Individual Power Plants Research Articles

Changes in Farm Supply Voltage Caused by Switching Operations at a Wind Turbine

Renewable electricity sources are now widely used worldwide. Currently, the most common sources are those that use energy contained in biomass, water, sun, and wind. When connected to a medium-voltage grid, individual wind power plants must meet specific conditions to maintain electricity quality. This article presents field study results on the impact of switching operations (turning the power plant on and off) at a 2 MW Vestas V90 wind turbine on the voltage parameters at the connection point of a farm located 450 m from the source. The analysis showed that the wind turbine under study significantly affects customers’ voltage near the source, causing it to increase by approximately 2.5%. Sudden cessation of generation during the afternoon peak causes a 3% voltage fluctuation, potentially affecting equipment sensitive to rapid voltage changes.

A worldwide analysis of stranded fossil fuel assets’ impact on power plants’ CO2 emissions

Will power plants emit less or more CO2 in anticipation of stronger climate policies that would strand fossil fuel reserves? Here, using a worldwide data source on individual power plants’ CO2 emissions and the value of countries’ at-risk fossil fuel assets, we show that between 2009 and 2018, plants emitted more CO2 in countries where more assets would be devalued under a 1.5 °C scenario, which we theorize is due to these countries’ regulatory leniency and plants’ vested interest in long-term fossil fuel contracts. Although the extra amount of carbon emitted each year trigged by imperiled assets is relatively small, it would exhaust a sizable portion of the electricity sector’s remaining carbon budget when added up over time. This is especially true in the U.S. and Russia where up to 16% and 12% of their budgets, respectively, could be spent within ten years due solely to the stranded asset effect.

Quantifying instantaneous nitrogen oxides emissions from power plants based on space observations

Thermal power plants are significant contributors to nitrogen oxides (NOx), impacting global atmospheric conditions and human health. Satellite observations, known for their continuity and global coverage, have become an effective means of quantifying power plant emissions. Previous studies, often accumulating long temporal data into integrated plumes, resulted in substantial errors in annual emissions at the individual power plant level due to neglecting variations in emissions and diffusion conditions. This study presents, for the first time, the quantification of instantaneous NOx emissions based on single overpass observations from the Tropospheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite. By addressing the temporal variability of power plant emissions, it effectively reduces annual estimation errors. Comparative analysis between the Exponentially-Modified Gaussian (EMG) and Gaussian Plume Model (GPM) simulations demonstrates the capability of EMG to provide instantaneous emission estimates based on actual plumes, exhibiting closer proximity to actual monitoring values than GPM. Applying the EMG method, we quantify the instantaneous emission rates of six power plants in the United States. Comparing annual emission estimations at individual power plants with traditional integrated plume results, our method demonstrates a 63.7 % improvement in annual emission estimations. This study offers more detailed data on power plant emissions, providing a new avenue for better understanding the emission behavior of thermal power plants.

Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches

Top-down constraints of CO2 emissions from coal-fired power plants are critical to improving the accuracy of CO2 emission inventory and designing carbon reduction strategies. Different top-down models based on satellite observation have been proposed in previous studies, but discrepancies between these models and the underlying drivers are rarely explored, limiting the confidence of their application for monitoring point-source CO2 emissions from satellite. Here, we apply three top-down models to estimate CO2 emissions from individual coal-fired power plants in the United States (US) and China in 2014–2021 from Orbiting Carbon Observatory 2 (OCO-2) satellite observations. The first one applies the Gaussian plume model to optimize emissions by fitting modeled CO2 enhancement to the observation. The second and third methods apply the same inversion framework using the maximum likelihood estimation, but with WRF-Chem and WRF-FLEXPART as forward models, respectively. We evaluate consistency between the three methods in estimating emissions of 10 power plants in the US, using daily reported values from the US Environmental Protection Agency (EPA) as a benchmark, and then apply the three methods to quantify emissions of 13 power plants in China. Results show that the WRF-Chem and WRF-FLEXPART based inversion results are consistent with the EPA reported emission rates, with correlation coefficients (r) of 0.76 and 0.85 and mean biases (MB) of 4.06 and 3.97 ktCO2/d relative to EPA reports at all 10 power plants, respectively. The Gaussian plume model driven by wind fields from WRF-Chem model without the wind rotation shows comparable ability in reproducing the EPA reported emission rates at 7 power plants (r = 0.82, MB = 6.17), but is not applicable for the other three cases. We find that application of the high-resolution three-dimensional wind fields can better capture the shape of observed plumes, especially under complex wind conditions, compared to the Gaussian plume model which relies on wind field at a single point, and thus the Gaussian plume model has difficulty to optimize emissions under inhomogeneous wind fields or when observations are far away from the power plant. In general, using the WRF-FLEXPART model as the forward model in the inverse analysis shows advanced capability to simulate narrow-shape plumes in the absence of numerical diffusion which is inherent in Eulerian model such as WRF-Chem. Emissions estimated by the three top-town methods show a moderate consistency at 13 coal-fired power plant cases in China, with 8 of 13 cases showing differences of <30% between at least two methods. However, large differences emerge when wind fields are inhomogeneous and number of available observations is limited. Using different meteorological wind fields and OCO-2 data versions can also bring substantial differences to the posterior emissions for all three approaches. We find that the posterior CO2 emissions, though only reflecting instantaneous emission rates at satellite overpass time, are not proportional to the reported capacities of these power plants, indicating that attributing CO2 emissions simply based on the capacity of power plants in some bottom-up approaches may have significant discrepancies. Our study exposes the capability and limitation of different top-down approaches in quantifying point-source CO2 emissions, advancing their application for better serving increasing constellations of point-source imagers in the future.

