阅读说明：本技术 量子粒子群算法的复合储能容量的能量调度方法及系统 (Energy scheduling method and system for composite energy storage capacity of quantum particle swarm optimization ) 是由 朱海鹏 张海 徐刚 刘宗杰 谢允红 张西鲁 周科 王一飞 魏春雪 孔平 秦昆 于 2019-08-21 设计创作，主要内容包括：本公开提出了量子粒子群算法的复合储能容量的能量调度方法及系统,针对复合储能容量的能量调度时所建立的目标函数及约束条件进行求解：粒子初始化,将群体中最优个体的位置记为全局初始最优位置；对粒子i的每一维,计算得到一个随机点的位置,并进化产生新粒子；在能量调度策略下,计算满足系统约束的适应度值,即目标函数值；分别更新粒子的个体和全局最佳位置；判断终止条件是否满足,满足则输出计算结果；反之,对粒子i的每一维,重新获得一个随机点的位置,再次更新粒子的个体和全局最佳位置。针对含复合储能的多种分布式电源主动配电网,提出应用具有收敛速度快、全局收敛能力强的量子行为粒子群优化算法配置复合储能的容量。(The invention provides an energy scheduling method and system of composite energy storage capacity of quantum particle swarm optimization, which solve the objective function and constraint condition established during energy scheduling of the composite energy storage capacity: initializing particles, and recording the position of an optimal individual in a group as a global initial optimal position; calculating the position of a random point for each dimension of the particle i, and generating a new particle; under an energy scheduling strategy, calculating a fitness value meeting system constraint, namely an objective function value; updating the individual and global optimal positions of the particles respectively; judging whether the termination condition is met, and outputting a calculation result if the termination condition is met; on the contrary, for each dimension of the particle i, the position of a random point is obtained again, and the individual and global optimal positions of the particle are updated again. Aiming at various distributed power source active power distribution networks containing composite energy storage, the quantum behavior particle swarm optimization algorithm with high convergence speed and strong global convergence capability is applied to configure the capacity of the composite energy storage.)

1. The energy scheduling method of the composite energy storage capacity of the quantum particle swarm algorithm is characterized by comprising the following steps of:

solving an objective function and a constraint condition established during energy scheduling of the composite energy storage capacity:

initializing particles, namely randomly generating M two-dimensional initial particles Xi (0) in a particle position constraint range, wherein each particle represents the energy storage configuration condition in the first iteration optimizing process;

setting the initial optimal position Pi (0) of each individual to be Xi (0), evaluating the fitness of each initial particle, and recording the position of the optimal individual in the population as a global initial optimal position;

calculating the position of a random point for each dimension of the particle i, and generating a new particle;

under an energy scheduling strategy, calculating a fitness value meeting system constraint, namely an objective function value;

updating the individual and global optimal positions of the particles respectively;

judging whether the termination condition is met, and outputting a calculation result if the termination condition is met; on the contrary, for each dimension of the particle i, the position of a random point is obtained again, and the individual and global optimal positions of the particle are updated again.

2. The energy scheduling method of composite energy storage capacity of quantum particle swarm optimization according to claim 1, wherein the process of the objective function and constraint condition established during the energy scheduling of the composite energy storage capacity is as follows:

respectively modeling a wind power generation unit, a photovoltaic power generation unit and an energy storage unit in an active power distribution network to obtain specific expressions of the power output by a single fan, the output power of a photovoltaic battery and the state of charge (SoC) of energy storage in the charging and discharging process;

during energy distribution, the super capacitor compensates high-frequency components in the deficit power, and the rest low-frequency components are compensated by an energy type storage battery;

when the actual charging and discharging power of the super capacitor and the actual charging and discharging power of the storage battery are obtained, power correction is carried out by considering the energy storage rated power limit value and the energy storage charge state;

establishing an objective function by taking the minimum daily average cost of the composite energy storage device as an optimization objective;

and solving the configuration capacities of the storage battery and the super capacitor by taking energy storage capacity constraint, energy storage maximum charge-discharge power constraint, instantaneous power balance constraint, load power shortage and energy overflow constraint as constraint conditions according to an objective function.

3. The energy scheduling method of composite energy storage capacity of quantum particle swarm optimization according to claim 2, wherein when obtaining the actual charge and discharge power of the storage battery, the method specifically comprises:

and obtaining the ideal charging and discharging power of the storage battery, then carrying out primary correction on the ideal charging and discharging power by considering the corresponding limit value of the energy storage power, and then carrying out secondary correction on the ideal power value by judging whether the energy storage after energy compensation is carried out by using the correction value is out of limit, namely obtaining the actual charging and discharging power value of the storage battery.

