• Journal of Korea Water Resources Association
• /
• v.53 no.spc1
• /
• pp.785-795
• /
• 2020

Recently, the integrated management of the quantity and quality of water was been derived by the Ministry of Environment in Korea. This reconstruction in the national government organization can be recognized as the major politic measure. For this IWRM (Integrated Water Resources Management), it is necessary to be able to fairly distribute, operate and manage water resources in a situation where related techniques are needed to fully support, such as measuring exactly the demand and supply of water resources. The reason why IWRM is difficult, despite the development of related technologies, is because the management entities are highly diverse and their interests are much complicated. Thus, this study is tried to suggest specific improvement for current policies by benchmarking the KPX (Korea Power Exchange). In the field of water management as similar to the electric industry, there is an essential need for a working-level organization that can manage, control, monitor, and regulate water resources with practical and plenipotentiary control like the non-profit organization, KPX. Such time has come for decisive policy changes through benchmarking the structure, system, productivity, and challenges of the electricity industry in the water policy.

• Korean Journal of Artificial Intelligence
• /
• v.12 no.1
• /
• pp.17-24
• /
• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.3
• /
• pp.375-383
• /
• 2015

The virtual power plant (VPP) is a new technology to achieve flexibility as well as controllability, like traditional centralized power plants, by integrating and operating different types of distributed energy resources (DER) with the information communication technology (ICT). Though small-sized DERs may not be controlled in a centralized manner, these are more likely to be utilized as power plants for centralized dispatch and participate in the energy trade given that these are integrated into a unified generation profile and certain technical properties such as dispatch schedules, ramp rates, voltage control, and reserves are explicitly implemented. Unfortunately, the VPP has been in a conceptual stage thus far and its common definition has not yet been established. Such a lack of obvious guidelines for VPP may lead to a further challenge of coming up with the business model and reinforcing the investment and technical support for VPP. In this context, this paper would aim to identify the definition of VPP as a critical factor in smart grid and, at the same time, discuss the details required for VPP to actively take part in the electricity market under the smart grid paradigm.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.10
• /
• pp.1399-1411
• /
• 2012

The grid frequency is controlled cooperatively by the governor of the Turbine-Generator and the automatic generation controller(AGC) of the KPX(Korea Power Exchange). It is a basic requirement that the reliability of the governor is verified to enhance the power system stability but it is not easy to confirm the response characteristics of the governor because all generators are operated in the grid system that has the constant voltage and frequency. Therefore, it is necessary to study a new test method in order to examine the governor dynamic characteristic in the similar fault conditions. A study has shown that it is verified to simulate the turbine-generator power control system, the governor response characteristic under limited conditions and contribution of AGC with the gas turbine generator simulation model as well as demonstrate the dynamic response of the governor with the developed governor dynamic characteristic tester based on digital controller while the turbine-generator is connected to the grid system. This tester is constructed by the built-in functions of the turbine-generator main controller. In this treatise, the theoretical background, development method and the results of both simulations and demonstrations are described as another way to verify the turbine-generator governor dynamic characteristics.

• Proceedings of the KIEE Conference
• /
• 2005.11b
• /
• pp.162-164
• /
• 2005

This paper presents an hour-ahead System Marginal Price (SMP) forecasting framework based on a neural network. Recently, the deregulation in power industries has impacted on the power system operational problems. The bidding strategy of market participants in energy market is highly dependent on the short-term price levels. Therefore, short-term SMP forecasting is a very important issue to market participants to maximize their profits. and to market operator who may wish to operate the electricity market in a stable sense. The proposed hybrid neural network is composed of tow parts. First part of this scheme is pattern classification to input data using Kohonen Self-Organizing Map (SOM) and the second part is SMP forecasting using back-propagation neural network that has three layers. This paper compares the forecasting results using classified input data and unclassified input data. The proposed technique is trained, validated and tested with historical date of Korea Power Exchange (KPX) in 2002.

• Proceedings of the KIEE Conference
• /
• 2005.11b
• /
• pp.148-150
• /
• 2005

Since the SMP(System Marginal Price) is a vital factor to the market participants who intend to maximize the their profit and to the ISO(Independent System Operator) who wish to operate the electricity market in a stable sense, the short-term marginal price forecasting should be performed correctly. This paper presents a methodology of a day-ahead SMP forecasting using ARIMA(Autoregressive Integrated Moving Average) based on the Time Series. And also we suggested a correction algorithm to minimize the forecasting error in order to improve efficiency and accuracy of the SMP forecasting. To show the efficiency and effectiveness of the proposed method, the numerical studies have been performed using Historical data of SMP in 2004 published by KPX(Korea Power Exchange).

• Proceedings of the KIEE Conference
• /
• 2001.05a
• /
• pp.230-232
• /
• 2001

This paper presents a model for short-term load forecasting using multiple time-series. We made one-hour ahead load forecasting without classifying load data according to daily load patterns(e.g. weekday. weekend and holiday) To verify its effectiveness. the results are compared with those of neuro-fuzzy forecasting model(5). The results show that the proposed model has more accurate estimate in forecasting.

• Proceedings of the KIEE Conference
• /
• 2003.11a
• /
• pp.438-440
• /
• 2003

This paper resents an application of an Artificial Neural Network(ANN) technique to forecast the short-term system marginal price(SMP). The forecasting of SMP is a very important factor in an electricity market for the optimal biddings of market participants as well as for the market stabilization of regulatory bodies. The proposed neural network scheme is composed of three layers. In this process, input data are set up to reflect market conditions. And the $\lambda$ that is the coefficient of activation function is modified in order to give a proper signal to each neuron and improve the adaptability for a neural network. The reposed techniques are trained validated and tested with the historical real-world data from korea Power Exchange(KPX).

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.7
• /
• pp.816-823
• /
• 2018

The Auto-synchronizer is essential equipment for synchronizing a generator to the power system. It is performing that measurement of the magnitude, frequency and phase of the voltage signal of the power system and generator. It is important to select the appropriate measurement algorithm for preventing various problem such as mechanical stress and Electrical problem. Teager Energy Operator(TEO) and Discrete separation algorithm(DESA) is measurable the instantaneous parameters of a sine wave using 5 samples and can be measured at a fast and with a simple operation. Therefore it has many advantages in measuring the parameters. In this paper, it confirmed measurement results using matlab simulations when there are synchronized in order of frequency, magnitude. Also it presented methods using digital filters and sample intervals to improve accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.12
• /
• pp.1765-1773
• /
• 2012

The accuracy of load forecast is very important from the viewpoint of economical power system operation. In general, the weekdays' load demand pattern has the continuous time series characteristics. Therefore, the conventional methods expose stable performance for weekdays. In case of special days or weekends, the load demand pattern has the discontinuous time series characteristics, so forecasting error is relatively high. Especially, weekdays near the thanksgiving day and lunar new year's day have the mixed load profile characteristics of both weekdays and weekends. Therefore, it is difficult to forecast these days by using the existing algorithms. In this study, a new load forecasting method is proposed in order to enhance the accuracy of the forecast result considering the characteristics of weekdays and weekends. The proposed method was tested with these days during last decades, which shows that the suggested method considerably improves the accuracy of the load forecast results.