Output Power Variation Research Articles

The Investigation of Carrier Mobility Effect on the Performance Characteristics of the InGaN-Based Vertical Cavity Surface Emitting Laser (VCSEL) by Solving the Rate Equations

Background: Among the parameters that play an important role in describing the performance of many devices is carrier mobility which is a criterion for the easy movement in semiconductor crystals. Objective: The effect of carrier mobility on the performance characteristics of InGaN quantum well vertical-cavity surface-emitting laser was analytically investigated. Methods: By solving the Poisson’s equation, current density equation, charge concentration continuity equation and carrier and photon rate equations, the variation of current density and carrier density with respect to the position and time and the effects of carrier mobility and temperature on these parameters were investigated. Furthermore, the effect of mobility on the variation of output power versus the injection current and on the time variation of photon and carrier density and the output power was investigated. Results: By increasing the carrier mobility, the threshold current is reduced and the output power is increased. In studying the effect of temperature on the desired parameters, the variation of carrier density with respect to time and position was affected by the temperature change. This phenomenon is due to the dependence of these parameters on the diffusion coefficients and consequently on the mobility of the carriers and the dependence of mobility on temperature. Conclusions: The output power increased, and the time delay in accruing the laser decreased. Consequently, the carrier recombination increased, further resulting in a rapid laser operation.

Machine Learning for Wind Energy Analysis and Forecasting

Abstract: Better prediction designs for the contributes directly of renewables are essential to reducing the requirement for controlling the energy associated with traditional power plants. For the extremely variable wind power output to be successfully integrated into the power system, a reliable forecast is especially crucial. For a wind inclusion study in the western United States, we use data from the National Renewable Energy Laboratory (NREL) and focus on summary wind power projection in this chapter. Our approach immediately derives functional connections from data, in contrast to physical procedures that depend on exceedingly complex differential equations. By reprising the regression model as a regression issue, we explore several regression strategies including regression models, k-nearest peers, and support vector regression. In our experiments, we look at forecasts for both individual wind turbines and entire wind parks, proving the viability of a machine learning technique for short-term wind power projection

Itô-theory-based multi-time scale dispatch approach for cascade hydropower-photovoltaic complementary system

The complementary operation of cascade hydropower (CHP) and photovoltaic (PV) can increase the integration of PV power and has become a trend in modern power systems. However, the randomness and variability of PV power output might cause challenges in precisely tracking the submitted generation plan of the CHP-PV complementary system. The safety and stability of the receiving-end power grid might deteriorate. In this paper, a multi-time scale dispatch framework with day-ahead and intra-day dispatch is established for the CHP-PV complementary system to address the PV uncertainties. The day-ahead dispatch is employed to provide reliable reference results and sufficient adjustment reservations for the intra-day dispatch. An Itô-theory-based stochastic optimization (ITB-SO) approach is proposed for the intra-day dispatch by modeling the PV prediction error with stochastic differential equations (SDE). The SDE model can be embedded into the ITB-SO approach without scenario generation, and thus computational burden can be significantly reduced compared with scenario-based methods. A complementary system in southwest China is chosen as a detailed case study. The simulation results demonstrate that real-time random PV fluctuations could be compensated by adjusting CHPs. Meanwhile, the target water level of cascade reservoirs can be tracked under the proposed approach.

Utilizing a Tunable Delay Line Interferometer to Improve the Sensing Accuracy of an FBG Sensor System

This paper proposes a novel sensing system based on a tunable delay line interferometer. The tunable delay line interferometer has been used to interpret strain, bringing us high accuracy as well as tunability. The shifted wavelength of the fiber Bragg grating (FBG) sensor caused by the applied strain can be visualized by an optical power meter (OPM) instead of an optical spectrum analyzer (OSA) by converting it to a power change using a tunable delay line interferometer (TDI). Different free spectral ranges (FSRs) are assigned to the TDI to investigate the accuracy and operation range of the proposed system. Thus, we achieve high accuracy and sensitivity by adjusting the FSR to 0.47 nm. Experimental results show that the maximum output power variation corresponding to a strain of 10 με is about 0.9 dB when the FSR is set to 0.47 nm. The proposed system is also cost-effective regarding the equipment utilized for interrogation: a tunable delay line interferometer and an optical power meter.