Probabilistic forecasting of regional solar power incorporating weather pattern diversity

Power grid stability depends on the ability to forecast solar power generation at the regional level. Most previous research on probabilistic forecasting has focused on the use of machine learning to predict the output of individual solar power plants rather than regional solar power generation, and few studies have considered the effects of seasonal weather patterns at the regional level. In this study, climate and geographic data were collected from 83 weather stations between 2019 and 2021 for use in developing a probabilistic model by which to predict regional solar power generation. The results of weather pattern analysis based on unsupervised machine learning and ensemble voting were used to build a probabilistic quantile regression model for the short-term prediction of regional solar power generation capacity. The efficacy of the model was assessed using data from 48 solar PV power plants, which included four sub-datasets pertaining to four target regions. Highly accurate results were consistently obtained across all regions in both the winter and summer seasons. The proposed probabilistic model outperformed conventional deterministic model by 6.55% and conventional probabilistic model by 4.03% in terms of total normalized mean absolute error (NMAE). Prediction intervals generated by the proposed model could be used as input parameters for regional power dispatch decisions.

Can transnational municipal networks mitigate the carbon pollution of the world’s power plants?: an empirical analysis

Transnational municipal networks (TMNs) have been heralded as actors that can avert a climate catastrophe by filling the “emission gaps” left by national climate policies. But can these networks reduce the carbon pollution of power plants, the world’s largest sites of climate-disrupting emissions? Using an international data source on individual power plants, we empirically analyze this issue. Findings reveal that after accounting for their structural properties and the national policies to which they are subject, power plants emit less CO2 when nested in cities that are members of TMNs and this is especially true of plants in less developed countries. In contrast, national climate policies are unrelated to plants’ environmental performance over time. Although our analyses suggest TMNs help to reduce the emissions of the typical power plant, they also indicate they have little bearing on the emissions of the world’s most egregious polluting plants.

Bi-decadal trend of atmospheric emissions from thermal power plants in Mainland Southeast Asia: Implications on acid deposition and climate change Mitigation

Steady increase in electricity generation and heavy reliance on coal in Mainland Southeast Asia (M-SEA) create huge pressure on the environment. This study used information collected from individual thermal power plants (TPPs) in M-SEA to calculate emissions of air pollutants and greenhouse gases (GHG) for 2010, 2015 and 2019. The emissions were projected to 2030 following the latest national Power Development Plans. The emission results were analyzed in relation to the power development by country and fuel type, and environmental impacts. The region collective annual TPP emissions in 2019, in Gg/yr, were 27 PM2.5, 77 PM10, 0.7 BC, 4.9 OC, 255 SO2, 451 NOx, 91 CO, 12 NMVOC, 0.4 NH3, 260 CO2, 13 CH4, and 26 N2O. Coal-fired TPPs dominated the emissions of most species while NG-fired contributed the largest amounts of NH3 and CH4. Bi-decadal increase in energy production from TPPs of nearly 3 times is accompanied by 2.7 times increase in emissions. The 2010–2019 period saw average emissions increase by 1.9 times (TPPs' energy production increased 1.6 times), slightly higher than the rate of 1.4 times projected for 2019–2030 (double TPPs’ energy production). The current intrusion rate of renewable energy accompanied by phasing-out of old TPPs are still by far insufficient to reverse the emission trend. Aggressive power development in Vietnam with its heavy coal reliance made it the largest emitter in 2019 and the projected for 2030, followed by Thailand. Spatially, higher emissions are seen over locations of large coal-fired TPPs in Vietnam and Thailand. Available rainwater composition monitoring data showed higher deposition amounts of sulfate and nitrate in areas located near or downwind of large TPPs. Significant GHG emissions projected for TPPs in 2030 indicated that TPPs should be the priority for emission reduction to achieve Nationally Determined Contribution targets. Emission database produced by this study can be used in dispersion modeling studies to assess impacts of TPPs on air quality, health, and acid deposition.

