Handbook of CO₂ in Power Systems

In this paper, we first provide an introduction of the electricity market and its major players. Then, equilibrium models of the CO₂ emission permits market are discussed. We consider a variational inequality (VI) approach to model an oligopolistic competition in the market of CO₂ emission permits and electric power. A Cournot model of electricity market is described and a variational inequality framework is developed for oligopolistic electricity models with marketable CO₂ permits. Properties of the model are provided and some solution methods are discussed. A simple example is presented and implemented in a PC and solved by a built-in solver powered by Newton’s method of VI. Discussions of possible extensions of the model are given, like using conjectured supply function approach and including non-electric power participants of CO₂ trading.

Electricity production accounts for around 40% of global energy-related CO₂ emissions and it is expected that the electricity demand increases to twice the current level in 2050. Therefore it is necessary to invest in low-carbon thermal power plants, nuclear and renewable energy for realizing low-carbon economy. These policies may require a large amount of investment costs, and additionally, the uncertainty increases in a situation surrounding power generation projects and their investments. On the other hand, environmental policy for encouraging use of low carbon emission generation power includes an internalization of the externality for CO₂ emissions such as carbon-emissions tax. In this study, we develop a real option model of power generation investments allowing for two uncertainties of the market risk and the introduction of the policy. We analyze the effect of the uncertainties on the power generation mix and the investment timing.

Models formulated as complementarity problems have been applied previously to assess the potential for market power and costs of environmental regulation in transmission-constrained electricity markets. One emerging use of these models is to study the impacts of cap-and-trade (C&T) policies on electricity markets. In this chapter, we first summarize the theoretical background on the choice of environmental instruments to regulate emissions from the power sector. The chapter then presents a mathematical formulation of a power market that incorporates a carbon dioxide C&T program. We illustrate the capability of the model by presenting the results from two analyses. The first analysis examines the impact of the European Union Emissions Trading Scheme (EU ETS) on the northwestern European electricity market. The second study investigates the energy and emissions implications of Maryland’s decision to join the Regional Greenhouse Gas Initiative (RGGI) by nesting a regional power sector model within a national model. In both cases, the larger firms in the electric market are modeled as Cournot oligopolists who pursue a quantity strategy while at the same time they act competitively in the C&T programs and transmission markets. We demonstrate how complementarity-based power market models can be easily modified to incorporate details of alternative policy designs.

Cap-and-trade and carbon taxes are two types of environmental policies that differ in their goals and approaches but both have the potential to reduce greenhouse gas emissions and promote renewable energy. We compare the effectiveness and efficiency of these policies in the context of generation capacity expansion. Game theoretic models are used to integrate the environmental policies in a generation expansion planning framework. The most efficient tax policies and variations are obtained using the inverse equilibrium models. We compare cap-and-trade and carbon taxes with respect to five criteria: carbon price and tax, renewable energy portfolio, total energy generation, generation companies’ and grid owner’s total profit, and government revenue. Numerical experiments show the relative advantages, disadvantages, similarities, differences, and limitations of the policies.

An agent-based approach is proposed in this book chapter to analyze interactions between the emission and electricity markets. A cap-and-trade system is assumed to be in place to regulate emissions from power generation. Generation companies are modeled as adaptive learning agents that can bid strategically into the electricity market by a Q-learning algorithm. These companies also participate in allowances trading in the emission market by adjusting their own allowances positions. In the simulation, generation companies can value their generation capacity and available allowances to maximize their profits. Three different trading strategies are modeled to mimic the decision-making process of generation companies. This modeling framework can help design a sound emission market by simulating market scenarios with different policies. It can also be used to investigate the operation strategies for generation companies in such an environment.

Climate change is one of the most important challenges of this century. Human activity, particularly that of the electric power sector, has played a dramatic role in exacerbating this scenario. This paper presents a brief survey of some of the policies that are being adopted around the world to tackle the challenge of emissions reductions, with specific focus on the power sector. We discuss some widely used policies such as renewable portfolio standards, feed-in tariffs, carbon taxes, and cap-and-trade, with more emphasis on the latter. We conclude the chapter with a detailed discussion of some of the simulation and mathematical programming models that have been developed to examine economic and environmental impacts of emissions control policies on electricity markets.

In this article, we explore the realm of carbon offset markets that have been set up to enable consumers to offset their share of carbon emissions. Though the market is a relatively new one, it has quickly spawned countless offset providers under both regulated and voluntary schemes. Our research points out that the market is widely unregulated and, furthermore, there is no common quality or certification structure for the offset providers. At this point in the evolution of the market, only a handful of offset provider ratings schemes exist; and even these schemes leave a vast void for consumers.

