The paper presents the problem of forecasting the operation condition of LM6000-PF gas turbine with a heat recovery hot water boiler in off-design mode. The presented unit is part of a real cogeneration heat and power plant. The maximum electrical power output for nominal conditions of the case study unit is 50 MWe. The proposed method is based on the thermodynamic modeling supplied with a data-driven model. The prediction model usesdetermined correction factors based on the real operating data. Correctionfactors for electricity production, heat rate, flue gas mass flow rate, and overall heat transfer coefficient in the boiler are proposed to increase the accuracy of the prediction results. The outcome from themodel can be used for short-term planning of energy production in the case studywithcombined heat and power plant. Here, fundamental production parameters such as electric power, heat production in the recovery boiler, and fuel consumption must be forecasted for the upcoming day in an hourly resolution. The results in the form of hourly parameter changes are presented and compared with real ones from two years of the system operation. The model's mean percentage error was reduced by using corrections factors to approximately 0.8% and 1.7% for electric power and recovered heat, respectively.
Greenhouse gas (GHG) emissions, particularly carbon dioxide, are the driving force for global climate change. Fossil fuel-fired power plants are one of the largest contributors to CO2 pollution. When carbon dioxide is released into the atmosphere, capturing and removing it is very difficult. Carbon capture systems (CCSs) can prevent the release of CO2 into the air. Recently, the use of microalgae photobioreactors as CCS has gained momentum. The produced microalgae can then be harvested for many applications including biofuels and other products. Closed photobioreactors are especially adept at this but at an increased price and energy input. Open photobioreactors are able to grow large amounts of algae at low prices and energies but with less control over the growth parameters. This paper briefly reviews the existing research literature in this field. Various systems, configurations, inefficiencies, and types of algae are reviewed and parameters impacting algae growth are presented.
This article presents a detailed study of the dynamic stability of microgrid-connected captive power plants linked to the agricultural sector located in western India. A refined modeling approach is developed to derive a reduced-order model using the Park-Gorev model along with Concordia and De Mello assumptions. This model contains six parameters, Kl to K6, that are described as the postulated constants associated with the power plant's initial operating conditions. Furthermore, the excitation regulator is modeled as a first order lag which improves the original IEEE Is model, providing the framework with detail. A detailed systematic dynamic stability analysis is performed for different operating conditions and identifies the stability and instability regions. Their consequences are noted for operational staff and focus on important factors that need to be considered prior to generator loading and commitment. This research is a detailed study of the dynamic behavior of captive power plants which is very useful to make operational decisions. Besides its primary purpose, this work will be useful for creating procedures and instructions on operational standards. The techniques used are crucial in improving the understanding of dynamic stability of power plants systems, which in turn modifies the existing knowledge and technological understanding in this area of concern.
Accurate power consumption (PN) forecasting under climate-sensitive operating conditions remains challenging due to the nonlinear, non-stationary, and dynamically coupled nature of demand and environmental factors. Existing studies predominantly rely on empirically selected auxiliary variables or static correlation analysis, which may fail to capture the temporal interaction mechanisms underlying PN dynamics. In this study, a recurrence-based dynamic characteristic analysis framework is proposed to systematically investigate both the intrinsictemporal behavior of PN and its variable-specific coupling with meteorological factors. Recurrence plots and cross recurrence plots are employed to visualize dynamic patterns and shared state evolution, while cross recurrence quantification analysis metrics are used to quantify coupling strength and temporal organization. To integrate heterogeneous dynamical indicators, a geometric-mean-based measure is introduced to rank the dynamic relevance of influencing variables in a unified manner. The recurrence-guided variable selection strategy is further validated through an echo state network (ESN) forecasting model, in which dynamically significant meteorological variables are incorporated as auxiliary inputs. Experimental results across three zones under high-load summer conditions of Tetouan city demonstrate that the proposed recurrence-guided ESN consistently outperforms the empirically configured multivariate ESN, achieving notable reductions in prediction error while maintaining a lightweight training structure. The proposed framework provides an interpretable and data-driven approach for identifying dynamically relevant influencing factors and improving PN forecasting under complex operating regimes.
The integration of advanced computational techniques, including artificial intelligence (AI), machine learning (ML), and genetic algorithms, is profoundly impacting the analysis and understanding of dynamic energy markets. This review article provides a comprehensive overview of how these methods are being applied to various economic and financial aspects within the energy sector. Drawing from a focused review of recent research, we explore their utility in areas such as pricing and cost, determination for commodities like electricity and crude oil, enhancing financial markets and investment strategies, and improving risk assessment capabilities. We synthesize key findings on the effectiveness and challenges associated with deploying these sophisticated computational tools, shedding light on emerging trends and highlighting promising avenues for future research. This synthesis aims to deepen the understanding of how AI, ML, and genetic algorithms are shaping strategic decision-making and fostering innovation within the evolving global energy landscape.
