Actual Generation Research Articles

SELECTION OF OPTIMAL SOLAR PANEL ORIENTATION PARAMETERS

The article investigates the issue of optimising the installation parameters of solar panels to maximise their efficiency in the conditions of modern Ukraine, where the traditional energy infrastructure is severely damaged due to military operations. Particular attention is paid to parameters such as the angle of inclination and orientation of solar panels relative to the sun, which affect the overall performance of the system. The study was carried out on the basis of a solar power plant located on the roof of the educational building of the Ukrainian State University of Science and Technology in Dnipro. A detailed analysis of the plant's operation was carried out with the solar arrays oriented to the southwest (azimuth 222˚) and the results were compared with the calculations obtained using the PVWatts® calculator developed by the US National Energy Laboratory (NREL). The results of the study showed that the actual annual electricity generation was 3917 kWh, which is close to the predicted 3973 kWh obtained using PVWatts®. The analysis also demonstrated that changing the orientation of the solar panels from southwest to south (180˚ azimuth) could increase the annual electricity generation to 4227 kWh, which confirms the maximum efficiency of the southern orientation. The findings can be used to optimise existing and future solar power plant projects, ensuring more efficient use of resources and improving the overall efficiency of power systems. This knowledge will help to reduce dependence on fossil energy sources, reduce greenhouse gas emissions and ensure uninterrupted electricity supply for the population and critical facilities in the face of the destruction of traditional energy infrastructure.

Assessment of Annual Litterfall of Woody Plant Community in Southern Thorn Forest, Tirunelveli, Peninsular India

The biological phenomenon, litterfall acts as a connection between tree canopy and substratum of the habitats, influencing the concentration of vital soil nutrients thereby contributing to the tree growth and forest productivity. Information on litter generation of tropical forests including tropical thorn forest is lacking. Therefore, the current study was conducted to find annual litterfall of tree community existing within a Reserve Forest in Tirunelveli, India. A field ecological study was carried out to find annual litter production of woody plant community. Litter traps were kept randomly across the forest to assess the litter production. The litter fallen in each trap was collected separately on monthly basis for one calendar year. The collected litter was separated in to four different classes viz., leaf, wood, amorphous and reproductive organs. The relationship of climatic variables with litterfall was estimated by Pearson’s simple correlation test. The annual litter generation of the study area was estimated at 8.058 tons ha-1 y-1. The amount of total fallen and four classes litter per month varied significantly. In addition, litterfall did not show any significant relationship with the mean monthly temperature (p = 0.128; r2= 0.216) and monthly rainfall (p = 0.817; r2 = 0.0056). The deciduous species accounted for 95% (3.449 tons ha-1) of total annual leaf litterfall. Among four litter classes, the leaf litter accounted for 45.05%. The quantity of annual litterfall recorded from present study area is comparable with other tropical dry forests. The present study concentrated on limited forest areas, further studies with larger study area are needed to quantify the actual annual litter generation of southern thorn forests flourishing in various districts of Tamil Nadu.

Examination of differences between actual and potential revenue generation in a pharmacist-run ambulatory clinic

BackgroundPharmacists are not billable health care providers under Medicare Part B or most insurance plans. Because of this, pharmacist services are relegated to incident-to-provider billing, despite pharmacists routinely providing services high in complexity. This discrepancy may negatively skew perceptions of pharmacists’ contributions to outpatient clinic care. ObjectiveThe objective of this study was to identify the potential revenue generation for pharmacist-delivered services at a single, rural South Dakota clinic if pharmacists were considered billable health care providers. MethodsThis retrospective, single center study utilized a chart review of first-quarter data from a single ambulatory clinic served by a 0.5 full time equivalent pharmacist serving Chronic Disease Management (CDM) and COVID-19 patients. For each appointment, the chart note was reviewed for elements that would satisfy requirements for Current Procedural Terminology billing codes. Medicare and Medicaid reimbursement was determined using official 2022 Physician Fee Schedules and private insurance reimbursement was set at a single rate of 60% of the fee schedule of the most common private payer. ResultsDuring the 3-month study period, 118 patients (206 appointments) were seen by the pharmacist. The amount paid to the clinic was estimated at $2174.91. The hypothetical amount paid to the clinic if pharmacists were considered billable health care providers is $10,415.31 for CDM clinic and $7953.48 for COVID-19 clinic, totaling $18,368.79. Excluding uninsured patients, the hypothetical total is $17,102.03, with total unrealized revenue of $16,193.88. ConclusionIf pharmacists were considered billable health care providers and their services were billed accordingly, the potential revenue generation is significantly higher than actually generated revenue. This estimated data can be used to better quantify and qualify appointment-related data for nonpharmacist clinic managers.