Short term wind speed forecasting using time series techniques

ABSTRACT Wind power is a renewable energy source that can be used in place of conventional fossil-fuel-based power. Although the integration of wind power has many benefits, the conventional electrical system requires a constant supply, i.e. the power supply ought to be equivalent to the power demand consistently. It’s tough to keep this equilibrium because of the variation of the wind power output. Improving wind speed predictions is one of the solutions to the balance problem. This paper centers around short-term wind speed forecasting using time series methods. A time series is a logically ordered succession of numerical data points that can be used to study any variable that changes over time. This paper applies a variety of time series forecasting techniques – Exponential Smoothing, ARIMA, LSTM, and a novel hybrid LSTM-ARIMA model – to three different time periods of hourly measured wind speed data. The performance of the models is compared using metrics such as MSE (Mean Squared Error), RMSE (Root Mean Squared Error), NSE (Nash – Sutcliffe model efficiency coefficient), MAE (Mean Absolute Error), and MAPE (Mean Absolute Percentage Error). The proposed LSTM-ARIMA model has the highest prediction accuracy and achieves the least error metrics at all time scales. It outperforms other architectures, achieving a MAPE of 24.78% for the 12-day scale, 9.30% for the 2-day scale, and 12.80% for the 1-day scale.

Dynamic modeling and coupling characteristics analysis of biomass power plant integrated with carbon capture process

Biomass direct-fired circulating fluidized bed boiler has distinguished advantages of strong fuel adaptability, high combustion efficiency and low NOX emission, thus has been regarded as one of the important pathways for biomass utilization. Retrofitting the biomass direct-fired circulating fluidized bed boiler power plant (BCFBP) with carbon capture can strongly support the goal of carbon neutrality, because negative CO2 emission is achieved during the power generation. Nevertheless, the biomass direct-fired circulating fluidized bed boiler is sensitive to the combustion condition, high in operating costs, slow in dynamic response and poor in regulating capability. The integration of carbon capture may pose significant changes on the operating safety, efficiency and flexibility. A comprehensive analysis is thus required to understand the interactions between the BCFBP and carbon capture under various operating conditions. To this end, this paper develops a plant-wide dynamic model for a 130 ton/h BCFBP integrated with solvent-based post-combustion carbon capture (PCC) system. Multi-stage models of biomass combustion, steam generation and steam turbine-feedwater heater systems are developed to represent the dynamic interactions among biomass, heat, electricity and carbon, so that the impacts of BCFBP operation on the PCC process can be evaluated. Detailed BCFBP-PCC connecting system model are also developed to reflect the dynamic changes of steam/water flowrate, pressure and temperature in the steam turbine system caused by the integration of PCC. The developed model is then used to analyze the system interactions, operating costs, negative carbon capability and load ramping flexibility of the integrated BCFBP-PCC. Results show that the integration of PCC 1) causes significant impacts on the water/steam parameters of the steam turbine-feedwater heaters behind the reboiler steam extraction point; 2) reduces the power output of BCFBP by 26.12 % under 95 % CO2 capture level condition; 3) expands the power output variation range of the BCFBP from 22.3 to 36.6 MWe and provides the negative CO2 emission capacity from 2.2 to 6.9 kg CO2/s; 4) offers a feasible way to improve the power ramping performance through flexible distribution of the water/steam in the steam turbine-feedwater heater system. This paper provides specific guidance for the design, retrofit and operation of the BCFBP-PCC plant.

The effect of exercising in different environments on heart rate and power output among older adults-a randomized crossover study.

