scholarly journals Optimal Operation Schedule of Semi-Fixed PV System and Its Effect on PV Power Generation Efficiency

2017 ◽  
Vol 37 (6) ◽  
pp. 69-77
Author(s):  
In-Kyu Kwak ◽  
Sun-Hye Mun ◽  
Jung-Ho Huh
Keyword(s):  
Power Generation ◽  
Optimal Operation ◽  
Generation Efficiency ◽  
Pv System ◽  
Operation Schedule ◽  
Pv Power Generation

scholarly journals Net demand short-term forecasting in a distribution substation with PV power generation

2020 ◽  
Vol 152 ◽  
pp. 01001 ◽  
Author(s):  
Eduardo Garcia-Garrido ◽  
Montserrat Mendoza-Villena ◽  
Pedro M. Lara-Santillan ◽  
Enrique Zorzano-Alba ◽  
Alberto Falces
Keyword(s):  
Power Generation ◽  
Solar Energy ◽  
Power Distribution ◽  
Distribution Systems ◽  
Electric Energy ◽  
Optimal Operation ◽  
Electric Energy Consumption ◽  
Short Term Forecasting ◽  
Artificial Neural Network Ann ◽  
Pv Power Generation

The integration of renewable energies, specifically solar energy, in electric distribution systems is increasingly common. For an optimal operation, it is very important to forecast the final net demand of the power distribution network, considering the variability of solar energy combined with the variability of the electric energy consumption habits of population. This paper presents the methodology followed to forecast the net demand in a power distribution substation. Two approaches are considered, the net demand direct prediction, and the indirect prediction with the forecasts of PV power generation and load demand. Artificial Neural Network (ANN) based models and autoregressive models with exogenous variables (ARX) are used to predict the net demand, directly and indirectly, for the 24 hours of the day-ahead. The methodology is applied to a medium voltage distribution substation and the direct and indirect forecasts are compared.

scholarly journals Performance Analysis of a Modernized Z-Source Inverter for Robust Boost Control in Photovoltaic Power Conditioning Systems

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 139 ◽  
Author(s):  
Nafis Subhani ◽  
Ramani Kannan ◽  
Md Mahmud ◽  
Mohd Romlie
Keyword(s):  
Power Generation ◽  
Common Ground ◽  
Single Stage ◽  
Solar Irradiation ◽  
Generation System ◽  
Power Conditioning ◽  
Pv System ◽  
Voltage Stress ◽  
Power Generation System ◽  
Pv Power Generation

In this paper, the performance of a new Z-source inverter (ZSI)-based single-stage power conditioning system (PCS) is analyzed for a standalone photovoltaic (PV) power generation system. The proposed ZSI-based PCS includes two main parts: one is the input from PV units and the other is the ZSI. In this work, a new topology, termed the switched inductor-assisted strong boost ZSI (SL-SBZSI), is introduced for improving the performance of the PCS. The proposed topology shows high boosting capability during the voltage sag in PV units due to variations in solar irradiation and temperature. Another key advantage is the reduced capacitor voltage stress and semiconductor switch voltage stress of the inverter bridge, which ultimately minimizes the size and cost of the single-stage PCS. The proposed ZSI topology falls under the doubly grounded category of inverter by sharing the common ground between the input and output. This is an additional feature that can minimize the leakage current of PV units at the ac output end. The operational principles, detailed mathematical modeling, and characteristics of the proposed SL-SBZSI for a standalone photovoltaic (PV) power generation system is presented in this paper for analyzing performance. The simulation results, which are performed in MATLAB/Simulink, demonstrate the improved performance of the proposed SL-SBZSI for the standalone PV system. The performance of the proposed topology is also evaluated through an experimental validation on a laboratory-based PV system.

Research on China's First Residential Grid-Connected Photovoltaic System

2013 ◽  
Vol 392 ◽  
pp. 563-567
Author(s):  
Yan Jie Dai ◽  
Chun Yan Sun ◽  
Xiao Yong Wang ◽  
Wei Hua Yang
Keyword(s):  
Power Generation ◽  
Power Quality ◽  
Electrical Power ◽  
Photovoltaic System ◽  
Pv System ◽  
Quality Analyzer ◽  
Low Efficiency ◽  
Power Metering ◽  
Pv Power Generation ◽  
Power Standard

