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Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed for these subsequences. Then, the network is optimized using the improved Newton–Raphson genetic algorithm (NRGA), and the innovative PAM is added to focus on the periodic characteristics of the data. Finally, the results of each component are summarized to obtain the final prediction results. A case study of the Australian DKASC Alice Spring PV power plant dataset demonstrates the performance of the proposed approach. Compared with other paper models, the MAE, RMSE, and MAPE performance evaluation indexes show that the proposed approach has excellent performance in predicting output power accuracy and stability. [ABSTRACT FROM AUTHOR]

Deploying renewable generation to replace conventional fossil-fuel-based energy supplies provides an important pathway for the decarbonization of steelmaking plants. Meanwhile, it is also crucial to improve the flexibility of blast-furnace-gas-fired combined-cycle power plants (BFGCCs) to ease the accommodation of uncertain renewable generation. To this end, this paper proposes the deployment of a molten salt heat storage (MSHS) system in BFGCCs to store the heat of gas turbine flue gas so that the power–heat coupling of these BFGCCs can be unlocked to enhance the flexibility of the energy supply. A stochastic capacity optimization of an integrated BFGCC–MSHS–wind–solar (BMWS) energy system is presented to determine the optimal installed capacities of a BFG holder, MSHS, wind turbine, and PV panel, aiming to achieve an economic and safe energy supply for the entire system. Multiple scenarios considering uncertain fluctuations in load demands and renewable generation are generated with the Monte Carlo method based on a typical scenario. These scenarios are then reduced to representative scenarios using the synchronous substitution and reduction method for stochastic capacity optimization to enhance the reliability of the results. The case study results demonstrate that configuring MSHS reduces the total annualized cost of the BMWS system by 2.28%. Furthermore, considering the uncertainties of the power/heating load and wind/PV generation can reduce the expected annualized total cost of the BMWS system and the corresponding standard deviation by 5.66% and 81.45%, respectively. The BMWS system can achieve 730.68 tons of equivalent CO2 reduction in 24 h due to the successful utilization of renewable energy. This paper provides an effective approach for the decarbonization of energy generation systems in steelmaking plants. [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]

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed method in this paper can be utilized to assess the enhanced accommodation capability of renewable energy sources following the coupling integration with retrofitted thermal power units. Thermal power units (TPUs) play a crucial role in accommodating the high penetration of renewable energy sources (RESs) like wind turbines (WTs) and photovoltaics (PVs). This paper proposes an evaluation framework to quantitatively analyze the flexibility potential of retrofitted TPUs in enhancing the accommodate capability of RESs through coupling integration and optimal scheduling. Firstly, the coordination framework for coupling TPUs with RESs is outlined, including a comprehensive analysis of benefits and implementation strategies. Secondly, an annual optimal scheduling model for TPUs and RESs is developed, incorporating deep peak regulation services, ladder-type constraints for retrofitted TPUs, and their operational characteristics before and after the coupling integration. Thirdly, indices to evaluate RES accommodation levels and TPU regulation capacities are proposed to quantify the performance of power sources. Finally, a real-world case study is conducted to demonstrate that integrating retrofitted TPUs with RESs through coupling significantly enhances RES utilization by 3.6% and boosts TPUs' downward regulation capabilities by 32%. [ABSTRACT FROM AUTHOR]

In city-integrated energy systems containing electric–thermal multi-energy sources, the uncertainty of renewable energy sources and the fluctuation of loads challenge the safe, efficient, economic and stable operation of the integrated energy systems. This paper introduces a novel approach for the operation of a carbon capture plant/CHP with PV accommodation within a city-integrated energy system. The proposed strategy aims to maximize the utilization of photovoltaic (PV) power generation and carbon capture equipment, addressing issues related to small-scale CHP climbing constraints and short-term output regulation. Additionally, this paper presents a multi-timescale optimal scheduling strategy, which effectively addresses deviations caused by PV fluctuations and load changes. This was achieved through a detailed analysis of the CHP climbing constraints, carbon capture equipment operation and real-time characteristics of PV power generation. This paper introduces a fully distributed neural dynamics-based optimization algorithm designed to address multi-timescale optimization challenges. Utilizing rolling cycles, this algorithm computes both day-ahead and real-time scheduling outcomes for urban integrated energy systems. Theoretical analyses and numerical simulations were conducted to validate the precision and efficacy of the proposed model and algorithm. These analyses quantitatively evaluate the scheduling performance of PV power generation and carbon capture CHP systems across various timescales. [ABSTRACT FROM AUTHOR]

In the realm of Natural Gas Combined Cycle (NGCC) power plants, it is crucial to prioritize the mitigation of CO2 emissions to ensure environmental sustainability. The integration of post-combustion carbon capture technologies plays a pivotal role in mitigating greenhouse gas emissions enhancing the NGCC's environmental profile by minimizing its carbon footprint. This research paper presents a comprehensive investigation into the integration of solar thermal energy into the Besmaya Natural Gas Combined Cycle (NGCC) power plant, located in Baghdad, Iraq. Leveraging advanced process simulation and modeling techniques employing Aspen Plus software, the study aims to evaluate the performance and feasibility of augmenting the existing NGCC facility with solar assistance for post-carbon capture. The primary objective of this research is to conduct a thorough simulation of the Besmaya NGCC power plant under its current operational conditions, thereby establishing a baseline for subsequent analyses. Subsequently, a solar-assisted post-combustion capture (PCC) plant is simulated and seamlessly integrated into the existing power infrastructure. To accurately estimate solar thermal power potential at the Baghdad coordinates, the System Advisor Model (SAM) is employed. The integration of solar thermal energy into the NGCC power plant is meticulously examined, and the resulting hybrid system's technical viability and performance metrics are rigorously evaluated. The paper contributes to the field by providing valuable insights into the technical feasibility and potential benefits of incorporating solar thermal energy into conventional natural gas power generation infrastructure, particularly in the context of the Besmaya NGCC plant in Baghdad. The power generation capacity of the plant was set at 750 MW. With this capacity, the annual CO2 generation was estimated at 2,119,318 tonnes/year which was reduced to 18,064 tonnes/year (a 99% reduction). The findings aim to inform future decisions in the pursuit of sustainable and efficient energy solutions, addressing both environmental concerns and energy security in the region. [ABSTRACT FROM AUTHOR]

Power systems are generally designed to be reliable when faced with low-impact, high-probability, and expected power outages. By contrast, the probability of extreme event (extreme weather or natural disasters) occurrence is low, but may seriously affect the power system, from long outage times to damage to major equipment such as substations, transmission lines, and power plants. As, in the short term, it is extremely difficult to completely avoid the damage caused by extreme events, it is important to enhance the resilience of power systems. This study has provided a comprehensive review of power system resilience by discussing its concepts, assessment, and enhancement measures. This article summarized possible impacts and quantitative indicators of various types of disasters on power grids, presented the concept of power system resilience, and analyzed the main characteristics that a resilient system should possess. Moreover, this article further distinguished the differences between the resilience, flexibility, and survivability of a power system. More importantly, this paper has proposed a novel framework and the corresponding metric for assessing resilience, which makes the evaluation of system resilience more accurate. Finally, this paper discussed various measures to enhance power system resilience and outlined potential challenges for future research. [ABSTRACT FROM AUTHOR]

