Renewable energy in electric utility capacity planning: a decomposition approach with application to a Mexican utility

Thesis/Dissertation · Tue Jan 01 00:00:00 EST 1985

OSTI ID:5762943

Staschus, K

In this dissertation, efficient algorithms for electric-utility capacity expansion planning with renewable energy are developed. The algorithms include a deterministic phase that quickly finds a near-optimal expansion plan using derating and a linearized approximation to the time-dependent availability of nondispatchable energy sources. A probabilistic second phase needs comparatively few computer-time consuming probabilistic simulation iterations to modify this solution towards the optimal expansion plan. For the deterministic first phase, two algorithms, based on a Lagrangian Dual decomposition and a Generalized Benders Decomposition, are developed. The probabilistic second phase uses a Generalized Benders Decomposition approach. Extensive computational tests of the algorithms are reported. Among the deterministic algorithms, the one based on Lagrangian Duality proves fastest. The two-phase approach is shown to save up to 80% in computing time as compared to a purely probabilistic algorithm. The algorithms are applied to determine the optimal expansion plan for the Tijuana-Mexicali subsystem of the Mexican electric utility system. A strong recommendation to push conservation programs in the desert city of Mexicali results from this implementation.

• Research Organization: Virginia Polytechnic Inst. and State Univ., Blacksburg (USA)
• OSTI ID: 5762943
• Resource Relation: Other Information: Thesis (Ph. D.)
• Country of Publication: United States
• Language: English

Related Subjects

29 ENERGY PLANNING
POLICY AND ECONOMY
ELECTRIC UTILITIES
PLANNING
RENEWABLE ENERGY SOURCES
MEXICO
ALGORITHMS
CAPACITY
COMPUTER CALCULATIONS
ENERGY CONSERVATION
DEVELOPING COUNTRIES
ENERGY SOURCES
LATIN AMERICA
MATHEMATICAL LOGIC
NORTH AMERICA
PUBLIC UTILITIES
296000* - Energy Planning & Policy- Electric Power
299000 - Energy Planning & Policy- Unconventional Sources & Power Generation