Long-term Power Research Articles

With the increasing demand for mineral resources, mining operations are gradually moving into deeper levels, leading to a rise in the severity of the high-temperature thermal hazard caused by geothermal heat in mines. To understand the distribution and characteristics of the temperature field in the surrounding rock of metal mines, this study derived a non-steady-state heat conduction differential equation suitable for the surrounding rock of underground metal mines based on the thermal property changes of rocks at high temperatures, thereby obtaining the distribution characteristics of the thermal insulation ring in the surrounding rock. However, the monitoring technology of the underground rock temperature field is constrained by factors such as long-term power supply of equipment and complex data collection. Therefore, based on high-precision temperature acquisition devices, this study developed and constructed a cloud monitoring system for the temperature field of the surrounding rock, realizing long-term online temperature field monitoring of the 930 m and 945 m horizontal underground roadway in the West Mountain mining area of Sanshandao. The monitoring data indicate that the system has stable data transmission and smooth operation, achieving remote long-term data collection and transmission. This has improved the efficiency of monitoring the temperature field of surrounding rocks in mines, providing a basis for the risk assessment of thermal environmental conditions in deep high-temperature areas of metal mines and the control of the thermal hazard in deep underground engineering.

This study investigates the power consumption of various media player software applications, comparing open-source and proprietary options. The experiment measured the average power consumption of CPU, GPU, and memory usage of media players such as Kodi, MPC, MPV, SMP, VLC, Windows Media Player, ACG, ALLPlayer, GOM, KMPlayer, LAPlayer, POTPlayer, and RealPlayer while playing 4K video. The results revealed that proprietary media players generally consume less power compared to their open-source counterparts. Statistical analysis, including descriptive statistics and independent samples t-tests, confirmed these findings. Long-term power consumption projections indicated substantial energy savings with more efficient media players. These findings underscore the importance of considering energy efficiency in software selection for sustainable computing.

Long-term power forecasting of photovoltaic plants using artificial neural networks

Power purchase agreements aim to secure long-term contracts between sellers and buyers, particularly in renewable energy transactions. However, successful negotiations for a fixed long-term price and energy volume while ensuring maximum utility for stakeholders remains a significant challenge. This paper introduces a comprehensive model for negotiating 24/7 power purchase agreements, focusing on hourly pricing to address deficits and surpluses throughout each day of the contract timeline. The model incorporates demand flexibility through battery storage, settling on the strike price using Nash Bargaining theory and optimal management of energy consumption relative to market price fluctuations. A soft margin support vector machine classification model determines the buyer’s maximum acceptable price. Moreover, Gaussian process classification is employed to calculate a probabilistic, risk-adjusted strike price, enabling a data-driven approach to power purchase negotiations. The proposed model’s performance is demonstrated through a detailed case study of Norway, illustrating how demand flexibility can significantly lower long-term power purchase agreement contract prices. The analysis of yearly price trends indicates that incorporating flexibility resources in long-term energy contracts may lead to a reduction in strike prices by around 25%. Moreover, such flexibility enhances demand-generation matching, thereby increasing renewable energy transactions within such agreements.

Hedging renewable power purchase agreements

To restrict the excessive short-circuit current (SCC), this letter proposes an SCC-constrained optimal topology adjustment model considering the N-1 security criterion. Topology adjustment, including transmission switching (TS), bus sectionalization (BS), lines operating out of breaker bay (LOB) and commitment status of units (CU), has nonlinear relationships with SCC and is difficult to express explicitly. In this letter, an explicit formulation of SCC considering topology adjustment is proposed based on the equation of admittance matrix and nodal self-impedance, which is further recast as a mixed-integer linear programming (MILP) model. In addition, the N-1 security criterion is included for avoiding jeopardizing system security by topology adjustment. Numerical case studies validate the effectiveness and advantages of the proposed model in SCC restriction.

