Predictability of price movements in deregulated electricity markets

In this paper we investigate predictability of electricity prices in the Canadian provinces of Alberta and Ontario, as well as in the US Mid-C market. Using scale-dependent detrended fluctuation analysis, spectral analysis, and the probability distribution analysis we show that the studied markets exhibit strongly anti-persistent properties suggesting that their dynamics can be predicted based on historic price records across the range of time scales from 1 h to one month. For both Canadian markets, the price movements reveal three types of correlated behavior which can be used for forecasting. The discovered scenarios remain the same on different time scales up to one month as well as for on- and off-peak electricity data. These scenarios represent sharp increases of prices and are not present in the Mid-C market due to its lower volatility. We argue that extreme price movements in this market should follow the same tendency as the more volatile Canadian markets. The estimated values of the Pareto indices suggest that the prediction of these events can be statistically stable. The results obtained provide new relevant information for managing financial risks associated with the dynamics of electricity derivatives over time frame exceeding one day.     

Comparative analysis of features of Polish and Lithuanian Day-ahead electricity market prices

The goal of this article is to better understand the processes of electricity market price formation in Poland and Lithuania through an analysis of the features (volatility and spikes) of Lithuanian and Polish day-ahead electricity market prices and to assess how acquired electricity price features could affect the achievement of the main goals of the national energy policy. The following indicators have been calculated to determine electricity market price volatility: the oscillation coefficient, the coefficient of variation, an adjusted coefficient of variation, the standard deviation indicator, the daily velocity indicator (based on the overall average price) and the daily velocity indicator (based on the daily average price). Critical values for electricity market price have been calculated to evaluate price spikes. This analysis reveals that electricity market-price volatility is moderate in Poland and high in Lithuania. Electricity price spikes have been an observable phenomenon both in Lithuanian and in Polish day-ahead electricity markets, but they are more common in Lithuania, encompassing 3.15% of the time period analysed in Poland and 4.68% of the time period analysed in Lithuania. Volatile, spiking and increasing electricity prices in day-ahead electricity markets in Lithuania and Poland create preconditions and substantiate the relevance of implementation of the national energy policies and measures.     

Characteristics of the prices of operating reserves and regulation services in competitive electricity markets

In this paper, characteristics of the prices of reserves and regulation services in the Ontario, New York and ERCOT electricity markets are studied. More specifically, price variability, price jumps, long-range correlation, and non-linearity of the prices are analyzed using the available measures in the literature. For the Ontario electricity market, the prices of 10-min spinning, 10-min non-spinning, and 30-min operating reserves for the period May 1, 2002 to December 31, 2007 are analyzed. For the New York market, prices of the same reserves plus regulation service are studied for the period February 5, 2005 to December 31, 2008. For the ERCOT market, we analyze the prices of responsive reserve, regulation up and regulation down services, for the period January 1, 2005 to December 31, 2009. The studied characteristics of operating reserve and regulation prices are also compared with those of energy prices. The findings of this paper show that the studied reserve and regulation prices feature extreme volatility, more frequent jumps and spikes, different peak price occurrence time, and lower predictability, compared to the energy prices.     

Energy resources’ seasonal/daily dependencies under coupled operation of day-ahead markets

ABSTRACT

Studies on the benefits of electricity markets integration are scarce. With particular attention to the Romanian day-ahead electricity market (DAM), we analyze electricity prices’ seasonality and volatility and observe whether changes occurred after the 4 M Market Coupling project implementation. The focus lies on assessing the seasonal and cyclical components of DAM prices and coal/wind-based electricity generation. We address the changes in time series’ models that occurred after the DAM coupling and determine stationary models for those time series. The Dickey–Fuller test, augmented with qualitative variables assigned to seasons, days of the week, and legal holidays is our proposed method. Market integration could not prevent price spikes. The post-coupling period is characterized by smaller differences between peak and off-peak prices. This might indicate a positive effect (ceteris paribus) of market coupling on reducing price volatility. Most price models show complex dependencies on seasons, days, and dummies.     

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.     

The future developments of the electricity prices in view of the implementation of the Paris Agreements: Will the current trends prevail,or a reversal is ahead?

