Fractal dimension steepest descent calculation method for valve point effect power grid economic dispatch problem
A technology of economic scheduling and valve point effect, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of low solution efficiency and large amount of calculation
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Embodiment 1
[0044] The present invention considers the fractal dimension steepest descent solution method of the grid economic dispatching problem considering the valve point effect, such as figure 1 shown, proceed as follows:
[0045] Step (1), obtain the output upper and lower limit data of each unit in the power system with N generator sets Coefficient data a of output-fuel cost function g ,b g ,c g ,e g , f g and system load data P d .
[0046] Step (2), establishing a mathematical optimization model of the power grid economic dispatching problem considering the unit valve point effect.
[0047] In the traditional economic dispatching problem, the unit output-fuel cost curve is approximated as a smooth quadratic polynomial. It should be noted that the output-fuel cost curve of the unit is obtained by multiplying the inherent output-fuel consumption curve of the unit by the fuel cost coefficient. However, the wire-drawing effect when the intake valve of the turbine is suddenl...
Embodiment 2
[0086] In order to make step (3) in the specific implementation of the present invention clearer, taking a 3-unit system with a total load of 850MW as an example, Table 1 gives the solution obtained in the whole process of solving step (3). From Table 1, it can be seen that the fuel cost unit decrease value D of unit 3 in the first iteration g is the largest, so its singular point position changes from 4 minus 1 to 3 in the second iteration, and the fuel cost unit of unit 1 decreases by D in the second iteration g is the largest, so its singular point position changes from 6 minus 1 to 5 in the third iteration, and so on. After 8 iterations, the total output of N units is closest to and greater than the system load. After 16 iterations All units are at the lower limit of output. It can be seen from Table 1 that the calculation of step (3) is very simple and intuitive. In the 13-machine system with a system load of 1800MW, step (3) stops after 47 iterations. In the 40-machine...
Embodiment 3
[0092] Taking a standard test system of 3 units, 13 units and 40 units as examples respectively, the problems are solved respectively by adopting the steepest descent method of the present invention. Among them, the total load of the 3-unit system is 850MW, the total load of the 13-unit system is 1800MW, and the total load of the 40-unit system is 10500MW. In step (4), the optimization method is implemented using the YALMIP toolbox in MATLAB. Since the goal of the 0-1 variable quadratic optimization problem shown in formula (7) is mainly to minimize the load imbalance, its solution is relatively simple. Therefore, the following settings are used in the solution of the optimization problem shown in step (4) and formula (7) of the three standard test systems: YALMIP adopts the branch and bound method solver, and the maximum number of iterations of the branch and bound solution method is set to 20. The continuous variable optimization subproblem in the branch and bound method ado...
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