Along with the continuous expansion of photovoltaic (PV) power generation, different capacity of grid connected PV system is gradually increased. China's first residential grid-connected PV system has interconnected successfully in QingDao and operated normally. This document analyzed electrical connected diagram of grid-connected PV system. Using power quality analyzer, the online power quality is monitored and analyzed. When PV power generation is low efficiency of operating state, harmonic current is over distortion limits. Monitoring data was simulated through electrical power standard source. To ensure power metering accuracy under harmonic, the watt-second method is proposed. Testing results show that smart electrical meter can meter accurately within 20 times harmonics.

scholarly journals Efficiency Measurement and Factor Analysis of China’s Solar Photovoltaic Power Generation Considering Regional Differences Based on a FAHP–DEA Model

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1936
Author(s):  
Jing Yang ◽  
Changhui Yang ◽  
Xiaojia Wang ◽  
Manli Cheng ◽  
Jingjing Shang
Keyword(s):  
Power Generation ◽  
Energy Structure ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Process Data ◽  
Generation Efficiency ◽  
Analytic Hierarchy ◽  
Dea Model ◽  
Hierarchy Process ◽  
Pv Power Generation

Driven by the transformation of the energy structure, China’s photovoltaic (PV) power generation industry has made remarkable achievements in recent years. However, there are more than 30 regions (cities/provinces) in China, and the economic, policy, technological, and the environmental conditions of each region are significantly different, which leads to a huge discrepancy in PV power generation efficiency. To address the imbalance in the development of PV industry, first, this paper employed the integrated fuzzy analytic hierarchy process–data envelopment analysis (FAHP–DEA) model to evaluate the PV power generation efficiency of 30 regions in China. Second, Tobit regression model was used to examine the effects of 9 potential influencing factors. Third, a concrete analysis was conducted, and discussion based on the efficiency rankings and regression results was made. Additionally, the FAHP–DEA model proposed in this study can also be applied to the efficiency evaluation issues of other types of renewable energy.

Benefit-Cost Analysis for Private PV System Investment with DSTATCOM Compensation

2014 ◽  
Vol 1030-1032 ◽  
pp. 2527-2531
Author(s):  
Shih Chieh Hsieh ◽  
Chao Shun Chen ◽  
Chia Hung Lin ◽  
Wei Lin Hsieh
Keyword(s):  
Power Generation ◽  
Cost Analysis ◽  
Distribution Systems ◽  
Voltage Control ◽  
Solar Irradiation ◽  
Pv System ◽  
Benefit Cost Analysis ◽  
Control Scheme ◽  
Benefit Cost ◽  
Pv Power Generation

This paper presents a benefit-cost analysis for private photovoltaic (PV) system investment with distribution static compensator (DSTATCOM) compensation to enhance the PV penetration in distribution systems. A hybrid voltage control scheme with reactive power compensation from DSTATCOMs and active power curtailment is applied to avoid the violation of voltage variation caused by large PV power injection. The PV power generation is estimated based on local solar irradiation and temperature data. The annual curve of PV power generation and annual energy delivered to the distribution system with the hybrid voltage control scheme are also determined. The annual revenue of PV power sales, the initial capital investment cost of a PV system with or without a DSTATCOM, and the operating and maintenance cost are then considered to evaluate the benefit and cost of the PV investment over its life cycle.

scholarly journals Multistep Finite Control Set Model Predictive Control of Photovoltaic Power Generation System with Harmonic Compensation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xuejiao Gong ◽  
Shifeng Hu ◽  
Ruijin Zhu
Keyword(s):  
Power Generation ◽  
Reactive Power ◽  
Power Grid ◽  
Dynamic Performance ◽  
Maximum Power ◽  
Pv System ◽  
Nonlinear Load ◽  
Harmonic Compensation ◽  
Power Point ◽  
Pv Power Generation

Photovoltaic (PV) power generation is the main aspect of new energy power generation, and it is an important means to achieve the goal of carbon neutrality. When the PV system is connected to the grid, the nonlinear load of the grid will affect the power quality and consume reactive power. This paper proposes a PV power generation grid-connected system to improve power quality, with an active power filter (APF) function. Through the maximum power point tracking (MPPT) method, PV power generation can operate at the maximum power point and play the function of harmonic and reactive power compensation at the load side. To improve the dynamic performance of the grid-connected PV system and harmonic compensation simultaneously, multistep finite control set model predictive control (FCS-MPC) is adopted for the grid-connected module. The whole system does not need additional equipment, as it plays the role of two devices and effectively reduces the input cost. In this paper, the proposed structure and multistep FCS-MPC are verified in MATLAB/Simulink. The results show that the system injects the maximum power into the power grid at the same time when the load changes and compensates the harmonic generated by the nonlinear load of the power grid so that the total harmonic distortion of the power grid can meet the operation standard, and the system has good dynamic performance and steady-state performance.

