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Author: Ansgar Geiger Publisher: KIT Scientific Publishing ISBN: 3866446330 Category : Mathematics Languages : en Pages : 338
Book Description
The profitability of power plant investments depends strongly on uncertain fuel and carbon prices. In this doctoral thesis, we combine fundamental electricity market models with stochastic dynamic programming to evaluate power plant investments under uncertainty. The application of interpolation-based stochastic dynamic programming and approximate dynamic programming allows us to consider a greater variety of stochastic fuel and carbon price scenarios compared to other approaches.
Author: Ansgar Geiger Publisher: KIT Scientific Publishing ISBN: 3866446330 Category : Mathematics Languages : en Pages : 338
Book Description
The profitability of power plant investments depends strongly on uncertain fuel and carbon prices. In this doctoral thesis, we combine fundamental electricity market models with stochastic dynamic programming to evaluate power plant investments under uncertainty. The application of interpolation-based stochastic dynamic programming and approximate dynamic programming allows us to consider a greater variety of stochastic fuel and carbon price scenarios compared to other approaches.
Author: Diana Kudakwashe Chiyangwa Publisher: ISBN: Category : Languages : en Pages : 86
Book Description
The purpose of this study is to develop a strategy for investment in power generation technologies in the future given the uncertainties in climate policy and fuel prices. First, such studies are commonly conducted using deterministic methods which assume a given likelihood of the carbon and gas price levels. In this study a probabilistic approach is used to address these uncertainties. Secondly, capacity expansion models conventionally apply average estimates to predict the amount of power that each generator will produce based on the technology chosen. I propose an alternate method which determines the actual generation hour-by-hour of a generator. Using this method, I also capture the variability of wind generation across the year. To accomplish this goal, I used the Electric Reliability Council of Texas (ERCOT) as a case study. I investigated the effect of different scenarios of generation technology investments projected over a period of twenty years. I conducted two sets of analysis; first assuming that Carbon Capture and Storage (CCS) technologies will be available after 2020, then assuming that they will not. Using a dispatch model, I simulated the hours of a load duration curve for 2020 and 2030. In the first period 2010-2020, I assumed the price of carbon to either be $0 or $50/ton CO2. In the second period, I take the carbon price to be at either a low of $25/ton of CO2 or a high of $100/ton of CO2 . The price of natural gas used was either a high of $15/MMBtu or a low of $3/MMBtu in both periods. Using a Monte Carlo, I sample the wind generation based on the season and the time of day. The system costs with the new investment scenarios were then evaluated in a decision tree to establish the socially optimal solution. I find that the optimal strategy to be taken today depends on the availability of CCS technologies in 2030. Assuming that there is CCS in 2030, the more dominant strategy would be to build natural gas generators today. If we assume that there is no CCS in 2030, the strategy would depend on the probabilities of the levels of gas and carbon prices in 2020.