A growing body of evidence suggests that exposure to nature is beneficial for human health. However, the observed health effect of nature may be mediated by physical activity and that humans are physically active at a higher intensity outdoors compared to when they are physical active indoors. This study examines the variation of heart rate and power output for a fixed rating of perceived exertion in a group of healthy older adults in three different environments representing three levels of exposure to nature. To this randomized, 3-by-3 crossover design study, healthy older adults (≥65 years) were recruited from local gyms. All participants participated in three experimental conditions; indoors, simulated outdoors and outdoor environments, in a randomized order. The participants exercised for 20 minutes at an intensity equivalent to a rating of 11-13 on the Borg scale for perceived exertion (RPE). Measurements of heart rate, power output (Watt) and ratings of perceived exertion were taken at minutes 1 to 6 and at minute 20. To examine the effect of the environment on heart rate and power, linear mixed models were used. In all, 48 participants (56% females) were included in the analysis. No significant main effects on the outcomes were observed for power output (p = 0.073, η2 = 0.04) or heart rate (p = 0.067, η2 = 0.04). No significant effect on the outcomes was observed. However, borderline significant outcomes for power output or heart rate outdoors in nature, along with previous studies in the field, indicates that such an effect cannot be completely ruled out, but any effect is likely to be small. Future research examining health benefits of the independent exposure to nature are encouraged to adjust for the dose of physical activity. ID: ISRCTN22230544.

Physiological Responses and Performance During a 3-Minute Cycle Time Trial: Standard Paced Versus All-Out Paced.

To compare performance and physiological responses between a standard-paced 3-minute time trial (TTSP, ie,pacing based on normal intention) and a consistently all-out-paced 3-minute time trial (TTAOP). Sixteen well-trained male cyclists completed the TTSP and TTAOP, on separate days of testing, on a cycling ergometer with power output and respiratory variables measured. Time trials were preceded by 7 × 4-minute submaximal stages of increasing intensity with the linear relationship between power output and metabolic rate used to estimate the contribution from aerobic and anaerobic energy resources. The time course of anaerobic and aerobic contributions to power output was analyzed using statistical parametric mapping. Mean power output was not different between the 2 pacing strategies (TTSP = 417 [43] W, TTAOP = 423 [41] W; P = 0.158). TTAOP resulted in higher peak power output (P < .001), mean ventilation rate (P < .001), mean heart rate (P = .044), peak accumulated anaerobically attributable work (P = .026), post-time-trial blood lactate concentration (P = .035), and rating of perceived exertion (P = .036). Statistical parametric mapping revealed a higher anaerobic contribution to power output during the first ∼30 seconds and a lower contribution between ∼90 and 170 seconds for TTAOP than TTSP. The aerobic contribution to power output was higher between ∼55 and 75 seconds for TTAOP. Although there was no significant difference in performance (ie,mean power output) between the 2 pacing strategies, differences were found in the distribution of anaerobically and aerobically attributable power output. This implies that athletes can pace a 3-minute maximal effort very differently but achieve the same result.

A novel cost‐effective voltage fluctuations management in a droop‐controlled islanded microgrid

This paper presents a new energy management system (EMS) for an islanded microgrid (MG) to increase power system security cost-effectively. The small size of MGs, variations in renewable energy sources (RESs) output power, and low inertia of power electronic interfaced distributed energy resources (DERs) convert voltage fluctuations into a vital issue for the MG EMS. This paper proposes a two-stage stochastic multiobjective framework to simultaneously attain economic, environmental, and technical aims. In the first stage, many scenarios related to RESs output power variations, load demand changes, and DERs contingency outages are generated using Monte Carlo simulation. Afterwards, generated scenarios are reduced and applied to the second stage. A novel objective function based on the expected voltage fluctuations (EVFs) caused by active and reactive power variations is proposed, which is minimized alongside the active and reactive costs and MG emission. The proposed model is applied to a test MG, and active and reactive power and primary and secondary reserve scheduling are correctly accomplished for five different cases. Precise analysis confirms the notability and effectiveness of MG voltage fluctuations management in energy and reserve scheduling. Numerical results show more than 60% reduction in voltage fluctuations.

