Investigating Load Regulation Characteristics of a Wind-PV-Coal Storage Multi-Power Generation System

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.

High Efficiency PCS with Scan-Type MPPT Control for a Grid-Connected PV Power Generation System

For a standalone PV （photovoltaic） power generation system, the author previously proposed a new MPPT （maximum power point tracking） control method in which the I-V characteristics are scanned with a detection interval control that operates at specified intervals and monitors the maximum power point. The author has obtained satisfactory results using this new MPPT control method. This paper investigates the application of the new MPPT control method for a PCS （power conditioning system） in a grid-connected type PV power generation system. The experimental results clearly demonstrate that the developed PCS offers outstanding effectiveness in tracking the maximum power point in partially shaded environments.

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

Calculation Method and Analysis for the Annual Power Generation of PV Faades in China

The application of PV facades 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 facade in terms of PV electricity generation in different arrangements and weather conditions of four major cities in China. The calculation models for PV facade 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 facades. 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 facades. Weather conditions nlav a very important role in terms of PV generation nerformance of PV facades.

Forecast of Power Generation for Grid-Connected Photo-voltaic System Based on Grey Theory and Verification Model

Being photovoltaic power generation affected by radiation strength, wind speed, clouds cover and environment temperature, the generating in each moment is fluctuating. The operational characteristics of grid-connected PV systems are coincided with gray theory application conditions. A gray theory model has been applied in short-term forecast of grid-connected photovoltaic system. The verification model of the probability of small error will help to check the accuracy of the gray forecast results. The calculated result shows that the ?model accuracy has been greatly enhanced.

Research on Multi-Scale Modeling of Grid-Connected Distributed Photovoltaic Power Generation

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.

Influence of Control Modes of Grid-Connected Solar Photovoltaic Generation on Grid Power Flow

Integration of Solar Photovoltaic (PV) generation into an existing distribution system has many impacts on the system, with the power flow being one of the major issues. This impact is not generic for any network, but it may manifest itself either positively or negatively, depending on the grid configuration, interface control modes, operation mode, and load profile. Grid-connected PV systems have three control options of the local voltage controller of the interface DC-AC converter. These control modes are Power Factor control, voltage control, and Droop Voltage control. This paper aims at evaluating and comparing the impacts of those control modes on the grid power flow. A set of evaluation criteria and indices is defined and mathematically formulated. Based on the requirements of the used program (Power Factory Dig Silent V14.1.3), a computation plan (algorithm) has been proposed. The algorithm has been applied to a typical weak network and a wide range of simulations has been carried out. Simulation results have been thoroughly discussed and important findings have been concluded.

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of the Wind Power and Photovoltaic Energy

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.

Impact of PV Distributed Generation on Loop Distribution Network

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.

Grid-Connected PV Solar Energy Converter with Active and Reactive Power Control

This work proposes a 12 kW three-phase grid-connected single stage PWM DC-AC converter destined to process the energy provided by a photovoltaic array composed of 57 KC200GT PV modules with high power factor for any solar radiation. The PWM inverter modeling and the control strategy, using dqO transformation, are proposed in order to also allow the system operation as an active power filter, capable to compensate harmonic components and react power generated by the non-linear loads connected to the mains grid. An input voltage clamping technique is proposed to impose the photovoltaic operation on the maximum power point. Simulation and experimental results are presented to validate the proposed methodology for grid connected photovoltaic generation system.

PV Power Short-Term Forecasting Model Based on the Data Gathered from Monitoring Network

The degree of accuracy in predicting the photovoltaic power generation plays an important role in appropriate allocations and economic operations of the power plants based on the generating capacity data gathered from the geographically separated photovoltaic plants through network. In this paper, a forecasting model is designed with an optimization algorithm which is developed with the combination of PSO （Particle Swarm Optimization） and BP （Back Propagation） neural network. The proposed model is further validated and the experiment results show that the predication model assures the prediction accuracy regardless the day type transitions and other relevant factors, in the proposed model, the prediction error rate is worth less than 20% in all different climatic conditions and most of the prediction error accuracy is less than 10% in sunny day, and whose precision satisfies the management requirements of the power grid companies, reflecting the significance of the proposed model in engineering applications.

Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

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.

Short-Term Prediction of Photovoltaic Power Based on Fusion Device Feature-Transfer

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.

Frequency Control of Power System with Solar and Wind Power Stations by Using Frequency Band Control and Deadband Control of HVDC Interconnection Line

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.