scholarly journals Evaluation of Photovoltaic Power Generation by using Deep Learning in Solar Panels Installed in Buildings

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3564 ◽  
Author(s):  
Wei
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Deep Learning ◽  
Power Output ◽  
Solar Panel ◽  
Second Phase ◽  
Solar Panels ◽  
Generation Efficiency ◽  
Southern Taiwan ◽  
Pv Power Generation

Southern Taiwan has excellent solar energy resources that remain largely unused. This study incorporated a measure that aids in providing simple and effective power generation efficiency assessments of solar panel brands in the planning stage of installing these panels on roofs. The proposed methodology can be applied to evaluate photovoltaic (PV) power generation panels installed on building rooftops in Southern Taiwan. In the first phase, this study selected panels of the BP3 series, including BP350, BP365, BP380, and BP3125, to assess their PV output efficiency. BP Solar is a manufacturer and installer of photovoltaic solar cells. This study first derived ideal PV power generation and then determined the suitable tilt angle for the PV panels leading to direct sunlight that could be acquired to increase power output by panels installed on building rooftops. The potential annual power outputs for these solar panels were calculated. Climate data of 2016 were used to estimate the annual solar power output of the BP3 series per unit area. The results indicated that BP380 was the most efficient model for power generation (183.5 KWh/m2-y), followed by BP3125 (182.2 KWh/m2-y); by contrast, BP350 was the least efficient (164.2 KWh/m2-y). In the second phase, to simulate meteorological uncertainty during hourly PV power generation, a surface solar radiation prediction model was developed. This study used a deep learning–based deep neural network (DNN) for predicting hourly irradiation. The simulation results of the DNN were compared with those of a backpropagation neural network (BPN) and a linear regression (LR) model. In the final phase, the panel of module BP3125 was used as an example and demonstrated the hourly PV power output prediction at different lead times on a solar panel. The results demonstrated that the proposed method is useful for evaluating the power generation efficiency of the solar panels.

scholarly journals Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities

Smart Cities ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 842-852
Author(s):  
Moein Hajiabadi ◽  
Mahdi Farhadi ◽  
Vahide Babaiyan ◽  
Abouzar Estebsari
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Large Scale ◽  
Smart Cities ◽  
Energy Generation ◽  
Loss Functions ◽  
Support Vector ◽  
Pv System ◽  
Pv Systems ◽  
Pv Power Generation

The demand for renewable energy generation, especially photovoltaic (PV) power generation, has been growing over the past few years. However, the amount of generated energy by PV systems is highly dependent on weather conditions. Therefore, accurate forecasting of generated PV power is of importance for large-scale deployment of PV systems. Recently, machine learning (ML) methods have been widely used for PV power generation forecasting. A variety of these techniques, including artificial neural networks (ANNs), ridge regression, K-nearest neighbour (kNN) regression, decision trees, support vector regressions (SVRs) have been applied for this purpose and achieved good performance. In this paper, we briefly review the most recent ML techniques for PV energy generation forecasting and propose a new regression technique to automatically predict a PV system’s output based on historical input parameters. More specifically, the proposed loss function is a combination of three well-known loss functions: Correntropy, Absolute and Square Loss which encourages robustness and generalization jointly. We then integrate the proposed objective function into a Deep Learning model to predict a PV system’s output. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via back propagation. We investigate the effectiveness of the proposed method through comprehensive experiments on real data recorded by a real PV system. The experimental results confirm that our method outperforms the state-of-the-art ML methods for PV energy generation forecasting.

scholarly journals The Methodology of Calculating the Optimal ESS Capacity according to PV Power Generation for Grid-Connected PV-ESS System through Optimal Operation

2021 ◽  
Vol 41 (6) ◽  
pp. 85-95
Author(s):  
Yong-Ha Kim ◽  
Gyu-Rim Han ◽  
Sang-Hwa Han ◽  
Hye-Seon Lee ◽  
Jong-Min Park ◽  
...  
Keyword(s):  
Power Generation ◽  
Optimal Operation ◽  
Pv Power Generation
Sign in / Sign up

Export Citation Format

Share Document