Evaluating the power investment options with uncertainty in climate policy

This paper uses a real options approach (ROA) for analysing the effects of government climate policy uncertainty on private investors’ decision-making in the power sector. It presents an analysis undertaken by the International Energy Agency (IEA) that implements ROA within a dynamic programming approach for technology investment choice. Case studies for gas, coal and nuclear power investment are undertaken with the model. Illustrative results from the model indicate four broad conclusions: i) climate change policy risks can become large if there is only a short time between a future climate policy event such as post-2012 and the time when the investment decision is being made; ii) the way in which CO₂ and fuel price variations feed through to electricity price variations is an important determinant of the overall investment risk that companies will face; iii) investment risks vary according to the technology being considered, with nuclear power appearing to be particularly exposed to fuel and CO₂ price risks under various assumptions; and iv) the government will be able to reduce investors' risks by implementing long-term (say 10 years) rather than short-term (say 5 years) climate change policy frameworks. Contributions of this study include: (1) having created a step function with stochastic volume of jump at a particular time to simulate carbon price shock under a particular climate policy event; (2) quantifying the implicit risk premium of carbon price uncertainty to investors in new capacity; (3) evaluating carbon price risk alongside energy price risk in investment decision-making; and (4) demonstrating ROA to be a useful tool to quantify the impacts of climate change policy uncertainty on power investment.     

Delaying the introduction of emissions trading systems—Implications for power plant investment and operation from a multi-stage decision model

Relying on real options theory, we employ a multistage decision model to analyze the effect of delaying the introduction of emission trading systems (ETS) on power plant investments in carbon capture and storage (CCS) retrofits, on plant operation, and on carbon dioxide (CO₂) abatement. Unlike previous studies, we assume that the investment decision is made before the ETS is in place, and we allow CCS operating flexibility for new power plant investments. Thus, the plant may be run in CCS-off mode if carbon prices are low. We employ Monte Carlo simulation methods to account for uncertainties in the prices of CO₂ certificates, other inputs, and output prices, relying on a realistic parameterization for a supercritical pulverized coal plant in China. We find that CCS operating flexibility lowers the critical carbon price needed to support CCS investment because it renders CCS investment less irreversible. For a low carbon price path, operating flexibility also implies that delaying the introduction of an ETS hardly affects plant CO₂ abatement since the plant operator is better off purchasing emission certificates rather than operating the plant in CCS mode. Interestingly, for low carbon prices we find a U-shaped relation between the length of the delay and the economic value of the plant. Thus, delaying the introduction of an ETS may make investors worse off.     

A real options–based CCS investment evaluation model: Case study of China’s power generation sector

This paper establishes a carbon capture and storage (CCS) investment evaluation model based on real options theory considering uncertainties from the existing thermal power generating cost, carbon price, thermal power with CCS generating cost, and investment in CCS technology deployment. The model aims to evaluate the value of the cost saving effect and amount of CO₂ emission reduction through investing in newly-built thermal power with CCS technology to replace existing thermal power in a given period from the perspective of power generation enterprises. The model is solved by the Least Squares Monte Carlo (LSM) method. Since the model could be used as a policy analysis tool, China is taken as a case study to evaluate the effects of regulations on CCS investment through scenario analysis. The findings show that the current investment risk of CCS is high, climate policy having the greatest impact on CCS development. Thus, there is an important trade off for policy makers between reducing greenhouse gas emissions and protecting the interests of power generation enterprises. The research presented would be useful for CCS technology evaluation and related policy-making.     

Assessing the effects of CO2 price caps on electricity investments—A real options analysis

This paper uses real options modeling to assess the impact of different climate change policy instruments on investment, profits and cumulative emissions in the electricity sector. Even though CO₂ price caps or “safety valves” have been suggested as methods to limit uncertainty emanating from fluctuating prices of CO₂ permits that would hurt the industry's profit and thereby also energy security, our analysis shows that price caps set at a too low level are detrimental to the adoption of e.g. modern biomass-fired capacity as a replacement for existing coal-fired power plants. We therefore conduct a series of experiments with different policy scenarios to analyze under which regime emissions are most effectively reduced. With respect to CO₂ price uncertainty, it turns out that even for moderately rising CO₂ prices, fluctuations frequently lead to investment into carbon capture and storage (CCS), while investment is often not triggered in the face of deterministic CO₂ prices.     

