There is a growing need to explore the potential of coal-fired power plants(CFPPs)to enhance the utilization rate of wind power(wind)and photovoltaic power(PV)in the green energy field.This study developed a load regu...There is a growing need to explore the potential of coal-fired power plants(CFPPs)to enhance the utilization rate of wind power(wind)and photovoltaic power(PV)in the green energy field.This study developed a load regulation model for a multi-power generation system comprising wind,PV,and coal energy storage using realworld data.The power supply process was divided into eight fundamental load regulation scenarios,elucidating the influence of each scenario on load regulation.Within the framework of the multi-power generation system with the wind(50 MW)and PV(50 MW)alongside a CFPP(330 MW),a lithium-iron phosphate energy storage system(LIPBESS)was integrated to improve the system’s load regulation flexibility.The energy storage operation strategy was formulated based on the charging and discharging priority of the LIPBESS for each basic scenario and the charging and discharging load calculation method of LIPBESS auxiliary regulation.Through optimization using the particle swarm algorithm,the optimal capacity of LIPBESS was determined to be within the 5.24-4.88 MWh range.From an economic perspective,the LIPBESS operating with CFPP as the regulating power source was 49.1% lower in capacity compared to the renewable energy-based storage mode.展开更多
The application of PV faades emerges greatly in recent years and however its calculation methods and analysis remains insufficient under the weather conditions of China. In such demand,this paper investigates PV fa...The application of PV faades emerges greatly in recent years and however its calculation methods and analysis remains insufficient under the weather conditions of China. In such demand,this paper investigates PV faade in terms of PV electricity generation in different arrangements and weather conditions of four major cities in China. The calculation models for PV faade are developed and validated by comparing the results with the measured data from the field experiments. A parametric study is carried out to provide a reference for the optimal design of the PV faades. The results show that with various cities,building orientations,building forms,materials and arrangements of PV modules,there is a distinct difference in the electrical output energy of PV faades. Weather conditions play a very important role in terms of PV generation performance of PV faades.展开更多
As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly crucial.The current state of research in renewable generation and power forecasting technology,such as w...As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly crucial.The current state of research in renewable generation and power forecasting technology,such as wind and photovoltaic power(PV),is described in this paper,with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting.The methods for forecasting wind power and PV production.The physical model,statistical learningmethod,andmachine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production.Moreover,the experiments demonstrated that cloud map identification has a significant impact on PV generation.With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes,this paper summarizes the classification of wind power and PV generation systems,as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.展开更多
The complexity of distribution network model mainly depends on the model scale of grid-connected distributed photovoltaic (PV) power generation. Therefore, the simulation performance of multi-scale PV model is the key...The complexity of distribution network model mainly depends on the model scale of grid-connected distributed photovoltaic (PV) power generation. Therefore, the simulation performance of multi-scale PV model is the key factor of the simulation accuracy in the specific operating scenarios of distribution network. In this paper, a multi-scale model of grid connected PV distributed generation system is proposed based on the mathematical model of grid-connected distributed PV power generation. It is analyzed that differences of simulation performance, such as adaptability of simulation step size, accuracy of output and the effect on voltage profile of distribution network, between PV models with different scales in IEEE 33 node example. Simulation results indicate that the multi-scale model is effective in improving the accuracy and efficiency of simulation under different operating conditions of distribution network.展开更多
The rapid spreading of the Photovoltaic (PV) Systems as Distributed Generation (DG) in medium and low voltage networks created many effects and changes on the existing power system networks. In this work, two methods ...The rapid spreading of the Photovoltaic (PV) Systems as Distributed Generation (DG) in medium and low voltage networks created many effects and changes on the existing power system networks. In this work, two methods have been used and applied to determine the optimal allocation and sizing of the PV to be installed as DGs (ranging from 250 kW up to 3 MW). The first one is to determine the location according to the maximal power losses reduction over the feeder. The second one is by using the Harmony Search Algorithm which is claimed to be a powerful technique for optimal allocation of PV systems. The results of the two techniques were compared and found to be nearly closed. Furthermore, investigation on the effects on the feeder in terms of voltage levels, power factor readings, and short circuit current levels has been done. All calculations and simulations are conducted by using the MATLAB Simulation Program. Some field calculations and observations have been expended in order to substantiate the research findings and validation.展开更多
Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal p...Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.展开更多
To attain the goal of carbon peaking and carbon neutralization,the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy.However,this approach is hindered...To attain the goal of carbon peaking and carbon neutralization,the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy.However,this approach is hindered by the lack of training data for predicting new grid-connected PV power stations.To overcome this problem,this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations.The proposed model integrates a structure model,heat-dissipation conditions,and the loss coefficients of PV modules.Clear-Sky entropy,characterizes seasonal and weather data features,describes the main meteorological characteristics at the PV power station.Taking gate recurrent unit neural networks as the framework,the open and shared PV-power data as the source-domain training label,and a small quantity of power data from a new grid-connected PV power station as the target-domain training label,the neural network hidden layer is shared between the target domain and the source domain.The fully connected layer is established in the target domain,and the regularization constraint is introduced to fine-tune and suppress the overfitting in feature transfer.The prediction of PV power is completed by using the actual power data of PV power stations.The average measures of the normalized root mean square error(NRMSE),the normalized mean absolute percentage error(NMAPE),and the normalized maximum absolute percentage error(NLAE)for the model decrease by 15%,12%,and 35%,respectively,which reflects a much greater adaptability than is possible with other methods.These results show that the proposed method is highly generalizable to different types of PV devices and operating environments that offer insufficient training data.展开更多
In recent years, environmental problems are becoming serious and renewable energy has attracted attention as their solutions. However, the electricity generation using the renewable energy has a demerit that the outpu...In recent years, environmental problems are becoming serious and renewable energy has attracted attention as their solutions. However, the electricity generation using the renewable energy has a demerit that the output becomes unstable because of intermittent characteristics, such as variations of wind speed or solar radiation intensity. Frequency fluctuations due to the installation of large scale wind farm (WF) and photovoltaics (PV) into the power system is a major concern. In order to solve the problem, this paper proposes two control methods using High Voltage Direct Current (HVDC) interconnection line to suppress the frequency fluctuations due to large scale of WF and PV. Comparative analysis between these two control methods is presented in this paper. One proposed method is a frequency control using a notch filter, and the other is using a deadband. Validity of the proposed methods is verified through simulation analyses, which is performed on a multi-machine power system model.展开更多
In an active distribution grid,renewable energy sources(RESs)such as photovoltaic(PV)and energy storage systems(e.g.,superconducting magnetic energy storage(SMES))can be combined with consumers to compose a microgrid(...In an active distribution grid,renewable energy sources(RESs)such as photovoltaic(PV)and energy storage systems(e.g.,superconducting magnetic energy storage(SMES))can be combined with consumers to compose a microgrid(MG).The high penetration of PV causes high fluctuations of tie-line power flow and highly affects power system operations.This can lead to several technical problems such as voltage fluctuations and excessive power losses.In this paper,a fuzzy logic control based SMES method(FSM)and an optimized fuzzy logic control based SMES method(OFSM)are proposed for minimizing the tie-line power flow.Consequently,the fluctuations and transmission power losses are decreased.In FSM,SMES is used with a robust fuzzy logic controller(FLC)for controlling the tie-line power flow.An optimization model is employed in OFSM to simultaneously optimize the input parameters of the FLC and the reactive power of the voltage source converter(VSC)of SMES.The objective function of minimizing the tieline power flow is incorporated into the optimization model.Particle swarm optimization(PSO)algorithm is utilized to solve the optimization problem while the constraints of the utility power grid,VSC,and SMES are considered.The simulation results demonstrate the effectiveness and robustness of the proposed methods.展开更多
