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In this paper, the effect of electric vehicles (EVs) on load frequency control (LFC) in the context of a deregulated market within an asymmetric three-area system featuring a novel combination of hybrid power plants is presented. The paper discusses load frequency control within a deregulated market in an unequal three-area system using a new combination of hybrid power plants. All the areas have one renewable energy source and a thermal power plant (TPP), and each area incorporates electric vehicles. Area 1 contains a combination of a wind turbine system (WTS) and thermal, Area 2 has a geothermal power plant (GTPP) and thermal, and Area 3 has a biogas power plant (BPP) and thermal. This proposed system is investigated. Conventional PID, PI, and I controllers are used because they are simple, cheap, and easily available. Their performance is observed and compared. The controller parameters undergo optimization by applying an innovative optimization method called the Mine Blasting algorithm, which utilizes an integral square error (ISE)-based fitness function. The analysis is done under bilateral and contract violation cases with and without generation rate constraints. Moreover, the state of charge (SoC) estimation concept under a deregulated environment and the significance of EVs in the proposed system, especially in the case of contract violation, is presented. [ABSTRACT FROM AUTHOR]

To address the issue of economic dispatch imbalance in virtual power plant (VPP) systems caused by the influence of operators and distribution networks, this study introduces an optimized economic dispatch method based on bi-level game theory. Firstly, a bi-level game model is formulated, which integrates the operational and environmental expenses of VPPs with the revenues of system operators. To avoid local optima during the search process, an enhanced reinforcement learning algorithm is developed to achieve rapid convergence and obtain the optimal solution. Finally, case analyses illustrate that the proposed method effectively accomplishes multi-objective optimization for various decision-making stakeholders, including VPP and system operators, while significantly reducing curtailment costs associated with the extensive integration of distributed renewable energy. Furthermore, the proposed algorithm achieves fast iteration and yields superior dispatch outcomes under the same modeling conditions. [ABSTRACT FROM AUTHOR]

The resilient control issue for the generation unit (GU) in a local power plant with unreliable communication is addressed in this article, where the communication may be jammed by denial-of-service (DoS) attacks. Based on the GU model of voltage and current at the point of common coupling, a demand-driven network communication protocol is proposed to decrease the number of scheduling signal transmissions, and an observer-based prediction method is provided to replenish the lack of dispatching data during transmission intervals when the demand has not changed. The closed-loop performance is analyzed for the GU system in the input-to-state stable framework with or without attack. According to the DoS attack model, which is described by the assumptions of frequency and duration, the conservativeness of the tolerable DoS attack index is reduced by using the thought of robustness to the maximum disturbance-induced error. Simulation examples are provided to verify the effectiveness of the approach proposed in this article. [ABSTRACT FROM AUTHOR]

The present paper reports the design of a new class of γ/γ′ containing high-strength CoNi-based superalloys in the Co–Ni–Al system by the addition of Ta and Ti. The designed alloys with compositions Co–30Ni–10Al–2Ta–xTi (where x = 0, 2, 4 at.%) exhibit a γ/γ′ microstructure suitable for moderate temperature applications (≤ 800 °C) requiring exceptional specific strength. The 3D APT compositional analysis of the γ and γ′ phases indicates a Co-rich matrix, while the ordered γ′ precipitates are Ni-rich with a stoichiometry of (Ni,Co)₃(Al,Ta). The subsequent addition of Ti up to 4 at.% increases the Ni partitioning to the γ′ precipitates, enhancing their stability. Furthermore, the addition of 4 at.% Ti increases the γ′ solvus temperature by 170 °C, from 950 to 1120 °C, and concurrently decreases the mass density from 8.50 to 8.34 g/cm3. A positive correlation between yield strength and temperature is observed in the Ti-containing alloys, with maximum yield strengths and specific yield strengths of 1025 ± 25 MPa and 122 MPa·g⁻1·cc, respectively, in the 2Ta2Ti composition, and 1030 ± 20 MPa and 123 MPa·g⁻1·cc, respectively, in the 2Ta4Ti composition. Long-term stability studies, including microhardness measurements and quantitative assessments of microstructural features, demonstrate excellent microstructural stability and resistance to coarsening in the 2Ta4Ti alloy at 900 °C. This work provides the opportunity to further design low-mass-density CoNi-based alloys with superior performance in the medium-temperature domain required for Advanced Ultra-Super-Critical (AUSC) power plants. This paper is part of a special volume in Journal of Materials Science in honor of Professor Kamanio Chattopadhyay, renowned for his research in our field and his significant contributions to the development of novel alloys for various engineering applications. In his early 60 s, Professor Chattopadhyay's group pioneered the discovery of low-density Co-based superalloys with a γ/γ' microstructure closely resembling that of Ni-based superalloys. This work involves some of the alloy compositions we conceived during our doctoral thesis work with him. [ABSTRACT FROM AUTHOR]