Inducing low-carbon investment in the electric power industry through a price floor for emissions trading

Uncertainty about long-term climate policy is a major driving force in the evolution of the carbon market price. Since this price enters the investment decision process of regulated firms, this uncertainty increases the cost of capital for investors and might deter investments into new technologies at the company level. We apply a real options-based approach to assess the impact of climate change policy in the form of a constant or growing price floor on investment decisions of a single firm in a competitive environment. This firm has the opportunity to switch from a high-carbon “dirty” technology to a low-carbon “clean” technology. Using Monte Carlo simulation and dynamic programming techniques for real data, we determine the optimal CO₂ price floor level and growth rate in order to induce investments into the low-carbon technology. We find that a carbon price floor can be used to induce earlier low-carbon technology investment and show this result to be robust to a large variety of input parameter settings.     

A real options evaluation model for the diffusion prospects of new renewable power generation technologies

This study presents a policy planning model that integrates learning curve information on renewable power generation technologies into a dynamic programming formulation featuring real options analysis. The model recursively evaluates a set of investment alternatives on a year-by-year basis, thereby taking into account that the flexibility to delay an irreversible investment expenditure can profoundly affect the diffusion prospects of renewable power generation technologies. Price uncertainty is introduced through stochastic processes for the average wholesale price of electricity and for input fuel prices. Demand for electricity is assumed to be increasingly price-sensitive, as the electricity market deregulation proceeds, reflecting new options of consumers to react to electricity price changes (such as time-of-use pricing, unbundled electricity services, and choice of supplier). The empirical analysis is based on data for the Turkish electricity supply industry. Apart from general implications for policy-making, it provides some interesting insights about the impact of uncertainty and technical change on the diffusion of various emerging renewable energy technologies.     

Uncertainties in key low carbon power generation technologies – Implication for UK decarbonisation targets

The UK government’s economy-wide 60% carbon dioxide reduction target by 2050 requires a paradigm shift in the whole energy system. Numerous analytical studies have concluded that the power sector is a critical contributor to a low carbon energy system, and electricity generation has dominated the policy discussion on UK decarbonisation scenarios. However, range of technical, social and market challenges, combined with alternate market investment strategies mean that large scale deployment of key classes of low carbon electricity technologies is fraught with uncertainty. The UK MARKAL energy systems model has been used to investigate these long-term uncertainties in key electricity generation options. A range of power sector specific parametric sensitivities have been performed under a ‘what-if’ framework to provide a systematic exploration of least-cost energy system configurations under a broad, integrated set of input assumptions. In this paper results of six sensitivities, via restricted investments in key low carbon technologies to reflect their technical and political uncertainties, and an alternate investment strategies from perceived risk and other barriers, have been presented.     

Determinants of the costs of carbon capture and sequestration for expanding electricity generation capacity

This study models the costs of electricity generation with carbon capture and sequestration (CCS), from generation at the power plant to carbon injection at the reservoir, examining the economic factors that affect technology choice and CCS costs at the individual plant level. The results suggest that natural gas and coal prices have profound impacts on the carbon price needed to induce CCS. To extend previous analyses we develop a "cost region" graph that models technology choice as a function of carbon and fuel prices. Generally, the least-cost technology at low carbon prices is pulverized coal, while intermediate carbon prices favor natural gas technologies and high carbon prices favor coal gasification with capture. However, the specific carbon prices at which these transitions occur is largely determined by the price of natural gas. For instance, the CCS-justifying carbon price ranges from $27/t CO₂ at high natural gas prices to $54/t CO₂ at low natural gas prices. This result has important implications for potential climate change legislation. The capital costs of the generation and CO₂ capture plant are also highly important, while pipeline distance and criteria pollutant control are less significant.     

Oil and natural gas prices and greenhouse gas emission mitigation

The hikes in hydrocarbon prices during the last years have lead to concern about investment choices in the energy system and uncertainty about the costs for mitigation of greenhouse gas emissions. On the one hand, high prices of oil and natural gas increase the use of coal; on the other hand, the cost difference between fossil-based energy and non-carbon energy options decreases. We use the global energy model TIMER to explore the energy system impacts of exogenously forced low, medium and high hydrocarbon price scenarios, with and without climate policy. We find that without climate policy high hydrocarbon prices drive electricity production from natural gas to coal. In the transport sector, high hydrocarbon prices lead to the introduction of alternative fuels, especially biofuels and coal-based hydrogen. This leads to increased emissions of CO₂. With climate policy, high hydrocarbon prices cause a shift in electricity production from a dominant position of natural gas with carbon capture and sequestration (CCS) to coal-with-CCS, nuclear and wind. In the transport sector, the introduction of hydrogen opens up the possibility of CCS, leading to a higher mitigation potential at the same costs. In a more dynamic simulation of carbon price and oil price interaction the effects might be dampened somewhat.     

