Section 2: Fossil Sector: Coal/Petroleum/Natural Gas
Table of Contents:
Fossil Sector: Coal/Petroleum/ Natural Gas
: Carbon Policies for Reducing Emissions in Power Plants through an Optimization Framework
Power generation systems have improved in both efficiency and profitability throughout the years. Nevertheless, addressing the environmental impact has not been a priority. Ол ег 66% of the global electricity production is from fossil fuel combustion (The World Bank, 2014). Consequently, the electricity and heat generation sector is the largest source of carbon dioxide emissions, accounting for about 42% of world emissions from fuel combustion (IEA. 2017). Since the beginning of the Industrial Revolution, the atmospheric CO, concentration has increased by more than a third and continues to do so. Specifically, in November 2017, its global concentration was 405.12 ppm and one year later it had already increased to 408.02 ppm (NOA, 2018). According to the Intergovernmental Panel on Climate Change, the human influence on the crment warming trend is clear and the scientific evidence for warming of the climate system is unequivocal (IPCC, 2013). The rise in carbon dioxide concentration in the atmosphere is a worldwide matter of concern due to its impact on global warming. The heat-trapping nature of carbon dioxide and other gases was already demonstrated in the mid-19th century. As fossil fuels are expected to remain the primary resource for producing electricity in the immediate future, there is a need to develop strategies to tackle this problem. These strategies must include alternatives to reduce emissions and simultaneously satisfy the increasing electricity demand.
From an optimization perspective, several macroscopic systems involving the integration of processes and the evaluation of different configurations have been proposed. These approaches consist of developing mathematical model formulations to find the optimal design of the system. The optimization model can be formulated considering a single objective (typically economic) or multiple objectives (e.g., economic, environmental and social). Through multi-objective optimization, tradeoffs between the objective functions can be found. Thus, the profitability and the other objectives of the system can be analyzed simultaneously by a set of optimal solutions. Particularly for reducing emissions in power plants, integrated systems involving processes to capture and utilize carbon har e been proposed. Furthermore, the combined use of fossil and non-fossil fuels along with novel configurations to satisfy power demands has been widely reported. This type of system is usually analyzed at a macroscopic level since the formulation can account for various possibilities, such as distinct energy resources or technologies, as well as its corresponding inlet and outlet flow rates, revenues and costs. To model this, the formulation can involve mass balances, discrete decisions and both linear and non-linear logical relationships. All of which can result in formulations of thousands of equations as well as continuous and integer variables.
When innovative technologies or other alternatives to reduce emissions are required, there can be high investment costs as well as variable costs involved. Hence, the relevance of carbon policies that aim to move towards these systems. In this regard, certain economic instruments involving penalizations and compensations have been developed by several governments (Vatn, 2015). Those policies have been addressed worldwide, for instance in China (Liu and Lu, 2015), South Africa (Alton et al., 2014) and the United States (Kaufman et al., 2016). The approaches include similar prices for the economic penalties. Moreover, other findings involve negative impacts, such as an increase in the urban-rural gap (Liang and Wei. 2012) and wealth redistribution if the penalization scheme is not well-designed (Chen et al., 2015).
The carbon tax is defined as a penalization for generated emissions given as a monetary cost per ton of carbon dioxide produced (Avi-Yonah and Uhlmann, 2009). An opposite parameter is the carbon tax credit, which consists of considering the avoided emissions and, based on the amount of reduction, some compensation that can be institutional, public, or private is given. The reductions occur after a change in technologies or in the production process (Graefe et al., 2011). These instruments have been approached in economics as the “polluter pays principle” and the "provider gets principle”, which means that those affecting the environmental qualities should pay while those improving them should be compensated (Vatn, 2015). Both carbon monetization strategies have been criticized for some challenges, including the correct setting of the tax rate, collecting the tax and using the resulting revenue (Matron and Toder,
2014). Additional issues causing uncertainty are that the tax credit has an arbitrary economic value (Hoel, 1996) and that the tax is standardized without accounting for economic sectors, industrial development and regional conditions (Newell et al., 2013). Whether the final decision is investing to reduce emissions or paying the economic penalty, there are associated costs (Clarkson et al.. 2015). This is the importance of analyzing the effect of carbon penalizations and compensations.