Improving land-use efficiency of solar power in China and policy implications

Improving the power output of solar photovoltaic (PV) farms is critical to maximize the potential of PV power and reduce extensive land use in the context of large-scale deployment of renewable energy. In this paper we developed an integrated solar power potential assessment framework to quantify the gap between technical potential and actual generation of solar PV farms on national, provincial, and plant scales, and identify the key factors that cause the underperformance of PV farms. Specifically, to assess technical potential, hourly meteorological reanalysis data is adopted and the power generation-related parameters are calculated (i.e. optimal panel tilt, array spacing, packing factor etc.) with consideration of their spatial variability. For actual power generation, a detailed plant-level dataset is first established by this study which integrates technical, operational, and geospatial information from 145 solar farms across seven provinces in China. Our results show that the actual PV power generation per square meter is only 1/3 of the estimated technical potential. Technological factor is the primary factor, accounting for 48.43% of the underperformance, followed by engineering and management factors, accounting for 38.55% and 13.02%, respectively. The novelty of our study is revealing the underperformance level of solar farms in reality, identifying the causing factors, and highlighting the importance of integrating land-use efficiency indicators into solar power planning and development.

Enhancing reliability assessment in distributed generation networks: Incorporating dynamic correlation of wind-solar power output uncertainty

Amidst escalating environmental concerns and energy scarcity, the integration of distributed generation (DG) within distribution networks (DN) has emerged as a pivotal developmental trend. The uncertainty inherent in renewable energy output often disrupts DG networks. Notably, the dynamic correlation between key renewable sources, such as wind and solar energy, significantly influences the reliability analysis of these networks.To comprehensively assess the impact of wind-solar power output uncertainty and its dynamic correlation on DN reliability, this study leverages copula theory to express the dynamic correlation coefficient between wind and solar power. This coefficient is formulated as the dynamic correlation of wind-solar power through copula dynamic correlation coefficient. Employing an auto-regressive moving average (ARMA) model with constraints solved using maximum likelihood kernel (MLK), we construct the wind-solar joint output (WSJO) model. Subsequently, utilizing sequential Monte Carlo simulation (MCS) with the WSJO model, we analyze DN reliability. In case of DN failure, the WSJO model generates random samples of the wind-solar joint output sequence. Subsequent power restoration to governed islands enables the calculation of DN reliability indices. The WSJO model constructed in this study accounts for wind resource output uncertainty and dynamic correlation, aligning more closely with actual distributed generation output and enhancing the accuracy of reliability assessment. Finally, we simulate the improved IEEE-RBTS-BUS6-F4 system to underscore the crucial role of considering wind-solar energy's dynamic correlation in DN reliability assessment.

An adaptive two-step staircase static reconfiguration method for improving the power generation of PV array

Partial shading of solar photovoltaic (PV) panels can significantly affect the performance of solar PV arrays. Various reconfiguration techniques have been explored in recent years. Still, their applicability to actual PV power generation is controversial due to the number of electrical switches, physical locations, interconnections and complexity. This study proposes an adaptive two-step staircase (A2SS) static reconfiguration method. The technique is experimentally validated in several conditions and compared with the conventional TCT connection, single-step staircase (1SS) static reconfiguration method, Arrow soduku, modified odd–even–prime (MOEP) and two-step staircase(2SS) static reconfiguration method. For the eight shading cases of LN, LW, LD, Ran, Cen, Cor, CD, and Plus at SET#1, after reconfiguring the PV array using A2SS, the power has a significant improvement of 17.6%, 17.0%, 13.4%, 13.4%, 20.6%, 20.2%, 3.1%, and 0.82% than TCT. In the four shading cases of Lr. C, Lr. O, Lr. T, and Lr. U at SET#2, the power showed a significant improvement of 11.8%, 9.2%, 10.7%, and 15.8% compared to TCT. It also has the best performance in various reconfiguration techniques, which are mentioned. In addition, the A2SS reconfiguration method can be better applied to various sizes of PV arrays. By optimizing the shading distribution and adjusting the row irradiance deviation, the power stability of PV power generation is improved while maximizing energy efficiency.

