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The incineration of high‐moisture solid residues generated at the recycling paper mills represents an energetically unfavourable method of resource utilization. Alternatively, hydrothermal pre‐treatment is considered. In this study, low‐value paper sludges from three different recycling streams were hydrothermally carbonized at 205, 225, and 245°C for 3 h. The raw feedstocks and derived hydrochars were analyzed for energy properties, chemical characteristics, surface morphology, functional groups, and combustion performance employing energy densification and mass yield quantification, scanning electron microscopy, elemental analyzer, Fourier‐transform infrared spectroscopy, and thermogravimetry. The increase in reaction temperature was reported to cause a decrease in mass yield and an increase in energy densification and calorific values corresponding to 5.98%–49.35% and 10.10%–58.51% for raw fibre rejects and final sludge‐derived hydrochar, respectively. Hydrothermal carbonization (HTC) had a non‐significant influence on the energy densification of primary clarifier sludge‐derived hydrochar. Higher reaction temperatures favoured the rate of dehydration and decarboxylation, leading to hydrochars with lower H/C and O/C ratios, thereby enhancing the overall fuel properties. The low‐nitrogen and low‐sulphur fuels obtained validated the effectiveness of HTC treatment to produce high‐quality cleaner solid fuel. The burnout temperature was mostly reduced with an increase in HTC temperature. At HTC‐205 and 225°C, the ignition temperature and the combustion performance increased as a result of the HTC reaction mechanisms. HTC effectively recovered hydrochar with increased carbon content, improved energy densification, and good combustion adequacy. Hydrochar derived from recycling mills may play a role in the energy sector as a substitute for coal or in co‐combustion at coal‐fired power plants. [ABSTRACT FROM AUTHOR]

Efficient, clean, and intelligent operation of coal-fired power plants plays a crucial role in the pursuit of energy conservation and carbon reduction. Online monitoring of pneumatically conveyed fuel is essential to furnish fundamental data for combustion optimization. This paper proposes a digital holographic particle analyzer (DHPA) with a portable design for monitoring pulverized fuel fineness. DHPA consists of a particle sampling and recycling probe, a digital inline holographic system, a controlling module, and software. The probe extracts representative fuel particles from the pipe, which are subsequently measured in a three-dimensional field by pulsed digital inline holography before being returned to the pipe. The performance tests of DHPA for coal fineness measurement encompass investigations into the dispersion of particles within the measurement volume, the minimum particle number necessary for reliable results, a comparison with the laser particle analyzer and sieving method, as well as sensitivity to mill parameter changes. It is recommended to capture at least 4 × 104 particles to ensure a credible outcome. DHPA exhibits comparability with the traditional sieving method, displaying an absolute error of 1.48% for fineness R 90 measurement. Moreover, DHPA has been successfully employed for measuring and optimizing the coal fineness at full-scale power plants. On-site measurements have unequivocally confirmed the feasibility and effectiveness of DHPA for monitoring pulverized fuel. [Display omitted] • A novel and portable digital holographic particle analyzer (DHPA) is developed. • DHPA can provide on-line, flexible, and closed-loop measurement. • DHPA is a viable alternative to the conventional off-line technique. • DHPA has been successfully applied in full-scale coal-fired plants. [ABSTRACT FROM AUTHOR]

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

The paper presents a specific method of environmental impact assessment applied in Strategic Environmental Assessment (SEA) for the National Emission Reduction Plan (NERP) in the Republic of Serbia, based on air quality. The specificity of the approach is in the application of a semiquantitative method of multicriteria evaluation based on air dispersion modeling and the integration of SEA goals, indicators and criteria for assessing the impact of the NERP on the quality of air and other environmental elements in this method. When predicting changes in air quality for the planning horizon to 2028, the physical, geographical and climatic characteristics of the area were taken into account, as well as technical measures to reduce SO2 emissions, since this was the dominant pollutant from the Serbian coal-fired power plants studied by the NERP. Air pollution modeling was carried out using the AERMOD software package based on the data collected, and the quantitative results obtained were used in a multicriteria evaluation as part of the SEA. The results of the research indicated the importance of applying this approach in order to significantly increase objectivity in the SEA process, since it is an important element of decision making at the strategic level. In addition, a comparative presentation of the modeling results before and after application of the NERP was an important part of the SEA process, and it provided a clear insight into expected changes in the air quality. This is a key argument for making appropriate policy decisions on spatial, energy, environmental and socio-economic development in the Republic of Serbia, which, like other developing countries, is sluggishly following global trends in energy transition. [ABSTRACT FROM AUTHOR]