The production of nitrogen oxides (NO x = NO + NO 2 ) is substantial in urban areas and from fossil fuel-fired power plants, causing both local and regional pollution, with severe consequences for human health. To estimate their emissions and implement air quality policies, authorities often rely on reported emission inventories. The island of Cyprus is de facto divided into two different political entities, and as a result, such emissions inventories are not systematically available for the whole island. We map NO x emissions in Cyprus for two 6-month periods in 2021 and 2022 with a flux-divergence scheme, using spaceborne retrievals of nitrogen dioxide (NO 2 ) columns at high spatial resolution from the TROPOMI instrument, as well as horizontal wind data to derive advection and concentrations of OH, NO, and NO 2 to derive chemical processes. Emissions are estimated under three different sets of parameters using ECMWF data and WRF-Chem simulations. These sets are chosen for their differences in spatial resolution and representation of wind and air composition. Exploiting the low emissions in Cyprus, we show that the flux-divergence method is limited by the resolution of wind and hydroxyl radical, the signal-to-noise ratio of the observed tropospheric column densities, and the NO x :NO 2 ratio above the main pollution sources. Such limitations lead to large discrepancies in the emissions calculated with the three different sets of parameters, making it difficult to estimate NO x emissions for the five power plants of the island without high uncertainties. Nevertheless, the obtained emissions display a higher seasonality than reported or inventory emissions. For the two power plants in the south, the different mean daytime output estimates appear to be significantly higher than the bottom-up estimates. They are also higher than those from the power plants in the south combined, despite a much lower production capacity, illustrating the application of different environmental norms and the use of different technologies and fuels in the two parts of Cyprus. [ABSTRACT FROM AUTHOR]

Hydrogen is a powerful enabler for a sustainable energy transition, offering benefits for both the power system and end-use applications. In this context, this work aims to determine the levelized cost of hydrogen (LCOH) for a hypothetical plant in Brazil with an installed capacity of 100 MW, calculated on an hourly basis. The plant has a long-term power purchase agreement (PPA) for the electrolysis process with two renewable power plants: a 210 MW wind power plant and a 120 MW photovoltaic (PV) solar plant. The energy generation data are sourced from the Brazilian National System Operator (ONS) website, while the costs associated with green hydrogen production come from secondary data. The study concludes that the LCOH is 5.29 US$/kg for an alkaline electrolyzer (AEL) and 5.92 US$/kg for a proton exchange membrane electrolyzer (PEMEL). Additionally, approximately 80% of the hydrogen is certified as 100% renewable on an hourly basis, and all hydrogen produced on a monthly basis meets this criterion with a utilization factor (UF) of 95%. [Display omitted] • The techno-economic analysis of a green hydrogen plant is performed. • Green hydrogen producers engage in power purchase agreements (PPAs). • 80% of the hydrogen is 100% renewable on an hourly basis. • Monthly hydrogen is 100% renewable at a utilization factor of 95%. • The LCOH is U$5.29/kg for AEL and US$5.92/kg for PEMEL. [ABSTRACT FROM AUTHOR]

Several coal-fired power plants (CFPPs) were built in South Africa, mainly in the central Mpumalanga Province, due to an increase in the demand for Eskom, the national power utility, to keep up with socio-economic growth. The CFPPs, of which 90% are owned by Eskom, generate a significant share of the country's electricity but contribute to the air pollution experienced in the country. The paper discusses sulphur dioxide (SO2), nitrogen dioxide (NO2) and particulate matter of size less than 10 micrometre (μm) in diameter (PM10), using data from 2014 to 2018. The statistics revealed higher PM10 concentrations during winter than in summer and spring at the Kriel and Komati sites; associated with the higher contribution of domestic burning. The study's results could influence legislation and policies and help to understand the source of poor ambient air quality by assessing the three pollutants within the area of the selected power plants. [ABSTRACT FROM AUTHOR]

Prolonged exposure to hot environments increases the probability of heat load that may cause occupational heat strain to workers. This study investigates the impact of individual and work-related factors on the heat-related consequences among power plant workers in a hot region. This cross-sectional study was conducted in 2020. The collecting data tool was a validated self-reported 26 item questionnaire and completed in 534 individuals. We used logistic regression, Adjusted odds ratio (AOR) and maximum likelihood evaluations for data analysis. The findings indicated that age, work environment, physical work demands and drinking fluids during work hours are significant with heat exposure perception, heat-related symptoms, and work performance (P-value<0.05). Further, the male workers aged 40-49 are more prone to heat-related symptoms (AOR 1.34, 95% CI 1.18-2.13). The importance of addressing heat stress in occupational settings is necessary and informing strategies to help workers adapt to heat in hot workplaces. [ABSTRACT FROM AUTHOR]

Aggregators are increasingly using portfolios of various end-user resources such as local renewable generation, batteries, EVs, and demand-side management to participate in the electricity markets as virtual power plants. Whilst the smart control of energy within buildings is well established for cost optimization, the prospect of an aggregator collaborating with the energy managers of buildings to trade electricity in the wholesale markets is under researched. This paper uses a state-of-the-art architectural simulator to model the thermal inertia of a typical high-rise building in London and represent the thermodynamics of its hourly occupancy and weather responses. The building is simulated as a virtual battery by manipulating the HVAC settings. Price arbitrage on the electricity wholesale market and contracting with the local distribution network for flexible reserve services are both feasible and profitable. It is concluded that aggregators can apparently use buildings as additional assets within their trading portfolios. JEL Classification: L85 and L94. [ABSTRACT FROM AUTHOR]

