Gas Fired Power Plants: Investment Timing, Operating Flexibility and Abandonment

We analyze investments in gas fired power plants under stochastic electricity and gas prices. We use a real options approach, taking into account the economic information in futures and forward prices. A simple but realistic two-factor model is used for price process, enabling analysis of the value of operating flexibility, the opportunity to sell and abandon the capital equipment, as well as finding thresholds for energy prices for which it is optimal to enter into the investment. Our case study, using real data, indicates that when the decision to build is considered, the plant’s flexibility and abandonment option do not have significant value.Real options, spark spread, gas fired power plant, forward prices

New renewable electricity capacity under uncertainty: The potential in Norway

Uncertainty affecting project values makes investors hesitate to build new capacity unless profitability is significant. When analysing the potential for new renewable power system capacity in a region, it is therefore necessary to properly capture both uncertainty effects and decision-making behaviour of investors. Important stochastic factors typically include wholesale electricity prices and certificate prices. We calculate trigger levels for the sum of these factors, and compare these with the current long-term contract prices to estimate the potential for new renewable electricity capacity. We take into account the cost and technical potential of small hydro and wind in Norway, the number of prenotifications, concession applications and grants, and the capacity targets of subsidising governmental bodies. With an electricity certificate policy target of 41 TWh per year of new renewables for Sweden and Norway combined until 2016, we estimate that 12 TWh wind power and 6.2 TWh hydropower will be built in Norway. Due to the option value of waiting, most of this capacity will come after 2010.Finance, Hydroelectric power generation, Power system planning, Stochastic processes, Uncertainty, Wind energy

Modeling long-term electricity forward prices

In contrast to forwards and futures on storable commodities, prices of long-term electricity forwards exhibit a dynamics different to that of short-term and mid-term prices. We model long-term electricity forward prices through demand and supply for electricity, adjusted with a risk premium. Long-term prices of electricity, oil, coal, natural gas, emission allowance, imported electricity and aluminum are modeled with a vector autoregressive model. To estimate the model we use weekly prices of far-maturity forwards relevant for Nordic electricity market. Electricity prices experienced few substantial shocks during the period analyzed, however, we found no evidence of a structural break. Cointegration analysis indicates two stationary cointegrating vectors. Nord Pool price is found significant in the short- and the long-run model, while the gas price is insignificant in both. Other variables are significant only in the long-run model. The model shows some influence of the risk premium, however not on the long-term electricity forwards at Nord Pool.Electricity prices; long-term forward prices; VAR modeling; cointegration

Investment timing and optimal capacity choice for small hydropower projects

This paper presents a method for assessing small hydropower projects that are subject to uncertain electricity prices. We present a real options-based method with continuous scaling, and we find that there is a unique price limit for initiating the project. If the current electricity price is below this limit it is never optimal to invest, but above this limit investment is made according to the function for optimal size. The connection between the real option and the physical properties of a small hydropower plant is dealt with using a spreadsheet model that performs a technical simulation of the production in a plant, based on all the important choices for such a plant. The main results of the spreadsheet are simulated production size and the investment costs, which are in turn used for finding the value of the real option and the price limit. The method is illustrated on three different Norwegian small hydropower projects.OR in Energy; Real Options; Continuous Scaling; Project Evaluation; Hydropower

Evaluation of hydropower upgrade projects - a real options approach

When evaluating whether to refurbish existing hydropower plants or invest in a new power plant, there are two important aspects to take into consideration. These are the capacity chosen for the production facilities and the timing of the investment. This paper presents an investment decision support framework for hydropower producers with production facilities due for restoration. The producer can choose between refurbishing existing power plants and investing in a new production facility. A real options framework is proposed to support the investment decision. Using a case from Norsk Hydro ASA, a Norwegian hydropower producer, we employ the framework to evaluate the investment opportunities. Our main contribution is an approach that combines hydropower scheduling and real options valuation, and the results from our analysis suggest feasible investment strategies for Norsk Hydro ASA.Electricity price uncertainty; reservoir management; hydroelectric scheduling; investment under uncertainty; electricity markets

Portfolio management emphasizing electricity market applications. A stochastic programming approach