Abstract The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

Prediction of long-term photovoltaic power generation in the context of climate change

Long-term power performance evaluation of vertical building integrated photovoltaic system

Ceramic membranes are widely used in microbial fuel cells (MFCs) owing to their cost-effectiveness and availability. However, these membranes often face challenges such as biofouling and negative mass-transfer effects. This study explored the use of a polymer layer to mitigate these issues, focusing on a polyvinylidene fluoride (PVDF) nanofibre membrane with various surface modifications. The modifications included alkaline treatment (PVDF-OH), rhamnolipids treatment (PVDF/BS), and a combination of both (PVDF-OH/BS). The ceramic membrane integrated with PVDF-OH/BS achieved the highest power density of 13.8 W m−3, which was 38 % higher than that of the unmodified ceramic membrane. Additionally, during the long-term study (days 90–101), the Ceramic + PVDF-OH/BS maintained a 64 % higher power performance compared to the unmodified ceramic membrane, indicating superior antifouling properties. Electrochemical and surface characterisation revealed that rhamnolipid-modified PVDF nanofibers enhanced fouling resistance. The findings demonstrate that natural biosurfactants which can be produced in situ within MFCs, can form a protective layer over membranes and significantly enhance their long-term power performance. This study represents the first instance of using natural microbial biosurfactants to improve membrane efficiency in a bioelectrochemical system.

SEAformer: frequency domain decomposition transformer with signal enhanced for long-term wind power forecasting

During the manufacturing of surgical forceps, the flashes of the blanks need to be removed. Manual production has problems such as high labor intensity, low efficiency, and high-risk factors. To solve this problem and realize fully automatic resection, a novel modular workstation was designed and a corresponding process method was proposed. The workstation adopts robots, non-standard automation equipment, and image recognition technology instead of manual loading and blanking, but the blank storage still needs to be performed manually. The critical components were selected according to the workstation design scheme and process method, and the control system design was completed. The reliability of the separation unit was studied through a test platform, and the failure problem caused by uneven force was solved using a blank locking device, which showed that the separation success rate was stabilized at 100%. The detection speed of the image recognition system can reach 100 ms/piece, and the product qualification rate can reach 95.7%. The advantages of the workstation in terms of output and productivity were further analyzed by comparing it to manual production, where the average daily output increased by 12.5% (4500 pieces). In addition, the results of long-term test experiments and power consumption comparison tests showed that the workstations are highly stable and consume little additional power.

In the current model, the unclear and unreasonable method of revenue sharing among wind-solar-storage hybrid energy plants may a lso hinder the effective measurement of energy storage power station costs. This lack of clarity discourages energy storage from effectively collaborating with renewable energy stations for greenpower trading and spot trading.Therefore, this study proposes an optimal revenue sharing model of wind-solar-storage hybrid energy plant under medium and long-term green power trading market to facilitate the coordinated operation and equitable revenue allocation. Firstly, a method for decomposing transaction volume of green power is introduced by considering the uncertainty of spot market prices and physical delivery characteristics of green power trading. Then, a coordinated scheduling strategy of hybrid renewable energy plant is proposed to maximize revenues generated from both the green power and spot markets. Consequently, a cost-benefit contribution index system is developed to quantify the contribution of energy storage in the wind-solar-storage hybrid power plant. The revenue sharing model based on the minimum cost-remaining savings (MCRS) method can significantly increase overall revenue for renewable energy plants by reducing deviation penalties. It also enhances the operating revenue of energy storage power stations by considering the contributions of both energy storage and renewable energy plant in the green power market. The superiority of the proposed cooperation revenue sharing m odel for profitability enhancement of energy storage is v alidated through comparative case studies.

Organic molecule-doped n-type single-walled carbon nanotube (SWCNT) networks are promising candidates for advanced energy applications, such as flexible thermoelectrics and photovoltaics. Yet charge transport in n-type SWCNTs is limited by two factors: i) charge localization impeding inter-tube transport caused by disordered mesostructure of randomly oriented SWCNTs and ii) reduction of charge carrier concentration driven by oxidation. Herein, studied the relationship between the mesostructure and thermoelectric properties of n-type SWCNTs obtained by surfactant-functionalization and polymer-dopant grafting. Surprisingly, the electrical conductivity of the polymer-doped SWCNTs keeps increasing with increasing polymer content, even after the saturation of carrier concentration, resulting in 12x higher conductivity on polymer-doping compared to surfactant-functionalization. While hopping transport typically dominates in disordered systems, it is shown that a bridging effect from the polymer causes unusual band-like conduction in polymer-doped SWCNTs. Additionally, since surfactants are essential to prevent oxidation and retain n-type over a long duration, shows that SWCNTs obtained through a dual-functionalization strategy using both polymer-dopant and surfactant, demonstrates a long-term stable high n-type thermoelectric power factor, when the surfactant amount is carefully controlled. Besides thermoelectrics, the findings are of general interest to developing stable and conductive n-type SWCNTs for various energy and electronic applications.