Virtual power plant needs to use advanced coordinated control technology to aggregate a large amount of new energy to reliably meet the regulatory needs of the superior power grid. Currently, virtual power plant aggregation technology considering reliability effectively alleviates the problems of low reliability of traditional virtual power plants and poor absorption capacity of new energy. However, in the process of solving the optimization scheme, the traditional optimization solution based on physical models is faced with great challenges due to the complex characteristics such as diversity and heterogeneity of virtual power plant aggregation models. Therefore, a data-driven virtual power plant aggregation method is proposed. The dispatching characteristics of different virtual power plant clusters can be effectively expressed by using the load data, the historical dispatching data of virtual power plant clusters and the data-driven technology. The packaging model reflecting the reliability difference of virtual power plant assemblies is established. The results show that the calculation results indicate that the root mean square error of the model is only 0.2134. Compared to LSTM training model and BP neural networks, the RMSE has decreased by 44.22% and 54.41%, respectively, while the MAE has decreased by 48.32% and 57.84%, respectively. This method has good accuracy. At the same time, this method provides a new method for complex and heterogeneous power system dispatching operation of China's new power system. [ABSTRACT FROM AUTHOR]

The article discusses the proposed federal policy platforms that include substantial changes to environmental regulation at the U.S. Environmental Protection Agency (EPA). Topics include the authority given by the Clean Air Act (CAA) to the EPA to develop policies to reduce emissions of major air pollutants, health benefits of air pollution control devices, and role of the CAA in healthy decarbonization.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

The 1–2 seat helicopters have developed considerably in recent years. They have a maximum take-off weight of 250 to 750 kg. Most of these helicopters have been converted into unmanned versions. Typically, such UAV models retain the rotor system, power plant, transmission, and empennage of the manned versions. For this reason, statistics and design methods for small manned helicopters are also applied to unmanned versions. The existing methods for selecting helicopter parameters in the preliminary design phase are based on statistical data for heavier-class helicopters. However, the lightest weight class helicopters differ significantly from their heavier counterparts. The analysis shows that the results of parameter selection at the preliminary design stage have an error rate of between 11 and 30%. The main reason for this difference is a scale factor. In this paper, a method for determining helicopter tail unit parameters at the preliminary design stage is presented. The proposed relationships for the horizontal stabilizer, fin, tail boom, and tail rotor parameters are based on an analysis of statistical data from 36 rotorcraft and the author's design experience. In particular, the article presents the relationships between the geometric parameters of the empennage and tail rotor from other helicopter data. The relationships presented also allow the mass of the tail unit to be determined. [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.)

The article "UK Project Management Round Up" by Dr. Miles Shepherd provides an overview of recent developments in the UK's energy sector, focusing on projects related to power transmission, renewable energy, and infrastructure upgrades. The article highlights successful project submissions in the energy sector, challenges in meeting government targets for green energy capacity, and ongoing efforts to keep the lights on amidst increasing demand. Additionally, the article discusses a major road upgrade project in Wales, as well as other project news related to electricity prices, recycling initiatives, and defense industry partnerships. [Extracted from the article]

Demand for low-carbon sources of hydrogen and power is expected to rise dramatically in the coming years. Individually, steam methane reformers (SMRs) and combined cycle gas power plants (CCGTs), when combined with carbon capture utilisation and storage (CCUS), can produce large quantities of on-demand decarbonised hydrogen and power respectively. The ongoing trend towards the development of CCUS clusters means that both processes may operate in close proximity, taking advantage of a common infrastructure for natural gas supply, electricity grid connection and the CO2 transport and storage network. This work improves on a previously described novel integration process, which utilizes flue gas sequential combustion to incorporate the SMR process into the CCGT cycle in a single "combined fuel and power" (CFP) plant, by increasing the level of thermodynamic integration through the merger of the steam cycles and a redesign of the heat recovery system. This increases the 2nd law thermal efficiency by 2.6% points over un-integrated processes and 1.9% points the previous integration design. Using a conventional 35% wt. monoethanolamine (MEA) CO2 capture process designed to achieve two distinct and previously unexplored CO2 capture fractions; 95% gross and, 100% fossil (CO2 generated is equal to the quantity of CO2 captured). The CFP configuration reduces the overall quantity of flue gas to be processed by 36%–37% and increases the average CO2 concentration of the flue gas to be treated from 9.9% to 14.4% (wet). This decreases the absorber packing volume requirements by 41%–56% and decreases the specific reboiler duty by 5.5% from 3.46–3.67 GJ/tCO2 to 3.27–3.46 GJ/tCO2, further increasing the 2nd law thermal efficiency gains to 3.8%–4.4% points over the un-integrated case. A first of a kind techno economic analysis concludes that the improvements present in a CO2 abated CFP plant results in a 15.1%–17.3% and 7.6%–8.0% decrease in capital and operational expenditure respectively for the CO2 capture cases. This translates to an increase in the internal rate of return over the base hurdle rate of 7.5%–7.8%, highlighting the potential for substantial cost reductions presented by the CFP configuration. [ABSTRACT FROM AUTHOR]