The article begins with a discussion on the state of the carbon markets including the mandate from the Kyoto Protocol. Next, the article will examine the concept of carbon offsets and provide examples of the market. Our purpose is to present the logic and the working of carbon markets, especially in the space of market instruments aimed at lowering of carbon emissions. Moreover, we ask how this market developed and where is it headed. The article will outline the standards environment for offset providers in order to illustrate the need for a single set of criteria among providers. It will then explore the differences among the providers and articulate the specific criteria upon which providers may be evaluated. Finally, we share the results of the data collection and highlight early findings. These finding allow us to compare providers effectively and efficiently on a common scale that services both providers as well as consumer stakeholders.

Jurisdictions across the globe are implementing CO₂ emissions reduction policies. These policies typically ignore most locational issues, probably because the consequences of greenhouse gas emissions do not depend on the exact emission location. However, the response to emission policies and the costs and effectiveness of emissions reduction in power systems are time-varying and locational in nature. The first part of the paper elaborates on the economic properties of the concept of locational marginal carbon intensity and formulates an allocation of the carbon footprint of the electrical grid to individual generating units, transmission facilities and end users on a real time basis. In the second part, the theory of the marginal carbon footprint is applied to the derivation of the optimal investment policy underlying Renewable Portfolio Standards (RPS). The argument is made that the existing RPS policies are at best sub-optimal in their goal to reduce emissions of Carbon Dioxide and other greenhouse gases. A proposed optimal investment rule could serve to improve the efficiency of RPS policies.

In the energy sector, the new millennium is bringing unprecedented challenges related to global warming, which are representing a major driver for change in energy system planning, design and operation. In this context, and on the basis of the state-of-the-art literature in the field, the aim of this chapter is to illustrate the fundamental aspects relevant to energy system operation in the presence of CO₂ emission-related issues and constraints, with an indicative time horizon from 1 day to 1 week. Within this time frame, the role of CO₂ emissions in optimal operational strategies to exploit the energy system equipment is opportunely identified, illustrated and modelled. In particular, this chapter discusses a number of optimisation problem formulations set up in different frameworks, with the aim of highlighting their characteristics and at the same time synthesising the main relevant points of the studies.

In this work the impact of greenhouse gas (GHG) emission reduction on Power Generation Expansion Planning (PGEP) is investigated. An overview of several PGEP models, which also consider environmental constraints and GHG emission limits, is presented. After a short introduction on regulations about GHG emission Cap and Trade System in Europe and in the United States and a survey on state of the art PGEP, a new approach to assess the effects of fuel and electric energy price volatility on long term generation planning by a GENCO (GENeration COmpany) is proposed. The objective function, to be maximized, consists of the total revenue obtained by the GENCO over a certain time horizon into the future. A general model is developed to find both present and future generation mixes of a given GENCO and the Lagrangian Relaxation method is used to solve the large scale mixed integer problem. However, its results will be not really suitable to define a generation planning strategy, unless the uncertainties of costs, prices and construction times are considered. For this reason, a Monte Carlo simulation procedure is implemented including an expansion planning computation at each step. In the model renewable resources, like off-shore and on-shore wind, biomass, mini hydro, geothermal, solar thermodynamic and photovoltaic power plants, are also taken into account. The results are presented with reference to an hypothetical GENCO, by setting all the scenario variables on the basis of available historical data.

The paper focuses on the market dynamics of the EU Emissions Trading Scheme (EU ETS), the cap-and-trade system implemented to reduce CO₂ emissions from electricity and heat plus some industrial sectors in the EU. An overview of carbon market models is presented and an analysis based on a particular model is used to illustrate some of the main price drivers in the market. The model results indicate that it is crucial to capture short and long term fuel switching in electricity generation and electricity demand response in order to forecast EU Allowance (EUA) prices. In addition, the impact of other policy measures is significant, e.g., support to renewable energy and compensation for industries at risk of carbon leakage. The applied model, together with the available data and market characteristics, imply that the dynamics of emission reductions from the heating sector is poorly understood, including the combined impact of the ETS and other policy measures such as the renewable energy target.

