Actual Generation Research Articles

Performance degradation analysis and fabrication guidance of μ-TEG from material to device

Micro thermoelectric generator (μ-TEG) attracts more and more attention due to its small size and high power density. Many two-dimensional thermoelectric materials with high performance have facilitated the development of μ-TEG. However, the performance of μ-TEG fabricated by these great thermoelectric materials is significantly degraded due to size effect, interfacial effects (include contact effect and boundary effect) and structure effect. To accurately assess the performance degradation degree from material to μ-TEG and guide the device fabrication, an experiment-verified mathematical model considering interfacial and size effects is proposed. Firstly, the phonon/electron temperature distribution in thermoelectric leg of μ-TEG is analyzed to investigate the device-level thermoelectric properties of material. Then based on the device-level thermoelectric properties, the actual power generation performance model of μ-TEG is established to conduct the influence analysis of these effects (boundary, size, contact and structure effects) on material and device. Finally, the thermoelectric leg thickness (Hte) is optimized to realize optimal power generation. The study results reveal that boundary and size effects weaken the device-level thermoelectric properties, and the reduction trend is more obvious when Hte is smaller, especially when Hte ≤ 20 μm. The decrease from the material intrinsic figure of merit ((ZT)m) to the device figure of merit ((ZT)D) is owing to the boundary effect, structure effect and contact effect, and the dominant factor of this decrease changes from structure effect (Hte＜7 μm)to contact effect (Hte ≥ 7 μm) as Hte increases, which points to a main optimization direction for (ZT)D for different Hte. As for contact effect, the electrical contact resistivity (re,c) has a more significant impact on weakening the performance of μ-TEG than thermal contact resistivity (rk,c), and their optimization goals are explored (re,c ≤ 5.1 × 10−12 Ω·m2, rk,c ≤ 9.3 × 10−8 K·m2/W). At given electrical and thermal contact resistivity, there exists an optimal Hte for achieving the optimal power generation (Popt) and a large range of Hte for achieving 95%Popt, and the optimal Hte increases with increasing electrical and thermal contact resistivity. This study can reduce the processing difficulty and save time and economic costs of μ-TEG fabrication, which can avoid the blind fabrication of μ-TEG.

A D-stacking dual-fusion, spatio-temporal graph deep neural network based on a multi-integrated overlay for short-term wind-farm cluster power multi-step prediction

The uncertainty of wind energy due to its non-stationary and random nature poses a major challenge to engineers responsible for power system scheduling. In the present research, a spatio-temporal graph deep neural network is used to learn the spatial characteristics and explore the strong correlation characteristics of wind-farm clusters. The Bagging, long short-term memory (LSTM), and random forest (RF) are superimposed based on the stacking integration algorithm. By analyzing the decomposed wind electron sequence characteristics, two Stacking models are used to predict the wind power of target wind-farms respectively and then a dual stacking model fusion strategy is formed. Finally, a multi-step prediction model with the spatio-temporal characteristics of D-stacking multi-integration fusion is designed for rolling multi-step prediction of wind-farm cluster power to obtain high-precision target wind-farm power. Through the actual wind power generation measured in northwest China to conduct case studies and comparative tests, it concludes that：(1) Spatio-temporal method in this paper can effectively extract the deep spatial features of wind farm clusters. (2) Dual fusion strategy improves Stacking effectively. (3) The proposed model can obtain accurate wind power prediction results, which is superior to 14 comparative algorithms proposed by other researchers.