The developed method, which consists in determining the installed power of distributed generation, in particular, a wind power plant, a solar power plant, a standby power plant and the technical parameters of an energy storage system, to ensure the security of the supply of electric energy to local consumers, is given. Achieving a balance between the daily amount of electricity produced by distributed generation and the amount of consumption ensures the determination of excess amounts of electricity, which mainly occur during periods of peak solar radiation and, accordingly, the required capacity of an energy storage system. According to the simulation results, it was determined that with the maximum daily consumption of electricity by local consumers during the year at the level of 96 MWh, the daily balance of electricity is achieved at the installed capacity of the wind power plant at the level of 3.6 MW; solar power plant is 14 MW; standby power plant is 3.7 MW and the nominal power of an four-hour energy storage system is 16 MW [ABSTRACT FROM AUTHOR]

Copyright of Umsatzsteuer-Rundschau is the property of De Gruyter 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.)

ABSTRACT Electricity production using fossil fuels contributes to air pollution and adverse health impacts. One option for decreasing fossil fuel consumption is replacing fossil fuel power plants with electricity produced from green hydrogen (H2, produced from renewable sources). Previous studies mainly focused on greenhouse gas emissions from two common H2 production methods, steam methane reforming (SMR) and water electrolysis. This study compares the estimated emissions and associated health outcomes of generating electricity from fossil fuels with electricity generated from H2 produced through SMR or electrolysis in various regions of the United States. Shifting from coal‐generated electricity to SMR‐produced H2‐generated electricity results in health benefits while shifting from natural gas‐generated electricity to electricity generated from H2 generated via water electricity results in health costs in all regions. Depending on the region, replacing a natural gas power plant with electricity generated from H2 produced via SMR or replacing a coal power plant with electricity generated from water electrolysis could result in either health benefits or costs. This study also considers the impact of plant location on human health outcomes as well as the impact of increasing renewable energy percentages on health outcomes associated with replacing a coal or natural gas power plant with electricity generated from H2 produced through grid‐based water electrolysis. The results indicate that a high renewable fraction (over 85% of the grid) is required to experience health benefits, emphasizing the challenges associated with moving toward electrolysis‐based H2 production for electricity generation. [ABSTRACT FROM AUTHOR]

The need to increase the level of municipal solid waste (MSW) utilization, combined with an increased focus on low-carbon energy, leads to an increase in interest in technologies for producing hydrogen from MSW, not only at the industrial level, but also at the state level. The availability of free space and excess generation capacity makes thermal power plants (TPPs) the most suitable objects for introducing such installations. The aim of the study is to analyze the potential and prospects for converting MSW to hydrogen based on TPPs using the example of the Ural Federal District. During the study, an assessment was carried out of the potential volume of hydrogen production and use of MSW, the parameters of a thermal power plant were calculated that could serve as a basis for introducing an MSW hydrogen installation. The expected change in technical and economic indicators of a thermal plant was analyzed, and recommendations were made on the choice of a site. Thus, under the conditions of the Ural Federal District, total hydrogen production can range from 11 to 31 tons per hour. At the same time, production of hydrogen will require water vapor ranging from 63 to 137 tons per hour and cooling water ranging from 32 to 7233 tons per hour. And it will lead to a decrease in electric power from 58 MW to 125 MW (while maintaining the load on steam-generating equipment constant), an increase in conventional fuel consumption from 6 tons per hour to 12 tons (with an increase in load on the steam generator), and an increase of 18 MW thermal capacity of thermal plants to 84 MW when disposing of waste heat generated by the MSW (hydrogen) installation. The expected margin profit change will vary from −32 thousand rubles per hour with a decrease in turbine power and utilization of hydrogen waste heat, or from +29 to −21 thousand rubles with an increase in steam generation unit fuel consumption and use of hydrogen production waste heat. (provided to the authors on 27.08.2024 for printing in IJHE from the archive of Fermaltech Montenegro Limited, made by A.L. Gusev using Designer. On the DALL E 3 platform.). [Display omitted] [ABSTRACT FROM AUTHOR]

This article introduces a novel approach for assessing microstructure, particularly its degradation after extended operation. The authors focus on creep processes in power plant components, highlighting the importance of diagnostics in this field. This article emphasizes the value of combining traditional microstructure observation techniques with image analysis. A non-destructive method of evaluating microstructure parameters (matrix replicas) is presented, and its accuracy is evaluated against the conventional destructive method. The assessment utilizes quantitative data derived from classical stereological principles and image analysis. Parameters like mean chord length, relative surface area, mean cross-sectional area, and mean equivalent diameter are compared for replica and metallographic specimens. The results show that the replica method accurately reproduces the microstructure. In their conclusions, the authors highlight the importance of developing visual methods alongside the application of artificial intelligence while indicating the challenges in achieving this goal. [ABSTRACT FROM AUTHOR]

