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Long-term contract is an important developing direction of China's coal industry coordination. This paper aims to discuss how to use contract for difference (CFD) to avoid risk and effectively increase the benefit of both coal and thermal power plants in the coal-electricity supply chain.

Based on prospect theory, this paper takes the risks and benefits of the coal and coal-fired power plants in the coal supply chain under CFD into balanced consideration to construct the contract coordination mechanism. In this mechanism, the coal demand in the coal supply chain equilibrium under centralized decision-making is regarded as the total annual volume of transactions needed to design the contract coordination mechanism and solve double marginalization. Then, based on prospect theory, in the construction of CFD, this paper takes the income of power and coal enterprises when they are in equilibrium under Stackelberg non-cooperative game as the reference point. In addition, considering that coal demand is a random variable, the CFD with a one-year trading session can be designed.

The research derives the coal price of the contract for difference, contract trading volume and its proportion of the total trading volume. A numerical example shows that the model above can be used to effectively avoid the risk of both coal and electricity sides.

To solve the conflict between coal enterprises and thermal power plants, let the coal-electricity supply chain be converted from non-cooperative game to cooperative game. Based on the prospect theory, this paper takes the income of the non-cooperative game of coal and thermal power plants as a reference point and considers how to design the coordination mechanism, the contract for difference, so as to make the two parties cooperate to solve the double marginal utility of the non-cooperative game in a chain supply. The main innovation of the work lies in the following: first, the coal demand when the coal-electrical supply chain is in balance under centralized decision-making is taken as the total annual trading volume needed to design the contract coordination mechanism and solve double marginalization. Second, based on prospect theory, in the construction of CFD, the benefits of coal-fired power plants and coal enterprises when both sides are in equilibrium under the Stackelberg non-cooperative game are taken as the reference points, and coal demand is taken as a random variable to design the CFD with a one-year transaction period. The price of coal that is not traded through CFD is calculated according to the daily market price. Third, this paper proposes the prospect M-V criterion of the risk-benefit equilibrium of both power and coal enterprises, which means that the risk-benefit equilibrium of both sides is the prospect variance effect of both sides relative to the reference point benefit divided by the prospect expectation effect.

Many power producers are looking for ways to develop smarter energy capabilities to tackle challenges in the sophisticated, non-linear dynamic processes due to the complicated operating conditions. One prominent strategy is to deploy advanced intelligence systems and analytics to monitor key performance indicators, capture insights about the behavior of the electricity generation processes, and identify factors affecting combustion efficiency. Thus, the purpose of this paper is to outline a way to incorporate a business intelligence framework into existing coal-fired power plant data to transform the data into insights and deliver analytical solutions to power producers.

The proposed ten-step business intelligence framework combines the architectures of database management, business analytics, business performance management, and data visualization to manage existing enterprise data in a coal-fired power plant.

The results of this study provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NO_x and consequently explain and predict the leading causes of variation in the emission of NO_x in the combustion process.

Once the framework is integrated into the power generation process, it is important to ensure that the top management and the data analysts at the plants have the same perceptions of the benefits of big data and analytics in the long run and continue to provide support and awareness of the use of business intelligence technology and infrastructure in operational decision making.

The key finding of this study helps the power plant prioritize the important factors associated with the emission of NO_x; closer attention to those factors can be promptly initiated in order to improve the performance of the plant.

The use of big data is not just about implementing new technologies to store and manage bigger databases but rather about extracting value and creating insights from large volumes of data. The challenge is to strategically and operationally reconsider the entire process not only to prepare, integrate, and manage big data but also to make proper decisions as to which data to select for the analysis and how to apply analytical techniques to create value from the data that is in line with the strategic direction of the enterprise. This study seeks to fill this gap by outlining how to implement the proposed business intelligence framework to provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NO_x and to explain and predict the leading causes of variation in the emission of NO_x in the combustion process.