The power quality issues in the power system have significantly affectedboth utilities and customers. Among the prevalent power quality problems, current harmonics pose a common challenge, often addressed through the application of shunt active filters. In this paper, a voltage source inverter (VSI) based distribution static compensator (D-STATCOM) is considered as a shunt active filter. To control the filter, this work proposes a hysteresis inner control and new bi-level least mean square (BLMS) outer control algorithm. The BLMS algorithm is used to extract the converged weight required for reference source current generation from both active and reactive power components of harmonic-rich load current. To achieve the tuned weight, a novel modeling and design method is presented based on mathematical equations by splitting active and reactive subnets separately. It aims for a better alternative solution for realizing the weight update without using the existing neural network (NN) toolbox. Finally, the power quality solution, like source current harmonic diminution, proper voltage adjustment, voltage normalizing and improved power factor, is analyzed by using the proposed technique. In addition to this, reduced sizing of the VSI is also analyzed. To demonstratethe better performance of the D-STATCOM with proposed algorithm, analytical evolution is performed in MATLAB/SIMULINK. The performance is also compared with an existing adaptive-based least mean square (ALMS) strategy.
The objective of this paper is to analyze the chemical, electrical and mechanical power flow of a Chevrolet Spark EV covering the UDDS, Hwy and US06 cycles. The experimental time series of instantaneous voltage and current of the battery are obtained from the chassis dynamometer measurements performed by ANL. By introducing a simple model for the conversion of energy chemical to electrical, and electric to mechanical, and vice-versa, and a model for aerodynamic and rolling losses, the time series of chemical, electric and mechanical power and energy are thus computed.1
The use of renewable energy such as wind energy is one of the affordable solutions to achieve the most capital electric power demand, because this resource is efficient as well as being the cleanest, renewable in nature. However, in Algeria, the electrical power system located in the highland region, which has considerable wind potential, is usually not strong enough and the voltage stability issues caused by a possible installation of wind farm may require dynamic compensation devices, such as distributed-flexible AC transmission system (D-FACTS). Therefore, this paper investigates the implementation of parallel D-FACTS systems, under grid fault conditions, with taking into account the grid requirements over low voltage ride through (LVRT) performance and the voltage stability issue for wind farm connected to the distribution network at the Algerian highland region. Then, two types of D-FACTS devices considered in this paper are both the distribution static VAR compensator (D-SVC) and the distribution static synchronous compensator (D-STATCOM). Some simulation results show a comparative study between the D-SVC and D-STATCOM devices connected at the point of common coupling (PCC) to support wind farm based on doubly fed induction generator (DFIG) under grid fault conditions. Finally, we present a solution to this problem by dimensioning and giving the appropriate choice of D-FACTS system, while offering feasibility study of this wind farm project by economic analysis.
This study evaluates the performance of small wind turbines (SWTs) under Central European wind conditions based on eleven years of hourly data from 59 Polish meteorological stations. The analysis covers various turbine technologies-Savonius (drag-based), Darrieus (lift-based), and hybrid designs-using experimentally derived power curves. The research introduces a novel comparative framework incorporating both seasonal and diurnal variability, distinguishing between 12/12-hour and seasonally variable day-night divisions. The results reveal that wind speeds peak in winter and spring, with daytime Values consistently higher due to thermal turbulence. Mean seasonal variations remain within 8%, indicating stable long-term wind conditions. Calculated capacity factors for all turbine types are relatively low, ranging between 4% and 21%, with averages comparable to photovoltaic systems. The Darrieus 300 W turbine demonstrates the highest efficiency in moderate-wind regimes, achieving capacity factors up to 21% during winter days. Winter months alone contribute up to 39% of annual energy output, confirming the importance of seasonally adaptive hybrid renewable systems. Although the analysis is based on meteorological data representative of Central European conditions, the dataset encompasses diverse geographical settings, including coastal, lowland, and upland areas. Consequently, the results provide insight into the relative performance of different small wind turbine technologies and may serve as a useful reference for assessing their applicability under a broad range of climatic conditions beyond the specific region considered.
