A structural risk-neutral model of electricity prices

The objective of this paper is to present a model for electricity spot prices and the corresponding forward contracts, which relies on the underlying fuels markets, thus avoiding the electricity non-storability restriction. The structural aspect of our model comes from the fact that the electricity spot prices depend on the dynamic of the electricity demand at the maturity $T$, and on the random available capacity of each production means. Our model allows to explain, in a stylized fact, how the different fuels prices together with the demand combine to produce electricity prices. This modeling methodology allows to transfer to electricity prices the risk-neutral probabilities of the fuels market and under the hypothesis of independence between demand, outages filtrations on one hand, and fuels prices filtration on the other hand, it provides a regression-type relation between electricity forward prices and fuels forward prices. Moreover, the model produces, by nature, the well-known peaks observed on electricity market data. In our model, spikes occur when the producer has to switch from one technology to the lowest cost available one. Numerical tests performed on a very crude approximation of the French electricity market using only two fuels (gas and oil) provide an illustration of the potential interest of this model.energy markets; electricity prices; fuels prices; risk-neutral probability; no-arbitrage pricing; forward contracts

A structural risk-neutral model of electricity prices

The objective of this paper is to present a model for electricity spot prices and the corresponding forward contracts, which relies on the underlying market of fuels, thus avoiding the electricity non-storability restriction. The structural aspect of our model comes from the fact that the electricity spot prices depend on the dynamics of the electricity demand at the maturity $T$, and on the random available capacity of each production means. Our model explains, in a stylized fact, how the prices of different fuels together with the demand combine to produce electricity prices. This modeling methodology allows one to transfer to electricity prices the risk-neutral probabilities of the market of fuels and under the hypothesis of independence between demand and outages on one hand, and prices of fuels on the other hand, it provides a regression-type relation between electricity forward prices and forward prices of fuels. Moreover, the model produces, by nature, the well-known peaks observed on electricity market data. In our model, spikes occur when the producer has to switch from one technology to the lowest cost available one. Numerical tests performed on a very crude approximation of the French electricity market using only two fuels (gas and oil) provide an illustration of the potential interest of this model.energy markets; electricity prices; fuel prices; risk-neutral probability; no-arbitrage pricing; forward contracts

Determinants of power spreads in electricity futures markets: A multinational analysis. ESRI WP580, December 2017

The growth in variable renewable energy (vRES) and the need for flexibility in power systems go hand in hand. We study how vRES and other factors, namely the price of substitute fuels, power price volatility, structural breaks, and seasonality impact the hedgeable power spreads (profit margins) of the main dispatchable flexibility providers in the current power systems - gas and coal power plants. We particularly focus on power spreads that are hedgeable in futures markets in three European electricity markets (Germany, UK, Nordic) over the time period 2009-2016. We find that market participants who use power spreads need to pay attention to the fundamental supply and demand changes in the underlying markets (electricity, CO2, and coal/gas). Specifically, we show that the total vRES capacity installed during 2009-2016 is associated with a drop of 3-22% in hedgeable profit margins of coal and especially gas power generators. While this shows that the expansion of vRES has a significant negative effect on the hedgeable profitability of dispatchable, flexible power generators, it also suggests that the overall decline in power spreads is further driven by the price dynamics in the CO2 and fuel markets during the sample period. We also find significant persistence (and asymmetric effects) in the power spreads volatility using a univariate TGARCH model

Understanding the fine structure of electricity prices

This paper analyzes the special features of electricity spot prices derived from the physics of this commodity and from the economics of supply and demand in a market pool. Besides mean reversion, a property they share with other commodities, power prices exhibit the unique feature of spikes in trajectories. We introduce a class of discontinuous processes exhibiting a "jump-reversion" component to properly represent these sharp upward moves shortly followed by drops of similar magnitude. Our approach allows to capture—for the first time to our knowledge—both the trajectorial and the statistical properties of electricity pool prices. The quality of the fitting is illustrated on a database of major U.S. power markets

Understanding the Fine Structure of Electricity Prices.

This paper analyzes the special features of electricity spot prices derived from the physics of this commodity and from the economics of supply and demand in a market pool. Besides mean-reversion, a property they share with other commodities, power prices exhibit the unique feature of spikes in trajectories. We introduce a class of discontinuous processes exhibiting a jump-reversion component to properly represent these sharp upward moves shortly followed by drops of similar magnitude. Our approach allows to capture - for the first time to our knowledge - both the trajectorial and the statistical properties of electricity pool prices. The quality of the fitting is illustrated on a database of major US power markets.Energy price risk; Simulation; Calibration; Statistical estimations; Jump diffusions; Electricity prices;

The incentive to invest in thermal plants in the presence of wind generation. WP446. December 2012

