Output Power Of Generator Research Articles

Multiple model predictive control for offshore wind turbines operating in the full‐load range

AbstractThis paper presents a multiple model predictive control (MMPC) strategy based on an individual blade pitch (IBP) mechanism to control floating offshore wind turbines (FOWTs) when operating in the full‐load range (above the rated wind speed). The strategy utilizes local model‐based predictive controllers (MPCs) to minimize generator output power fluctuations, generator speed variations, and alleviate tower mechanical loads, particularly fore‐aft and side‐to‐side shear forces. To ensure smooth transitions between local controllers, a soft‐switching technique is employed. The IBP actuation and generator torque control fix generator power and speed at the rated values, mitigating platform motions induced by wind and waves. The proposed control strategy is applied to a 5‐MW baseline wind turbine with a spar‐buoy platform developed by the National Renewable Energy Laboratory. Furthermore, a comparative analysis is conducted by designing an MMPC based on the collective blade pitch (CBP) mechanism. Through comprehensive simulation and analysis, the effectiveness of the IBP‐based MMPC strategy is demonstrated.

In Situ Observation of Orientational Ordering in Polyimide Triboelectric Generators by Using Optical Second-Harmonic Generation Measurement.

By using in situ optical second-harmonic generation (SHG) measurement, dynamical changing of polar molecular orientations caused by mechanical rubbing of polyimide triboelectric generators is observed. The polar orientational order is enhanced during rubbing, which relaxes with the time constant τ = 7.1 s. Analysis of s- and p-polarized SHG intensity under s-polarized laser incidence showed that molecules align along the rubbing direction with the tilt angle θ0 = 51° from the surface normal. By using the obtained time constant and the tilt angle, the output power in the I-V measurement is discussed. That is, the SHG result gives a theoretical maximum power of 2.0 μW/cm2, while the effective power observed in the I-V measurement is 0.26 μW/cm2, suggesting that merely part of produced power is received at external loads. The in situ SHG measurement gives helpful ways to discuss the power output of triboelectric generators using polar materials.

Fine-grained simulation model for PV power output interval based on two-stage scenario clustering and dual-ensemble compatible learning

Photovoltaic (PV) power simulation is indispensable for the planning and operation of renewable-rich power systems. Among various simulation methods, data-driven indirect simulation methods are the most efficient, primarily involving scenario extraction and power output generation. Typical scenario extraction methods often feature coarse characterizations, which also cannot be expressed by traditional power output generation models lacking the ability of quantifying the uncertainty of the output results. This paper proposes a fine-grained simulation model for PV power output interval, i.e., upper bound and lower bound, based on two-stage clustering of multiple typical scenarios and dual-ensemble compatible learning. The first stage of scenario extraction, considering the impact of total daily irradiance on PV output, employs a line distance index for preliminary scenario division of daily PV output. These scenarios are further divided into five characteristic scenarios with an angular distance index considering the impact of intra-day irradiance variations on power output. PV power output interval is generated from dual-ensemble compatible learning including Stacking and Bagging, which are adaptively selected with a Bias-Variance Selection Index (BS) calculated using the value of scenario characteristics. Stacking and Bagging are driven by three neural network base learners with Bayesian layers, which can output the lower bound and upper bound with confidence at a certain level, thus quantifying the uncertainty of the result accurately. The actual data of Daxing and Gangjialing power stations are used in case study. Results form case study show that compared with the existing methods, the proposed method can improve the Power Coverage Rate (PCR) and Average Power Simulation Accuracy (APA) of uncertainty simulation results by 27.44% and 5.39%, respectively. Meanwhile, the Average Daily Maximum Power Simulation Accuracy (AMPA) is similar to that of existing methods.

