的主要敏感因素，其中固定资产通过规模化降低成本后会带来电价的减少。而原料成本仅对秸秆发电技术的影响较大。