• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.12
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• pp.1791-1800
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• 2012

This paper presents effective design schemes for a photovoltaic (PV) and battery hybrid system that includes state-of-the-art technologies such as maximum power point tracking scheme for PV arrays, an effective charging/discharging circuit for batteries, and grid-interfacing power inverters. Compared to commonly-used PV systems, the proposed configuration has more flexibility and autonomy in controlling individual components of the PV-battery hybrid system. This paper also proposes an intelligent coordination scheme for the components of the PV-battery hybrid system to improve the efficiency of renewable energy resources and peak-load management. The proposed algorithm is based on a rule-based expert system that has excellent capability to optimize multi-objective functions. The proposed configuration and algorithms are investigated via switching-level simulation studies of the PV-battery hybrid system.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.1
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• pp.121-125
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• 2016

KEPCO has completed the installation and demonstration of a 52 MW battery energy storage system (BESS) for frequency regulation. Especially, 24 MW BESS is for Automatic Generation Control (AGC) in Shin-Yongin substation. Recently, KEPCO Research Institute has operated it connected to EMS of KPX. This paper discussed the operation strategy of EMS through a study on using 24 MW BESS in AGC operation and propose the improvement of AGC target. It is expected that this paper helps a safe and reliable operation and control of ESS for AGC through its continuous update.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.8
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• pp.1099-1106
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• 2012

Microgrid is a new electrical energy system that composed of various generators, renewable energy, batteries and loads located near the electrical customers. When Microgrid is interconnected with large power system, Microgrid don't need to control the frequency. But in case of the outage or faults of power system, Microgrid should control the frequency to prevent the shutdown of Microgrid. This paper presents the frequency control methods using the droop function, being used by synchronous generators and EMS(Energy Management System). Using droop function, two battery systems could share the load based on locally measured signals without any communications between batteries. Also, we suggest that EMS should control the controllable distributed generators as P/Q control modes except batteries to overcome the weakness of droop function. Finally we suggest the two batteries systems to prolong the battery's life time considering the economical view. The validation of proposed methods is tested using PSCAD/EMTDC simulations and field test sites at the same time.

• Journal of Electrical Engineering and Technology
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• v.11 no.5
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• pp.1195-1201
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• 2016

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.63-66
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• 1991

With the diversity of life patterns and the improvement of level in life which have been resulted from the economic development, people have showed the tendency to pursue the comfortable life as well as the home automation or intelligent house. Furthermore, the clean energy supply and management system have been introduced for the solution of environmental problem on earth and the effective utilization of energy. This study is to describe the battery energy storage device, which is one of the clean energy supply and management systems that are economically efficient in both sides of supply and demand. and able to solve the problem of energy crisis.

• Advances in Energy Research
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• v.4 no.1
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• pp.47-68
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• 2016

A well designed hybrid power system (HPS) can deliver electrical energy in a cost effective way. In this paper, model for HPS consisting of photo voltaic (PV) module and wind mill as renewable energy sources (RES) and solar lead acid battery as storage device connected to unidirectional grid is developed for peak demand reduction. Life time energy cost of the system is evaluated. One year hourly site condition and load pattern are taken into account for analysing the HPS. The optimal HPS is determined for least life time energy cost subject to the constraints like state of charge of the battery bank, dump load, renewable energy (RE) generation etc. Optimal solutions are also found out individually for PV module and wind mill. These three systems are compared to find out the most feasible combination. The results show that the HPS can deliver energy in an acceptable cost with reduced peak consumption from the grid. The proposed optimization algorithm is suitable for determining optimal HPS for desired location and load with least energy cost.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.116-124
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• 2015

Smart grids propose new solutions for electricity consumers as a means to help them use energy in an efficient way. In this paper, we consider the demand-side management issue that exists for a group of consumers (houses) that are equipped with renewable energy (wind turbines) and storage units (battery), and we try to find the optimal scheduling for their home appliances, in order to reduce their electricity bills. Our simulation results prove the effectiveness of our approach, as they show a significant reduction in electricity costs when using renewable energy and battery storage.

• Proceedings of the Safety Management and Science Conference
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• 2002.11a
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• pp.279-284
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• 2002

All-vanadium redox flow battery(VRFB) has been studied actively as one of the most promising electrochemical energy storage systems for a wide range of applications such as electric vehicles, photovoltaic arrays, and excess power generated by electric power plants at night time. CPCS has been shown to have the characteristics as an excellent current collector for VRFB and electrochemical properties of specific resistivity 0.31 $\Omega$cm, which were composed of G-1028 80 wt%, PVC 10 wt%, DBP 5 wt% and FS 5 wt%. Energy efficiencies of VRFB with the CPCE and the existing electrode assembly were 84.14 % and 77.24 % respectively, in charge/discharge experiments at constant current of 200 mA, and the CPCE was confirmed to be suitable as the electrode of VRFB.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• Resources Recycling
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• v.31 no.4
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• pp.3-11
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• 2022

Owing to the increasing demand for electric vehicles (EVs), appropriate management of their waste batteries is required urgently for scrapped vehicles or for addressing battery aging. With respect to technological developments, data-driven diagnosis of waste EV batteries and management technologies have drawn increasing attention. Moreover, robot-based automatic dismantling technologies, which are seemingly interesting, require industrial verifications and linkages with future battery-related database systems. Among these, it is critical to develop and disseminate various advanced battery diagnosis and assessment techniques to improve the efficiency and safety/environment of the recirculation of waste batteries. Incorporation of lithium-related chemical substances in the public pollutant release and transfer register (PRTR) database as well as in-depth risk assessment of gas emissions in waste EV battery combustion and their relevant fire safety are some of the necessary steps. Further research and development thus are needed for optimizing the lifecycle management of waste batteries from various aspects related to data-based diagnosis/classification/disassembly processes as well as reuse/recycling and final disposal. The idea here is that the data should contribute to clean design and manufacturing to reduce the environmental burden and facilitate reuse/recycling in future production of EV batteries. Such optimization should also consider the future technological and market trends.