The Clean-Development Mechanism,stochastic permit prices and energy investments

We analyze the impact on energy investments stemming from different emission permit classes, by considering permits that are allocated inside the European Emission Trading Scheme and secondary Certified Emission Reduction (sCER) permits originating from the Clean Development Mechanism. One price taking firm which is subject to emission regulation has the choice to invest in gas or wind power plant. The firm faces uncertainty regarding stochastically evolving permit prices, while it receives a premium on the electricity price for wind energy. As a first step, we determine the value of the option to invest into a gas power plant over time. Then, we calculate the investment probability of a gas power investment in a range of policy scenarios. We find that allowing the usage of sCER permits in the present policy framework has a positive impact on gas power investment. Decoupling the price processes has a similar effect. If the quota of sCER permits is doubled, the decrease in the investment probability for wind power is large. We carry out sensitivity tests for different parameter values, and find that investment behavior changes significantly with differing interest rates, the wind energy premium and volatility.     

The value of green energy under regulation uncertainty

We examine investments in power generation projects under policy uncertainty, when the investor has the choice between two alternative technologies, a gas-fired plant and a wind plant. Increased likelihood of subsidy withdrawal reduces the payoff from and postpones investments in the wind technology. Simultaneously, it accelerates investments in gas, thereby eliminating or further postponing investments in wind capacity. We show that this substitution phenomenon can be of first order importance: it can have a significant impact on the timing of investment, the wind premium, and the probability of investing in the wind technology. Our results provide new insights about the scope and impact of green energy regulation.     

Coevolution of policy,market and technical price risks in the EU ETS

Within the EU, there have been calls for governments to provide greater certainty over carbon prices, even though it is evident that their price risk is not entirely due to policy uncertainty. We develop a stochastic simulation model of price formation in the EU ETS to analyse the coevolution of policy, market and technology risks under different initiatives. The current situation of a weak (20%) overall abatement target motivates various technology-support interventions, elevating policy uncertainty as the major source of carbon price risk. In contrast, taking a firm decision to move to a more stringent 30% cap would leave the EU–ETS price formation driven much more by market forces than by policy risks. This leads to considerations of how much risk mitigation by governments would be appropriate, and how much should be taken as business risk by the market participants.     

Incentives of carbon dioxide regulation for investment in low-carbon electricity technologies in Texas

This paper compares the incentives a carbon dioxide emissions price creates for investment in low carbon dioxide-emitting technologies in the electricity sector. We consider the extent to which operational differences across generation technologies – particularly, nuclear, wind and solar photovoltaic – create differences in the incentives for new investment, which is measured by the operating profits of a potential entrant. First, astylized model of an electricity system demonstrates that the composition of the existing generation system may cause electricity prices to increase by different amounts over time when a carbon dioxide price is imposed. Differences in operation across technologies therefore translate to differences in the operating profits of a potential entrant. Then, a detailed simulation model is used to consider a hypothetical carbon dioxide price of $10–$50 per metric ton for the Electric Reliability Council of Texas (ERCOT) market. The simulations show that, for the range of prices considered, the increase in electricity prices is positively correlated with output from a typical wind unit, but the correlation is much weaker for nuclear and photovoltaic. Consequently, a carbon dioxide price creates much stronger investment incentives for wind than for nuclear or photovoltaic technologies in the Texas market.     

Optimal investment strategies in decentralized renewable power generation under uncertainty

This paper presents a method for evaluating investments in decentralized renewable power generation under price un certainty. The analysis is applicable for a client with an electricity load and a renewable resource that can be utilized for power generation. The investor has a deferrable opportunity to invest in one local power generating unit, with the objective to maximize the profits from the opportunity. Renewable electricity generation can serve local load when generation and load coincide in time, and surplus power can be exported to the grid. The problem is to find the price intervals and the capacity of the generator at which to invest. Results from a case with wind power generation for an office building suggests it is optimal to wait for higher prices than the net present value break-even price under price uncertainty, and that capacity choice can depend on the current market price and the price volatility. With low price volatility there can be more than one investment price interval for different units with intermediate waiting regions between them. High price volatility increases the value of the investment opportunity, and therefore makes it more attractive to postpone investment until larger units are profitable.     

Uncertainty modeling of CCS investment strategy in China’s power sector

The increasing pressure resulting from the need for CO₂ mitigation is in conflict with the predominance of coal in China’s energy structure. A possible solution to this tension between climate change and fossil fuel consumption fact could be the introduction of the carbon capture and storage (CCS) technology. However, high cost and other problems give rise to great uncertainty in R&D and popularization of carbon capture technology. This paper presents a real options model incorporating policy uncertainty described by carbon price scenarios (including stochasticity), allowing for possible technological change. This model is further used to determine the best strategy for investing in CCS technology in an uncertain environment in China and the effect of climate policy on the decision-making process of investment into carbon-saving technologies.     