КОНЦЕПЦІЯ ПОБУДОВИ СТРУКТУРНО МІНЛИВОЇ ЕЛЕКТРОЕНЕРГЕТИЧНОЇ СИСТЕМИ УКРАЇНИ

To support the sustainable functioning and development of the energy sector in the conditions of terrorist and military threats, the concept of building a structurally variable electric power system (EPS) is proposed. Structural variability is defined as the ability of the EPS to reproduce such a variety of subsystems and electrical connections between them, which enables the operator to manage the structure of the EPS and, in this way, ensure the stable operation of the electric power industry in conditions of purposeful destructive actions. The concept of a structurally changing EPS is a strategy for anticipating challenges and threats to the sustainable functioning and development of the electric power industry. In order to build a structurally variable EPS, it is proposed to create regional EPSs with their own capacities for production, storage, distribution and supply of electricity in volumes sufficient for consumption by the population, housing and communal services, transport and agriculture within each region. The establishment of the territorial dimensions of individual subsystems, i.e. regional EPS, is determined on the basis of a compromise between the necessary number of such subsystems and connections between them, which ensure the desired degree of variability of the EPS, and the capitalization levels of the regions sufficient to support investment and operating costs for the corresponding regional EPS. It is assumed that the national EPS supports the regional EPS with agreed amounts of maneuvering and reserve capacities and ensures the supply of electricity to enterprises of industrial, construction, transport and other types of economic activity that are of national importance and do not join the regional EPS. In cases of destruction of individual regional power plants, the national power plant, as well as undamaged neighboring regional power plants, together provide the electricity needs of the affected regions. In the structurally variable EPS, uniform rules of behavior of all energy companies as market participants interacting at the national and regional levels should apply. For the organization of such interaction, it is proposed to apply the decomposition of trading platforms and form a distributed electricity market from interconnected upper-level electricity market and regional electricity markets. References 12, figures 6.

German atlas of Thermal Storage Power Plants (TSPP) - A first approach

The paper assesses the potential of transforming conventional power plants to Thermal Storage Power Plants (TSPP) in Germany on a macro scale. The resulting atlas is based on a scenario of the power sector published before that assumes that current conventional power plants will be transformed to TSPP with a capacity of 70 GW in 2040, providing reliable power supply for the residual load. In order to achieve this, several criteria are introduced that rank all power plants in Germany in terms of suitability for such conversion. Moreover, a method of finding the necessary land areas for PV and biomass required as primary energy sources for the TSPP is described with the help of a geographic information system. The analysis is divided into 2 parts: The first part is to consider the whole country as a single entity, and all power plants are analyzed according to their ranking list in order to find out if the scenario is consistent with existing PV and biomass potentials. The second part focuses on individual power plants without considering the ranking list, in order to find the best potential PV and biomass supply areas for each potential power plant that could be transformed on a first-come-first-save basis. The findings of this study include mapping of the transformation potential of individual power plants, and also provides an integrated framework for a TSPP transition at the national level. By providing a comprehensive overview of the TSPP-atlas across the country, these maps may serve as a helpful tool for policymakers and power plant owners in making decisions regarding the deployment of TSPP.

Tracking Carbon Flows from Coal Mines to Electricity Users in China Using an Ensemble Model.