Author: Merrill Jones Barradale Publisher: ISBN: Category : Languages : en Pages : 206
Book Description
This dissertation examines the influence of attitudes, beliefs, and preferences of energy industry practitioners on investment decision-making with regard to fuel choice for new electric power plants. The conclusions are based on in-depth interviews and an extensive online survey I conducted of 600-800 energy professionals in the U.S. power sector. Chapter 1 analyzes the impact of policy uncertainty on investment decision-making in renewable energy, using the federal production tax credit (PTC) and wind energy investment as an example. It is generally understood that the pattern of repeated expiration and short-term renewal of the PTC causes a boom-bust cycle in wind power plant investment in the U.S. This on-off pattern is detrimental to the wind industry, since ramp-up and ramp-down costs are high, and players are deterred from making long-term investments. The widely held belief that the severe downturn in investment during "off" years implies that wind power is unviable without the PTC turns out to be unsubstantiated: this chapter demonstrates that it is not the absence of the PTC that causes the investment downturn during "off" years, but rather the uncertainty over its return. Specifically, it is the dynamic of power purchase agreement negotiations in the face of PTC renewal uncertainty that drives investment volatility. This suggests that reducing regulatory uncertainty is a crucial component of effective renewable energy policy. The PTC as currently structured is not the only means, existing or potential, for encouraging wind power investment. Using data from my survey, various alternative policy incentives are considered and compared in terms of their perceived reliability for supporting long-term investment. Chapter 2 introduces the concept of expected payment of carbon as a factor in investment decision-making. The notion of carbon risk (the financial risk associated with CO2 emissions under potential climate change policy) is usually incorporated into investment decision-making by including a cost of carbon in the budget analysis. Most existing literature uses the expected price of carbon as a proxy for this cost, where expected price is a weighted average of various scenarios, often comparing policy proposals and representing either the price of traded permits or level of carbon tax, depending on the type of policy. The literature focuses on the minimum price of carbon required to influence power plant investment decisions. In contrast, this chapter introduces expected payment as a more accurate measure of carbon cost as it is perceived by industry practitioners. The expected payment of carbon is the expected price of carbon times the probability that this cost would actually be faced in the case of a particular investment. This concept helps explain both the 2005-2006 surge of activity in coal-fired power plant development and the subsequent decline in that interest. The energy industry has been slow to move away from fossil fuels and towards renewable resources. In chapter 3 I find evidence for a cognitive bias that plays a role in this momentum. Energy executives' expectations of future energy prices are strongly correlated with their own preferences, which I document for the case of natural gas prices. This is an example of wishful expectations, a form of overconfidence in which people are excessively optimistic over uncontrollable future outcomes. This implies energy executives with strong exposure to fossil fuels are excessively optimistic on future prices and so continue to invest despite the presence of superior alternatives.
Author: National Research Council Publisher: National Academies Press ISBN: 0309143748 Category : Science Languages : en Pages : 48
Book Description
The U.S. Capitol Complex in Washington, D.C., comprises some of the most historic and symbolic buildings in the nation. The steam and chilled water required to heat and cool these buildings and related equipment is generated and distributed by the Capitol Power Plant (CPP) district energy system. Portions of the CPP system are now 50 to 100 years old and require renewal so that reliable utility services can be provided to the U.S. Capitol Complex for the foreseeable future. Evaluation of Future Strategic and Energy Efficient Options for the U.S. Capitol Power Plant provides comments on an interim set of publicly available consultant-generated options for the delivery of utility services to the U.S. Capitol Complex. The report provides recommendations to bring the interim options to completion, including suggestions for additional analyses, so that the CPP can be best positioned to meet the future strategic and energy efficiency requirements of the U.S. Capitol Complex.
Author: Yihsu Chen Publisher: Springer Nature ISBN: 3030448665 Category : Business & Economics Languages : en Pages : 278
Book Description
This book covers game-theoretic approaches to analyzing policies for environmental regulation in the power sector. The scope includes operational and investment decisions in imperfectly competitive electricity markets as well as transmission planning and policy design. Given this context, this book synthesizes equilibrium and bi-level modeling to address challenging research questions such as: • How are power-plant operations affected by carbon policy, such as cap-and-trade (C&T) systems? • How does market power in electricity generation affect market outcomes and CO2 emissions? • How does a strategic firm with first-mover advantage manipulate both electricity and C&T permit prices? • How does a strategic firm with first-mover advantage invest in new generation capacity under a C&T system? • How does sustainable transmission planning adapt to an imperfectly competitive power sector? • How should a renewable portfolio standard (RPS) target be revised in an imperfectly competitive power sector? This book includes plenty of illustrative examples to facilitate the concepts’ comprehension. It is intended to make equilibrium and bi-level models adapted for policy assessment accessible to graduate students, academic researchers, industry practitioners, and policy analysts.