This article presents the use of radar images from the ERS-1 and ERS-2 satellites to detect environmental changes in the Kujawy and Dobrzy & nacute; Lake District region, covering an area of approximately 3012 km & sup2; in central Poland. The aim of the analysis is to identify spatial changes (e.g., shifts in object boundaries) and physical changes (e.g., terrain moisture) resulting from natural and anthropogenic factors. The ERS satellites, as the first European platforms for environmental monitoring in the microwave spectrum, enable observations independent of weather conditions, which confirms their usefulness in detecting surface changes. The methodology includes processing images from 1995 and 1998, including filtering. geometrization, and multi-temporal composition (difference/1995/1998), classification into unchanged and changed areas, taking into account land cover (CORINE Land Cover), soils, and geomorphological forms. The results indicate that most of the area has undergone changes, mainly due to more intense rainfall in 1995, affecting the moisture content of forests, meadows, and arable land. The most sensitive areas are sandy plains, hilly marginal zones, and undulating moraine uplands. The analysis confirms the occurrence of environmental changes in the region, such as droughts and desertification, consistent with climate observations. The conclusions emphasize the value of color composition in facilitating interpretation, enabling the detection of changes in moisture and terrain roughness. This technique has potential in monitoring floods, land subsidence, and land cover, providing a basis for creating thematic maps. The paper contains a comprehensive summary of potential sCO2 cycle applications being considered for power generation. The authors give examples of different sCO2 based cycles used in combination with conventional energy sources like fossil fuels or nuclear as well as renewable energy sources like solar. The article presents SCO2 recompression cycle simulation model results and - using this example, cycle flexibility and parameters-discusses potential application of the cycle.
In the existing UHV (Ultra High Voltage) transmission line lightning shielding failure modeling, due to the inability to perform high-precision dynamic grid control on the electromagnetic field mutation area, key physical quantities such as electric field and current density in the transient propagation process have insufficient spatial resolution and error accumulation problems, which affects the model's ability to resolve the shielding failure response in time and space. This paper introduced a posteriori error-driven adaptive finite element modeling method, which embedded the grid error control mechanism into the distributed parameter modeling process of lightning shielding. During the modeling process, the physical field is distributed in the space in the form of a continuous function, and the conductor electrical parameters, field strength vector and charge density are spatially coupled and solved inside each unit, reflecting the continuous propagation characteristics of electromagnetic behavior at the structural scale. Firstly, this paper uses the three-dimensional Maxwell equations to establish a multi-conductor transmission channel model and performs time integration in the Newmark-format. Secondly, the discrete error distribution is locally calibrated by the residual error estimator. It combines unit encryption with least squares field value projection for adaptive grid adjustment, and finally iteratively solves the transient electric field and current vector distribution in each time step. In the experimental simulation, the adaptive finite element model with a threshold of le-4 achieved an accurate analysis of the maximum electric field strength of 550kV/m in the lightning strike area, and the lightning current response at the top of the tower reached a peak of 28.5kA at 0.60 mu s. The electric field error in the key area of the scheme with an error threshold of le-2 dropped from the initial 0.2 to within 0.003, and the total simulation time of the modeling was controlled within 53.0 seconds. The results show that this method significantly optimizes the modeling calculation efficiency and the analytical ability of lightning transient evolution while ensuring physical accuracy, providing a more reliable numerical tool for UHV lightning protection design and evaluation.
Smart substations are the key to the intelligent construction of the power grid. Efficient data processing and inspection optimization are of great significance for the safe and stable operation of the power grid. However, current data -processing in smart substations suffers from problems such as the mismatch between processing and loading time and long redundant time, which restricts its in-depth digital development. To address these issues, this paper first analyzes the relationship between the types and capacities of data in smart substations and identifies the main causes of redundant data -processing time. Based on this, a cache partitioning strategy is formulated according to the data capacity characteristics, and the relationship among the inspection sequence, cache space, and redundant time is further explored. Combining the importance of equipment and historical failure rates, a screening scheme for the inspection sequence is established to reducedata-processing redundancy and optimize the inspection duration. Finally, taking the data capacity of a 500 kV smart substation as an example, the feasibility of this scheme is verified.