Hydro–Wind–PV–Integrated Operation Optimization and Ultra-Short-Term HESS Configuration

In order to address the challenges associated with optimizing multi-timescale operations and allocating ultra-short-term energy storage for HWP integration, this study takes into account both the economic and reliability aspects of the HWP integration base. It proposes a model for optimizing operations and allocating energy storage capacity, achieving optimization across long-term, short-term, and ultra-short-term operations for an MECB. Initially, operation optimization is implemented for an entire group of terraced hydropower plants by regulating them with annual regulating capabilities on a long-term timescale. The objectives are to maximize the daily average minimum output and annual power generation. Subsequently, short-term operation optimization focuses on maximizing HWP power feed-in, minimizing new energy power curtailment, and reducing residual load standard deviation while ensuring the guaranteed output optimization results for the long term. Finally, to mitigate ultra-short-term fluctuations in new energy, a HESS with specified capacity and power is configured with the goal of minimizing comprehensive costs. Additionally, to address the challenge of smoothing negative fluctuations, which is hindered by charging and discharging efficiency limitations, a variable baseline is introduced, deviating from the conventional 0 MW baseline. A simulation study based on data from the hydro–wind–PV hybrid project in the Beipanjiang River Basin, China, demonstrates the following: (1) after long-term system optimization, the total power generation capacity of the system increases by 9.68%, while the peak-to-valley difference in output is significantly reduced; (2) short-term system optimization significantly reduces both the average variance in residual loads and the amount of power curtailed over five representative days; (3) the system incorporates 398.62 MWh of lithium-ion battery storage with a power of 412.47 MW and 51.09 MWh of supercapacitor storage with a power of 223.32 MW, which, together, completely smooth out the ultra-short-term fluctuations in new energy output.

Piezo-to-piezo (P2P) conversion: simultaneous β-phase crystallization and poling of ultrathin, transparent and freestanding homopolymer PVDF films via MHz-order nanoelectromechanical vibration.

An unconventional yet facile low-energy method for uniquely synthesizing neat poly(vinylidene fluoride) (PVDF) films for energy harvesting applications by utilizing nanoelectromechanical vibration through a 'piezo-to-piezo' (P2P) mechanism is reported. In this concept, the nanoelectromechanical energy from a piezoelectric substrate is directly coupled into another polarizable material (i.e., PVDF) during its crystallization to produce an optically transparent micron-thick film that not only exhibits strong piezoelectricity, but is also freestanding-properties ideal for its use for energy harvesting, but which are difficult to achieve through conventional synthesis routes. We show, particularly through in situ characterization, that the unprecedented acceleration associated with the nanoelectromechanical vibration in the form of surface reflected bulk waves (SRBWs) facilitates preferentially-oriented nucleation of the ferroelectric PVDF β-phase, while simultaneously aligning its dipoles to pole the material through the SRBW's intense native evanescent electric field . The resultant neat (additive-free) homopolymer film synthesized through this low voltage method, which requires only -orders-of-magnitude lower than energy-intensive conventional poling methods utilizing high kV electric potentials, is shown to possess a 76% higher macroscale piezoelectric charge coefficient d33, together with a similar improvement in its power generation output, when compared to gold-standard commercially-poled PVDF films of similar thicknesses.

Establishing Lightweight and Robust Prediction Models for Solar Power Forecasting Using Numerical–Categorical Radial Basis Function Deep Neural Networks

As green energy technology develops, so too grows research interest in topics such as solar power forecasting. The output of solar power generation is uncontrollable, which makes accurate prediction of output an important task in the management of power grids. Despite a plethora of theoretical models, most frameworks encounter problems in practice because they assume that received data is error-free, which is unlikely, as this type of data is gathered by outdoor sensors. We thus designed a robust solar power forecasting model and methodology based on the concept of ensembling, with three key design elements. First, as models established using the ensembling concept typically have high computational costs, we pruned the deep learning model architecture to reduce the size of the model. Second, the mediation model often used for pruning is not suitable for solar power forecasting problems, so we designed a numerical–categorical radial basis function deep neural network (NC-RBF-DNN) to replace the mediation model. Third, existing pruning methods can only establish one model at a time, but the ensembling concept involves the establishment of multiple sub-models simultaneously. We therefore designed a factor combination search algorithm, which can identify the most suitable factor combinations for the sub-models of ensemble models using very few experiments, thereby ensuring that we can establish the target ensemble model with the smallest architecture and minimal error. Experiments using a dataset from real-world solar power plants verified that the proposed method could be used to build an ensemble model of the target within ten attempts. Furthermore, despite considerable error in the model inputs (two inputs contained 10% error), the predicted NRMSE of our model is still over 10 times better than the recent model.