Lignite coal-fired power plants are the main electricity generators in the province of Saskatchewan, Canada. Although burning lignite coal to generate power is economical, it produces significant greenhouse gases making it a big challenge to Canada’s international commitment on emission reduction. However, abundant agricultural crops and sawdust produced in Saskatchewan put the province in a good position to produce and use agri-pellets as an alternative fuel to generate electricity. This study aims to conduct an economic and environmental analysis of the replacement of lignite coal by agri-pellets as the fuel for Saskatchewan’s coal-fired power plants.

The study estimates the economic and environmental costs and benefits of two alternative fuels for power plants. The economic analysis is based on the pellet production and transportation costs from farms to production sites and from the production sites to power plants. In the production process, biomass precursors are densified with and without additives to produce fuel agri-pellets with appropriate mechanical durability and high heating value per volume unit. The environmental analysis involves estimation of greenhouse gas emissions and their social costs for lignite coal and different types of agri-pellets under different scenarios for pellet production and transportation.

The results show that although the total cost of electricity is lower for coal than agri-pellets, the gap shrinks when social costs and specifically a carbon price of $50/tonne are included in the model. The cost of electricity in lignite coal-fired power plants would also be on par with agri-pellets-fired power plants if the carbon price is between U$68 and $78 per tonne depending on the power plant locations. Therefore, a transition from coal to agri-pellet fuels is feasible if a high-enough price is assigned to carbon. The method and the results can be generalized to other places with similar conditions.

There are a few caveats in this study as follows. First, the fixed costs associated with the transformation of the existing coal-fired power plants to pellet-fired plants are not considered. Second, the technological progress in the transportation sector, which would favor the net benefits of using pellets versus coal, is not included in the analysis. Finally, the study does not address the possible political challenges facing the transition in the context of the Canadian federal system.

The study results indicate that the current carbon price of $50 per tonne is not sufficient to make the agri-pellets a feasible source of alternative energy in Saskatchewan. However, if carbon pricing continues to rise by $15 annually starting in 2022, as announced, a transition from coal to agri-pellets will be economically feasible.

Canada is committed to reduce its emission according to the Paris agreement, and therefore, needs to have a concrete policy to find alternative energy sources for its coal-fired power plants. This study examines the challenges and benefits of such transition using the existing agri-pellet resources in Saskatchewan, a province with abundant agricultural residues and coal-fired power plants. The findings indicate that a significant emission reduction can be achieved by using agri-pellets instead of coal to produce electricity. The study also implies that the transition to renewable energy is economical when social costs of carbon (carbon tax) is included in the analysis.

As far as the authors know, this is the first study providing a socio-economic analysis for a possible transition from the coal-fired power plants to a more clean and sustainable renewable energy source in one of the highest carbon dioxide (CO₂) producer provinces in Canada: Saskatchewan. The study builds upon the technical production of three agri-pellets (oat hull, canola hull and sawdust) and estimates the economic and environmental costs of alternative fuels under different scenarios.

This study aims to focus on the submicron particles (with diameter of 0.2-1.0 μm) of the ambient air from a coal-fired power plant. A systematic examination of their morphology, particle size and chemical element will be analyzed, so as to provide more scientific information and theoretical basis for the formation and control method of inhalable particles, as well as data support for environmental impact and ecological effects assessments.

In this paper, the morphology, size distribution and elemental characteristics of submicron particles from ambient air of a coal-fired power plant are studied by single particle analysis.

The results show that atmospheric particles in coal-fired power plant are mainly spherical particles, and most of them are soot aggregates adhered or coated with other particles with few rectangle particles. The particles collected in the afternoon and evening are mainly of spherical particles, and small-sized particles collected in the morning are mainly spherical ones, while the overall concentration is larger than that of the spherical particles in the size range above 0.5 μm. The results indicated that the larger-sized spherical particles have a lower concentration.

Coal-fired power plants are still the main supply of electricity in China, but the inhalable particles, especially sub-micron particles (0.1-1.0 μm) cannot be effectively captured by the dust removal device from the coal-fired power plant. Thus, a large amount of inhalable particles is emitted into the atmosphere, becoming the major air pollutants in China.