4. The energy scheduling method of composite energy storage capacity of quantum particle swarm optimization according to claim 3, wherein the power of the storage battery is corrected at one time: comparing the ideal charging and discharging power value of the storage battery with the rated power value of the storage battery, if the ideal charging and discharging power value of the storage battery is larger than or equal to the rated power value of the storage battery, giving the rated power value of the storage battery to a primary correction value of the ideal charging and discharging power of the storage battery, and otherwise, giving the ideal charging and discharging power value of the storage battery to a primary correction value of the;

and obtaining the energy storage charge state of the storage battery after energy compensation is carried out by the primary corrected value of the ideal charge-discharge power of the storage battery.

5. The energy scheduling method of composite energy storage capacity of quantum particle swarm optimization according to claim 4, wherein the secondary correction of the power of the storage battery comprises: and comparing the energy storage state of charge of the storage battery obtained by the primary correction with the minimum value of the energy storage state of charge of the storage battery, if the energy storage state of charge of the storage battery is smaller than or equal to the minimum value of the energy storage state of charge of the storage battery, calculating the actual charge and discharge power of the storage battery according to a calculation formula, and otherwise, taking the primary correction value of the ideal charge and discharge power of the storage battery as the actual charge and.

6. The energy scheduling method of the composite energy storage capacity of the quantum particle swarm algorithm according to claim 2, wherein when the actual charge and discharge power of the super capacitor is obtained, the specific process is as follows:

obtaining the ideal charging and discharging power of the super capacitor, wherein the ideal charging and discharging power of the super capacitor is the difference between the ideal compensation value of the mixed energy storage power and the actual charging and discharging power value of the storage battery;

primary correction of the power of the super capacitor: comparing the ideal charging and discharging power value of the super capacitor with the rated power value of the super capacitor, if the ideal charging and discharging power value of the super capacitor is larger than or equal to the rated power value of the super capacitor, giving the rated power value of the super capacitor to a primary correction value of the ideal charging and discharging power of the super capacitor, and otherwise, giving the ideal discharging power value of the super capacitor to a primary correction value of the ideal charging and discharging power of the super;

and obtaining the energy storage charge state of the super capacitor after energy compensation is carried out by the primary corrected value of the ideal charge-discharge power of the super capacitor.

7. The energy scheduling method of composite energy storage capacity of quantum particle swarm optimization according to claim 6, wherein the super capacitor power is corrected twice: and comparing the energy storage charge state of the super capacitor obtained by the primary correction with the minimum value of the energy storage charge state of the super capacitor, if the energy storage charge state of the super capacitor is smaller than or equal to the minimum value of the energy storage charge state of the super capacitor, calculating the actual charging and discharging power of the super capacitor according to a calculation formula, and otherwise, taking the primary corrected value of the ideal charging and discharging power of the super capacitor as the actual charging and discharging power of the super capacitor.

8. The energy scheduling system of the composite energy storage capacity of the quantum particle swarm algorithm solves a target function and a constraint condition which are established during energy scheduling of the composite energy storage capacity: the method is characterized by comprising the following steps:

a particle initialization module configured to: randomly generating M two-dimensional initial particles Xi (0) in a particle position constraint range, wherein each particle represents the energy storage configuration condition in the first iteration optimization process;

setting the initial optimal position Pi (0) of each individual to be Xi (0), evaluating the fitness of each initial particle, and recording the position of the optimal individual in the population as a global initial optimal position;

a global optimal location acquisition module configured to: calculating the position of a random point for each dimension of the particle i, and generating a new particle;

under an energy scheduling strategy, calculating a fitness value meeting system constraint, namely an objective function value;

updating the individual and global optimal positions of the particles respectively;

judging whether the termination condition is met, and outputting a calculation result if the termination condition is met; on the contrary, for each dimension of the particle i, the position of a random point is obtained again, and the individual and global optimal positions of the particle are updated again.

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program implements the steps of the method for energy scheduling of composite energy storage capacity of quantum-behaved particle swarm optimization algorithm of any of claims 1-7.

10. A computer readable storage medium, having stored thereon a computer program, characterized in that the program, when being executed by a processor, is adapted to carry out the steps of the method for energy scheduling of composite energy storage capacity of quantum-behaved particle swarm optimization as claimed in any one of the claims 1 to 7.

Technical Field

The disclosure relates to the technical field of distribution networks, in particular to an energy scheduling method and system for composite energy storage capacity of a quantum particle swarm algorithm.

Background

At present, storage batteries are generally selected as energy storage equipment in a power distribution network, and the storage batteries are used as energy type energy storage equipment, so that the energy distribution network has the characteristics of high energy density, strong storage capacity and the like, but also has the defects of long charge-discharge period, short service life, high cost and the like, and therefore the application of an energy storage technology is limited. The super capacitor is used as a power type energy storage element which is most widely applied at present, has the characteristics of high power density, short charging and discharging period, high energy storage efficiency, long cycle life and the like, and can effectively stabilize short-time and small-amplitude power fluctuation in the power generation of renewable energy sources.