A low‐carbon economic dispatch model for electricity market with wind power based on improved ant‐lion optimisation algorithm

AbstractIntroducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value. However, the randomness of wind power generation puts forward higher requirements for electricity market transactions. Therefore, the carbon trading market is introduced into the wind power market, and a new form of low‐carbon economic dispatch model is developed. First, the economic dispatch goal of wind power is be considered. It is projected to save money and reduce the cost of power generation for the system. The model includes risk operating costs to account for the impact of wind power output variability on the system, as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment. The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions, and analyze the impact of different carbon trading prices on the system. A low‐carbon economic dispatch model for the wind power market is implemented based on the following two goals. Finally, the solution is optimised using the Ant‐lion optimisation method, which combines Levi's flight mechanism and golden sine. The proposed model and algorithm's rationality is proven through the use of cases.

Adaptive control strategy of fuel cell voltage stabilization for long-term operation

ABSTRACT An adaptive control strategy for fuel cell stabilization is proposed to alleviate the coupling effects of power output variation and performance degradation in the whole service life. An improved semi-empirical model is firstly developed to describe and analyze the direct methanol fuel cell (DMFC) performance under different operating conditions while considering degradations. Multi-objective optimizations are then utilized for the adaptive generation of control strategy to stabilize the voltage. Experiments are implemented to validate the effectiveness of the adaptive control strategy at different time periods during long-term operation. The results show that the improved semi-empirical model can provide a dynamic description of DMFC I-V relationships with the time. The adaptive control strategy developed from this model is effective for voltage stabilization that the deviation could be constrained in ±10% at different power outputs during long-term operation.

Optimal Microgrid System Operating Strategy Considering Variable Wind Power Outputs and the Cooperative Game among Subsystem Operators

In recent years, the microgrid system (MGS) has become an important method for the consumption of renewable energy sources (RES). In the actual operation process, the uncertainties of RES add to the complexity of the MGS operation. Furthermore, the MGS is often operated by multiple subsystem operators. The benefit distribution among subsystem operators is an important factor affecting the overall stable operation of the MGS. In order to resist the interference of the above factors, a two-stage optimization method is proposed in this paper, which includes a bi-level robust optimization (BRO) model in the overall scheduling stage of the MGS and a nucleolus-based cooperative game (NCG) model in the internal cost allocation stage among the subsystem operators. The simulations demonstrated the following outcomes: (1) the P2G device can reduce the operating cost of the MGS by converting electricity into natural gas when the electricity price is low; (2) the two-stage optimization method can ensure the stable operation of the MGS by resisting the disturbance of uncertain wind power outputs in the overall scheduling stage and realizing a reasonable cost allocation among the subsystem operators in the internal cost allocation stage.

Transient performance prediction of solar dish concentrator integrated with stirling and TEG for small scale irrigation system: A case of Ethiopia

The global population growth, climate change effects, and the rapid decline in the stock of fossil fuels increase the demand for food, water, and energy. Irrigation technologies are essential in negating poverty and food insecurity in a developing country while promoting sustainable development goals. However, rain-fed agricultural activities and the lack of modern irrigation technologies in Ethiopia aggravate the problem. This work presents a transient performance investigation of a solar dish concentrator coupled with a Stirling engine and thermoelectric generator for the small-scale irrigation system. A solar dish concentrator with a 2.8 m aperture diameter and 0.4 m depth was used, and Stirling engine analysis was performed using a second-order adiabatic model. System performance was investigated at different operating parameters to predict output power and pump flow rate variation with solar time for a selected irrigation season. Results show that at a heat source temperature of 413.8 K, the thermoelectric unit gives maximum electrical power of 5.2 W at an efficiency of 2.78%. At the same time, the Stirling engine-driven pump provides a cumulative flow rate of 173,594.95 L per day at a thermal efficiency of 18.61%. The output power and pump flow rate reach their maximum at noontime for all selected irrigation seasons. The effect of regenerator effectiveness on the thermal efficiency of the Stirling engine was also examined, and the findings indicate that the Stirling engine's thermal efficiency rises with regenerator effectiveness. Using a solar thermal irrigation system in a location with a high solar radiation potential allows small farmers to generate more income and contribute to the country's food security.