In this chapter I provide an overview of the theoretical and applied literature dealing with mean-variance portfolio analysis used to study the efficiency of portfolios of power generation assets. The relevant literature focuses on the risk-mitigating benefits of technological diversification vis-a-vis single-technology analysis with conventional levelized cost analysis, to varying degrees taking into account real-world constraints. Part of the cutting-edge research deals with the benefits that accrue from intra-technology diversification and geographical dispersion. Some studies also take into account country-specific differences in national regulatory framework conditions and local resource potentials (esp. in the case of wind power). Other research has focused on technical and system-related aspects, such as load dispatch and portfolio restrictions, e.g., arising from grid constraints and the intermittent nature of many renewable energy sources. Complementary approaches to mean-variance portfolio analysis, such as real options analysis and fuzzy modeling, as well as alternative measures of risk (e.g. Value at Risk – VaR, Conditional Value at Risk – CVaR, and (semi-) mean absolute deviation), are briefly discussed as well, thus acknowledging some of the most important recent developments in this research area that have not been reviewed elsewhere yet.

In recent years there has been a move in the majority of industrialized countries to invest in renewable resources for the production of energy. This move has come about as people worldwide are more aware of negative effects of fossil fuel sources of energy on the environment, including the release of green house gases such as CO₂. Utilization of renewable sources of energy, for instance, harnessing wind power in electricity production, is deemed to be reducing the use of fossil fuels and hence results in the reduction of CO₂. Mechanisms that promote and facilitate utilization of renewable sources of energy are being developed. In particular, recently stochastic programming market clearing mechanisms have been suggested that would seemingly allow for a more efficient use of wind energy hence reduction of fossil fuel use, that ultimately would result in a reduction of CO₂. In this paper we will examine the steady state behaviour of participants in an electricity market to fully analyze the hypothesis that the stochastic programming market clearing mechanism is less fossil fuel (and hence CO₂) intensive than a conventional two settlement market through some simple examples.

Short term power system operation has traditionally been planned using optimization models that represent a cost minimization problem referred to as the unit commitment problem. Computational advances observed from the 1970s have made possible to solve increasingly complex and large scale formulations of this model, which usually are mixed-integer problems. More recently, the restructuring of many electricity markets in several countries has introduced competition among generators, and increased the interest in incorporating uncertainty into the unit commitment problem. Typical sources of uncertainty can be future demand, fuel prices, and unit availability. However, under competition, electricity spot price has also become an important uncertain variable, and has forced electric power generation companies to focus on operation strategies that may lead to the maximization of benefits. Short-term operation planning is not exempted from this shift and this has lead to the self-scheduling problem, where individual companies want to determine the unit commitment that would maximize their benefits. In this article, we review recent advances for the stochastic unit commitment and self-scheduling problems. After reviewing several recent modeling approaches we consider the relevance of environmental considerations, particularly CO₂ emissions, and review relevant propositions for modeling and incorporating emission constraints into the unit commitment problem.

It is generally accepted that CO₂ capture and storage technologies (CCS) will play an essential role in the reduction of greenhouse gases emission in a medium-large term. Despite the research efforts devoted to the development of more efficient capture processes, two of the main challenges of CCS are the efficiency penalty caused by the CO₂ separation, compression and conditioning, and the economic cost. Consequently, the minimizations of the energy requirements and/or the CO₂ avoided cost are the research priorities for the future implementation of CCS technology.

The objective of this chapter is to describe some examples of minimizing the CO₂ avoided cost in several applications of CCS. The first example illustrates a preliminary analysis for the selection of the appropriate option to overcome the energy requirement for regeneration in an amine scrubbing CCS application. The second case presents a problem for minimizing CCS cost depending on several operational variables in an emerging and promising option for CO₂ capture. The last example shows a formal optimization problem with a different objective function, minimizing the cost penalties associated to CO₂ compression. It is concluded that optimization will provided essential information to select the adequate process layout and the proper operational variables supported by the concepts of the Second Law Analysis of Thermodynamics.

This paper explores the fundamental concepts required to model intertemporal carbon capture costs. A technical overview of post-combustion, pre-combustion, and alternative combustion carbon capture technologies is followed by a discussion of cost measures that allow for side-by-side comparison of one technology to the next. Carbon capture cost measures include capital cost per unit output power, levelized electricity cost (LEC) and CO₂ avoidance cost. The relationships between these cost measures are mathematically explored and cost data for several carbon capture technologies obtained from a survey of the literature are provided. We then discuss the most significant drivers of intertemporal carbon capture cost reduction, which include learning by doing (LBD), production returns to scale and research and development (R&D).

This chapter presents a new advancement in energy minimization and CO₂ mitigation research. It describes the development of a combined technique to simultaneously synthesize energy recovery network and offers fuel switching options to satisfy CO₂ emission reduction target using graphical and mathematical programming approaches. The approach is illustrated using petroleum refinery and palm oil refining process as the case study. The application of this technique yields significant CO₂ emission reduction with short payback period with or without clean development mechanism (CDM).