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Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper explores the potential of a new source of fly ash, deposited on the site of a coal-fired power plant in Ireland dating from 1985 to 1995, as a cement replacement material. A series of X-ray diffraction (XRD) analyses on binder samples with cement replacement levels of 0, 10, 25 and 35% was undertaken to determine the fly ash's mineralogical composition and to determine its suitability as a supplemental cementitious material (SCM). The XRD results reveal a unique mineral composition with promising characteristics for enhancing the strength and durability of concrete. The experimental results were used to calibrate a thermodynamic model to predict changing phase assemblage and hydration behaviour over time and per replacement level. Thermodynamic models have been shown to give credible predictions of the long-term performance of cements, including SCMs. The initial experimental results' thermodynamic modelling demonstrates the feasibility of this fly ash source as a sustainable alternative to traditional cement, paving the way for more eco-friendly construction. Ash deposits dating from 1995 to 2005 and from 2005 to the present will be presented in subsequent publications. [ABSTRACT FROM AUTHOR]

Background: The main goal of the paper is to define the level of energy efficiency in the economies of selected countries in the Balkan region that have opted for the EU Green Deal, a circular economy, and a transition to carbon neutrality. Energy efficiency, as a determinant of carbon neutrality, was selected as an indicator for analysis because it records particularly unfavorable indicators in the region under observation. The research was carried out on a sample of seven Balkan countries and their surrounding areas. An initial qualitative analysis was followed by a quantitative analysis based on a combination of statistical methods and soft computing. Six indicators were selected for the analysis covering a period of 30 years (1990–2020). Results: A significant obstacle to the green transition and the region's transition to a circular economy and carbon neutrality is energy efficiency and energy related pollution—the reliance of most countries on coal-fired thermal power plants for electricity generation. The research results showed the following: (a) the degree of economic development and membership in the European Union are not significantly related to the level of energy efficiency; (b) most of the sampled countries are in the initial stages of introducing activities to achieve carbon neutrality; and (c) only Slovenia has documented consistent indicators and evident advancements in its efforts to achieve carbon neutrality. Based on the research findings, proposals for improvements were made in the direction of policymaking and in a methodological sense. Conclusions: The implementation of circularity and carbon neutrality as a long-term goal of the European Union is not necessarily related to the level of economic development, nor can its trajectory be exclusively ascertained by means of data processing and monitoring. A more precise understanding of a carbon-neutral future can be achieved through the incorporation of qualitative data to a greater extent, a realistic evaluation of historical facts and their repercussions, as well as projections of the effects that reality and global developments after 2022 will have on each country. [ABSTRACT FROM AUTHOR]

In coal-fired power plants, coal piles serve as the fundamental management units. Acquiring point clouds of coal piles facilitates the convenient measurement of daily coal consumption and combustion efficiency. When using servo motors to drive Light Detection and Ranging (LiDAR) scanning of large-scale coal piles, the motors are subject to rotational errors due to gravitational effects. As a result, the acquired point clouds often contain significant noise. To address this issue, we proposes a Rapid Point Cloud Stitching–Constrained Particle Filter (RPCS-CPF) method. By introducing random noise to simulate servo motor rotational errors, both local and global point clouds are sequentially subjected to RPCS-CPF operations, resulting in smooth and continuous coal pile point clouds. Moreover, this paper presents a coal pile boundary detection method based on gradient region growing clustering. Experimental results demonstrate that our proposed RPCS-CPF method can generate smooth and continuous coal pile point clouds, even in the presence of servo motor rotational errors. [ABSTRACT FROM AUTHOR]

With a significant share of renewable power generation integrated into the power supply, it is crucial to timely adjust the regulating peak load for coal-fired power plants equipped with CO2 capture to ensure the stable operation of the multi-energy supply system. In this paper, the effects of varying boiler loads on the techno-economic performance of the retrofitted power plant were studied. Furthermore, the potential for reducing the cost of CO2 capture was investigated, and early opportunities for demonstration were discussed. Results showed that when the boiler load decreased from 100% turbine heat acceptance condition to 50% turbine heat acceptance condition, the cost of CO2 capture increased from 37.0 $/t CO2 to 57.0 $/t CO2, cost contribution of energy penalty and extra capital investment also increased from 20.6 $/t-CO2 to 25.7 $/t-CO2, and from 16.4 $/t-CO2 to 31.3 $/t-CO2, respectively. Furthermore, by improving separation efficiency from 0.15 to 0.5, a 25% to 30% reduction in CO2 capture cost can be achieved. The cost of CO2 capture could decrease by 42.2–50.5% when the cumulative capacity reaches 250 GW under the high investment learning rate scenario. According to the distribution of coal prices and renewable energy sources in China, the early demonstration projects of multi-energy supply systems should prioritize the northern region. The results of this work can provide informative references for making roadmaps and policies for CO2 emission reduction toward carbon neutrality. [ABSTRACT FROM AUTHOR]

