• 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.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.

• KIEE International Transactions on Power Engineering
• /
• v.5A no.3
• /
• pp.303-308
• /
• 2005

The restructure of the electrical power industry is accompanied by the extension of the electrical power exchange. One of the key pieces of information used to determine how much power can be transferred through the network is known as available transfer capability (ATC). The traditional ATC deterministic approach is based on the severest case and it involves a complex procedure. Therefore, a novel approach for A TC calculation is proposed using cost optimization in this paper. The Jeju Island interconnected HVDC system has inland KEPCO (Korean Electric Power Corporation) systems, and its demand is increasing at the rate of about $\10[%]$ annually. To supply this increasing demand, the capability of the HVDC system must be enlarged. This paper proposes the optimal transfer capability of the HVDC system between Haenam in the inland and Jeju in Cheju Island through cost optimization. The cost optimization is based on generating cost in Jeju Island, transfer cost through Jeju-Haenam HVDC system and outage cost with one depth (N-1 contingency).

• Proceedings of the KIEE Conference
• /
• 2003.11a
• /
• pp.294-296
• /
• 2003

본 논문에서는 우리나라 7개 지역의 시송전 선로를 구성하여 전력계통안정화장치(Power System Stabilizer)의 특성을 분석한다. Black start 발전기의 전력은 각 지역의 시송전 경로를 통하여 우선공급발전기에 공급한다. 이는 한국전력거래소(KPX)에서 규정하고 있으며, 주선로와 예비선로로 구성되어 있다. PSS의 성능 분석은 PSS1A type을 부착하여 수행하였다. 시뮬레이션 패키지는 EMTDC를 사용하여 그 특성을 검토하였다.

• Journal of Electrical Engineering and Technology
• /
• v.5 no.1
• /
• pp.54-60
• /
• 2010

This paper presents a new algorithm for determining generator operation point for maintaining stability considering generator faults in Jeju-Haenam HVDC system. As the HVDC system consumes reactive power for the transmission of active power substantially, compensation of reactive power is essential. And the HVDC system is operated on frequency control mode. That is to say, the HVDC system almost manages system frequency. Therefore, we recognized that the Jeju system could be unstable if the reactive power consumed by the HVDC is insufficient when out-of-step occurs with large generators. When the solution of power flow analysis does not converge due to the unstable system phenomenon, we have difficulty in establishing countermeasures as the post-fault information is not available. In this paper, for the purpose of overcoming this difficulty in establishing countermeasures, we introduce the CPF(Continuation Power Flow) algorithm. This paper suggests an algorithm for calculating the output limitation of the generator to maintain the stability in case of generator fault in the Jeju system.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.473-474
• /
• 2007

The Jeju power grid experienced several major power disturbances over the last decade. The postmortem studies of the incidents indicated that some of the generating units did not respond as predicted by system analysis & studies. Consequently, the Korean Power Exchange (KPX) mandated that all units (generators, excitation, governor and turbine systems) in the Korean network greater than or equal to 20MVA be tested to verify the generator reactive power limits as well as the dynamic model data being used for system studies. This paper presents field experiences of the authors in testing and modeling of steam turbines and their associated governors during the generator and model validation.

• International Journal of Precision Engineering and Manufacturing-Green Technology
• /
• v.5 no.5
• /
• pp.623-630
• /
• 2018

The renewable energy systems have been in the spotlight as an alternative for environmental issues. Therefore, the governmental policies are being implemented to spread of promote power generation system using renewable energy in various countries around the world. In addition, Korea has also developed a policy called the power trading contract which can profit from electricity produced from renewable power generation system through Korea Electric Power Corporation (KEPCO) and Korea Power Exchange (KPX). As a result, the power trading contracts can trade power after self-consuming in-house by using small-scale renewable power system for residential customers as well as electricity retailers. The power trading contracts applicable as a small-scale power system have a 'Net metering (NM)' and a 'Power Purchase Agreement (PPA)', and these two types of power trading contracts trade surplus power, but payment method of each power trading is different. The microgrid proposed in this paper is based on grid connected microgrid using Photovoltaic (PV) system and Energy Storage System (ESS), that supplied power to residential demand, we evaluate the operation cost of microgrid by power demand in each power trading contracts and propose the appropriate power trading contracts according to electricity demand.