Accurately tracking carbon flows is crucial for preventing carbon leakage and allocating responsibility for reducing CO2eq emissions. In this study, we developed an ensemble model to effectively track carbon flows within China's power system. Our approach integrates coal quality tests, individual power plant datasets, a dynamic material-energy flow analysis model, and an extended version of an interconnected power grid model that incorporates transmission and distribution (T&D) losses. Our results not only provide accurate quantification of unit-based CO2eq emissions based on coal quality data but also enable the assessment of emissions attributed to T&D losses and emission shifts resulting from interprovincial coal and electricity trade. Remarkably, for CO2eq emissions from coal-fired units, the disparity between the guideline and our study can be as high as [-95%, 287%]. We identify Guangdong, Hebei, Jiangsu, and Zhejiang provinces as the major importers of both coal and electricity, responsible for transferring nearly half of their user-based emissions to coal and power bases. Significantly, T&D losses, often overlooked, contribute to 15-20% of provincial emissions at the user side. Our findings emphasize the necessity of up-to-date life cycle emissions and spatial carbon shifts in effectively allocating emission reduction responsibilities from the national level to provinces.

A regional solar forecasting approach using generative adversarial networks with solar irradiance maps

The intermittent and stochastic nature of solar resource hinders the integration of solar energy into modern power system. Solar forecasting has become an important tool for better photovoltaic (PV) power integration, effective market design, and reliable grid operation. Nevertheless, most existing solar forecasting methods are dedicated to improving forecasting accuracy at site-level (e.g. for individual PV power plants) regardless of the impacts caused by the accumulated penetration of distributed PV systems. To tackle with this issue, this article proposes a novel generative approach for regional solar forecasting considering an entire geographical region of a flexible spatial scale. Specifically, we create solar irradiance maps (SIMs) for solar forecasting for the first time by using spatial Kriging interpolation with satellite-derived solar irradiance data. The sequential SIMs provide a comprehensive view of how solar intensity varies over time and are further used as the inputs for a multi-scale generative adversarial network (GAN) to predict the next-step SIMs. The generated SIM frames can be further transformed into PV power output through a irradiance-to-power model. A case study is conducted in a 24 × 24 km area of Brisbane to validate the proposed method by predicting of both solar irradiance and the output of behind-the-meter (BTM) PV systems at unobserved locations. The approach demonstrates comparable accuracy in terms of solar irradiance forecasting and better predictions in PV power generation compared to the conventional forecasting models with a highest average forecasting skill of 10.93±2.35% for all BTM PV systems. Thus, it can be potentially used to assist solar energy assessment and power system control in a highly-penetrated region.

The impact of greenhouse gases on climate change

Climate change has become a threatening problem for all countries of the world. For Kazakhstan, one of the world’s largest grain exporters, with its previously predominantly arid climate, developed mining industry, metallurgy and mainly coalfired heat and electricity generation, slowing down warming is becoming an urgent task. The purpose of this study is to summarise various aspects of the impact of greenhouse gas emissions on climate change in the Republic of Kazakhstan and the possibilities of reducing greenhouse gas emissions through Kazakhstan’s transition to a new economic model based on the use of renewable energy sources. During the study, various methods of analysis were used – analytical selection based on a given or identified feature, comparative analysis of similar characteristics of various objects, statistical analysis of dynamic series, synthesis, representing the primary theoretical generalisation of empirical data. As a result of the study, a noticeable dependence of changes in average annual temperature and precipitation on total greenhouse gas emissions was identified. Moreover, the identified similarity of the dynamics of the main climatic indicators with the dynamics of individual greenhouse gases in Kazakhstan requires further research. It is also concluded that individual innovations in the form of individual renewable energy power plants or a Quota trading system determined by the highest values will not lead to a noticeable reduction in greenhouse gas emissions. Only the transition to a green economy, which should become a state priority, and its principles and main characteristics should be the reference when making any government decisions. This study can become a starting point for many theoretical and practical studies and show the need to reduce greenhouse gas emissions not as a purely mechanical process, but in conjunction with other phenomena, which can be useful in making managerial decisions

Low-carbon transition model for power generation companies in China: A case study

The power sector is the largest emitter of carbon dioxide in China, and the coal-based energy structure presents an additional challenge to its decarbonization. Several studies have been conducted to find cost-optimal pathways for the low-carbon transition of the power sector or individual power plants, but few studies have taken power generation companies as a research topic. In this paper we addressed the issue by building an optimization model that can characterize power generation companies, and selected a big company as a case to study how economic and technological factors drive the company to adjust its plant allocation and affect its profitability. We find that the transformation trajectory of the company is similar to that of the power sector as a whole. The share of coal power plants will remain stable at 53% during this decade and then decline rapidly after 2030. At the same time, the proportion of renewables will rise rapidly from 35% in 2020 to 90% by 2060. The total cost of achieving the carbon neutrality target is approximately RMB 4.6 trillion during 40 years, and the annual cost is approximately RMB 114.9 billion, which is equivalent to about 40% of its total revenue in 2021. Analysis of different scenarios suggests that achieving carbon neutrality 5 to 10 years ahead of time is financially and technologically feasible. Energy storage will play a significant role whereas CCS will have little impact on this company.

