CN113346526B - Multi-node energy storage system configuration method based on discrete-continuous hybrid method - Google Patents

Multi-node energy storage system configuration method based on discrete-continuous hybrid method Download PDF

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CN113346526B
CN113346526B CN202110563474.7A CN202110563474A CN113346526B CN 113346526 B CN113346526 B CN 113346526B CN 202110563474 A CN202110563474 A CN 202110563474A CN 113346526 B CN113346526 B CN 113346526B
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周喜超
王楠
赵鹏翔
李建林
李振
崔宜琳
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North China University of Technology
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Abstract

本发明涉及一种基于离散‑连续混合法的多节点储能系统配置方法。包括以下步骤：在各节点典型日功率曲线下建立系统及经济模型，综合考虑系统运行性能和经济性得出离散‑连续混合法目标函数；充分考虑离散部分和连续部分，定义编码串的形式和数量，初始化编码串；对初始编码串进行排序，利用离散‑连续混合法进行编码串的迭代；达到最大迭代次数结束，求得使得目标函数最优的储能控制与选址配置。本发明专利综合考虑储能系统运行性能和经济性，考虑遗传算法容易与其他算法相结合、粒子群算法虽简单但不能有效解决离散问题等特点，结构简单，对储能系统的研究推广具有重要意义。

Figure 202110563474

The invention relates to a method for configuring a multi-node energy storage system based on a discrete-continuous hybrid method. It includes the following steps: establishing a system and economic model under the typical daily power curve of each node, comprehensively considering the operating performance and economy of the system to obtain the discrete-continuous hybrid method objective function; fully considering the discrete part and the continuous part, defining the form and number, initialize the code string; sort the initial code string, and use the discrete-continuous hybrid method to iterate the code string; when the maximum number of iterations is reached, the energy storage control and location configuration that optimizes the objective function is obtained. The patent of the invention comprehensively considers the operation performance and economy of the energy storage system, considers that the genetic algorithm is easy to combine with other algorithms, and the particle swarm algorithm is simple but cannot effectively solve discrete problems. significance.

Figure 202110563474

Description

一种基于离散-连续混合法的多节点储能系统配置方法A configuration method of multi-node energy storage system based on discrete-continuous hybrid method

技术领域：Technical field:

本发明涉及一种储能系统，进一步涉及一种基于离散-连续混合法的多节点储能系统配置方法。The invention relates to an energy storage system, and further relates to a multi-node energy storage system configuration method based on a discrete-continuous hybrid method.

背景技术：Background technique:

对储能系统进行规划时需要考虑储能接入的位置是否恰当，储能作为一个双向电力元件，在电力系统中的接入位置会直接影响系统潮流流向，改变线路负载，影响网络损耗，影响系统的功率水平。所以，选择合理的布局来提高系统运行安全稳定性尤为关键。由于储能价格相对较高，所以选择合理的配置来提升储能应用的经济性水平也成为重要的研究内容。此外，近年来新能源装机规模不断提高，新能源发电具有随机性和间歇性，储能系统可以双向输出，且输出功率稳定、可控、动态响应速度较快，可以促进新能源消纳以及平抑新能源功率波动，并在新能源发电不足时为负荷持续供电，因此，综合考虑储能出力特性与经济性，对储能系统控制与选址配置方法进行研究，不仅对增强供电可靠性、提高电能质量以及促进新能源消纳有影响，而且从长远来看，更是对促进我国新能源产业发展、转变电力的发展方式等具有重要作用。When planning the energy storage system, it is necessary to consider whether the location of the energy storage access is appropriate. As a two-way power element, the access location of the energy storage in the power system will directly affect the power flow of the system, change the line load, affect the network loss, and affect the power flow. The power level of the system. Therefore, it is particularly critical to choose a reasonable layout to improve the security and stability of system operation. Due to the relatively high price of energy storage, choosing a reasonable configuration to improve the economic level of energy storage applications has also become an important research content. In addition, in recent years, the installed capacity of new energy has been continuously increased, and the power generation of new energy is random and intermittent. The energy storage system can output in two directions, and the output power is stable, controllable, and has a fast dynamic response speed, which can promote the consumption and stabilization of new energy. The power of new energy fluctuates, and the load is continuously supplied when the power generation of new energy is insufficient. Therefore, comprehensively considering the output characteristics and economy of energy storage, the control and site selection and configuration methods of energy storage system are studied, which not only enhances the reliability of power supply, improves the Power quality and the promotion of new energy consumption have an impact, and in the long run, it will play an important role in promoting the development of my country's new energy industry and changing the development mode of electric power.

发明内容：Invention content:

考虑经济性是实现储能技术推广应用的必然发展趋势，储能系统的应用受到技术经济性水平、市场环境与相关政策等因素的影响。储能成本来源可以包括:初始购置成本、运维成本等，可以此成本最低为目标来实现储能选址以及优化配置。Considering economy is an inevitable development trend to realize the promotion and application of energy storage technology. The application of energy storage system is affected by factors such as the level of technical economy, market environment and related policies. The sources of energy storage cost can include: initial purchase cost, operation and maintenance cost, etc., and the energy storage location and optimal configuration can be achieved with the goal of the lowest cost.

