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CN114725971A - Operation decision method and system based on hybrid energy storage system - Google Patents

Operation decision method and system based on hybrid energy storage system Download PDF

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CN114725971A
CN114725971A CN202210650713.7A CN202210650713A CN114725971A CN 114725971 A CN114725971 A CN 114725971A CN 202210650713 A CN202210650713 A CN 202210650713A CN 114725971 A CN114725971 A CN 114725971A
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陈新江
何冠楠
杨煜
宋洁
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Abstract

本发明提供一种基于混合储能系统的运行决策方法及系统，该方法包括：首先建立以最大化运营收益的目标函数，以及多种储能方式约束的约束条件；再基于所述目标函数及所述约束条件构建混合储能系统的时空决策模型；最后利用所述时空决策模型对混合储能系统的运行进行决策。通过本发明提供的基于混合储能系统的运行决策方法及系统，能够增加混合储能系统在电池储能运行过程中的灵活性，提高了电池储能的效率，并提高了运营收益。

Figure 202210650713

The present invention provides an operation decision-making method and system based on a hybrid energy storage system. The method includes: firstly establishing an objective function to maximize operating benefits and constraints constrained by multiple energy storage modes; and then based on the objective function and The constraints construct a spatiotemporal decision-making model of the hybrid energy storage system; finally, the spatiotemporal decision-making model is used to make decisions on the operation of the hybrid energy storage system. The operation decision method and system based on the hybrid energy storage system provided by the present invention can increase the flexibility of the hybrid energy storage system during the operation of the battery energy storage, improve the efficiency of the battery energy storage, and improve the operating income.

Figure 202210650713

Description

一种基于混合储能系统的运行决策方法及系统An operation decision-making method and system based on a hybrid energy storage system

技术领域technical field

本发明涉及储能领域，具体涉及一种基于混合储能系统的运行决策方法及系统。The invention relates to the field of energy storage, in particular to an operation decision-making method and system based on a hybrid energy storage system.

背景技术Background technique

随着间歇性可再生能源大规模接入和交通电气化，能源供给与消费侧的不确定性增加，对能源与交通系统的灵活性提出了更高的要求，能源与交通系统面临重大转型。转型后的低碳能源、交通等各行业的主流技术与生态都将发生深刻的变化。电化学储能电池作为可再生能源集成的关键技术，有望促进碳能源系统的发展，并有望广泛分布于能源系统中的电动汽车中，构成一个耦合能源与交通系统的电池网络。With the large-scale access of intermittent renewable energy and the electrification of transportation, the uncertainty on the energy supply and consumption side has increased, which has put forward higher requirements for the flexibility of the energy and transportation system, and the energy and transportation system is facing a major transformation. After the transformation, the mainstream technologies and ecology of various industries such as low-carbon energy and transportation will undergo profound changes. As a key technology for renewable energy integration, electrochemical energy storage batteries are expected to promote the development of carbon energy systems, and are expected to be widely distributed in electric vehicles in the energy system, forming a battery network that couples energy and transportation systems.

在现有技术中，为实现电动汽车的储能，电动汽车可以通过采用电池储能电站等固定储能系统进行储能，也可以采用装载电池的移动储能车等移动储能系统进行储能。然而，目前现有的大多数关于电池储能系统的研究主要集中在单一储能系统的充放电或更换电池优化研究，使得电池储能运行过程中不够灵活以及效率较低。In the prior art, in order to realize the energy storage of electric vehicles, electric vehicles can use fixed energy storage systems such as battery energy storage power stations for energy storage, or use mobile energy storage systems such as mobile energy storage vehicles loaded with batteries for energy storage . However, most of the current research on battery energy storage systems mainly focuses on the optimization of charging and discharging or replacing batteries of a single energy storage system, which makes the battery energy storage inflexible and low in efficiency during operation.

发明内容SUMMARY OF THE INVENTION

因此，本发明要解决的技术问题在于克服现有技术中单一电池储能系统在电池储能过程中不够灵活以及效率较低的缺陷，从而提供一种基于混合储能系统的运行决策方法及系统。Therefore, the technical problem to be solved by the present invention is to overcome the defects of inflexibility and low efficiency of the single battery energy storage system in the battery energy storage process in the prior art, so as to provide an operation decision-making method and system based on a hybrid energy storage system .