This paper presents technical and economic analysis of hybrid ac microgrid (MG). The MG comprises conventional sources like diesel generators (DiGen), renewable energy sources such as roof-top solar photo-voltaic (PV) installation, and battery energy storage systems (BESS). The primary objective of this analysis is to study and compare levelized cost-of-energy (LCoE) and net-present value (NPV) for blend of energy sources catering electrical energy to various set-ups along with other vital economic instruments like internal rate of return (IRR), return on investment (ROI) and simple payback period (SPP). The analysis has been performed with existing utility-grid in operation. To compare the combination of micro-sources, same average annual electricity consumption has been considered as reference for comparison for various load profiles, viz. academic institutional, residential pocket, commercial, community and small/medium industrial loads. Moreover, the two tariff structures, with grid selling and non-grid selling, are considered. The model has been developed in HOMER application software. The reference location for academic institution selected for study is situated in southern part of Gujarat, India and has longitude of 21.17 degrees N and latitude of 72.83 degrees E.
In this paper, an application of Elephant Herding Optimization technique has been proposed to determine the parameter of PIDcontroller for load frequency control of two area non-reheat power system. This algorithm is one of the new meta-heuristics-based algorithms. The parameter of the PID controller is optimized by minimizing the objective function as an Integral Square Error.A 1% step load perturbation is applied in the load demand in area-1 for analyzing the dynamic performance of the power system. This paper utilizes the EHO tuned PID to improve the stability and dynamic response of the LFC power system. The superiority of the proposed EHO tuned PID controller has been proved by comparing the performances of change in frequency and change in power in tie-line power system with PID controller in literature. In addition to these, the robust analysis of proposed controller is applied to the power system by changing the system parameters during loading conditions in the range of +25% to -25% of their standard values.
In this research, two solution methods including an Artificial Neural Network (ANN) as well as an ANN combined with Genetic Algorithm (GA) are developed for forecasting wind speeds in the city of Binalood in Iran considering long, medium, and short-term periods. The input data included site atmospheric pressure, mean sea level atmospheric pressure, temperature at a height of 100 meters, soil temperature at a depth of 0-10cm, absolute humidity, relative humidity, total accumulated precipitation, total cloud cover, plus speed and direction of wind at a height of 100 meters, which were collected using windPRO software. The models were run in MATLAB with all mentioned parameters mentioned as the inputs, and wind speed at of 100 m height considered as the output. Hourly meteorological data pertaining to a prolonged period of time from 2008 to 2019 were used for training the ANN models for forecasting. Mean Squared Error (MSE) and correlation coefficient between outputs and targets were used as the measure of accuracy. After a sensitivity analysis, the ANN-GA hybrid, with an MSE of 0.29192 and a correlation coefficient of 0.99750, was found to be the most accurate method for wind speed forecasting in the study area.
The ultimate objective of this project is to develop coal-and carbon-fueled fuel cells for portable applications. Since direct carbon fuel cells are nowhere near commercialization and the fuel gasification process requires a large amount of water (an undesirable characteristic for portable applications), the only remaining solution is to use the partial oxidation process to produce CO-rich gaseous fuel. This fuel then can be fed to direct carbon monoxide fuel cells (DCMFCs). These fuel cells require the development of CO-resistant anode catalysts. To develop these catalysts, direct alcohol fuel cells were used as the source of inspiration. Ten different catalysts were fabricated and tested. The results indicated that while the homemade Pt-Ru membrane-electrode assembly (MEA) outperformed the commercial Pt-Ru MEA it could not compete with the Pt-Sn MEA. While the performance of the MEAs with Pt-Ru-Ni, Pt-Ru-Sn-Ni, and Pt-Ru-Sn-Co was not satisfactory, the Pt-Ru-Sn-Co-W MEA achieved the best performance among all MEAs with Nafion (R) 117 membrane. The last MEA was made of the Pt-Sn anode catalyst and Nafion (R) 212 membrane and outperformed all other MEAs by a wide margin. The experiments demonstrated that the performance of the best-developed MEA was three times better than the performance of the commercial Pt-Ru MEA. This means the performance of DCMFCs can be significantly improved by developing a suitable anode electrocatalyst, using a thinner membrane, and operating at optimum conditions. These results are not only applicable for DCMFCs but are also very useful for other proton exchange membrane fuel cells (PEMFCs) operating with CO-containing fuels.
