• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.991-994
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• 2016

This paper presents a load management method based on OpenADR of smart grid. Previous demand side algorithm is restricted on reducing peak power. But, in this paper we suggest a method of performing the energy-saving control according to the power price utilizing building automatic control system installed on the customer side in the case of hourly differential pricing signal is transmitted to the open automated demand response system. And, we showed the integrated demand management software for 3 buildings.

• The Journal of Society for e-Business Studies
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• v.24 no.4
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• pp.119-133
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• 2019

Recently, the importance prediction of photovoltaic power (PV) is considered as an essential function for scheduling adjustments, deciding on storage size, and overall planning for stable operation of PV facility systems. In particular, since most of PV power is generated in peak time, PV power prediction in a peak time is required for the PV system operators that enable to maximize revenue and sustainable electricity quantity. Moreover, Prediction of the PV power output in peak time without meteorological information such as solar radiation, cloudiness, the temperature is considered a challenging problem because it has limitations that the PV power was predicted by using predicted uncertain meteorological information in a wide range of areas in previous studies. Therefore, this paper proposes the LSTM (Long-Short Term Memory) based the PV power prediction model only using the meteorological, seasonal, and the before the obtained PV power before peak time. In this paper, the experiment results based on the proposed model using the real-world data shows the superior performance, which showed a positive impact on improving the PV power in a peak time forecast performance targeted in this study.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2202-2205
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• 2011

Demand side management is used to maintain the reliability of power systems and to increase the economic benefits by avoiding power plant construction. This paper presents a systematic method to calculate the quantity of seasonal demand side management using time series. A numerical example is presented to calculate the quantity of demand side management in winter season using time series.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.43-50
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• 2016

NAND flash based storage devices adopts multi-channel and multi-way architecture to improve performance using parallel operation of multiple NAND devices. However, multiple NAND devices consume higher current and peak power overlap problem influences on the system stability and data reliability. In this paper, current waveform is measured for erase, program and read operations, peak current and model is defined by profiling method, and estimated probability of peak current overlap among NAND devices. Also, system level TLM simulator is developed to analyze peak overlap phenomenon depending on various simulation scenario. In order to remove peak overlapping, token-ring based simple power management method is applied in the simulation experiments. The optimal peak overlap ratio is proposed to minimize performance degradation based on relationship between peak current overlapping and system performance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.3
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• pp.95-99
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• 2013

The maximum power analysis simulator took advantage of the facilities and power consumption reduction simulator test scenario development and testing of improvement in the scenario. As a maximum demand power controller, Maximum power analysis simulator performs control and disperasion of maximum demand power by calculating base power, load forecast, and present power which are based on signal of watt-hour meter to keep the electricity under the target. In addition, various algorithms to select appropriate control methode on each of the light installations through the peak demand power is configured to management. The simulation shows the success of control power for the specified target controlled by five sequential lighting installations.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.5
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• pp.207-217
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• 2018

Recently, the importance of energy demand management, particularly peak load control, has been increasing due to the policy changes of the Second Energy Basic Plan. Even though the installation of distributed generation systems such as Photovoltaic and energy storage systems (ESS) are encouraged, high initial installation costs make it difficult to expand their supply. In this study, the power consumption of a university building was measured in real time and the measured power consumption data was used to calculate the optimal installation capacity of the Photovoltaic and ESS, respectively. In order to calculate the optimal capacity, it is necessary to analyze the operation methods of the Photovoltaic and ESS while considering the KEPCO electricity billing system, power consumption patterns of the building, installation costs of the Photovoltaic and ESS, estimated savings on electric charges, and life time. In this study, the power consumption of the university building with a daily power consumption of approximately 200kWh and a peak power of approximately 20kW was measured per minute. An economic analysis conducted using these measured data showed that the optimal capacity was approximately 30kW for Photovoltaic and approximately 7kWh for ESS.

• The Journal of Bigdata
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• v.5 no.1
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• pp.227-239
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• 2020

This paper introduces a method for predicting the maximum power demand despite communication errors in industrial sites. Due to the recent policy of de-nuclearization in Korea, the price of electricity is inevitable, and the amount of electricity used and maximum load management for the management of power demand are becoming important issues. Accordingly, it is important to predict and manage peak power. However, problems such as loss and modulation of measured power data occur at industrial sites due to noise generated by various facilities and sensors. It is difficult to predict the exact value when measured effective power data are lost. The study presents a model for predicting and correcting anomalies and missing values when measured effective power data are lost. The models used in this study are expected to be useful in predicting peak power demand in the event of communication errors at industrial sites.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.891-897
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• 2009

This paper presents a novel load management technique that can lower the peak demand caused by package airconditioner loads in large apartment complex. An intelligent hierarchical load management system composed of a Central Intelligent Management System(CIMS) and multiple Local Intelligent Management Systems(LIMS) is proposed to implement the proposed technique. Once the required amount of the power reduction is set, CIMS issues tokens, which can be used by each LIMS as a right to turn on the airconditioner. CIMS creates and maintains a queue for fair and proper allocation of the tokens among the LIMS requesting tokens. By adjusting the number tokens and queue management policies, desired power reduction can be achieved smoothly. The Markov Birth and Death process and the Balance Equations utilizing the Diffusion Model are employed for evaluation of queue performances during transient periods until the static balances among the states are achieved. The proposed technique is tested using a summer load data of a large apartment complex and give promising results demonstrating the usability in load management while minimizing the customer inconveniences.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2668-2670
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• 2004

This paper presents an advanced load control method in Direct Load Control(DLC) system. It is important to aggregate a various demand side resource which is surely controllable at the peak power time for a successful DLC system. Because the DLC system use simple On/Off control that may cause a harmful effect on a plant to reduce a peak power load, there are some restriction on deriving a voluntary participation of demand side resource. So it needs a new approach to direct load control method, and this paper describes an advanced load control method using control logic which is based on load properties. This method is easy to take account of a various characteristic of load, it can be use as a dynamic control logic which is good for adaptive control. The suggested control logic method is verified by modeling a control logic for a turbo refrigerator which affects on peak power in summer season.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.349-360
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• 2013

Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.