Investment In Power Generation Research Articles

The importance of project finance for renewable energy projects

Given the magnitude of investment needs into low-carbon power generation, the availability and cost of capital is crucial for successful energy transitions. Recently, a strong increase of non-recourse project finance (as compared to corporate finance on a project sponsor's balance sheets) could be observed for power generation projects. Classical economic motivations for project finance are the prevention of contamination risk, and agency conflicts – however, these reasons do not apply for comparably small projects in low-risk environments, such as many renewable energy projects being realized today. This paper therefore assesses the importance of project finance for renewable energy projects in investment-grade countries, and the underlying drivers to use this kind of finance. Eight potential reasons for using project finance are distilled from economic and finance theory, and then empirically evaluated using a novel dataset for new power plant investments in Germany 2010–2015. Results show that in this extreme case with particularly low investment risks, project finance has much larger importance for renewables than for fossil fuel-based power plants. It is not used to reduce contamination risk or agency conflicts, but, instead driven by the “debt overhang” of non-utility sponsors such as independent project developers. We discuss implications for policy makers, the financial sector, as well as energy scholars concerned with power generation investment decisions.

Investments in Power Generation Under Uncertainty—a MIP Specification and Large-Scale Application for EU

Investments in power generation constitute a typical budget allocation problem in the context of multiple objectives, while all factors influencing investor’s decisions for power plants are subject to considerable uncertainties. The paper introduces a multi-objective stochastic model designed to optimize budget allocation decisions for power generation in the context of risk aversion taking into account several sources of uncertainty, especially with regard to volatility of fossil fuel and electricity prices, technological costs, and climate policy variability. Probability distributions for uncertain factors influencing investment decisions are directly derived from the stochastic global energy model PROMETHEUS and thus they take into account complex interactions between variables in the systemic context. In order to fully incorporate stochastic characteristics of the problem, the model is specified as an optimization problem in which the probability that an objective exceeds a given threshold is maximized (risk aversion) subject to a set of deterministic and probabilistic constraints. The model is formulated as a mixed integer program providing complete flexibility on the joint distributions of rates of return of technologies competing for investments, as it can handle non-symmetric distributions and take automatically into account complex covariance patterns as emerging from comprehensive PROMETHEUS stochastic results. The analysis shows that risk is a crucial factor for power generation investments with investors not opting for technologies subject to uncertainty related to climate policies and fossil fuel prices. On the other hand, combination of options with negative covariance tends to benefit in the context of risk-hedging behavior.

Capacity expansion games with application to competition in power generation investments

We consider competitive capacity investment for a duopoly of two distinct producers. The producers are exposed to stochastically fluctuating costs and interact through aggregate supply. Capacity expansion is irreversible and modeled in terms of timing strategies characterized through threshold rules. Because the impact of changing costs on the producers is asymmetric, we are led to a nonzero-sum timing game describing the transitions among the discrete investment stages. Working in a continuous-time diffusion framework, we characterize and analyze the resulting Nash equilibrium and game values. Our analysis quantifies the dynamic competition effects and yields insight into dynamic preemption and over-investment in a general asymmetric setting. A case-study considering the impact of fluctuating emission costs on power producers investing in nuclear and coal-fired plants is also presented.

Optimal wind power generation investment, considering voltage stability of power systems

Studies show that improper sizing and placement of wind farms (WFs) lead to undesired investment and operation costs as well as the risk of voltage instability. Thus, optimal placement of WFs and enough loading margin (LM) are important factors which ensure the voltage stability of system as well as optimal investment and expenditure for WFs development. In this paper, modal analysis is used to determine the optimal place of WFs from the voltage stability viewpoint. Moreover, a new voltage stability constrained wind energy planning (VSC-WEP) model is proposed to determine the optimal yearly wind power penetration while satisfying voltage stability constraints. A 10-years horizon is considered and the net profit from the energy procurement via the WFs' installed optimally, is maximized. Furthermore, the added capacity of WFs and the net profit are analyzed by sensitivity analyzes to investigate the impact of various technical and financial factors on the obtained results. The proposed VSC-WEP model is implemented on the IEEE New-England 39-bus test system, and solved by General Algebraic Modeling System (GAMS) optimization package. The simulation results demonstrate the capability of the proposed model for optimal determination of WFs capacity while preserving a proper LM of system.