Ensemble learning soft sensor method of endpoint carbon content and temperature of BOF based on GCN embedding supervised ensemble clustering

Abstract The endpoint control of Basic Oxygen Furnace (BOF) steelmaking depends on the prediction of the endpoint carbon content and temperature. However, predicting these variables is challenging because of the numerous working conditions in the industrial field and the volatility of the sensor data collected during BOF steelmaking. The accuracy of prediction models in ensemble learning depends significantly on the initial distribution of data. However, the complex nature of BOF steelmaking data makes it challenging to generate diverse subsets, which ultimately affects the accuracy of predictions. This paper presents a new approach called Graph Convolutional Network Node Embedding Supervised Ensemble Clustering (GESupEC) for soft sensor modelling in ensemble learning to tackle these issues. GESupEC utilizes a similarity graph derived from a co-association matrix and employs graph convolutional networks to extract structural information among nodes. By optimising the clustering loss within the network, GESupEC learns compact node representations that are useful for the clustering task. Furthermore, it generates a reconstruction matrix based on the similarity of node embeddings. This matrix helps with the extraction of a suitable subset of data for BOF steelmaking through matrix decomposition. After that, the gradient boosting decision tree regression sub-model is established based on the data subset. An ensemble strategy called Gray Relational Analysis Weighted Average is proposed, which assigns weights based on the grey relation similarity between test samples and different data subsets. This weighted average strategy aims to enhance the accuracy of carbon content and temperature predictions. When tested with actual BOF steelmaking generation process data, the prediction accuracy of carbon content reached 88.6% within the error range of ±0.02%, and the prediction accuracy of temperature reached 92.6% within the error range of ±10 °C.

Monthly electricity generation forecast based on similar month screening and STL decomposition

In order to solve the problems of monthly electricity generation forecasting being limited by the lack of actual data source, and the large errors caused by the influence of various factors such as weather and holidays, and the limitations of the applicable scenarios of the existing research results, a monthly electricity generation forecasting model based on similar month screening and Seasonal and Trend decomposition using Loess (STL) was proposed in this paper. The complementary advantages of Multiple Linear Regression (MLR) and Improved Random Forest Regression (RFR) are utilized to achieve the monthly electricity generation prediction in the province. This prediction model does not require a large number of data to obtain a better prediction accuracy, and breaks through the limitations of the existing monthly electricity prediction model that are only suitable for a certain industry or a certain region. Experiments performed on an actual electric power generation series validate the efficiency of the proposed model.

Design comparison and effects of a chamber structure on wave dissipation

ABSTRACT Among all the ocean energy sources that have been widely studied, chamber structures such as oscillating water column (OWC) technologies are effective and low-cost methods to develop hundreds of wave power generation devices as the disadvantage is its lack of efficiency. Even if the OWC system does not perform any power generation operations, it is a wave-absorbing structure. A large part of the wave energy may be blocked by the structure’s acting chamber or could be dissipated during the interaction of the waves in the air chamber, which affects the actual power generation benefit evaluation. Nevertheless, a better wave attenuation structure may present a relatively poor design of the OWC generator chamber. A series of experiments were conducted with regular and irregular wave conditions to study the effects of energy dissipation caused by a chamber structure under varied barrier and orifice designs.

Evaluation of a grid-connected PV power plant: performance and agrivoltaic aspects

AbstractThe performance ratio, a globally recognized metric that correlates with reported global solar radiation values, serves as a crucial indicator for evaluating the efficiency of grid-connected PV plants. Also, a large scale PV power plant alone can afford some agricultural irrigation energy requirement of a region. In this study, the actual generation data from a power plant located in Bursa province in northwestern Türkiye, during its initial six years of operation have been analyzed. The analysis reveals that the annual electricity production of the power station reaches approximately 10 GWh. Notably, the time period between April and September witnesses a monthly electricity generation exceeding 1 GWh, with September emerging as the most productive month, characterized by an average performance ratio of 94.5% during this six-year period. However, over the span of six years, the highest average electricity generation occurs in July, peaking at 1.34 GWh. Also, the power plant alone can meet the agricultural irrigation energy requirement of the region in the range of 6.7–2.3%. From an environmental impact and global warming perspective, it is noteworthy that during the 36-month period in the summer season, the performance ratio exceeded 100% only three times. However, within the 32-month period in the winter season, the performance ratio exceeded 100% 19 times. This situation indicates that while the reported radiation rates by the managements are consistent with the actual values for the summer months, they need to be revised, especially for the winter months.

Enhanced Day-Ahead Electricity Price Forecasting Using a Convolutional Neural Network–Long Short-Term Memory Ensemble Learning Approach with Multimodal Data Integration

Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model’s robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel “policy-versus-policy” strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R2, MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF.

