Bayesian estimation of stable CARMA spot models for electricity prices

We develop a Bayesian estimation procedure for the electricity spot price model in Benth et al. (2014). This model incorporates a trend and seasonality component, a stable CARMA process for the price spikes, and an additional Lévy process for mid-range price level changes. Our MCMC algorithm has two advantages over the existing stepwise estimation procedure presented in Benth et al. (2014): First, since our algorithm produces samples from the full posterior distribution over all parameters, we can estimate the parameters much more accurately, which is shown in simulation studies. Second, we can provide accuracy measures as credibility intervals in addition to the point estimates. The approach is quite general, so that it can be adapted also to other similar pricing models. For illustration, we analyse spot and future prices from the EEX using the new Bayesian method and provide estimates for the risk premium together with credibility regions.     

A new index for electricity spot markets

Different indexes are used in electricity markets worldwide to represent the daily behavior of spot prices. However, the peculiarities of these markets require a careful choice of the index, based on mathematical formulation and its statistical properties. Choosing a bad index may influence the financial policies of market players, since derivative pricing and hedging performance can be deeply affected.     

Efficient pricing and investment in electricity markets with intermittent resources

Facing growing technological and environmental challenges, the electricity industry needs effective pricing mechanism to promote efficient risk management and investment decisions. In a restructured electricity market with competitive wholesale prices and traditionally regulated retail rates, however, there are technical and institutional barriers that prevent dynamic pricing with price responsive demand. In regions with limited energy storage capacity, intermittent renewable resources present special challenges. This could adversely affect the effectiveness of public policies causing inefficient investments in energy technologies. In this paper, we present an updated economic model of pricing and investment in restructured electricity market and use the model in a simulation study for an initial assessment of renewable energy strategy and alternative pricing mechanisms. A key objective of the study is to shed light on the policy issues so that effective decisions can be made to improve efficiency.     

A new approach to congestion pricing in electricity markets: Improving user pays pricing incentives

Electricity pricing has traditionally been based on average cost pricing where consumers pay a ‘flat’ tariff based upon the average cost of production and transportation of electricity. The introduction of new ‘smart’ meters allows electricity providers to differentiate tariffs on the basis of time. Utilising congestion pricing theory, the energy industry has embraced ‘time-of-use’ (ToU) tariffs with a view to more efficiently pricing electricity. This paper demonstrates that pricing as a function of demand variability (reflecting capacity utilisation) is a more appropriate alternative to existing ToU tariffs for more efficiently allocating costs to end users. We call this new alternative pricing model ‘first derivative ratio’ FDR pricing. This new approach to congestion pricing could be applied to markets other than electricity, such as road transportation.     

Modelling price and volatility inter-relationships in the Australian wholesale spot electricity markets

This paper examines the inter-relationships of wholesale spot electricity prices among the four regional electricity markets in the Australian National Electricity Market (NEM): namely, New South Wales, Queensland, South Australia and Victoria using the constant conditional correlation and Tse and Tsui's (Tse, Y.K., Tsui, A.K.C., 2002. A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20 (3), 351–362.) and Engle's (Engle, R., 2002. Dynamic conditional correlation: a sample class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics 20 (3), 339–350.) dynamic conditional correlation multivariate GARCH models. Tse and Tsui's (Tse, Y.K., Tsui, A.K.C., 2002. A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20 (3), 351–362.) dynamic conditional correlation multivariate GARCH model which takes account of the Student t specification produces the best results. At the univariate GARCH(1,1) level, the mean equations indicate the presence of positive own mean spillovers in all four markets and little evidence of mean spillovers from the other lagged markets. In the dynamic conditional correlation equation, the highest conditional correlations are evident between the well-connected markets indicating the presence of strong interdependence between these markets with weaker interdependence between the not so well-interconnected markets.     

Fitting semiparametric Markov regime-switching models to electricity spot prices

Recently regime-switching models have become the standard tool for modeling electricity prices. These models capture the main properties of electricity spot prices well but estimation of the model parameters requires computer intensive methods. Moreover, the distribution of the price spikes must be fully specified although the high volatility of the spikes makes it difficult to check such distributional assumptions. Consequently, there are a number of competing proposals for the distribution in the spike regime. As an alternative, we propose a semiparametric Markov regime-switching model that leaves the distribution under the spike regime unspecified. We show that the model parameters can be estimated by employing robust statistical techniques. This presents an alternative to the existing estimation methods that are based on computer intensive numerical maximization of the likelihood function. The model in combination with the estimation framework is easier to estimate, needs less computation time and distributional assumptions. To show its advantages we compare the proposed model with a well-established Markov regime-switching model in a simulation study. Furthermore, we apply the model to log-prices for the Australian electricity market. The results are in accordance with the results from the simulation study, indicating that the proposed model might be advantageous whenever the distribution of the spike process is not sufficiently known. The results are thus encouraging and suggest the use of our approach when modeling electricity prices and pricing derivatives.     