A new cable-damper stopper equipped for Coal-Fired Thermal Power Plants (CFTPPs) was proposed in our previous paper, where a series of shaking table tests were carried out to investigate its seismic performance, so this paper aims to reveal the working mechanism of the stopper from the theoretical and numerical simulation aspects. The stopper is composed of three main components, i.e. a steel cable, a steel block and a viscous damper. First, the nonlinear restraint stiffness of the steel cable to the boiler was derived; second, the critical damping of the stopper, which makes the vibration of the boiler decay exponentially with no oscillation, was derived; third, an iterative method was proposed to globally tune the CFTPP structure, which makes it possible to obtain the optimal parameters of the cable-damper stopper, including the diameter of the steel cable and damping coefficient of the damper. Finally, finite element models were built and time history analyses were conducted to verify the proposed optimization approach. Firstly, the finite element models were proved valid through the comparison between the results of the simulations and the experiments. Then, based on the results of the simulations, the theoretical procedure was validated. The results revealed that the proposed cable-damper stopper reduced the seismic responses of displacements of the top floor and the boiler significantly, about 30.18% and 60.01% respectively compared with their counterparts of CFTPPs without stoppers. [ABSTRACT FROM AUTHOR]

The integration of renewable generation presents a promising venue for displacing fossil fuels, yet integration remains a challenge. This paper investigates Demand Response (DR) as a means of economically integrating Renewable Energy Resources (RERs). We propose Incentive-Based DR (IBDR) programs, particularly suitable for small customers. The uncertainties in the electricity market price pose a challenge to IBDR programs, which is addressed in this paper through a novel and robust IBDR approach that considers both the electricity market price uncertainties and customer responses to incentives. In this paper, scenarios are simulated premised on the Western Electricity Coordinating Council (WECC) 240-bus system in which coal-fired power plants become inactivated, while the RER contribution increases in the span of one year. The simulation results indicate that the proposed IBDR program mitigates the issues associated with renewable expansion, such as utility benefit loss and market price volatility. In addition, the proposed IBDR effectively manages up to 30% of errors in day-ahead wind forecasts that significantly reduce financial risks linked to IBDR programs. [ABSTRACT FROM AUTHOR]

Background: The main goal of the paper is to review the existing state and propose a model solution for the introduction of the waste-to-energy concept in the Republic of Serbia and Bosnia and Herzegovina, as these Balkan countries are a source of high pollution due to the inefficient use of fossil fuels and the operation of coal-fired power plants. Besides, these countries have very low level of waste management, which results in the uncontrolled disposal of a large amount of waste which consists of plastic and microplastic materials which are difficult to decompose in natural ecosystems. Methods: Considering the type and objectives of the study, a mixed research method was chosen as a combination of exploratory research, descriptive research, explanatory research, and modeling. Results: The main result of the research shows the unfavorable situation in the field of waste management in the Republic of Serbia, as well as in Bosnia and Herzegovina. Both countries have adopted adequate strategies and plans, but waste management is not implemented accordingly, Nevertheless, numerous problems create considerable opportunities for improvement, especially regarding the potential for energy production from waste, which is an important approach to implementing the circular economy model. The study showed that the situation is particularly unfavorable in rural areas. Hence, the research proposes (a) a novel model for waste-to-energy governance and (b) a novel model for waste-to-energy management in rural areas. The research was done, and models were developed based on the examples of the Republic of Serbia, and Bosnia and Herzegovina. However, the results can be used in countries with a similar level of waste management and with a larger share of rural areas. Conclusions: The paper emphasizes the importance of a holistic and systemic approach to waste management, with emphasis on using waste-to-energy concept as particularly applicable in the transition to circular economy. This study proposes a model for the integration of waste management (with emphasis on plastic and microplastic) and energy efficiency, presenting a model of approach that can be used in countries that are at the beginning of introducing a circular economy. [ABSTRACT FROM AUTHOR]