This study aims to propose an approach to assess the security of supply (SS) in a coal-fired electricity generation supply chain subject to public price regulation in Brazil. This study characterizes the Brazilian scenario of coal-fired electricity generation, which represents less than 3.5% of the energy sources.

Data from six mining companies that supply a coal plant were analyzed in a case study. The risks were characterized and objectively estimated through a synthetic multidimensional index. Structural changes in the earnings before interest, taxes, depreciation, amortization and exploration indicator time series of coal companies (CC) were statistically detected.

Empirical evidence demonstrates that the supply chain has a low disruption risk (SS index equal to 0.74). However, when suppliers are individually analyzed, 48.64% of all coal shows moderated disruption risk, and 2.51% is under high risk. In addition, this study finds a drop in the financial results of CC related to public regulation of coal prices. This impacts the security of coal supply.

This study discusses the influence of legal and regulatory policy risks in a coal power generation supply chain and the implications of the SS index as a management tool.

A novel SS index is presented and empirically operationalized, and its dimensions – environmental, occupational, operational, economic-financial and supply capacity – are analyzed.

Human-induced changes in climate have affected the environment to the extent that any more economic development at the cost of the environment will be too costly. Thus, sustainable development options posing no additional harm to the environment are the only viable option. This study aims to examine the likely environmental impacts of infrastructural developments through the China–Pakistan Economic Corridor (CPEC).

There is a scarcity of academic debate and discussion on the environmental impact of CPEC developments in laws and policies on the environment. The qualitative approach is followed in this study and official documents and reports are used to investigate the environmental challenges posed by CPEC.

The findings show three possible environmental concerns which could increase the climate change vulnerability of Pakistan. The coal-fired power plants are the most prominent threat based on their CO₂ contributions and smog. Second, cutting more than 54,000 trees for roads infrastructure will increase CO₂ concentration along the CPEC route. Third, increasing vehicle trafficking by up to 7,000 trucks per day on Karakorum Highway alone will release 36.5 million tons of additional CO₂.

It is essential to rethink the environmental cost of CPEC. The study suggests economic and legal cooperation between Pakistan and China as a way forward to deal with climate change issues. Environmental laws should be a vital part of CPEC projects to ensure their safety, security and sustainability.

This paper aims to study the changes in productivity for 25 state‐owned coal‐fired power plants (CFPPs) over a period of seven years (2003‐2010).

The methodology that is utilized in the study follows a non‐parametric approach of data envelopment analysis (DEA) and uses the Malmquist index to estimate the change in productivity of panel samples. In the calculations, the study considers installed capacity, fuel, labour, electricity used, and average operational time as inputs and considers net electricity produced as output.

The results indicate that the average total factor productivity regressed during the investigation period at an annual rate of 2 percent. The decrease in productivity is equally attributed to the technical efficiency change and technological change components, with an average decline in productivity of 1 percent per year. A plant‐wise analysis demonstrates that the Parichha plant recorded an average increase in productivity of 3.9 percent per year that was mainly driven by the technical efficiency change component (4.2 percent).There is little variation in the productivity of small‐size plants when compared with medium and large‐size plants. The productivity of multivaried plants is comparatively lower than BHEL (Bharat Heavy Electricals Limited) make plants.

The impact of size, make and region on change in productivity is examined. This study recommends specific policies that can be implemented to increase the productivity of power plants. The study also provides a contemporary overview of Indian CFPPs that can aid energy planners and plant operators in the monitoring and detection of changes in productivity.

The purpose of this paper is to present a framework to assess the feasibility of fuel cells in both monetary and non‐monetary terms. It aims to develop an index is to compare different sources of energy in the residential market of the rural Appalachian region of the USA.

An index is developed to measure sustainable criteria and the factors that influence the user's decision‐making process of adopting an alternative energy. The sustainable criteria are obtained through a literature review, and the factors that influenced the decision‐making process are found through surveys to individuals who may potentially adopt alternative energies. After the index is developed, it is used to compare fuel cells and the conventional grid system using the specific case of a typical rural house.