In order to improve grid security while pursuing a grid operation economy and new energy consumption rates, this paper proposes a short-term optimal scheduling method based on security quantification for the grid containing a pumped-storage power plant. The method first establishes a grid security evaluation model to evaluate grid security from the perspective of grid resilience. Then, a short-term optimal dispatch model of the grid based on security quantification is constructed with the new energy consumption rate and grid loss as the objectives. In addition, an efficient intelligent optimization algorithm, Dung Beetle Optimization, is introduced to solve the scheduling model, dynamically updating the evaluation intervals during the iterative solution process and evaluating the grid security level and selecting the best result after the iterative solution. Finally, the improvement in the term IEEE 30-bus grid connected to a pumped-storage power plant is used as an example to verify the effectiveness of the proposed method and model. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) integrate diverse energy resources using advanced communication technologies and intelligent control strategies. This integration enhances the utilization and efficiency of distributed generation. This paper explores the incorporation of VPPs into load frequency control (LFC) systems. It includes an analysis of VPP-aggregated resources' frequency regulation characteristics and a VPP-inclusive LFC model. Additionally, a decentralized automatic generation control strategy is proposed to distribute power outputs effectively, enabling swift grid frequency adjustments. This study uses MATLAB simulations to demonstrate the benefits and efficacy of VPPs in LFC, underscoring their role in advancing grid management and stability. [ABSTRACT FROM AUTHOR]

The University of Newcastle (UON) and Jord International Pty Ltd. (Jord) have jointly developed a novel concept for the storage of energy from renewable and fossil fuel sources. The process, referred to as the pressurized regenerative calcium cycle (PRC2), relies on cyclic carbonation and calcination of CaO/CaCO3, in which low-cost electrical energy (i.e., off-peak, or excess generation from renewables) drives the calcination reaction and electricity is generated as required through the carbonation reaction. Initial proof-of-concept testing of the process was previously conducted within an existing fluid bed reactor at UON. The PRC2 concept was successfully demonstrated by maintaining the fluid bed reactor at a constant temperature by using the heat released during the reaction of calcium oxide and carbon dioxide. Following proof-of-concept testing, further refinement of the PRC2 process, which is the subject of this paper, was conducted to address its shortcomings and, importantly, facilitate the detailed design, construction, and operation of a large-scale demonstration plant. Nine different configurations were examined for the PRC2 process, for each of which a combined experimental, process modelling, and techno-economic assessment was completed. Experimental investigations were conducted to determine the suitability of carbonate materials for the PRC2 process. Process modelling and levelized cost of storage (LCOS) calculations were concurrently conducted and revealed that the molten salt approach (Option 9) was the most promising, having superior round-trip efficiency and lowest LCOS. For practical reasons (e.g., technical difficulties of working with molten salts), Option 3 (indirect power generation using a fluid bed reactor) was deemed the most feasible option for a demonstration scale plant. The LCOS for Option 3 (assuming a 100 MWe capacity) was calculated to be AUD 245 per MWh, which is on par with the cost of batteries for peak power replacement applications (the cost associated with lithium-ion batteries is AUD 370 per MWh). [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]

The harmonics of photovoltaic power plants are affected by various factors including temperature, weather, and light amplitude. Traditional power harmonic prediction methods have weak non-linear mapping and poor generalization capability to unknown time series data. In this paper, a Kernel Extreme Learning Machine (KELM) model power harmonic prediction method based on Gray Relational Analysis (GRA) with Variational Mode Decomposition (VMD) coupled with Harris Hawk Optimization (HHO) is proposed. First, the GRA method is used to construct the similar day set in one screening, followed by further using K-means clustering to construct the final similar day set. Then, the VMD method is adopted to decompose the harmonic data of the similar day set, and each decomposition subsequence is input to the HHO-optimized KELM neural network for prediction, respectively. Finally, the prediction results of each subseries are superimposed and numerical evaluation indexes are introduced, and the proposed method is validated by applying the above method in simulation. The results show that the error of the prediction model is reduced by at least 39% compared with the conventional prediction method, so it can satisfy the function of harmonic content prediction of a photovoltaic power plant. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources in the European power system is one of the main goals set by the European Union. In order to ease this integration, in recent years, Renewable Energy Communities (RECs) have been introduced that aim to increase the exploitation of renewable energy at the local level. This paper presents an Energy Management System (EMS) for an industrial microgrid owned and operated by a greentech company located in the north of Italy. The company is a member of an REC. The microgrid is made of interconnected busbars, integrating photovoltaic power plants, a fleet of electric vehicles, including company cars and delivery trucks supporting Vehicle-to-Grid (V2G), dedicated charging stations, and a centralized battery energy storage system. The industrial site includes two warehouses, an office building, and a connection to the external medium-voltage network. The EMS is designed to optimize the operation of the microgrid and minimize the operating costs related to the sale and purchase of energy from the external network. Furthermore, as the company is a member of an REC, the EMS must try to follow a desired power exchange profile with the grid, suggested by the REC manager, with the purpose of maximizing the energy that is shared within the community and incentivized. The results demonstrate that, when minimizing only costs, local self-consumption is favored, leading to a Self-Sufficiency Rate (SSR) of 65.37%. On the other hand, when only the adherence to the REC manager's desired power exchange profile is considered in the objective function, the SSR decreases to 56.43%, net operating costs increase, and the energy shared within the REC is maximized. [ABSTRACT FROM AUTHOR]

An integrated zero-carbon power plant aggregates uncontrollable green energy, adjustable load, and storage energy resources into an entity in a grid-friendly manner. Integrated zero-carbon power plants have a strong demand response potential that needs further study. However, existing studies ignore the green value of renewable energy in power plants when participating in demand response programs. This paper proposed a mathematical model to optimize the operation of an integrated zero-carbon power plant considering the green value. A demand response mechanism is proposed for the independent system operator and the integrated zero-carbon power plants. The Stackelberg gaming process among these entities and an algorithm based on dichotomy are studied to find the demand response equilibrium. Case studies verify that the mechanism activates the potential of the integrated zero-carbon power plant to realize the load reduction target. [ABSTRACT FROM AUTHOR]

