Long-term Power Research Articles

Medium- and Long-Term Power System Planning Method Based on Source-Load Uncertainty Modeling

In order to consider the impact of source-load uncertainty on traditional power system planning methods, a medium- and long-term optimization planning method based on source-load uncertainty modeling and time-series production simulation is proposed. First, a new energy output probability model is developed using non-parametric kernel density estimation, and the spatial correlation of the new energy output is described using pair-copula theory to model the uncertainty analysis of the new energy output. Secondly, a large number of source-load scenarios are generated using the Markov chain Monte Carlo simulation method, and the optimal selection method for discrete state numbers is provided, and then the scenario reduction is carried out using the fast forward elimination technology. Finally, the typical time-series curves of the source-load uncertainty characteristics obtained are incorporated into the optimization planning method together with various flexible resources, such as the demand-side response and energy storage, and the rationality of the planning scheme is judged and optimized based on key indicators such as the cost, wind–light abandonment rate, and loss-of-load rate. Based on the above methods, this paper offers an example of the power supply planning scheme for a certain region in the next 30 years, providing effective guidance for the development of new energy in the region.

Medium- and Long-Term Distributed Photovoltaic Power Prediction Based on Multiple Time Series Feature and Multiple-Model Fusion

Distributed photovoltaic power stations have advantages such as local direct power supply and reduced transmission energy consumption, and whose demands are constantly being developed. Conducting research on medium- and long-term distributed photovoltaic prediction will have significant value for applications such as the electricity trade market, power grid operation, and the planning of new power stations. Due to characteristics such as long time dependence, disperse power stations, and strong randomness, making accurate and stable predictions becomes very difficult. In this research, we propose a multiple time series feature and multiple-model fusion-based ensemble learning model for medium- and long-term distributed photovoltaic power prediction (M2E-DPV). Considering the wave influence and the differences in distributions in different areas of photovoltaic power, multiple feature combinations are designed to increase feature expression ability and adaptability. Based on the boost ensemble learning model, trained on a single model of different time scale features, the optimal scoring strategy is used for multiple model fusion in the rolling prediction process, and finally, time-segmented probabilistic correction is performed. The experiment results show the effectiveness of the M2E-DPV under multiple feature combinations and multi-model fusion strategies. The average MAPE, R2, and ACC indicators are 0.15, 0.96, and 0.91, respectively. Compared with other methods, there is a significant improvement, indicating that the prediction ability of the model framework proposed in this paper is advanced and robust.

СТАТИСТИЧНИЙ АНАЛІЗ БІЗНЕС РИЗИКІВ В УКРАЇНІ В УМОВАХ ВОЄННОГО СТАНУ ТА ШЛЯХИ ЇХ ПОДОЛАННЯ

The article analyzes the main risks faced by business entities in Ukraine with a full-scale Russian invasion of Ukraine in 2022. The modern dynamics of registration of new business entities since the beginning of the full-scale war for March - August 2022 were analyzed. The main problems that made it difficult to do business in Ukraine since the beginning of the full-scale invasion were identified, namely consumer insolvency, a drop in demand for the products of enterprises, and a lack of funding. , destruction of supply chains, long-term power outages due to military attacks on energy facilities, rising costs of raw materials, materials and energy resources, and a shortage of skilled workers. The article also analyzed the rating of doing business in strategic cities of Ukraine, namely Lviv, Odesa, Kharkiv and Dnipro. Preventive measures were also proposed to reduce business risks in Ukraine under martial law.Among the main recommendations: analysis of the insurance policy and the possibility of asset protection; development of crisis management and evacuation plans; strengthening relations with other companies and authorities to provide support in crisis; analysis of risks in the market and adaptation of production in accordance with changes in demand; development of different planning and budgeting scenarios for different market conditions; diversification of supply chains and search for alternative sources of raw materials; monitoring of financial markets and timely response to changes; development of a strategy to reduce currency risks and other financial losses; development of alternative sources of financing and investments; developing an emergency plan and having alternative suppliers of raw materials; ensuring the stability of infrastructure and transport links; monitoring of global events and geopolitical risks to predict possible supply interruptions; development of emergency power plans and backup solutions - to ensure power supply in case of energy shortage; reducing dependence on centralized energy sources and developing alternative energy sources, etc.

