語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Participation of distributed loads i...
~
Bilgin, Enes.
FindBook
Google Book
Amazon
博客來
Participation of distributed loads in power markets that co-optimize energy and reserves.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Participation of distributed loads in power markets that co-optimize energy and reserves./
作者:
Bilgin, Enes.
面頁冊數:
191 p.
附註:
Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.
Contained By:
Dissertation Abstracts International75-09B(E).
標題:
Engineering, System Science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3581007
ISBN:
9781321077476
Participation of distributed loads in power markets that co-optimize energy and reserves.
Bilgin, Enes.
Participation of distributed loads in power markets that co-optimize energy and reserves.
- 191 p.
Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.
Thesis (Ph.D.)--Boston University, 2014.
As the integration of Renewable Generation into today's Power Systems is progressing rapidly, capacity reserve requirements needed to compensate for the intermittency of renewable generation is increasing equally rapidly. A major objective of this thesis is to promote the affordability of incremental reserves by enabling loads to provide them through demand response. Regulation Service (RS) reserves, a critical type of bi-directional Capacity Reserves, are provided today by expensive and environmentally unfriendly centralized fossil fuel generators. In contrast, we investigate the provision of low-cost RS reserves by the demand-side. This is a challenging undertaking since loads must first promise reserves in the Hour Ahead Markets, and then be capable of responding to the dynamic ISO signals by adjusting their consumption effectively and efficiently. To this end, we use Stochastic Control, Optimization Theory, and Approximate Dynamic Programming to develop a decision support framework that assists Smart Neighborhood Operators or Smart Building Operators (SNOs/SBOs) to become demand-side-providers of RS reserve.
ISBN: 9781321077476Subjects--Topical Terms:
1018128
Engineering, System Science.
Participation of distributed loads in power markets that co-optimize energy and reserves.
LDR
:04397nmm a2200313 4500
001
2055287
005
20141203121509.5
008
170521s2014 ||||||||||||||||| ||eng d
020
$a
9781321077476
035
$a
(MiAaPQ)AAI3581007
035
$a
AAI3581007
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Bilgin, Enes.
$3
3168928
245
1 0
$a
Participation of distributed loads in power markets that co-optimize energy and reserves.
300
$a
191 p.
500
$a
Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.
500
$a
Adviser: Michael C. Caramanis.
502
$a
Thesis (Ph.D.)--Boston University, 2014.
520
$a
As the integration of Renewable Generation into today's Power Systems is progressing rapidly, capacity reserve requirements needed to compensate for the intermittency of renewable generation is increasing equally rapidly. A major objective of this thesis is to promote the affordability of incremental reserves by enabling loads to provide them through demand response. Regulation Service (RS) reserves, a critical type of bi-directional Capacity Reserves, are provided today by expensive and environmentally unfriendly centralized fossil fuel generators. In contrast, we investigate the provision of low-cost RS reserves by the demand-side. This is a challenging undertaking since loads must first promise reserves in the Hour Ahead Markets, and then be capable of responding to the dynamic ISO signals by adjusting their consumption effectively and efficiently. To this end, we use Stochastic Control, Optimization Theory, and Approximate Dynamic Programming to develop a decision support framework that assists Smart Neighborhood Operators or Smart Building Operators (SNOs/SBOs) to become demand-side-providers of RS reserve.
520
$a
We first address the SNO/SBO short time scale operational task of responding to the Independent System Operator's (ISO) dynamic RS requests. We start by developing a model-based Markovian decision problem that trades off ISO RS tracking against demand response related utility loss. Starting with a model based approach we obtain near optimal operational policies through a novel approximate policy iteration technique and an actor critic approach which is robust to partial knowledge of the underlying system dynamics. We then abandon the model based terrain and solve the dynamic operational problem through reinforcement learning that is capable of modeling a population of duty cycle appliances with realistic thermodynamics. We finally propose a smart thermostat design and develop an adaptive control policy that can drive the smart thermostat effectively. The latter approach is particularly suited for systems whose dynamics and dynamically changing consumer preferences are not known or observed beyond the total power consumption.
520
$a
We then address the SNO/SBO task of bidding RS reserves to the hour ahead market. This task determines the maximal RS reserves that the SNO/SBO can promise based on information available at the beginning of an hour, so as to maximize the associated hour-ahead revenues minus the expected average operating cost that will be incurred during the operational task to follow. To accomplish this task, we (i) develop probabilistic constraints that model the feasible maximum reserves which can be offered to the market without exceeding the SNO/SBO's ability to later track the unanticipated dynamic ISO RS signal, and (ii) calibrate a describing function that approximates the average operational cost as a function of the maximal reserves that can be feasibly offered in the day ahead market. The above is made possible by statistical analysis of the controlled system's stochastic dynamics and properties of the optimal dynamic policies that we derive.
520
$a
The contribution of the thesis is twofold: The solution of a difficult stochastic control problem that is crucial for effective demand-response-based provision of regulation service, and, the characterization of key properties of the stochastic control problem solution, which allow its integration into the hour-ahead market bidding problem.
590
$a
School code: 0017.
650
4
$a
Engineering, System Science.
$3
1018128
650
4
$a
Operations Research.
$3
626629
690
$a
0790
690
$a
0796
710
2
$a
Boston University.
$3
1017454
773
0
$t
Dissertation Abstracts International
$g
75-09B(E).
790
$a
0017
791
$a
Ph.D.
792
$a
2014
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3581007
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9287766
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入