本发明专利综合考虑储能系统运行性能和经济性，考虑遗传算法与其他算法相结合比较容易、粒子群算法虽简单但不能有效解决离散问题等特点，使用粒子群算法进行连续优化，实现对多节点储能系统的储能额定功率及额定容量配置。具体技术方案如下：The patent of the present invention comprehensively considers the operation performance and economy of the energy storage system, considers that the genetic algorithm is easy to combine with other algorithms, and the particle swarm algorithm is simple but cannot effectively solve discrete problems. The energy storage rated power and rated capacity configuration of the node energy storage system. The specific technical solutions are as follows:

一种基于离散-连续混合法的多节点储能系统配置方法，包括如下过程：A method for configuring a multi-node energy storage system based on a discrete-continuous hybrid method, comprising the following processes:

步骤1：在各节点典型日功率曲线下建立系统模型；具体包括：Step 1: Establish a system model under the typical daily power curve of each node; the details include:

步骤1.1：根据各节点的功率情况建立系统模型；Step 1.1: Establish a system model according to the power situation of each node;

Pk(t)＝Py(t)+PBk(t)，P k (t)=P y (t)+P Bk (t),

SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，SOE(t+1)=SOE(t)+P Bk (t)×Δt×η÷Q e ,

储能系统能量状态的限制：SOEL≤SOE(t)≤SOEU，Limitation of energy state of energy storage system: SOE L ≤SOE(t)≤SOE U ,

储能系统功率的限制：-Pe≤PBk(t)≤Pe，Limitation of energy storage system power: -P e ≤P Bk (t)≤P e ,

储能系统初始购置成本的限制：CPPe+CQQe≤A，Limitation on the initial acquisition cost of the energy storage system: C P P e +C Q Q e ≤A,

每日初始时段储能系统能量状态与结束时段储能系统能量状态相同限制：SOE(Ts)＝SOE(Te)，The energy state of the energy storage system in the initial period of the day is the same as the energy state of the energy storage system in the end period: SOE(T s )=SOE(T e ),

电力系统节点编号为j，0≤j≤J，k为其中安装储能的节点，Pk(t)为第t时刻k节点安装储能后的功率，Py(t)为第t时刻k节点安装储能前的功率，PBk(t)为第t时刻k节点储能系统的功率，SOE(t)为第t时刻的储能系统能量状态，Δt为采样时间，η为储能系统充放电效率，Qe为储能系统额定容量，Pe为储能系统额定功率，Qe为储能系统额定容量，CP为储能系统单位功率造价，CQ为储能系统单位容量造价，A为储能系统初始购置成本上限，SOE(Ts)为每日初始时刻储能系统能量状态，SOE(Te)为每日结束时刻储能系统能量状态，SOEL代表储能系统能量状态下限，SOEU代表储能系统能量状态下限；The node number of the power system is j, 0≤j≤J, k is the node where the energy storage is installed, P k (t) is the power after the energy storage is installed at the node k at the t-th time, and P y (t) is the t-th time k The power of the node before energy storage is installed, P Bk (t) is the power of the energy storage system at node k at time t, SOE(t) is the energy state of the energy storage system at time t, Δt is the sampling time, and η is the energy storage system Charge and discharge efficiency, Q e is the rated capacity of the energy storage system, P e is the rated power of the energy storage system, Q e is the rated capacity of the energy storage system, C P is the cost per unit power of the energy storage system, and C Q is the cost per unit capacity of the energy storage system , A is the upper limit of the initial purchase cost of the energy storage system, SOE(T s ) is the energy state of the energy storage system at the beginning of the day, SOE(T e ) is the energy state of the energy storage system at the end of the day, and SOE L represents the energy of the energy storage system The lower limit of the state, SOE U represents the lower limit of the energy state of the energy storage system;

得出储能系统相关功率函数f1：

Figure BDA0003079862830000021
The relevant power function f 1 of the energy storage system is obtained:
Figure BDA0003079862830000021

Ts为每日采样的最初一个时刻，Te为每日采样的最后一个时刻；T s is the first moment of daily sampling, and T e is the last moment of daily sampling;

步骤1.2根据各节点的成本情况建立经济模型；Step 1.2 Establish an economic model according to the cost of each node;

储能系统的初始购置成本Cc：Cc＝CP×Pe+CQ×Qe，The initial acquisition cost of the energy storage system C c : C c =C P ×P e +C Q ×Q e ,

储能系统的运维成本Cy:Cy＝CPy×Pe+CQy×Qe，The operation and maintenance cost of the energy storage system C y :C y =C Py ×P e +C Qy ×Q e ,

资金回收系数By：

Figure BDA0003079862830000031
Fund recovery factor By :
Figure BDA0003079862830000031

CPy为储能系统单位功率运维造价，CQy为储能系统单位容量运维造价，r为贴现率，Y为储能系统的运行年限，通过电池类型可知总的循环寿命，再由功率损耗可知等效的每日循环寿命，如用雨流计数法，用总的循环寿命除每日的循环寿命得出的值再除365，便可得到储能系统使用寿命年限；C Py is the operation and maintenance cost per unit power of the energy storage system, C Qy is the operation and maintenance cost per unit capacity of the energy storage system, r is the discount rate, Y is the operating life of the energy storage system, the total cycle life can be known from the battery type, and then the power The equivalent daily cycle life can be known from the loss. For example, using the rain flow counting method, divide the value obtained by dividing the daily cycle life by the total cycle life and then divide by 365 to obtain the service life of the energy storage system;

经济模型的约束条件：Constraints of the economic model:

Pk(t)＝Py(t)+PBk(t)，P k (t)=P y (t)+P Bk (t),

SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，SOE(t+1)=SOE(t)+P Bk (t)×Δt×η÷Q e ,

SOEL≤SOE(t)≤SOEU，SOE L ≤SOE(t)≤SOE U ,

-Pe≤PBk(t)≤Pe -P e ≤P Bk (t)≤P e

CPPe+CQQe≤AC P P e +C Q Q e ≤A

SOE(Ts)＝SOE(Te)；SOE(T s )=SOE(T e );