Simulating climate and energy policy with agent-based modelling: The Energy Modelling Laboratory (EMLab)

We present an approach to simulate climate and energy policy for the EU, using a flexible and modular agent-based modelling approach and a toolbox, called the Energy Modelling Laboratory (EMLab). The paper shortly reviews core challenges and approaches for modelling climate and energy policy in light of the energy transition. Afterwards, we present an agent-based model of investment in power generation that has addressed a variety of European energy policy questions. We describe the development of a flexible model core as well as modules on carbon and renewables policies, capacity mechanisms, investment behaviour and representation of intermittent renewables. We present an overview of modelling results, ongoing projects, a case study on current reforms of the EU ETS, and we show their relevance in the EU context.

Accelerating investments in power in sub-Saharan Africa

Private sector investments in African power generation play an increasingly important role in addressing the continent's electricity supply shortages. Our analysis of investment trends in sub-Saharan Africa reveals some key success factors.

Capacity Expansion Games with Application to Competition in Power Generation Investments

We consider competitive capacity investment for a duopoly of two distinct producers. The producers are exposed to stochastically fluctuating costs and interact through aggregate supply. Capacity expansion is irreversible and modeled in terms of timing strategies characterized through threshold rules. Because the impact of changing costs on the producers is asymmetric, we are led to a nonzero-sum timing game describing the transitions among the discrete investment stages. Working in a continuous-time diffusion framework, we characterize and analyze the resulting Nash equilibrium and game values. Our analysis quantifies the dynamic competition effects and yields insight into dynamic preemption and over-investment in a general asymmetric setting. A case-study considering the impact of fluctuating emission costs on power producers investing in nuclear and coal-fired plants is also presented.

Capacity adequacy in power markets facing energy transition: A comparison of scarcity pricing and capacity mechanism

This article analyses how a capacity mechanism can address security of supply objectives in a power market undergoing an energy transition that combines energy efficiency efforts to stabilise demand and a rapid increase in the proportion of renewables. To analyse this situation, power markets are simulated over the long term with a System Dynamics model integrating new investment and closure decisions. This last trait is relevant to studying investment in power generation in mature markets undergoing policy shocks. The energy-only market design with a price cap, with and without a capacity mechanism, is compared to scarcity pricing in two investment behaviour scenarios with and without risk aversion. The results show that the three market designs lead to different levels of risk for peaking unit investment and results thus differ according to which risk aversion hypothesis is adopted. Assuming a risk-neutral investor, the results indicate that compared to an energy-only market with a price cap at 3 000 €/MWh, an energy-only market with scarcity pricing and the market design with a capacity mechanism are two efficient options to reach similar levels of load loss. But under the hypothesis of risk aversion, the results highlight the advantage of the capacity mechanism over scarcity pricing.

Assessing Potential of Power Generation Investment in ASEAN Countries

The Association of Southeast Asian Nations (ASEAN) has been considered as one of the attracting regions for foreign direct investment. As developing nations are growing, they tend to consume more electricity and need more power generation, which becomes one of attractive businesses in this region. However, not many studies focus on an assessment of business environment of power generation and might deter unfamiliar investors from venture. Therefore, we assessed the relative attractiveness of power generation investment in this region in five aspects: economy, business, risk, infrastructure, and finance, which were taken up from the framework of the Global Infrastructure Investment Index. The assessment can provide an overview of strengths and weaknesses of power generation investment and serve as guidelines for investment decisions and policy making to investors and government in each ASEAN country.

Optimal feed-in tariff for solar photovoltaic power generation in China: A real options analysis

The feed-in tariff policy is widely used to promote the development of renewable energy. China also adopts feed-in tariff policy to attract greater investment in solar photovoltaic power generation. This study employs real options method to assess the optimal levels of feed-in tariffs in 30 provinces of China. The uncertainties in CO2 price and investment cost are considered. A method that integrates the backward dynamic programming algorithm and Least-Squares Monte Carlo method is used to solve the model. The results demonstrate that the feed-in tariffs of 30 provinces range from 0.68 RMB/kWh to 1.71 RMB/kWh, and the average level is 1.01 RMB/kWh. On this basis, we find that the levels of sub-regional feed-in tariff announced in 2013 are no longer appropriate and should be adjusted as soon as possible. We have also identified the implications of technological progress and carbon emission trading schemes, as well as the importance of strengthening electricity transmission. It has been suggested that the Chinese government takes diverse measures, including increasing research and development investment, establishing and improving a nationwide carbon emission trading scheme and accelerating the construction of electricity-transmission infrastructure, to reduce the required feed-in tariff and promote the development of solar photovoltaic power generation.