The pebble bed reactor design is inherently safe and generally considered meltdown-proof. The reactor also benefits from: a constant refueling process which limits shutdown periods, a modular design which can adapt to increasing energy loads, and a relatively simple design which can be easily explained. These are essential features for a newer technology being used within a combined heat and power (CHP) network. The purpose of this paper is to assess the possibility of utilizing a nuclear pebble bed reactor for CHP generation, with a focus on the benefit that this could bring to Central and Eastern Europe (CEE). For a high efficiency CHP scheme, a consistent load of heat must be provided and utilized. Due to the lack of shutdown time, the PBMR provides this consistent load which would be best used in colder regions of the world, such as CEE. The technology used for CHP must also be accepted by members of the public. This is a major hurdle to cross for a nuclear CHP scheme, as nuclear power is perceived somewhat negatively within certain regions of the world. For this report, a survey has been conducted, assessing public perceptions of nuclear power and how these can be altered. The results show that, with the provision of a short paragraph of information describing the safety features of the pebble bed reactor, respondents became increasingly positive about a nuclear pebble bed reactor CHP scheme. This goes some way to prove that, given time and provision of information, a pebble bed reactor for CHP network could be accepted among communities of people. The idea of using a nuclear pebble bed reactor for CHP is not a new one. A review of the ACACIA design for cogeneration has been conducted. It was calculated that the designed, small reactor has the potential to provide electricity for 21,973 and heat for 13,706 UK homes within a year. The design also considers and eradicates the risk of potential hazards such as steam/air ingress and radioactive egress from the reactor core. Overall, the nuclear pebble bed reactor has been found to be suited to use for CHP generation.
The governance of green circular economy is an important way to promote sustainable development of the life cycle. However, existing research has not fully revealed the impact of each stage of the life cycle on the governance of green circular economy, which has become a key issue in understanding and practicing sustainable development of the life cycle. Therefore, this study aims to explore a green circular economy governance model from a life cycle perspective and quantify the impact of each stage of the life cycle on green circular economy governance. This study used a spatial lag model for empirical analysis, selecting a series of core variables, such as indicators of production stage, consumption stage, abandonment stage, and recycling stage, as well as green service facilities. The results indicated that the p-values for all years (except 2020) were greater than 0.05, indicating insufficient evidence of autocorrelation in the life cycle. The p-values of LM spatial lag test and robust LM error lag test were both 0, showing that the spatial lag model was significant at the 1% level. This discovery revealed a high correlation in the analysis of the impact of green circular economy governance. The average correlation degree of all indicators was 0.6632, while the correlation degree of all indicators was 0.7010. In addition, developed and resource rich regions could better achieve green governance, while open areas were more attractive to green investment. The various stages of the life cycle have a significant impact on the governance of green circular economy, and this quantitative evidence helps to scientifically and effectively promote the sustainable development of green circular economy governance in the life cycle.
This paper proposes a novel dual three-phase SVM for six-phase multilevel inverter to control a six-phase induction machine (SPIM). The main idea is to control the six-phase multilevel Inverter as two three-phase multilevel inverters separately by space vector modulation of the N-level three-phase separate DC source (SDCS) inverter. This enables a great simplification of the algorithm control for six-phase multilevel (N level) inverter drive. In six-phase SVM, N degrees vectors ( N = 2 two level, 3 level or 4 level, implies 64 vectors, 729 vectors, 15625 vectors respectively) were used. However, in a proposed dual three-phase SVM, we use N-3 vectors N = 2 level, 3 level or 4 level implies 8 vectors, 27 vectors, 125 vectors respectively) to control the six-phase multilevel inverters as two three-phase multilevel inverters with the same three-phase multilevel SVM. Whereas the first three-phase Inverter is composed of 1,9, and 5 phases, and the second three pimseiscomposed of 24, and opimses. The simulationresults of the indirect field-oriented control (IFOC) of six-phase induction machine drive fed by stacked multilevel inverters are given to highlight the performance of the proposed dual three-phase SVM. Moreover, multilevel topologies are used to reduce the current THD distortion as well as the total semiconductor losses.
The rapid growth of smart grids and renewable energy systems has augmented the demand for secure, efficient, and low-power edge-based computing solutions. These systems rely on real-time data exchanges between distributed energy. resources, substations, and control units necessitating cryptographic techniques that maintain data integrity without compromising performance or energy efficiency. An optimized hardware-software co-design of the Sitein cryptographic hash algorithm is presented in this work, specifically designed for FPGA-based System-on-Chip (SoC) platforms. The system. achieves quick Threefish operations with minimal power and delay by carefully partitioning the various computing tasks between the Processing System (PS) and the Programmable Logic (PL). A Xilinx Zynq-7000 platform is used for experimental evaluation, which shows a throughput of 7168 Mbps, a peak frequency of 140 MHz, and a delay of 10 cycles, all while using only 0.533 watts. These findings demonstrate the applicability of the suggested method for safe, instantaneous communication in smart grid edge devices, including microgrid controllers and smart meters, providing a scalable and energy-efficient cryptographic foundation for energy systems of the future.