Pricing of reserves: Valuing system reserve capacity against spot prices in electricity markets

This paper models the interdependencies between markets for secondary reserve capacity and spot electricity to derive the pricing of reserves under equilibrium conditions. Starting with the indifference condition between offering in both markets, the reservation price is derived from the opportunity cost consideration and the unit commitment conditions in a fundamental interrelated market framework. The reserve market examined compares widely to the German market for secondary reserves, but the general approach may also be used to investigate other reserve markets. The approach explores and formalizes the influence of reserve capacity on the spot market supply function. A numerical solution procedure is provided to this non-trivial case of market interaction. The model is used to estimate the expected reservation price development over the last years in Germany.     

Bayesian calibration and number of jump components in electricity spot price models

We find empirical evidence that mean-reverting jump processes are not statistically adequate to model electricity spot price spikes but independent, signed sums of such processes are statistically adequate. Further we demonstrate a change in the composition of these sums after a major economic event. This is achieved by developing a Markov Chain Monte Carlo (MCMC) procedure for Bayesian model calibration and a Bayesian assessment of model adequacy (posterior predictive checking). In particular we determine the number of signed mean-reverting jump components required in the APXUK and EEX markets, in time periods both before and after the recent global financial crises. Statistically, consistent structural changes occur across both markets, with a reduction of the intensity and size, or the disappearance, of positive price spikes in the later period. All code and data are provided to enable replication of results.     

Retail competition in electricity markets

The introduction of competition into retail electricity supply gave rise to great expectations. However, to date, its performance has proven less than stellar, owing primarily to the theoretical concepts underpinning this reform, which draw heavily on the Austrian school. Neither consumers’ decision processes nor this sector's technical paradigm were adequately accounted for, leading to an uncorrect estimation of the expected impact of opening to competition. Short- and medium-term prospects for the evolution of retail markets must be reconsidered from the perspective of greater stability: not a generalization of competition, but rather a persistent segmentation between active and inactive clients; not a large and rapid diffusion of radical innovations in commercialisation, with the potential for undermining the incumbents’ positions.     

Volatility spillover from world oil spot markets to aggregate and electricity stock index returns in Turkey

This study examines the inter-temporal links between world oil prices, ISE 100 and ISE electricity index returns unadjusted and adjusted for market effects. The traditional approaches could not detect a causal relationship running from oil returns to any of the stock returns. However, when we examine the causality using Cheung–Ng approach we discover that world oil prices Granger cause electricity index and adjusted electricity index returns in variance, but not the aggregate market index returns. Hence, our results show that the Cheung–Ng procedure with the use of disaggregated stock index returns can uncover new information that went unnoticed with the traditional causality tests using aggregated market indices.     

Forecasting realized volatility in electricity markets using logistic smooth transition heterogeneous autoregressive models

We apply the non-parametric realized volatility technique and the associated jump detection test to measure volatility and jumps in electricity prices. Then, we propose a group of logistic smooth transition heterogeneous autoregressive (LSTHAR) models of realized volatility. The models can simultaneously approximate long memory behavior and describe sign and size asymmetries. They differ in the underlying heterogeneous autoregressive structure and the transition variable specification. The out-of-sample forecast accuracy of the LSTHAR models is evaluated through the Diebold–Mariano test and the superior predictive ability test, in terms of the mean square error and the mean absolute error. Using high-frequency prices from the Australian New South Wales (NSW) electricity market as empirical data, we draw the following conclusions. 1) Introducing the logistic smooth transition structure with appropriate transition variable specification to the heterogeneous autoregressive models improves volatility forecasts. 2) Overall, the LSTHAR model that uses the sum of Beta function weighted past returns as the transition variable and includes past daily jumps as a predictor is the superior model for predicting volatility in the NSW market. This model significantly outperforms the others.     

Supply adequacy in electricity markets based on hydro systems—the Brazilian case

This article examines the practical perspective for introducing the deregulation model in systems with a strong predominance of hydroelectric generation, such as the Brazilian electricity system. In order to do this, the article describes the process of establishing short-term prices in systems with such characteristics, concluding that this economic signal is inefficient for stimulating a sustained generation expansion. As a result of this analysis, the article proposes, as a regulatory policy, a competitive process of energy contracting that favors the making of decisions with long-term horizons, ensuring the adequacy of supply, and, additionally, permitting a satisfactory management of market risk by generation and distribution companies.     