Transient analysis of megawatt-level space gas-cooled reactor coupled with He-Xe Brayton cycle system

The development of the aerospace industry and advancements in aerospace technology have created an urgent need for space power supply. A self-developed thermal hydraulic system analysis program is utilized to examine the transient response safety of gas-cooled reactors and investigate the performance of the coupled Brayton cycle overall system. The typical transient accident conditions were simulated, including positive reactivity insertion, turbine-alternator-compressor shaft speed drop, and loop coolant flow rate reduction. By considering cladding integrity as the safety criterion, it is determined that the reactor studied in this work has a safety margin of 0.152$ when introducing reactivity. The introduction of positive reactivity enhances reactor power output and heat generation. As more heat is carried away by He-Xe gas into the turbine, both operating temperature and heat transfer efficiency within the thermal cycle system are further improved. With an introduction of 0.13 % positive reactivity into the core, reactor thermal power can be stabilized at 110 % of its rated value while increasing system thermoelectric conversion efficiency by 2.7 %. During a continuous turbine-alternator-compressor shaft speed drop incident (from 100 % to 80 % rated speed), the total electrical power drops to 580 kWe along with reductions in temperature ratio and pressure ratio for both turbine and compressor. System cycle efficiency shows a positive correlation with turbine-alternator-compressor shaft speed within an appropriate range. In a sudden coolant flow rate drop incident, when the loop flow rate decreases to 60 % of its rated value, both reactor power and turbine-alternator-compressor shaft power decline significantly: the total electrical power drops from 1096 kWe to 216 kWe while the system’s cycle efficiency decreases from 32.1 % to 12.13 %. This work provides valuable insights into conceptual design considerations as well as safety assessment aspects pertaining to megawatt-class space.

Small-Sample Short-Term Photovoltaic Output Prediction Model Based on GRA-SSA-GNNM Method

The precision of photovoltaic (PV) output forecasting results is crucial to the reliability of the intelligent distribution network and multi-energy supplementary system. This work aims to address problems of insufficient research related to the short-term prediction of small-sample PV power generation and the low prediction accuracy in the previous research. A hybrid prediction model based on grey relation analysis (GRA) combined with the sparrow search algorithm (SSA) and the grey neural network model (GNNM) is proposed. In this paper, GRA is utilized to reduce the dimension of meteorological features of the samples. Then, the GNNM is used to perform regression analysis on the input features after reducing the dimension of meteorological features of the samples, and the parameters of the GNNM are optimized via SSA. A limited dataset was used to compare several models in different seasons and weather conditions. The prediction results agree well with the data from the PV power plant in Xinjiang, indicating that the GRA-SSA-GNNM model developed in this work effectively achieves a high precision estimation in short-term PV power generation output prediction and has a promising application in this field.

Analysis of Optical Window for Constant Cooling Heat Flux‐Based Spectral Splitting in Concentrator Photovoltaic System

The solar cells cooled to constant temperature at different concentration ratios (CR) and spectral bands (SB) require the same cooling heat flux (CHF), but the output power varies significantly. Thus, it is necessary to clarify the relationship of CHF‐CR‐SB to provide a reference for photovoltaic systems to select the output power generation of the spectral band under constant cooling heat flux. In this article, a selecting spectra model of a centralized photovoltaic (CPV) system is established and the selection of the spectrum based on CHF and the CR is analyzed. Theory shows that the wider the spectral division of the same CR, the larger the cooling heat flux consumed. The narrower the spectrum division, the higher the CR that the cell receiver can withstand. The experiments show that the higher the photoelectric conversion efficiency (PCE), the lower the cooling heat flux to be consumed. The cooling heat flux of 650 filter consumes 22.74% more than the UV700 filter, which means the demand for CHF is more severe when the heat spectrum distribution is wide. The spectral division should be carried out according to the requirements of high‐energy flux but a small thermal energy proportion to achieve efficient PCE.

Modeling and forecasting air conditioning loads using BP neural network

Abstract Power load forecasting plays an extremely important role in the safety and stability of the power system. Due to the immediate use of the power system, grid workers need to develop power generation plans based on load forecasting report data to maintain a balance between power generation output and load. Accurate prediction reports can assist power companies in planning and managing the production and distribution of electricity, further improving energy utilization efficiency and reducing costs, as well as improving the reliability and stability of the power system. Regarding the modeling and prediction of wide-area air conditioning loads, this article uses the method of regional decomposition to establish air conditioning prediction models for different cities. Finally, the prediction results of the models for different cities are added together to obtain the air conditioning load prediction results. The BP prediction model developed in this paper demonstrates relatively accurate results, offering a method for forecasting air conditioning load.

