• Energy Research

Abstract Generation adequacy is a key ingredient to security of electricity supply. We develop a stochastic model of demand and supply (from different technologies) for measuring it from a physical or technical point of view. We adopt several metrics of supply shortfalls. Next we demonstrate the model by example. Because of limited interconnections with neighboring countries, Spain can be considered an electric island. We get numerical estimates of the parameters underlying the model. We then resort to Monte Carlo simulation to derive the risk profile of the adequacy metrics in the coming decades. We consider up to ten scenarios, with different demand paths and generation parks. The proposed deployment of renewable technologies and the envisaged closure of coal-fired and nuclear stations result in greater risk of shortages. For one, assuming that demand grows at 1.36% per year, from 2020 to 2030 the expected energy not served increases more than 400-fold as coal and nuclear capacities are reduced; the factor runs into the tens of thousands by 2040 and 2050 when those technologies cease to operate. These results are potentially important for policy makers, system operators, and power companies involved in the construction of the European internal electricity market.

Coal-fired power plants may enjoy a significant advantage relative to gas plants in terms of cheaper fuel cost. Still, this advantage may erode or even turn into disadvantage depending on CO2 emission allowance price. This price will presumably rise in both the Kyoto Protocol commitment period (2008-2012) and the first post-Kyoto years. Thus, in a carbon-constrained environment, coal plants face financial risks arising in their profit margins, which in turn hinge on their so-called "clean dark spread". These risks are further reinforced when the price of the output electricity is determined by natural gas-fired plants' marginal costs, which differ from coal plants' costs. We aim to assess the risks in coal plants' margins. We adopt parameter values estimated from empirical data. These in turn are derived from natural gas and electricity markets alongside the EU ETS market where emission allowances are traded. Monte Carlo simulation allows to compute the expected value and risk profile of coal-based electricity generation. We focus on the clean dark spread in both time periods under different future scenarios in the allowance market. Specifically, bottom 5% and 10% percentiles are derived. According to our results, certain future paths of the allowance price may impose significant risks on the clean dark spread obtained by coal plants.

Abstract This paper addresses the valuation of an operating coal-fired power plant and a natural gas power plant when they operate as base load, independently of margins, and when the plant is cycling, running only when the electricity price is higher than the variable costs (fuel, emissions and variable O&M). Three sources of risk are considered: electricity prices, the fuel used and carbon allowances. Parameters are calibrated with market data and well-known valuation techniques such as contingent claim analysis are used. The results show the importance of operating flexibility in the appraisal of power plants as a result of price volatility. The effect of setting an increasing floor for emission allowance prices is also analysed. The model used is a general one which includes the existence of correlations also obtained via market trading prices.

This paper proposes the use of a novel multivariate, dynamic approach wavelet local multiple correlation (WLMC) (Fernández-Macho, 2018) to analyse the relationship between oil time series in the time-scale domain. This approach is suitable for use with energy data of any kind that change over time and involve heterogeneous agents who make decisions across different time horizons and operate on different time scales. The study of the links between multivariate oil time series is of great importance in energy research, e.g., it is extremely important for petroleum industry refiners and investors to know the relationships and margins between output prices and crude oil costs. The estimation of wavelet correlations in a multivariate framework between such prices is a suitable way to analyse crude oil and petroleum products as a system. To exemplify the use of WLMC, we analyse the relationships between the prices of seven commodities: West Texas Intermediate crude oil and six distilled products (conventional gasoline, regular gasoline, heating oil, diesel fuel, kerosene and propane) from 10/06/2006 to 17/01/2017. The results reveal that the wavelet correlations are strong throughout the period studied and there is a strong decay in correlation values from 2013 to 2015. The most plausible explanation for this decay is overproduction of tight oil in the U.S. and a slowdown in global demand for oil. Furthermore, our results also reveal that heating oil, diesel and kerosene maximise the multiple correlation with respect to the other oil variables on different scales, indicating that these products are the most dependent variables in the crude-product/price system. WLMC offers new opportunities for applications in energy research and other fields. © 2018 Elsevier Ltd

A quantification of present and future mean annual losses due to extreme coastal events can be crucial for adequate decision making on adaptation to climate change in coastal areas around the globe. However, this approach is limited when uncertainty needs to be accounted for. In this paper, we assess coastal flood risk from sea-level rise and extreme events in 120 major cities around the world using an alternative stochastic approach that accounts for uncertainty. Probability distributions of future relative (local) sea-level rise have been used for each city, under three IPPC emission scenarios, RCP 2.6, 4.5 and 8.5. The approach allows a continuous stochastic function to be built to assess yearly evolution of damages from 2030 to 2100. Additionally, we present two risk measures that put low-probability, high-damage events in the spotlight: the Value at Risk (VaR) and the Expected Shortfall (ES), which enable the damages to be estimated when a certain risk level is exceeded. This level of acceptable risk can be defined involving different stakeholders to guide progressive adaptation strategies. The method presented here is new in the field of economics of adaptation and offers a much broader picture of the challenges related to dealing with climate impacts. Furthermore, it can be applied to assess not only adaptation needs but also to put adaptation into a timeframe in each city. The authors acknowledge funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 603906, Project: ECONADAPT and Horizon 2020 Project RESIN (grant agreement no. H2020-DRS-9-2014). LMA and IG are grateful for the financial support received from the Basque Government for support via project GIC12/177-IT-399-13. LMA also thanks financial support from the Spanish Ministry of Science and Innovation (ECO2015-68023).

Learning the dynamics of power prices in a given market is important for a number of players (e.g., producers, consumers, and policy makers) at both macro- and microeconomic levels. This paper analyzes the recent behavior of spot prices in eight Western European countries. The sample period coincides with the COVID-19 pandemic for the most part: it starts in April 2020 and runs until May 2023; it includes the start of the Russia–Ukraine war. We introduce a new model for the hourly spot price of electricity. The deterministic component includes yearly, weekly, and daily seasonalities; the stochastic component accounts for volatility, mean reversion, and discrete jumps. We estimate the model with publicly available hourly data. Regarding the development of the internal market for electricity, we find that core mainland power markets now move closer in step with one another than before, but the integration process of the Iberian Peninsula seems to have kicked into reverse. As for the dynamics of power prices, in the last part of the sample period the speed of reversion falls everywhere, and price volatility increases noticeably; the expected number of jumps per hour decreases, but their average size turns to positive and they become more volatile.

In this paper we analyze the valuation of options stemming from the flexibility in an Integrated Gasification Combined Cycle (IGCC) Power Plant. First we use as a base case the opportunity to invest in a Natural Gas Combined Cycle (NGCC) Power Plant, deriving the optimal investment rule as a function of fuel price and the remaining life of the right to invest. Additionally, the analytical solution for a perpetual option is obtained. Second, the valuation of an operating IGCC Power Plant is studied, with switching costs between states and a choice of the best operation mode. The valuation of this plant serves as a base to obtain the value of the option to delay an investment of this type. Finally, we derive the value of an opportunity to invest either in a NGCC or IGCC Power Plant, that is, to choose between an inflexible and a flexible technology, respectively. Numerical computations involve the use of one- and two-dimensional binomial lattices that support a mean-reverting process for the fuel prices. Basic parameter values refer to an actual IGCC power plant currently in operation. Chamorro acknowledges financial support from research grant 9/UPV00I01.I01-14548/2002.