第一方面，本发明提供一种基于混合储能系统的运行决策方法，所述方法包括：In a first aspect, the present invention provides an operation decision-making method based on a hybrid energy storage system, the method comprising:

建立以最大化运营收益的目标函数，以及多种储能方式约束的约束条件；Establish an objective function to maximize operating benefits, and constraints on multiple energy storage methods;

基于所述目标函数及所述约束条件构建混合储能系统的时空决策模型；constructing a spatiotemporal decision-making model of the hybrid energy storage system based on the objective function and the constraints;

利用所述时空决策模型对混合储能系统的运行进行决策。The operation of the hybrid energy storage system is decided by using the spatiotemporal decision-making model.

在该方式中，以最大化运营收益为目标函数，为实现收益的最大化，多种储能方式混合储能，以多种储能方式约束为约束条件，建立时空决策模型，以对混合储能系统的运行进行决策，增加了电池储能方式的灵活性，提高了电池储能的效率，并以最大限度提高了运营收益。In this method, the objective function is to maximize the operating income. In order to maximize the income, a variety of energy storage methods are mixed with energy storage. It increases the flexibility of battery energy storage methods, improves the efficiency of battery energy storage, and maximizes operating benefits.

在一实施例中，所述目标函数包括市场收益函数和成本函数，目标函数为市场收益函数与成本函数之差，所述成本函数包括运输成本函数、更换电池成本函数以及电池老化成本函数。In one embodiment, the objective function includes a market revenue function and a cost function, the objective function is the difference between the market revenue function and the cost function, and the cost function includes a transportation cost function, a battery replacement cost function, and a battery aging cost function.

在该方式中，根据成本函数和市场收益函数的差值，以便于计算目标的值。In this way, the value of the target is easily calculated based on the difference between the cost function and the market return function.

在一实施例中，所述市场收益函数为：

Figure 350613DEST_PATH_IMAGE001
， In one embodiment, the market return function is:
Figure 350613DEST_PATH_IMAGE001
,

其中，REV表示市场收益，

Figure 471016DEST_PATH_IMAGE002
表示电池网络节点-时间对集合，
Figure 188436DEST_PATH_IMAGE003
表示节点g在t时刻的节点边际电价，
Figure 786907DEST_PATH_IMAGE004
、
Figure 70121DEST_PATH_IMAGE005
分别表示t时刻位于节点g处的固定储能系统的充电 量和放电量，
Figure 627005DEST_PATH_IMAGE006
表示移动储能车辆的集合，
Figure 362879DEST_PATH_IMAGE007
、
Figure 30621DEST_PATH_IMAGE008
分别表示t时刻移动储能车辆v在 节点g处的充电量和放电量； where REV is the market return,
Figure 471016DEST_PATH_IMAGE002
represents the set of battery network node-time pairs,
Figure 188436DEST_PATH_IMAGE003
represents the node marginal electricity price of node g at time t ,
Figure 786907DEST_PATH_IMAGE004
,
Figure 70121DEST_PATH_IMAGE005
respectively represent the charging and discharging capacity of the fixed energy storage system located at node g at time t ,
Figure 627005DEST_PATH_IMAGE006
represents a collection of mobile energy storage vehicles,
Figure 362879DEST_PATH_IMAGE007
,
Figure 30621DEST_PATH_IMAGE008
respectively represent the charging and discharging capacity of the mobile energy storage vehicle v at node g at time t ;

所述成本函数为：

Figure 168341DEST_PATH_IMAGE009
， The cost function is:
Figure 168341DEST_PATH_IMAGE009
,

其中，

Figure 364967DEST_PATH_IMAGE010
表示运输成本，
Figure 56980DEST_PATH_IMAGE011
表示更换电池成本，
Figure 997254DEST_PATH_IMAGE012
表示电池老化成本。 in,
Figure 364967DEST_PATH_IMAGE010
represents the transportation cost,
Figure 56980DEST_PATH_IMAGE011
represents the cost of replacing the battery,
Figure 997254DEST_PATH_IMAGE012
Indicates the battery aging cost.