Morocco, despite its heavy reliance on imported fossil fuels, which made up 68% of electricity generation in 2020, has recognised its significant renewable energy potential. The Nationally Determined Contribution (NDC) commitment is to reduce emissions by 45.5% from baseline levels with international assistance and abstain from constructing new coal plants. Moreover, the Green Hydrogen Roadmap aims to export 10 TWh of green hydrogen by 2030, as well as use it for local electricity storage. This paper critically analyses this Roadmap and Morocco's readiness to reach its ambitious targets, focusing specifically on an energy trilemma perspective and using OSeMOSYS (Open-Source energy Modelling System) for energy modelling. The results reveal that the NDC scenario is only marginally more expensive than the least-cost scenario, at around 1.3% (approximately USD 375 million), and facilitates a 23.32% emission reduction by 2050. An important note is the continued reliance on existing coal power plants across all scenarios, which challenges both energy security and emissions. The assessment of the Green Hydrogen Scenarios highlights that it could be too costly for the Moroccan government to fund the Green Hydrogen Roadmap at this scale, which leads to increased imports of polluting fossil fuels for cost reduction. In fact, the emission levels are 39% higher in the green hydrogen exports scenario than in the least-cost scenario. Given these findings, it is recommended that the Green Hydrogen Roadmap be re-evaluated, with a suggestion for a postponement and reduction in scope. [ABSTRACT FROM AUTHOR]

With the rapid development and technological innovation in the energy market, peer-to-peer (P2P) energy trading, as a decentralised and efficient trading model, has been widely studied and practically applied. However, in P2P energy transactions involving multiple prosumers, there are challenges such as information asymmetry, trust issues, and transaction transparency. To address these challenges, blockchain technology, as a distributed ledger technology, provides solutions. In this paper, we propose a blockchain technology-based prosumer–virtual power plant (VPP) two-tier interactive energy management framework to assist P2P energy transactions between multiple prosumers. In this framework, the virtual power plant acts as a leader and sets differentiated tariffs for different prosumers to equal the distribution of social welfare. The various prosumers act as followers and respond to the leader's decisions in a cooperative manner. Blockchain's immutability and transparency enable prosumers to participate in P2P energy trading with greater trust, share idle energy, and share revenues based on contribution. In addition, given the uncertainty of renewable energy, this paper employs a stochastic planning approach with conditional value at risk (CVaR) to describe the expected loss of VPP. Ultimately, as verified by the arithmetic simulation, the blockchain co-governance transaction model effectively supports energy coordination and optimization of complementarities while ensuring the utility of each transaction node. This model promotes the application of renewable energy in local consumption, while facilitating the innovation and sustainable development of the energy market. [ABSTRACT FROM AUTHOR]

This paper aims to develop a novel method for the dynamic equivalence of a renewable power plant, ultimately contributing to power system modeling and enhancing the integration of renewable energy sources. In order to address the challenge posed by clusters of renewable generation units during the equivalence process, the paper introduces the degree of similarity to assess similarity features under data. After leveraging the degree of similarity in conjunction with data-driven techniques, the proposed method efficiently entails dividing numerous units in a large-scale plant into distinct clusters. Additionally, the paper adopts practical algorithms to determine the parameters for each aggregated cluster and streamline the intricate collector network within the renewable power plant. The equivalent model of a renewable power plant is thereby conclusively derived. Comprehensive case studies are conducted within a practical offshore wind plant setting. These case studies are accompanied by simulations, highlighting the advantages and effectiveness of the proposed method, offering an accurate representation of the renewable power plant under diverse operating conditions. [ABSTRACT FROM AUTHOR]

In recent years, there have been several harmonic resonance accidents around the world that involve renewable energy power plants. The frequency scanning method is the most widely used technique in engineering practice to evaluate the severity of resonance due to its simple operation and clear physical meaning. However, when establishing electromagnetic transient simulation models and conducting frequency scans, a single generation unit or a few renewable generation units are usually used to represent the original power plant for the purpose of reducing model complexity and improving the simulation efficiency. Such a practice has been found to be effective in dynamic studies around the fundamental frequency. However, its feasibility in harmonic resonance studies has not yet been fully investigated. Because of this research gap, a feasibility study is conducted in this paper by using a real-life photovoltaic power plant. The detailed harmonic model of the plant is first established using the harmonic linearization method, and the equivalent harmonic model is then developed using the power loss conservation method. The feasibility of the equivalent model was investigated in detail, and the impact of the different impedance models on the resonance analysis was analyzed. The results indicate that the conventional dynamic equivalent model can effectively reflect the harmonic resonance characteristics of photovoltaic power plants. Furthermore, a more simplified model that ignores the inductance of the collector line is recommended in this paper to further reduce the modeling complexity. [ABSTRACT FROM AUTHOR]