As the greenhouse effect intensifies, China faces pressure to manage CO2 emissions. Coal-fired power plants are a major source of CO2 in China. Traditional CO2 emission accounting methods of power plants are deficient in computational efficiency and accuracy. To solve these problems, this study proposes a novel RF-RFE-DF-Optuna (random forest–recursive feature elimination–deep forest–Optuna) framework, enabling accurate CO2 emission prediction for coal-fired power plants. The framework begins with RF-RFE for feature selection, identifying and extracting the most important features for CO2 emissions from the power plant, reducing dimensionality from 46 to just 5 crucial features. Secondly, the study used the DF model to predict CO2 emissions, combined with the Optuna framework, to enhance prediction accuracy further. The results illustrated the enhancements in model performance and showed a significant improvement with a 0.12706 increase in R2 and reductions in MSE and MAE by 81.70% and 36.88%, respectively, compared to the best performance of the traditional model. This framework improves predictive accuracy and offers a computationally efficient real-time CO2 emission monitoring solution in coal-fired power plants. [ABSTRACT FROM AUTHOR]

This paper reviews the possible contributions of Hybrid Power Plants (HPPs) to support weak grids while maintaining the desired system stability. Moving towards a converter-dominant power system with less inherent inertia and distant connections to the nearest synchronous generator, frequency and voltage controls are becoming more critical to ensure the stability of the weak grid. In this regard, state-of-the-art literature is reviewed for frequency and voltage controllers in single-technology power plants, like wind and solar power plants. The contribution of this paper lies in providing a clear overview of available literature in terms of frequency and voltage control stages, regardless of the utilized control method. On the other hand, focus has been put on the increased utilization of HPPs to provide more flexibility, increased availability, and reduced variability through the combination of various sources, i.e., wind, solar, and storage. Furthermore, investigating the specific capabilities and challenges of HPPs, this review shows that very little literature has been conducted on voltage control using HPPs. Finally, the aspect of relevance of HPPs is discussed in the control and stability of modern power systems. [ABSTRACT FROM AUTHOR]

The accurate prediction of photovoltaic (PV) power generation is crucial for improving virtual power plant (VPP) efficiency and power system stability. However, short-term PV power forecasting remains highly challenging due to the significant impact of weather changes, especially the complexity of cloud motion. To this end, this paper proposes an end-to-end innovative deep learning framework for data fusion based on multimodal learning, which utilizes a new auto cross modal correlation attention (ACMCA) mechanism designed in this paper for feature extraction and fusion by combining historical PV power generation time-series data and sky image data, thereby enhancing the model's prediction performance under complex weather conditions. In this paper, the effectiveness of the proposed model was verified through a large number of experiments, and the experimental results showed that the model's forecast skill (FS) reached 24.2% under all weather conditions 15 min in advance, and 24.32% under cloudy conditions with the largest fluctuations. This paper also compared the model with a variety of existing unimodal and multimodal models, respectively. The experimental results showed that the model in this paper outperformed other benchmark methods in all indices under different weather conditions, demonstrating stronger adaptability and robustness. [ABSTRACT FROM AUTHOR]

Most Nuclear Power Plants (NPPs) are designed for baseload operations, maintaining a steady power output at 100%, except during planned maintenance and refueling. However, in countries like France, Slovakia, and Korea, where nuclear power is a major source of electricity, integrating nuclear energy with intermittent renewables is crucial for stable power generation. This integration necessitates daily power adjustments by NPPs in response to grid demands, a process known as a Load Follow Operation (LFO). Such a process introduces strong interdependencies between thermal–hydraulic and neutron–kinetic parameters, coupled with the three-dimensional movement of Control Element Assemblies (CEAs) and Xenon dynamics, which pose safety challenges due to shifts in core power distribution. To address these complexities, a multi-physics approach is employed using the multi-physics package RELAP5/3DKIN and implementing two strategies. The first strategy uses a mechanical shim, adjusting the reactor power exclusively through CEAs. The second strategy combines CEA movement with adjustments in soluble boron concentration. Both strategies are evaluated against axial offset and 3D power peaking safety limits to ensure compliance with operational safety requirements. [ABSTRACT FROM AUTHOR]

The Polish economy, and especially the energy sector, is facing an energy transformation. For decades, most electricity in Poland has been generated from hard coal, but in recent years, renewable energy sources have been gaining an increasing share of the market. The aim of the energy transformation is to reduce the carbon footprint in electricity production, which translates into the decarbonization of the economy, including manufactured products. Currently (2024), increasing the share of renewable energy sources raises major challenges in terms of energy storage or other activities and forces cooperation with flexible sources of electricity generation. One of the challenges is to determine what a decarbonized energy mix in Poland could look like in 2050, in which there would be sources (with a smaller share of coal sources in the mix than currently) of electricity generation based on hard coal with CCS technology. In order to do this in an economically efficient manner, there are aspects related to the location of power plants that would remain in operation or repower current generating units. The added value of the study is the simulation approach to the analysis of the problem of assessing the effectiveness of CCS technology implementation together with the transport and storage infrastructure, as well as the multi-aspect scenario analysis, which can determine the limits of CCS technology effectiveness for a given power unit. Positive simulation results (NPV amounted to 147 million Euro) and the knowledge obtained in the scope of the correlated and simultaneous impact of many important cost factors and prices of CO2 emission allowances make this analysis and its results close to reality. Examples of analyses of the effectiveness of CCS system implementations known from the literature are most often limited to determining linear relationships of single explanatory variables with a specific forecasted variable, even if these are multifactor mathematical models. [ABSTRACT FROM AUTHOR]