在该方式中，考虑节点边际电价以及车辆在节点处的充电量和放电量，以综合计算出市场收益，以提高市场收益的准确性。In this method, the marginal electricity price of the node and the charging and discharging capacity of the vehicle at the node are considered to comprehensively calculate the market revenue, so as to improve the accuracy of the market revenue.

在一实施例中，所述运输成本函数为：

Figure 452463DEST_PATH_IMAGE013
， In one embodiment, the transportation cost function is:
Figure 452463DEST_PATH_IMAGE013
,

其中，

Figure 616728DEST_PATH_IMAGE014
表示由电池网络各节点组成的路径集合，
Figure 327195DEST_PATH_IMAGE015
表示移动储能车辆v在 路径
Figure 71160DEST_PATH_IMAGE016
上的运输成本；
Figure 449052DEST_PATH_IMAGE017
为第一决策变量，当移动储能车辆经过路径
Figure 253060DEST_PATH_IMAGE016
时，
Figure 450823DEST_PATH_IMAGE018
，当移动 储能车辆不经过路径
Figure 998479DEST_PATH_IMAGE016
时，
Figure 699719DEST_PATH_IMAGE019
； in,
Figure 616728DEST_PATH_IMAGE014
represents the path set composed of each node of the battery network,
Figure 327195DEST_PATH_IMAGE015
Indicates that the mobile energy storage vehicle v is on the path
Figure 71160DEST_PATH_IMAGE016
the cost of transportation;
Figure 449052DEST_PATH_IMAGE017
is the first decision variable, when the mobile energy storage vehicle passes the path
Figure 253060DEST_PATH_IMAGE016
hour,
Figure 450823DEST_PATH_IMAGE018
, when the mobile energy storage vehicle does not pass the path
Figure 998479DEST_PATH_IMAGE016
hour,
Figure 699719DEST_PATH_IMAGE019
;

所述更换电池成本函数为：

Figure 940207DEST_PATH_IMAGE020
， The replacement battery cost function is:
Figure 940207DEST_PATH_IMAGE020
,

其中，

Figure 359687DEST_PATH_IMAGE021
表示单位换电成本，
Figure 711034DEST_PATH_IMAGE022
、
Figure 797939DEST_PATH_IMAGE023
分别表示t时刻移动储能车辆v在节点g 更换电池后产生的充电量和放电量； in,
Figure 359687DEST_PATH_IMAGE021
represents the unit replacement cost,
Figure 711034DEST_PATH_IMAGE022
,
Figure 797939DEST_PATH_IMAGE023
respectively represent the charge and discharge of the mobile energy storage vehicle v after the battery is replaced at node g at time t ;

所述电池老化成本函数为：The battery aging cost function is:

Figure 209329DEST_PATH_IMAGE024
，
Figure 209329DEST_PATH_IMAGE024
,

其中，

Figure 116105DEST_PATH_IMAGE025
表示电池边际老化成本。 in,
Figure 116105DEST_PATH_IMAGE025
Represents the marginal battery aging cost.

在该方式中，以运输成本函数、更换电池成本函数及电池成本老化函数，共同确定成本函数，以计算出成本，以提高成本的准确性。In this method, the cost function is jointly determined by the transportation cost function, the battery replacement cost function and the battery cost aging function to calculate the cost, so as to improve the accuracy of the cost.

在一实施例中，所述多种储能方式约束的约束条件，包括：移动储能系统约束、固定储能系统约束以及混合储能联合约束条件。In an embodiment, the constraints of the multiple energy storage manner constraints include: mobile energy storage system constraints, fixed energy storage system constraints, and hybrid energy storage combined constraints.

在该方式中，以多种储能方式约束为约束条件，以提高电池储能在电网中的效率，并增加电池储能在电网中的灵活性。In this method, the constraints of various energy storage modes are used as constraints to improve the efficiency of battery energy storage in the grid and increase the flexibility of battery energy storage in the grid.

在一实施例中，所述移动储能系统约束包括：路径约束函数、第一容量约束函数以及充放电约束函数；所述固定储能系统约束包括：第二容量约束函数；所述混合储能联合约束包括：第三容量约束函数。In one embodiment, the mobile energy storage system constraints include: a path constraint function, a first capacity constraint function, and a charge-discharge constraint function; the fixed energy storage system constraints include: a second capacity constraint function; the hybrid energy storage The joint constraints include: a third capacity constraint function.

