• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.14-19
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• 2008

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.804-806
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• 2005

In this paper, a new probabilistic generation modeling method which can address the characteristics of changed electricity industry is proposed. The major contribution of this paper can be captured in the development of a probabilistic generation modeling considering generator maintenance outage and in the classification of market demand into multiple demand clusters for the applications to electricity markets. Conventional forced outage rates of generators are conceptually combined with maintenance outage of generators and, consequently, effective outage rates of generators are new iy defined in order to properly address the probabilistic characteristic of generation in electricity markets. Then, original market demands are classified into several distinct demand clusters, which are defined by the effective outage rates of generators and by the inherent characteristic of the original demand. We have found that generators have different effective outage rates values at each classified demand cluster, depending on the market situation. From this, therefore, it can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be more efficiently analyzed in electricity markets perspectives, for this classification can be widely applicable to other technical problems in power systems such as generation scheduling, power flow analysis, price forecasts, and so on.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.33-40
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• 2024

Introducing renewable energy sources, such as wind and photovoltaic arrays, in microgrids that supply remote regions with electricity represents a significant leap in electricity generation. Combining photovoltaic panels and diesel engines is one of the most common ways to supply electricity to rural communities. Such hybrid systems can reduce the cost of electricity generation in these remote power systems because they use free energy to balance the power generated by diesel engines. However, the combination of renewable energy sources and diesel engines tends to complicate the sizing and control of the entire system due to the intermittent nature of renewable energy sources. This study sought to investigate this issue in depth. It proposes a robust hybrid controller that can be used to facilitate optimum power sharing between a PV power source and diesel generators based on the dynamics of the available PV energy at any given time. The study also describes a hybrid PV-diesel power plant's essential functional parts that produce electricity for a microgrid using a renewable energy source. Power control needs to be adjusted to reduce the cost of power generation.

• Asian Journal of Atmospheric Environment
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• v.8 no.1
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• pp.59-68
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• 2014

The Fukushima nuclear accident in 2011 had an extensive impact on the national electricity plans. This paper outlines alternative electricity scenarios that meet the goals of nuclear phase-out and greenhouse gas (GHG) emission reduction. This paper also analyzes the results of each scenario in respect to the electricity mix, GHG emissions, costs and employment effects. The Long-range Energy Alternatives Planning system (LEAP) model was used to simulate the annual electricity demand and supply system from 2011 to 2030. The reference year was 2009. Scenarios are reference (where existing plans are continued), A1, A2, B1, B2, and C2 (where the levels of demand management and nuclear phase-out are different). The share of renewable energy in the electricity mix in 2030 for each scenario will be increased from about 1% in 2009 to 8% in the reference scenario and from 11% to 31% in five alternative scenarios. Total cumulative cost increases up to 14% more than the reference scenario by replacing nuclear power plants with renewable energy in alternative scenarios could be affordable. Deploying enough renewable energy to meet such targets requires a roadmap for electricity price realization, expansion of research, development and deployment for renewable energy technologies, establishment of an organization dedicated to renewable energy, and ambitious targets for renewable energy.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.6
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• pp.560-568
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• 2005

The cost accounting of electricity and heat produced from an energy system is important in evaluating the economical efficiency and deciding the reasonable sale price. The OECOPC method, suggested by the author, was applied to a 650 MW combined cycle cogeneration system having 4 operating modes, and each unit cost of electricity and heat products was calculated. In case that a fuel cost is ${\\}400/kg$ and there are no direct and indirect cost, they were calculated as follows; electricity cost of ${\\}23,700/GJ$ at gas-turbine mode, electricity cost of ${\\}15,890/GJ$ at combined cycle mode, electricity cost of ${\\}14,146/GJ$ and heat cost of ${\\}6,466/GJ$ at cogeneration mode, and electricity cost of ${\\}14,387/GJ$ and heat cost of ${\\}4,421/GJ$ at combined cycle cogeneration mode. Further, these unit costs are applied to account benefit on this system. Since the suggested OECOPC method can be applied to any energy system, it is expected to contribute to cost accounting of various energy systems.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.327-340
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• 2021

Purpose: The purpose of this study is to propose a system predicting whether an electricity distribution system is abnormal by analyzing the temperature of the deteriorated system. Traditional electricity distribution system abnormality diagnosis was mainly limited to post-inspection. This research presents a remote monitoring system for detecting thermal images of the deteriorated electricity distribution system efficiently hereby providing safe and efficient abnormal diagnosis to electricians. Methods: In this study, an object detection algorithm (YOLOv5) is performed using 16,866 thermal images of electricity distribution systems provided by KEPCO(Korea Electric Power Corporation). Abnormality/Normality of the extracted system images from the algorithm are classified via the limit temperature. Each classification model, Random Forest, Support Vector Machine, XGBOOST is performed to explore 463,053 temperature datasets. The process capability index is employed to indicate the quality of the electricity distribution system. Results: This research performs case study with transformers representing the electricity distribution systems. The case study shows the following states: accuracy 100%, precision 100%, recall 100%, F1-score 100%. Also the case study shows the process capability index of the transformers with the following states: steady state 99.47%, caution state 0.16%, and risk state 0.37%. Conclusion: The sum of caution and risk state is 0.53%, which is higher than the actual failure rate. Also most transformer abnormalities can be detected through this monitoring system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1019-1023
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• 2018

In this paper sensitivity analysis of temperature on special day electricity demand of land and Jeju Island is performed. The basic electricity demand per 3 hours is defined as electricity demand that reflects the GDP effect without the temperature influence. The temperature sensitivity per 3 hours is calculated through the relationship between special day electricity demand normalized to basic electricity demand and temperature. In the future, forecast error will be improved if the temperature sensitivity per 3 hours is applied to the special day load forecasting.

• New & Renewable Energy
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• v.3 no.4
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• pp.31-37
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• 2007

Because of their low operating and maintaining costs, ground-source heat pump(GSHP) systems are an increasingly popular choice for providing heating, cooling and water heating to public and commercial buildings. Despite these advantages and the growing awareness, GSHP systems to residential sectors have not been adopted in Korea until recently. A feasibility study of a residential GSHP system was therefore conducted using the traditional life cycle cost(LCC) analysis within the current electricity price framework and potential scenarios of that framework. As a result, when the current residential electricity costs for running the GSHP system are applied, the GSHP system has weak competitiveness to conventional HV AC systems considered. However, when the operating costs are calculated in the modified price frameworks of electricity, the residential GSHP system has the lower LCC than the existing cooling and heating equipments. The calculation results also show that the residential GSHP system has lower annual prime energy consumption and total pollutant emissions than the alternative HVAC systems considered in this work.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.64-69
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• 2009

Because of their low operating and maintaining costs, ground-source heat pump(GSHP) systems are an increasingly popular choice for providing heating, cooling and water heating to public and commercial buildings. Despite these advantages and the growing awareness, GSHP systems to residential sectors have not been adopted in Korea until recently. A feasibility study of a residential GSHP system was therefore conducted using the traditional life cycle cost(LCC) analysis within the current electricity price framework and potential scenarios of that framework. As a result, when the current residential electricity costs for running the GSHP system are applied, the GSHP system has weak competitiveness to conventional HVAC systems considered. However, when the operating costs are calculated in the modified price frameworks of electricity, the residential GSHP system has the lower LCC than the existing cooling and heating equipments. The calculation results also show that the residential GSHP system has lower annual prime energy consumption and total greenhouse gas emissions than the alternative HVAC systems considered in this work.