Optimized predictive maintenance in photovoltaic (PV) systems is crucial for ensuring prolonged operational performance and cost‐effective operation and maintenance (O&M). Even though failure detection methods have already been developed, the main challenge remains the lack of predictive maintenance strategies to accurately forecast underperformance conditions. The scope of this work is to develop a predictive maintenance and failure detection routine for assessing the health status of PV systems. The workflow consists of the eXtreme gradient boosting algorithm for modeling the PV performance, the one‐class support vector machine algorithm for fault detection, and the Facebook Prophet algorithm for forecasting PV performance trends and generating maintenance alerts. The developed data‐driven routine analyzes performance trend deviations and it is validated using a historical dataset from a utility‐scale PV power plant in Greece. The obtained results show the effectiveness of the developed workflow in detecting fault conditions, achieving a sensitivity of 96.9%. Additionally, the results demonstrate the workflow's ability to generate predictive maintenance alerts up to 7 days in advance, yielding a sensitivity of 92.9%. Finally, the study provides useful insights that enhance operators' efficiency in conducting O&M activities. [ABSTRACT FROM AUTHOR]

Combined Cycle Power Plants (CCPPs) generate electrical power through gas turbines and use the exhaust heat from those turbines to power steam turbines, resulting in 50% more power output compared to traditional simple cycle power plants. Predicting the full-load electrical power output ( P E ) of a CCPP is crucial for efficient operation and sustainable development. Previous studies have used machine learning models, such as the Bagging and Boosting models to predict P E . In this study, we propose employing Super Learner (SL), an ensemble machine learning algorithm, to enhance the accuracy and robustness of predictions. SL utilizes cross-validation to estimate the performance of diverse machine learning models and generates an optimal weighted average based on their respective predictions. It may provide information on the relative contributions of each base learner to the overall prediction skill. For constructing the SL, we consider six individual and ensemble machine learning models as base learners and assess their performances compared to the SL. The dataset used in this study was collected over six years from an operational CCPP. It contains one output variable and four input variables: ambient temperature, atmospheric pressure, relative humidity, and vacuum. The results show that the Boosting algorithms significantly influence the performance of the SL in comparison to the other base learners. The SL outperforms the six individual and ensemble machine learning models used as base learners. It indicates that the SL improves the generalization performance of predictions by combining the predictions of various machine learning models. [ABSTRACT FROM AUTHOR]

Modern power systems are developing rapidly, with distributed energy, energy storage devices, adjustable loads, and other flexible resources consolidated through microgrids, virtual power plants, and integrated source–network–load–storage systems. This consolidation under various balancing zone models facilitates synergistic operations and has become critical to enhancing distributed power consumption and ensuring the reliability of electricity supply. Therefore, in light of the challenges of inadequate economic efficiency, reduced accommodation of renewable energy, and poorer operational reliability in distribution networks, this study proposes a category selection and flexibility resource scheduling method that considers the differences in multiple balancing zone models and modes. Firstly, the approach establishes a multi-dimensional characteristic evaluation index and multiple balancing zone operation models. The characteristic evaluation indicators are then utilized to assess the unique properties of the balancing zone system and eliminate unreasonable operating modes. Finally, through analyzing the effectiveness and differences of various balancing zone operation modes, an optimal operation mode is selected, and a scheduling plan is formulated. We conclude that the scheduling plan optimization method considering the operation mode can realize a reasonable choice of operation modes and achieve benefit optimization. [ABSTRACT FROM AUTHOR]

Powering and communicating with wearable devices on bio-interfaces is challenging due to strict weight, size, and resource constraints. This study presents a sunflower-like plant-wearable sensing device that harnesses solar energy, achieving complete energy self-sustainability for long-term monitoring of plant sap flow, a crucial indicator of plant health. It features foldable solar panels along with all essential flexible electronic components, resulting in a compact system that is lightweight enough for small plants. To tackle the low-energy density of solar power, we developed an ultralow-energy light communication mechanism inspired by fireflies. Together with unmanned aerial vehicles and deep learning algorithms, this approach enables efficient data retrieval from multiple devices across large agricultural fields. With its simple deployment, it shows great potential as a low-cost plant phenotyping tool. We believe our energy and communication solution for wearable devices can be extended to similar resource-limited and challenging scenarios, leading to exciting applications. [ABSTRACT FROM AUTHOR]