ShaPRS: Leveraging shared genetic effects across traits or ancestries improves accuracy of polygenic scores

We present shaPRS, a method that leverages widespread pleiotropy between traits or shared genetic effects across ancestries, to improve the accuracy of polygenic scores. The method uses genome-wide summary statistics from two diseases or ancestries to improve the genetic effect estimate and standard error at SNPs where there is homogeneity of effect between the two datasets. When there is significant evidence of heterogeneity, the genetic effect from the disease or population closest to the target population is maintained. We show via simulation and a series of real-world examples that shaPRS substantially enhances the accuracy of polygenic risk scores (PRSs) for complex diseases and greatly improves PRS performance across ancestries. shaPRS is a PRS pre-processing method that is agnostic to the actual PRS generation method, and as a result, it can be integrated into existing PRS generation pipelines and continue to be applied as more performant PRS methods are developed over time.

Short-term wind power prediction using a novel model based on butterfly optimization algorithm-variational mode decomposition-long short-term memory

The precise forecasting of wind power output is crucial for the integration of large-scale renewable energy generation into the power grid. It plays a vital role in ensuring safe and stable operation of power grid, diminishing fuel consumption, and minimizing environmental impacts. This research presents a machine learning prediction model that integrates intelligent optimization algorithms and data decomposition techniques for short-term wind power output forecasting. The initial phase of this research develops a prediction model utilizing variational mode decomposition and long short-term memory networks. To mitigate the randomness and uncertainty of wind energy, and improve prediction accuracy, the butterfly optimization algorithm is introduced to optimize the parameters of variational mode decomposition and long short-term memory networks. Several in-depth case studies are carried out on the actual wind power generation dataset in mainland China to confirm the feasibility and effectiveness of the proposed hybrid model. Experimental results demonstrate that the proposed model outperforms the compared model in terms of prediction accuracy across different seasons, showing good practicality and generalizability.

Influence of mudstone on coal spontaneous combustion characteristics and oxidation kinetics analysis

To explore the spontaneous combustion characteristics and hazards of the low-temperature oxidation (LTO) stage in the process of spontaneous combustion of coal and mudstone, the pore structure, spontaneous combustion characteristic parameters, and exothermic characteristics of coal and mudstone were tested and studied, and the oxidation kinetic parameters were calculated. The results show that mudstone has a larger specific surface area and pore volume than coal. From the fractal characteristics, the pore structure of mudstone is more complex than that of coal. According to the comparison of theoretical and actual gas generation and oxygen consumption rate curves, it is found that there is an interaction between coal and mudstone in the LTO process. With the increase of mudstone mass ratio, gas production, and its oxygen consumption rate increase. Among them, CM-4 (Coal:Mudstone = 1:1) has the highest exothermic intensity and the exothermic factor (A) and fire coefficient (K) increase with the increase of mudstone content. The apparent activation energy of the mudstone sample is lower than that of the raw coal, indicating that the sample after adding mudstone is more likely to have spontaneous combustion in the LTO stage.

Hydrokinetic resource assessment in Colombia’s Non-Interconnected Zones: Case study

Energy accessibility in Non-Interconnected Zones (NIZ), together with the energy transition context, converges on exploring non-conventional renewable energy sources and the technology to harness them. Hydrokinetics is an interesting and abundant resource in Colombia, present in many cases near NIZ communities but with unavailable information about (flow velocity) its actual power generation potential. This work presents a method to assess the hydrokinetic resource in NIZ based on the estimation of river flow velocity from river discharge, river level, and cross-sectional profile data available from the Colombian Institute of Hydrology, Meteorology and Environmental Studies (IDEAM). It is applied in a case study where a year of data was studied to characterise the hydrokinetic resource and match a local household load demand with the potential power generation of a hydrokinetic turbine. An average river flow velocity of 1.14 m/s was obtained, showing a consistent behaviour that means reliability in potential energy production. This approach could be helpful for researchers or power suppliers who aim to harness hydrokinetic resources near NIZ users with hydrokinetic technology in the project planning stages.

A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data

The performance of wind turbines directly determines the profitability of wind farms. However, the complex environmental conditions and influences of various uncertain factors make it difficult to accurately assess and monitor the actual power generation performance of wind turbines. A data-driven approach is proposed to intelligently monitor the power generation performance evolution of wind turbines based on operational data. Considering the inherent nonlinearity and structural complexity of wind turbine systems, a data-derived characteristic construction and dimensionality reduction method based on KPCA is adopted as a prerequisite. Additionally, an AdaBoost-enhanced regressor is applied to wind power prediction with adequate inputs, and day-oriented deviation indicators are further constructed for quantifying performance fluctuations. The final validation phase includes two application cases: In the first case, the results show that the proposed method is sensitive enough to capture the early characteristics of blade damage faults. In the second case, an uncertainty error within ±0.5% demonstrates that the proposed method has high-level accuracy in the quantitative assessment of the power performance and good practical effectiveness in real engineering applications.