Inequitable Exposures to U.S. Coal Power Plant-Related : 22 Years and Counting.

Emissions from coal power plants have decreased over recent decades due to regulations and economics affecting costs of providing electricity generated by coal vis-à-vis its alternatives. These changes have improved regional air quality, but questions remain about whether benefits have accrued equitably across population groups. We aimed to quantify nationwide long-term changes in exposure to particulate matter (PM) with an aerodynamic diameter () associated with coal power plant emissions. We linked exposure reductions with three specific actions taken at individual power plants: scrubber installations, reduced operations, and retirements. We assessed how emissions changes in different locations have influenced exposure inequities, extending previous source-specific environmental justice analyses by accounting for location-specific differences in racial/ethnic population distributions. We developed a data set of annual source impacts ("coal ") associated with emissions at each of 1,237 U.S. coal-fired power plants across 1999-2020. We linked population-weighted exposure with information about each coal unit's operational and emissions-control status. We calculate changes in both relative and absolute exposure differences across demographic groups. Nationwide population-weighted coal declined from in 1999 to in 2020. Between 2007 and 2010, most of the exposure reduction is attributable to scrubber installations, and after 2010 most of the decrease is attributable to retirements. Black populations in the South and North Central United States and Native American populations in the western United States were inequitably exposed early in the study period. Although inequities decreased with falling emissions, facilities in states across the North Central United States continue to inequitably expose Black populations, and Native populations are inequitably exposed to emissions from facilities in the West. We show that air quality controls, operational adjustments, and retirements since 1999 led to reduced exposure to coal power plant related . Reduced exposure improved equity overall, but some populations continue to be inequitably exposed to associated with facilities in the North Central and western United States. https://doi.org/10.1289/EHP11605.

INFORMATION-MEASURING SYSTEM FOR CONTROL AND ACCOUNTING OF ELECTRIC ENERGY

The issues of building an information-measuring system for control and accounting of electric energy based on electronic meters are considered. In the process of supplying consumers with energy, losses of electricity inevitably occur. Voltage fluctuations, the coefficient of non-sinusoidality, the coefficient of asymmetry do not sufficiently characterize the mode of operation of power plants, substations and power lines. Power and energy are among the most important measured quantities characterizing both the load of the power system as a whole and the distribution of load between individual power plants. The principles of construction of measuring power transducers, which form the basis of electronic meters, with the use of frequency-pulse multipliers are analyzed.

Coupling of the Simulation Planning and Power Flow Calculation Tools: Case Study of the Republic of Serbia

Planning of the national energy system by looking at alternative investment decisions through annual simulations (using the EnergyPlan tool) due to its (combinatorial) complexity leaves little room for explicitness regarding the network's impact on planning decisions and therefore there is a need to look at the power flows in the network. The investment decisions that are made remain with a degree of vagueness in the technical aspect, which can be removed with this connection. The CASE tool is used for numerous system calculations of power flows, shortcircuit currents, series faults, stability to small disturbances and transient stability. Calculations based on the calculation of power flows are also implemented in the program: network reduction, security (N-1 and N-X), NTC and OTDF/PTDF. The coupling of the CASE program and EnergyPlan was achieved through the output text file of EnergyPlan, which contains the total production of power plants by type, the total consumption of the system divided into several subtypes, export and import from the regulatory area. CASE loads the output file of EnergyPlan and distributes the total production by power plant types to each individual power plant in the system according to predefined distribution coefficients, while the total consumption is distributed proportionally to the existing loads by network nodes. Thus, a system model for power flow calculation is defined for each hour of the year. Calculation results for each hour are stored in the working memory of the CASE program and can be sorted by the most loaded element of the system, the largest exchange of the observed control area, losses in the control area, production by a certain type of power plant, consumption, etc. In this way, it is possible to efficiently analyse the operation of the system according to different criteria. First of all, the goal of this work is to couple the two software tools, which has not been realized until now, as well as the presentation of some of the most significant results selected from among the numerous options of state sorting. The result of the work is the integration of software tools that serve the benefit of planning activities in terms of their technical feasibility, that is, to examine more precisely whether a prospective scenario is realistic from the aspect of power flows in transmission grid.