The inventor finds in research that, at present, a traditional PSO algorithm is selected for solving the energy scheduling of the composite energy storage, and the traditional PSO algorithm has a problem that the traditional PSO algorithm cannot converge on a global or even a local optimal solution, so that a final solution result cannot meet requirements, and therefore, the solving algorithm of the energy scheduling of the composite energy storage needs to be adjusted adaptively.

Disclosure of Invention

The purpose of the embodiments of the present specification is to provide an energy scheduling method for a composite energy storage capacity of a quantum particle swarm algorithm, which can quickly obtain a solution result.

An embodiment of the present specification provides an energy scheduling method for a composite energy storage capacity of a quantum particle swarm algorithm, including:

solving an objective function and a constraint condition established during energy scheduling of the composite energy storage capacity:

initializing particles, namely randomly generating M two-dimensional initial particles Xi (0) in a particle position constraint range, wherein each particle represents the energy storage configuration condition in the first iteration optimizing process;

setting the initial optimal position Pi (0) of each individual to be Xi (0), evaluating the fitness of each initial particle, and recording the position of the optimal individual in the population as a global initial optimal position;

calculating the position of a random point for each dimension of the particle i, and generating a new particle;

under an energy scheduling strategy, calculating a fitness value meeting system constraint, namely an objective function value;

updating the individual and global optimal positions of the particles respectively;

judging whether the termination condition is met, and outputting a calculation result if the termination condition is met; on the contrary, for each dimension of the particle i, the position of a random point is obtained again, and the individual and global optimal positions of the particle are updated again.

The embodiment of the present specification further provides an energy scheduling system of composite energy storage capacity of a quantum particle swarm algorithm, which solves a target function and a constraint condition established during energy scheduling of the composite energy storage capacity: the method comprises the following steps:

a particle initialization module configured to: randomly generating M two-dimensional initial particles Xi (0) in a particle position constraint range, wherein each particle represents the energy storage configuration condition in the first iteration optimization process;

setting the initial optimal position Pi (0) of each individual to be Xi (0), evaluating the fitness of each initial particle, and recording the position of the optimal individual in the population as a global initial optimal position;

a global optimal location acquisition module configured to: calculating the position of a random point for each dimension of the particle i, and generating a new particle;

under an energy scheduling strategy, calculating a fitness value meeting system constraint, namely an objective function value;

updating the individual and global optimal positions of the particles respectively;

judging whether the termination condition is met, and outputting a calculation result if the termination condition is met; on the contrary, for each dimension of the particle i, the position of a random point is obtained again, and the individual and global optimal positions of the particle are updated again.

Compared with the prior art, the beneficial effect of this disclosure is:

the method aims at various distributed power source active power distribution networks containing composite energy storage, and provides the method for configuring the capacity of the composite energy storage by applying a quantum behavior particle swarm optimization algorithm with high convergence speed and strong global convergence capability.

The average best position of C is introduced in the QPSO algorithm during solving, so that a waiting effect exists among particles, the particles do not have a set rule before measurement and are scattered in any position of a solution space in a probability mode, and the synergy among the particles is improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.

FIG. 1 is a schematic wind speed-power curve of a wind turbine according to an exemplary embodiment of the present disclosure;

FIG. 2(a) is a graph showing the illumination intensity versus external current-voltage characteristic of an example of the present disclosure;

FIG. 2(b) is a schematic diagram of temperature-external current-voltage characteristics of an example of the present disclosure;

FIG. 3 is a flowchart of a battery power correction process according to an exemplary embodiment of the disclosure;

FIG. 4 is a flowchart of a supercapacitor power correction process according to an embodiment of the disclosure;

5(a) -5 (b) are schematic diagrams illustrating the difference between QPSO and conventional PSO state transition in the embodiment of the present disclosure;

FIG. 6 is a schematic view of wind/solar power output and load curves of an embodiment of the present disclosure;

fig. 7 is a schematic diagram of a hybrid energy storage power compensation ideal value P HESS according to an embodiment of the disclosure;

fig. 8 is a graph showing the results of P × HESS spectrum analysis according to an example of the present disclosure;

FIG. 9 is a schematic diagram of charging and discharging power of a hybrid energy storage system according to an exemplary embodiment of the disclosure;

fig. 10 is a graph illustrating the curves of the sampling points 1-48 of the composite energy storage actual power and P × HESS according to the embodiment of the disclosure;

fig. 11 is a schematic diagram of the sampling point 150-180 composite energy storage actual power and P × HESS curve according to the embodiment of the disclosure;

fig. 12 is a comparison diagram of the optimization process of two algorithms according to the embodiment of the disclosure.

Detailed Description

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.