This paper was conceived to investigate some central issues related to the upheaval of current energy scenarios in Sicily. New power connection lines that are about to be built in the Mediterranean area, planned with a view to a constantly increasing renewable generation, encourage the carrying out of analyses on how the Sicilian electric power system will be able to make itself ready to support large power injections, especially due to new renewables plants that will be established in the region soon. This study, carried out in close collaboration with the Italian TSO Terna S.p.A and the University of Palermo, defines what the impacts of new renewable power plants will be on the Sicilian power transmission grid under intact and non-intact grid conditions. This study consists of steady-state simulations carried out using WinCreso® software version 7.62.1-3 in two energy scenarios estimated for the years 2024 and 2027, based on real connection requests by producers to Terna, and allows one to go beyond the studies conducted so far on a 2030 basis through the precise identification of network nodes or lines in difficulty. Finally, as well as presenting an interesting case study due to Sicily's strategic position in the Mediterranean Sea, this article proposes a methodological approach that can easily be adopted in other contexts and by other TSOs to analyze similar situations. [ABSTRACT FROM AUTHOR]

Featured Application: Featured Application: This research offers a practical methodology for designing biogas plants, improving renewable energy production, and promoting sustainable agricultural practices. This paper aims to enhance the efficiency of biogas plants for the production of electrical and thermal energy by optimizing substrate selection and digester heating techniques. The study presents a comprehensive project of a biogas plant, including all necessary installations and a detailed methodology for equipment selection. Specific substrates used include animal flour, corn silage, and molasses, each chosen for their high dry matter content and biogas production efficiency. The digester heating technique was analyzed to maintain optimal fermentation temperatures, ensuring a stable and efficient biogas production process. The projected daily biogas production is estimated to be 5688 m3. The analysis shows that maintaining a constant digester temperature significantly improves biogas yield. Seasonal variations in thermal energy requirements were identified, highlighting the need for effective insulation and heating mechanisms, particularly during colder months. Additionally, the implementation of advanced heating techniques demonstrated a reduction in overall energy consumption for maintaining the desired digester temperature. This research provides valuable insights for the design and operation of efficient biogas plants. By optimizing substrate mixtures and digester heating, the study contributes to sustainable energy production, reduced greenhouse gas emissions, and enhanced biogas plant efficiency, supporting both environmental protection and economic development. [ABSTRACT FROM AUTHOR]

Among renewable resources, solar energy is abundant and cost effective. However, the efficiency and performance of photovoltaic panels (PVs) are adversely affected by the rise in the surface temperature of solar cells. This paper analyzes the idea of utilizing thermoelectric modules (TEMs) to enhance the efficiency and performance of PV panels. The proposed hybrid solar thermoelectric generation (HSTEG) system employs TEMs as thermoelectric coolers (TECs) to enhance panel efficiency and as thermoelectric generators (TEGs) to convert excess heat into additional electricity. This study includes an extensive evaluation of the proposed idea using MATLAB Simulink and experimental validation in indoor as well as outdoor environments. The use of TECs for the active cooling of the PV system leads to an increase in its efficiency by 9.54%. Similarly, the passive cooling by TECs along with the additional power generated by the TEGs from the excessive heat led to an increase in the efficiency of the PV system of 15.50%. The results demonstrate the HSTEG system's potential to significantly improve PV panel efficiency and energy generation, offering a promising avenue for advancing solar energy technology. [ABSTRACT FROM AUTHOR]

Pavement photovoltaic (PV) is an innovative energy-harvesting technology that seamlessly integrates into road surfaces, merging established PV power generation methods with conventional roadway infrastructure. This fusion optimally utilizes the extensive spatial assets inherent in road networks. This paper offers an exhaustive examination of the literature concerning the physical models and performance evaluation of photovoltaic pavements. This study delineates the essential three-tier structure of pavement modules and juxtaposes the advantages and drawbacks of design models across these strata, thereby facilitating the development of more suitable solutions for varying application scenarios. The importance of accommodating fluctuations in shadows and countering the heat island effect (HIE) is emphasized. Nevertheless, the technology remains in its nascent research phase, characterized by challenges associated with limited long-term durability and efficacy. Building upon these findings, this study addresses the challenges confronting pavement PV from three perspectives and outlines future prospects and recommendations for its progression. [ABSTRACT FROM AUTHOR]

While technical optimization focuses on maximizing the annual energy yield of utility-scale PV parks, the ultimate goal for power plant owners is to maximize investment profit. This paper aims to bridge the gap between technical and economic approaches by using simulation data from a real-case utility-scale PV park. It analyzes how changes in configuration parameters such as the DC–AC ratio and string length and PV technologies like solar tracking systems and bifacial modules impact the economic metrics of the project, i.e., net present value (NPV) and internal rate of return (IRR). PVSyst software was utilized as a simulation tool, while in-house developed software implementing appropriate technical and economic models served as a comparison platform and was used to validate the outputs generated through PVSyst. Results indicate that the commonly used horizontal single-axis tracking configuration may economically underperform compared with fixed-tilt setups. The optimal DC–AC ratio fell within the range of 1.30 to 1.35. Extending the string length from 25 to 28 modules improved economic indexes. Additionally, fixed-tilt bifacial modules can enhance project economics if a 10% cost premium compared with standard monofacial PV modules is considered. [ABSTRACT FROM AUTHOR]

In the business of growing and selling ornamental plants, it is important to keep track of plants from nursery to distribution. Radio Frequency Identification (RFID) technology provides an easier tracking method for inventories of plants by attaching tags with unique identifiers. Due to the vast area of most nurseries, there is a need to have an efficient method of scanning RFID tags. This paper investigates the use of drones and RFID, specifically, the effects of RFID reader power and flight altitude on tag counts. The experimental setup evaluated three RFID reader power levels (15 dBm, 20 dBm, and 27 dBm), three flight altitudes (3 m, 5 m, and 7 m), the number of passes (one or two), and two plant types ('Green Giant' arborvitae and 'Sky Pencil' holly). For RFID tags, four types were used (L5, L6, L8, and L9), with two antenna types (dog-bone and square-wave) and two attachment types (loop-lock and stake). For each power level, the UAV was flown to three different altitudes of 3 m, 5 m, and 7 m above the ground. At each altitude, two scan passes were performed at a constant speed of approximately 1.5 m/s. Each plot of plants (two in total) was randomly tagged with a total of 40 RFID tags per plot. Field data were collected from September to December 2023 (on a total of eight dates). The data showed that a power level of 15 dBm and an altitude of 3 m yielded a tag count of 53%, while counts of 34% and 16% were achieved at 5 m and 7 m, respectively. At 20 dBm and an altitude of 3 m, the count accuracy across all tag types and both plants was 90%. When the altitude was increased to 5 m and 7 m, tag-count accuracy dropped to 75% and 33%, respectively. The highest count accuracy was observed at 27 dBm and an altitude of 3 m, with a reading accuracy of 98%. Tag types L6 and L9 performed better at any power level and altitude, while L5 and L8 performed well at a higher power level and lower altitude. In this experiment, canopy properties (size and shape) had no effect on the number of tags read. This study aimed to evaluate the RFID power and UAV altitude achieving the highest accuracy in scanning the RFID tags. Furthermore, it also assessed the effects of plant growth on the scanning efficiency and accuracy of the system. [ABSTRACT FROM AUTHOR]

