Long-Term Reserve Expansion of Power Systems With High Wind Power Penetration Using Universal Generating Function Methods

Ding, Yi; Wang, Peng; Goel, L.; Loh, Poh Chiang; Wu, Qiuwei

doi:10.1109/tpwrs.2010.2054841

Cited by 135 publications

(63 citation statements)

References 35 publications

(52 reference statements)

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“…Consequently, the second stage of the operation problem can be derived as the following optimization problem (13)(14)(15)(16)(17)(18)(19). In the second stage problem, the CLC model [16,17] has been considered to analyze the impact of various EV charging scenarios with different charging patterns.…”

Section: Two-stage Decomposed Stochastic Integrated Generation and Trmentioning

confidence: 99%

“…In the second stage problem, the CLC model [16,17] has been considered to analyze the impact of various EV charging scenarios with different charging patterns. Integrated generation and transmission investment decisions are determined at intervals of one year, while the time scale of operation problem is one hour interval.…”

Section: Two-stage Decomposed Stochastic Integrated Generation and Trmentioning

confidence: 99%

“…In this paper, the probabilistic distribution of EV charging loads is estimated by LHRS [15] to reduce the computational effort of the proposed method. In this paper, the hourly chronological load curve (CLC) [16,17] is used to study the effect of hourly charging patterns of EVs on the integrated generation and transmission planning problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic Integrated Generation and Transmission Planning Incorporating Electric Vehicle Deployment

Moon¹,

Kong²,

Joo³

et al. 2013

Journal of Electrical Engineering and Technology

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-The power industry is currently facing many challenges, due to the new environment created by the introduction of smart grid technologies. In particular, the large-scale deployment of electric vehicles (EVs) may have a significant impact on demand for electricity and, thereby, influence generation and transmission system planning. However, it is difficult to deal with uncertainties in EV charging loads using deterministic planning methods. This paper presents a two-stage stochastic decomposition method with Latin-hyper rectangle sampling (LHRS) to solve the integrated generation and transmission planning problem incorporating EV deployment. The probabilistic distribution of EV charging loads is estimated by Latin-hyper rectangle sampling (LHRS) to enhance the computational performance of the proposed method. Numerical results are presented to show the effectiveness of the proposed method.

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Section: Two-stage Decomposed Stochastic Integrated Generation and Trmentioning

confidence: 99%

Section: Two-stage Decomposed Stochastic Integrated Generation and Trmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic Integrated Generation and Transmission Planning Incorporating Electric Vehicle Deployment

Moon¹,

Kong²,

Joo³

et al. 2013

Journal of Electrical Engineering and Technology

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“…However, the intermittent nature of renewable energy systems calls for the integration of conventional sources and appropriate energy storage systems in order to maintain system reliability. In this way, reliability evaluation studies of renewable energy systems integrated with conventional sources [13][14][15][16][17] and storage systems [18][19][20][21][22][23] have been extended in the literature. In this regard, the simulation-based method is computationally cumbersome, in which the wind speed is predicted through a time series analysis of the past data [23,24].…”

Section: Introductionmentioning

confidence: 99%

Reliability assessment of a standalone wind-conventional/energy storage system using probabilistic production simulation method

Delavaripour¹,

Dehkordi²

2015

Turk J Elec Eng & Comp Sci

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This paper presents a reliability evaluation technique for a small standalone system including wind and conventional resources integrated with an energy storage system. A battery bank is considered as the energy storage system. The focus in this analysis is on the effect of battery modeling and its performance on system reliability. In this way, this paper presents an accurate model for the electrochemical batteries, which takes into account all the influential characteristics of the battery related to reliability studies, such as depth of charge, efficiency of charge, and rate of discharge. The proposed reliability evaluation method is based on the combination of a probabilistic production simulation technique as an analytical method and time series technique, in the domain of adequacy studies. By using the probabilistic production simulation technique, not only can the reliability of wind-conventional resources be assessed, but the fuel consumption can also be analyzed. On the other hand, by using the time series approach, the time variation of wind speed and operating constraints of the battery bank can be considered in evaluating the reliability indices. In this paper, a typical lead-acid battery bank is used. It is shown how the accurate modeling of the battery bank can affect the reliability indices. Beneficial results are obtained for different operating strategies and wind penetrations and are then compared.

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“…Compared with Monte Carlo simulation techniques [1][2][3][4] and analytical methods [5][6][7][8], an Universal Generating Function (UGF) technique [9,10] is the most promising analysis tool for the probabilistic production simulation of power systems, and has been widely used in the probabilistic production simulation analysis of power systems.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic production simulation of a power system with wind power penetration based on improved UGF techniques

Wang

Liu

Wang

2013

J. Mod. Power Syst. Clean Energy

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Universal Generating Function (UGF) techniques have been applied to Multi-State System (MSS) reliability analysis, such as long term reserve expansion of power systems with high wind power penetration. However, using simple steady-state distribution models for wind power and large generating units in reliability assessment can yield pessimistic appraisals. To more accurately assess the power system reliability, UGF techniques are extended to dynamic probabilistic simulation analysis on two aspects of modelling improvement. Firstly, a principal component analysis (PCA) combined with a hierarchal clustering algorithm is used to achieve the salient and time-varying patterns of wind power, then a sequential UGF equivalent model of wind power output is established by an apportioning method. Secondly, other than the traditional two-state models, the conventional generator UGF equivalent model is established as a four discrete-state continuous-time Markov model by Lztransform. In the construction process of such a UGF model, the state values are transformed into the integral multiples of one common factor by choosing proper common factors, thus effectively restraining the exponential growth of its state number and alleviating the explosion thereof. The method is suitable for reliability assessment with dynamic probabilistic distributed random variables. In addition, by acquiring the clustering information of wind power, the system reliability indices, such as fuel cost and CO 2 emissions through different seasons and on different workdays, are calculated. Finally, the effectiveness of the method is verified by a modified IEEE-RTS 79 system integrated with several wind farms of historical hourly wind power data of Zhangbei wind farm in North China.

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Long-Term Reserve Expansion of Power Systems With High Wind Power Penetration Using Universal Generating Function Methods

Cited by 135 publications

References 35 publications

Stochastic Integrated Generation and Transmission Planning Incorporating Electric Vehicle Deployment

Stochastic Integrated Generation and Transmission Planning Incorporating Electric Vehicle Deployment

Reliability assessment of a standalone wind-conventional/energy storage system using probabilistic production simulation method

Probabilistic production simulation of a power system with wind power penetration based on improved UGF techniques

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