The most relevant sustainable criteria found in literature are environmental impact, energy consumption, and cost. The factors that influenced the user's decision to adopt an alternative energy are cost, environmental impact, space, reliability, and safety. Those factors are combined in an index used to compare fuel cells and the coal‐fired grid system. According to the index, fuel cells have a slightly better rating than the grid system powered by a coal‐fired power plant; however, further development of fuel cells will be needed in order to be fully competitive.

The index developed is based on the rural Appalachian region of the USA. If the index is used in other locations, factors, and weights should be adjusted accordingly.

The framework discussed in the paper includes both the sustainable performance of the system, and the factors that influenced the user's decision to adopt an alternative energy. The latter has previously not been taken into consideration in the assessment of these types of technologies.

The factors considered in the paper will make a significant difference if alternative energies are to be considered as a viable alternative to traditional energy forms. The framework took a holistic approach, and considered factors such as cost, environment, energy consumption, reliability, maintenance, space and safety, that are deemed important by the final user.

The purpose of this paper is to analyze the fuzzy reliability of the compressor house unit (CHU) system in a coal fired thermal power plant under vague environment by reducing the accumulating phenomenon of fuzziness and accelerating the computation process. This paper uses different fuzzy membership functions to quantify uncertainty and access the system reliability in terms of different fuzzy reliability indices having symmetric shapes.

This study analyses the fuzzy reliability of the CHU system in a coal fired thermal power plant using T_ω-based generalized fuzzy Lambda-Tau (TBGFLT) technique. This approach applies fault tree, Lambda-Tau method, different fuzzy membership functions and α-cut coupled T_ω-based approximate arithmetic operations to compute various reliability parameters (such as failure rate, repair time, mean time between failures, expected number of failures, availability and reliability) of the system. The effectiveness of TBGFLT technique has been demonstrated by comparing the results with results obtained from four different existing techniques. Moreover, this paper applies the extended Tanaka et al. (1983) approach to rank the critical components of the system when different membership functions are used.

The adopted TBGFLT technique in the present study improves the shortcomings of the existing approaches by reducing the accumulating phenomenon of fuzziness, accelerating the computation process and getting symmetric shapes for computed reliability parameters when different membership functions are used to quantify data uncertainty.

In existing fuzzy reliability techniques which are developed for repairable systems either triangular fuzzy numbers, triangle vague sets or triangle intuitionistic fuzzy sets have been used for quantifying uncertainty. These approaches do not examine the systems for components with different membership functions. The present study is an effort in this direction and evaluates the fuzzy reliability of the CHU system in a coal fired thermal power plant for components with different membership functions. This is the main contribution of the paper.

The purpose of this study is to benchmark the performance of state-owned coal-fired power plants (CFPPs) and test whether plant-specific knowledge in terms of quality of coal, size, age and make of plant contribute to an improvement in plant efficiency.

The methodology that is utilized in the study follows a nonparametric approach of data envelopment analysis (DEA) with sensitivity analysis and Tobit regression model. The input-oriented DEA models are applied to evaluate the overall, pure technical and scale efficiencies of the CFPPs. Further, slack analysis is conducted to identify modes to improve the efficiency of the inefficient plants. Sensitivity analysis based on peer count and the removal of variables is carried out to identify the benchmark power plant. Through Tobit and bootstrap-truncated regression model, the paper investigates whether a plant's specific knowledge influences its efficiency.

The DEA analysis demonstrates that nine plants are technically purely efficient.The slack analysis reveals that reducing the consumption of oil is the most effective way to improve the efficiency of inefficient plants. Mattur plant is the benchmark for most of the inefficient plants. Regression result suggests that quality of coal and size of plant significantly affect the inefficiency of the sample plants. Bharat Heavy Electrical Limited MAKE plant achieved higher efficiency in comparison to mixed MAKE.

This study is one of the few published studies that benchmark the performance of state-owned CFPPs. This research carried out taking some new uncontrollable parameters of power plant utilities of India. Research work also identifies the possible causes of inefficiency and provides measures to improve the efficiency of the inefficient power plant.