Carbon Policies through Optimization Approaches
Recently, several optimization studies have addressed different energy systems through multi-objective models and monetization of carbon externalities. In this regard, Sauchez-Bautista et al. (2017) reported the tradeoffs among economic, environmental and social objectives for an integrated energy system including fuel and biofuel production along with carbon capture through forest plantations. Other mathematical models have addressed the impact of carbon policies on promoting the generation of clean energy (Wong et al.. 2010) and on fostering investment for carbon capture in coal-fired power plants (Guillen-Gosalbez et al., 2012). Similarly, comparative scenarios with penalizations or compensations har e been evaluated in the design of combined heat and power systems involving biogas use by means of a mathematical programming model, which gives compromise solutions (Fuentes-Cortes et al., 2017). Moreover, Pascual-Gonzalez et al. (2016) proposed a decision-support tool including a multi-objective model, environmentally extended input-output tables and life cycle assessment, with the objective of minimizing emissions at a global macroeconomic scale through modifications in the economic sectors. Galau-Martin et al. (2018) proposed an optimization approach based on the study of the effect of interregional cooperation in meeting the emissions targets with cost-effective solutions. A mixed- integer linear programming (MILP) model has been developed by Ren and Gao (2010) for the integrated planning and evaluation of distributed energy resources, where different effects (economic, energetic and environmental) as well as economic policies (carbon tax and energy prices) are considered.
An analysis of carbon policies has been proposed for a macroscopic system that integrates power plants involving chemical looping combustion systems with algae cultivation to utilize CO,. This optimization work includes the formulation of a general MILP model and the obtained results show important economic benefits as well as reductions in emissions, specifically with the economic compensations (Munguia- Lopez et al., 2018). Furthermore, the impact of economic penalties and compensations on a system of water distribution networks involving power-desalination plants has been reported. The study includes the use of renewable fuels to reduce emissions, while through an optimization framework economic, environmental and social benefits are obtained. The results refer to a case study related to a water and energy management problem in Sonora, Mexico (Munguia-Lopez et al., 2019). Both approaches will be further described in the next sections.
The carbon penalizations and compensations evaluated in the aforementioned studies have been previously reported and are the following. For the carbon taxes, predicted values for monetizing the externalities in the USA in the future are considered (Figure 1) as well as the prices of 10 and 15 $/ton CO, that are expected to be included in future Mexican regulations (SEMARNAT and INECC, 2012). The perspective of carbon prices in the USA is based on assuming a policy that applies a fee on carbon dioxide emissions starting at 25 S/ton in 2015 and rising by 5% per year (Kaufman et al., 2016). On the other hand, the values for the carbon tax credits vary fr om 0.3 to 130 S/ton CO, avoided. These extreme prices are evaluated along with intermediate compensations including 1,4, 7, 10, 80 and, 120 S/ton CO,, which represent the variation of the carbon price for world emissions. The average carbon price for tax credits has changed in the last years from 4 to 7 S/ton CO, and most of the world emissions are priced at 10 $/ton CO,. Furthermore, economic models have estimated that the prices 80 and 120 S/ton CO, are needed to meet climate stabilization goals (Kossoy et al., 2015). Note that the considered taxes and tax credits have been already used or predicted to be used in the future. However, the impact of carbon policies that change over tune has not been evaluated. As future work, an approach including the effect of variable penalizations and compensations over a time horizon would be useful to identify optimal policies and recommend carbon prices for the next years.
Besides carbon taxes and tax credits, alternative policies for reducing emissions in the electricity sector have been proposed and actively implemented, such as Cap and Trade and Renewables Portfolio Standards. Cap and Trade is a market-based policy tool that establishes an emission cap (the maximum quantity of authorized emissions) and allowances to emit a specific quantify (e.g., 1 ton) of a pollutant. The total number of allowances equals the level of the cap. Allowances can be bought or sold (traded) in an allowance market (Schreifels and Kruger, 2003). There are some differences between the Cap and Trade program and the taxation approach. For instance, the first one would establish a price on emissions indirectly by limiting total emissions and issuing tradable emissions allowances, while a carbon tax would directly establish a price on emissions (Kaufman et al., 2016). On the other hand. Renewables Portfolio Standards (RPS) are regulations that require electricity supply companies to obtain specific amounts of renewable energy generation (such as wind, solar, biomass and geothermal) over tune. It has been suggested that these policies have played an important role in driving U.S. renewable electricity growth (Barbose et al., 2015).
Figure 1. Tlie perspective of carbon prices in the USA for different years.