在一实施例中，所述移动储能系统的路径约束函数为：In one embodiment, the path constraint function of the mobile energy storage system is:

Figure 271143DEST_PATH_IMAGE026
，
Figure 271143DEST_PATH_IMAGE026
,

其中，

Figure 212554DEST_PATH_IMAGE027
、
Figure 794845DEST_PATH_IMAGE028
分别表示移动储能车辆进、出节点的路径集合，
Figure 188917DEST_PATH_IMAGE029
、
Figure 147646DEST_PATH_IMAGE030
分别表 示移动储能车辆的起始节点和终止节点，n表示t时刻移动储能车辆所在的节点位置； in,
Figure 212554DEST_PATH_IMAGE027
,
Figure 794845DEST_PATH_IMAGE028
respectively represent the path set of the mobile energy storage vehicle entering and exiting the node,
Figure 188917DEST_PATH_IMAGE029
,
Figure 147646DEST_PATH_IMAGE030
represent the starting node and the end node of the mobile energy storage vehicle, respectively, and n represents the node position where the mobile energy storage vehicle is located at time t ;

所述移动储能系统的第一容量约束函数为：

Figure 677984DEST_PATH_IMAGE031
， The first capacity constraint function of the mobile energy storage system is:
Figure 677984DEST_PATH_IMAGE031
,

其中，

Figure 431177DEST_PATH_IMAGE032
表示t时刻移动储能车辆v的荷电状态，
Figure 312545DEST_PATH_IMAGE033
表示
Figure 74965DEST_PATH_IMAGE034
时刻移动储能车辆v 的荷电状态，
Figure 725389DEST_PATH_IMAGE035
表示自放电率，
Figure 383903DEST_PATH_IMAGE036
表示充电或放电效率； in,
Figure 431177DEST_PATH_IMAGE032
represents the state of charge of the mobile energy storage vehicle v at time t ,
Figure 312545DEST_PATH_IMAGE033
express
Figure 74965DEST_PATH_IMAGE034
the state of charge of the mobile energy storage vehicle v at all times,
Figure 725389DEST_PATH_IMAGE035
represents the self-discharge rate,
Figure 383903DEST_PATH_IMAGE036
Indicates charging or discharging efficiency;

所述移动储能系统约束的充放电约束函数为：

Figure 752568DEST_PATH_IMAGE037
， The charge-discharge constraint function constrained by the mobile energy storage system is:
Figure 752568DEST_PATH_IMAGE037
,

其中，

Figure 53099DEST_PATH_IMAGE038
为第二决策变量，当t时刻移动储能车辆v在节点g处充电或放电或更换 电池时，
Figure 823609DEST_PATH_IMAGE039
，当t时刻移动储能车辆v在节点g处不充电、不放电、且不更换电池时，
Figure 653025DEST_PATH_IMAGE040
，
Figure 508985DEST_PATH_IMAGE041
表示节点g处能同时进行充电、放电和更换电池的移动储能车辆的数量； in,
Figure 53099DEST_PATH_IMAGE038
is the second decision variable, when the mobile energy storage vehicle v is charged or discharged or the battery is replaced at the node g at time t ,
Figure 823609DEST_PATH_IMAGE039
, when the mobile energy storage vehicle v does not charge, discharge, and replace the battery at node g at time t ,
Figure 653025DEST_PATH_IMAGE040
,
Figure 508985DEST_PATH_IMAGE041
Represents the number of mobile energy storage vehicles that can simultaneously charge, discharge and replace batteries at node g ;

所述固定储能系统的第二容量约束函数为：The second capacity constraint function of the fixed energy storage system is:

Figure 347628DEST_PATH_IMAGE042
，
Figure 347628DEST_PATH_IMAGE042
,

其中，

Figure 707066DEST_PATH_IMAGE043
表示t时刻位于节点g处的固定储能系统的荷电状态，
Figure 238541DEST_PATH_IMAGE044
表示t-1时刻 位于节点g处的固定储能系统的荷电状态。 in,
Figure 707066DEST_PATH_IMAGE043
represents the state of charge of the fixed energy storage system located at node g at time t ,
Figure 238541DEST_PATH_IMAGE044
Represents the state of charge of the fixed energy storage system located at node g at time t -1.