Optimal operation strategies of compressed air energy storage (CAES) on electricity spot markets with fluctuating prices

Compressed air energy storage (CAES) technologies can be used for load levelling in the electricity supply and are therefore often considered for future energy systems with a high share of fluctuating renewable energy sources, such as e.g. wind power. In such systems, CAES plants will often operate on electricity spot markets by storing energy when electricity prices are low and producing electricity when prices are high. In order to make a profit on such markets, CAES plant operators have to identify proper strategies to decide when to sell and when to buy electricity. This paper describes three independent computer-based methodologies which may be used for identifying the optimal operation strategy for a given CAES plant, on a given spot market and in a given year. The optimal strategy is identified as the one which provides the best business-economic net earnings for the plant. In practice, CAES plants will not be able to achieve such optimal operation, since the fluctuations of spot market prices in the coming hours and days are not known. Consequently, two simple practical strategies have been identified and compared to the results of the optimal strategy. This comparison shows that, in practice, a CAES plant can be expected to earn 80-90 per cent of the optimal earnings.     

Volatility spillovers in Australian electricity markets

Electricity markets are significantly more volatile than other comparable financial or commodity markets. This study examines volatility spillover effects across different regions in the Australian National Electricity Market (NEM), aiming at providing a better understanding of the transmission of risks in a multi-regional context. Our analysis is based on the econometric framework originally proposed by Diebold and Yilmaz (2009, 2012). We conduct both a static and a dynamic assessment of aggregated spillover effects as well as their directional decomposition between the individual regions. We find that volatility spillovers are typically more pronounced between physically interconnected markets. We further relate the dynamic spillover patterns to specific short-term market events as well as long-term changes in the share of renewable energy, fuel mix, generation capacity, and the implementation of a Carbon Pricing Mechanism. Our findings provide important insights to market participants and regulators with regard to cross-regional trading of electricity, developing risk management strategies, and building additional interconnector infrastructure to facilitate regional market integration in the NEM.     

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.     

Capacity choices in liberalised electricity markets

This paper addresses the issue of investment in electricity generation in the context of a liberalised market. We use the main results derived from a theoretical model where firms invest strategically to simulate the Spanish electricity system with real-world data. Our results indicate that, under reasonable parameter constellations regarding the number of agents, the level of capacity resulting from private decisions falls well short of the social optimum. Last, we show that two regulatory mechanisms that have been used to generate additional incentives for private agents to install capacity (capacity payment and price-adder) are ineffective and/or prohibitively costly.     

Renewable energy in competitive electricity markets

A restructured electricity system may undervalue renewable energy. We remain wary of unregulated markets, but urge the renewable energy community to begin considering how they could adapt to increasing customer choice.     

The effects of electricity pricing on PHEV competitiveness

Plug-in hybrid electric vehicles (PHEVs) will soon start to be introduced into the transportation sector, thereby raising a host of issues related to their use, adoption and effects on the electricity sector. Their introduction has the potential to significantly reduce carbon emissions from the transportation sector, which has led to government policies aimed at easing their introduction. If their widespread adoption is set as a target it is imperative to consider the effects of existing policies that may increase or decrease their adoption rate. In this study, we present a micro level electricity demand model that can gauge the effects of PHEVs on household electricity consumption and the subsequent economic attractiveness of the vehicles. We show that the electricity pricing policy available to the consumer is a very significant factor in the economic competitiveness of PHEVs. Further analysis shows that the increasing tier electricity pricing system used in California will substantially blunt adoption of PHEVs in the state; and time of use electricity pricing will render PHEVs more economically attractive in any state.     

The effect of counter-trading on competition in electricity markets

In a competitive electricity market, nodal pricing is the most efficient way to manage congestion. Counter-trading is inefficient as it gives the wrong long term signals for entry and exit of power plants. However, in a non-competitive market, additional entry will improve the competitiveness of the market, and will increase social benefit by reducing price–cost margins. This paper studies whether the potential pro-competitive entry effects could make counter-trading more efficient than nodal pricing. We find that this is unlikely to be the case, and expect counter-trading to have a negative effect on overall welfare. The potential benefits of additional competition (more competitive prices and lower production cost) do not outweigh the distortions (additional investment cost for the entrant, and socialization of the congestion cost to final consumers).     

Independent regulatory authorities in European electricity markets

Liberalisation of the electricity market has taken place in most European countries within the last decade. It is considered a precondition of successful liberalisation to establish so-called independent regulatory authorities. In this article, we compare the status and practice of them in 16 European countries, and discuss the relationship between the organisation of the regulation and the market outcome.