Biomass Power Generation Investment in China: A Real Options Evaluation

This paper proposes a real options model for evaluating the biomass power generation investment in China. The uncertainties in the market price of electricity, CO2 price and straw price are considered. Meanwhile the dynamic relationship between installed capacity and fuel cost, as well as the long-term reduction of subsidy are described. Two scenarios, i.e., with the carbon emission trading scheme existent and non-existent, respectively, is built to empirically analyze the investment of a 25-MW straw-based power generation project. The results show that investors should undertake the investment in 2030 under two scenarios. Investment values are 14,869,254.8 and 37,608,727 Chinese Yuan (RMB), respectively. The implementation of the carbon emission trading scheme theoretically helps improve investment value and advance the most likely optimal investment time. However, the current CO2 price is not sufficient to advance the most likely optimal investment time. The impacts of several factors, including subsidy policy, CO2 price, straw price, installed capacity, correlation structure and the validity period of investment, on the optimal investment strategy are also examined. It is suggested that governments take some measures, including increasing subsidy, setting the growth pattern of subsidy and establishing and perfecting a nationwide carbon trading market, to improve the investment environment and attract more investments.

System Dynamics Modelling of Renewable Power Generation Investment Decisions Under Risk

System Dynamics Modelling of Renewable Power Generation Investment Decisions Under Risk

Climate Policy Uncertainty and Power Generation Investments: A Real Options-CVaR Portfolio Optimization Approach

A decision support framework has been provided to assist investors with long-term decision-making for investment choices in power generation assets under uncertain climate policy. The model combines real options analysis and modern portfolio optimization theory. A long-term correlation between carbon and renewable portfolio standard certificate prices is used to model the interaction of climate policies, with a case study being developed to investigate the optimal choice of capacity additions to an existing mix of power generation assets in Australia. The findings show that there is potential for investors to fully hedge their existing fossil fuel based generation assets through the addition of on-shore wind capacity. The model developed allows for (1) the investigation of investment risk and return under uncertain climate policies, and (2) the study of interaction among green policies.

Energy-efficient distribution transformers in Europe: impact of Ecodesign regulation

This paper assesses the impact of energy-efficient distribution transformers through the implementation of energy efficiency classes, as well as through mandatory standards that remove the worst transformers from the market. Energy-efficient distribution transformers can not only save about 1 % of all the electricity generated but can also save very large investments in power generation and transmission power systems. Technical solutions to reduce energy consumption of transformers are available, but the market penetration of high-efficient transformers is significantly lower than it could be. The paper analyses the main market and regulatory failures and identifies that the introduction of efficiency classes and minimum energy performance standards (MEPS) is the best way to foster energy performance in distribution transformers. Policies and measures supporting energy-efficient distribution transformers around the world are reviewed. The use of Ecodesign methodology do define possible transformer MEPS regulation is discussed for different types of distribution transformers, as well as the associated impacts. Sensitivity analysis in relation to key parameters (load factor and electricity prices) is analyzed. The energy saving potential and the environmental impacts are calculated until the year 2050 in the European Union, as well as at world level. The potential savings in the year 2050 represent about 35 TWh of electricity in Europe, equivalent to about four million tons of reduced CO2 emissions. For the world, the estimated impact in 2050 is about 450 TWh equivalent to about 180 million tons of reduced CO2 emissions.

SMART-SREC: A stochastic model of the New Jersey solar renewable energy certificate market

Markets for solar renewable energy certificates (SRECs) are gaining in prominence in many states, stimulating growth of the US solar industry. However, SREC market prices have been extremely volatile, causing high risk to participants and potentially less investment in solar power generation. Such concerns necessitate the development of realistic, flexible and tractable models of SREC prices that capture the behavior of participants given the rules that govern the market. We propose an original stochastic model called SMART-SREC to fill this role, building on established ideas from the carbon pricing literature, and including a feedback mechanism for generation response to prices. We calibrate the model to the New Jersey market and backtest it, analyzing parameter sensitivity and demonstrating its ability to reproduce historical dynamics. Finally, we run simulations to investigate the role and impact of regulatory parameters, thus providing insight into the crucial role played by market design.