A long-term power system state calculation method using sequential power and holomorphic embedding

Traditional Power Flow (PF) calculations struggle to support the data analysis requirements as the power system develops. Therefore, this paper introduces a non-sequential and non-iterative PF calculation method to enhance the computational speed and analytical efficiency of power system state variables over long time scales. Firstly, a Power Sequential Division (PSD) method is proposed, representing the power curve using hierarchical energy with temporal characteristics. Next, based on the Fast and Flexible Holomorphic Embedding (FFHE) method, the paper explores the incremental properties of Voltage Power Series Coefficients (VPSC) and proposes an Incremental Holomorphic Embedding (IHE) method. Finally, by combining PSD and IHE, this study introduces a Power Sequential Flow (PSPF) method for rapid computation of power system state variables over long time scales. Unlike traditional PF methods, PSPF method shifts away from time-series calculations and instead analyzes the distribution of power system state variables over long time scales from an energy perspective. Various test cases are set up to analyze the performance characteristics of IHE and PSPF from multiple perspectives. The results indicate that the proposed methods significantly outperform traditional PF methods in terms of computational speed, adaptability to test cases, and analytical efficiency.

Optimal strategies for virtual power plants to participate in medium- and long-term power trading in the context of contracts for difference (CFDs)

Abstract In the context of virtual power plants gradually joining the medium-and long-term power market transactions, the decision-making problem of this new subject in the transaction process has attracted wide attention. Since virtual power plants possess distributed power sources on the supply side, as well as variable loads, energy storage, and other resources on the customer side, they are able to participate in both the sale and purchase of electricity in the medium-and long-term electricity market at the same time, which leads to the need for the relevant analysis to be divided into the two cases of sale and purchase of electricity. At the same time, due to the wide application of the CFD mechanism, power transactions should also take into account the compensation adjustment brought by this mechanism in addition to the market price. To sum up, this paper constructs a static game model for the virtual power plant’s power sales and purchasing strategies in the medium-and long-term power market under the premise of the CFD mechanism. The analysis shows that the optimal trading volume is related to the CFD volume, the total volume of power supplied by the bidding, the market price, etc. The relevant conclusions will help market managers to grasp the market price. The conclusions of the analysis help market managers grasp the key transaction information and assist virtual power plants to participate in medium-and long-term power transactions in an orderly manner.

Flexibility potential quantification of electric vehicle charging clusters

A significant obstacle to providing flexibility services with electric vehicles (EVs) is the uncertainty surrounding the profitability and flexibility potential of charging clusters when utilised as a flexible load. Currently, there is a lack of comprehensive and easily applicable methods for quantifying flexibility in the literature. This paper introduces an evaluation tool and a set of flexibility indexes to assess the capability of charging clusters to deliver flexibility services. The method is designed to evaluate and quantify the flexibility potential of charging clusters in terms of short-term and long-term power adjustments and charge scheduling. Through sensitivity analysis, we examine how connection capacity, EV battery capacities, power capabilities, and the number of daily charging sessions influence the flexibility potential of charging clusters. Our findings highlight a direct relationship between the grid connection capacity of clusters and their ability to perform short-term power adjustments. Moreover, while larger batteries tend to reduce energy and time flexibility, their increased storage capability facilitates managing and scheduling a larger energy volume. Furthermore, for the days analysed, the flexibility potential showed minimal sensitivity to the number of daily charging sessions. Instead, the amount of energy requested and connection patterns emerge as key determinants of overall flexibility. In summary, this research provides valuable insights that can inform the design, monitoring, and assessment of EV charging clusters when evaluating their suitability for various flexibility services.