Regulatory capture reflects a phenomenon in which a regulatory agency, initially established to cater the interests of the public, formulates a policy based on private or 'special' interests. Actors within the legislative process are influenced by external parties or may even fall for regulatory capture as they have engraved a mindset that sides with the regulated industry. Regulatory capture is more likely to occur when there is a lack of public participation, transparency, and imbalance access to information. It is also much more prominent when the regulation is made to govern a profitable sector. Although regulatory capture has been numerously addressed, this paper focuses specifically on the challenges posed by regulatory capture in Indonesia's energy sector, particularly with respect to one of its profitable sectors – the coal-fired power plant industry. Despite Indonesia's expressed intention to transition to renewable energy and the apparent environmental impacts of coal-fired power plants, regulations regarding such a sector are still made to favour businesses. This relates to the fact that many political actors who hold positions as legislators and regulators also wield important positions in Indonesia's coal-fired power plant companies. This paper illustrates the tension and struggle between the two contrasting interests of businesses and the public by looking into the theoretical framework of regulatory capture and public interests. Subsequently, it extracts the elements of regulatory capture and utilizes it to identify whether regulatory capture is inherent in Indonesia's coal-fired power plant industry. Ways to prevent regulatory capture are further explored. In doing so, this paper resorts to a doctrinal and normative method. The analysis conducted leads to the conclusion that there are apparent signs of political influence in the coal-fired power plant industry, indicating the existence of regulatory capture. As such, measures to prevent and eradicate regulatory capture should be implemented by the government. [ABSTRACT FROM AUTHOR]

The maintenance strategy used in an electric power plant plays a crucial role in its overall performance and operational efficiency. An effective maintenance strategy describes the approach to exploiting various forms of maintenance (corrective, preventive, predictive, proactive, etc.) in an electric power plant. In this paper, the effective and ineffective impacts of maintenance strategies on power plants were examined. Also, the distinction between corrective, preventive, and aggressive maintenance was considered. In terms of effective impacts, a well-designed and executed maintenance strategy enhances the reliability and availability of the electric power plant by minimising unplanned downtime. It extends the lifespan of critical equipment, improves safety measures, increases energy efficiency, and contributes to long-term cost savings. However, in terms of ineffective impacts, poorly planned or executed maintenance strategies can result in increased downtime, higher repair costs, safety risks, decreased efficiency, and regulatory compliance issues. Neglecting maintenance can lead to equipment failures, reduced productivity, and potential environmental incidents. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid economic development, the development speed of all walks of life has entered a new level, and the power industry has also developed rapidly. Driven by market demand, China's power transmission range and power transmission capacity will enter a new level. At the same time, the problems brought about by the development of the power system are equally severe. Due to the large load density in individual areas, the detection of short-circuit current must be improved as an important issue. The purpose of this paper is to study how to improve the practical model of short-circuit current calculation of photovoltaic power plants, so that it can be well applied to the current high-density current detection in China. Therefore, this paper improves the recursive least squares (RLS) algorithm and applies it to the practical model of short-circuit current calculation of photovoltaic power plants and describes the improvement process of the algorithm in detail. At the same time, this paper designs relevant experiments and analysis to count the data of the improved RLS algorithm in the short-circuit current calculation of the actual photovoltaic power station and combines the data of this part to test and analyze the ability of the algorithm. The experimental results in this paper show that the improved RLS algorithm has a very good improvement in the calculation accuracy of the short-circuit current calculation of photovoltaic power plants in the actual model calculation. At the same time, the calculation efficiency is also improved, and the current tracking effect is also improved by 7%. [ABSTRACT FROM AUTHOR]

In the context of global sustainable development, solar energy is very widely used. The installed capacity of photovoltaic panels in countries around the world, especially in China, is increasing steadily and rapidly. In order to obtain accurate information about photovoltaic panels and provide data support for the macro-control of the photovoltaic industry, this paper proposed a hierarchical information extraction method, including positioning information and shape information, and carried out photovoltaic panel distribution mapping. This method is suitable for large-scale centralized photovoltaic power plants based on multi-source satellite remote sensing images. This experiment takes the three northwest provinces of China as the research area. First, a deep learning scene classification model, the EfficientNet-B5 model, is used to locate the photovoltaic power plants on 16-m spatial resolution images. This step obtains the area that contains or may contain photovoltaic panels, greatly reducing the study area with an accuracy of 99.97%. Second, a deep learning semantic segmentation model, the U2-Net model, is used to precisely locate photovoltaic panels on 2-m spatial resolution images. This step achieves the exact extraction results of the photovoltaic panels from the area obtained in the previous step, with an accuracy of 97.686%. This paper verifies the superiority of a hierarchical information extraction method in terms of accuracy and efficiency through comparative experiments with DeepLabV3+, U-Net, SegNet, and FCN8s. This meaningful work identified 180 centralized photovoltaic power plants in the study area. Additionally, this method makes full use of the characteristics of different remote sensing data sources. This method can be applied to the rapid extraction of global photovoltaic panels. [ABSTRACT FROM AUTHOR]

Summary: Coal‐fired thermal power plants play important roles in daily life but are vulnerable to earthquakes. This paper presents a novel cable‐damper stopper with linear damping and carries out a series of shaking table tests to evaluate its performance on a 1/25‐scaled coal‐fired thermal power plant. The stopper consists of a cable, a linear viscous damper and a steel block attached to the boiler. The seismic responses of the structure equipped with different sets of cable‐damper stoppers which are of different damping coefficients and diameters of cables were studied and evaluated, where three seismic ground motions with two intensities were selected as input motions. The obtained acceleration and displacement responses of the support structure and the boiler reveal that the stopper cable effectively restrains the relative displacement between the boiler and the support structure. Furthermore, it can be found that the damping affects the seismic protection performance of the stopper depending on the intensity of the boiler vibration. In the paper, the working mechanism of the stopper is also analyzed based on the test data. It turns out that the stopper is more effective when it works under severe earthquakes. [ABSTRACT FROM AUTHOR]