得出储能系统每日成本函数f2：The daily cost function f 2 of the energy storage system is obtained:

f2＝(Cc×By+Cy)÷365，f 2 = (C c ×By +C y )÷365,

步骤1.3：根据步骤1.1和步骤1.2得到的储能系统功率损耗函数以及储能系统每日成本函数，得到综合考虑系统运行性能和经济性且用于离散-连续混合法的目标函数f3：Step 1.3: According to the power loss function of the energy storage system and the daily cost function of the energy storage system obtained in Step 1.1 and Step 1.2, the objective function f 3 for the discrete-continuous hybrid method that comprehensively considers the operating performance and economy of the system is obtained:

Figure BDA0003079862830000032
Figure BDA0003079862830000032

其中F[f1]表示与函数f1相关的系统功率损耗公式；where F[f 1 ] represents the system power loss formula related to the function f 1 ;

步骤2：充分考虑离散部分和连续部分，定义编码串的形式和数量，初始化编码串；Step 2: Fully consider the discrete part and the continuous part, define the form and quantity of the encoding string, and initialize the encoding string;

步骤2.1：充分考虑离散部分和连续部分，定义储能安装节点、该节点中储能额定功率及额定容量、该节点储能功率PBk(t)，在第g次迭代中，编码串为

Figure BDA0003079862830000041
编码串长度等于编码位数m+n，其中离散部分二进制编码串为
Figure BDA0003079862830000042
离散部分二进制编码串长度等于编码位数n，
Figure BDA0003079862830000043
到
Figure BDA0003079862830000044
之间的二进制编码位对应储能所选节点，其取值为零到储能节点编号J的任意整数，1≤p≤n-1，
Figure BDA0003079862830000045
到
Figure BDA0003079862830000046
之间的二进制编码位对应
Figure BDA0003079862830000047
到
Figure BDA0003079862830000048
之间的二进制编码位所对应的节点安装储能的额定功率及额定容量，需满足n-p为偶数，额定功率及额定容量取值均为零到
Figure BDA0003079862830000049
连续部分编码串长度为
Figure BDA00030798628300000410
其中bkd为该安装储能的k节点的第d时刻的储能系统的功率，初始随机生成S个编码串
Figure BDA00030798628300000411
e＝1,2,…,F；Step 2.1: Fully consider the discrete part and the continuous part, define the energy storage installation node, the rated power and rated capacity of the energy storage in the node, and the energy storage power P Bk (t) of the node. In the gth iteration, the code string is
Figure BDA0003079862830000041
The length of the coded string is equal to the number of coded bits m+n, and the discrete part of the binary coded string is
Figure BDA0003079862830000042
The length of the discrete part binary code string is equal to the number of code bits n,
Figure BDA0003079862830000043
arrive
Figure BDA0003079862830000044
The binary coded bits in between correspond to the node selected by the energy storage, and its value is any integer from zero to the node number J of the energy storage, 1≤p≤n-1,
Figure BDA0003079862830000045
arrive
Figure BDA0003079862830000046
The binary-coded bit correspondence between
Figure BDA0003079862830000047
arrive
Figure BDA0003079862830000048
The rated power and rated capacity of the installed energy storage node corresponding to the binary coded bits between the two must satisfy that np is an even number, and the rated power and rated capacity are both from zero to
Figure BDA0003079862830000049
The length of the continuous part of the encoded string is
Figure BDA00030798628300000410
where b kd is the power of the energy storage system at the d-th moment of node k where energy storage is installed, and S code strings are randomly generated initially.
Figure BDA00030798628300000411
e=1,2,...,F;

步骤2.2初始化编码串的最大迭代次数、选择率、交叉率、变异率、粒子速度和位置等；Step 2.2 Initialize the maximum number of iterations, selection rate, crossover rate, mutation rate, particle velocity and position of the encoding string, etc.;

步骤3：对初始编码串进行排序，利用离散-连续混合法进行编码串的迭代；Step 3: Sort the initial code string, and use the discrete-continuous hybrid method to iterate the code string;

步骤4：达到最大迭代次数结束，求得使得目标函数最优的储能控制与选址配置。Step 4: When the maximum number of iterations is reached, the energy storage control and site selection configuration that optimizes the objective function is obtained.

与最接近的现有技术相比，本发明的有益效果是：Compared with the closest prior art, the beneficial effects of the present invention are:

本发明技术方案中，借鉴粒子群算法中的速度更新、位置更新思想与遗传演化过程中的编码、选择、交叉以及变异思想，综合考虑储能系统运行性能和经济性，综合考虑遗传算法容易与其他算法相结合、粒子群算法虽简单但不能有效解决离散及组合优化问题等特点，每次编码串进行优化迭代时，分裂成离散部分和连续部分，使用粒子群算法进行连续部分迭代，而使用遗传算法进行离散部分迭代，各自迭代完成合成编码串为一次完整迭代，达到要求不再继续寻优时，输出使系统运行性能和经济性最优的编码串，进而实现电力系统其中一个节点的储能系统控制与选址配置，且构建单层模型，结构简单，有利于储能系统的研究推广。In the technical scheme of the present invention, the speed update and position update ideas in the particle swarm algorithm and the coding, selection, crossover and mutation ideas in the genetic evolution process are used for reference, the operation performance and economy of the energy storage system are comprehensively considered, and the genetic algorithm is easy to integrate with Combining other algorithms, the particle swarm optimization algorithm is simple but cannot effectively solve the discrete and combinatorial optimization problems. The genetic algorithm performs discrete partial iterations, and each iteration completes the synthetic code string as a complete iteration. When the requirements are not continued, the code string that optimizes the operating performance and economy of the system is output, and then the storage of one node of the power system is realized. Energy system control and site selection configuration, and the construction of a single-layer model, the structure is simple, which is conducive to the research and promotion of energy storage systems.

