Forecasting the intra-day spread densities of electricity prices

Intra-day price spreads are of interest to electricity traders, storage and electric vehicle operators. This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast the German electricity price spreads between different hours of the day, as revealed in the day-ahead auctions. The four specifications of the density functions are dynamic and conditional upon exogenous drivers, thereby permitting the location, scale and shape parameters of the densities to respond hourly to such factors as weather and demand forecasts. The best fitting and forecasting specifications for each spread are selected based on the Pinball Loss function, following the closed form analytical solutions of the cumulative distribution functions

A Stochastic Latent Moment Model for Electricity Price Formation

The wide range of models needed to support the various short-term operations for electricity generation demonstrates the importance of accurate specifications for the uncertainty in market prices. This is becoming increasingly challenging, since electricity hourly price densities exhibit a variety of shapes, with their characteristic features changing substantially within the day and over time, and the influx of renewable power, wind and solar in particular, has amplified these effects. A general-purpose, analytically tractable representation of the stochastic price formation process would have considerable value for operations control and trading, but existing empirical approaches for the application of standard density functions are unsatisfactory. We develop a general four parameter stochastic model for hourly prices, in which the four moments of the density function are dynamically estimated as latent state variables and furthermore modelled as functions of several plausible exogenous drivers. This provides a transparent and credible model that is suffciently flexible to capture the shape-shifting effects, particularly with respect to the wind and solar output variations causing dynamic switches in the upside and downside risks. Extensive testing on German wholesale price data, benchmarked against quantile regression and other models in out-of-sample backtesting, validated the approach and its analytical appeal

Expansion of the investor base for the energy transition

Despite the emergence of the green bond market, the Energy Service Company (ESCO) model and green investment banks, the opportunities which the world’s capital markets present to increase the pool of potential investors and reduce project financing costs for renewable, energy efficient and low carbon assets remain under-exploited. This has been a persistent concern for policy-makers. We review the appeal of this sector to different classes of investor and assess the successes and failures of several innovative products including securitisations, yieldcos, green bonds, green investment banks and crowdfunding. We analyse the experiences with these products and suggest that policy needs to recognise how fiscal initiatives can leverage their inherent appeal

Analysis of the fundamental predictability of prices in the British balancing market

This research analyses the non-linear and complex effects of drivers of system imbalance prices in the GB electricity market. Unlike day-ahead prices, the balancing settlement prices are comparatively under-researched, yet their importance is growing with greater market risks. The fundamental drivers of these prices are analysed over 2016-2019. The result of a nonlinear modelling approach reveals that system imbalance price exhibits a regime-switching behaviour, driven by weather and demand forecast errors, as well as other system effects. Surprisingly, balancing prices are predictable out of sample and a regime switching specification is more accurate than a linear model for prediction

Systematic analysis of the evolution of electricity and carbon markets under deep decarbonization

The decarbonization of electricity generation presents policy makers in many countries with the delicate task of balancing initiatives for technological change with a commitment to market liberalization. Despite the theoretical attractions, it has become doubtful whether carbon markets by themselves can offer the desired solution. We address this question through an integrated modeling framework, stylized for the Great Britain (GB) power market within the EU ETS, which includes three distinct components: (a) long-term least-cost capacity planning, similar in functionality to many used in policy analysis, but innovative in providing the endogenous calculation of carbon prices; (b) short-term price risk analysis producing hourly dispatch and pricing outputs, which are used to test the annual financial performance metrics implied by the longer-term investments; and (c) agent-based computational learning to derive pricing behavior in imperfect markets. The results indicate that the risk/return profile of electricity markets may deteriorate substantially as a result of decarbonization, reducing the propensity of companies to invest in the absence of increased government support and/or more beneficial market circumstances. If allowed, markets may adjust by deferring investment until conditions improve, consolidating to increase market power, or operating in a tighter market with reduced spare capacity. To the extent that each of these “market-led” solutions may be politically unpalatable, policy design will need to sustain a delicate regulatory regime, moderating the possible increased market power of companies while maintaining low-carbon subsidies for longer than expected. This paper considers some of the modeling implications for this compromise

