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 ◽  
...  
2020 ◽  
Vol 152 ◽  
pp. 01001 ◽  
Author(s):  
Eduardo Garcia-Garrido ◽  
Montserrat Mendoza-Villena ◽  
Pedro M. Lara-Santillan ◽  
Enrique Zorzano-Alba ◽  
Alberto Falces

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.


2021 ◽  
Vol 7 ◽  
pp. 3703-3725
Author(s):  
Mohammad Ehteram ◽  
Fatemeh Barzegari Banadkooki ◽  
Chow Ming Fai ◽  
Mohsen Moslemzadeh ◽  
Michelle Sapitang ◽  
...  

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1717
Author(s):  
Wanxing Ma ◽  
Zhimin Chen ◽  
Qing Zhu

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.


2021 ◽  
Vol 11 (2) ◽  
pp. 727 ◽  
Author(s):  
Myeong-Hwan Hwang ◽  
Young-Gon Kim ◽  
Hae-Sol Lee ◽  
Young-Dae Kim ◽  
Hyun-Rok Cha

In recent years, photovoltaic (PV) power generation has attracted considerable attention as a new eco-friendly and renewable energy generation technology. With the recent development of semiconductor manufacturing technologies, PV power generation is gradually increasing. In this paper, we analyze the types of defects that form in PV power generation panels and propose a method for enhancing the productivity and efficiency of PV power stations by determining the defects of aging PV modules based on their temperature, power output, and panel images. The method proposed in the paper allows the replacement of individual panels that are experiencing a malfunction, thereby reducing the output loss of solar power generation plants. The aim is to develop a method that enables users to immediately check the type of failures among the six failure types that frequently occur in aging PV panels—namely, hotspot, panel breakage, connector breakage, busbar breakage, panel cell overheating, and diode failure—based on thermal images by using the failure detection system. By comparing the data acquired in the study with the thermal images of a PV power station, efficiency is increased by detecting solar module faults in deteriorated photovoltaic power plants.


Author(s):  
Cheng-Ting Hsu ◽  
Hung-Ming Huang ◽  
Tsun-Jen Cheng ◽  
Yih-Der Lee ◽  
Yung-Ruei Chang ◽  
...  

Author(s):  
Dongsu Kim ◽  
Heejin Cho ◽  
Rogelio Luck

This study evaluates potential aggregate effects of net-zero energy building (NZEB) implementations on the electrical grid in simulation-based analysis. Many studies have been conducted on how effective NZEB designs can be achieved, however the potential impact of NZEBs have not been explored sufficiently. As significant penetration of NZEBs occurs, the aggregated electricity demand profile of the buildings on the electrical grid would experience dramatic changes. To estimate the impact of NZEBs on the electrical grid, a simulation-based study of an office building with a grid-tied PV power generation system is conducted. This study assumes that net-metering is available for NZEBs such that the excess on-site PV generation can be fed to the electrical grid. The impact of electrical energy storage (EES) within NZEBs on the electrical grid is also considered in this study. Finally, construction weighting factors of the office building type in U.S. climate zones are used to estimate the number of national office buildings. In order to consider the adoption of NZEBs in the future, this study examines scenarios with 20%, 50%, and 100% of the U.S. office building stock are composed of NZEBs. Results show that annual electricity consumption of simulated office buildings in U.S. climate locations includes the range of around 85 kWh/m2-year to 118 kWh/m2-year. Each simulated office building employs around 242 kWp to 387 kWp of maximum power outputs in the installation of on-site PV power systems to enable NZEB balances. On a national scale, the daily on-site PV power generation within NZEBs can cover around 50% to 110% of total daily electricity used in office buildings depending on weather conditions. The peak difference of U.S. electricity demand typically occurs when solar radiation is at its highest. The peak differences from the actual U.S. electricity demand on the representative summer day show 9.8%, 4.9%, and 2.0% at 12 p.m. for 100%, 50%, and 20% of the U.S. NZEB stocks, respectively. Using EES within NZEBs, the peak differences are reduced and shifted from noon to the beginning of the day, including 7.7%, 3.9%, and 1.5% for each percentage U.S. NZEB stock. NZEBs tend to create the significant curtailment of the U.S. electricity demand profile, typically during the middle of the winter day. The percentage differences at a peak point (12 p.m.) are 8.3%, 4.2%, and 1.7% for 100%, 50%, and 20% of the U.S. NZEB stocks, respectively. However, using EES on the representative winter day can flatten curtailed electricity demand curves by shifting the peak difference point to the beginning and the late afternoon of the day. The shifted peak differences show 7.4%, 3.7%, and 1.5% at 9 a.m. for three U.S. NZEB stock scenarios, respectively.


Author(s):  
Jiacong Cao ◽  
Hong Fang

Building cooling, heating and power generation (BCHP) is important for the sustainable energy strategy in China because of its contribution to energy conservation and the reduction of CO2 emissions. The number of BCHP or small-scaled combined cooling, heating and power generation systems that have been put to use or are in the course of construction is steadily increasing in China. However, in many cases the performance of BCHP systems is not good enough, i.e., the average real exergetic efficiency of whole system is much lower than expected and the economic effect is not satisfactory. This is a problem that perplexes designers and plant owners and need be investigated so as to increase the knowledge of optimizing the operation of BCHP systems. In this paper the performance of a typical BCHP system is investigated using thermodynamic and thermoeconomic analyses based on the simulating results of off-design operation and the solution of performance optimization of the system. With the help of a great number of real running data of the system and the master data supplied by manufacturers, a model of the system operation is developed to simulate the whole domain of operation on off-design conditions. In order to shorten computer time the operation domain is described by a set of functions obtained by curve fitting using the numerical data from the simulation. Two models of optimization, of which the objective functions are the exergetic efficiency and gross benefit of the whole BCHP system separately, are established in virtue of these fitted functions. The simulation of off-design operation and the solution of the optimization problems supply a great number of useful data that form various graphs, which are to be the references to energy conservation and economic operation of the systems. The investigation indicates that there are some differences between the optimum working conditions obtained by the two optimization models, whereas it is inevitable that the system runs with some lower efficiency and less gross benefit when working at high cooling or heating load factors. By analyzing the data some significant conclusions are obtained, which will be helpful for the BCHP industry in China.


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