Distributed Solar PV System for Industrial Application

The paper presents the design and field test of a distributed solar PV system for industrial application （DGPVi）. DGPVi utilizes HyPV （hybrid PV） system which generates solar power for self-consumption in lighting and air conditioning in a production line of a factory when solar energy is available. It does not feed the excess PV power to the grid. HyPV will be switched to grid power supply when solar energy is not available. A 3 kWp DGPVi is installed in a factory for field demonstration. The test results show that the solar PV power generated can be utilized immediately. The solar energy generation efficiency （kWh/day per kWp PV installation） of DGPVi is close to that of grid-tied PV system without self-consumption and battery storage. The yearly return on investment of DGPVi is 2.0% at the present installation cost or 3.3% at further cost-down cost. The payback time will be 14.3 years at the present installation cost or 12.1 years at cost-down cost. The present study verifies the economic feasibility of DGPVi.

Comparison between the Energy Required for Production of PV Module and the Output Energy Througout the Product Life Time

Electrical energy consumption is growing and is necessary to improve the technologies related to energy production. We have carried out a pilot study about environmental impacts during the manufacturing process of PV （photovoltaic） modules and compared between the energy requirement for the production of PV cells and modules and generation throughout the life time of the finished good that is PV module. It was taken into account the generation of environmental aspects and impacts in the manufacture of monocrystalline silicon PV modules （consisting of three components： silicon cell, fiat tempered glass and aluminum frame）, and an analysis of a grid-connected PV system using an energetic alternative in residences was considered. Results show that, this kind of renewable energy is really clean and can be considered as a way to change the energy technology.

A Simulation Research on the Grid-Connected Control Technology of Single-Phase Inverters Based on MATLAB

This paper primarily discusses the main circuit of single-phase inverter circuits.It begins by introducing the research context and the significance of the subject,then discusses the topology of grid-connected single-phase inverter circuits,continues by discussing the control strategy for grid-connected single-phase inverter circuits,realizes a sinusoidal pulse width modulation(SPWM)signal generation circuit and an inverse control algorithm program,and finally ensures good output waveform and fast dynamic response.In view of the hysteresis feature of the grid voltage’s synchronous signal sampling circuit,the acquisition function in digital signal processing(DSP)control chips is applied,and the reasons for the hysteresis phenomenon are thoroughly investigated.The reliability of the SPWM control algorithm is revealed through the results.

Novel Wind Power Grid-connection System Using Inductive Filtering Technology

Herein,a novel wind power grid-connection system based on inductive filtering is proposed to improve grid-connection compatibility,and is implemented in a 50-MW real system.First,the topology and wiring configuration of the proposed system are discussed.Thereafter,an equivalent circuit and mathematical model are established to reveal the filtering characteristics and resonance damping mechanism of the proposed system.Finally,a 50-MW wind farm-based experimental study,which is conducted to validate the effectiveness and availability of the system is discussed.The experimental results show that the main harmonics,power factor,voltage fluctuation,and flicker satisfy national standards.

Short-term photovoltaic power forecasting based on a new hybrid deep learning model incorporating transfer learning strategy

The accurate prediction of photovoltaic(PV)power generation is an important basis for hybrid grid scheduling.With the expansion of the scale of PV power plants and the popularization of distributed PV,this study proposes a multilayer PV power generation prediction model based on transfer learning to solve the problems of the lack of data on new PV bases and the low accuracy of PV power generation prediction.The proposed model,called DRAM,concatenates a dilated convolutional neural network(DCNN)module with a bidirectional long short-term memory(BiLSTM)module,and integrates an attention mechanism.First,the processed data are input into the DCNN layer,and the dilation convolution mechanism captures the spatial features of the wide sensory field of the input data.Subsequently,the temporal characteristics between the features are extracted in the BiLSTM layer.Finally,an attention mechanism is used to strengthen the key features by assigning weights to efficiently construct the relationship between the features and output variables.In addition,the power prediction accuracy of the new PV sites was improved by transferring the pre-trained model parameters to the new PV site prediction model.In this study,the pre-training of models using data from different source domains and the correlations between these pre-trained models and the target domain were analyzed.

Research on large-scale dispatchable grid-connected PV systems

This paper describes the research on a largescale dispatchable grid-connected photovoltaic(PV)system for supplying power to the grid for dispatch instead of supplying the electricity to a local load.In order to maximise the value of the solar energy,a hybrid electricity storage consisting of batteries and supercapacitors is used with the PV system.This paper proposes a control strategy focusing on theDCpower at theDClink rather than at the grid-connected inverter.Two typical sets of real data,collected from existing sites,are used to demonstrate the practicality of the system.Finally,the simulation results are used to demonstrate the good performance and feasibility of the proposed system together with the proposed control strategy.