A high-performance all-day vertical thermoelectric generator based on a double-sided reflective structure

Harvesting environmental energy sources is crucial for achieving renewable energy generation and net-zero emissions. However, ensuring uninterrupted electricity generation from environmental energy sources remains challenging. In this study, we present a simple, compact, and expandable all-day vertical passive thermoelectric generator (V-TEG) with a double-sided reflective structure that simultaneously harnesses solar and space cold energy. Outdoor experimental measurements, combined with finite element simulation analysis, revealed that the optimal angles of the solar reflector relative to the TEG range from 30° to 50°. Additionally, the TEG placed in the north–south orientation was found to enhance its daytime performance. The use of low-cost black paint combined with the solar reflector achieved equivalent spectral selectivity, effectively minimizing the infrared heat loss of the hot end of the TEG. During a 48-h outdoor test, the V-TEG achieved an average daytime power of 112.00 mW/m2 and a peak of 363.42 mW/m2. Comparative experiments demonstrated that the V-TEG outperformed horizontally placed TEG during the daytime. Furthermore, scaling the system by connecting three TEGs in series significantly increased the daily power output. This compact V- TEG system offers a promising solution for long-term power generation in low-power sensors and off-grid communities, supporting renewable energy and carbon neutrality goals.

High-efficiency Yb:YAG thin-disk chirped pulse amplifier delivering 884-femtosecond laser with tunable repetition rates and high stability

We experimentally demonstrated a compact, highly efficient, stable, chirped pulse amplification (CPA) thin-disk regenerative system with a tunable repetition rate. The laser head consists of a 9 at. % Yb:YAG thin-disk with a diameter of 8.8 mm and a thickness of 150 μm, is designed for a 48-pump pass configuration. In CPA-based regenerative amplifier configuration, a maximal output power of 85 W at 500 kHz is achieved with a conversion efficiency of 53.1 %. This corresponds to a pulse energy of 170 μJ. To the best of our knowledge, this is the highest conversion efficiency in CPA thin-disk regenerative amplifiers. The pulse width is compressed to 884 fs with a pair of grating, resulting in a peak power of 192 MW. At 85 W, a near diffraction limit beam quality factor M2 of 1.40 is measured, which is essential in precision applications. In addition, remarkable long-term power stability is confirmed with a root-mean-square (RMS) fluctuation of 0.12 % over a 24-hour duration. The reported femtosecond amplifier is believed to be a promising tool for various applications such as extreme manufacturing, terahertz spectroscopy, etc.

Impact of repetitive ELM transients on ITER divertor tungsten monoblock top surfaces

Abstract Owing to the high stored energy of ITER plasmas, the heat pulses due to uncontrolled Type I edge localized modes (ELMs) can be sufficient to melt the top surface of several poloidal rows of tungsten monoblocks in the divertor strike point regions. Coupled with the melt motion associated with tungsten in the strong tokamak magnetic fields, the resulting surface damage after even a comparatively small number of such repetitive transients may have a significant impact on long-term stationary power handling capability. The permissible numbers set important boundaries on operation and on the performance required from the plasma control system. Modelling is carried out with the recently updated MEMENTO melt dynamics code, which is tailored to tackle melt motion problems characterized by a vast spatio-temporal scale separation. The crucial role of coupling between surface deformation and shallow angle heat loading in aggravating melt damage is highlighted. As a consequence, the allowable operational space in terms of ELM-induced transient heat loads is history-dependent and once deformation has occurred, weaker heat loads, incapable of melting a pristine surface, can further extend the damage.

A Comparative Analysis of Power Consumption While Using Open-Source and Proprietary Media Players

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

Increasing the robustness of forecasting energy production models from a photovoltaic plant is crucial in investment decisions, while profitability is closely linked to its location since its energy production depends on the local climate conditions. This study aimed to mitigate uncertainty regarding the installation of photovoltaic systems by addressing the challenge of forecasting electrical energy production over the long term. Artificial Neural Network models were used for this purpose, predicting the power output of a photovoltaic plant based on the ambient temperature, cell temperature, and solar irradiance. Data recorded every minute over one year at an experimental photovoltaic plant revealed a strong correlation between energy production and the input variables. This research compared the performance of multilayer perceptron, feedforward, long short-term memory, and modular artificial neural networks architectures. Validation was carried out using mean squared error, mean absolute error, mean absolute percentage error, root mean squared error, and coefficient of determination as key metrics. Notably, the long short-term memory network demonstrated the highest accuracy in energy forecasting achieving a mean squared error of 0.0089 p.u, a mean absolute error of 0.0527 p.u, a root mean squared error of 0.0944 p.u, and a mean absolute percentage error of 49.0124 %. Nonetheless, the modular model came close to long short-term memory accuracy but with lower computational cost.