Author: Yusheng Xue Publisher: Springer Nature ISBN: 9819900638 Category : Technology & Engineering Languages : en Pages : 884
Book Description
This book includes original, peer-reviewed research papers from the 7th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control(PMF2022), held in Nanjing, China, on August 14-15, 2022. The accepted papers cover the following topics: 1. Advanced power transmission technology2. AC/DC hybrid power grid technology3. Power Internet of Things Technology and Application4. Operation, control and protection of smart grid5. Active distribution network technology6. Power electronic technology and application7. New technology of substation automation8. Energy storage technology and application9. Application of new technologies such as artificial intelligence, blockchain, and big data10. Application of Information and Communication Technology11. Low-carbon energy planning and security12. Low-carbon operation of the power system13. Low-carbon energy comprehensive utilization technology14. Carbon trading and power market15. Carbon emission stream and carbon capture technology16. Energy saving and smart energy technology17. Analysis and evaluation of low-carbon efficiency of power system18. Carbon flow modelling in power system operationThe papers included in this proceeding share the latest research results and practical application examples on the methodologies and algorithms in these areas, which makes the book a valuable reference for researchers, engineers, and university students.
Author: Nidhi Santen Publisher: ISBN: Category : Languages : en Pages : 315
Book Description
Effectively balancing existing technology adoption and new technology development is critical for successfully managing carbon dioxide (CO2) emissions from the fossil-dominated electric power generation sector. The long infrastructure lifetimes of power plant investments mean that deployment decisions made today will influence carbon dioxide emissions long into the future. New technology development and R&D decisions can help reduce the overall costs of reducing emissions, but there are multiple technology investments to choose from, and returns to R&D are inherently uncertain. These features of the technology "deployment versus development" question create unique challenges for decision makers charged with managing cumulative carbon dioxide emissions from the electricity sector. Unfortunately, current quantitative decision support tools ultimately lack one or more of three overarching features jointly necessary to provide useful insights about an optimal balance between R&D program and power plant investments. They lack (1) resolution of the critical structure of the electricity sector, (2) an explicit endogenous representation of the effects of learning-by-searching technological change, and/or (3) an efficient decision-analytic framework to explore multiple technology investment options under uncertainty in the returns to R&D. This dissertation presents a new quantitative decision support framework that allows for the study of socially optimal R&D and capital investment decisions for the power generation sector. Through a novel integration of classical electricity generation investment planning methods, economic modeling of endogenous R&D-driven technological change, and emerging numerical stochastic optimization techniques, the new framework (1) explicitly accounts for the complementary roles that generating technologies play within the electric power system, (2) considers the characteristics of the uncertainty in the technology innovation process, and (3) identifies flexible, adaptive R&D investment strategies for multiple technologies for decision makers to consider. A series of numerical experiments with the new model reveal that (1) the optimal near-term R&D investment strategy under technological change uncertainty and adapting between decisions can be different than the optimal strategy assuming perfect foresight, and may be higher or lower; (2) the timing that a technology should be deployed to meet a specific carbon target dictates the direction and magnitude of the difference in these decisions; (3) increasing the level of uncertainty tends to increase near-term R&D investments; and (4) increasing right-skewness of the uncertainty (i.e., decreasing the likelihood of higher than average returns), reduces R&D spending throughout the planning horizon.
Author: Sebastian Rothe Publisher: Springer ISBN: 3658243791 Category : Business & Economics Languages : en Pages : 292
Book Description
The liberalization process, tightening environmental standards and the need for replacing aged power plants force European utilities to optimize their future generation mix. Power plants are real assets and as a consequence the power plant park of a utility firm equals a portfolio of different generation assets. This thesis adds to the understanding how to identify an efficient generation portfolio through time by assuming a non-constant feasible set. According to our results a combination of conventional thermal and renewable energies turn out to be efficient in terms of expected value and risks. Therefore, implementing a strategy based on renewable energies which cause less CO2 per MWh generated electricity clearly pays off. Potential readership includes scholars from energy economics and energy finance as well as interested practitioners involved in these areas.