A run‐of‐the‐river power plant is a renewable energy source used for electricity production. Its power output depends on the varying water flow rate over time, which can impact the reliability of the electric network. Previous research has not studied the effects of water flow rate and temperature variations on the hazard rate of the plant's components. This paper addresses this gap by investigating the impact of these variations on the reliability of electric networks with run‐of‐the‐river power plants. The analysis considers the hazard rates of the plant's components, incorporating the relationship between hazard rate and temperature based on the Arrhenius law. Parameters such as power output, current, power loss, operating temperature, and hazard rate are calculated for different water flow rates and ambient temperatures. Numerical simulations on a test system are conducted to examine the influence of water flow rate and temperature on the reliability indices of the electric network. The results demonstrate that water flow rate and temperature significantly affect the hazard rates of run‐of‐the‐river power plants. This highlights the need to consider these factors in the reliability analysis of electric networks incorporating these renewable resources. [ABSTRACT FROM AUTHOR]

In the context of a low‐carbon economy, considering the output uncertainties of the renewable energy sources and the fluctuation of electrical price, the cost–benefit analysis is proposed for multi‐investor virtual power plant (VPP) under different risk preferences, and multi‐investor VPP capacity sizing problem is researched. First, the structural organization and flexible regulation characteristics of multi‐investor VPPs are analyzed. Second, integrated model and cost–benefit analysis indexes of VPP are built. Third, an optimal capacity sizing model of multi‐investor VPP is established, aiming at income maximization. Game theory is used to configure the distributed generators in the VPP under various cooperative modes and noncooperative modes of multiple investors. Fourth, according to the installed proportion of each distributed generator and the risk preference coefficient of each investor, the income allocation correction model based on the improved Shapley value method is proposed to stabilize the optimal VPP alliance. Finally, the demonstration project in the animation industry park of Sino‐Singapore Tianjin Eco‐City is employed as representative scenarios, which is used to validate the effectiveness of the proposed model. The results prove that the method proposed in this paper can guide knowledge for the investors with different risk preferences when planning the optimal capacity of multiple distributed generators in the VPP. [ABSTRACT FROM AUTHOR]

The double-carbon goal proposal has made it imperative for China's power industry to address the urgent issue of reducing greenhouse gas emissions from coal-fired power plants and promoting their clean and efficient use. A new approach to achieving peak-shaving and improving grid stability is the combination of carbon capture and storage (CCS) facilities with coal-fired power plants, which offers a strategic solution for mitigating climate change. Nonetheless, the viability of CCS retrofitting for coal-fired power plants in China hinges on its economic feasibility. The technical characteristics, economic analysis, and policy implications of clean coal-fired power plant equipped with CCS (CCP-CCS) technologies are evaluated in the paper. A comparison of the characteristics of different clean coal power generation technologies, including capacity, efficiency, coal consumption, and environmental performance, is conducted. The paper analyzes the economic feasibility of four typical clean coal power generation technologies, namely Supercritical (SC), Ultra-supercritical (USC), Integrated Gasification Combined Cycle (IGCC), and Circulating Fluidized Bed (CFB), both with and without CCS, using the levelized cost of electricity (LCOE) model. Financial parameters, investment parameters, operation and maintenance parameters, tax parameters, and capacity parameters are all considered in the LCOE model. The results indicate: (1) If CCS is not installed, the LCOE of USC power plants is the lowest at 0.37 yuan/kWh, while the LCOE of IGCC power plants is the highest at 0.44 yuan/kWh. (2) If CCS is installed, the LCOE of CFB units is the highest at 0.74 yuan/kWh, while the LCOE of USC units is the lowest at 0.60 yuan/kWh. (3) In sensitivity analysis, coal prices have the most significant impact on LCOE, followed by expected annual operating hours, while the cost of carbon capture equipment is the least sensitive. (4) The cost of retrofitting CCS on clean coal-fired power plants (CCP) is currently high, with an LCOE increase of around 40 to 70%, which is comparable to that of renewable energy electricity. This paper presents policy recommendations, including preferential loans, tax incentives, and R&D funding, to promote the development of CCP in China based on the economic analysis results and challenges faced by CCP. Additionally, the paper proposes relevant policy implications such as preferential loan policies, a diversified financing system, R&D and innovation, and the establishment of a carbon trading market for CCS in China to provide practical guidance for the promotion of CCP-CCS technologies in China. [ABSTRACT FROM AUTHOR]

