Energy Management in PV Battery Grid Connected System in MATLAB============================================================Join this channel to get access to...

Energy Management in PV Battery Grid Connected System in MATLAB============================================================Join this channel to get access to...

Reactive Power Compensation in Grid Connected PV System Using STATCOM and Fixed CapacitorIntegration of Distributed Energy Resources (DER) in conventional po...

Demand-side management in Grid-Connected Battery System using Neural Network A novel energy management system to improve the efficiency of grid-connected energy storage systems using a deep neural network is developed. The high penetration of renewable energy and decentralization of the grid has led to an increase in the instability of the grid. To reduce this instability, a balance between the consumption demand and production rate needs to be maintained. For this objective, electric vehicle batteries can be integrated with demand-side management techniques using a deep neural network. The controller can be programmed with the timing of the peak and the off-peak hours obtained from the demand curve data and state of charge of the battery. The controller will take two inputs: The time of the day and the State of Charge of the battery. The NN controller will detect the arrival of the peak and will send a message to the EV battery to supply a programmed percentage of power to the household appliances. The direct communication between the grid and the battery can be eliminated to reduce the infrastructure requirements and data processing. The grid can operate successfully during normal working hours and can supply the total power consumption by the loads at any time of the day. The peak to average power ratio can be reduced by operating the EV battery during peak hours for providing that programmed percentage to the appliances for better grid operation. This drained battery will be further fully charging during low loading of the grid and keep ready for the following days’ operation. According to the results of simulation studies, it is demonstrated that our proposed model not only enhances users’ utility but also reduces energy consumption costs.

A 100-kW PV array is connected to a 0.4 kV grid via a DC-DC boost converter and a three-phase three-level Voltage Source Converter (VSC). Maximum Power Point Tracking (MPPT) is implemented in the boost converter by means of a Simulink® model using the 'PO + Proportional Integral Regulator' technique. The detailed model contains the following components: PV array delivering a maximum of 100 kW at 1000 W/m^2 sun irradiance. 5-kHz DC-DC boost converter increasing voltage from PV natural voltage (290 V DC at maximum power) to 700 V DC. The switching duty cycle is optimized by an MPPT controller that uses the 'Incremental Conductance + Integral Regulator' technique. This MPPT system automatically varies the duty cycle in order to generate the required voltage to extract maximum power. 1980-Hz 3-level 3-phase VSC. The VSC converts the 700 V DC link voltage to 230 V AC and keeps the unity power factor. The VSC control system uses two control loops: an external control loop that regulates DC link voltage to +/- 250 V and an internal control loop that regulates Id and Iq grid currents (active and reactive current components). Id current reference is the output of the DC voltage external controller. Iq current reference is set to zero in order to maintain the unity power factor. Vd and Vq voltage outputs of the current controller are converted to three modulating signals Uabc_ref used by the PWM Generator. The control system uses a sample time of 100 microseconds for voltage and current controllers as well as for the PLL synchronization unit. Pulse generators of Boost and VSC converters use a fast sample time of 1 microsecond in order to get an appropriate resolution of PWM waveforms.

Reactive Power Compensation in Grid Connected PV System Using STATCOM and Fixed CapacitorIntegration of Distributed Energy Resources (DER) in conventional po...

STEP By STEP Implementation of Three Phase Grid Connected Solar PV System in MATLAB=========================================================A 100-kW PV array...

Demand-side management in Grid-Connected Battery System using Neural Network A novel energy management system to improve the efficiency of grid-connected energy storage systems using a deep neural network is developed. The high penetration of renewable energy and decentralization of the grid has led to an increase in the instability of the grid. To reduce this instability, a balance between the consumption demand and production rate needs to be maintained. For this objective, electric vehicle batteries can be integrated with demand-side management techniques using a deep neural network. The controller can be programmed with the timing of the peak and the off-peak hours obtained from the demand curve data and state of charge of the battery. The controller will take two inputs: The time of the day and the State of Charge of the battery. The NN controller will detect the arrival of the peak and will send a message to the EV battery to supply a programmed percentage of power to the household appliances. The direct communication between the grid and the battery can be eliminated to reduce the infrastructure requirements and data processing. The grid can operate successfully during normal working hours and can supply the total power consumption by the loads at any time of the day. The peak to average power ratio can be reduced by operating the EV battery during peak hours for providing that programmed percentage to the appliances for better grid operation. This drained battery will be further fully charging during low loading of the grid and keep ready for the following days’ operation. According to the results of simulation studies, it is demonstrated that our proposed model not only enhances users’ utility but also reduces energy consumption costs.

Grid-connected solar PV system with Battery Energy Storage SystemThe penetration of renewable sources in the power system network in the power system has bee...

A 100-kW PV array is connected to a 0.4 kV grid via a DC-DC boost converter and a three-phase three-level Voltage Source Converter (VSC). Maximum Power Point Tracking (MPPT) is implemented in the boost converter by means of a Simulink® model using the 'PO + Proportional Integral Regulator' technique. The detailed model contains the following components: PV array delivering a maximum of 100 kW at 1000 W/m^2 sun irradiance. 5-kHz DC-DC boost converter increasing voltage from PV natural voltage (290 V DC at maximum power) to 700 V DC. The switching duty cycle is optimized by an MPPT controller that uses the 'Incremental Conductance + Integral Regulator' technique. This MPPT system automatically varies the duty cycle in order to generate the required voltage to extract maximum power. 1980-Hz 3-level 3-phase VSC. The VSC converts the 700 V DC link voltage to 230 V AC and keeps the unity power factor. The VSC control system uses two control loops: an external control loop that regulates DC link voltage to +/- 250 V and an internal control loop that regulates Id and Iq grid currents (active and reactive current components). Id current reference is the output of the DC voltage external controller. Iq current reference is set to zero in order to maintain the unity power factor. Vd and Vq voltage outputs of the current controller are converted to three modulating signals Uabc_ref used by the PWM Generator. The control system uses a sample time of 100 microseconds for voltage and current controllers as well as for the PLL synchronization unit. Pulse generators of Boost and VSC converters use a fast sample time of 1 microsecond in order to get an appropriate resolution of PWM waveforms.

Demand-side management in Grid-Connected Energy Storage System using Deep Neural Network============================================================A novel e...