DocumentCode :
1714508
Title :
Physically-based load demand models for assessing electric load control actions
Author :
Gomes, Alvaro ; Antunes, C.H. ; Martins, A.G.
Author_Institution :
Dept. of Electr. Eng. & Comput., Univ. of Coimbra, Coimbra, Portugal
fYear :
2009
Firstpage :
1
Lastpage :
8
Abstract :
Simulation, by resorting to suitable models, is often used by electric utilities in studies related with load forecasting, system reliability, power flow and demand-side management activities, among others. Nowadays, in restructured electricity market scenarios, activities involving buying and selling energy can also be studied through simulation. Different goals for those studies require different load representation and modeling. Since some activities at the demand-side level may lead to changes in load demand shape and levels, it is necessary to foresee such impacts before their implementation. Moreover, whenever such actions involve the remote control of end-use loads a careful assessment must be done in order to avoid undesirable effects, such as payback or strong reduction in revenues without other counterparts. The use of suitable load models contributes to avoid or greatly reduce both the need for pilot programs, which may be a costly and time consuming activity, and the risk of reducing revenues/profits. This work presents the results of using physically-based air conditioner models to simulate load control actions and to analyze the impacts of such actions on the demand, on the revenues, and on the comfort of consumers.
Keywords :
load flow; load forecasting; load regulation; power markets; power system management; power system reliability; power system simulation; demand-side management activities; electric load control; electric utilities; electricity market; load forecasting; physically-based load demand models; power flow; remote control; system reliability; Energy management; Load flow; Load flow control; Load forecasting; Load modeling; Power industry; Power system management; Power system modeling; Predictive models; Reliability; Air conditioning; Load management; Load modeling; Load shedding; Monte Carlo methods; Power demand; Power systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
PowerTech, 2009 IEEE Bucharest
Conference_Location :
Bucharest
Print_ISBN :
978-1-4244-2234-0
Electronic_ISBN :
978-1-4244-2235-7
Type :
conf
DOI :
10.1109/PTC.2009.5281800
Filename :
5281800
Link To Document :
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