Enhancing bargaining power for energy communities in renewable power purchase agreements using Gaussian learning and fixed price bargaining

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

This paper uses closed-form and copula models to analyze short-term and medium-term financial instruments' hedging effectiveness and tail risk reduction in long-term renewable Power Purchase Agreements (PPAs). Using monthly and yearly exchange-traded electricity futures, we study PPA contracts from California, Germany, France, Italy, and Spain. The overall low or negative hedging effectiveness and inconsistent tail risk reductions indicate that traditional linear hedging strategies may not consistently achieve the desired risk mitigation. Monthly contracts are generally more effective in California, Germany, and France, while yearly contracts are preferable in Italy and Spain, highlighting the need for market-specific hedging strategies.

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

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

Accurate long-term prediction of power generation in photovoltaic (PV) power stations is crucial for preparing generation plans and future planning. Quantitative prediction of future power generation from PV stations not only contributes to the stable operation of the local power system but also assists managers in formulating regional energy policies to promote renewable energy consumption. We utilized the NEX-GDDP-CMIP6 high-resolution climate dataset and employed the Vine Copula method for post-downscaling. This approach enabled high-resolution forecasts of key meteorological factors under different shared socioeconomic pathways (SSPs) scenarios (SSP245 and SSP585) for a PV power station in Yunnan, China. Additionally, we developed the KM-PSO-SVR power generation prediction model, which enables future accurate long-term PV power generation prediction. The results show that the Vine Copula multi-model ensemble downscaling model can effectively simulate the changes in key meteorological factors in the PV power station area. The KM-PSO-SVR model exhibited good simulation performance, with a mean absolute error of 0.843, root mean square error of 1.136, and correlation coefficient of 0.874 during the validation period. The results indicate that during the decade spanning from January 1, 2025, to December 31, 2034, radiation and wind speed will be decrease, while the temperature is expected to increase. In the SSP245 scenario, there is a 1.585 % increase in the average annual power generation during the future carbon peaking period (2025–2034). However, the SSP585 scenario, representing higher future emissions, shows a lower increase of 1.479 %.

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

As the mandatory scope of zero-energy building certification has expanded, the integration of renewable energy systems into building designs and energy operations has significantly increased. The long-term maintenance of these systems is crucial for sustaining zero-energy goals. The primary key to maintaining renewable energy systems is to assess quantitative performance factors, such as degradation and malfunction. This study presents a comprehensive evaluation of the long-term power performance of a large-scale vertical building-integrated photovoltaic system, which has been monitored over 9 years (2012–2020). The system, comprising 22 arrays with a total installed capacity of 142.654 kWp, was analyzed to derive quantitative performance indicators that highlight the importance of continuous maintenance. The photovoltaic system's arrays were investigated to separate them into analysis groups, and simulation-based analysis was carried out for the detailed assessments. The findings revealed that 19 of 22 arrays were operating normally, while three experienced malfunctions. In 2020, a 19.12 % performance degradation was observed in normally operating arrays compared to 2012. Post-malfunction, the affected arrays performed at approximately half the efficiency of the normal arrays. Despite theses issues, inverter efficiency remained stable across all arrays. However, a noticeable decline in array efficiency indicated performance degradation due to specific array issues. When compared to simulation-based theoretical performance, in 2020, the malfunctioned arrays exhibited nearly triple the performance degradation of the normally operating arrays. These results underscore the critical role of quantitative performance indicators in maintenance strategies and demonstrate how inadequate maintenance can lead to substantial power generation losses.

Enhancing performance and durability of PVDF-ceramic composite membranes in microbial fuel cells using natural rhamnolipids

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

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

Novel Workstation Module and Method for Automatic Blanking of Surgical Forceps

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.

Unusual Electrical Conductivity Enhancement in Stable n-Type Carbon Nanotube Networks.

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.