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Due to the uncertainty energy resources, the distributed renewable energy supply usually leads to the highly unstable reliability of power system. For instance, power system reliability can be affected by the high penetration of large-scale wind turbine generators (WTG). Therefore, energy storage system (ESS) is usually installed with the distributed renewable energy generation to improve the power system reliability by smoothing out the fluctuations and improve the supply and demand balance. This paper aims to analyze the power system reliability by developing the multi-ESSs coordinated with WTGs model, which is each ESS linked with each WTG. The main indices for reliability evaluation are loss of load expectation, expected energy not served and energy index of reliability. Monte Carlo simulation method is used in this study. Furthermore, this paper demonstrates various sensitivity analysis of multi-ESSs model include the impact of various ESS capacity, peak load, WTG dispatch restriction index in China power system among case study. In addition, the capacity credit of WTG with multi-ESSs were evaluated in this study.

In general, resiliency of the power system can be defined as "the ability of the power system to quickly return to its original state from external shocks, such as natural disasters characterized by great influence and low probability of occurrence " Data is needed to study resiliency of the power system However, even though the probability of an accident such as the Chernobyl nuclear accident and the Fukushima nuclear accident is extremely low, it is difficult to establish a database of actual accidents that once occurred accidents can bring a crisis to the survival of mankind System resiliency, therefore, is very hot issue in recent This paper develops new function and models in order to identify characteristics of the resiliency for COVID-19 confirmer data for seven countries © 2021 Korean Institute of Electrical Engineers All rights reserved

This paper proposes a novel dynamic voltage instability expectation index which takes into account dynamic voltage stability characteristics, the long-term phenomena and probability state of power system contingency. This index is a meter to measure the general robust power system under critical cases which are identified by contingency analysis. The critical contingencies are precisely analyzed by steady-state power load flow in perturbations to figure out violation cases, the weakness of the power system. The probability states of each of violation cases are calculated through availability and unavailability values of devices. The degree of voltage instability is estimated newly by the smallest determinant of dynamic load flow Jacobian matrix in evolution time of power system simulation. The variables of dynamic devices such as under load tap changer of the transformer, excitation limiters of generator unit, affected on a long-term characteristic of the power system, are considered in dynamic load flow Jacobian matrix. Combining the voltage instability rate and the probability state, a novel index, namely voltage instability expectation in dynamic index, is proposed. This index reflects the mutual correlation between the voltage instability rate and probable contingency cases which result in either voltage violation, overload flow, or voltage instability. The new index is demonstrated in IEE 9-bus test system and actual Vietnam power system. It should be a new measure to make a decision of transmission system expansion planning.

This study aims to analyze the reliability of wind power generator in Jeju Island power system in order to maintain the operational state of power system at the reliability levels. It is also described an algorithm for probabilistic production cost credit evaluation of Wind Turbine Generators (WTG). An analysis on generated capacity of wind power generator with Tamla offshore wind farm according to two different average wind speeds such as 11.8 [m/s] and 12.8 [m/s], and without Tamla offshore wind farm have been done in terms of Effective Load Carrying Capability (ELCC) and Capacity Credit (C.C.) in power system from the perspective of Loss of Load Expectation (LOLE). According to the changing of wind speed in the Tamla offshore wind farm, this paper demonstrates how it changes the ELCC and C.C. shown as case studies.

This paper proposes a conversion function and a method to transform LOLED (days/year) using daily peak load curve into LOLEH (hours/year) using hourly load curve and describes a relationship between LOLED and LOLEH. The indices cannot only be just transformed arithmetically but also have different characteristics physically because of using their different load curves. The conversion function is formulated as variables of capacity and a forced outage rate of generator, hourly load daily load factor, daily peak load yearly load factor and etc. Therefore, the conversion function (γ = φ(.)) is not simple. In this study, therefore, the function is formulated as linear times of separated two functions. One is an exponential formed conversion function of daily load factor and the other is a formulated with an exponential typed conversion function of daily peak load yearly load factor. Furthermore, this paper presents an algorithm and a flow chart for transforming LOLED into LOLEH. The proposed conversion function is applied to the actual KPS (Korea Power System) in 2015. The exponent coefficients of the conversion functions are assessed using the proposed method. Finally, assessment errors using the conversion function for case study of the actual system are evaluated to certify as a first proposed method.