• 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.

• Proceedings of the KIEE Conference
• /
• 2011.07a
• /
• pp.380-381
• /
• 2011

전력거래소는 2014년 나주본사이전 이후 1억kW 대용량 전력계통을 운영하기 위한 차기 계통운영시스템(이하 차기EMS) 구축을 계획하고 있다. EMS(Energy Management System)는 전국의 발, 변전소에서 계통정보를 실시간으로 취득하여 전력계통을 감시하며, 연료비 기반의 최적 경제점을 찾아 발전기를 제어하고, 전력계통을 수식화한 상태추정 결과를 기반으로 상정사고분석, 고장전류계산 등 전력계통운영을 위한 종합시스템이다. 국내 EMS의 역사는 1979년 미국의 L&N 시스템 도입을 시작으로 1988년 일본의 Toshiba EMS, 2001년 Alstom사의 NEMS를 구축하여 현재 운영중에 있다. 하지만, 외산 제품 도입에 따른 기술이전, 유지보수의 어려움을 타개하기 위해 2004년 한국형 EMS(이하 K-EMS) 연구개발계획을 수립하고 전력거래소를 주축으로 한 산학연을 구성하여 2010년 K-EMS 개발을 성공적으로 완료하였다. 차기 EMS는 국내 기술력으로 개발된 정부연구과제 성과물인 K-EMS를 기반으로 구축이 이루어지며, 총 3년간의 개발 및 시험과정을 거쳐 실계통운영을 담당할 예정이다. 차기EMS가 설치되어 운영예정인 급전소는 전력수급 균형유지와 발전소 운영 총괄 지휘 및 765kV, 345kV 송전망 운영역할을 담당할 나주급전소와 154kV 비수도권 송전망 운영을 담당할 천안급전소, 154kV 수도권 송전망 운영을 책임질 서울급전소 이상 3곳이다. 차기EMS는 발전 및 송, 변전 설비의 대형화, 다양한 FACTS 설비, 신재생에너지원으로 대표되는 분산전원의 등장과 같은 급변하는 전력계통 변화에 능동적인 역할을 성공적으로 수행할 것으로 기대하고 있다.

• Journal of Korean Society for Atmospheric Environment
• /
• v.33 no.4
• /
• pp.393-402
• /
• 2017

In this study, greenhouse gas (GHG) reductions from bioenergy (biogas, biomass) have been estimated in Korea, 2015. This study for construction of reduction inventories as direct and indirect reduction sources was derived from IPCC 2006 guidelines for national greenhouse gas inventories, guidelines for local government greenhouse inventories published in 2016, also purchased electricity and steam indirect emission factors obtained from KPX, GIR respectively. As a result, the annual GHG reductions were estimated as $1,860,000tonCO_{2eq}$ accounting for 76.8% of direct reduction (scope 1) and 23.2% of indirect reduction (scope 2). Estimation of individual greenhouse gases (GHGs) from biogas appeared that $CO_2$, $CH_4$, $N_2O$ were $90,000tonCO_2$ (5.5%), $55,000tonCH_4$ (94.5%), $0.3tonN_2O$ (0.004%), respectively. In addition, biomass was $250,000tonCO_2$ (107%), $-300tonCH_4$ (-3.2%), $-33tonN_2O$ (-3.9%). For understanding the values of estimation method levels, field data (this study) appeared to be approximately 85.47% compared to installed capacity. In details, biogas and biomass resulting from field data showed to be 76%, 74% compared to installed capacity, respectively. In the comparison of this study and CDM project with GHG reduction unit per year installed capacity, this study showed as 42% level versus CDM project. Scenario analysis of GHG reductions potential from bioenergy was analyzed that generation efficiency, availability and cumulative distribution were significantly effective on reducing GHG.