Coal remains an important fuel for use in thermal power plants. However, coal-burning power plants produce large amounts of CFA, which contains TEs such as B, F, and Se, which are leached upon contact with water and act as potential polluters of aquifer systems and soil. To study the transformation of TEs, a thermodynamic calculation (FactSage 7.2) was used. Paper sludge ash was used as a calcium compound additive. The influence of blending a high-calcium additive on the environmental safety of TEs was investigated based on the effect of the mass addition ratio of PS ash. This study's results confirmed that the leaching processes of TEs, namely (CaO)2(Al2O3), CaF2, Ca10(PO4)F2, and CaSeO4(H2O)2, were caused by the formation of B, F, and Se compounds during the leaching process. Thus, it is clear that calcium has the greatest influence on the transformation of TEs due to their reaction, which, in turn, minimizes the effects of the TEs' release into the environment. The concentrations of TEs from the sample and addition of PS ash decreased slightly, indicating that the inhibition of TEs was enabled through the addition of PS ash. Although the PS ash YB had the highest calcium content, the PS ash YC gave the best results during the B and Se inhibition processes. The experimental observation was also evaluated for comparison. For the analysis of TEs' leaching ratios using the thermodynamic calculation and experiment, the experimental results were lower than those initially predicted. These results will help us to choose the best available control technology to minimize the effects of TEs released into the environment. [ABSTRACT FROM AUTHOR]

Photovoltaic power stations utilizing solar energy, have grown in scale, resulting in an increase in operational maintenance requirements. Efficient inspection of components within these stations is crucial. However, the large area of photovoltaic power generation, coupled with a substantial number of photovoltaic panels and complex geographical environments, renders manual inspection methods highly inefficient and inadequate for modern photovoltaic power stations. To address this issue, this paper proposes a method and system for hot spot detection on photovoltaic panels using unmanned aerial vehicles (UAVs) equipped with multispectral cameras. The UAVs capture visible and infrared images of the photovoltaic power plant, which are then processed for photogrammetry to determine imaging position and attitude. The infrared images are stitched together using this information, forming a geographically referenced overall image. Hot spot detection is performed on the infrared images, enabling the identification of faulty photovoltaic panels and facilitating efficient inspection and maintenance. Experimental trials were conducted at a photovoltaic power station in Qingyuan, Guangdong Province China. The results demonstrate the effectiveness of the proposed method in accurately detecting panels with hot spot faults. [ABSTRACT FROM AUTHOR]

In the case of new investment in RES technology, there are two issues related to efficiency assessment. The first one is how to join technical, financial and economic efficiency assessments in one. An investment feasibility study is usually conducted through a financial analysis to obtain the internal rate of return and the net present value. However, a new RES investment is typically financially unprofitable unless the environmental and social aspects are included. The second one is a lack of required financial data. The new RES investment is often innovative and neither the owner nor other entities have financial data on the operating costs and expenditure for the last periods. Therefore, in this paper, we proposed two methods of efficiency assessment. The first one is based on the avoided costs theory. Furthermore, the second one belongs to heuristic methods and is based on the experts' assessment of different kinds of parameters. The purpose of this study is to assess the efficiency of the pilot project of VPPs using two recommended methods. This paper emphasizes the advantages and disadvantages of each method. The actual technical and financial data for the period of six months from the pilot study were calculated. [ABSTRACT FROM AUTHOR]