Energy, exergy, exergoeconomic, environmental (4E) evaluation and multi-objective optimization of a novel SOFC-ICE-SCO2-HRSG hybrid system for power and heat generation

To achieve sustainable development of the global energy system, it is essential to develop efficient and cost-effective cogeneration systems. This paper introduces a new cogeneration system named SOFC-ICE-SCO2-HRSG, which combines solid oxide fuel cells (SOFC), internal combustion engines (ICE), supercritical carbon dioxide (SCO2) power cycles, and heat recovery steam generators (HRSG) to generate electricity and heating simultaneously. The proposed system offers a promising solution for achieving high efficiency and cost-effective clean power generation and heating. Energy, exergic, exergoceconomic, and environmental analyses were conducted to evaluate the system's performance. The impact of fuel flow, steam carbon ratio, and other parameters on the system's efficiency was also analyzed. The system was optimized using a genetic algorithm, and the optimal operating condition was determined using the TOPSIS method. The results indicate that the proposed system can achieve a total output power and net output power of 345.58 kW and 288.94 kW, respectively, under optimal conditions, with a 1:1 fuel flow ratio between the SOFC and ICE systems. The total unit cost of the product can be as low as 42.98 $/GJ, while the theoretical and actual generation efficiencies can reach 48.00% and 40.13%, respectively. The overall energy efficiency and exergy efficiency were found to be 65.82% and 42.28%, respectively. Additionally, the system can achieve a CO2 emission rate of 0.4712 kg/kW·h, and the social cost for pollutant emissions was estimated to be 3.34 $/GJ. This study provides evidence that the proposed hybrid system has excellent performance in terms of efficiency, cost, and environmental impact. As an advanced energy conversion technology, it holds great potential for practical applications in meeting energy demands while reducing environmental pollution.

Offshore wind power generation system control using robust economic MPC scheme

With the increasing penetration of wind power in the modern power system, the economic model predictive control (EMPC) has been widely employed in the control of the offshore wind power generation system for promoting the dynamic economy. A robust EMPC strategy, aiming to minimizing damage to the turbine while maximizing the electric power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A linear model is utilized to approximate the original nonlinear behavior of the wind power generation system, while the consequent linearization error is taken as the external disturbance of the control system. A linear feedback controller with a robust control invariant set is designed to restrict the deviation between the nominal linear system and the actual nonlinear wind power generation system. Plant simulations, as well as NREL FAST code testing, are given to demonstrate the proposed controller's effectiveness throughout the overall operating region.

Vision Transformer-Based Photovoltaic Prediction Model

Sensing the cloud movement information has always been a difficult problem in photovoltaic (PV) prediction. The information used by current PV prediction methods makes it challenging to accurately perceive cloud movements. The obstruction of the sun by clouds will lead to a significant decrease in actual PV power generation. The PV prediction network model cannot respond in time, resulting in a significant decrease in prediction accuracy. In order to overcome this problem, this paper develops a visual transformer model for PV prediction, in which the target PV sensor information and the surrounding PV sensor auxiliary information are used as input data. By using the auxiliary information of the surrounding PV sensors and the spatial location information, our model can sense the movement of the cloud in advance. The experimental results confirm the effectiveness and superiority of our model.

Day-Ahead Forecasting of the Theoretical and Actual Wind Power Generation in Energy-Constrained Island Systems

Grid operators of islands with limited system tolerance are often challenged by the need to curtail wind energy in order to maintain system stability and security of supply. At the same time, and in the absence of storage facilities and/or other means of flexibility such as demand-side management, wind park owners face the problem of rejected wind energy production that varies considerably within the year. In the prospect of a more dynamic market operation in island grids, estimation of the anticipated wind energy curtailments may allow the evaluation of different options for wind park owners, such as short-term leasing of energy storage and/or direct, bilateral power purchase agreements with flexible demand entities. To enable such options, effective wind energy forecasting is necessary not only in terms of theoretical production, but also in terms of actual production being absorbed by the system. In this direction, the current research works on the prediction of day-ahead wind energy production in island grids, aiming to generate both theoretical (expected) and actual wind power forecasts. To that end, we use artificial neural networks for the development of different day-ahead forecasting models of hourly granularity, and we then test their performance in a large-scale non-interconnected island system, where annual wind energy curtailments for local wind parks may exceed 25% of the respective theoretical yield. Our results indicate that models developed provide a fair accuracy of day-ahead wind energy predictions, which is further elaborated by initiating a discussion on the emergence of alternative actor schemes in similar systems.

