Energystorage multitarget control method and system considering windpower ramp rate and operating cost
Energystorage multitarget control method and system considering windpower ramp rate and operating cost
 CN 104,573,847 A
 Filed: 12/09/2014
 Published: 04/29/2015
 Est. Priority Date: 12/09/2014
 Status: Active Application
First Claim
1. consider an energy storage multi objective control method for windpowered electricity generation climbing and operation cost, it is characterized in that, the method comprises the steps:
 A, the parameter of particle cluster algorithm is set;
B, the position generating each particle in primary group and speed;
C, determine the adaptive value of each particle;
The adaptive value of D, more each particle, finds the particle position of local optimum with global optimum particle position E, the speed upgrading each particle and position;
F, according to mutation probability determine whether mutation operation is carried out to each particle;
G, judge whether population meets iterations requirement or convergent requirement, if meet arbitrary requirement, then stop iteration, export net result;
Otherwise, jump to step C and continue iteration, till meeting arbitrary requirement.
Chinese PRB Reexamination
Abstract
The invention provides an energystorage multitarget control method and system considering windpower ramp rate and operating cost. The method comprises the following steps: setting parameters of a particle swarm optimization; generating an initial particle swarm and an initial speed of the particle swarm optimization; determining adaptive values of particles; finding respectively corresponding particle positions of local optimum and global optimum; updating the speeds and the positions of the particles and performing mutation operation on the particles; judging whether the particle swarm meets the iterations requirement or the convergence requirement or not, and if the particle swarm meets one of the iterations requirement and the convergence requirement, stopping iteratively outputting the final result, otherwise, continuing iteration. The system comprises a particle swarm generation module, a calculation module, an updating module, a variation module and an execution module. A battery energy storage system for inhibiting the windpower ramp rate is subjected to charging and discharging power optimization through the particle swarm optimization, so that the operating cost is reduced while the windpower ramp rate is inhibited from being too high.

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17 Claims

1. consider an energy storage multi objective control method for windpowered electricity generation climbing and operation cost, it is characterized in that, the method comprises the steps:

A, the parameter of particle cluster algorithm is set; B, the position generating each particle in primary group and speed; C, determine the adaptive value of each particle; The adaptive value of D, more each particle, finds the particle position of local optimum with global optimum particle position E, the speed upgrading each particle and position; F, according to mutation probability determine whether mutation operation is carried out to each particle; G, judge whether population meets iterations requirement or convergent requirement, if meet arbitrary requirement, then stop iteration, export net result;
Otherwise, jump to step C and continue iteration, till meeting arbitrary requirement.


2. the method for claim 1, is characterized in that, in steps A, described parameter comprises minimum and maximum value, the mutation probability p of inertial factor ω
 _{m}, population size N and iterations C.

3. the method for claim 1, is characterized in that, in step B,
First, generate primary group, i.e. 1st generation population, the position of ith particle in 1st generation population and the expression form of speed as follows:

4. the method for claim 1, is characterized in that, in step C, is determined the adaptive value of each particle by the target function value F of following formula:

5. method as claimed in claim 4, is characterized in that,
Determine that described wind storage commingled system goes out force value P to electrical network according to following formula _{grid, t}: 
P _{grid，t}＝
P _{wind，t}+P _{bess，t}Described wind power output climbing rate P is determined according to following formula _{ramp, t};


6. the method for claim 1, is characterized in that, in step D,
Find out ith particle iterate to h for time, adaptive value maximum in front h generation is as local optimum particle position corresponding to it is the particle position of local optimum Find out iterate to h for time all particle local optimums in maximum adaptive value as global optimum particle position corresponding to it is the particle position of global optimum

7. the method for claim 1, is characterized in that, in step e, by the following formula more speed of new particle and position:

8. the method for claim 1, is characterized in that, in step F, by comparing mutation probability p _{m}with random number r _{5}size, determine whether carrying out mutation operation, described r to particle each in population _{5}for the random number of 0 to 1 of stochastic generation;

If mutation probability p _{m}>
r _{5}, then mutation operation is not carried out;
If mutation probability p _{m}≤
r _{5}, then a certain element in each particle position of Stochastic choice carries out mutation operation.