We assess the impact on the European electricity market of the European Union “Clean energy for all Europeans” package, which implements the EU Nationally Determined Contribution in Paris COP 21. We focus on the year 2030, which is the year with defined climate targets. For the assessment, we employ a game-theoretic framework of the wholesale electricity market, with high technical detail. The model is applied to two core scenarios, a Base scenario and a Low Carbon scenario to provide insights regarding the future electricity capacity, generation mix, cross-border trade and electricity prices. We also assess three additional variants of the core scenarios concerning different levels of: a) fossil and CO₂ prices; b) additional flexibility provided by batteries; c) market integration. We find that the electricity prices in 2030 substantially increase from today's level, driven by the increase in fuel and CO₂ prices. The flexibility from batteries helps in mitigating the price peaks and the price volatility. The increased low marginal cost electricity generation, the expansion of non-dispatchable and distributed capacities, and the higher market integration further reduce the market power from producers in the electricity markets from today's level.     

Good volatility,bad volatility: What drives the asymmetric connectedness of Australian electricity markets?

Efficient delivery of network services and the electricity infrastructure to meet the long-term consumer's interests are the main objectives and the strategies of a national electricity market, while the main interests of generators are to maximize their profit through pricing strategies. Therefore, the objective of this study is to explore whether electricity prices across the four Australian States display symmetric price volatility connectedness. The study is the first attempt in the literature to make use of intraday 5-min Australian dispatch electricity prices, spanning the period December 8th, 1998 to May 5th, 2016 to quantify asymmetries in volatility connectedness emerging from good, and bad volatility. The results provide supportive evidence that the Australian electricity markets are connected asymmetrically implying the presence of some degree of market power that is exercised by generators across regional electricity markets.     

Large-scale solutions

For more than a century, we have accepted the premise that once electricity is produced, it cannot be stored. This lack of storage causes extreme electricity price volatility (compared with other commodities) and hourly fluctuations in wholesale market prices, and has prompted specialized real-time markets that provide price fluctuations in 5- or 10-min intervals. The hourly volatility reflects the widely disparate costs of production from different resources that lead to a steep supply curve in most markets; that steep supply curve, coupled with highly variable demand and an inelastic demand curve in today's markets, makes for high volatility.     

Price risk and hedging strategies in Nord Pool electricity market evidence with sector indexes

Electricity markets become more competitive due to their liberalization; therefore, electricity prices are considerably more volatile compared to other commodity prices. As the electricity is an integral part of production and economic growth processes, the electricity price may influence the stock market through affecting the real output and consequently the sum of cash flows. Hence, investors are facing electricity price risks, and need to protect their benefits. This paper investigates the impacts of electricity market variations on the Nordic stock market returns using hourly observations of electricity spot prices pairwise in aggregate market index and some sector indexes. Our sample is divided into three sub-periods according to the electricity volatility structure. A generalized long memory model is adopted to estimate the conditional mean of the studied time series, and the FIGARCH process is used to model the conditional variance. Thereafter, a VaR, c-DCC-FIGARCH, CVaR and ΔCVaR models are applied to assess electricity market exposure. Moreover, in order to evaluate the optimal portfolio, we calculated the optimal portfolio weights, the optimal hedge ratios and the hedge effectiveness index of the electricity market commodity in several sectors stock portfolios. Our results show evidence of long run dependence between electricity market returns and sectoral stock market returns, and they indicate that the tail dependence is significant and varies across sectors and over periods. Finally, the optimal weights and hedge ratios for electricity/stock portfolio holdings are sensitive to the considered sectors. Therefore, electricity market commodities can be adopted to diversify and hedge against stock market risks.     

Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape

In the context of the liberalized and deregulated electricity markets, price forecasting has become increasingly important for energy company's plans and market strategies. Within the class of the time series models that are used to perform price forecasting, the subclasses of methods based on stochastic time series and causal models commonly provide point forecasts, whereas the corresponding uncertainty is quantified by approximate or simulation-based confidence intervals. Aiming to improve the uncertainty assessment, this study introduces the Generalized Additive Models for Location, Scale and Shape (GAMLSS) to model the dynamically varying distribution of prices. The GAMLSS allow fitting a variety of distributions whose parameters change according to covariates via a number of linear and nonlinear relationships. In this way, price periodicities, trends and abrupt changes characterizing both the position parameter (linked to the expected value of prices), and the scale and shape parameters (related to price volatility, skewness, and kurtosis) can be explicitly incorporated in the model setup. Relying on the past behavior of the prices and exogenous variables, the GAMLSS enable the short-term (one-day ahead) forecast of the entire distribution of prices. The approach was tested on two datasets from the widely studied California Power Exchange (CalPX) market, and the less mature Italian Power Exchange (IPEX). CalPX data allow comparing the GAMLSS forecasting performance with published results obtained by different models. The study points out that the GAMLSS framework can be a flexible alternative to several linear and nonlinear stochastic models.     

Analyzing the impact of futures trading on spot price volatility: Evidence from the spot electricity market in France and Germany

This paper examines the impact of the introduction of electricity futures on the spot-price volatility of the French (Powernext) and German (EEX) electricity markets, as well as the degree of their price correlation over the period 2002–2011. Our working hypotheses were tested based on a bivariate VECM-GARCH model. The results indicate that the introduction of futures contracts in the French electricity market, as well as the launch of the joint futures market in these countries in 2009, has decreased spot price volatility. However, this effect was not as explicit for the German market, due to data specificities. Other interesting results are: the German market dominates and leads the long run price relationship; the impact of cooling needs on demand is greater than the impact of heating needs; there is a substantial systematic pattern of electricity prices and their respective volatilities during weekdays and holidays. Overall, results are supportive of policy making at the European Commission regarding electricity market integration.     

Price volatility in commodity markets with restricted participation

In commodity markets, price volatility may rise significantly if the product granularity increases. To gain insights into the underlying drivers, we analyze price volatility based on the example of German electricity markets. We develop a theoretical model to reproduce the price formation in the day-ahead and intraday auction which are sequential short-term electricity markets with 60-minute and 15-minute products. As cross-border trade is allowed in the day-ahead but not in the intraday auction, the model accounts for the impact of restricted market participation. The theoretical model is then transferred into an empirical analysis to first validate the modeling approach and second to comparatively assess the impact of increasing product granularity and restricted market participation. The empirical results indicate that the disproportional rise in quarter-hourly price volatility is mainly triggered by limited market participation and not only by the high volatility of renewable supply and demand. Since restricted market participation refers to a lack of market coupling, we derive a proxy for efficiency losses ranging from EUR 55 million to EUR 108 million that may be reduced if markets are coupled.     

The impact of wind power on electricity prices

This paper investigates the impact of wind power on electricity prices using a production cost model of the Independent System Operator – New England power system. Different scenarios in terms of wind penetration, wind forecasts, and wind curtailment are modeled in order to analyze the impact of wind power on electricity prices for different wind penetration levels and for different levels of wind power visibility and controllability. The analysis concludes that electricity price volatility increases even as electricity prices decrease with increasing wind penetration levels. The impact of wind power on price volatility is larger in the shorter term (5-min compared to hour-to-hour). The results presented show that over-forecasting wind power increases electricity prices while under-forecasting wind power reduces them. The modeling results also show that controlling wind power by allowing curtailment increases electricity prices, and for higher wind penetrations it also reduces their volatility.     

A dynamic network analysis of spot electricity prices in the Australian national electricity market

This study applies dynamic network analysis to the power sector, examining the relationship between regional spot electricity prices in the Australian National Electricity Market (NEM). In particular, we employ principal component analysis and generate Granger causality networks to examine the degree of interconnectedness of the NEM in a time-varying setting. We find that the derived measures of interdependence can be related to actual market events such as price spikes, unexpected high demand for electricity, sudden increases in price volatility, rebidding of dominant generators, the temporary or permanent outage of major power stations, and upgrades and limitations in transmission capacity. In the analysed network, we find that stronger dependence is exhibited by regional markets that are linked through interconnectors, while the direction of Granger causality can be related to interregional trade. We further examine the usefulness of the derived measures for forecasting distributional characteristics of spot prices such as the maximum price, volatility, price spreads, or upcoming periods of price spikes. Our results suggest that the derived network measures have predictive power, albeit limited, for the behaviour of spot electricity prices in the NEM.     