Fault Detection and Power Loss Assessment for Rooftop Photovoltaics Installed in a University Campus, by Use of UAV-Based Infrared Thermography

In contrast to commercial photovoltaic (PV) power plants, PV systems at universities are not actively monitored for PV module failures, which can result in a loss of power generation. In this study, we used thermal imaging with drones to detect rooftop PV module failures at a university campus before comparing reductions in power generation according to the percentage of module failures in each building. Toward this aim, we adjusted the four factors affecting the power generation of the four buildings to have the same values (capacities, degradations due to aging, and the tilts and orientation angles of the PV systems) and calibrated the actual monthly power generation accordingly. Consequently, we detected three types of faults, namely open short-circuits, hot spots, and potential-induced degradation. Furthermore, we found that the higher the percentage of defective modules, the lower the power generation. In particular, the annual power generation of the building with the highest percentage of defective modules (12%) was reduced by approximately 25,042 kWh (32%) compared to the building with the lowest percentage of defective modules (4%). The results of this study can contribute to improving awareness of the importance of detecting and maintaining defective PV modules on university campuses and provide a useful basis for securing the sustainability of green campuses.

Research on Potential Equilibrium of Dynamic Combustion Performance in Electrothermal-Chemical Launching

The interior ballistic potential equilibrium theory is used to analyse the parameters at the potential equilibrium points of pressure curves of 30mm conventional launching experiment and electrothermal-chemical (ETC) launching experiments. Analysis shows that: compared to the conventional launching experiment, (1) the time for ETC launching experiment to arrive the potential equilibrium point is apparently shortened; (2) the relative burnt percentages of the propellants at the potential equilibrium points are smaller in ETC launching tests. And the curves of gas generation brisance in the three ETC launching experiments are consistent. Then based on the measured pressure curves, the actual gas generation functions in chamber for conventional launching experiment and ETC launching experiments are fitted respectively. Using the average actual gas generation functions to replace the shape functions which are derived from the hypothesis of Geometric Combustion Law, the interior similarity equations of ETC Gun are established with the potential equilibrium point as the reference state. The calculated pressure curve obtained by solving the interior ballistic similarity equations of electrothermal chemical gun is in good agreement with the experimental pressure curve. Therefore, it is expected that the interior similarity equations of ETC Gun could predict the experimental pressure curve.

Intelligent Small Sample Defect Detection of Water Walls in Power Plants Using Novel Deep Learning Integrating Deep Convolutional GAN

Thermal power generation is one of main forms of the electricity generation in the world. However, power plants are often shut down due to boiler accidents caused by the damage of water walls. Therefore, a novel intelligent small sample defect detection method of water walls in power plants based on the deep learning integrating deep convolutional GAN (DCGAN) and seam carving algorithm is proposed. The proposed method uses the seam carving algorithm to solve the overfitting of the DCGAN for generating high-quality images. Then the intelligent small sample defect detection model is built by convolutional neural networks (CNNs). Finally, the proposed method is used in the defect detection of water walls in actual thermal power generation plants. Experimental results demonstrate that the proposed method can achieve the detection accuracy of 98.43%. which is higher than different GANs, different detection networks integrating different processes used and not used data expand methods.

Non-invasive accurate time resolved inverse battery calorimetry

Precise knowledge of the battery heat generation rate and the internal temperature rise allows accurate thermal regulation required for enabling fast charging while minimizing side reactions and avoiding thermal runaway. With the necessity of high energy density, the size of batteries is constantly increasing. Because of poor thermal conduction, the thermal lag in large batteries is significant resulting in a considerable temperature gradient within the cell. Existing calorimetry methods only account for the external heat flow measurement and the external temperature rise and therefore cannot account for the thermal lag inside the cell. Additionally, with these methods, the temporal (time-resolved) information of the heat generation rate is compromised, which makes observation and attribution of electrochemical- thermal signatures impossible. As a potential solution, various intrusive techniques such as embedded thermal sensors have been proposed in the literature. However, these techniques are limited in scope. To enable accurate measurement of time-resolved heat flux measurement in commercial batteries, we instead propose a new battery calorimetry approach by combining state-of-the-art commercial calorimetry with an inverse heat transfer algorithm. This inverse calorimetry is completely non-intrusive with no change to commercial calorimeters. The inverse calorimetry reduces the error in heat generation rate to within 10% of the actual heat generation rate compared to 50% from the lumped capacitance method (best existing method). This method enables time resolved observation of electrochemical-thermal signatures such as a negative heat generation rate due to entropy change, which could not be observed from existing calorimetry methods.