In response to escalating challenges in energy conservation and emission reduction, this study delves into the complexities of heat transfer in two-phase flows and adjustments to combustion processes within coal-fired boilers. Utilizing a fusion of hybrid modeling and automation technologies, we develop soft measurement models for key combustion parameters, such as the net calorific value of coal, flue gas oxygen content, and fly ash carbon content, within the Distributed Control System (DCS). Validated with performance test data, these models exhibit controlled root mean square error (RMSE) and maximum absolute error (MAXE) values, both within the range of 0.203. Integrated into their respective automatic control systems, these models optimize two-phase flow heat transfer, fine-tune combustion conditions, and mitigate incomplete combustion. Furthermore, this paper conducts an in-depth exploration of the generation mechanism of nitrogen oxides (NO) and low oxygen emission reduction technology in coal-fired boilers, demonstrating a substantial reduction in furnace exit NO generation by 30% to 40% and the power supply coal consumption decreased by 1.62 g/(kW h). The research outcomes highlight the model's rapid responsiveness, enabling prompt reflection of transient variations in various economic indicator parameters. This provides a more effective means for real-time monitoring of crucial variables in coal-fired boilers and facilitates timely combustion adjustments, underscoring notable achievements in boiler combustion. The research not only provides valuable and practical insights into the intricacies of two-phase flow heat transfer and heat exchange but also establishes a pioneering methodology for tackling industry challenges. Graphic Abstract [ABSTRACT FROM AUTHOR]

Alternative solvents based on aqueous solutions of amino acids have been recently developed as possible substitutes for Mono Ethanol Amine (MEA) for CO2 removal from flue gas streams. The potassium taurate solvent has the advantages of degradation resistance, low toxicity and low energy requirements for its regeneration. With any type of solvent, CO2 removal applied to a power production plant decreases the revenues obtained from selling electricity because of the energy requirements. Operating the CO2 removal section in flexible mode avoids significant effects on the profits of the power plant, while accomplishing environmental regulations. This work is the first journal paper focusing on the application in flexible mode of the potassium taurate system for treating a flue gas stream from a 500 MW coal-fired power plant. Techno-economic evaluations are performed to determine the best operating conditions considering the variation in the electricity demand and its price, and different values of carbon tax. In the summer period, with high electricity prices and demands, carbon tax values between 45 EUR/tCO2 and 60 EUR/tCO2 favor CO2 absorption in the flexible mode, without periods of full CO2 emissions during the day. [ABSTRACT FROM AUTHOR]

Offshore photovoltaic power stations (OPVPS) greatly help solve energy problems and land resource scarcity. A crucial phase of the OPVPS project lifecycle is site selection. To select the optimal location of OPVPS with many difficulties such as the uncertainty of the environment, the compensating relationships among criteria, and the different attributes of the alternatives, this paper proposed a fuzzy multi-criteria decision-making framework based on Pythagorean fuzzy Elimination et Choix Traduisant la Realité-III (ELECTRE-III) method. First, the comprehensive criteria system for siting OPVPS was constructed, which included veto and evaluation criteria. Second, the Pythagorean fuzzy set was used to express the uncertain evaluation of experts. Third, considering the actual situation that experts had different experiences and abilities, this paper proposed a novel expert weighting method. Fourth, entropy weighting method, best–worst method, and combination weighting of game theory were introduced to calculate the criteria weights. Fifth, considering the compensation between criteria, ELECTRE-III was used for ranking. Finally, to verify the applicability and robustness of the proposed framework, a China case study was conducted; the results showed that Haizhou Bay is the best alternative. [ABSTRACT FROM AUTHOR]

Vietnam currently has 26 coal-fired power plants in operation, emitting a total amount of coal ash about 16 million tons/year of Fly Ash (FA) and Coal Bottom Ash (CBA). FA and CBA increase the costs of landfill space, and negatively affect the environment. Now, concrete lagging with steel arches is the biggest proportion of the type of current structural support in underground mining (about 70÷80%) in Quang Ninh coal area, Vietnam. The main objective of this research is to propose an optimized proportion of the FA and CBA of thermal power plants used in concrete mixture on making concrete lagging in SVP steel arches. This paper also presents the results of an experimental study using FA and CBA for making concrete lagging with the optimized proportion as cement and sand by the Advantest 9 (Control-Italy) system at the Laboratory of Underground and Mining Construction of Hanoi University of Mining and Geology (HUMG). The study result can be concluded that cement and sand replacement by FA and CBA are useful in lower grades of concrete such as M200 in making laggings of SVP steel arches of the underground mines in Quang Ninh coal area and in contributing to the environment protection. [ABSTRACT FROM AUTHOR]