附图说明：Description of drawings:

图1是本发明方法流程图。Fig. 1 is the flow chart of the method of the present invention.

图2是实施例中编码串示意图。FIG. 2 is a schematic diagram of an encoded string in an embodiment.

图3是实施例中步骤2、步骤3、步骤4的过程流程示意图Fig. 3 is the process flow schematic diagram of step 2, step 3, step 4 in the embodiment

图4是实施例中遗传交叉运算过程示意图。FIG. 4 is a schematic diagram of a genetic crossover operation process in an embodiment.

图5是实施例中遗传变异运算过程示意图。FIG. 5 is a schematic diagram of the genetic variation calculation process in the embodiment.

具体实施方式：Detailed ways:

下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整描述。The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.

一种基于离散-连续混合法的多节点储能系统配置方法，包括如下过程：A method for configuring a multi-node energy storage system based on a discrete-continuous hybrid method, comprising the following processes:

步骤1：在各节点典型日功率曲线下建立系统模型；具体包括：Step 1: Establish a system model under the typical daily power curve of each node; the details include:

步骤1.1：根据各节点的功率情况建立系统模型；Step 1.1: Establish a system model according to the power situation of each node;

Pk(t)＝Py(t)+PBk(t)，P k (t)=P y (t)+P Bk (t),

SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，SOE(t+1)=SOE(t)+P Bk (t)×Δt×η÷Q e ,

储能系统能量状态的限制：SOEL≤SOE(t)≤SOEU，Limitation of energy state of energy storage system: SOE L ≤SOE(t)≤SOE U ,

储能系统功率的限制：-Pe≤PBk(t)≤Pe，Limitation of energy storage system power: -P e ≤P Bk (t)≤P e ,

储能系统初始购置成本的限制：CPPe+CQQe≤A，Limitation on the initial acquisition cost of the energy storage system: C P P e +C Q Q e ≤A,

每日初始时段储能系统能量状态与结束时段储能系统能量状态相同限制：SOE(Ts)＝SOE(Te)，The energy state of the energy storage system in the initial period of the day is the same as the energy state of the energy storage system in the end period: SOE(T s )=SOE(T e ),

电力系统节点编号为j，0≤j≤J，k为其中安装储能的节点，Pk(t)为第t时刻k节点安装储能后的功率，Py(t)为第t时刻k节点安装储能前的功率，PBk(t)为第t时刻k节点储能系统的功率，SOE(t)为第t时刻的储能系统能量状态，Δt为采样时间，η为储能系统充放电效率，Qe为储能系统额定容量，Pe为储能系统额定功率，Qe为储能系统额定容量，CP为储能系统单位功率造价，CQ为储能系统单位容量造价，A为储能系统初始购置成本上限，SOE(Ts)为每日初始时刻储能系统能量状态，SOE(Te)为每日结束时刻储能系统能量状态，SOEL代表储能系统能量状态下限，SOEU代表储能系统能量状态下限；The node number of the power system is j, 0≤j≤J, k is the node where the energy storage is installed, P k (t) is the power after the energy storage is installed at the node k at the t-th time, and P y (t) is the t-th time k The power of the node before energy storage is installed, P Bk (t) is the power of the energy storage system at node k at time t, SOE(t) is the energy state of the energy storage system at time t, Δt is the sampling time, and η is the energy storage system Charge and discharge efficiency, Q e is the rated capacity of the energy storage system, P e is the rated power of the energy storage system, Q e is the rated capacity of the energy storage system, C P is the cost per unit power of the energy storage system, and C Q is the cost per unit capacity of the energy storage system , A is the upper limit of the initial purchase cost of the energy storage system, SOE(T s ) is the energy state of the energy storage system at the beginning of the day, SOE(T e ) is the energy state of the energy storage system at the end of the day, and SOE L represents the energy of the energy storage system The lower limit of the state, SOE U represents the lower limit of the energy state of the energy storage system;

得出储能系统相关功率函数f1：

Figure BDA0003079862830000061
The relevant power function f 1 of the energy storage system is obtained:
Figure BDA0003079862830000061

Ts为每日采样的最初一个时刻，Te为每日采样的最后一个时刻；T s is the first moment of daily sampling, and T e is the last moment of daily sampling;

步骤1.2根据各节点的成本情况建立经济模型；Step 1.2 Establish an economic model according to the cost of each node;

储能系统的初始购置成本Cc：Cc＝CP×Pe+CQ×Qe，The initial acquisition cost of the energy storage system C c : C c =C P ×P e +C Q ×Q e ,

储能系统的运维成本Cy:Cy＝CPy×Pe+CQy×Qe，The operation and maintenance cost of the energy storage system C y :C y =C Py ×P e +C Qy ×Q e ,

资金回收系数By：

Figure BDA0003079862830000062
Fund recovery factor By :
Figure BDA0003079862830000062

CPy为储能系统单位功率运维造价，CQy为储能系统单位容量运维造价，r为贴现率，Y为储能系统的运行年限，通过电池类型可知总的循环寿命，再由功率损耗可知等效的每日循环寿命，如用雨流计数法，用总的循环寿命除每日的循环寿命得出的值再除365，便可得到储能系统使用寿命年限；C Py is the operation and maintenance cost per unit power of the energy storage system, C Qy is the operation and maintenance cost per unit capacity of the energy storage system, r is the discount rate, Y is the operating life of the energy storage system, the total cycle life can be known from the battery type, and then the power The equivalent daily cycle life can be known from the loss. For example, using the rain flow counting method, divide the value obtained by dividing the daily cycle life by the total cycle life and then divide by 365 to obtain the service life of the energy storage system;