Using a stochastic programming approach, we consider portfolio management problems in the electricity and insurance businesses. Traditional portfolio management models assume that the markets in which the manager operates are perfectly competitive. There is reason to question this in the case of deregulated electricity markets, which are often dominated by large vertically integrated firms. Employing a two-stage stochastic Cournot-type game model of the Scandinavian electricity market, we investigate the potential for use of market power by large producers. The model takes into account the commitment effect of hydroelectric generation and forward contract decisions. We find that Statkraft, the largest pure hydro producer, has no market power on the seasonal level due to the aggregate hydro capacity of the large number of smaller producers. However, Vattenfall, the largest producer, has incentives to withhold thermal capacity in order to raise prices. Leaving the potential problems related to market power aside, we consider a price-taking hydropower producer facing uncertainty both in prices and reservoir in ow. Taking the view of a risk averse producer, we propose a portfolio management model for the purpose of hedging the financial risks using electricity contracts and the exibility of the production assets. This is a multistage stochastic programming model, and studying a case with real data from Norway, we find that such a model has the potential to improve the portfolio management processes currently used. Model verification is a challenging task in stochastic programming. For a portfolio management problem in a Norwegian insurance company, we compare two alternative model approaches. The first is a stochastic programming model, and the second is one where the asset mix is assumed constant, called the fixed mix approach. We explain how such a comparison can be done, considering the fact that in actual use, the models will be rerun before each decision is made. We find that the stochastic programming approach performs only slightly better than the fixed mix approach. With a stochastic programming approach it is possible to model portfolio management problems where the investment universe can contain derivative assets. We have applied this in an analysis of a casualty insurer's problem where we investigate the use of financial reinsurance, through selected options, in cases where the insurance company considers bearing catastrophe risks. Particular attention has been paid to the prices of the derivatives used in this model, using both arbitrage and equilibrium concepts.dr.ing.dr.ing

Electricity futures prices: time varying sensitivity to fundamentals

This paper provides insight in the time-varying relation between electricity futures prices and fundamentals in the form of prices of contracts for fossil fuels. As supply curves are not constant and different producers have different marginal costs of production, we argue that the relation between electricity futures prices and futures prices of underlying fundamentals such as natural gas, coal and emission rights are not constant and vary over time. We test this view by applying a model that linearly relates electricity futures prices to the marginal costs of production and calculate the log-likelihood of different time-varying and constant specifications of the coefficients. To do so, we formulate the model in state-space form and apply the Kalman Filter to observe the dynamics of the coefficients. We analyse historical prices of futures contracts with different delivery periods (calendar year and seasons, peak and off-peak) from Germany and the U.K. The results indicate that analysts should choose a time-varying specification to relate the futures price of power to prices of underlying fundamentals

How to proceed with competing alternative energy technologies: A real options analysis

Concerns about CO2 emissions create incentives for the development and deployment of energy technologies that do not use fossil fuels. Indeed, such technologies would provide tangible benefits in terms of avoided fossil-fuel costs, which are likely to increase as restrictions on CO2 emissions are imposed. However, a number of challenges need to be overcome prior to market deployment, and the commercialisation of alternative energy technologies may require a staged approach given price and technical risk. We analyse how a firm may proceed with staged commercialisation and deployment of competing alternative energy technologies. An unconventional new alternative technology is one possibility, where one could undertake cost-reducing production enhancement measures as an intermediate step prior to deployment. By contrast, the firm could choose to deploy a smaller-scale existing renewable energy technology, and, using the real options framework, we compare the two projects to provide managerial implications on how one might proceed.acceptedVersion© 2010. This is the authors’ accepted and refereed manuscript to the article. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0

Stochastic programming in energy

We give the reader a tour of good energy optimization models that explicitly deal with uncertainty. The uncertainty usually stems from unpredictability of demand and/or prices of energy, or from resource availability and prices. Since most energy investments or operations involve irreversible decisions, a stochastic programming approach is meaningful. Many of the models deal with electricity investments and operations, but some oil and gas applications are also presented. We consider both traditional cost minimization models and newer models that reflect industry deregulation processes. The oldest research coincides with the birth of linear programming, and most models within the market paradigm have not yet found their final form.stochastic programming, energy, regulated markets, deregulation, uncertainty, electricity, natural gas, oil

Investment timing and capacity choice under rate-of-return regulation for renewable energy support

This study analyzes a renewable energy (RE) support scheme recently introduced in Russia and compares it to the most frequently applied policy measures, feed-in tariff (FiT) and feed-in premium (FiP) schemes. In particular, we present an analytical formulation of the problem set-up and study optimal investment timing and capacity choice employing a real options approach. In addition, we conduct detailed sensitivity analyses to highlight how different policies shape investment behavior. The contributions of this paper include modeling the Russian RE support mechanism in a dynamic programming framework that allows us to show that such a RE support design offers a strong case for transferring market risks away from the investor and has potential for a unique combination of effectiveness and cost efficiency.Post-print / Final draf