China's 2060 carbon neutrality target requires coal-fired power generation and coal consumption to shrink dramatically, or to introduce carbon capture, storage and utilization technologies (CCUS). In the provinces which produce a lot of coal and export it to other regions such as Shanxi Province, there are many severe challenges to achieve carbon neutrality goal. This paper constructs a provincial level Computable General Equilibrium Model for Shanxi Province to estimate the impacts on economy based on different decarbonization scenarios in power sector. Through comparison among the quantified scenarios, a sustainable pathway to coal phasing-out is also discussed in this research. The results indicate that in the simple phasing-out of coal thermal power scenario and single deployment of CCUS technology scenario, it will be a shock to GDP due to its coal-reliance economy. In the scenario which utilizes CCUS technologies and coal phasing out in the future, it proves that CCUS will mitigate economic impacts from 2025 to 2050. So, instead of coal phasing out, a sustainable pathway with accelerating change to thermal coal with CCUS is discussed in this research. It found that in this scenario, Shanxi province can keep a lower carbon intensity with a lowest carbon reduction cost of 3.62 k RMB/t. [ABSTRACT FROM AUTHOR]

Power generation comprises high environmental and ecological impacts. The global power industry is under pressure to develop more efficient ways to operate and reduce the impacts of inherent process variability. With the rapid development of technologies within the energy sector, large volumes of data are available due to in-time operational measurements. With increased computer processing capabilities, machine learning is applied to explore these in-time operational measurements for improved process understanding. This research paper investigates machine learning algorithms for energy efficiency improvement at coal-fired thermal power plants by conducting a systematic literature review. This research is essential since it provides guidelines for applying machine learning towards sustainable energy supply and improved decision-making. Subsequently, efficient processes result in the reduction of fuel usage, which results in lower emission levels for equivalent power generation capacity. Furthermore, this study contributes towards future research by providing valuable insights from academic and industry-related studies. [ABSTRACT FROM AUTHOR]

The accumulation of snow is one of the essential reasons for the significant decrease in the power generation efficiency of PV stations in winter. For this reason, this paper firstly provides a detailed review of the current research into the physical processes and shedding reasons for the natural melting of snow on PV module surfaces. Then, several common techniques to promote snow and ice melting on PV surfaces are discussed, and the feasibility and shortcoming of ultrasonic technology in removing high‐density snow and ice are analyzed in detail. Finally, this paper proposes two snow sensing technologies for PV modules, namely, image processing method and interdigital capacitance method. Among them, the image method based on fixed camera and UAV detection is more suitable for wide area PV power stations, while the interdigital capacitance method is helpful in obtaining snow characteristic parameters, such as icing type and LWC. The relevant research has certain reference significance for PV power stations to formulate a more reasonable snow removal plan and power generation forecast. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

One of the most frequent causes of coal fired power plant shutdown is boiler tube leak. Superheaters are crucial components of boilers operating under high temperatures and pressures. Understanding the superheater potential damages is essential to define the rectification action needed to keep and elevate overall power plant productivity. This paper highlights the analysis of platen superheater tube failure in a 2x150 MW coal-fired power plant in South Sumatera. Various test including chemical composition, hardness, tensile, metallography, SEM fracture surface examination, and XRD compound analysis were conducted for investigation purposed. The failure was initiated by the plugging of the tube elbow due to deposits and ashes adhering to the tube's interior. This obstruction prevented saturated steam flow inside the tube, leading to overheating and a subsequent drop in mechanical strength. Overheating was confirmed by the presence of spheroid particles in the ferrite matrix. Prolonged overheating resulted in the formation of microvoids, leading to creep failure and crack formation in the tube. The improvements made by adding refractory material give positive results. The platen superheater tubes which previously exceeded temperature limits, currently operate within normal range temperatures, reduced spray water consumption 6-10 tons/day, and significantly increased boiler efficiency from 83.19% to 83.54%. [ABSTRACT FROM AUTHOR]