Energy markets are crucial for integrating Distributed Energy Resources (DER) into modern power grids. However, this integration presents challenges due to the inherent variability and decentralized nature of DERs, as well as poorly adapted regulatory environments. This paper proposes a medium-term decision-making approach based on a comprehensive suite of computational tools for integrating DERs into Colombian energy markets. The proposed framework consists of modular tools that are aligned with the operation of a Commercial Virtual Power Plant (CVPP). The tools aim to optimize participation in bilateral contracts and short-term energy markets. They use forecasting, uncertainty management, and decision-making modules to create an optimal portfolio of DER assets. The suite's effectiveness and applicability are demonstrated and analyzed through its implementation with heterogeneous DER assets across various operational scenarios. [ABSTRACT FROM AUTHOR]

Featured Application: The research presented by the authors in this article points to the need for changes in the construction of agricultural biogas plants to optimise electricity production. Energy from biogas is widely available, inexpensive, and often contributes to waste management, making it one of the most promising renewable energy sources. The main factors influencing this process' efficiency include the substrates' chemical composition, temperature, and digester load. This paper presents the possibilities offered by a biogas plant built at a farm specialising in dairy cows. The dependence of the power generated in the micro biogas plant on its technical parameters was analysed in detail. Studies carried out by the authors in an agricultural microgas plant (with an electrical output of 40 kW) have shown that they are designed to maintain continuous energy production, despite changing process parameters such as digester mass level, biogas height, temperature or slurry flow into the digester. However, from the point of view of the amount of electricity generated, changes would have to be made to the design of the biogas plant. Firstly, a more powerful generator would have to be installed to cover the electricity requirements of the equipment installed in the biogas plant so that power close to the rated capacity of the biogas plant is still sent to the grid. Secondly, replacing the two existing agitators of the digestion mass (9 kW each) with more agitators of lower power (e.g., four agitators of 4.5 kW each) would be necessary. These should be programmed so that one of the agitators operates at any given time (the operating time of a given agitator should depend on the composition of the digestate). [ABSTRACT FROM AUTHOR]

The development of a cost-effective and accurate model for predicting the fatigue life of materials is essential for designing thermal power plants and assessing their structural reliability under operational conditions. This paper reports a novel energy-based approach for developing unified models that predict the fatigue life of boiler tube materials in ultra-supercritical (USC) power plants. The proposed method combines the Masing behavior with a cyclic stress–strain relationship and existing stress-based or strain-based fatigue life prediction models. Notably, the developed models conform to the structure of the modified Morrow model, which incorporates material toughness (a temperature compensation parameter) into the Morrow model to account for the effects of temperature. A significant advantage of this approach is that it eliminates the need for tensile tests, which are otherwise essential for assessing material toughness in the modified Morrow model. Instead, all material constants in our models are derived solely from fatigue test results. We validate our models using fatigue data from three promising USC boiler tube materials—Super304H, TP310HCbN, and TP347H—and their welded joints at operating temperatures of 500, 600, and 700 °C. The results demonstrate that approximately 91% of the fatigue data for all six materials fall within a 2.5× scatter band of the model's predictions, indicating a high level of accuracy and broad applicability across various USC boiler tube materials and their welded joints, which is equivalent to the performance of the modified Morrow model. [ABSTRACT FROM AUTHOR]

The number of XLPE cables being used near or beyond their design life is increasing. The importance of timely cable replacement is necessary. Therefore, research is actively being conducted using VLF Tan δ diagnostic technology to assess the insulation condition of cables. Present studies lack in measuring and analyzing the VLF Tan δ of service-aged XLPE cables. Additionally, there is a research gap considering the operating environment. Therefore, it is needed to diagnose and analyze the insulation condition of the same service-aged cable when it is operated in a different environment. This paper assesses and analyzes the insulation condition of cables installed in the BFP, cooling tower, and deaerator booster pump of a combined heat and power plant. Each cable was evaluated by measuring the VLF Tan δ, the dielectric breakdown test of the cable, and the tensile strength, elongation at break, crystallinity, and dielectric strength of the XLPE specimens. Additionally, the correlation between the VLF Tan δ and other characteristics was also analyzed. It was found that degradation progressed in the order of the BFP, the cooling tower, and the deaerator booster pump. Therefore, it was confirmed that even for the same cable, deterioration varies depending on the installation location. [ABSTRACT FROM AUTHOR]

Renewable energies have become pivotal in the global energy landscape. Their adoption is crucial for phasing out fossil fuels and promoting environmentally friendly energy solutions. In recent years, the energy management system (EMS) concept has emerged to manage the power grid. EMS optimizes electric grid operations through advanced metering, automation, and communication technologies. A critical component of EMS is power forecasting, which facilitates precise energy grid scheduling. This research paper introduces a deep learning hybrid model employing convolutional neural network–long short-term memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting. The proposed method integrates the variational mode decomposition (VMD) algorithm with the CNN-LSTM model to predict PV power output from a solar farm in Boussada, Algeria, spanning 1 January 2019, to 31 December 2020. The performance of the developed model is benchmarked against other deep learning models across various time horizons (15, 30, and 60 min): variational mode decomposition–convolutional neural network (VMD-CNN), variational mode decomposition–long short-term memory (VMD-LSTM), and convolutional neural network–long short-term memory (CNN-LSTM), which provide a comprehensive evaluation. Our findings demonstrate that the developed model outperforms other methods, offering promising results in solar power forecasting. This research contributes to the primary goal of enhancing EMS by providing accurate solar energy forecasts. [ABSTRACT FROM AUTHOR]

In the modern power grid, characterized by the increased penetration of power electronics and extensive utilization of renewable energy, inverter-based power plants play a pivotal role as the principal interface of renewable energy sources (RESs) and the grid. Considering the stability characteristics of grid-following (GFL) inverters when the grid is relatively weak, the application of grid-forming (GFM) controls becomes imperative in enhancing the stability of the entire power plant. Thus, there is an urgent need for suitable and effective models to study the interaction and stability of the paralleled inverters employing GFL and GFM controls. Thus, the small-signal modeling with full-order state-space model and eigenvalues analysis are presented in this paper. First, the small-signal state-space model of the individual GFL and GFM inverters is obtained, considering the control loop, interaction, reference frame, transmissions, and time delays. Then, the models of the individual inverter are extended to the hybrid inverters to study the effects of the GFM inverters on the small-signal stability of the entire system. And the impacts of the inertia and damping are analyzed by the eigenvalues of the state-transition matrix. A case comprising three parallel GFL inverters and two GFL inverters with one GFM inverter, respectively, are studied to examine the effectiveness and accuracy of the model. Finally, the stability margin obtained from the eigenvalue analysis of the entire system is verified by time-domain simulations. [ABSTRACT FROM AUTHOR]

