Page 138 - Mathematical Models and Algorithms for Power System Optimization

Load Optimization for Power Network 129

Table 5.2 Variable processing principles for linear programming model of maximizing LSC

Variables Replace LC variable P C with load variable P L ; set the original P L
as the upper limit of P C .
Objective function Replace LC variable P C with load variable P L .
Equality constraints Delete LC variable P C .
Range constraints Delete LC variable P C .
Bound constraints Replace LC variable P C with load variable P L ;
replace load variable P L with its upper limit.
(2) Specific expressions:

Objective function:

P L
min C 0Š (5.14)
½
P G
Equality constraints:

P L
½ 11Š ¼ 1 P L (5.15)
P G
Range constraints:

B 0 1 B 0 1 B 0 1 P 0 B 0 1
0
B b R P P b B b R B b R L B b R P + P b (5.16)
0
L
L
0 0 0 P 0 G 0
Bound constraints:
" #

0 P L P L
(5.17)
P G P G P G
5.4 Calculation Procedure of Minimizing LCO

For a real urban power grid structure with several hundred buses,the data size is large and the format
is stringent. To avoid tedious manual data input and possible errors, this chapter directly uses
the Bonneville Power Administration’s (BPA’s) data format, adding two undefined fields in the
BPA procedures for generator output lower limit in columns 81–85 and load bus weight in columns
86–90 of bus data card (B card). The calculation result is in form of text output that gives the
optimal LC results in a specific system state. The basic calculation procedure is shown in the
following section.

Therefore, the LCO is defined as a process of avoiding LCs, if possible, or minimizing the total
LCs, if unavoidable, by rescheduling generation outputs and load transfer when the system
is in a state of N k.
Step 1: Input data, including the grid structure and the load level of all the nodes.