• Journal of IKEEE
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• v.25 no.2
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• pp.388-398
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• 2021

This paper proposes a particle swarm optimization (PSO)-based peak shaving scheme using energy storage system (ESS) for electricity tariff reduction. The proposed scheme compares the actual load with the estimated load consumption, calculates the additional output power that the ESS needs to discharge additionally to reduce peak load, and adds the input. In addition, in order to compensate for the additional power, the process of allocating power to the determined point is performed, and an optimization that minimizes the average of the load expected at the active power allocations using PSO so that the allocated value does not affect the peak load. To investigated the performance of the proposed scheme, case study of small and large load prediction errors was conducted by reflecting actual load data and load prediction algorithm. As a result, when the proposed scheme is performed with the ESS charge and discharge control to reduce electricity tariff, even when the load prediction error is large, the peak load is successfully reduced, and the peak load reduction effect of 17.8% and electricity tariff reduction effect of 6.02% is shown.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.137-143
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• 2014

Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, load forecasting of the unit of seconds using the Exponential Smoothing Methods, ARIMA model, Kalman Filter is proposed. Also simulation of load forecasting of the unit of the seconds methods and existing forecasting methods is performed and analyzed the accuracy. As a result of simulation, the accuracy of load forecasting methods in seconds is higher.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.8
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• pp.626-633
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• 2002

The electric power load during the peak time in summer is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. And also the method of forecasting the cooling load using neural network is suggested. For the simulation, the cooling load is calculated using actual temperature and humidity, The forecast of the temperature, humidity and cooling load are simulated. As a result of the simulation, the forecasted data is approached to the actual data.

• International Journal of Air-Conditioning and Refrigeration
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• v.11 no.4
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• pp.170-177
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• 2003

The electric power load during the summer peak time is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. The method of forecasting the cooling load using neural network is also suggested. The daily cooling load is mainly dependent on actual temperature and humidity of the day. The simulation is started with forecasting the temperature and humidity of the following day from the past data. The cooling load is then simulated by using the forecasted temperature and humidity data obtained from the simulation. It was observed that the forecasted data were closely approached to the actual data.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.435-437
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• 2001

The change of the electricity charge from cost base to price base due to the introduction of the electricity market competition causes consumer to choose a variety of charge schemes and a portion of loads to be affected by this change. Besides, it is required the index that consolidate the price volatility experienced on the power exchange with gaming and strategic bidding by suppliers to increase profits. Therefore, in order to find a mathematical model of the sensitively-responding-to-price loads, the price-sensitive load model is needed. And the development of state-of-the-art technologies affects the electricity price, so the diffusion of high-efficient end-uses and these price affect load patterns. This paper shows the analysis on learning curves algorithms which is used to investigate the correlation of the end-uses' price and load patterns.

• Proceedings of the KIEE Conference
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• summer
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• pp.165-167
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• 2004

End-user electric power consumption trends shows various load curves dependant on industry, contract, season, day and time. Analysis of end-user electric power consumption trends has a key role to efficiently meet electricity demand. There are several factors of change in electricity demand such as the change of weather, international conflict, and industrial trends during summer. This paper has analyzed the analysis the end-user electric power consumption trends using the load curve during international conflict. We observed that international conflict decreased electric demand by $5.4\%$. This increase is not significant, and therefore we conclude that the international conflict has not greatly affected Korea's electricity demands. This paper provides useful information so as to mon: efficiently perform demand side management.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.801-803
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• 2005

Fundamentally, success of the competitive electricity market is dependent on efficient market design. However, since electricity incorporates various physical constraints as other commodities, the resource assignment (i.e., dispatch scheduling) is also one of requisites for the successful operation of electricity market. Therefore, efficient dispatch scheduling is an important issue to succeed in the deregulated electricity market and the efficiency of this electricity market may be considerably increased by systematic studies on dispatch scheduling algorithm and corresponding constraints, especially system security. Moreover, contrary to traditional vertically-integrated electric power industry condition, since various decision-makings in deregulated electricity market are directly connected with market participants' benefits, only rational dispatch scheduling algorithm can convince these participants. Therefore, it can provide a basis of grievance prevention. In this paper, we propose an algorithm for security constrained dispatch scheduling with respect to load curtailment. Proposed algorithm decomposes the dispatch problem into a master problem corresponding to basecase optimal power flow (OPF) and several subproblems corresponding a series of contingencies using two-stage optimization technique.

• International Journal of Air-Conditioning and Refrigeration
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• v.9 no.1
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• pp.58-64
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• 2001

This study is to examine the emission quantity of $CO_2$ gas as the environmental load in office buildings. After the investigation of monthly consumption of each energy source(electricity and natural gas), it is analyzed that the $CO_2$ emission quantity of 34 office buildings surveyed is 22.4 kg-c/$m^2$-year, which consists of 17.5 kg-c/$m^2$-year by consurunlelectncif. and 4.9 kg-c/$m^2$-year by consuming natural gas. And the $CO_2$ emission quantity of each load in those buildings consists of 68% emitted by general electricity, 16% by cooling load and 16% by beating load. It is also proposed that the $CO_2$ emission quantity of cooling and heating load is profoundly pertinent to the variation of outdoor temperature.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.61-64
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• 2001

The electric power load during the peak time in summer is strongly affected by cooling load. which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system etc are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice-storage system is suggested. And also the method of forecasting the cooling load using neural network is suggested. For the simulation, the cooling load is calculated using actual temperature and humidity. The forecast of the temperature, humidity and cooling load are simulated. As a result of the simulation, the forecasted data approached to the actual data.

• The Korean Journal of Food & Health Convergence
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• v.5 no.4
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• pp.19-23
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• 2019

In recent years, the Internet was a lot of information on the use of Tesla coils bifilar [1] in conjunction with induction cooker, for "free" electricity during power resistive load, including different incandescent lamps. With the development of scientific and technological progress, the demand for electricity in each year is increasing, and at the same time, the need to increase the capacity of electricity production, which entails large investments and, consequently, increase of prices for consumers. As before today, the search for free electric energy is an up-to-date and still open topic. The purpose of research - simplification of information on the possibility of using bifilar coils as an electric current source in conjunction with induction cooker. Consequently, it can be concluded from the above that the use of Tesla coils in combination with induction cooker has very low k.k.d. and it is not possible to obtain excessive electric energy using the Tesla coil under the connection schemes considered.