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.

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.

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.

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.

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.

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.

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.

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.