所述混合储能系统的第三容量约束函数为：The third capacity constraint function of the hybrid energy storage system is:

Figure 316218DEST_PATH_IMAGE045
，
Figure 316218DEST_PATH_IMAGE045
,

其中，

Figure 976130DEST_PATH_IMAGE046
表示t时刻节点g的最大充电量或放电量。 in,
Figure 976130DEST_PATH_IMAGE046
Represents the maximum charge or discharge amount of node g at time t .

在该方式中，以路径约束函数、第一容量约束函数以及充放电约束函数共同确定移动储能系统的约束条件，以增强移动储能系统约束条件的准确性，以第二容量约束函数确定固定储能系统约束条件，以增强固定储能系统约束条件的准确性，以第三容量约束函数确定混合储能系统约束条件，以增强混合储能系统约束条件的准确性。In this way, the path constraint function, the first capacity constraint function and the charge and discharge constraint function are used to jointly determine the constraints of the mobile energy storage system to enhance the accuracy of the constraints of the mobile energy storage system, and the second capacity constraint function is used to determine the fixed The energy storage system constraints are used to enhance the accuracy of the fixed energy storage system constraints. The third capacity constraint function is used to determine the hybrid energy storage system constraints to enhance the accuracy of the hybrid energy storage system constraints.

第二方面，本发明提供一种基于混合储能系统的运行决策系统，所述系统包括：In a second aspect, the present invention provides an operation decision-making system based on a hybrid energy storage system, the system comprising:

建立模块，用于建立以最大化运营收益的目标函数，以及多种储能方式约束的约束条件；Build a module for establishing an objective function to maximize operating benefits and constraints for multiple energy storage methods;

构建模块，用于基于所述目标函数及所述约束条件构建混合储能系统的时空决策模型；a building module for building a spatiotemporal decision-making model of the hybrid energy storage system based on the objective function and the constraints;

决策模块，用于利用所述时空决策模型对混合储能系统的运行进行决策。A decision-making module is used for making decisions on the operation of the hybrid energy storage system by using the spatiotemporal decision-making model.

第三方面，本发明提供一种计算机设备，包括存储器和处理器，所述存储器和所述处理器之间互相通信连接，所述存储器中存储有计算机指令，所述处理器通过执行所述计算机指令，从而执行第一方面及其可选实施方式中任一项的基于混合储能系统的运行决策方法。In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory and the processor are connected in communication with each other, the memory stores computer instructions, and the processor executes the computer by executing the computer instructions. instruction, thereby executing the operation decision method based on the hybrid energy storage system of any one of the first aspect and its optional embodiments.

第四方面，本发明提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机指令，所述计算机指令用于使所述计算机执行第一方面及其可选实施方式中任一项的基于混合储能系统的运行决策方法。In a fourth aspect, the present invention provides a computer-readable storage medium, where computer instructions are stored in the computer-readable storage medium, and the computer instructions are used to cause the computer to execute any one of the first aspect and its optional implementation manners. Item-based operation decision-making method of hybrid energy storage system.

附图说明Description of drawings

为了更清楚地说明本发明具体实施方式或现有技术中的技术方案，下面将对具体实施方式或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施方式，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to illustrate the specific embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description The drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

图1是本发明实施例提出的一种基于混合储能系统的运行决策方法的流程图。FIG. 1 is a flowchart of an operation decision-making method based on a hybrid energy storage system proposed by an embodiment of the present invention.

图2是本发明实施例提出的一种基于混合储能系统的运行决策系统的结构框图。FIG. 2 is a structural block diagram of an operation decision-making system based on a hybrid energy storage system proposed by an embodiment of the present invention.

图3是本发明实施例提出的一种计算机设备的硬件结构示意图。FIG. 3 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.

具体实施方式Detailed ways

下面将结合附图对本发明的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

为了提高电池储能系统在电池储能运行过程中的灵活性，以及提高电池储能的效率，本发明实施例中提供一种基于混合储能系统的运行决策方法，如图1所示，该方法包括如下步骤S1至步骤S3。In order to improve the flexibility of the battery energy storage system during the operation of battery energy storage and improve the efficiency of battery energy storage, an embodiment of the present invention provides an operation decision method based on a hybrid energy storage system. As shown in FIG. 1 , the The method includes the following steps S1 to S3.

