DocumentCode :
1250673
Title :
A New Technique for Simulating the Operation of Multiple Assigned-Energy Generating Units Suitable for Use in Generation System Expansion Planning Models
Author :
Manhire, Brian ; Jenkins, R.Taber
Author_Institution :
Ohio University, Athens, OH
Issue :
10
fYear :
1982
Firstpage :
42
Lastpage :
42
Abstract :
A new technique is presented for probabilistically simulating the operation of power systems which includes multiple assigned-energy units (e.g., conventional hydroelectric and, under certain circumstances, both nuclear units and cogeneration units). Within the context of probabilistic simulation, the new technique is completely rigorous. Statistical cumulants are used to represent equivalent load. The new technique has been successfully applied in an advanced technology evaluation and expansion model???the TVA TARANTULA model. This application has been accomplished with-out sacrificing other simulation capabilities such as: random partial outages, maintenance based on LOLP and load forecast uncertainty, and the ability to aggregate (without loss of rigor) groups of many small units for the purpose of computational expediency. A description is provided of tests performed to evaluate the new technique. Probabilistic simulation is a load duration oriented technique for simulating the operation of electric power systems. The technique is particularly amenable both to power systems consisting entirely of demand-energy units and to rigorous simulation of the random forced outage effects of these units. In this paper a demand-energy unit is defined as any generating unit which serves consumer load upon demand???provided the unit is available to do so and the dispatcher calls on it to operate. Demand-energy units include coalfired units, oil-fired units, gas turbines, and nuclear units early in the refueling cycle. Certain types of generating resources are unable to serve load upon demand (even if they are available to do so) because the supply of energy is limited.
Keywords :
Cogeneration; Computational modeling; Context modeling; Hydroelectric power generation; Load forecasting; Nuclear power generation; Power system modeling; Power system planning; Power system simulation; Predictive models;
fLanguage :
English
Journal_Title :
Power Engineering Review, IEEE
Publisher :
ieee
ISSN :
0272-1724
Type :
jour
DOI :
10.1109/MPER.1982.5519900
Filename :
5519900
Link To Document :
بازگشت