The development of a steady-state model of an Integrated Plasma Gasification Combined Cycle (IPGCC) power plant is presented here. The model includes the plasma gasifier, syngas conditioning units, and the power generation unit. Furthermore, the model of each component implemented in Aspen Plus is described in detail (thermodynamic method, chemical reactions, and operative conditions). The proposed model was validated by comparing the plasma gasification results with experimental and numerical data from the literature; the relative error was 6.23% and 5.24%, respectively. The model was then used to perform a two-part sensitivity analysis. In the first part, simulations with municipal solid waste (MSW) with a moisture content varying from 20% to 60% were performed. The moisture content increment reduced the torch-specific power consumption by 53%. However, because of the increasing specific fuel consumption, the thermal efficiency of the IPGCC power plant also decreased by 28% as the MSW moisture content increased from 20% to 60%. In the second part, it was determined that the IPGCC power plant reached the best performance (32.5%) when a high plasma temperature (5000 °C) and a low gasification temperature (2000 °C) were used. At these highest efficiency conditions, the 1000 t/day IPGGC power plant's net power generation was 62 MWe. [ABSTRACT FROM AUTHOR]

The virtual power plant integrating the flexible resources in the distribution network can provide additional adjustment capacity for the auxiliary services of distribution network. However, the actual internal situation of distribution network including insufficient adjustable capacity of energy storage, unreasonable power allocation, and voltage overrun leads to the difficulties in optimization scheduling. Therefore, this paper proposes a power allocation optimization strategy of distributed electricity‐H2 virtual power plants (EHVPPs) with aggregated flexible resources. Specifically, a distributed EHVPP division method based on the granular K‐medoids clustering algorithm is proposed to realize the independent autonomy and coordinated interaction between EHVPPs, and in order to quantify the operation and regulation capacity of distributed EHVPPs, an aggregation approach of regulating feasible domains of flexibility resources based on the improved zonotope approximations is developed. Moreover, a power allocation strategy based on the flexibility weight factor is proposed to handle the calculated minimum deviation between the total active output of PV and the dispatching power command, realizing the self‐consistency of distributed EHVPPs. Comparative studies have demonstrated the superior performance of the proposed methodology in economic merits and self‐consistency efficiency. [ABSTRACT FROM AUTHOR]

AbstractDue to the change of working load caused by peak shaving and frequency modulation in power plants, the traditional fixed ejector cannot meet the requirements of high-performance operation under variable load conditions. However, the adjustable ejector with spindle has problems such as the length of the spindle is too long, and the anti-vibration measures are not in place. The spindle vibrates and deflects during the operation of the ejector. In this research, the influence of the structure deviation of the spindle on the ejector performance is evaluated by numerical simulation. The results show that the spindle of the adjustable ejector can obtain the maximum secondary fluid mass flow by controlling the primary fluid. Under different operating conditions, the entrainment ratio of the adjustable ejector is improved by 114.7% compared with that of the fixed ejector. The structural deviation will reduce the entrainment ratio and anti-back pressure capability, and the ejector will change from the critical mode to the sub-critical mode. The deviation between the spindle and the primary nozzle will change the flow field inside the primary nozzle, thus affecting the primary fluid ejection direction and the entrainment ratio. [ABSTRACT FROM AUTHOR]

Heat of exhaust gases from stations operating with internal combustion engines (ICE) and heat of cooling water have been investigated in the paper. In such situations, 60% of the fuel heat is lost through the exhaust gas from the engines and cooling water. Various schemes are proposed for using this heat. When implementing these schemes, steam, hot water and cold can be obtained from waste gases. Then the efficiency of the station increases, specific fuel consumption and the amount of flue gases emitted into the atmosphere and environmental loads are reduced. [ABSTRACT FROM AUTHOR]

Large‐scale energy storage solutions are crucial to ensure grid stability and reliability in the ongoing energy transition towards a low‐carbon, renewable energy based electricity supply. This article presents the evaluation of a novel low‐head pumped hydro storage system designed for coastal environments and shallow seas. The proposed system addresses some of the challenges of low‐head pumped hydro storage including the need for larger flow rates and reservoirs as well as the requirement of machinery with high efficiencies across a wide operating range to accommodate larger changes in gross head during storage cycles. It includes several units of contra‐rotating reversible pump‐turbines connected to axial‐flux motor generators within a ring dike, as well as dedicated machine‐ and grid‐side control. The technology allows for independent control of each runner, making it possible to adapt to the specific operating conditions of low‐head systems. In this work, a numerical approach is used to simulate the system's performance and dynamic behaviour under various operational conditions, including energy generation, storage, and grid support of a 1 GW system with 4 GWh of storage capacity. The potential system performance for energy balancing cycles is evaluated, and a sensitivity analysis is conducted to assess the influence of scaling the motor‐generators on performance and footprint of the plant. Additionally, the capability and limitations of the system to respond to grid demand fluctuations and provide frequency regulation services are assessed. The results demonstrate that the low‐head pumped hydro storage system is a viable large‐scale energy storage solution, capable of round‐trip efficiencies above 70% across a wide operating range. By increasing the maximum power of the electric machines, the maximum head range of the whole system is increased which correlates with a threefold increase in energy density per unit area. The dynamic simulations further show that the system can rapidly change its power output allowing it to provide frequency regulation services. Allocating 20% of its nominal power as a reserve, the new power setpoints can be reached within a maximum of 5 s independent of its initial state of charge. [ABSTRACT FROM AUTHOR]