The new energy structure needs to balance energy security and dual carbon goals, which has brought major challenges to coal-fired power plants. The pollution reduction and carbon emissions reduction in coal-fired power plants will be a key task in the future. In this paper, an optimization technique for the operation of an electrostatic precipitator is proposed. Firstly, the voltage-current model is constructed based on the modified dust charging mechanism; the modified parameters are trained through the gradient descent method. Then, the outlet dust concentration prediction model is constructed by coupling the mechanism model with the data model; the data model adopts the long short-term memory network and the attention mechanism. Finally, the particle swarm optimization algorithm is used to achieve the optimal energy consumption while ensuring stable outlet dust concentration. By training with historical data collected on site, accurate predictions of the secondary current and outlet dust concentration of the electrostatic precipitator have been achieved. The mean absolute percentage error of the voltage-current characteristic model is 1.43%, and the relative root mean-squared error is 2%. The mean absolute percentage error of the outlet dust concentration prediction model on the testing set is 5.2%, and the relative root mean-squared error is 6.9%. The optimization experiment is carried out in a 330 MW coal-fired power plant. The results show that the fluctuation of the outlet dust concentration is more stable, and the energy saving is about 43% after optimization; according to the annual operation of 300 days, the annual average carbon reduction is approximately 2621.34 tons. This method is effective and can be applied widely. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) can aggregate distributed energy resources (DER) and controllable loads to participate in the bidding of electricity market and carbon trading market. However, the uncertainty of renewable power production brings high transaction risks to VPP. Given this background, this paper proposes a novel Wasserstein‐distance based two‐stage distributionally robust chance constrained (DRCC) bidding model for VPP participating in the electricity‐carbon coupled market. The uncertainties are modelled as an ambiguity set based on Wasserstein distance, in which the two‐sided chance constraints are guaranteed satisfied. In the first stage, VPP's revenue is maximized according to the forecast information. In the second stage, the re‐dispatch measures are determined to hedge against the perturbation of uncertainties under the worst‐case distribution within the ambiguity set. Finally, a reformulation approach based on strong duality theory and conditional value‐at‐risk (CVaR) approximation is proposed to transform the DRCC problem into a tractable mixed‐integer linear programming (MILP) framework. Case studies are carried out on the IEEE 30‐bus system to verify the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The low-low-temperature electrostatic precipitator (LLT-ESP) is considered one of the mainstream technological approaches for achieving ultra-low ash emissions and has already been applied in many coal-fired power plants. Particulate matter and SO3 can both be removed by LLT-ESP. However, the removal performance of SO3 is relatively lower than that of particulate matter, which is caused by the condensation characteristics of SO3. In this paper, the condensation characteristics of SO3 were investigated on a simulated experimental system, and several measurement and characteristic methods were used to investigate mechanisms. After reducing the flue gas temperature with a heat exchanger, the size distribution of particulate matter, the mass concentration of SO3 on different sizes of particulate matter, as well as the microscopic morphology and elemental composition of particulate matter, were all experimentally studied. The results indicate that gaseous SO3 transformed into a liquid phase by heterogeneous or homogeneous condensation and then adhered to the surface of particulate matter through nucleation–condensation, collision–coalescence, and adsorption reactions. Furthermore, the removal efficiency of SO3 in LLT-ESP was also investigated under various conditions, such as ash concentration and flue gas temperature drop, suggesting that a higher ash concentration and a more significant temperature drop were beneficial for improving SO3 removal efficiency. Nevertheless, it is worth noting that the impact was limited by a further increase in ash concentration and a drop in flue gas temperature. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Generation of photovoltaic power plants is growing rapidly in the last ten years in the world. One of the key factors for the construction of floating photovoltaic power plants is to provide space for their construction. This paper presents statistical indicators of installed capacities of floating photovoltaic power plants, as well as a detailed description of the components of these power plants. Approaches to construction and maintenance recommendations are described in more detail. The basic results of simulations are presented on a concrete example of a floating photovoltaic 1 MW power plant on Lake Modrac. The available areas of artificial lakes in Bosnia and Herzegovina were analysed, and it was shown that the installation of floating photovoltaic power plants on 5% of the surface of artificial lakes would provide around 10% of the total electricity consumption in Bosnia and Herzegovina. [ABSTRACT FROM AUTHOR]

Introduction: Model predictive control (MPC) is an advanced control strategy which can achieve fast reference tracking response and deal with process constraints, time delay and multivariable problems. However, thermal processes in coal-fired power plants are usually difficult to model accurately, which limits the application of MPC to thermal power plants. Methods: To solve the problem, this paper proposes a process-model-free method for MPC via a reference model (RM)-based controller, i.e., a desired dynamic equational (DDE) proportional-integral-derivative (PID) controller (DDE-PID). Results and Discussion: The DDE-PID can provide the design model and enhance the disturbance rejection ability for MPC. Simulations and results of field tests on a coal-fired unit show the superiorities of the proposed controller in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of the field application of the MPC with DDE-PID, or MPC-DDE in short, to thermal power plants. [ABSTRACT FROM AUTHOR]