Effects of Carbohydrate and Sodium Chloride Mouth Rinses on Repeated Sprint Performance

Aim: The purpose of this research is to examine the effects of oral rinsing of CHO and NaCl on repeated sprint performance in trained athletes.&#x0D; Methods: Fifteen trained athletes (5 women; 10 men) voluntarily participated in the repeated, single-blind, placebo-controlled and crossover design study. Athletes came to the laboratory with a night fasting four times with an interval of at least 48 hours and participated in the repeated sprint test (10 sec × 6, 40 sec intervals) after 30 minutes of endurance exercise (70% maxVO2). At the 0th, 10th, 20th and 30th minutes of the endurance exercise, it was requested to MR with CHO (6.4% maltodextrin), sodium chloride (6.4%) solution and water (placebo) or no rinsing (control).&#x0D; Results: As a result of the analyzes performed with 3 × 4 ANOVA, the power output variables obtained by repeated sprint performance [peak power, average power, minimum power (W, W/kg) and fatigue index (%)] and fatigue variables (heart rate, blood lactate level and rate of perceived exertion) between sessions were not found to be significantly different.&#x0D; Conclusion From the obtained results, it may be concluded that the method and stimuli used in this study seem insufficient to affect the outcome variables of physical performance.

부하에 의한 출력 변화가 둔감한 6.78 MHz Class E 전력증폭기 설계

A power amplifier structure is presented that has a low output power variation against the load impedance variation at 6.78 MHz using two Class E power amplifiers and a λ/4 transmission line. A Class E amplifier with a low output impedance and another Class E amplifier with a high output impedance are connected in parallel with a phase difference of 90° using a λ/4 transmission line. Each amplifier generates the same output power under nominal load conditions to achieve the maximum efficiency. The output power variation can be reduced during operation, which increases the output power from one amplifier and decreases the output power from another at the same time when the load impedance is changed from the nominal value. Therefore, the efficiency can be maintained better against the load impedance variation compared with the conventional balanced structure. The experiments show that the proposed structure has an output power variation of 16.18 %–18.4 6% less than that of the balanced structure. For the high-power mode using both power amplifiers, an output power of 13.54–21.53 W and an efficiency of 75.41 %–80.16 % were obtained. An output power of 4.67–8.76 W and efficiency of 77.89 %–82.48 % were obtained in the low-power mode, in which only one power amplifier operates.

Variability of wave power production of the M4 machine at two energetic open ocean locations: Off Albany, Western Australia and at EMEC, Orkney, UK

Since intermittent and highly variable power supply is undesirable, quantifying power yield fluctuations of wave energy converters (WECs) aids with assessment of potential deployment sites. This paper presents analysis of 3-hourly, monthly, seasonal, and inter-annual variability of power output of the M4 WEC. We compare expected performance from deployment at two wave energy hotspots: off Albany on the south-western coast of Australia and off the European Marine Energy Centre (EMEC) at Orkney, UK. We use multi-decadal wave hindcast data to predict the power that would have been generated by M4 WEC machines. The M4 machine, as a floating articulated device which extracts energy from flexing motion about a hinge, is sized according to a characteristic wavelength of the local wave climate. Using probability distributions, production duration curves, and coefficients of variation we demonstrate larger variability of the 3-hourly power yield at Orkney compared to Albany. At longer timescales, seasonal trends are highlighted through average monthly power values. From a continuity of supply perspective, we investigate occurrences of low production at three different threshold levels and calculate duration and likelihood of such events. Orkney is found to suffer from more persistent lows, causing a more intermittent power output. We also consider the effect of machine size on its power performance. Smaller machines are found to more effectively smooth out the stochastic nature of the underlying wave resource.

Output power smoothing of wind power plants using unified inter-phase power controller equipped with super-capacitor

Due to the growing concerns regarding environmental and economic issues in power systems, nowadays the application of renewable energy sources (RES), especially wind energy has been increased. However, the increasing penetration of wind power plants (WPPs) into the grid has raised numerous challenges including output power fluctuations as well as the significant role these sources play in low-voltage ride-through (LVRT) in case of short circuit fault conditions. Energy storage systems (ESSs) are commonly employed to enhance WPP output power smoothing. On the other side, flexible AC transmission systems (FACTS) can be utilized to enhance dynamic transients of the WPPs under short circuit contingencies. This paper presents the utilization of unified inter-phase power controller (UIPC) and the super-capacitor to fulfill the aforementioned challenges in connection of WPPs to the grid. To achieve this, a novel power control scheme is proposed to compensate for output power variations. The WPP model studied here is of doubly-fed induction generator (DFIG) type wind turbine. Simulations and comparisons are performed in the MATLAB/Simulink® environment to demonstrate the effectiveness of the proposed scheme in smoothening WPP output power as well as enhancing their LVRT capability.