In China, systemic techno-economic analysis for solar tracker has been absent. To fill the blank, by taking the economic analysis of solar tracker application as the research object and using the LCOE method widely us...In China, systemic techno-economic analysis for solar tracker has been absent. To fill the blank, by taking the economic analysis of solar tracker application as the research object and using the LCOE method widely used internationally, the techno-economic analysis model of solar tracker was established according to conditions in China. Influence factors on LCOE were analyzed by using the established model, and the relationship between each cost factor and the cost component of energy leveling of tracker was further studied. In addition, the calculation method of investment payback period based on energy leveling analysis was established, and the influence of various factors on investment payback period was revealed through an example calculation. The research results will help to measure the economy of tracker application more accurately and comprehensively, and promote the popularization and application of solar tracker. The economic analysis model of solar tracker application was established by using LCOE method. The influence factors and cost component of LCOE were analyzed with the model. The payback period of solar tracker investment was also analyzed based on LCOE method.展开更多
The power output of the photovoltaic(PV) system having multiple arrays gets reduced to a great extent when it is partially shaded due to environmental hindrances. The maximum power trackers which are conventionally us...The power output of the photovoltaic(PV) system having multiple arrays gets reduced to a great extent when it is partially shaded due to environmental hindrances. The maximum power trackers which are conventionally used may not be competent enough to find the maximum power point(MPP) during partially shaded conditions. The sensible reason for the failure of conventional trackers is during partial shaded conditions the PV arrays exhibit multi peak power curves, thereby making simple maximum power point tracking(MPPT) algorithms like perturb and observe(P&O) to get stuck with local maxima instead of capturing global maxima.Therefore, global search MPPT aided by evolutionary and swarm intelligence algorithms will be conducive to find global power point during partially shaded conditions. This work suggests a unified controller which feeds control signal to its power electronic conditioner placed at each module. The evolutionary algorithm which is taken into consideration in this work is differential evolution(DE).The performance of the proposed method is compared to the classical un-dimensional search controller and it is evident from the Matlab/Simulink results that the unified controller prevails over the distributed counterpart.展开更多
基金supported by the Natural Science Foundation of China(Grant Nos.52076079,52206010)Natural Science Foundation of Hebei Province,China(Grant No.E2020502013)the Fundamental Research Funds for the Central Universities(2021MS076,2021MS079).
文摘There is a growing need to explore the potential of coal-fired power plants(CFPPs)to enhance the utilization rate of wind power(wind)and photovoltaic power(PV)in the green energy field.This study developed a load regulation model for a multi-power generation system comprising wind,PV,and coal energy storage using realworld data.The power supply process was divided into eight fundamental load regulation scenarios,elucidating the influence of each scenario on load regulation.Within the framework of the multi-power generation system with the wind(50 MW)and PV(50 MW)alongside a CFPP(330 MW),a lithium-iron phosphate energy storage system(LIPBESS)was integrated to improve the system’s load regulation flexibility.The energy storage operation strategy was formulated based on the charging and discharging priority of the LIPBESS for each basic scenario and the charging and discharging load calculation method of LIPBESS auxiliary regulation.Through optimization using the particle swarm algorithm,the optimal capacity of LIPBESS was determined to be within the 5.24-4.88 MWh range.From an economic perspective,the LIPBESS operating with CFPP as the regulating power source was 49.1% lower in capacity compared to the renewable energy-based storage mode.
基金Sponsored by the National Natural Science Foundation of China (Grant No.51008136)the Graduates’ Innovation and Enterprise Fund of HUST (Grant No.HF-11-12-2013)
文摘The application of PV faades emerges greatly in recent years and however its calculation methods and analysis remains insufficient under the weather conditions of China. In such demand,this paper investigates PV faade in terms of PV electricity generation in different arrangements and weather conditions of four major cities in China. The calculation models for PV faade are developed and validated by comparing the results with the measured data from the field experiments. A parametric study is carried out to provide a reference for the optimal design of the PV faades. The results show that with various cities,building orientations,building forms,materials and arrangements of PV modules,there is a distinct difference in the electrical output energy of PV faades. Weather conditions play a very important role in terms of PV generation performance of PV faades.