Optimum Power Forecasting Technique for Hybrid Renewable Energy Systems Using Deep Learning

Power forecasting in large-scale electrical systems, comprising photovoltaic (PV), solar, and wind power, faces challenges due to geographical diffusion and temporal variations. Despite numerous studies, the disparity between predicted and actual generation remains a significant issue. This study utilizes historical power and atmospheric data from diverse plants, employing preprocessing techniques to enhance quality and reduce noise. K-Means clustering is applied to the dataset, optimizing deep learning training periods and increasing accuracy. A resilient hybrid deep learning model is proposed for microgrid (MG) power forecasting, encompassing preprocessing, model training, and assessment stages. Mathematical models for PV systems, battery storage, and wind systems, along with a K-means clustering algorithm, contribute to accurate forecasting. The recurrent neural network based on gated recurrent unit architecture outperforms traditional algorithms, demonstrating superior accuracy, and reduced errors in extensive experimental analyses. Pearson coefficients reveal associations between different power production forms, emphasizing the potential of hybrid renewable energy clusters to enhance forecasting. Case studies illustrate the partial controllability of concentrated solar power production, reducing overall renewable energy cluster unpredictability. The proposed method showcases the efficacy of the hybrid model in addressing challenges and improving accuracy in large-scale power forecasting.

Peak load estimation of renewable energy generation based on imitator dynamic algorithm

AbstractIn the field of renewable energy generation forecasting, it is crucial to accurately estimate the peak load. However, due to the complex nonlinear characteristics of the data, the traditional long short-term memory network performs poorly in processing these data. This study introduces the imitator dynamic algorithm, which is able to generate samples close to the real situation by learning the change pattern of the data. Extensive experimental tests show that with the number of iterations increasing to 200, the prediction accuracy of the model reaches 62.35%, which is significantly better than that of the long short-term memory network, although it is decreased compared with the initial iteration. The imitator dynamic algorithm accurately learns the unknown data distribution according to two metrics of probability density and cumulative distribution within 5% error, demonstrating good generalization ability and robustness. These research results are of great significance for predicting the actual generation capacity of renewable energy. It not only enables grid operators to accurately predict and schedule power generation, but also supports sustainable energy development by improving grid stability and promoting the use of renewable energy.

A new optimal PV installation angle model in high-latitude cold regions based on historical weather big data

PV technologies are regarded as one of the most promising renewable options for the transition towards Net Zero. Despite the rapid development of PV systems in recent years, achieving the necessary goals requires more than a threefold increase in annual capacity deployment by 2030. However, current PV systems often fall short of optimal performance due to improper installation angles. In high-latitude cold regions, the actual PV generation capacity is frequently overestimated due to the omission of snow conditions. This study introduces a novel model designed for high-latitude regions to predict local optimal PV installation angle that maximizes PV power generation, utilizing historical weather big data, including snowfall and melting effects. A case study is presented within a Swedish context to demonstrate the implementation of these methods. The results highlight the crucial role snow conditions play in determining PV performance, resulting in an average reduction of 14.7% in annual PV power generation. Optimal installation angle could yield approximately a 4.8% improvement compared to common installation angles. The study also explores the application of snow removal agents, which could potentially increase PV generation by 0.1–2.3%. Additionally, the new PV installation angle successfully captures the impact of the local weather changes on PV power generation, potentially serving as a bridge between climate change adaptation and future PV power generation endeavors.

Estimation of Energy Storage Requirements in an Independent Power System from an Energy Perspective

Taiwan’s power system operates as an isolated grid, preventing the export of surplus energy. Excess electricity is either stored or discarded (curtailed). This study aims to estimate the energy storage requirement for the day with the most extreme electricity consumption behavior in a year without energy curtailment. Based on the installed capacity and actual power generation of renewable energy sources in 2022, this research estimates the power generation per GW of the installed capacity at full load. Integrating the government’s annual installation capacity plans, this study forecasts the annual power generation of renewable sources. Using the electrical load during the 2023 Spring Festival as a baseline and assuming an annual electricity consumption growth rate of 2%, combined with a minimum gas power output of approximately 6 GW, this study calculates the unused power generation, which represents the pumped-storage hydroelectricity and battery energy storage systems requirements for that day under a no-curtailment scenario. Considering the semi-annual adjustments in installation planning, this study’s code is open-sourced and designed to provide updated results with new planning data input, facilitating ongoing adjustments.