Shading on photovoltaic (PV) modules due to shadows, covering, dust, etc., usually characterized as semi-transparent, will significantly affect the power generation capacity. No systematic study has considered the impact of semi-transparent coverings on the power generation capacity of PV modules. This paper covers a single cell in the PV module using a covering with a transmittance of 18.55% and systematically investigates its impact on the power generation capacity. The open-circuit voltage (Voc) of the PV module is nearly unaffected by semi-transparent coverings because the covered cell can be considered as working at a lower irradiance and thus can output a voltage close to that of the uncovered cell. The short-circuit current (Isc) is significantly affected by coverings because it is co-contributed by the photocurrent (evaluated based on the covering ratio R and transmittance) and the reverse bias current ΔIsc (the covered cell is in a reverse bias state). The ΔIsc increases with R because more charge accumulates at the bi-ends of the covered cell; but, it decreases at full covering, which implies that in a partially covered case the uncovered part contributes more to ΔIsc than the covered part. The fill factor (FF) of the PV module first increases and then decreases with R, as the equivalent resistance of the covered cell increases rapidly with R, which replaces the wire resistance in dominating the series resistance of the PV module when R > 0.6. This work is of great theoretical significance in analyzing the output characteristics of PV modules under real conditions. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of the abolition of annual net metering in Slovenia and compares the electric energy costs for the studied active user after the abolition. The article also provides an exploration of the role played by an aggregator, which serves as a central entity that enables individuals to participate in the electric energy market. An analysis of the case study of an active user was made, where an analysis was made of the measurements of household consumption and photovoltaic plant production for the year 2022. This article presents an economic analysis with and without net metering and an analysis of the aggregator involvement strategies. In addition, a battery energy storage system was also considered in the analysis. An important part of the article is the identification of the flexibility potential for shiftable loads, which enable an aggregator to acquire insight into the energy consumption profile and energy production profile of active users. The following indicators were used to compare the strategies: annual electric energy cost and the indicators including self-sufficiency, self-consumption, and grid dependency. The findings indicate that, even in the absence of annual net metering, the active user can lower their costs for electric energy with the help of an aggregator. [ABSTRACT FROM AUTHOR]

Thermal power plant slag is a waste that is presently obtained from many power stations all over the world. A possible method for its utilization is using it to produce concrete. This paper analyses the effect of thermal power plant slag on the technological properties of concrete mixtures and the mechanical properties of concrete subjected to heat–moisture processing. Quantitative estimates of the investigated factors' influence on the concrete mixture's water demand and the strength of steamed concrete were obtained. The influences of TPP slag content and its water demand on concrete composition features as well as concrete strength are shown. The novelty of the work lies in the use of an experimental–statistical model to optimize the composition of steamed concrete using slag from the viewpoint of maximum strength per kilogram of cement. It has been demonstrated that the optimal part of slag in aggregate, which provides maximum strength at 4 h and 28 days after steaming, is 0.5–0.55 and 0.45–0.55, respectively. A method for the design of concrete composition using slag from thermal power plants is proposed. [ABSTRACT FROM AUTHOR]

In the context of an insulated area with a subtropical climate, such as La Réunion island, it is crucial to reduce the energy consumption of buildings and develop local renewable energy sources to achieve energy autonomy. Direct current (DC) nanogrids could facilitate this by reducing the energy conversion steps, especially for solar energy. This article presents the deployment and efficiency evaluation of a 48 VDC low-voltage direct current (LVDC) nanogrid, from conception to real-world installation within a company. The nanogrid consists of a photovoltaic power plant, a lithium–iron–phosphate (LFP) battery, and DC end-use equipment, such as LED lighting and DC fans, for two individual offices. For identical test conditions, which are at an equivalent cabling distance and with the same final power demand, the total power consumed by the installation is measured for several stages from 50 to 400 W, according to a 100% DC configuration or a conventional DC/AC/DC PV configuration incorporating an inverter and AC/DC converter. The methodology used enables a critical view to be taken of the installation, assessing both its efficiency and its limitations. Energy savings of between 23% and 40% are measured in DC for a power limit identified at 150 W for a distance of 25 m. These results show that it is possible to supply 48 VDC in an innovative way to terminal equipment consuming no more than 100 W, such as lighting and air fans, using the IEEE 802.3 bt power over ethernet (PoE) protocol, while at the same time saving energy. The nanogrid hardware and software infrastructure, the methodology employed for efficiency quantification, and the measurement results are described in the paper. [ABSTRACT FROM AUTHOR]

Efficient integration of wind energy requires accurate wind power forecasting. This prediction is critical in optimising grid operation, energy trading, and effectively harnessing renewable resources. However, the wind's complex and variable nature poses considerable challenges to achieving accurate forecasts. In this context, the accuracy of wind parameter forecasts, including wind speed and direction, is essential to enhancing the precision of wind power predictions. The presence of missing data in these parameters further complicates the forecasting process. These missing values could result from sensor malfunctions, communication issues, or other technical constraints. Addressing this issue is essential to ensuring the reliability of wind power predictions and the stability of the power grid. This paper proposes a long short-term memory (LSTM) model to forecast missing wind speed and direction data to tackle these issues. A fractional-order neural network (FONN) with a fractional arctan activation function is also developed to enhance generated wind power prediction. The predictive efficacy of the FONN model is demonstrated through two comprehensive case studies. In the first case, wind direction and forecast wind speed data are used, while in the second case, wind speed and forecast wind direction data are used for predicting power. The proposed hybrid neural network model improves wind power forecasting accuracy and addresses data gaps. The model's performance is measured using mean errors and R2 values. [ABSTRACT FROM AUTHOR]

Energy communities are key actors in the energy transition since they optimally interconnect renewable energy capacities with the consumers. Despite versatile objectives, they usually aim at improving the self-consumption of renewable electricity within low-voltage grids to maximize revenues. In addition, energy communities are an excellent opportunity to supply renewable electricity to regional and national energy systems. However, effective price signals have to be designed to coordinate the needs of the energy infrastructure with the interests of these local stakeholders. The aim of this paper is to demonstrate the integration of energy communities at the national level with a bottom–up approach. District energy systems with a building scale resolution are modeled in a mixed-integer linear programming problem. The Dantzig–Wolfe decomposition is applied to reduce the computational time. The methodology lies within the framework of a renewable energy hub, characterized by a high share of photovoltaic capacities. Both investments into equipment and its operation are considered. The model is applied on a set of five typical districts and weather locations representative of the Swiss building stock. The extrapolation to the national scale reveals a heterogeneous photovoltaic potential throughout the country. Present electricity tariffs promote a maximal investment into photovoltaic panels in every region, reaching an installed capacity of 67.2 GW and generating 80 TWh per year. Placed in perspective with the optimal PV capacity forecast at 15.4 GW   p e a k at the national level, coordinated investment between local and national actors is needed to prevent dispensable expenses. An uncoordinated design is expected to increase the total costs for residential energy systems from 12% to 83% and curtails 48% of local renewable electricity. [ABSTRACT FROM AUTHOR]

