Fast Thermal Generation Rescheduling

Abstract

A new dynamic programming algorithm for f~s~heduling thermal generation is presented. The savings in computational times are brought about by the introduction of two new techniques: the variable truncation dynamic programming and the limitation of the solution space to be searched. Several examples on a 20 machine system are used to illustrate the application of the algorithm and to show that optimal solutions are obtained at significantly reduced computational times.

Reserve from wind power potential in system economic loading

“…The minimum operating cost found by the proposed heuristic algorithm is $186 090, compared with $186 040 for dynamic programming in [19]. Our heuristic algorithm results in an important feature: unit power output exhibits little volatility.…”

“…To validate the performance of the above heuristic scheduling algorithm, a comparison with dynamic programming and Lagrangian relaxation on a 16-unit system is carried out. The detailed implementation of dynamic programming can be found in [19], whereas the implementation of Lagrangian relaxation is introduced in this section. Further, the benefits of active curtailment over passive curtailment are illustrated using data from the Irish power system.…”

“…The performance of the heuristic loading algorithm was tested on the 16-unit system over 24 h presented in [19]. The system characteristics are listed in Table 1.…”

“…The system characteristics are listed in Table 1. The heuristic algorithm was compared with dynamic programming [19] and Lagrangian relaxation to demonstrate its suitability in the search for the minimum operating cost.…”

“…To compare the operating cost saving between the heuristic algorithm and dynamic programming, the experimental conditions were set to be consistent with those used in [19]:…”