基金This project is supported by the National Natural Science Foundation of China(NSFC)(Nos.61806087,61902158).
文摘As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly crucial.The current state of research in renewable generation and power forecasting technology,such as wind and photovoltaic power(PV),is described in this paper,with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting.The methods for forecasting wind power and PV production.The physical model,statistical learningmethod,andmachine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production.Moreover,the experiments demonstrated that cloud map identification has a significant impact on PV generation.With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes,this paper summarizes the classification of wind power and PV generation systems,as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.
文摘The complexity of distribution network model mainly depends on the model scale of grid-connected distributed photovoltaic (PV) power generation. Therefore, the simulation performance of multi-scale PV model is the key factor of the simulation accuracy in the specific operating scenarios of distribution network. In this paper, a multi-scale model of grid connected PV distributed generation system is proposed based on the mathematical model of grid-connected distributed PV power generation. It is analyzed that differences of simulation performance, such as adaptability of simulation step size, accuracy of output and the effect on voltage profile of distribution network, between PV models with different scales in IEEE 33 node example. Simulation results indicate that the multi-scale model is effective in improving the accuracy and efficiency of simulation under different operating conditions of distribution network.
文摘The rapid spreading of the Photovoltaic (PV) Systems as Distributed Generation (DG) in medium and low voltage networks created many effects and changes on the existing power system networks. In this work, two methods have been used and applied to determine the optimal allocation and sizing of the PV to be installed as DGs (ranging from 250 kW up to 3 MW). The first one is to determine the location according to the maximal power losses reduction over the feeder. The second one is by using the Harmony Search Algorithm which is claimed to be a powerful technique for optimal allocation of PV systems. The results of the two techniques were compared and found to be nearly closed. Furthermore, investigation on the effects on the feeder in terms of voltage levels, power factor readings, and short circuit current levels has been done. All calculations and simulations are conducted by using the MATLAB Simulation Program. Some field calculations and observations have been expended in order to substantiate the research findings and validation.
基金supported by National Science Foundation of China(61563032,61963025)Project supported by Gansu Basic Research Innovation Group(18JR3RA133)+1 种基金Industrial Support and Guidance Project for Higher Education Institutions of Gansu Province(2019C-05)Open Fund Project of Key Laboratory of Industrial Process Advanced Control of Gansu Province(2019KFJJ02).
文摘Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.
基金supported by the NationalNatural Science Foundation of China(No.6180802161)the Educational Commission of Liaoning Province of China(No.JZL201915401)We thank TopEdit(www.topeditsci.com)for its linguistic assistance during the preparation of this manuscript.
文摘To attain the goal of carbon peaking and carbon neutralization,the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy.However,this approach is hindered by the lack of training data for predicting new grid-connected PV power stations.To overcome this problem,this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations.The proposed model integrates a structure model,heat-dissipation conditions,and the loss coefficients of PV modules.Clear-Sky entropy,characterizes seasonal and weather data features,describes the main meteorological characteristics at the PV power station.Taking gate recurrent unit neural networks as the framework,the open and shared PV-power data as the source-domain training label,and a small quantity of power data from a new grid-connected PV power station as the target-domain training label,the neural network hidden layer is shared between the target domain and the source domain.The fully connected layer is established in the target domain,and the regularization constraint is introduced to fine-tune and suppress the overfitting in feature transfer.The prediction of PV power is completed by using the actual power data of PV power stations.The average measures of the normalized root mean square error(NRMSE),the normalized mean absolute percentage error(NMAPE),and the normalized maximum absolute percentage error(NLAE)for the model decrease by 15%,12%,and 35%,respectively,which reflects a much greater adaptability than is possible with other methods.These results show that the proposed method is highly generalizable to different types of PV devices and operating environments that offer insufficient training data.