With the integration of large-scale renewable energy generation, some new problems and challenges are brought for the operation and planning of power systems with the aim of mitigating the adverse effects of integrating photovoltaic plants into the grid and safeguarding the interests of diverse stakeholders. In this paper, a methodology for allotting capacity is introduced, which takes into account the active involvement of multiple stakeholders in the energy storage system. The objective model for maximizing the financial proceeds of the PV plant, the system for the storage of energy, and a power grid company is studied. Then, in order to maximize the benefit of three stakeholders, a modified particle swarm optimization algorithm is devised, employing the prevailing typical allocation strategy. Finally, a case study is provided based on the modified IEEE 14-bus and the actual power grid from South Xinjiang, China. The simulation results and findings of the case study conclusively illustrate that the proposed methodology adeptly ensures the maximization of interests for the triad of stakeholders. [ABSTRACT FROM AUTHOR]

This paper presents methodologies and equipment for SI-traceable inline measurements of water content (a critical quality parameter) in solid biofuels. Inline measurement systems for water content are commonly used at CHP plants, providing continuous real-time data. However, the accuracy of these systems is in most cases unsatisfactory, mainly because the systems are not calibrated representatively for the relevant material, and until now, calibrations traceable to the SI system have not been available. To provide reliable and accurate inline water content data, new procedures and equipment for calibrating measurement systems were developed. Two reference methods for the determination of water content were developed; one measures the airflow and dewpoint of desorbed water in the air passing a test sample, while the other uses a P2O₅-sensor. Additionally, a transfer standard based on a cavity resonance sensor was developed for fast onsite calibration of the inline sensor at the power plant. This new instrument allows for quick and accurate measurements. The transfer standard is made metrologically traceable to the primary measurement standards. The entire system was demonstrated by calibrating an inline microwave-based (MW) sensor at the CHP plant of VERDO in Randers, Denmark. Thus, a complete metrological traceability chain was established from an industrial to a primary standard. [ABSTRACT FROM AUTHOR]

The study deals with the possibilities of using alternative types of fuels to produce electricity. Power Plant Vojany (PPV) is a thermal power plant (TPP) in eastern Slovakia, which is part of the company Slovenské elektrárne, a. s. (SE). PPV primarily used black coal to produce electricity, which had to be imported from abroad (the Russian Federation). This activity has become inefficient both economically and environmentally, due to the high price of CO2 permits and the high emission factor of this type of fuel. PPV decided to co-combust biomass and refuse-derived fuel (RDF), which resulted in much better economic conditions due to their price, economic efficiency, and partly closed CO2 cycle. The aim of the paper is to explore the possibilities related to the production of energy in the cleanest possible way and with the least possible damage to the environment in coal thermal power plants using the example of operating Power Plant Vojany located in eastern part of Slovakia and to inspire each other for the modern transformation. For the purposes of hypothesis verification, analytical methods focused on overview studies of average fuel prices, comparisons, and the balance of fuels in connection with eliminated CO2 emissions, as well as municipal waste (MW) management in the EU and V4 countries, were used. The authors also focused on the energy recovery and combustion of MW and tracking the achieved CO2 savings in connection with the development of fuel sources in PPV. The results point to the fact that PPV is one of the power plants that could use biomass and RDF as fuel, which confirms the economic advantages of this procedure. The results confirm that the potential of RDF production in Slovakia is sufficient to ensure the operation of PPV at planned, even higher volumes of electricity production. The transformation to cleaner operation of coal thermal power plants represents a significant contribution of this study. [ABSTRACT FROM AUTHOR]

As the global demand for renewable energy continues to rise, biogas production has emerged as a promising solution for sustainable energy generation. This review article presents the advantages of biogas technologies (mainly agricultural, based on waste of animal and plant origin) and extensively discusses the main principles of biogas production in the anaerobic digestion (AD). In this respect, the main parameters of the process, which require monitoring and decisive for its efficiency are described, therefore: temperature, pH value, retention time and organic loading rate (OLR). The principles of substrate selection are also discussed and the necessity and advantages of the use of organic waste according to the model of a circular economy and the concept of sustainable development, are indicated. It is emphasized that according to the new European regulations, the crops classified as food cannot be considered energy crops. The part on biogas production is summarised with an explanation of the necessity to treat and purify biogas. Biogas purification is important from the point of view of the efficiency of its conversion into electricity. A special place in this paper is devoted to the design, construction, functioning and operation of biogas plants, based on both scientific and practical aspects. In conclusion of this chapter, the economic aspects and profitability of operating biogas plants are discussed. Cost and benefit analyses are the major tool used for the systematic evaluation of the financial costs and potential benefits associated with the operation of biogas plants. The important fact is that the return on investment can be achieved within a few years, provided the activities are well-planned and executed. In addition to the fundamental issues of the operation of biogas plants, this article presents the global situation regarding the development of biogas plants, discussing in detail the specific needs and limitations on different continents. It is a interesting and extensive part of this article. The global agricultural biogas market is at very different levels of development. Most such installations are located in Asia and Europe. China has the highest number of biogas plants, with more than 100,000 biogas plants, followed by Germany with over 10,000 plants. In addition to the 100,000 biogas plants, China also has a large number of household biogas units, which gives a total of approx. 40 million operating units. The article concludes with a discussion of opportunities and barriers to the development of biogas plants, pointing to: financial issues, access to feedstock, political regulations, public awareness and the geopolitical situation. The most frequently cited reasons for investment failure include economic problems, lack of professional knowledge. [ABSTRACT FROM AUTHOR]