经济模型的约束条件：Constraints of the economic model:

Pk(t)＝Py(t)+PBk(t)，P k (t)=P y (t)+P Bk (t),

SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，SOE(t+1)=SOE(t)+P Bk (t)×Δt×η÷Q e ,

SOEL≤SOE(t)≤SOEU，SOE L ≤SOE(t)≤SOE U ,

-Pe≤PBk(t)≤Pe -P e ≤P Bk (t)≤P e

CPPe+CQQe≤AC P P e +C Q Q e ≤A

SOE(Ts)＝SOE(Te)；SOE(T s )=SOE(T e );

得出储能系统每日成本函数f2：The daily cost function f 2 of the energy storage system is obtained:

f2＝(Cc×By+Cy)÷365，f 2 = (C c ×By +C y )÷365,

步骤1.3：根据步骤1.1和步骤1.2得到的储能系统功率损耗函数以及储能系统每日成本函数，得到综合考虑系统运行性能和经济性且用于离散-连续混合法的目标函数f3：Step 1.3: According to the power loss function of the energy storage system and the daily cost function of the energy storage system obtained in Step 1.1 and Step 1.2, the objective function f 3 for the discrete-continuous hybrid method that comprehensively considers the operating performance and economy of the system is obtained:

Figure BDA0003079862830000075
Figure BDA0003079862830000075

其中F[f1]表示与函数f1相关的系统功率损耗公式；where F[f 1 ] represents the system power loss formula related to the function f 1 ;

步骤2：充分考虑离散部分和连续部分，定义编码串的形式和数量，初始化编码串；Step 2: Fully consider the discrete part and the continuous part, define the form and quantity of the encoding string, and initialize the encoding string;

步骤2.1：充分考虑离散部分和连续部分，定义储能安装节点、该节点中储能额定功率及额定容量、该节点储能功率PBk(t)，如图2所示，在第g次迭代中，编码串为

Figure BDA0003079862830000071
编码串长度等于编码位数m+n，其中离散部分二进制编码串为
Figure BDA0003079862830000072
离散部分二进制编码串长度等于编码位数n，
Figure BDA0003079862830000073
到
Figure BDA0003079862830000074
之间的二进制编码位对应储能所选节点，其取值为零到储能节点编号J的任意整数，1≤p≤n-1，
Figure BDA0003079862830000081
到
Figure BDA0003079862830000082
之间的二进制编码位对应
Figure BDA0003079862830000083
到
Figure BDA0003079862830000084
之间的二进制编码位所对应的节点安装储能的额定功率及额定容量，需满足n-p为偶数，额定功率及额定容量取值均为零到
Figure BDA0003079862830000085
连续部分编码串长度为
Figure BDA0003079862830000086
其中bkd为该安装储能的k节点的第d时刻的储能系统的功率，初始随机生成S个编码串
Figure BDA0003079862830000087
e＝1,2,…,F；Step 2.1: Fully consider the discrete part and the continuous part, define the energy storage installation node, the rated power and rated capacity of the energy storage in the node, and the energy storage power P Bk (t) of the node, as shown in Figure 2, in the gth iteration , the encoded string is
Figure BDA0003079862830000071
The length of the coded string is equal to the number of coded bits m+n, and the discrete part of the binary coded string is
Figure BDA0003079862830000072
The length of the discrete part binary code string is equal to the number of code bits n,
Figure BDA0003079862830000073
arrive
Figure BDA0003079862830000074
The binary coded bits in between correspond to the node selected by the energy storage, and its value is any integer from zero to the node number J of the energy storage, 1≤p≤n-1,
Figure BDA0003079862830000081
arrive
Figure BDA0003079862830000082
The binary-coded bit correspondence between
Figure BDA0003079862830000083
arrive
Figure BDA0003079862830000084
The rated power and rated capacity of the installed energy storage node corresponding to the binary coded bits between the two must satisfy that np is an even number, and the rated power and rated capacity are both from zero to
Figure BDA0003079862830000085
The length of the continuous part of the encoded string is
Figure BDA0003079862830000086
where b kd is the power of the energy storage system at the d-th moment of node k where energy storage is installed, and S code strings are randomly generated initially.
Figure BDA0003079862830000087
e=1,2,...,F;

步骤2.2初始化编码串的最大迭代次数、选择率、交叉率、变异率、粒子速度和位置等；包括如下具体过程：Step 2.2 Initialize the maximum number of iterations, selection rate, crossover rate, mutation rate, particle speed and position of the encoding string, etc.; including the following specific processes:

步骤2.2.1：初始化编码串的最大迭代次数G，初始化离散部分交叉率Pc、变异率Pm，设定学习因子C1和C2、惯性权重w，初始化连续部分粒子的速度和位置，第d维度位置矢量代表第d时刻的储能系统的功率，第d维度速度矢量代表第d时刻的储能系统的功率的改变量，约束条件如下：Step 2.2.1: Initialize the maximum number of iterations G of the coding string, initialize the discrete part crossover rate P c , the mutation rate P m , set the learning factors C 1 and C 2 , the inertia weight w, and initialize the velocity and position of the continuous part particle, The position vector of the dth dimension represents the power of the energy storage system at the dth time, and the velocity vector of the dth dimension represents the change of the power of the energy storage system at the dth time. The constraints are as follows:

粒子位置限制：

Figure BDA0003079862830000088
Particle position constraints:
Figure BDA0003079862830000088

Figure BDA0003079862830000089
为粒子f在第j次迭代中第d维的位置，xmin为位置的最小值，对应-Pe，由离散部分可知-Pe，xmax为位置的最大值，对应Pe，由离散部分可知Pe，
Figure BDA0003079862830000089
is the position of particle f in the d-th dimension in the jth iteration, x min is the minimum value of the position, corresponding to -Pe, from the discrete part to know -Pe, x max is the maximum value of the position, corresponding to Pe, from the discrete part to know Pe ,

粒子速度限制：

Figure BDA00030798628300000810
Particle Velocity Limits:
Figure BDA00030798628300000810

Figure BDA00030798628300000811
为粒子f在第j次迭代中第d维的速度，vmin为速度的最小值，vmax为速度的最大值；
Figure BDA00030798628300000811
is the velocity of the d-th dimension of the particle f in the jth iteration, v min is the minimum value of the velocity, and v max is the maximum value of the velocity;

步骤2.2.2：根据编码串编码位数值选择对应的储能安装节点、该节点中储能额定功率及额定容量、该节点储能功率PBk(t)，代入步骤1所建立的系统及经济模型的约束条件，判断随机生成的F个初始编码串是否满足约束条件，然后去掉不满足约束条件的编码串并再次随机产生与去掉的编码串数目相同的编码串，直到F个编码串全部判断满足约束条件为止，初始化迭代次数为0，即g＝0；Step 2.2.2: Select the corresponding energy storage installation node, the rated power and rated capacity of the energy storage in the node, and the energy storage power P Bk (t) of the node according to the code bit value of the encoding string, and substitute it into the system and economy established in step 1. Constraints of the model, determine whether the randomly generated F initial code strings meet the constraints, then remove the code strings that do not meet the constraints, and randomly generate the same number of code strings as the removed code strings again, until all F code strings are judged Until the constraints are met, the number of initialization iterations is 0, that is, g=0;

步骤3：对初始编码串进行排序，利用离散-连续混合法进行编码串的迭代；包括如下具体过程：Step 3: Sort the initial code string, and use the discrete-continuous hybrid method to iterate the code string; including the following specific processes:

步骤3.1：得到F个全部满足约束条件的编码串后，根据编码位数值选择对应的储能安装节点及该节点储能额定容量、额定功率、该节点储能功率PBk(t)，代入f3，计算F个编码串对应的函数值，若

Figure BDA0003079862830000091
则对应储能选址节点数的二进制编码串大于的部分默认对应储能节点0，按目标函数进行优劣排序；Step 3.1: After obtaining F code strings that all meet the constraints, select the corresponding energy storage installation node and the rated energy storage capacity, rated power of the node, and energy storage power P Bk (t) of the node according to the value of the encoded bits, and substitute f 3. Calculate the function value corresponding to the F code strings, if
Figure BDA0003079862830000091
Then the part of the binary code string corresponding to the number of energy storage site selection nodes is larger than the energy storage node 0 by default, and is sorted according to the objective function;

步骤3.2：每次编码串

Figure BDA0003079862830000092
进行迭代时，分成离散部分
Figure BDA0003079862830000093
和连续部分
Figure BDA0003079862830000094
迭代，连续部分迭代使用粒子群算法的速度更新、位置更新，离散部分迭代使用遗传算法的选择、交叉、变异，两部分均迭代完成且满足约束条件的编码串为下一代编码串；如图3所示，包括如下具体过程：Step 3.2: Encode the string each time
Figure BDA0003079862830000092
When iterating, divide into discrete parts
Figure BDA0003079862830000093
and the continuation part
Figure BDA0003079862830000094
Iteration, continuous part iteration uses speed update and position update of particle swarm optimization, discrete part uses genetic algorithm selection, crossover, mutation, the code string that is completed iteratively and meets the constraints is the next generation code string; as shown in Figure 3 shown, including the following specific processes:

步骤3.2.1：对按目标函数进行优劣排序的编码串进行迭代，分为离散部分迭代和连续部分迭代；Step 3.2.1: Iterate on the encoded string sorted by the objective function, which is divided into discrete part iteration and continuous part iteration;

步骤3.2.2：离散部分对粒子群按公式Step 3.2.2: Discrete part for particle swarm according to formula

Figure BDA0003079862830000095
进行速度更新，
Figure BDA0003079862830000095
Do speed updates,

按公式

Figure BDA0003079862830000096
进行位置更新；by formula
Figure BDA0003079862830000096
make location updates;

Figure BDA0003079862830000097
为粒子f在第d维的个体极值点的位置，
Figure BDA0003079862830000098
为整个种群在第d维的全局极值点的位置，r1、r2为0-1的随机数；
Figure BDA0003079862830000097
is the position of the individual extreme point of particle f in the d-th dimension,
Figure BDA0003079862830000098
is the position of the global extreme point of the entire population in the d-th dimension, and r 1 and r 2 are random numbers of 0-1;

步骤3.2.3：连续部分对各个二进制编码串的二进制编码位数值进行选择运算、交叉运算、变异运算，如图4、图5所示；Step 3.2.3: The continuous part performs selection operation, crossover operation, and mutation operation on the binary code bit value of each binary code string, as shown in Figure 4 and Figure 5;