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.     

How competitive are EU electricity markets? An assessment of ETS Phase II

This paper studies the interactions between electricity and carbon allowance prices in the year-ahead energy markets of France, Germany, United Kingdom and the Nordic countries, during Phase II of the EU ETS. VAR and Granger-causality methods are used to analyze causal interfaces, whereas the volatility of electricity prices is studied with basic and asymmetric AR-GARCH models. Among the main results, the marginal rate at which carbon prices feed into electricity prices is shown to be ca. 135% in the EEX and Nord Pool markets, where electricity and carbon prices display bidirectional causality, and 109% in the UK. Therefore, generators in these markets internalized the cost of freely allotted emission allowances into their electricity prices considerably more than the proportionate increase in costs justified by effective carbon intensity. Moreover, electricity prices in France are found to Granger-cause the carbon price. This study also shows how European electricity prices are deeply linked to coal prices among other factors, both in terms of levels and volatility, regardless of the underlying fuel mix, and that coal was marginally more profitable than gas for electricity generation. EU policies aimed at increasing the carbon price are likely to be crucial in limiting the externalities involved in the transition to a low-carbon system.     

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。     

Power trade,welfare, and air quality

We use detailed microdata from all generators in the Ontario wholesale electricity market to investigate cross-border electricity trade and its impact on air emissions and welfare (consumer and producer surpluses) in Ontario. Using the technical characteristics of the generators and financial data we run a competition model every hour. We examine how trade expansion across different parts of the interconnected power grid affects the efficiency in the Ontario market. We show that there is a significant welfare gain from power trade. The air emissions savings are also considerable. For instance, when hourly imports double from current levels CO₂ emissions decrease around 13%, and market prices reduce 5.4%. In autarky, CO₂, SO₂, NOx emissions increase 12%, 22%, 16%, resp., the prices go up 5.8%, and the price volatility rises 12%. However, the impact of negative wholesale prices on market outcomes is small.     

Are electricity prices affected by the US dollar to Euro exchange rate? The Spanish case

The objective of this paper is to investigate the relationships between Spanish electricity spot prices and the US dollar/Euro (USD/Euro) exchange rate during the period 2005–2007, taking into account the study of the association between dollar and oil prices, in order to better understand the evolution of the former over time. The first finding in this study is that Spanish electricity spots prices, the USD/Euro exchange rate and oil prices are cointegrated; therefore there is a long-run equilibrium relationship between the three variables. Short-run relationships have been detected between oil prices and Spanish electricity prices and USD/Euro exchange rate in the sense that Spanish electricity prices and USD/Euro exchange rate are affected by oil prices in the short run. There is a transmission of volatility between USD/Euro exchange rate and oil prices to Spanish electricity prices; so although Spanish electricity prices are not affected in level by the movements of USD/Euro exchange rate, they are in volatility. In this kind of scenario the conclusions confirm that for countries so dependent on external causes as Spain, one possible solution for guarantying the energy security would be the promotion of the renewable energies. Therefore we cannot ignore the impact in the internal expenses of the cost of installation and generation of green energies so there must be a balance between the increase in renewables and the reasonable market price of the electricity.     

Quantifying multiscale inefficiency in electricity markets

One of the basic features of efficient markets is the absence of correlations between price increments over any time scale leading to random walk-type behavior of prices. In this paper, we propose a new approach for measuring deviations from the efficient market state based on an analysis of scale-dependent fractal exponent characterizing correlations at different time scales. The approach is applied to two electricity markets, Alberta and Mid Columbia (Mid-C), as well as to the AECO Alberta natural gas market (for purposes of providing a comparison between storable and non-storable commodities). We show that price fluctuations in all studied markets are not efficient, with electricity prices exhibiting complex multiscale correlated behavior not captured by monofractal methods used in previous studies.