The Effect of the Spanish Nuclear Phase-out on the Electricity Market

Portugal and Spain are integrated into what is known as the Iberian Electricity Market. Spain participates in this market with significant contribution of energy produced by nuclear plants. In 2022, nuclear energy accounted for 22 % of the total energy generated in Spain. Nuclear power plants sell their energy largely through bilateral contracts, and approximately 20 % is offered in the day-ahead electricity market. By 2035, Spain will phase-out nuclear power producing a structural change of its generation mix. In this paper, we investigate the effect on the day-ahead Iberian electricity market that the gradual removal of nuclear power plants will have on the daily electricity market. The methodology is based on a ceteris paribus approach, where generation conditions are modified by removing nuclear plants and keeping the rest of the variables constant (demand and other generation). Under these conditions, the market is reproduced, estimating how electricity prices change in the day-ahead electricity market. To evaluate the market electricity prices under this new scenario with a total or a partial elimination of nuclear energy generation, it is necessary to use a market model. The existing literature shows various approaches to replicate the electricity market, among which are agent-base modelling, optimization algorithms, artificial intelligence techniques or metaheuristic merit-order methods. Our approach is based on the latter methods, by using actual generation and demand data, the new market generation and demand curves are reconstructed and the new clearing price is obtained. For the most conservative scenario, the results show a price increase of more than 5 % for the first 6 months of 2021. The reason for this increase is that nuclear generation participates in the market by offering its energy at prices close to zero. When this generation is withdrawn, there is a shift to the left of the hourly generation curve producing a new market equilibrium at a point of higher price and lower energy.

Research on optimization of power dispatching under the constraint of deviation electricity

In the medium- and long-term electricity market, uncertain power output, and power load will lead to the deviation between the actual generation and the signed medium- and long-term contract electricity. In order to reduce the system deviation balance cost and ensure the balance of power supply and demand, it is necessary to find a suitable dispatch method. First, a monthly electricity decomposition model of thermal power units is proposed in this paper. After that, we propose a power dispatching model under the constraint of deviated electricity quantity to minimize the cost of electricity deviation. The simulation results of numerical examples show that the proposed dispatching model and method can effectively reduce the monthly contract power deviation and the balance cost.

Probabilistic Estimation of PV Generation at Customer and Distribution Feeder Levels Using Net-Demand Data

Reliable operation of the electricity grid requires the grid operator to know detailed information about the system at any given time. This information usually comes from readings of different devices, or from different estimation algorithms. However, having accurate estimations become more challenging due to increasing amount of photovoltaic (PV) generation, especially widespread integration of rooftop PV into distribution systems with only the net demand being recorded. This paper proposes a novel probabilistic approach that estimates behind-the-meter gross PV generation using only net-metered demand and local weather data. It also provides a way to measure the uncertainty around the PV estimations. In areas with low PV penetration, a second probabilistic approach is proposed to estimate aggregated PV generation using feeder-level aggregated net-demand data. Our proposed approaches combine a physics-based PV model, machine learning based clustering or classification methods, and Bayesian framework to estimate hourly PV generation at both customer and feeder levels. Both approaches are validated using real demand and generation data, where the results show reliable PV estimations with the estimated 95% confidence band covers 92% of the actual PV generation data. The median PV estimation also reduces the mean squared error of the existing model by 77%.