步骤S1：建立以最大化运营收益的目标函数，以及多种储能方式约束的约束条件。Step S1: Establish an objective function to maximize operating benefits, and constraints constrained by multiple energy storage methods.

在本发明实施例中：目标函数是指优化的目标，在约束条件的约束下，不断优化目标函数的值，最终得到最优解，实现目标函数值最大化。In the embodiment of the present invention, the objective function refers to the objective of optimization. Under the constraints of constraints, the value of the objective function is continuously optimized, and an optimal solution is finally obtained to maximize the value of the objective function.

在一具体实施例中，可以先建立目标函数，再建立约束条件；也可以先建立约束函数，再建立目标函数，本实施例对此不做限定。In a specific embodiment, the objective function may be established first, and then the constraint conditions may be established; or the constraint function may be established first, and then the objective function may be established, which is not limited in this embodiment.

以最大化运营收益为目标函数，能够使得目标函数在满足约束函数的约束条件时，求得目标函数的极值，以实现运营商的运营收益最大化为目标，从而最大限度提高运营商的运营收益，进而实现时空套利。Taking maximizing the operating income as the objective function, can make the objective function obtain the extreme value of the objective function when it satisfies the constraints of the constraint function, in order to maximize the operating income of the operator, so as to maximize the operation of the operator. Profits, and then realize space-time arbitrage.

步骤S2：基于目标函数及约束条件构建混合储能系统的时空决策模型。Step S2: Constructing a spatiotemporal decision-making model of the hybrid energy storage system based on the objective function and constraints.

在本发明实施例中：时空决策模型是在一定约束条件的约束下，求解最优解，以使得目标函数取得期望极值。In the embodiment of the present invention, the spatiotemporal decision-making model is to solve the optimal solution under the constraints of certain constraints, so that the objective function obtains the desired extreme value.

步骤S3：利用时空决策模型对混合储能系统的运行进行决策。Step S3: Use the spatiotemporal decision-making model to make decisions on the operation of the hybrid energy storage system.

在本发明实施例中：利用时空决策模块，提高电池储能的效率，提高运营收益。In the embodiment of the present invention, the space-time decision-making module is used to improve the efficiency of battery energy storage and increase the operating income.

通过上述实施例，以最大化运营收益为目标函数，为实现收益的最大化，多种储能方式混合储能，以多种储能方式约束为约束条件，建立时空决策模型，以对混合储能系统的运行进行决策，增加了电池储能方式的灵活性，并提高了电池储能的效率，同时提高了运营收益。Through the above embodiment, taking maximizing operating income as the objective function, in order to realize the maximization of income, a variety of energy storage methods are mixed with energy storage, and the constraints of various energy storage methods are used as constraints to establish a time-space decision-making model. It increases the flexibility of battery energy storage methods, improves the efficiency of battery energy storage, and improves operating income.

在一实施例中，目标函数包括市场收益函数和成本函数，目标函数为市场收益函数与成本函数之差，成本函数包括运输成本函数、更换电池成本函数以及电池老化成本函数。In one embodiment, the objective function includes a market revenue function and a cost function, the objective function is the difference between the market revenue function and the cost function, and the cost function includes a transportation cost function, a battery replacement cost function, and a battery aging cost function.

具体地，市场收益函数为：Specifically, the market return function is:

Figure 455653DEST_PATH_IMAGE047
（1）
Figure 455653DEST_PATH_IMAGE047
(1)

