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The purpose of this paper is to present the simulation method of power plants and storage system capacity design.

Owing to solar irradiation, wind speed and water flow are highly and randomly changeable, time variation of the signals needs to be taken into consideration as well as some features of the power plants and storage system. A Matlab/Simulink model of the given system – DC microgrid has been developed. The model allows simulation of a few years static simulations of the power balance. Hence, it can be used to size the plants.

An effective method of the power system design has been developed. It allows sizing the plants taking into consideration resources and load profiles, year changes in profiles and future development of the system. The storage system can be optimized to avoid high power unbalance and power cost increasing.

The model describes only static power behaviour of the modelled power system. It does not allow simulating local voltage changes and dynamic properties of the plants and storage.

This technique helps to size the plants and, first of all, storage system taking into consideration several technical and economical issues.

The method gives opportunity to design a storage system's capacity and power and optimize them. The authors have not found similar methods in the literature.

The aim of the study is to identify main failure phenomena and to evaluate reparation costs, reparation time, loss of profit and their relationship with power plant and faulty components age. In this work, several machinery breakdowns occurred in thermal power plants fed by solid biomass, biodiesel, biogas and municipal solid waste, have been investigated. In the period between 2004 and 2012, 23 faults have been analyzed.

Each fault has been classified considering: power plant technical specifications, type of damage, reparation cost, reparation time and loss of profit (when data are available). The whole data have been, therefore, compared to find out significant information.

It has been pointed out that relevant property damages are mainly caused by old components failure. In addition, the loss of profit is generally much higher than the property damage (six times on average basis).

The study provides useful information that can be of interest for personnel of energy utilities, banks and insurance companies in managing power plants risks and in planning the availability of energy services.

Carbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.

This paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO₂ and PM_2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.

Results suggest that the carbon trading mechanism induces 2.7% of CO₂ emission reduction and 6.7% of PM_2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.

This paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.

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.

People rely on power plants to generate the electricity needed to run much of their lives. Power plants, though, are typically not the domain of the average citizen. Even if they stand near homes, schools, and other important places, the operations inside, not to mention the many social and environmental impacts outside, largely lack the scrutiny of most citizens. Is this a problem, especially when some governmental oversight already regulates the plants’ operations? The National Council for the Social Studies (NCSS) defines the main purpose of social studies education as creating effective citizens. This article describes an interdisciplinary unit of study by middle-grades youth about a proposed power plant in their city of Lansing, Michigan. It shows students scrutinizing the complex power plant issue through a variety of experiences and from different angles. While supporting NCSS’ stance on the teaching of citizenship, we call for a conception of citizenship extending beyond human communities and structures to the community of the earth and all living beings. We also encourage social studies teachers to take up the work of teaching for ecological citizenship.

The purpose of this paper is to propose a new multi-criteria decision making (MCDM) technique to determine the priority of renewable power plants construction conceding technical, economic, social, political and environmental aspects.

First, a comprehensive set of 5 main criteria of technical, economic, social, political and environmental are considered for renewable power plants construction, each including 5 sub-criteria (a total of 25 sub-criteria). Then, the analytic hierarchy process method is used to determine the weight of the criteria. Finally, a new MCDM technique proposed to prioritize the construction of renewable power plants, named TOPKOR. To demonstrate the capability of the proposed method, a case study is conducted in which six types of renewable power plants are evaluated.

Comparison results of the main criteria weights show that the “economic” [0.403], “environmental” [0.296] and “technical” [0.17] aspects have the highest importance, respectively. The results also show that solar, hydroelectric and wave and tidal power plants have the highest priority for construction, respectively.

The result of this research could be useful for related decision makers in construction of the renewable power plants to have a comprehensive set of criteria in technical, economic, social, political and environmental aspects in their decision process.

This research provides a comprehensive set of criteria and sub-criteria for prioritizing the renewable power plants. Moreover, a new hybrid MCDM technique is introduced for prioritizing the construction of power plants.

Biomass waste can be used as fuel in biomass power plants to generate electricity. It is a type of renewable energy widely available in Malaysia because 12 million tons of the biomass waste is produced every year. At present, only 5 per cent of the total biomass waste in Sabah, one of the states in Malaysia, is used to generate electricity for on-site consumption. The remaining 95 per cent of the biomass waste has not been utilized because the transportation cost for shifting the waste from the plantations to the power plants is substantial, hence making the cost of the biomass generated electricity to be high. Therefore, a methodology is developed and presented in this paper to determine the optimum geographic distribution and capacities of the biomass power plants around a region so that the cost of biomass generated electricity can be minimized. The paper aims to discuss these issues.

The methodology is able to identify the potential locations of biomass power plants on any locations on a region taking into account the operation and capital costs of the power plants as well as the cost of connecting the power plants to the national grid. The methodology is programmed using Fortran.

This methodology is applied to Sabah using the real data. The results generated from the methodology show the best locations and capacities of biomass power plants in Sabah. There are 20 locations suitable for biomass power plants. The total capacity of these biomass power plants is 4,996 MW with an annual generation of 35,013 GWh. This is sufficient to meet all the electricity demand in Sabah up to 2030.

The methodology is an effective tool to determine the best geographic locations and sizes of the biomass power plants around a region.

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.

The purpose of this paper is to analyse the performance of a wind electric generating power plant through the study of reliability measures. The enhancement of the performance of the wind power plant using various approaches is also an objective of this paper.

This paper describes two models of a wind electric generating power plant using the Markov process and supplementary variable technique and solved with the help of Laplace transformation. The first model has been analyzed without fault coverage and Gumbel-Hougaard family of copula, while the second model of the wind power plant employs fault coverage and Gumbel-Hougaard family of copula which are used to enhance the performance. The proposed methodology is then illustrated in detail considering numerical examples.

Numerous reliability characteristics such as availability, reliability and mean time to failure to examine the performance of the wind power plant have been investigated. Through the comparative study of both the models, the authors concluded that the plant can generate electricity over long periods of time by covering more and more detected faults, which is made possible with two types of repair facility.

In this work, the authors have developed a mathematical model based on a wind electric generating power plant. This work incorporates not only the component failures that stop or degrade the working of the plant but also deals with the catastrophic and repair strategy of the plant.

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.