Quality performance gaps and minimal electricity losses in East Africa

The electricity sector in East Africa is characterized by high levels of electricity losses. The literature has extensively focused on investments and policy reforms that can potentially reduce losses. In this paper, we follow another approach by nonparametrically estimating the minimal losses given the actual inputs, outputs and electricity generation process. Minimal losses are then compared to actual losses to construct quality performance indicators. Using a tailored database for six East African countries over 10 years, we show that electricity losses could be reduced by 8%, representing savings of approximately $60 million per year.

Techno-economic evaluation of green hydrogen production with low-temperature water electrolysis technologies directly coupled with renewable power sources

It is obvious that green hydrogen production needs to increase to solve climate change issues, but green hydrogen has not been actively produced due to economic disadvantages. In order to secure the economic feasibility of green hydrogen production, it is necessary to find the optimal combination of renewable power sources and water electrolysis types. In this study, the combinations of three types of renewable power plant such as offshore wind, onshore wind, and onshore photovoltaic and two types of water electrolysis such as alkaline water electrolysis (AWE) and polymer electrolyte membrane water electrolysis (PEMWE) are analyzed in the economic evaluation. Previous researches on economic evaluation of green hydrogen production have only been conducted under rated operating conditions, but this study conducted realistic calculations by considering partial load operation according to fluctuating power generation. Accurate hydrogen production efficiency was derived by using actual renewable energy generation data for year with sample time of 1hr, and water electrolysis system simulation results according to power input. The offshore wind power generation data was generated using a wind power generation prediction model using a machine learning method. LCOH for each case was calculated using cost data of system components, and the impact of each price component on LCOH was analyzed through sensitivity analysis. As a result of the calculations, the onshore wind power-AWE case has the lowest LCOH with 7.25 $/kg while PV-PEMWE case has the highest LCOH with 13.44 $/kg. The capacity factor of renewable energy is shown to have the greatest impact on LCOH by sensitivity analysis, followed by the capital expenditure (CAPEX) of the renewable power plant or the operating expenditure (OPEX). This study provides an insight to establish the methodology finding out the most efficient green hydrogen production system based on water electrolysis dedicated to renewable power sources.

Transaction decision optimization of new electricity market based on virtual power plant participation and Stackelberg game.

This study intends to optimize the trading decision-making strategy of the new electricity market with virtual power plants and improve the transmission efficiency of electricity resources. The current problems in China's power market are analyzed from the perspective of virtual power plants, highlighting the necessity of reforming the power industry. The generation scheduling strategy is optimized in light of the market transaction decision based on the elemental power contract to enhance the effective transfer of power resources in virtual power plants. Ultimately, value distribution is balanced through virtual power plants to maximize the economic benefits. After 4 hours of simulation, the experimental data shows that 75 MWh of electricity is generated by the thermal power system, 100 MWh by the wind power system, and 200 MWh by the dispatchable load system. Comparatively, the new electricity market transaction model based on the virtual power plant has an actual generation capacity of 250MWh. In addition, the daily load power of the models of thermal power generation, wind power generation, and virtual power plant reported here are compared and analyzed. For a 4-hour simulation run, the thermal power generation system can provide 600 MW of load power, the wind power generation system can provide 730 MW of load power, and the virtual power plant-based power generation system can provide up to 1200 MW of load power. Therefore, the power generation performance of the model reported here is better than other power models. This study can potentially encourage a revised transaction model for the power industry market.

Application of Photovoltaic Systems in Field Observation and Research Stations: Research on the Relationship between Power Generation Scale and Electricity Consumption to Improve Photovoltaic Application in Field Observation Stations