Temperature control is important in second harmonic generation (SHG) based on non-critical phase matching, which is widely used in the accelerator field to generate drive lasers. To further improve the stability of the drive laser for the DC-SRF photocathode electron gun at Peking University, a high-precision temperature control oven for lithium borate (LBO) crystals was developed. The oven’s structure was designed to minimize heat exchange with the external environment. The temperature control circuit uses a thermoelectric cooler to ensure the temperature stability of the sampling circuit. The program utilizes a cascaded proportional-integral-derivative and an anti-saturation integral algorithm to achieve high-precision temperature control. Experiments showed that fluctuation at the working temperature of the LBO crystal in this oven was within ±0.009 °C, corresponding to a root mean square (RMS) jitter of 0.003 °C, and the long-term power fluctuation of the 13.7 W green laser generated with SHG was less than 1%.

Informer model with season-aware block for efficient long-term power time series forecasting

ABSTRACT The stable operation of the power grid requires accurate predictions of wind power generation and the stabilization of its fluctuations through the integration of other energy sources. An increasing number of deep learning methods are now being employed in the field. However, due to the instability of wind power data, existing methods struggle to uncover deep spatiotemporal dependencies. We propose a novel method named SD-STGNN (Series Decomposition and Spatio-Temporal Graph Neural Network). SD-STGNN first decomposes unstable wind power data into seasonal and trend components. For the seasonal data reflecting short-term fluctuation patterns, we employ a Gated Temporal Convolutional Network and Graph Convolutional Network to capture spatiotemporal relationships. Additionally, for trend data reflecting long-term fluctuation patterns, we introduce a Temporal-Feature Enhancement module, utilizing Multi-Layer Perceptrons to extract deep information along both temporal and feature dimensions. Extensive experiments were conducted on the SDWPF public dataset. Compared to existing state-of-the-art baseline methods, our proposed SD-STGNN model achieves a notable average reduction in Mean Absolute Error by approximately 6.26%, in Root Mean Squared Error by 7.55%, and in Mean Absolute Percentage Error by 2.65%. Additionally, there is an average improvement of about 4.74% in the coefficient of determination.

The economy of a country is directly proportional to the power sector of that country. An unmanaged power sector causes instability in the country. Pakistan is also facing this phenomenon due to uncontrolled power outage and circular debt. Pakistan’s power sector is analyzed as a case study to find out the root cause for the unmanaged power sector and for proposing the most effective data-driven solution. After a literature review and discussion with domain experts, it was found that inaccurate power demand forecast is one of the main reasons for power crisis in Pakistan. Under-forecasting caused load shedding, and over-forecasting increased circular debt due to idle capacity payments. Previously, traditional statistical methods were used for power demand forecasting. The multiple linear regression model that is being used since 2018 (IGCEP) uses features such as previous year load and demographic and economic variables for long-term peak power demand forecasting till 2030. The problem is that the independent variables used in existing models are manipulated and cause a gap between actual and forecasted power demand. Moreover, even yearly peak power demand is not absolutely linear in nature; hence, it is necessary to apply AI-based techniques that can handle nonlinearity effectively. Not using system-generated data, not using the most appropriate features, not using an appropriate forecasting time horizon, and not using the appropriate forecasting model are main reasons for inaccurate peak power demand forecasting. The issue can be resolved by forecasting monthly peak power demand for the next 5 years by using the National Power Control Center’s (NPCC) system-generated data. Accurate monthly peak load forecasting leads to accurate yearly peak power demand. The monthly peak load forecasting strategy not only helps in managing operational issues of the power sector such as fuel scheduling and power plant maintenance scheduling but also guides decision-makers toward power and transmission expansion or contraction in the long term. More accurate monthly peak power demand forecasting can be achieved by applying nonlinear AI models in a comprehensive dataset comprising new engineered features, climate features, and the number of consumers. All these features are mostly system-generated and cannot be manipulated. As a result, the accuracy is improved and the results are more reliable than those of the existing models. The new features can be engineered from recent monthly peak load data generated by the system operator (NPCC). Climate features are collected from the Meteorological Department of Pakistan through sensors or database connectivity. The number of electricity consumers can be extracted from NEPRA’s state-of-industry report. All three datasets are combined on a common key (month–year) to a comprehensive dataset, which is passed through different AI models. In the experimental setup, it is found that support vector regression (SVR) produces the most accurate results, with an R-square of 99%, RMSE of 28, and MAPE of 0.1355, which are the best results compared to the literature reviewed.