The conversion of coal-fired power plants to nuclear power stations is a potential method for decarbonizing coal power and offers a pathway for low-carbon development in China's power industry. This paper focuses on retrofitting China's coastal coal-fired power stations and compares the potential nuclear reactor technologies for the retrofit: China's mainstream pressurized water reactor and the commercially operated fourth-generation high-temperature gas-cooled reactor (HTGR). The analysis compares the degree of matching between the two technologies and coal-fired power stations in terms of unit capacity, thermal system parameters, unit speed, structural dimensions, and weight, which significantly impact the retrofit scheme. The results indicate that HTGR is more compatible with coal-fired power plants and is recommended as the type of nuclear reactor technology to be retrofitted. The study selected the 210 MWe High-Temperature Gas-Cooled Reactor Pebble-Bed Module (HTR-PM) as the reactor technology for retrofitting a typical 300 MW class subcritical coal-fired unit. Based on the concept of subcritical parameters upgrading, the potential analysis and strategy study of retrofit is carried out in terms of the turbine, the main heat exchange equipment, the main pumps, and the main thermal system pipelines in the conventional island. The results indicate that the conventional island of the HTR-PM nuclear power plant has significant potential for retrofitting, which can be a crucial research direction for nuclear retrofitting of coal-fired power plants. [ABSTRACT FROM AUTHOR]

Gas flaring has been identified as a major contributor to global warming and climate change. It is used either as a safety measure or as a means of disposal for technical or economic reasons. Over 250 toxins have been directly/indirectly associated with gas flaring and its associated emissions. Most of these toxins have been known to have significant inimical impacts on humans' health, plant biodiversity, and the environment. With the recent rise in global energy insecurity, several EU countries have either returned to coal power generation or extended the lifetime of their coal-fired plants thereby increasing anthropogenic carbon emissions. This increase in carbon emission has necessitated the re-evaluate of gas flare practices vis-à-vis the environmental challenges and the financial potentials. This paper presents a holistic review of gas flaring, its types, composition, systems design, estimation methods, social and environmental challenges, the abatement measures, and the re-utilization strategies. It identified the potential to save a minimum of US$10.4 billion globally if more stringent gas flare abatement measures were pursued. Furthermore, the paper highlights the recent trends in flare gas re-utilization technologies such as the production of bioproducts which has been reported to hold a potential for an annual production of about 148 million bbl of biocrude and 67 million metrics of algae protein from 140 bcm of globally flared gas. Finally, it explored the possible way forward and stringent measures that can be pursued to disincentivize gas flare and also increase investments in gas processing technologies. [ABSTRACT FROM AUTHOR]

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The outbreak of the 2022 Russia-Ukraine conflict exacerbated the natural gas supply shortage in European countries. European countries restarted coal-fired power plants to maintain economic and social operations. The uneven distribution of coal resources in the world makes coal international trade inevitable. The intricate trade relations between trading countries have formed a coal trade network. When a country's coal exports are limited due to geopolitical factors, it will cause coal supply risks. The risk will spread to more countries along the trade network, eventually leading to the collapse of the trade network. This paper builds a crisis propagation model of the coal supply under the Russia-Ukraine conflict using the cascading failure model. The results showed that the Czech Republic, Ireland, Portugal, and Bulgaria become abnormal as the proportion of coal exports β increases. When the Russian Federation reduced its coal exports by 80% and countries maintained only 10% coal exports against crisis, 23 European countries were the worst. Iceland, Ireland, Turkey and other countries were spread by the indirect risk and became abnormal countries. The Czech Republic and Bulgaria were spread by multiple risk and became abnormal countries. [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]

Reducing CO 2 emissions from coal-fired power plants is an urgent global issue. Effective and precise monitoring of CO 2 emissions is a prerequisite for optimizing electricity production processes and achieving such reductions. To obtain the high temporal resolution emissions status of power plants, a lot of research has been done. Currently, typical solutions are utilizing Continuous Emission Monitoring System (CEMS) to measure CO 2 emissions. However, these methods are too expensive and complicated because they require the installation of a large number of devices and require periodic maintenance to obtain accurate measurements. According to this limitation, this paper attempts to provide a novel data-driven method using net power generation to achieve near-real-time monitoring. First, we study the key elements of CO 2 emissions from coal-fired power plants (CFPPs) in depth and design a regression and physical variable model-based emission simulator. We then present Emission Estimation Network (EEN), a heterogeneous network-based deep learning model, to estimate CO 2 emissions from CFPPs in near-real-time. We use artificial data generated by the simulator to train it and apply a few real-world datasets to complete the adaptation. The experimental results show that our proposal is a competitive approach that not only has accurate measurements but is also easy to implement. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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