In large-scale water conservancy and hydropower projects, complex rock structures are considered to be the main factor controlling the stability of hydraulic structures. The classification of rock mass structure plays an important role in the safety of all kinds of large buildings, especially underground engineering buildings. As a quantitative classification index of rock mass, the rock block index is very common in the classification of borehole and dam foundation rock mass structures. However, there are few studies on the classification of underground engineering rock masses. Moreover, their classification criteria have disadvantages in spatial dimension. Therefore, this paper takes the long exploratory cave CPD1 in the water transmission and power generation system of the Qingtian pumped storage power station in Zhejiang Province as the research object and launches a study on the structural classification of the rock mass of a flat cave based on the 3D rock block index. According to the group distribution of joints, the sections are statistically homogeneous. Additionally, the Monte Carlo method is used to carry out random simulations to generate a three-dimensional joint network model. The virtual survey lines are arranged along the center of the shape of the three different orthogonal planes of the 3D joint network model to represent the boreholes, and the RBI values of the virtual survey lines on each orthogonal plane are counted to classify the rock mass structure of the flat cave in a refined manner using the rock block index of the rock mass in 3D. The above method realizes the application of the 3D rock block index in underground engineering and overcomes the limitations of traditional rock mass classification methods in terms of classification index and dimension. The results show that: (1) Three-dimensional joint network simulations built on statistical and probabilistic foundations can visualize the structure of the rock mass and more accurately reflect the structural characteristics of the actual rock mass. (2) Based on the 3D rock block index, the rock mass structure of the long-tunnel CPD1 is classified, from that of a continuous structure to a blocky structure, corresponding to the integrity of the rock mass from complete to relatively complete. The classification results are consistent with the evaluation results of horizontal tunnel seismic wave geophysical exploration. (3) Based on the 3D joint network model, it is reasonable and feasible to use the 3D rock block index as a quantitative evaluation index to determine the structure type of flat cave rock masses. The above method is helpful and significant in the classification of underground engineering rock mass structures. [ABSTRACT FROM AUTHOR]

For political and economic reasons, renewable sources of energy have gained much importance in establishing a sustainable energy economy. By their very nature, however, their benefits depend on changeable weather conditions, and are unrelated to the generation and consumption patterns in industrial or home environments. This generation–dissipation disparity induces price fluctuations and threatens the stability of the supply system, yet can be alleviated by installing energy depots. While the classic methods of energy storage are hardly cost-effective, they may be supplemented, or replaced, by a distributed system of small-scale hydropower plants with ponds used as energy reservoirs. In this paper, following a rigorous mathematical argument, a dynamic model of a multi-cascade of hydropower plants is constructed, and a cost-optimal controller, with formally proven properties, is designed. On the one hand, it allows for an increase in the owners' revenue by as much as 30% (compared to a free-flow state); on the other hand, it reduces the load fluctuation imposed on the grid and the legacy supply system. Moreover, the risk of floods and droughts downstream resulting from inappropriate use of the plants is averted. [ABSTRACT FROM AUTHOR]

Due to the high penetration of virtual power plants (VPPs), the bi-directional power flow between VPPs and active distribution grids makes the grid operation complex. Without congestion management, the operation schedule only considers the economic benefits, and power flow constraints might be violated. Hence, it is necessary to conduct power interaction within the operation constraints. This paper proposes a coordinated economic dispatch method under voltage security constraints. The linear expressions were derived by simplifying the AC power flow equations to reduce the computation complicity. Then, optimal economic dispatch models with voltage security constraints were established for the active distribution grid and VPPs, respectively. Meanwhile, the transacted power and clearing price were set as the communication variables, and a coordinated strategy was proposed for the overall optimal goal. The modified IEEE 33-node and PG&E-node distribution grids were utilized for the simulations, and the results affirmed the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of accurately modeling the operational constraints of Combined-Cycle Gas Turbines (CCGTs) within a unit-commitment framework. In practice, in Colombia, when given an initial dispatch by the Independent System Operator, CCGT plants are operated according to the results of heuristic simulation codes. Such heuristics often omit technical operating constraints, including hot, warm, or cold startup ramps; the minimum operation hours required for a gas turbine to start a steam turbine; the relationship between the dispatched number of steam and gas turbines; the load distribution among gas turbines; and supplementary fires. Most unit-commitment models in the literature represent standard technical constraints like startup, shutdown, up/down ramps, and in some cases, supplementary fires. However, they typically overlook other real-life CCGT operating constraints, which were considered in this work. These constraints are crucial in integrated energy systems to avoid equipment damage, which can potentially put CCGT plants out of service and ultimately lead to lower operating costs. [ABSTRACT FROM AUTHOR]

The increase in renewable generation of a distributed nature has brought significant new challenges to power and energy system management and operation. Self-consumption in buildings is widespread, and with it rises the need for novel, adaptive and intelligent building energy management systems. Although there is already extensive research and development work regarding building energy management solutions, the capabilities for adaptation and contextualization of decisions are still limited. Consequently, this paper proposes a novel contextual rule-based system for energy management in buildings, which incorporates a contextual dimension that enables the adaptability of the system according to diverse contextual situations and the presence of multiple users with different preferences. Results of a case study based on real data show that the contextualization of the energy management process can maintain energy costs as low as possible, while respecting user preferences and guaranteeing their comfort. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) are susceptible to cyber anomalies due to their extensive communication layer. FL-trust, an improved federated learning (FL) approach, has been recently introduced as a mitigation system for cyber-attacks. However, current FL-trust enhancements, relying solely on proportional-integral (PI), exhibit drawbacks like sensitivity to controller gain fluctuations and a slow response to sudden disturbances, and conventional FL-trust is not directly applicable to the non-independent and identically distributed (non-IID) datasets common in VPPs. To address these limitations, we introduce an artificial neural network (ANN)-based technique to adapt FL-trust to non-IID datasets. The ANN is designed as an intelligent anomaly mitigation control method, employing a dynamic recurrent neural network with exogenous inputs. We consider the effects of the most common VPP attacks, poisoning attacks, on the distributed cooperative controller at the secondary control level. The ANN is trained offline and tested online in the simulated VPP. Using MATLAB simulations on a HOMER-modeled VPP, the proposed technique demonstrates its superior ability to sustain normal VPP operation amidst cyber anomalies, outperforming a PI-based mitigation system in accuracy and detection speed. [ABSTRACT FROM AUTHOR]

Enhancing the energy efficiency of building envelopes is one of the key strategies for energy conservation and reducing consumption in buildings. This study employs numerical research methods to explore the impact of crucial factors such as solar cell coverage, air channel height, indoor relative humidity, and indoor wind speed on the power generation performance and thermal comfort of a photovoltaic (PV)—Trombe wall. The dynamic changes in optical and thermal performance and energy efficiency matching mechanisms of this system are also discussed in hot summer and warm winter regions. The research findings indicate that the periods of good thermal comfort and power generation efficiency for humans are from 9:00 to 17:00 in winter. In summer, these periods are from 5:00 to 8:00 as well as from 17:00 to 20:00. When the system height is 2 m, the electricity price for power supplied by the PV—Trombe wall system is 25% lower than the residential price, with an annual energy generation of 322.5 kWh/m2 of solar panel, which can save USD 6.35 in costs. Moreover, an experiment is conducted to investigate the thermoelectric correlation by constructing a traditional Trombe wall and an external PV—Trombe wall. When the coverage reached 52.08%, the overall system efficiency was maximized. At a coverage of 78.12%, the system's thermal efficiency was at its lowest, while the maximum power generation was 510.3 W. It can be seen that the PV—Trombe wall possesses good economic benefits and energy saving as well as emission reduction potential in hot summers and warm winters regions, and the smooth implementation of related works will effectively promote its applications and promotions. [ABSTRACT FROM AUTHOR]