步骤3.2.4：判断离散部分、连续部分都迭代完成后的编码串是否满足步骤1建立的系统及经济模型的约束，然后去掉不满足约束的编码串并再次随机产生与去掉的编码串数目相同的编码串，直到F个编码串全部判断满足约束为止，此为一次完整迭代，迭代次数增加1，即g＝g+1；Step 3.2.4: Determine whether the code string after iterative completion of discrete part and continuous part satisfies the constraints of the system and economic model established in step 1, then remove the code strings that do not meet the constraints and randomly generate the same number of code strings as the removed code strings until all the F code strings are judged to satisfy the constraints, this is a complete iteration, and the number of iterations increases by 1, that is, g=g+1;

步骤4：达到最大迭代次数结束，求得使得目标函数最优的储能控制与选址配置，包括如下具体过程：Step 4: When the maximum number of iterations is reached, the energy storage control and site selection configuration that optimizes the objective function is obtained, including the following specific processes:

步骤4.1：判断当前迭代次数是否达到最大迭代次数G，若是，则达到要求不再继续寻优，输出使系统运行性能和经济性最优的编码串，否则回到步骤3.2.1；Step 4.1: Determine whether the current number of iterations has reached the maximum number of iterations G, if so, then it will not continue to search if the requirements are met, and output the code string that optimizes the system performance and economy, otherwise, go back to Step 3.2.1;

步骤4.2：通过步骤4.1输出的编码串，进而得到电力系统其中一个节点的储能功率控制情况、储能安装节点、该节点的储能额定功率及额定容量。Step 4.2: Through the code string output in step 4.1, the energy storage power control status of one node in the power system, the energy storage installation node, the energy storage rated power and rated capacity of the node are obtained.

Claims (5)