Electrical Characteristics of Photovoltaic Cell in Solar-Powered Aircraft During Cruise

Aiming to study the electrical characteristics of photovoltaic cells during the flight of solar-powered unmanned aerial vehicles, this work combines a photovoltaic cell equivalent circuit model and a thermodynamic model. The influence of wing surface temperature and its influencing factor-solar radiation is of primary concern. A solar radiation model is established to explore the impact of solar irradiance on temperature and photovoltaic cell output. Atmospheric temperature and four basic parameters of photovoltaic cell, including open-circuit voltage, short-circuit current, voltage, and current at maximum power point under standard conditions are treated as input parameters. The surface temperature, the variation of output voltage, current, and power are studied with the altitude changing from 0 to 35 km and time from 0 to 24 h in spring equinoxes. Results find that with the increase in altitude, the surface temperature of the photovoltaic cell decreases first and then increases. The voltage of the photovoltaic cell decreases as the temperature increases, and the voltage-time curve varies at altitudes below 25 km and above 30 km. The peak power is available at an altitude between 15 and 20 km. The above findings can be applied to study energy generations and flows of solar-powered vehicles.

Wind Energy Analysis and Forecast using Machine Learning

Better prediction tools for future solar and wind power are crucial to reducing the requirement for controlling energy associated with the conventional power facilities. For optimal power grid integrating of highly variable wind power output, a strong forecast is extremely crucial. In this part, we concentration on wind power for the near run projections and conduct a wind unification study in the western United States using data from the National Research Conducted by the university (NREL). Our approach derives functional connections directly from data, unlike physical systems that rely on exceedingly difficult differential calculus. By recasting the prediction problem as a regression problem, we investigate several regression methodologies such as regression models, knearest strangers, and regression algorithms. In our testing, we look at projections for specific machines along with power from the wind parks, proving that a classification algorithm for predicting short-term electricity generation is feasible.

General Nash bargaining based direct P2P energy trading among prosumers under multiple uncertainties

This paper presents a transaction scheme for the direct peer-to-peer (P2P) energy trading problem among prosumers under uncertainties. Prosumers, in the distribution network, coordinate with neighbors to share the idle energy and maximize the cooperative benefit. Most of the existing researches focus on mechanism design with the assumption of accurate forecast. Nevertheless, the P2P energy trading problem is challenging due to multiple uncertainties, caused by the market price, intermittent power output and load demand variation. Although stochastic or robust optimization approaches are introduced and investigated to manage uncertainties, there are limitations for their application, such as large scenario sizes in the stochastic programming, coordination of the conservativeness and economy in the robust optimization. To address these challenges, a hybrid stochastic/robust optimization model is adopted to manage uncertainties which makes trade-offs between computational time and conservatism. The uncertain price is modeled via various scenarios based on forecast values while a robust optimization is developed to take into account the uncertainties of renewable generation and load. Based on the above uncertainties description, the direct P2P energy trading among prosumers is formulated as a general Nash bargaining (GNB) problem which is decomposed into two subproblem: operation cost minimization problem (P1) and bargaining problem (P2). As P1 and P2 are nonconvex problem, linearization techniques including KKT conditions and logarithmic transformation are employed to transform them as mixed-integer linear programming problem. A linear distflow model is formulated to address an OPF problem considering voltage limits of an AC network in a radial structure. Furthermore, a graph theoretic method is proposed to construct the incident matrix about buses and branches in the network constraints which considerably enhances the solution efficiency. Numerical case studies demonstrate that the constructed model can effectively incentivize direct P2P energy trading among prosumers, and multiple uncertainties affect the operation costs and energy transaction decisions. Additionally, the efficiency of the graph theory based modeling approach is proved for solving the energy trading model with OPF constraints.