期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 30, 期 4, 页码 1815-1824出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2014.2358237
关键词
Dynamic programming; generation expansion; geographic information systems; mixed-integer linear programming; power network planning; routing
资金
- National Natural Science Foundation of China [51347003]
- Fundamental Research Funds for the Central Universities [12MS19]
- U.S. National Science Foundation [ECCS-1102064, ECCS-1254310]
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1102064] Funding Source: National Science Foundation
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1254310] Funding Source: National Science Foundation
This paper introduces an efficient approach on static spatial power network expansion planning integrated with generation expansion, while considering complicated environments based on the raster map in geographic information systems (GIS). Candidate plants could be built on any cell in the map, which means that terminals of candidate lines connected to candidate plants are not fixed. This is a remarkable difference from the literature in which the terminals of candidate lines are fixed. The objective is to minimize the total system cost, subject to prevailing investment and operation constraints. The model is formulated as a mixed-integer linear programming (MILP) problem via integer algebra techniques. A two-step approach is proposed to address the computational complexity. The first step searches optimal electric line routes via dynamic programming, while the second step solves a simplified MILP problem for obtaining final optimal generation and transmission planning strategies based on optimal line routes derived from the first step. In most cases, the proposed two-step approach would derive the same global optimal solutions as those by solving the original formulation directly. Thus, the proposed two-step approach can significantly improve the computational efficiency while maintaining the solution optimality. Numerical examples demonstrate the effectiveness of the proposed approach.
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