Transient Stability-Constrained Unit Commitment Using Input Convex Neural Network.

This article proposes a transient stability-constrained unit commitment (TSC-UC) model using input convex neural networks (ICNNs). An ICNN is trained to learn the transient function that maps prefault operation conditions (e.g., generator power output) to the transient stability index (TSI), which can be further utilized to identify the transient status (e.g., stable or unstable). Transient stability evaluation is conducted using the learned ICNN, without discretizing differential-algebraic equations (DAEs) or interacting with the time-domain simulation tools. Based on the convexity of ICNNs, the trained ICNN is exactly encoded as a linear programming (LP) model and integrated into conventional UC models to form a TSC-UC model. To impose transient stability constraints and expedite the solution process, the proposed TSC-UC model is decomposed into a UC master problem and two subproblems (i.e., network feasibility check subproblems and transient stability check subproblems). The decomposed problem is then iteratively solved using the Benders decomposition. Simulation tests are conducted in the New England 39-bus test system and IEEE 118-bus test system to verify the validity of the proposed approach.

Aligning the Western Balkans power sectors with the European Green Deal

Abstract Located in Southern Europe, the Drina River Basin is shared between Bosnia and Herzegovina, Montenegro, and Serbia. The power sectors of the three countries have an exceptionally high dependence on coal for power generation. In this paper, we analyse different development pathways for achieving climate neutrality in these countries and explore the potential of variable renewable energy (VRE) and its role in power sector decarbonization. We investigate whether hydro and non-hydro renewables can enable a net-zero transition by 2050 and how VRE might affect the hydropower cascade shared by the three countries. The Open-Source Energy Modelling System (OSeMOSYS) was used to develop a model representation of the countries’ power sectors. Findings show that the renewable potential of the countries is a significant 94.4 GW. This potential is 68% higher than previous assessments have shown. Under an Emission Limit scenario assuming net zero by 2050, 17% of this VRE potential is utilized to support the decarbonization of the power sectors. Additional findings show a limited impact of VRE technologies on total power generation output from the hydropower cascade. However, increased solar deployment shifts the operation of the cascade to increased short-term balancing, moving from baseload to more responsive power generation patterns. Prolonged use of thermal power plants is observed under scenarios assuming high wholesale electricity prices, leading to increased emissions. Results from scenarios with low cost of electricity trade suggest power sector developments that lead to decreased energy security.

Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences

Abstract. This study investigates how blade aerodynamic modifications, including leading edge roughness (LER), influence wind turbine performance over their operational lifespan. It introduces a methodology developed to examine the intricate relationship between blade erosion, blade enhancements, operations and maintenance (O&M) events, control programmable logic controller (PLC) parameter updates, and their cumulative impact on turbine efficiency. Analysing data from 12 multi-megawatt offshore turbines over a 12-year period, the investigation hinges on the integration of supervisory control and data acquisition (SCADA) data, O&M records, and air density corrections. A key contribution is the development of the turbine performance integral (TPI) method, which, for the investigated turbines, leverages generator speed and power output data to track performance trajectories. Seasonal trend decomposition using locally estimated scatterplot smoothing (STL) further isolates long-term trends and seasonal variations in performance. Despite data availability and quality limitations, the study reveals significant findings concerning the impact of manufacturer software updates on turbine control strategies, resulting in improved performance; the variable effects of blade repairs and enhancements; and the complex interaction between O&M events and performance. This work applies a methodical approach and statistical rigour, offering a path forward for effectively monitoring wind turbine efficiency and advancing renewable energy.