In a deregulated market, the decision to build new thermal power plants rests with private investors and they will decide whether to invest on the basis of expected profits. This paper evaluates how such profits are affected by the increasing presence of wind generation. We use hourly historical data for the Irish Single Electricity Market, a compulsory pool market with capacity payments, and simulate future series of electricity shadow prices, bids of representative plants and wind generation. We estimate the correlation between shadow price and installed wind capacity on the basis of past data, finding a negative correlation. We then evaluate the effects of increased wind capacity on thermal power plants' expected profits. We find that increasing installed wind from the current level of 2000MW to about 3000MW causes a larger decrease in profits for baseload gas plants and a smaller decrease for less flexible coal-fuelled plants. The decrease in profits is of the order of 1 to 2 per cent

Forecasting Spikes in Electricity Prices

In many electricity markets, retailers purchase electricity at an unregulated spot price and sell to consumers at a heavily regulated price. Consequently the occurrence of extreme movements in the spot price represents a major source of risk to retailers and the accurate forecasting of these extreme events or price spikes is an important aspect of effective risk management. Traditional approaches to modeling electricity prices are aimed primarily at predicting the trajectory of spot prices. By contrast, this paper focuses exclusively on the prediction of spikes in electricity prices. The time series of price spikes is treated as a realization of a discrete-time point process and a nonlinear variant of the autoregressive conditional hazard (ACH) model is used to model this process. The model is estimated using half-hourly data from the Australian electricity market for the sample period 1 March 2001 to 30 June 2007. The estimated model is then used to provide one-step-ahead forecasts of the probability of an extreme event for every half hour for the forecast period, 1 July 2007 to 30 September 2007, chosen to correspond to the duration of a typical forward contract. The forecasting performance of the model is then evaluated against a benchmark that is consistent with the assumptions of commonly-used electricity pricing models.Electricity Prices, Price Spikes, Autoregressive Conditional Duration, Autoregressive

Market-based Options for Security of Energy Supply

Energy market liberalization and international economic interdependence have affected governments’ ability to react to security of supply challenges. On the other side, whereas in the past security of supply was largely seen as a national responsibility, the frame of reference has increasingly become the EU in which liberation increases security of supply mainly by increasing the number of markets participants and improving the flexibility of energy systems. In this logic, security of supply becomes a risk management strategy with a strong inclination towards cost effectiveness, involving both the supply and the demand side. Security of supply has two major components that interrelate: cost and risk. This paper focus the attention on costs in the attempt to develop a market compatible approach geared towards security of supply.Energy supply, Market-based options

Three Essays on Energy Economics

This dissertation focuses on the economics of electricity generation. I aim to answer three main questions: After controlling for outside market forces, how did acid rain regulation impact Eastern coal production? How have the fundamental relationships in the natural gas market changed since deregulation, especially given the rise of production from shale resources? And how have sub-state policies affected the adoption of residential solar generation installations? For each question, I use economic tools to provide empirical answers which will contribute both to the academic literature as well as energy policy.;My first essay looks at the coal production in the Eastern US from 1983-2012. It is widely understood that the quantity of coal produced in this region declined during this time period, though its causes are debated. While some have identified the cause to be outside economic forces, the prevailing view is that federal regulation was the main driver. By controlling for outside market forces, this paper is able to estimate the effect that the differing regulatory periods have had on coal production. Results demonstrate how in general the regulatory phases of the Acid Rain Program are associated with decreases in production in the Illinois and Appalachian basins, however with varying magnitudes. Further, there are some areas that saw some increases. The essay also measure the mitigating impact that the installation of \u27scrubber\u27 units had on production. Overall, this essay provides a more nuanced look at the relationship between coal production and regulation during this time period.;The second essay in this dissertation models the natural gas market. Since the complete deregulation of the market in 1993, there have been significant changes. Most notably, the rapid rise of production from shale resources has greatly increased the supply and decreased the price of the commodity. Where for many years a net importer, the US is now predicted to be a net exporter of natural gas within the next year. This massive change has altered the fundamental relationships in the market. This essay utilizes recently developed methodology to estimate how these relationships have changed over time. Further, given our research design we are able to estimate how the supply and demand elasticities have been influenced in the new era of abundant and cheap natural gas. Results provide a more nuanced view of the natural gas market, and allow for a better understanding of its drivers.;My third essay measures the impact that certain policies have had in the residential solar market. Specifically, I estimate the impact on residential solar adoption associated with sub-state policies, enacted at the municipal, county, or utility level. To capture the clustering and peer effects in the adoption of residential solar that have been described in the literature, I utilize spatial econometric methods. To better model the nested nature of state and county renewable policies, a Bayesian hierarchical model is used. Results suggest that sub-state policies are associated with positive and significant increases in per-capita residential solar installations and capacity additions