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Scheduling and Control of Energy Storage for Improving the Frequency Response of Power Systems.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Scheduling and Control of Energy Storage for Improving the Frequency Response of Power Systems./
作者:	Pulendran, Shuthakini.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	201 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10980039
ISBN:	9781085761888

Scheduling and Control of Energy Storage for Improving the Frequency Response of Power Systems.
Pulendran, Shuthakini.

Scheduling and Control of Energy Storage for Improving the Frequency Response of Power Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 201 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2019.

This item must not be sold to any third party vendors.

This thesis proposes a control strategy along with three resource scheduling methods to improve the frequency response of power systems using an energy storage system (ESS). The control strategy prevents transient under-frequency load shedding (TUFLS), unnecessary load disconnections caused by temporary declines in frequency, and operates in two stages: first, it injects maximum scheduled ESS power to immediately arrest frequency deviations, and then it switches to a model predictive controller for the safe withdrawal of support and energy recovery. The controller uses local measurements and has two distinct capabilities: (i) adapting to system changes by employing an online parameter estimator based on extended Kalman filter, and (ii) recovering energy to maintain energy neutrality in the ESS without causing TUFLS.The proposed resource scheduling methods use optimization models based on mixed integer linear programs to allocate resources for the purpose of (i) TUFLS prevention, (ii) frequency regulation, and (iii) the combination of the two. In particular, frequency regulation scheduling is based on compliance to Control Performance Standard 1 (CPS1) in the presence of stochastic load variations and uses a simplified model whose parameters are estimated by matching the response of a comprehensive model implemented in PSS/E. The salient feature is the development of two piecewise linear curves to represent the relationship between (i) thermal generation and ESS power capacities and (ii) ESS power and energy capacities, that could result in satisfactory CPS1 scores. These relationships are then included in the optimization model to solve for resource allocations by minimizing total capacity cost. As such, the formulation avoids complex algorithms that are otherwise required to model frequency dynamics in the optimization problem. The models and strategies implemented individually for both TUFLS prevention and frequency regulation are then combined to schedule a multi-functional ESS that is primarily used for frequency regulation.The proposed control and scheduling methods are evaluated using simulations of 24- bus one-area and/or 48-bus two-area IEEE reliability test systems. The results prove the effectiveness of the proposed methods. In addition, a simulation model for area control error based on autoregressive moving average (ARMA) is also developed and tested.

ISBN: 9781085761888Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Dynamic frequency support

Scheduling and Control of Energy Storage for Improving the Frequency Response of Power Systems.
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500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
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506 $a This item must not be sold to any third party vendors.
520 $a This thesis proposes a control strategy along with three resource scheduling methods to improve the frequency response of power systems using an energy storage system (ESS). The control strategy prevents transient under-frequency load shedding (TUFLS), unnecessary load disconnections caused by temporary declines in frequency, and operates in two stages: first, it injects maximum scheduled ESS power to immediately arrest frequency deviations, and then it switches to a model predictive controller for the safe withdrawal of support and energy recovery. The controller uses local measurements and has two distinct capabilities: (i) adapting to system changes by employing an online parameter estimator based on extended Kalman filter, and (ii) recovering energy to maintain energy neutrality in the ESS without causing TUFLS.The proposed resource scheduling methods use optimization models based on mixed integer linear programs to allocate resources for the purpose of (i) TUFLS prevention, (ii) frequency regulation, and (iii) the combination of the two. In particular, frequency regulation scheduling is based on compliance to Control Performance Standard 1 (CPS1) in the presence of stochastic load variations and uses a simplified model whose parameters are estimated by matching the response of a comprehensive model implemented in PSS/E. The salient feature is the development of two piecewise linear curves to represent the relationship between (i) thermal generation and ESS power capacities and (ii) ESS power and energy capacities, that could result in satisfactory CPS1 scores. These relationships are then included in the optimization model to solve for resource allocations by minimizing total capacity cost. As such, the formulation avoids complex algorithms that are otherwise required to model frequency dynamics in the optimization problem. The models and strategies implemented individually for both TUFLS prevention and frequency regulation are then combined to schedule a multi-functional ESS that is primarily used for frequency regulation.The proposed control and scheduling methods are evaluated using simulations of 24- bus one-area and/or 48-bus two-area IEEE reliability test systems. The results prove the effectiveness of the proposed methods. In addition, a simulation model for area control error based on autoregressive moving average (ARMA) is also developed and tested.
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