The accurate prediction of photovoltaic (PV) power generation is an important basis for hybrid grid scheduling. With the expansion of the scale of PV power plants and the popularization of distributed PV, this study proposes a multilayer PV power generation prediction model based on transfer learning to solve the problems of the lack of data on new PV bases and the low accuracy of PV power generation prediction. The proposed model, called DRAM, concatenates a dilated convolutional neural network (DCNN) module with a bidirectional long short-term memory (BiLSTM) module, and integrates an attention mechanism. First, the processed data are input into the DCNN layer, and the dilation convolution mechanism captures the spatial features of the wide sensory field of the input data. Subsequently, the temporal characteristics between the features are extracted in the BiLSTM layer. Finally, an attention mechanism is used to strengthen the key features by assigning weights to efficiently construct the relationship between the features and output variables. In addition, the power prediction accuracy of the new PV sites was improved by transferring the pre-trained model parameters to the new PV site prediction model. In this study, the pre-training of models using data from different source domains and the correlations between these pre-trained models and the target domain were analyzed. [ABSTRACT FROM AUTHOR]

Pressurized water reactors (PWRs) are the most common and widely used type of reactor, and ensuring the stability of the reactor is of utmost importance. The challenges lie in effectively managing power fluctuations and sudden changes in reactivity that could result in unsafe situations. Reactor power fluctuations can cause changes in behavior. At the same time, the transfer of heat from the fuel to the coolant and reactivity changes resulting from differences in fuel and coolant temperatures can also make the system unpredictable. The primary goal of a power controller used in a nuclear reactor is to sustain the specified power level, which guarantees the security of the power plant. To address these challenges, this paper presents a dynamic model of a PWR and applies several control techniques to the system for power level control. Specifically, a traditional PID controller, a neural network controller, a fuzzy self-tuned PID controller, and a neurofuzzy self-tuned PID controller were individually designed and evaluated to enhance the performance of the reactor power control system under constant and variable reference power. In addition, the robustness of each controller was assessed against time delays and external disturbances. The system was tested with various initial power values to evaluate its performance under different conditions. The results demonstrate that the neuro-fuzzy self-tuned PID controller has the best performance, as well as the fastest response time compared to the other controllers. Furthermore, the intelligent controllers were found to exhibit good robustness against time delays and external disturbances. The system's stability was not significantly affected by changes in the initial power value, although it had a minor effect on the transient response. Overall, the findings of this study can inform the design and optimization of control systems for PWRs, with the ultimate goal of improving their safety, reliability, and performance. [ABSTRACT FROM AUTHOR]

In order to study the low-carbon economic operation strategy of virtual power plant, this paper proposes a multi- objective multi-timescale optimization scheduling model for virtual power plant based on three phases: day-ahead, intra-day, and real-time. First, the day-ahead optimization model for low-carbon and economic operation of virtual power plant is constructed based on energy flow structure and carbon flow measurement. Then, the day-ahead schedule is corrected by the intraday optimization model and real-time correction model to reduce the influence of uncertainty factors. The augmented-constraint method is used to solve the nonlinear multi-objective planning model. The simulation results show that the scheduling strategy can realize the low-carbon and economic operation of the virtual power plant while guaranteeing more than 95% accuracy of carbon flow calculation. [ABSTRACT FROM AUTHOR]

In systems in which many heterogeneous agents operate autonomously, with competing goals and without a centralized planner or global information repository, safety and performance can only be guaranteed by "social" rules imposed on the behavior of individual agents. Social laws are structured in a way that they can be verified just by using local information made available to an agent by a small number of its neighbors. Automobile mobility with traffic rules and logistics robots in warehouses are canonical examples of such "regulated autonomy", but many other fairly-competing autonomous systems are to be expected shortly. In these systems, detecting whether an agent is not abiding by the rules is crucial for raising an alert and taking appropriate countermeasures. However, the limited visibility due to the local nature of the information makes the problem of misbehavior detection hard for any single agent, and only an exchange of information between agents can provide sufficient clues to arrive at a decision. This paper attacks the misbehavior detection problem for a class of socially organized autonomous systems, where the behavior of agents depends on the presence or absence of other neighbors. We propose a solution involving a "local monitor", which runs on each agent and includes a hybrid observer and a set-valued consensus node. Based on whatever visibility is available, it reconstructs a set-valued occupancy estimate of nearby regions and combines it with communicating neighbors to reach a shared view and a mismatch discovery. We provide a formal framework for describing social rules that unify many different applications and a tool to generate code automatically for local monitors. The technique is demonstrated in various systems, including self-driving cars, autonomous forklifts, and distributed power plants. [ABSTRACT FROM AUTHOR]