Observations on "Risk Transmission Across Supply Chains"

This is an invited commentary on the perspective paper by Sheridan Titman concerning the drivers of risk transmission across supply chains that have fundamental commodity inputs

Fundamental and financial influences on the co-movement of oil and gas prices

As speculative flows into commodity futures are expected to link commodity prices more strongly to equity indices, we investigate whether this process also creates increased correlations amongst the commodities themselves. Considering U.S. oil and gas futures, we investigate whether common factors, derived from a large international data set of real and nominal macroeconomic variables by means of the large approximate factor models methodology, are able to explain both returns and whether, beyond these fundamental common factors, the residuals remain correlated. We further investigate a possible explanation for this residual correlation by using some proxies for trading intensity derived from CFTC publicly available data, showing most notably that the proxy for speculation in the oil market increases the oil-gas correlation. We thus identify the central role of financial activities in shaping the link between oil and gas returns

Short-term electricity price forecasting with recurrent regimes and structural breaks

This paper develops a new approach to short-term electricity forecasting by focusing upon the dynamic specification of an appropriate calibration dataset prior to model specification. It challenges the conventional forecasting principles which argue that adaptive methods should place most emphasis upon recent data and that regime-switching should likewise model transitions from the latest regime. The approach in this paper recognises that the most relevant dataset in the episodic, recurrent nature of electricity dynamics may not be the most recent. This methodology provides a dynamic calibration dataset approach that is based on cluster analysis applied to fundamental market regime indicators, as well as structural time series breakpoint analyses. Forecasting is based upon applying a hybrid fundamental optimisation model with a neural network to the appropriate calibration data. The results outperform other benchmark models in backtesting on data from the Iberian electricity market of 2017, which presents a considerable number of market structural breaks and evolving market price drivers

Renewable power and electricity prices: the impact of forward markets

Increasing variable renewable power generation (e.g., wind) is expected to reduce wholesale electricity prices by virtue of its low marginal production cost. This merit-order effect of renewables displacing incumbent conventional (e.g., gas) generation forms the theoretical underpinning for investment decisions and policy in the power industry. This paper uses a game-theoretic market model to investigate how intermittently available wind generation affects electricity prices in the presence of forward markets, which are widely used by power companies to hedge against revenue variability ahead of near-real-time spot trading. We find that in addition to the established merit-order effect, renewable generation affects power prices through forward-market hedging. This forward effect reinforces the merit-order effect in reducing prices for moderate amounts of wind generation capacity but mitigates or even reverses it for higher capacities. For moderate wind capacity, uncertainty over its output increases hedging, and these higher forward sales lead to lower prices. For higher capacities, however, wind variability conversely causes power producers to behave less aggressively in forward trading for fear of unfavorable spot-market positions. The lower sales counteract the merit-order effect, and prices may then paradoxically increase with wind capacity despite its lower production cost. We confirm the potential for such reversals in a numerical study, suggesting new empirical questions while providing potential explanations for previously contradictory observed effects of market fundamentals. We conclude that considering the conventional merit-order effect alone is insufficient for evaluating the price impacts of variable renewable generation in the presence of forward markets

Statistical Arbitrage and Information Flow in an Electricity Balancing Market

Motivated by the events following a natural experiment in 2015, when the market rules for electricity spot trading were changed in Britain, we analyse the operational effects of market participants responding to price incentives for spillage and shortage positions in a single price, real-time market. We develop an analytical model for optimal real-time decisions by generators and speculators based upon forecasts of the conditional distribution of the total system imbalance between instantaneous supply and demand. From this, we examine the effects of time delays in information transparency for the consequent statistical arbitrage positions. We backtested this model empirically to the Austrian system imbalance settlements process within the German/Austrian integrated market. Results suggest that permitting additional intraday flexibility from a physical generator or a non-physical trader can be beneficial for the agents themselves, the system operator and market efficiency