Most field scientific observation and research stations are located at the end of power grids which are usually not extended to such areas. Consequently, the power supply of equipment in field observation stations cannot be guaranteed. Meanwhile, regions with poor ecosystem stability are relatively sensitive to environmental changes and thus prone to degradation and succession due to external interference. In this paper, the photovoltaic (PV) power generation system of a grassland ecohydrological field scientific observation and research station was taken as the research object. Two kinds of distributed PV power generation systems were simulated and analyzed by use of PVsyst software. The total power of laboratory equipment, PV power generation efficiency, and system cost of the field observation station were calculated and analyzed. The design scheme and scale of PV power generation systems suitable for field observation stations were determined. Finally, a PV power generation test system was set up, and PV power generation data were sorted out. The feasibility of the design scheme of PV power generation systems was verified by analyzing the relationship between the simulated and actual power generation of systems and that between the daily energy use proportions of field observation stations. Besides, the environmental benefits of PV systems were analyzed, and their amount of energy saving and emission reduction was calculated. This study can solve the issue of the low power supply guarantee rate of field observation stations, provide a design basis and beneficial reference for the construction of environment-friendly field laboratory stations, and realize green energy saving and the sustainable use of energy while protecting the ecosystem from being destroyed.

Control strategies of energy storage limiting intermittent output of solar power generation: Planning and evaluation for participation in electricity market

AbstractRenewable energy generation cannot be consistently predicted or controlled. Therefore, it is currently not widely used in the electricity market, which requires dependable production. In this study, reliability‐ and variance‐based controls of energy storage strategies are proposed to utilize renewable energy as a steady contributor to the electricity market. For reliability‐based control, photovoltaic (PV) generation is assumed to be registered in the power generation plan. PV generation yields a reliable output using energy storage units to compensate for PV prediction errors. We also propose a runtime state‐of‐charge management method for sustainable operations. With variance‐based controls, changes in rapid power generation are limited through ramp rate control. This study introduces new reliability and variance indices as indicators for evaluating these strategies. The reliability index quantifies the degree to which the actual generation realizes the plan, and the variance index quantifies the degree of power change. The two strategies are verified based on simulations and experiments. The reliability index improved by 3.1 times on average over 21 days at a real power plant.

A Post-Evaluation System for Smart Grids Based on Microservice Framework and Big Data Analysis

Wind energy, as a clean energy source, has been experiencing rapid development in recent years. However, there is often a significant difference between the designed electricity generation capacity and the actual electricity generation capacity during the construction of wind farms, making it difficult to assess the economic benefits of wind farms. Therefore, the development post-evaluation technology is required to support the renovation of old wind farms. In addition, traditional data analysis techniques are unable to handle and analyze massive data in a timely manner. Therefore, big data technology must be developed to improve processing efficiency. To address these issues and meet actual business needs, this paper designs an intelligent grid electricity generation post-evaluation platform for wind farms based on a microservice framework and big data analysis technology. The platform evaluates the operating status of wind farms by analyzing their operational and design data and visualizes relevant big data information. It provides technical support and improvement solutions for wind farm renovation and new wind farm construction. The platform has been tested and proven to meet the requirements for processing and analyzing massive data, post-evaluating electricity generation, and visualization.

The value of forecasts for PV power plants operating in the past, present and future Scandinavian energy markets

When participating in the Scandinavian wholesale energy markets with intermittent PV power, imbalances between market-committed and actual energy generation need to be economically accounted for in the imbalance settlement. The market-committed energy is based on forecasts issued at specific timestamps defined by the market. The economic value of such forecasts can be quantified for the past, present and future energy market regulations by comparing the achieved income using perfect forecasts to the income with imperfect forecasts. Differences between these regulations include changing from 60 min to 15 min market intervals, and from a dual-price to a single-price imbalance settlement structure.PV power production data were modelled from meteorological measurements carried out at 11 locations in each of the Nord Pool bidding areas. Forecasts generated from a smart persistence model and a more sophisticated numerical weather prediction (NWP) model were compared for these locations, and the value of these forecasts was estimated from historical market data for each of the 11 bidding areas. The results show that the estimated income, and income losses, are dependent on the electricity mix of the bidding areas. In addition, the transition from the dual-price to the single-price imbalance settlement structure reduces the value of providing accurate forecasts. Moreover, the added incentive to provide accurate forecasts when settling the markets and imbalances on 15 min instead of 60 min intervals is negligible with the single-price imbalance settlement. Finally, the results show that intraday trading is generally beneficial for PV power plants in Scandinavia.