Light‐Assisted Polyproton Dissociated PAAm‐PA Hydrogel‐Based Moisture‐Driven Electricity Generator with a Broad Operating Range

AbstractDue to its widespread availability and spontaneity, moisture electricity generation (MEG) holds unique advantages in self‐powered systems. However, it faces challenges, including the limitations of relying on a single kind of power generation and insufficient output performance. Inspired by the mechanisms of water absorption of plants, this paper explores a light‐moisture coordinated electricity generating hydrogel (L‐MEGH) device with flexible, scalable, and highly efficient energy conversion performance, which is obtained through the UV polymerization of hydrophilic acrylamide (AAM) and phytic acid (PA) in the presence of photosensitizers. The obtained hydrogel demonstrates superior moisture absorption and remarkable electricity generation stability across a range of humidity conditions. Notably, the open‐circuit voltage (Voc) of the L‐MEGH increased from 0.675 to 0.838 V after the addition of photosensitizers (Erythrosin B, E) (the significant enhancement, up to 24%), and the short‐circuit current (Isc) reaching 635.543 µA. This L‐MEGH can realize stable electrical output even under extreme temperatures, producing 0.5 V at −20 °C for 45 h. The scalable L‐MEGHs (connected on‐demand in series/parallel) can power various commercial electronics, including nighttime illumination, mobile phones, and health monitoring sensors. This work pioneers a sustainable power generation pathway capable of enhancing performance through the hybrid collection of multiple natural energy sources.

A systematic literature review of optimal placement of fast charging station

Electric vehicles (EV) have increased in the last few decades due to their ability to reduce greenhouse gas emissions (GHG). Support for the electrification of the transportation sector has encouraged researchers to investigate the optimal placement of fast charging stations (FCS). In this study, we conducted a systematic literature review of 84 primary studies between 2019 and 2024 by identifying objective function and solution techniques, uncertainty, stakeholders, and network classification. We identified the objective functions most commonly used by authors related to technical and cost-solving problems using techniques: conventional (41.7%), metaheuristic (33.3%), hybrid (22.6%), and other (2.4%). Several researchers have also considered various uncertainty parameters from EV, FCS demand, and distributed generation (DG) power output with the most popular probabilistic method to solve problems. Furthermore, the role of stakeholders and network classification is also reviewed in this article. Our study contributes to the field by providing a comprehensive overview of the most significant journals and highlighting future research on the optimal placement of FCS. Future work must focus on improving parameters, models, methods, and using real data from various factors related to FCS demand.

Energy, environmental, economic, and social assessment of photovoltaic potential on expressway slopes: A case in Fujian Province, China

Expressway slopes within the boundary of expressway construction land have the potential to be transformed from conventional land use to valuable utilization through the installation of photovoltaic (PV) systems. Considering the continuous increase in expressway mileage, the PV potential could be enormous; therefore, a method to quantify such potential is urgently needed. This study proposes a potential assessment method for expressway slope photovoltaics. First, the suitable locations of expressway slopes for installing PV systems are identified on the basis of publicly available digital maps, combined with the principles of PV site selection and geographic information systems. Then, the PVsyst model is used to simulate the power generation efficiency and power output of the PV systems and to evaluate their PV potential, contribution, and economic benefits. The implementation of the proposed method in Fujian Province, China, indicates that the installed capacity of PV systems could reach 16.3 GW, with an annual power output of 17,940 GWh, contributing to the peak shaving of power grids and regional energy supply. The reductions in CO2, SOx, and NOx emissions are 16.65 Mt, 8970 t, and 3947 t per year, respectively. Considering carbon trading, the payback period is shortened to 11.7 years, with the return on investment of 65.38 % and the net present value of 52.6 billion RMB over a 25-year operational period. Additionally, this project could create 19.88 % of jobs in the energy sector in Fujian Province, providing 27,861 jobs during the construction phase and 18,248 jobs during the operation and maintenance phase. Thus, this project is economically viable and generates positive energy, environmental, and social benefits, contributing to the promotion of local energy transition and sustainable development.

Experimental evaluation and optimization of the heat pipes integrated thermoelectric generator using response surface methodology