Due to the simplified assumptions or unascertained equipment parameters, traditional mechanism models of boiler system in coal-fired power plant usually have predictive errors that cannot be ignored. In order to further improve the predictive accuracy of the model, this paper proposes a novel recurrent neural network-based hybrid modeling method for digital twin of boiler system. First, the mechanism model of boiler system is described through recurrent neural network (RNN) to facilitate training and updating parameters, while the interpretability of the model does not degenerate. Second, for the time-varying parameters in the mechanism model, the functional relationship between them and the state variables is constructed by neurons to improve the predictive accuracy. Third, the long short-term memory (LSTM) neural network model is established to describe the unascertained dynamic characteristics to compensate the predictive residual of the mechanism model. Fourth, the update architecture and training algorithm applicable to the hybrid model are established to realize the iterative optimization of model parameters. Finally, experimental results show that the hybrid modeling method proposed in this paper can improve the predictive performance of traditional models effectively. [ABSTRACT FROM AUTHOR]

During the desulfurization process of coal-fired power plants, there are many related parameters that affect the desulfurization efficiency. So it is very difficult to measure the desulfurization efficiency directly. In this paper, in order to reflect the relationship between monitoring process parameters and desulfurization efficiency better, a novel prediction model of desulfurization efficiency based on improved FCM-PLS-LSSVM is established. Firstly, cluster analysis is used by improved an fuzzy C-Means (FCM) clustering algorithm to implement the preliminary classification. Secondly, in order to extract the principal components as optimal inputs of the model, the partial least squares (PLS) regression method is adopted to analyze the process factors which affect the efficiency of wet desulfurization of the flue. Then, using the least square support vector machine (LSSVM) to carry out regression prediction according to above optimization methods. Finally, numerical simulation results show that the mean absolute error (MAE) of the prediction result of the improved FCM-PLS-LSSVM model based on cluster analysis is less than 0.026. [ABSTRACT FROM AUTHOR]

Large amounts of cooling water are required for the cooling process in coal-fired or nuclear power plants. Because the primary use of water in a power plant is to condense steam and remove waste heat as part of a Rankine cycle, seawater temperature (SWT) is of critical importance for electrical output in power plant applications installed at the seaside. Moreover, the analysis of SWT is an important criterion for researching sea life and global climate, and it also serves as an important indicator of climate change. In this paper, multilayer perceptron, which is in the class of a feed-forward artificial neural network, deep learning approach based on long short-term memory and bidirectional long short-term memory neural networks and data-driven methods, such as adaptive neuro-fuzzy inference system (ANFIS) accompanied by fuzzy c-means, ANFIS with grid partition and ANFIS with subtractive clustering methods were applied to make 1-day ahead SWT predictions. Analyses were conducted using 5-year daily mean SWTs measured by the Turkish State Meteorological Service for Antalya province between 2014 and 2018. The models were evaluated using mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (R). According to the daily SWT prediction, the best MAE, RMSE, and R values were obtained with the ANFIS-SC model, which were 0.1877 °C, 0.2683 °C, and 0.99814, respectively. [ABSTRACT FROM AUTHOR]

This paper presented the effect of the activity of the coal fired power plants „Republika" and „Bobov dol" in Bulgaria and the influence of the wastewaters discharge on the ecological status of Razmetanitsa River (Struma River catchment - West Aegean region) and Sokolitsa River (Maritsa River catchment, East Aegean region). The data of the biological quality elements macrozoobenthos, phytobenthos and fish fauna; basic physicochemical parameters, dissolved oxygen, oxygen saturation, conductivity, biological oxygen demand, ammonium nitrogen, nitrate nitrogen, nitrite nitrogen, total nitrogen, orthophosphates as phosphorus, total phosphorus, calcium (calcium carbonate hardness), total suspended substances; specific pollutants - iron, manganese, copper, zinc, aluminum, chromium total, arsenic, free cyanides, polychlorinated biphenyls, phenols, uranium, radium-226, total a-activity, total ß-activity, etc.; sulphates, chlorides; the priority substances cadmium, lead, mercury and nickel from Annex 1 of Directive 2013/39/EU were reviewed and assessed for monitoring sites before and after the discharge points from coal fired power plants in the Struma and Maritsa catchments during the period 2013-2022. A relation between the worsened biological quality elements, the exceeded values of the monitored basic physicochemical parameters and the exceeded environmental quality standards of the specific pollutants and the deterioration of the ecological status of the observed surface water bodies was established. [ABSTRACT FROM AUTHOR]

Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coal-fired power plants. However, the combustion of coal in the coal-fired power plant emits pollutants such as sulfur oxides (SOx) and nitrogen oxides (NOx) which are suspected to cause damage to the environment and also be harmful to humans. For this reason, most countries have been strengthening regulations on coal-consuming industries. Therefore, the coal-fired power plant should also follow these regulations. This study focuses on the prediction of harmful emissions when the coal is mixed with high-quality and low-quality coals during combustion in the coal-fired power plant. The emission of SOx and NOx is affected by the mixture ratio between high-quality and low-quality coals so it is very important to decide on the mixture ratio of coals. To decide the coal mixture, it is a prerequisite to predict the amount of SOx and NOx emission during combustion. To do this, this paper develops a deep neural network (DNN) model which can predict SOx and NOx emissions associated with coal properties when coals are mixed. The field data from a coal-fired power plant is used to train the model and it gives mean absolute percentage error (MAPE) of 7.1% and 5.68% for SOx and NOx prediction, respectively. [ABSTRACT FROM AUTHOR]

