NDLI: Reliability Constrained Multi-Area Adequacy Planning Using Stochastic Programming With Sample-Average Approximations

Content Provider	IEEE Xplore Digital Library
Author	Jirutitijaroen, P. Singh, C.
Copyright Year	1969
Abstract	This paper proposes a mixed-integer stochastic programming approach to the solution of generation and transmission line expansion planning problem including consideration of system reliability. Favorable system reliability and cost trade off is achieved by the optimal solution. The problem is formulated as a two-stage recourse model where random uncertainties in area generation, transmission lines, and area loads are considered. Reliability index used in this problem is expected cost of load loss as this index incorporates duration and magnitude of load loss. The objective is to minimize the expansion cost in the first stage and the operation and expected cost of load loss in the second stage. Due to exponentially large number of system states (scenarios) in large power systems, direct application of the L-shaped algorithm seems impractical. The expected cost of load loss is therefore approximated by considering only sampled scenarios and evaluated in the optimization. The estimated objective value is called sample-average approximation (SAA) of the actual expected value. In this paper, Monte Carlo sampling and Latin hypercube sampling techniques are implemented. Confidence intervals of upper and lower bound are discussed. The method is implemented to an actual 12-area power system for generation expansion planning and transmission line expansion planning.
Sponsorship	IEEE Power Engineering Society
Starting Page	504
Ending Page	513
Page Count	10
File Size	450627
File Format	PDF
ISSN	08858950
Volume Number	23
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Power transmission lines Power system planning Reliability Cost function Stochastic systems Uncertainty Power systems Monte Carlo methods Hypercubes two-stage recourse model Latin hypercube sampling Monte Carlo sampling multi-area power system power system optimization reliability sample-average approximation stochastic programming
Content Type	Text
Resource Type	Article
Subject	Energy Engineering and Power Technology Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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