Wind energy stands out as a prominent renewable energy source, characterized by its high efficiency, feasibility, and wide applicability. Nonetheless, the integration of wind energy into the electrical system encounters significant obstacles due to the unpredictability and variability of wind speed. Accurate wind speed prediction is essential for estimating the short-, medium-, and long-term power output of wind turbines. Various methodologies and models exist for wind speed time series prediction. This research paper proposes a combination of two approaches to enhance forecasting accuracy: deep learning, particularly Long Short-Term Memory (LSTM), and the Autoregressive Integrated Moving Average (ARIMA) model. LSTM, by retaining patterns over longer periods, improves prediction rates. Meanwhile, the ARIMA model enhances the likelihood of staying within predefined boundaries. The study utilizes daily average wind speed data from the Gelibolu district of Çanakkale province spanning 2014 to 2021. Evaluation using the root mean square error (RMSE) shows the superior forecast accuracy of the LSTM model compared to ARIMA. The LSTM model achieved an RMSE of 6.3% and a mean absolute error of 16.67%. These results indicate the potential utility of the proposed approach in wind speed forecasting, offering performance comparable to or exceeding other studies in the literature.

Erythrocyte dysfunction is a characteristic of diabetes mellitus (DM). However, erythrocyte-associated biomarkers do not adequately explain the high prevalence of DM. Here, we describe red blood cell distribution width to albumin ratio (RAR) as a novel inflammatory biomarker for evaluating an association with DM prevalence and prognosis of all-cause mortality. Data analyzed in this study were extracted from the National Health and Nutrition Examination Survey (NHANES) 1999-2020. A total of 40,558 participants (non-DM and DM) were enrolled in the study; RAR quartiles were calibrated at Q1 [2.02,2.82] mL/g, Q2 (2.82,3.05] mL/g, Q3 (3.05,3.38] mL/g, and Q4 (3.38,12.08] mL/g. A total of 8,482 DM patients were followed (for a median of 84 months), of whom 2,411 died and 6,071 survived. The prevalence and prognosis associated with RAR and DM were analyzed; age and sex were stratified to analyze the prevalence of RAR in DM and the sensitivity of long-term prognosis. Among non-DM (n=30,404) and DM (n=10,154) volunteers, DM prevalence in RAR quartiles was 8.23%, 15.20%, 23.92%, and 36.39%. The multivariable odds ratio (OR) was significant for RAR regarding DM, at 1.68 (95% CI 1.42, 1.98). Considering Q1 as a foundation, the Q4 OR was 2.57 (95% CI 2.11, 3.13). The percentages of DM morbidity varied across RAR quartiles for dead (n=2,411) and surviving (n=6,071) DM patients. Specifically, RAR quartile mortality ratios were 20.31%, 24.24%, 22.65%, and 29.99% (P<0.0001). The multivariable hazard ratio (HR) for RAR was 1.80 (95% CI 1.57, 2.05). Considering Q1 as a foundation, the Q4 HR was 2.59 (95% CI 2.18, 3.09) after adjusting for confounding factors. Sensitivity analysis revealed the HR of male DM patients to be 2.27 (95% CI 1.95, 2.64), higher than females 1.56 (95% CI 1.31, 1.85). DM patients who were 60 years of age or younger had a higher HR of 2.08 (95% CI1.61, 2.70) as compared to those older than 60 years, who had an HR of 1.69 (95% CI 1.47, 1.94). The HR of RAR in DM patients was optimized by a restricted cubic spline (RCS) model; 3.22 was determined to be the inflection point of an inverse L-curve. DM patients with a RAR >3.22 mL/g suffered shorter survival and higher mortality as compared to those with RAR ≤3.22 mL/g. OR and HR RAR values were much higher than those of regular red blood cell distribution width. The predictive value of RAR is more accurate than that of RDW for projecting DM prevalence, while RAR, a DM risk factor, has long-term prognostic power for the condition. Survival time was found to be reduced as RAR increased for those aged ≤60 years among female DM patients.