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The object of this study is the processes of formation and changes of dispersed particles in fresh, make-up, cooling, and return water in open recirculating cooling systems (RCS) with an assessment of the influence of suspended substances in discharge waters on the aquatic ecosystem. The study was carried out on the example of the Rivne Nuclear Power Plant (RNPP) and the Styr River. Dispersed particles (DPs) pose technological obstacles in the RCS of power plants, and their content in discharge waters determines the ecological quality of water bodies. This paper describes the results of studying the formation and changes of DP in raw, make-up, cooling, and return waters of RNPP RCS with an assessment of the impact of suspended substances in discharge waters on the aquatic ecosystem of the Styr River. It was found that the formed dispersed particles after water treatment by liming contain DP consisting of calcium carbonate and have a size of 10–30 μm. As a result of agglomeration of DP in RCS, they aggregate to 120–150 μm, and due to low sedimentation resistance (sedimentation time 0.97 h), they settle in RCS. As a result of the deposition of DP in RCS, their significant decrease in return water (min–max=7.31–16.12 mg/dm³) is observed, despite the increase in their content in make-up water after water treatment (min–max=10.22–49.46 mg/dm³). According to the ecological classification, according to the content of suspended substances, the water of the Styr River in the zone of influence of RNPP discharges belongs to the II class, category 2, which characterizes the quality of the water as “very good” in terms of its state, and “clean” in terms of its degree of purity. It was concluded that the content of suspended solids does not exceed the established maximum permissible concentration (25 mg/dm³), the increase in the concentration of suspended solids does not exceed the established ecological standard of 0.25 mg/dm³ and does not have a negative impact on surface water. The results of the research could be used for other power plants equipped with an open RCS. [ABSTRACT FROM AUTHOR]

Micropiling is an extensively used method for improving the stability of building foundations and load-carrying capacity of the soil. Unlike regular piling, it can be employed without pile driving equipment and, hence, it is a versatile option of soil reinforcement under space-restricted situations. Since, it is an economical and easy to implement option for soil improvement, this paper intends to study the improvement in bearing capacity and settlement behaviour of a fly-ash bed using fine micropiles under isolated footings, for low-cast and lightweight structures. Fly-ash, a by-product of coal-fired power plants, is used in the experiment because it is extensively used as a filling material for housing projects in low-lying areas and pavements. Micropiles were installed underneath the footing as well as beyond the periphery of footing with varied spacing and extent ratio. The results are depicted through two dimensionless constants, namely bearing capacity ratio (BCR) and settlement reduction factor (SRF). Significant improvements have been observed in load-carrying capacity and settlement potential due to decrease in spacing and increase in lateral extent of micropiles. The optimum configuration of spacing and lateral extend achieved 135% bearing capacity improvement with 71% decrease in settlement. [ABSTRACT FROM AUTHOR]

The operation of the national carbon emission trading market has brought new pressure to coal power plants in China. How to control the carbon emissions of the plants and ensure their power generation has become an important issue. This paper proposed a new production profit model based on risk constraint to explore a decision-making method for the plant's carbon emission and production. A branch-and-cut algorithm is applied in the optimization solver to achieve a large-scale constraint linear programming, where the conditional value-at-risk is evaluated by Monte Carlo simulation. Results from the numerical analyses of the coal plant of 4520 MW capacity show that, the carbon emission according to our method is reduced by 3.94%, while the annual profit is increased by 1.25% and the overall cost is decreased by 1.06%. The proposed approach is helpful for the coal plants to manage their carbon emissions and optimize their power production. [ABSTRACT FROM AUTHOR]

To realize the green scenario, foreseen in the Energy Development Strategy in the Republic of North Macedonia until 2040, the existing coal-fired thermal power plants with an installed capacity of 800 MW should be successively closed in the coming period. For the same reason, the Republic of North Macedonia became a member of the PPCA Alliance (Powering Past Coal Alliance) and the first country from the Western Balkans that, together with Spain committed to abandon the production of electricity from coal by 2030. Hence, the problem that has been elaborated is very relevant because it deals with the continuation of the existence of MPC "Oslomej" through energy transition, that is, the replacement of electricity production from coal, with the production from solar energy. The analysis in this paper is focused and aims to address the importance of the further operation of MPC "Oslomej", with the construction and commissioning of the first photovoltaic plant PVPP Oslomej 1 of 10 MW. Afterward, MPC "Oslomej" will continue with the construction of PVPP Oslomej 2 of 10 MW and PVPP Oslomej 3 of 100 MW, which will completely replace the existing coal-fired thermal power plant TPP "Oslomej" of 120 MW. The construction of these photovoltaic power plants in the mine of the Oslomej plant, during the energy crisis is the first example in SouthEastern Europe, for which the European energy community reacted positively and expressed support to JSC ESM. [ABSTRACT FROM AUTHOR]