In this study, integration of heat pipes with thermoelectric generators is proposed for the recovery of waste heat. Heat energy is transferred from a heat source to thermoelectric elements via heat pipes, enabling energy conversion. To dissipate excess heat from the system after conversion, additional heat pipes are positioned on the cold sides of thermoelectric elements. Cooling of the condenser sections of these heat pipes is facilitated using air flow provided by a fan. A novel approach in this study involves the innovative integration of heat pipes with thermoelectric generators. This integration provides a new pathway for optimizing waste heat recovery systems by combining advanced thermal management techniques with thermoelectric technology. Experiments conducted at different power inputs (12–42 W) and air velocities (0.4, 0.8, 1.2 m/s) determine the performance of the thermoelectric generator. Furthermore, a power usage efficiency analysis was conducted to discuss the applicability of the proposed method in electronics. At low air velocities, values below the ideal value of 1 were achieved. Experimental data were analyzed using response surface methodology to assess the impact of varying input parameters on the power output and efficiency of the thermoelectric generator. Optimization studies highlighted the importance of effectively cooling the cold side of thermoelectric elements. The highest power output and efficiency are achieved under conditions of 42 W power input, 1.2 m/s air velocity, and use of 4 heat sinks, yielding approximately 0.8 W and 2 %, respectively. Furthermore, the response surface methodology analysis indicated that the most significant effect on system outputs was the power input. Moreover, the results of this study lay the groundwork for future strategic advancements in thermoelectric generators, enhancing their role in sustainable energy solutions.

Body heat energy driven knitted thermoelectric garments with personal cooling

Wearable, thermoelectric (TE) devices have gained significant interest in recent years as they can both generate output power from converting body heat into electrical energy and be utilized in personal cooling through the Peltier effect. Currently, researched wearable TE cooling devices focus on devices that can be attached to the human body in specific locations and use heat sinks or elongated thermoelectric pillars to increase their effectiveness, making them hard and bulky. In addition, these devices are attached, not integrated, to wearable garments, making their manufacturing a multi-step process. To address this issue, we developed a knitted TE garment through a single-step manufacturing process that offers energy generation and personal cooling capabilities. The functioning of the cooling device was mathematically modelled and analyzed to understand its scalability, optimization, and to show its potential. This garment features excellent thermoelectric generation of 7.48 mV and 0.46 mA on the human body, and the ability to cool the human body up to 1.5 °C. The observations proved that the mathematical model could predict the behavior of the TE device closely. The results showed that the proposed method of knitted TE cooling garment manufacturing is a viable and scalable process.

Integrating dual heat sources to enhance thermoelectric generator power output

The escalating demand for sustainable power sources for wearable electronics necessitates innovative solutions. This study tackles the challenge of sustainably powering wearable electronics, addressing the limitations of traditional batteries. The approach utilizes a thermoelectric generator with a flexible and stretchable substrate that integrates the photothermal effect and human body temperature as a heat source. A unique strategy is employed to achieve this, involving the combination of 10 g of polydimethylsiloxane, 0.5 g of carbon nanotubes powder, 1 g of base curing agent, and 1.15 g of ethyl acetate mixture used as the novel substrate material. The proposed composite substrate is paired with p-type and n-type thermoelectric legs of bismuth antimony telluride (Bi0.4Sb1.6Te3) and bismuth selenium telluride (Bi1.7Te3.7Se0.3), respectively. This innovative design ensures high flexibility, stretchability, and conformability, facilitating seamless integration into wearable electronic devices. Experimental validation and simulation-based studies reveal a power density of 166.29 μW/cm2, sufficient for powering small-scale wearable electronics. The thermoelectric generator coupled with a novel composite substrate showed 65.6 % stretchability, further underscoring its durability and adaptability. The critical finding lies in the dual heat source integration, which collectively boosts power output and ensures year-round performance, pushing the boundaries of wearable energy harvesting technologies.

A Transient Damping Improvement Strategy for Enhancing Grid-Connected Active Power Response Performance of VSG

The given power and grid frequency disturbances can cause transient oscillations and steady-state deviations in the output power of a virtual synchronous generator (VSG), which can be effectively addressed by adding transient damping. However, this approach may result in significant power overshoot. This article proposes an improved VSG control strategy based on transient electromagnetic power feedback compensation and a small-signal model reduction scheme. Firstly, the grid-connected active closed-loop small-signal models of typical VSG control and transient damping VSG control are established, respectively. The transient oscillation suppression mechanism of active power is revealed through root locus and frequency response analyses, and the power overshoot characteristics of the two control strategies are analysed by combining them with the system of zero points. Secondly, the active transient feedback compensation method and the small-signal model reduction design method are introduced in detail. Finally, comparative analysis experiments are conducted using the Matlab/Simulink and hardware-in-the-loop experimental platform. It is verified that the proposed control strategy can suppress transient oscillations in active power, prevent steady-state deviations, and effectively mitigate the power overshoot problem of the system.