9. method as claimed in claim 8, is characterized in that, the method that in described Stochastic choice particle position, a certain element carries out mutation operation comprises the steps:

Determined the element needed in the particle position of variation by following formula, namely determine to need the accumulator system chargedischarge electric power to which moment to carry out mutation operation; t＝
[T*r _{6}]During mutation operation, by following formula to P _{bess, i, t}^{h}again value;
P _{bess，i，t}^{h}＝
P _{bess，chmax}+r _{7}×
(P _{bess，dismax}P _{bess，chmax})All aforesaid operations is carried out to all particles, to complete whole mutation process. In formula, hop count when T is optimization, r _{7}be the random number of 0 to 1, square bracket [] represent gets the most contiguous integer towards positive infinity;
P _{bess, i, t}^{h}represent the chargedischarge electric power of ith particle position t in h generation.


10. the method for claim 1, is characterized in that, in step G,
Described iterations requires to arrange according to actual conditions; 
Described convergent requirement is; max[f(P _{bess}^{h})]min[f(P _{bess}^{h})]＜
α
P _{bess}^{h}＝
{P _{bess，1}^{h}，
P _{bess，2}^{h}，
…
，
P _{bess，t}^{h}，
…
，
P _{bess，T}^{h}}In formula, P _{bess}^{h}represent that h is for the set of all particles of population after h iteration, f (P _{bess}^{h}) represent the set of h for all particle adaptive values, then max [f (P _{bess}^{h})] represent the maximal value of all particle adaptive values in h generation, min [f (P _{bess}^{h})] representing the minimum value of all adaptive values in h generation, α
is a minimum number being greater than zero.


11. methods as described in claim 1 or 10, it is characterized in that, described output net result for:
 from the population obtained after meeting arbitrary requirement, pick out the particle corresponding to global optimum, recalculate adaptive value and the wind power output climbing rate of this particle, and export the chargedischarge electric power in accumulator system each moment.

12. Consider windpowered electricity generation climbing and the energy storage multi objective control system of operation cost for 12. 1 kinds, it is characterized in that, this control system comprises:

Population generation module, for arranging the parameter of particle cluster algorithm;
And generate position and the speed of each particle in primary group;Computing module, for determining the adaptive value of each particle and comparing, finds out the particle position of local optimum and global optimum respectively; Update module, for upgrading speed and the position of each particle; Variation module, for carrying out mutation operation to the position of each particle;
WithExecution module, for judging whether population meets iterations requirement or convergent requirement, until export net result after meeting arbitrary requirement.


13. systems as claimed in claim 12, it is characterized in that, described population generation module comprises:

Optimum configurations submodule, for arranging the parameter of particle cluster algorithm according to actual conditions; First calculating sub module, for the maximal value according to accumulator system charge and discharge power, the position of each particle and speed in stochastic generation primary group.


14. systems as claimed in claim 12, it is characterized in that, described computing module comprises:

Second calculating sub module, for calculate the wind storage wind power output value in commingled system each moment, wind power output climbing rate and wind power output climbing rate outoflimit time penalty term, and determine the adaptive value of each particle further by the objective function F of wind storage commingled system generating optimization; Local search submodule, for find out ith particle iterate to h for time maximum adaptive value be local optimum, the particle position corresponding to it is the particle position of local optimum; Global search submodule, for find out iterate to h for time all particle local optimums in maximum adaptive value be global optimum, the particle position corresponding to it is the particle position of global optimum.


15. systems as claimed in claim 12, it is characterized in that, described update module comprises:

Speed upgrades submodule, for upgrading the speed of each particle in population according to the particle position of local optimum and global optimum; Location updating submodule, for upgrading the position of each particle in population according to the position of last iteration particle and the speed of current iteration particle.


16. systems as claimed in claim 12, it is characterized in that, described variation module comprises:

First comparison submodule, by comparing mutation probability p _{m}with random number r _{5}size, determine whether carrying out mutation operation, described r to particle each in population _{5}for the random number of 0 to 1 of stochastic generation;
Chooser module, if desired carries out mutation operation, then first determine to carry out mutation operation to the accumulator system chargedischarge electric power in which moment;
And when mutation operation, according to the maximal value of accumulator system charge and discharge power to the accumulator system chargedischarge electric power value again in this moment.


17. systems as claimed in claim 12, it is characterized in that, described execution module comprises:

Judge submodule, for judging whether population meets iterations requirement or convergent requirement; Output submodule, for selecting particle position corresponding to global optimum from the population meeting iterations requirement or convergent requirement, and recalculates adaptive value and the wind power output climbing rate of this particle by computing module;
For exporting the chargedischarge electric power in accumulator system each moment.

Specification(s)