Under the global goal of "carbon peaking and carbon neutrality", new power system is facing a new energy development trend of increasing the capacity of new energy and reducing the capacity and quantity of coal power. Furthermore, new power systems are also moving towards new models such as the Energy Internet and energy storage diversification. This paper combines the needs of new power systems' "source-load interaction" and a differentiated analysis of the specific carbon emission characteristics of each subject of the Energy Internet "source-network-load-storage". This paper analyzes the energy efficiency coefficient and carbon emission factors of each link of "source-grid-load-storage". Therefore, the paper proposes a carbon control model for the Energy Internet. This paper focuses on the characteristics of coal power with the largest carbon reduction. Therefore, this article constructs a multi-dimensional low-carbonization evaluation index system for coal-fired power plants. In addition, this article designs a fusion weight calculation method for subjective and objective indicators based on CRITIC-G1. Additionally, this paper uses the ideal solution–gray relational method to evaluate the specified samples. Finally, this article verifies the effectiveness and feasibility of the implementation of the low-carbonization evaluation index system for coal-fired power plants. [ABSTRACT FROM AUTHOR]

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In order to accommodate more intermittent renewable energy in coal-dominated power systems, conventional thermal power plants need to improve their operational flexibility to balance the energy system at all times. However, flexible operation of coal-fired power plants could reduce energy efficiency and increase CO2 and pollutant emission, so it is important to consider environmental implications and optimize the dispatch of wind and coal power units in the system. In this paper, based on the output profile of wind power, a wind power peak (T, H) simulation model based on Gaussian distribution was established. Using a high-proportion wind power wind–coal combined base load power generation system as an example, the economical and environmentally friendly unit operation based on different wind power penetration was studied by simulation, and the decision strategy was established. Wind energy curtailment boundary was determined with power generation cost, energy consumption, CO2 and pollutant emissions as decision targets, respectively. Weekly scale results indicate that incorporating energy consumption and pollutant emissions into consideration will lead to different decision-making strategies compared with only targeting minimizing wind curtailment. This paper established a decision-making model of wind–coal system operation strategy based on economy and environmental criteria. This work directly contributes to real system operation and is of great significance for future scheduling/dispatch studies of actual power systems. [ABSTRACT FROM AUTHOR]

Since the liberalisation of India's energy sector began in the 1990s, the government has developed several novel techniques to make coal an attractive asset for investors. The pharaonic Ultra Mega Power Plant Project (UMPP) program is one such technique which secures finance capital, land, and environmental clearances for coal‐fired power plants. Drawing upon the analytic of assemblage, this paper tracks how the Cheyyur UMPP, one of the largest proposed coal‐fired power plants in the world, has been disassembled by a range of interdependent elements, from Right to Information (RTI) activism and environmental litigation to the perception of litigation risk and the materiality of coal. In pursuing an analysis of the events and elements which have led to the Cheyyur UMPP being disassembled, this paper calls for greater attention to the vulnerabilities, limits, and instabilities of coal assemblages. [ABSTRACT FROM AUTHOR]

To make up for the lack of domestic research on the dynamic model of the unit in a gas-steam combined cycle power plant, the thesis researched and established a real-time digital simulation model of the combined cycle power plant unit. Firstly, the theory found a non-real-time digital simulation model in MATLAB/SIMULINK environment. Then the paper converts it into C language code and uses the C Builder tool to encapsulate it into a simulation model that can run in real-time. Finally, the thesis conducted a simulation test on the model on real-time digital simulation and compared it with the field test results. The simulation results show that the combined cycle power plant unit model we have established can meet the real-time simulation requirements of the power system in terms of computational efficiency and model accuracy. [ABSTRACT FROM AUTHOR]

Coal is expected to remain a significant power supply source worldwide and shifting to carbon-neutral fuels will be challenging because of growing electricity demand and booming industrialization. At the same time, coal consumption results in severe air pollution and health concerns. Improvement in emission control technologies is a key to improving air quality in coal power plants. Many scientists reported removing air pollutants individually via conventional control methods. However, controlling multiple pollutants combinedly using the latest techniques is rarely examined. Therefore, this paper overviews the current and advanced physical technologies to control multi-air pollutants synergistically, including carbon control technologies. Also, the paper aims to examine how potential air pollutants (e.g., PM2.5, SO2, NOx, CO2), including mercury from the coal-fired power plants, cause environmental impacts. The data synthesis shows that coal quality is the most significant factor for increasing air emissions, regardless of power plant capacity. It is found that selecting techniques is critical for new and retrofitted plants depending on the aging of a power plant and other socio-economic factors. Considering the future perspective, this paper discusses possible pathways to transform from linear to a circular economy in a coal power plant sector, such as utilizing energy losses through energy-efficient processes and reuse of syngas. The article provides an in-depth analysis of advanced cost-effective techniques that would help to control the air pollution level. Additionally, a life cycle assessment-based decision-making framework is proposed that would assist the stakeholders in achieving net-zero emissions and offset the financial burden for air pollution control in coal-fired power plants. [ABSTRACT FROM AUTHOR]