Option value of electricity demand response

As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.     

Initiating a sustained diffusion of wind power: The role of public–private partnerships in Spain

The literature on policy approaches for the market support of renewable electricity is dominated by narrow conceptualizations of policy, referring mostly to direct instruments for economic feasibility. Such approaches often led to unsatisfactory explanations of diffusion results. This is the case of wind power diffusion in Spain, the success of which is typically credited to the ‘feed-in-tariff’ instrument. This paper offers an alternative explanatory account for wind power diffusion in Spain. It is argued that diffusion can be explained by a less obvious policy of stimulating investments by means of public–private partnerships (PPPs). The three legal frameworks for economic feasibility applicable up to 2004 harbored high economic risks. Although projects could have high profitability because of generous investment subsidies, up to mid 1990s most investments were based on PPPs, to address the risk perceptions of early investors. Fully-private partnerships now dominate investments, though PPPs have not disappeared. Next to winning investors’ confidence, the PPP policy led to an investment culture whereby partnership investments dominate. By 2000, 95.7% of the installed wind capacity was owned by partnerships, and only 4.3% by individual companies. Partnerships invest in larger projects, have ambitious investment plans, and these lead to a high diffusion tempo.     

An equilibrium pricing model for wind power futures

Generation from wind power plants is intermittent and affects profits of wind power generators and conventional generators alike. Currently, generators have limited options for transferring the resulting wind-related volume risks. The European Energy Exchange (EEX) recently introduced exchange-traded wind power futures to address this market imperfection. We propose a stylized equilibrium pricing model featuring two representative agents and analyze equilibrium prices as well as the mechanics behind risk premia for wind power futures. We calibrate and simulate stochastic models for wind power generation, power prices, electricity demand, as well as other relevant sources of uncertainty and use the resulting scenarios to conduct a case study for the German market; analyzing prices, hedging effectiveness, and risk premia. Our main result suggests that wind generators are willing to pay an insurance premium to conventional generators to reduce their risks. We conduct a thorough sensitivity analysis to test the influence of model parameters and find that our results on risk premia hold for a broad range of reasonable inputs.     

Carbon capture and storage as a corporate technology strategy challenge

Latest estimates suggest that widespread deployment of carbon capture and storage (CCS) could account for up to one-fifth of the needed global reduction in CO₂ emissions by 2050. Governments are attempting to stimulate investments in CCS technology both directly through subsidizing demonstration projects, and indirectly through developing price incentives in carbon markets. Yet, corporate decision-makers are finding CCS investments challenging. Common explanations for delay in corporate CCS investments include operational concerns such as the high cost of capture technologies, technological uncertainties in integrated CCS systems and underdeveloped regulatory and liability regimes. In this paper, we place corporate CCS adoption decisions within a technology strategy perspective. We diagnose four underlying characteristics of the strategic CCS technology adoption decision that present unusual challenges for decision-makers: such investments are precautionary, sustaining, cumulative and situated. Understanding CCS as a corporate technology strategy challenge can help us move beyond the usual list of operational barriers to CCS and make public policy recommendations to help overcome them.     

Investment in wind power and pumped storage in a real options model

Promoting renewable energy has been a key ingredient in energy policy seeking to de-carbonize the energy mix and will continue to do so in the future given the European Union's high ambitions to further curb carbon emissions. A wide range of instruments has been suggested and implemented in various countries of the EU. A prominent policy promoting investment in renewable technologies is the use of feed-in tariffs, which has worked well at large scale in, e.g. Germany, but which has only been implemented in a very limited way in countries such as the UK.Being subject to environmental uncertainties, however, renewables cannot be seen in isolation: while renewables-based technologies such as wind and solar energy, for example, suffer from uncertain loads depending on environmental conditions, hydropower allows for the storage of water for release at peak prices, which can be treated as a premium (partially) offsetting higher upfront investment costs. In addition, electricity prices will respond to changes in electric capacity in the market, which is often neglected in standard investment models of the electricity sector.This paper contributes to the existing literature in two ways: it provides a review of a renewables-based technology in a specific policy context and provides additional insight by employing a real options approach to investigate the specific characteristics of renewables and their associated uncertainties in a stylized setting taking explicitly into account market effects of investment decisions. The prices of the model are determined endogenously by the supply of electricity in the market and by exogenous electricity price uncertainty. The inclusion of market effects allows us to capture the full impact of public incentives for companies to invest into wind power and hydro pumped storage installations.