Integration of Contingency Analysis With Systematic Transmission Capacity Expansion Planning: ERCOT Case Study
In this paper, we propose a method for [Formula Omitted] contingency constrained transmission capacity expansion planning (TCEP), which is formulated as a mixed-integer programming (MIP) problem. In relatively well-designed power systems, a single outage of a majority of lines will not usually cause...
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| Published in: | IEEE transactions on power systems Vol. 31; no. 3; pp. 2234 - 2245 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
01.05.2016
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| Subjects: | |
| ISSN: | 0885-8950, 1558-0679 |
| Online Access: | Get full text |
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| Summary: | In this paper, we propose a method for [Formula Omitted] contingency constrained transmission capacity expansion planning (TCEP), which is formulated as a mixed-integer programming (MIP) problem. In relatively well-designed power systems, a single outage of a majority of lines will not usually cause overload on other lines in most loading conditions. Thus they will not affect the feasible region and the optimal answer of the TCEP optimization problem, and can be safely removed from contingency analysis if we can identify them. A contingency identification index is developed to detect these lines and create variable contingency lists (VCL) for different network loading conditions. In our proposed method, we use results of a relaxed version of the original problem as a lower bound answer in the first step, and integrate contingencies into TCEP in the next steps to solve this optimization problem faster while still satisfying [Formula Omitted] criterion. For solving TCEP with contingencies, two options are offered, i.e., option A that uses an updated system as its base case (original existing network together with selected lines by the relaxed problem) and option B that uses the original existing network as its base case (without results of the relaxed problem). Option A is faster than option B because it usually should select fewer new lines compared to B, but cannot guarantee optimality. Option B provides the optimal answer while taking more computational time. An ERCOT case study is used to show capabilities of the proposed method for solving large scale problems, and the numerical result demonstrates this method is much faster than the integrated MIP method that directly incorporates all contingencies. |
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| Bibliography: | ObjectType-Case Study-2 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-4 ObjectType-Report-1 ObjectType-Article-3 |
| ISSN: | 0885-8950 1558-0679 |
| DOI: | 10.1109/TPWRS.2015.2443101 |