A power system has become fragile due to climate change. The weather events, for example, Hurricanes Katrina, Sandy in the US and Tohoku Earthquake in Japan, caused enormous economic losses and have imprinted people in making the power system more resilient under natural disasters. In order to evaluate the impacts of natural disasters on the power system, resiliency is necessarily needed. But in contrast to power system reliability, power system resiliency is less known relatively and there is no widely accepted concept for itself. Therefore, this paper shows the fundamental concept for power system resiliency, some reasons those make power system vulnerable including natural disasters, and the method to evaluate the resiliency.

This paper presents an application of time series analysis in hourly wind speed simulation and forecast in Jeju Island, Korea. Autoregressive – moving average (ARMA) model, which is well in description of random data characteristics, is used to analyze historical wind speed data (from year of 2010 to 2012). The ARMA model requires stationary variables of data is satisfied by power law transformation and standardization. In this study, the autocorrelation analysis, Bayesian information criterion and general least squares algorithm is implemented to identify and estimate parameters of wind speed model. The ARMA (2,1) models, fitted to the wind speed data, simulate reference year and forecast hourly wind speed in Jeju Island.

This paper presents a study on reliability assessment and new contribution function development of power system including Wind Turbine Generator(WTG) combined with Battery Energy Storage System(BESS). This paper develops and proposes new reliability contribution function of BESS installed at wind farms. The methodology of reliability assessment, using Monte Carlo Simulation(MCS) method to simulate sample state duration, is proposed in detail. Forced Outage Rate(FOR) considered probabilistic approach for conventional generators is modelled in this paper. The penetration of large wind power can make risk to power system adequacy, quality and stability. Although the fluctuation of wind power, BESS installed at wind farms may smooth the wind power fluctuation. Using small size system as similar as Jeju island power system, a case study of reliability evaluation and new proposed contribution function of power system containing WTG combined with BESS is demonstrated in this paper, which would contributes to BESS reliability contribution and assessment tools of actual power system in future. Key Words : Wind turbine generator(WTG), Battery energy storage system(BESS), Monte carlo simulation(MCS), Reliability evaluation, ESS reliability contribution † Corresponding Author : Dept. of Electrical Eng., RIGET, ERI, Gyeongsang National University, Korea E-mail : jschoi@gnu.ac.kr* Dept. of Electrical Engineering, Gyeonsang National University , Korea ** KEPRI, KEPCO*** Dept. of Electrical Engineering, Daejin University, Korea Received : September 9, 2015; Accepted : February 19, 2016

This paper describes an alternative model, methodology, and case study for probabilistic reliability-based planning for High-Voltage Direct-Current (HVDC) grid expansion in a power system that includes wind turbine generators (WTGs). Ensuring the reliability of such a power system requires multi-state simulation because of the intermittent characteristics of wind speed. The power system includes an AC/DC converter and DC/AC inverter substation. The substation is composed of many thyristor devices, which can potentially fail. This problem was formulated as a hierarchical level II (HLII) reliability evaluation because HVDC transmission lines are considered. Using the proposed method, new HVDC contact bus points with the best reliability level can be chosen. The methodology was applied to a small-scale model power system.

This paper demonstrated a program that establishes Generator Maintenance Scheduling(GMS) that considers economic feasibility, environmentality, reliability, and supply reserve rate in a variety of aspects. This program aims for helping decision-makers to set up GMS by integrating various opinions using Fuzzy method. Probabilistic generation simulation in this program calculated the amounts of generation using Booth-Baleriaux Method and drew a value approximating to an optimal solution using GA in order to calculate a non-linear GMS value possessing infinite numbers. A case study set a standard value calculated by crisp in the system similar with the Korean system to aspiration level of fuzzy method and showed searching for satisfaction level by changing generation costs, the amounts of CO 2 emission, LOLE, and SRR. On top of that, it established GMS and showed the condition of generation system.

This paper proposes visualization system of generator maintenance scheduling considering various kinds of objective functions. The various kinds of objective functions include maximizing the reserve rate, maximizing probabilistic reliability and minimizing probabilistic production cost, minimizing CO 2 air pollution and maximizing satisfaction level of decision maker using fuzzy set theory. The probabilistic reliability objective includes LOLE (Loss of load expectation), EENS (Expected energy not served) and EIR(Energy index of reliability). Visualization of generator maintenance scheduling simulation systems is to compare each of the reliability, production cost and environmental according to each objective function. The practicality and effectiveness of the proposed approach are demonstrated in the simulation for a real-size power system model in Korea South-East Power CO. (KOSEP).