1. A multi-node energy storage system configuration method based on a discrete-continuous hybrid method is characterized by comprising the following processes:
step 1: establishing a system model under a typical daily power curve of each node; the method specifically comprises the following steps:
step 1.1: establishing a system model according to the power condition of each node;
Pk(t)＝Py(t)+PBk(t)，
SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，
limitation of energy state of energy storage system: SOEL≤SOE(t)≤SOEU，
Limitation of energy storage system power: -Pe≤PBk(t)≤Pe，
Limitation of initial acquisition cost of energy storage system: cPPe+CQQe≤A，
The energy state of the energy storage system in the initial period of each day is limited to be the same as that in the ending period: SOE (T)s)＝SOE(Te)，
The node number of the power system is J, J is more than or equal to 0 and less than or equal to J, k is a node in which energy storage is installed, Pk(t) is the power after the energy storage is installed at the k node at the t moment, Py(t) is the power before the k node is installed with energy storage at the t moment, PBk(t) is the power of the k-node energy storage system at the t-th moment, SOE (t) is the energy state of the energy storage system at the t-th moment, delta t is sampling time, eta is the charge-discharge efficiency of the energy storage system, and QeFor rating the capacity of the energy storage system, PeFor rating of energy storage systems, QeFor rating the capacity of the energy storage system, CPFor cost per unit power of the energy storage system, CQFor the cost of the energy storage system per unit capacity, A is the upper limit of the initial acquisition cost of the energy storage system, SOE (T)s) For the energy state of the energy storage system at the initial moment of the day, SOE (T)e) For the energy state of the energy storage system at the end of the day, SOELRepresenting the lower limit of energy state, SOE, of the energy storage systemURepresents the energy state lower limit of the energy storage system;
obtaining the related power function f of the energy storage system1：
Figure FDA0003079862820000011
TsFor the first moment of daily sampling, TeThe last moment of sampling per day;
step 1.2, establishing an economic model according to the cost condition of each node;
initial acquisition cost C of energy storage systemc：Cc＝CP×Pe+CQ×Qe，
Operation and maintenance cost C of energy storage systemy:Cy＝CPy×Pe+CQy×Qe，
Coefficient of capital recovery By：
Figure FDA0003079862820000021
CPyFor the unit power operation and maintenance cost of the energy storage system, CQyThe unit capacity operation and maintenance cost of the energy storage system is calculated, r is the current rate, Y is the operation age of the energy storage system, the total cycle life can be known through the battery type, the equivalent daily cycle life can be known through the power loss, and the service life of the energy storage system can be obtained by dividing 365 by the value obtained by dividing the daily cycle life by the total cycle life by a rain flow counting method;
constraint conditions of the economic model:
Pk(t)＝Py(t)+PBk(t)，
SOE(t+1)＝SOE(t)+PBk(t)×Δt×η÷Qe，
SOEL≤SOE(t)≤SOEU，
-Pe≤PBk(t)≤Pe
CPPe+CQQe≤A
SOE(Ts)＝SOE(Te)；
obtaining a daily cost function f of the energy storage system2：
f2＝(Cc×By+Cy)÷365，
Step 1.3: according to the energy storage system power loss function and the energy storage system daily cost function obtained in the steps 1.1 and 1.2, obtaining an objective function f comprehensively considering the system operation performance and the economy and used for a discrete-continuous hybrid method3：
Figure FDA0003079862820000022
Wherein F [ F ]1]Representation and function f1The associated system power loss formula;
step 2: fully considering the discrete part and the continuous part, defining the form and the number of the coding strings, and initializing the coding strings;
step 2.1: fully considering discrete part and continuous part, defining energy storage installation node, energy storage rated power and rated capacity in the node, and energy storage power P of the nodeBk(t) in the g-th iteration, the code string is
Figure FDA0003079862820000031
The code string length is equal to the number of code bits m + n, wherein the discrete part binary code string is
Figure FDA0003079862820000032
The discrete part binary code string length is equal to the number of code bits n,
Figure FDA0003079862820000033
to
Figure FDA0003079862820000034
The binary coding bit between the nodes corresponds to the selected nodes of the energy storage, the value of the selected nodes is any integer from zero to the number J of the energy storage nodes, p is more than or equal to 1 and less than or equal to n-1,
Figure FDA0003079862820000035
to
Figure FDA0003079862820000036
Binary coded bit correspondence between
Figure FDA0003079862820000037
To
Figure FDA0003079862820000038
The rated power and the rated capacity of the node installation energy storage corresponding to the binary coding bit between the two nodes need to satisfy the condition that n-p is an even number, and the values of the rated power and the rated capacity are from zero to zero
Figure FDA0003079862820000039
The length of the continuous partial code string is
Figure FDA00030798628200000310
Wherein b iskdFor the power of the energy storage system at the d-th moment of the k node for installing the energy storage, S code strings are initially randomly generated
Figure FDA00030798628200000311
Step 2.2, initializing the maximum iteration times, the selection rate, the crossing rate, the variation rate, the particle speed and the position of the coding string;
and step 3: sequencing the initial coding strings, and performing iteration of the coding strings by using a discrete-continuous mixed method;
and 4, step 4: and finishing the maximum iteration times, and solving the energy storage control and address selection configuration which enables the target function to be optimal.
2. A multi-node energy storage system configuration method based on a discrete-continuous hybrid method according to claim 1, characterized in that the step 2.2 comprises the following processes:
step 2.2.1: initializing the maximum iteration number G of the code string, and initializing the discrete part cross rate PcThe rate of variation PmSetting a learning factor C1And C2The inertia weight w initializes the speed and the position of the continuous part of particles, the d-dimension position vector represents the power of the energy storage system at the d-th moment, the d-dimension speed vector represents the change amount of the power of the energy storage system at the d-th moment, and the constraint conditions are as follows:
particle position limitation:
Figure FDA0003079862820000041
Figure FDA0003079862820000042
for the position of the particle f in the d-dimension, x, in the j-th iterationminIs the minimum value of position, corresponding to-Pe, known from the discrete part-Pe, xmaxIs the maximum value of the position, corresponding to Pe, which is known from the discrete part,
particle velocity limitation:
Figure FDA0003079862820000043
Figure FDA0003079862820000044
is the d-dimensional velocity, v, of the particle f in the j iterationminIs the minimum value of velocity, vmaxIs the maximum value of the speed;
step 2.2.2: selecting corresponding energy storage installation node, energy storage rated power and rated capacity in the node and energy storage power P of the node according to the number value of the coding bits of the coding stringBk(t) substituting the constraint conditions of the system and economic model established in the step 1 to judge F initial coding strings generated randomlyAnd if the constraint condition is met, removing the coding strings which do not meet the constraint condition and randomly generating the same number of coding strings as the removed coding strings again until all the F coding strings are judged to meet the constraint condition, wherein the initialization iteration number is 0, namely g is 0.
3. The method for configuring the multi-node energy storage system based on the discrete-continuous hybrid method as claimed in claim 1, wherein the step 3 comprises the following processes:
step 3.1: after F coding strings which all meet the constraint condition are obtained, the corresponding energy storage installation node, the energy storage rated capacity and rated power of the node and the energy storage power P of the node are selected according to the coding bit valueBk(t) substitution of f3Calculating the function values corresponding to the F code strings, if so
Figure FDA0003079862820000045
If the binary code string corresponding to the energy storage addressing node number is larger than the default corresponding energy storage node 0, sorting the advantages and disadvantages according to the objective function;
step 3.2: each time coding string
Figure FDA0003079862820000046
When performing the iteration, dividing into discrete parts
Figure FDA0003079862820000051
And a continuous portion
Figure FDA0003079862820000052
And (3) iteration, wherein the continuous part uses the speed update and the position update of the particle swarm algorithm in an iteration mode, the discrete part uses the selection, the intersection and the variation of the genetic algorithm in an iteration mode, and the encoding string which is finished in an iteration mode and meets the constraint condition is the next generation encoding string.
4. A method for configuring a multi-node energy storage system based on a discrete-continuous hybrid method according to claim 3, wherein the step 3.2 comprises the following steps:
step 3.2.1: performing iteration on the code string which is sorted according to the advantages and disadvantages of the target function, and dividing the iteration into discrete part iteration and continuous part iteration;
step 3.2.2: discrete part to particle group according to formula
Figure FDA0003079862820000053
The speed is updated in a way that the speed is updated,
according to the formula
Figure FDA0003079862820000054
Carrying out position updating;
Figure FDA0003079862820000055
the position of the particle f at the individual extreme point in dimension d,
Figure FDA0003079862820000056
is the position of the global extreme point of the whole population in the d-dimension1、r2A random number from 0 to 1;
step 3.2.3: the continuous part carries out selection operation, cross operation and mutation operation on binary coding bit values of each binary coding string;
step 3.2.4: and (3) judging whether the code strings after the iteration of the discrete part and the continuous part meet the constraints of the system and the economic model established in the step (1), then removing the code strings which do not meet the constraints and randomly generating the code strings with the same number as the removed code strings again until all the F code strings meet the constraints, wherein the iteration number is increased by 1, namely g is g + 1.
5. The method for configuring the multi-node energy storage system based on the discrete-continuous hybrid method as claimed in claim 4, wherein the step 4 comprises the following processes:
step 4.1: judging whether the current iteration number reaches the maximum iteration number G, if so, outputting a coding string which enables the system operation performance and the economy to be optimal and not continuing to optimize, otherwise, returning to the step 3.2.1;
step 4.2: and 4.1, obtaining the energy storage power control condition of one node of the power system, the energy storage installation node, and the energy storage rated power and rated capacity of the node through the encoding string output in the step 4.1.
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