Operational Adequacy Studies of Power Systems With Wind Farms and Energy Storages

Wang, Peng; Gao, Zhiyong; Bertling, Lina

doi:10.1109/tpwrs.2012.2201181

Cited by 125 publications

(88 citation statements)

References 31 publications

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“…Using this assumption for the probability density function, two probabilistic indices are formulated: EENS [8] and expected energy not used (EENU) [18]. The former measures the incidence of power shortages, and the latter is a metric for power surpluses.…”

Section: Probabilistic Indices Of Power Shortage and Surplusmentioning

confidence: 99%

Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis

Konishi

Takahashi

2017

Energies

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There is a growing need to connect renewable energy systems (REs), such as photovoltaic systems (PVs), to the power grid for solving environmental problems such as global warming. However, an electricity grid with RE is vulnerable to problems of power shortage and surplus owing to the uncertainty of RE outputs and grid failures. Energy storage systems (ESSs) can be used to solve supply reliability problems, but their installation should be minimized considering their high costs. This study proposes a method to optimize the allocations of PVs and ESSs based on vulnerability analysis, and utilizes our proposed concept of "slow" and "fast" ESSs, which can reflect the influences of both uncertainties: PV outputs and grid failures. Accordingly, this paper demonstrates an optimal allocation of PVs and ESSs that minimizes the amount of ESSs while satisfying the PV installation target and the constraints on supply reliability indices for power shortage and power surplus in the event of a grid failure.

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Section: Probabilistic Indices Of Power Shortage and Surplusmentioning

confidence: 99%

Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis

Konishi

Takahashi

2017

Energies

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“…Studies in Ref. [9] show that the changes in the FOR of the WTG do not have a significant impact on the calculated system reliability indices. It is, therefore, assumed in this paper that the wind farm consists of identical WTG with zero forced outage rates.…”

Section: Reliability Assessment Of Power Systems Incorporating Wind Pmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8]. Therefore, wind farms and generating capacity assessment of power system incorporating wind energy [9][10][11][12][13][14][15][16] should be conducted with previously published wind speed distribution analysis.…”

Section: Introductionmentioning

confidence: 99%

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

Li²,

Yang

et al. 2016

J. Mod. Power Syst. Clean Energy

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An accurate probability distribution model of wind speed is critical to the assessment of reliability contribution of wind energy to power systems. Most of current models are built using the parametric density estimation (PDE) methods, which usually assume that the wind speed are subordinate to a certain known distribution (e.g. Weibull distribution and Normal distribution) and estimate the parameters of models with the historical data. This paper presents a kernel density estimation (KDE) method which is a nonparametric way to estimate the probability density function (PDF) of wind speed. The method is a kind of data-driven approach without making any assumption on the form of the underlying wind speed distribution, and capable of uncovering the statistical information hidden in the historical data. The proposed method is compared with three parametric models using wind data from six sites.The results indicate that the KDE outperforms the PDE in terms of accuracy and flexibility in describing the longterm wind speed distributions for all sites. A sensitivity analysis with respect to kernel functions is presented and Gauss kernel function is proved to be the best one. Case studies on a standard IEEE reliability test system (IEEE-RTS) have verified the applicability and effectiveness of the proposed model in evaluating the reliability performance of wind farms.

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“…Most of these studies have focused on the operation and control strategy of the BESS for the integration with renewable energy, energy management, power quality, and etc. [1][2][3][4][5][6][7][8][9][10][11][12][13]. However, few protection studies in power systems with BESSs have been performed.…”

Section: Introductionmentioning

confidence: 99%

Reclosing scheme using synchronism checking for utilization of BESS in distribution system

Seo¹,

Rhee

2017

J. Mod. Power Syst. Clean Energy

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This paper proposes a reclosing scheme using synchronism checking for utilization of battery energy storage system (BESS) in a distribution system. The algorithm disconnects the faulty phase and keeps the power supply from the BESS to the healthy phase. Synchronism checking between the main source side and the load side is applied to minimize the transients at the reclosing instant. The BESS at the faulty phase is reconnected after checking the successful reclosing. The distribution system including BESS and proposed reclosing scheme are modeled by the electromagnetic transients program (EMTP)/ATPDraw. The various simulations by varing the fault clearing time are conducted and the simulation results are discussed. Also, the relation between proposed reclosing scheme and reliability is discussed.

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Operational Adequacy Studies of Power Systems With Wind Farms and Energy Storages

Cited by 125 publications

References 31 publications

Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis

Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

Reclosing scheme using synchronism checking for utilization of BESS in distribution system

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