This paper proposes probabilistic generation assessment system introduction of renewable energy generators. This paper is focused on wind turbine generator and solar cell generator. The proposed method uses an assessment model based on probabilistic model considering uncertainty of resources (wind speed and solar radiation). Equivalent generation function of the wind and solar farms are evaluated. The equivalent generation curves of wind farms and solar farms are assessed using regression analysis method using typical least square method from last actual generation data for wind farms. The proposed model is applied to Korea Renewable Generation System of 8 grouped 41 wind farms and 9 grouped around 600 solar farms in South Korea.

This paper proposes a probabi listic generatio n assessment mo del of renewable energy generators(REGs) considering uncertainty of resources, mainly focused on Wind Turbine Genera tor(WTG) and Solar Cell Generator(SCG) which are dispersed widely in South Korea The propos ed numerical analysis method as sesses the one day-ahead generation by combining equivalent generation characteristics function and probabilisti c distribution function of wind speed(WS) and solar radiation(S R) resources. The equivalent generat ion functions(EGFs) of the win d and solar farms are established by grouping a lot of the farms appropriately centered on W eather Measurement Station(WMS ). First, the EGFs are assessed by using regression analysis method based on typical least squ are method from the recorded a ctual generation data and hist orical resources(WS and SR). Second, the generation of the REGs is assessed by adding the on e day-ahead resources forecast, announced by WMS, to the EGFs which are formulated as third order degree polynomials usi ng the regression analysis. Third, a Renewable Energy Generation Assessment System(REGA S) including D/B of recorded a ctual generation data and hist orical resources is developed using the model and algorithm pre dicting one day-ahead power ou tput of renewable energy generators.

This paper proposes probabilistic reliability evaluation model of power system considering Wind Turbine Generator(WTG) integrated with Energy Storage System(ESS). Monte carlo sample state duration simulation method is used for the evaluation. Because the power output from WTG units usually fluctuates randomly, the power cannot be counted on to continuously satisfy the system load. Although the power output at any time is not controllable, the power output can be utilized by ESS. The ESS may make to smooth the fluctuation of the WTG power output. The detail process of power system reliability evaluation considering ESS cooperated WTG is presented using case study of Jeju island power system in the paper.

This paper proposes power supply assessment model of renewable energy generators(REG). The REGs have uncertainty of resource supply. Therefore, the REGs have probabilistic power characteristics. The analytical probabilistic power assessment model of the REGS is developed in this paper. This paper is focused on Wind Turbine Generator(WTG) and Solar Cell Generator(SCG). The assessment model based on probabilistic model considering uncertainty of resources(wind speed and solar radiation) is developed. The power is assessed from expected value based the proposed model. The proposed model is applied two simple systems, which have a WTG or a SCG in this paper.

This study develops alternative user friendly generator maintenance scheduling(GMS) considering not only probabilistic reliability maximization but also probabilistic production cost minimization. Furthermore, the proposed GMS system has various kinds of objective functions as like as CO2 minimization. The probabilistic reliability objective includes LOLE(Loss of load expectation), EENS(Expected energy not served) and EIR(Energy index of reliability). Production cost is evaluated with consideration of uncertainties of generators. In actual system case study describes effectiveness and user friendly of generator maintenance scheduling simulation systems proposed in this paper. Additionally, the practicality and effectiveness of the proposed approach are demonstrated in the simulation for a real-size power system model in Korea South-East Power CO.(KOEN).

This paper develops a conversion function and method transforming from daily peak load curve used LOLE D [days/year] to hourly load curve used LOLE H [hours/year] firstly. The indices can not only be conversed just arithmetically but also have different characteristics physically because of using their different load curves. The conversion function is formulated as variables of capacity and forced outage rate of generator, hourly load daily load factor and daily peak load yearly load factor, etc. Therefore, the conversion function (γ = ϕ(·)) can not be formulated in simple but in complex and difficult. In this study, therefore, the function is formulated as linear times of separated two functions. One is exponential formed conversion function of daily load factor. Another is formulated exponential typed conversion function of daily peak load yearly load factor. Furthermore, this paper presents algorithm and flow chart for conversing from LOLE D [days/year] to LOLE H [hours/year]. The proposed conversion function is applied to sample system and actual KPS(Korea Power System) in 2015. The exponent coefficients of the conversion functions are assessed using proposed method. Finally, assessment errors using conversion function for case studies of sample system and actual system are evaluated to certify the firstly proposed method.