文摘In recent years, environmental problems are becoming serious and renewable energy has attracted attention as their solutions. However, the electricity generation using the renewable energy has a demerit that the output becomes unstable because of intermittent characteristics, such as variations of wind speed or solar radiation intensity. Frequency fluctuations due to the installation of large scale wind farm (WF) and photovoltaics (PV) into the power system is a major concern. In order to solve the problem, this paper proposes two control methods using High Voltage Direct Current (HVDC) interconnection line to suppress the frequency fluctuations due to large scale of WF and PV. Comparative analysis between these two control methods is presented in this paper. One proposed method is a frequency control using a notch filter, and the other is using a deadband. Validity of the proposed methods is verified through simulation analyses, which is performed on a multi-machine power system model.
文摘In an active distribution grid,renewable energy sources(RESs)such as photovoltaic(PV)and energy storage systems(e.g.,superconducting magnetic energy storage(SMES))can be combined with consumers to compose a microgrid(MG).The high penetration of PV causes high fluctuations of tie-line power flow and highly affects power system operations.This can lead to several technical problems such as voltage fluctuations and excessive power losses.In this paper,a fuzzy logic control based SMES method(FSM)and an optimized fuzzy logic control based SMES method(OFSM)are proposed for minimizing the tie-line power flow.Consequently,the fluctuations and transmission power losses are decreased.In FSM,SMES is used with a robust fuzzy logic controller(FLC)for controlling the tie-line power flow.An optimization model is employed in OFSM to simultaneously optimize the input parameters of the FLC and the reactive power of the voltage source converter(VSC)of SMES.The objective function of minimizing the tieline power flow is incorporated into the optimization model.Particle swarm optimization(PSO)algorithm is utilized to solve the optimization problem while the constraints of the utility power grid,VSC,and SMES are considered.The simulation results demonstrate the effectiveness and robustness of the proposed methods.
文摘In China, systemic techno-economic analysis for solar tracker has been absent. To fill the blank, by taking the economic analysis of solar tracker application as the research object and using the LCOE method widely used internationally, the techno-economic analysis model of solar tracker was established according to conditions in China. Influence factors on LCOE were analyzed by using the established model, and the relationship between each cost factor and the cost component of energy leveling of tracker was further studied. In addition, the calculation method of investment payback period based on energy leveling analysis was established, and the influence of various factors on investment payback period was revealed through an example calculation. The research results will help to measure the economy of tracker application more accurately and comprehensively, and promote the popularization and application of solar tracker. The economic analysis model of solar tracker application was established by using LCOE method. The influence factors and cost component of LCOE were analyzed with the model. The payback period of solar tracker investment was also analyzed based on LCOE method.
文摘The power output of the photovoltaic(PV) system having multiple arrays gets reduced to a great extent when it is partially shaded due to environmental hindrances. The maximum power trackers which are conventionally used may not be competent enough to find the maximum power point(MPP) during partially shaded conditions. The sensible reason for the failure of conventional trackers is during partial shaded conditions the PV arrays exhibit multi peak power curves, thereby making simple maximum power point tracking(MPPT) algorithms like perturb and observe(P&O) to get stuck with local maxima instead of capturing global maxima.Therefore, global search MPPT aided by evolutionary and swarm intelligence algorithms will be conducive to find global power point during partially shaded conditions. This work suggests a unified controller which feeds control signal to its power electronic conditioner placed at each module. The evolutionary algorithm which is taken into consideration in this work is differential evolution(DE).The performance of the proposed method is compared to the classical un-dimensional search controller and it is evident from the Matlab/Simulink results that the unified controller prevails over the distributed counterpart.