The increasing adoption of renewable energy, particularly photovoltaic (PV) power, has highlighted the importance of accurate PV power forecasting. Despite advances driven by deep learning (DL), significant challenges remain, particularly in capturing the long-term dependencies essential for accurate forecasting. This study presents TransPVP, a novel transformer-based methodology that addresses these challenges and advances PV power forecasting. TransPVP employs a deep fusion technique alongside a multi-task joint learning framework, effectively integrating heterogeneous data sources and capturing long-term dependencies. This innovative approach enhances the model's ability to detect patterns of PV power variation, surpassing the capabilities of traditional models. The effectiveness of TransPVP was rigorously evaluated using real data from a PV power plant. Experimental results showed that TransPVP significantly outperformed established baseline models on key performance metrics including RMSE, R2, and CC, underscoring its accuracy, predictive power, and reliability in practical forecasting scenarios. [ABSTRACT FROM AUTHOR]

In Indonesia, the power generation sector is the primary source of carbon emissions, largely due to the heavy reliance on coal-fired power plants, which account for 60% of electricity production. Reducing these emissions is essential to achieve national clean energy transition goals. However, achieving this initiative requires careful consideration, especially regarding the complex interactions among multiple stakeholders in the Indonesian electricity market. The electricity market in Indonesia is characterized by its non-competitive and heavily regulated structure. This market condition often requires the PLN, as the system operator, to address multi-objective and multi-constraint problems, necessitating optimization in the generation dispatch scheduling scheme to ensure a secure, economical, and low-carbon power system operation. This research introduces a multiparadigm approach for GS optimization in a regulated electricity market to support the transition to clean energy. The multiparadigm integrates multi-agent system and system dynamic paradigms to model, simulate, and quantitatively analyze the complex interactions among multiple stakeholders in the Indonesian regulated electricity market. The research was implemented on the Java–Madura–Bali power system using AnyLogic 8 University Researcher Software. The simulation results demonstrate that the carbon policy scheme reduces the system's carbon emissions while increasing the system's cost of electricity. A linear regression for sensitivity analysis was conducted to determine the relationship between carbon policies and the system's cost of electricity. This research offers valuable insights for policymakers to develop an optimal, acceptable, and reasonable power system operation scheme for all stakeholders in the Indonesian electricity market. [ABSTRACT FROM AUTHOR]

This study investigates a dual-stage axial-flow fan within a specific power plant context. Numerical simulations encompassing both steady-state and stall conditions were conducted utilizing the Reynolds-averaged Navier–Stokes (RANS) equations coupled with the Realizable k–ε turbulence model. The findings reveal that, under normal operating conditions, there exists a positive correlation between the mass flow rate and outlet pressure with gas density while displaying a negative correlation with dynamic viscosity. Regardless of the changes in air density, the volumetric flow rate at the maximum outlet pressure of the fan remains essentially the same. When a stall occurs, the volumetric flow rate rapidly decreases to a specific value and then decreases slowly. The analysis of the three-dimensional flow field within the first-stage rotor was performed before and after the rotational stall occurrence. Notably, stall inception predominantly manifests at the blade tip. As the flow rate diminishes, the leakage area at the blade tip within a passage expands, directing the trajectory of the leakage vortex toward the leading edge of the blade. Upon reaching a critical flow rate, the backflow induced by the blade tip leakage vortex obstructs the entire passage at the blade tip, progressively evolving into a stall cell, thereby affecting flow within both passages concurrently. [ABSTRACT FROM AUTHOR]

Creating an effective deep learning technique for accurately diagnosing leak signals across diverse environments is crucial for integrating artificial intelligence (AI) into the power plant industry. We propose an automatic weight redistribution ensemble model based on transfer learning (TL) for detecting leaks in diverse power plant environments, overcoming the challenges of site-specific AI methods. This innovative model processes time series acoustic data collected from multiple homogeneous sensors located at different positions into three-dimensional root-mean-square (RMS) and frequency volume features, enabling accurate leak detection. Utilizing a TL-driven, two-stage learning process, we first train residual-network-based models for each domain using these preprocessed features. Subsequently, these models are retrained in an ensemble for comprehensive leak detection across domains, with control weight ratios finely adjusted through a softmax score-based approach. The experiment results demonstrate that the proposed method effectively distinguishes low-level leaks and noise compared to existing techniques, even when the data available for model training are very limited. [ABSTRACT FROM AUTHOR]

The maintenance of photovoltaic (PV) power plants is of central importance for their yield. To reach higher efficiencies in PV parks, it is helpful to detect soiling such as dust deposition and to apply this information to optimize cleaning strategies. Furthermore, a periodic inspection of the PV modules with infrared (IR) imagery is of advantage to detect and potentially remove faulty PV modules. Soiling can be erroneously interpreted as PV module defects and hence spatially resolved soiling measurements can improve the results of IR-based PV inspection. So far, soiling measurements are mostly performed only locally in PV fields, thus not supporting the above-mentioned IR inspections. This study presents a method for measuring the soiling of PV modules at cell resolution using RGB images taken by aerial drones under sunny conditions. The increase in brightness observed for soiled cells under evaluation, compared to clean cells, is used to calculate the transmission loss of the soiling layer. Photos of a clean PV module and a soiled module for which the soiling loss is measured electrically are used to determine the relation between the brightness increase and the soiling loss. To achieve this, the irradiance at the time of the image acquisitions and the viewing geometry are considered. The measurement method has been validated with electrical measurements of the soiling loss yielding root mean square deviations in the 1% absolute range. The method has the potential to be applied to entire PV parks in the future. [ABSTRACT FROM AUTHOR]

Combustion optimization is an effective way to improve the efficiency of thermal power generation and reduce carbon and NOx emissions. Real-time and precise NOx emission prediction is the basis for combustion optimization control of thermal power plants. To construct an accurate NOx concentration prediction model, a novel just-in-time learning (JITL) method based on random forest (RF) is proposed in the present work. With this method, first, an improved permutation importance algorithm is proposed to extract important variables. In addition, a similarity index that incorporates temporal and spatial measures is defined to select a local training set representative of the process data. Moreover, considering the influence of model parameters on prediction performance under different working conditions, a process monitoring method based on a moving window (MW) is used to monitor the change in working conditions and guide online updating. The experimental results show that the proposed method has excellent prediction accuracy, with a coefficient of determination of 0.9319, a root-mean-square error of 3.6960 mg/m3, and an average absolute error of 2.7718 mg/m3 on the test set, making it superior to other traditional methods. [ABSTRACT FROM AUTHOR]