Incorporating Uncertainty into World Energy Modelling: the PROMETHEUS Model

In general, policy and most economic decisions like investments are formulated in a non-deterministic context. Their analysis can be considerably enhanced if probability information on future outcomes is available, especially in terms of unbiased estimates of the extent of unpredictability and stochastic dependence. For the sake of transparency, it is also important to be able to trace the justification of variability and its structure. This paper introduces PROMETHEUS, a stochastic model of the world energy system that is designed to produce joint empirical distributions of future outcomes concerning many variables that are important in terms of the evolution of the world energy system. The model methodology is based on Monte Carlo techniques, and the joint distributions of the model inputs are derived to a large extent not only from statistical econometric analysis but also from specialised studies. The emphasis is placed on the exhaustive coverage of variability including omitted variables. By incorporating detailed coverage of uncertainty into a comprehensive large-scale global energy system model, PROMETHEUS can be used to quantify probabilistic assessments of future model outcomes, which constitute critical parameters in formulating robust energy and climate policies. The description of the main model characteristics is complemented with an analytical example that illustrates the usefulness of stochastic PROMETHEUS results in the context of power generation investments under uncertainty.

A valuation of wind power projects in Germany using real regulatory options

This study evaluates a proposed investment in wind power generation using real options from the regulatory framework applicable to the German market (Europe's largest producer of renewable energy). For this evaluation, we have modelled the primary uncertainties affecting the project and we have incorporated the uncertainties arising from public support for renewable energy. These mechanisms generate RROs (real regulatory options) – not in the hands of the project's promoter – that affect the net present value of the project. The inclusion of these mechanisms enriches and improves the valuation methodology of such projects. The comparative analysis of the case study, using other countries, reinforces the idea that the existing RROs depreciate the net present value of renewable generation projects. We believe that the methodology we propose in our paper can help policy makers to improve their policies to promote renewable energy.

Public policies can reduce tropical deforestation: Lessons and challenges from Brazil

Reducing carbon emissions from deforestation and forest degradation now constitutes an important strategy for mitigating climate change, particularly in developing countries with large forests. Given growing concerns about global climate change, it is all the more important to identify cases in which economic growth has not sparked excessive forest clearance. We address the recent reduction of deforestation rates in the Brazilian Amazon by conducting a statistical analysis to ascertain if different levels of environmental enforcement between two groups of municipalities had any impact on this reduction. Our analysis shows that these targeted, heightened enforcement efforts avoided as much as 10,653km2 of deforestation, which translates into 1.44×10−1PgC in avoided emissions for the 3 y period. Moreover, most of the carbon loss and land conversion would have occurred at the expense of closed moist forests. Although such results are encouraging, we caution that significant challenges remain for Brazil's continued success in this regard, given recent changes in the forestry code, ongoing massive investments in hydro power generation, reductions of established protected areas, and growing demand for agricultural products.

Power Generation Investment Planning in a Modern Power System With High Share of Renewables

Abstract This article is focused on investment planning in power generation industry, which becomes an important direction of research under the conditions of newly formed environment of electricity market. The authors emphasize the key role of effective instruments and methods for estimation of the costs and economic efficiency of a generating capacity and its adequacy in a modern power system. In the paper, cost-based concepts are analyzed which form a basis for decision-making and long-term planning of investments in the power generation sector for a power system with high share of renewable energy sources.

An indicative assessment of investment opportunities in the African electricity supply sector

In the coming decades, demand for electricity will increase considerably on the African continent. Investment in power generation, transmission and distribution is necessary to meet this demand. In this paper a cost-optimization tool is used to assess investment opportunities under varying scenarios of GDP growth, electricity trade and CO2 taxation. Business as usual fuel price outlooks are assumed, and related assumptions are relatively conservative. The goal is to find if there are economic indications that renewable energy might play a significant role in the expansion of the African electricity system. The results show that there is potential of renewable energy (RE) resources to have a significant share in the generation mix. By 2030, 42% and 55% of the total generation is powered by renewables in the high and low GDP scenarios respectively. Promotion of interregional trade can assist in unlocking RE potential across the continent, such as hydro in Central Africa and wind in East Africa; these regions are projected to be net exporters of electricity. Additionally, generation by off-grid technologies increases over time, reaching 12% of the total generation by 2030 in Sub-Saharan Africa.