An integrated analysis of air pollution from US coal-fired power plants

The United States is one of the world's leaders in electricity production, generating about 4116 billion kWh in 2021, of which coal accounted for 21.8% of the total. This study applies an integrated approach using both terrestrial and satellite data to specifically examine emissions from coal-fired power plants and its spatial extent. The study also highlights the effectiveness of government policies to reduce emissions. It was found that emission of pollutants from the country's energy sector has been steadily declining, with annual emissions of sulfur dioxide (SO2) and nitrogen oxides (NOx) decreasing from the US electric power sector between 1990 and 2020 by 93.4% and 84.8%, respectively, and carbon dioxide (CO2) by 37% between 2007 and 2020. Although overall emissions from coal-fired power plants are declining, some individual plants have yet to install environmental equipment to control emissions. According to US government data, major emitters of SO2, NOx, and CO2 in the US are the Martin Lake power plant in East Texas, the Labadie power plant near St. Louis, Missouri, and the James H Miller Jr plant near Birmingham, Alabama. This study also integrates TROPOMI satellite data to detect point emissions from individual power plants. While the highest levels of measured pollutants were over the country's major cities and areas of fossil fuel extraction, TROPOMI could clearly distinguish the pollution caused by power plants in more rural areas. Although the US has made great strides in reducing emissions from coal-fired power plants, these plants still represent a major source of pollution and remain a major concern. Totally eliminating coal as a power source will be difficult with the higher power demands resulting from the transition to electric automobiles.

Global and regional models for identification of cooling technology in thermal power generation for water demand estimations in water-energy nexus studies

Water-energy nexus studies aim to connect the process of power generation with the corresponding water demand. The lack of information on the currently installed cooling systems at individual power plants is a challenge for the assessment of the water use on the power plant-level, which complicates decision-making in water management, especially in water-stressed regions. In this study, we investigate the spatial and temporal trends in cooling technology installations globally. Based on that, we propose a machine learning model for cooling technology identification on a regional and global level, which uses a combination of feature selection and classification algorithms. The global model demonstrates an average test set accuracy of 85.42%, which corresponds to only a minor underestimation of the actual global water footprint of 1.78% when the cooling technology and water footprint of individual power plant units is unknown. Apart from that, a special emphasis was placed on regions characterized by high and extremely high water stress, where mistakes in water policy planning and water management may lead to an unsustainable water use or even to an overexploitation of water resources. In these regions, the calculated test set accuracy was 80.83%, which is significantly larger than the average accuracy of a majority class model. The results and the method proposed in this study enable cooling system identification in individual power units using information available from other sources, such as the water stress score or seasonal freshwater availability in the region.

Costs of achieving emission limits in coal-burning power plants under the recent best available techniques regulation amendment: Evidence from national microeconomic data

Emissions limits for coal-burning power plants are gradually being tightened around the world, with additional requirements and consequently significant costs placed on existing power plants. Design that conforms to regulatory requirements require credible estimation of the associated abatement costs. However, the existing methods for estimating abatement costs do not estimate those costs using microeconomic data or take the need to retrofit current abatement technologies into sufficient consideration. Therefore, they fail to calculate the actual costs of compliance accurately and thus are not suitable for assessment of cost proportionality, which is the key principle of regulatory design. This paper introduces a straightforward and innovative methodological approach to quantification of abatement costs that can reflect the real-world situation at an individual power plant and allows assessment of proportionality in meeting emission standards. The approach is based on collection of detailed data at the level of individual plants and on the comparison of costs associated with various levels of emission reduction in defined scenarios. The approach is demonstrated by application to nearly 50% of the installed thermal power capacity of the Czech Republic. The results show that recent European regulations for coal-burning plant emissions incur high abatement costs for most power plants, despite having very little effect in terms of emission reductions.

Life cycle assessment shows that retrofitting coal-fired power plants with fuel cells will substantially reduce greenhouse gas emissions

Life cycle assessment shows that retrofitting coal-fired power plants with fuel cells will substantially reduce greenhouse gas emissions