其中，REV表示市场收益，

Figure 158030DEST_PATH_IMAGE048
表示电池网络节点-时间对集合，
Figure 723004DEST_PATH_IMAGE049
表示节点g 在t时刻的节点边际电价，
Figure 903449DEST_PATH_IMAGE050
、
Figure 237479DEST_PATH_IMAGE051
分别表示t时刻位于节点g处的固定储能系统的充电量和 放电量，
Figure 110757DEST_PATH_IMAGE052
表示移动储能车辆的集合，
Figure 163026DEST_PATH_IMAGE053
、
Figure 881584DEST_PATH_IMAGE054
分别表示t时刻移动储能车辆v在节点g 处的充电量和放电量。 where REV is the market return,
Figure 158030DEST_PATH_IMAGE048
represents the set of battery network node-time pairs,
Figure 723004DEST_PATH_IMAGE049
represents the node marginal electricity price of node g at time t ,
Figure 903449DEST_PATH_IMAGE050
,
Figure 237479DEST_PATH_IMAGE051
respectively represent the charging and discharging capacity of the fixed energy storage system located at node g at time t ,
Figure 110757DEST_PATH_IMAGE052
represents a collection of mobile energy storage vehicles,
Figure 163026DEST_PATH_IMAGE053
,
Figure 881584DEST_PATH_IMAGE054
respectively represent the charging amount and the discharging amount of the mobile energy storage vehicle v at node g at time t .

具体地，成本函数为：

Figure 335699DEST_PATH_IMAGE055
，其中，
Figure 114299DEST_PATH_IMAGE056
表示运输成本，
Figure 919444DEST_PATH_IMAGE057
表示更换电 池成本，
Figure 441692DEST_PATH_IMAGE058
表示电池老化成本。 Specifically, the cost function is:
Figure 335699DEST_PATH_IMAGE055
,in,
Figure 114299DEST_PATH_IMAGE056
represents the transportation cost,
Figure 919444DEST_PATH_IMAGE057
represents the cost of replacing the battery,
Figure 441692DEST_PATH_IMAGE058
Indicates the battery aging cost.

具体地，运输成本函数为：Specifically, the transportation cost function is:

Figure 750314DEST_PATH_IMAGE059
（2）
Figure 750314DEST_PATH_IMAGE059
(2)

其中，

Figure 699815DEST_PATH_IMAGE060
表示由电池网络各节点组成的路径集合，
Figure 461098DEST_PATH_IMAGE061
表示移动储能车辆v在 路径上
Figure 52616DEST_PATH_IMAGE062
的运输成本；
Figure 215744DEST_PATH_IMAGE063
为第一决策变量，当移动储能车辆经过路径
Figure 336147DEST_PATH_IMAGE062
时；
Figure 850305DEST_PATH_IMAGE064
，当移动储 能车辆不经过路径
Figure 714356DEST_PATH_IMAGE062
时，
Figure 997570DEST_PATH_IMAGE065
； in,
Figure 699815DEST_PATH_IMAGE060
represents the path set composed of each node of the battery network,
Figure 461098DEST_PATH_IMAGE061
Indicates that the mobile energy storage vehicle v is on the path
Figure 52616DEST_PATH_IMAGE062
the cost of transportation;
Figure 215744DEST_PATH_IMAGE063
is the first decision variable, when the mobile energy storage vehicle passes the path
Figure 336147DEST_PATH_IMAGE062
Time;
Figure 850305DEST_PATH_IMAGE064
, when the mobile energy storage vehicle does not pass the path
Figure 714356DEST_PATH_IMAGE062
hour,
Figure 997570DEST_PATH_IMAGE065
;

具体地，更换电池成本函数为：Specifically, the replacement battery cost function is:

Figure 554453DEST_PATH_IMAGE066
（3）
Figure 554453DEST_PATH_IMAGE066
(3)

其中，

Figure 290328DEST_PATH_IMAGE067
表示单位换电成本，
Figure 223649DEST_PATH_IMAGE068
、
Figure 95790DEST_PATH_IMAGE069
分别表示t时刻移动储能车辆v在节点g 更换电池后产生的充电量和放电量； in,
Figure 290328DEST_PATH_IMAGE067
represents the unit replacement cost,
Figure 223649DEST_PATH_IMAGE068
,
Figure 95790DEST_PATH_IMAGE069
respectively represent the charge and discharge of the mobile energy storage vehicle v after the battery is replaced at node g at time t ;

具体地，电池老化成本函数为：Specifically, the battery aging cost function is:

Figure 823574DEST_PATH_IMAGE070
（4）
Figure 823574DEST_PATH_IMAGE070
(4)

其中，

Figure 46745DEST_PATH_IMAGE071
表示电池边际老化成本。 in,
Figure 46745DEST_PATH_IMAGE071
Represents the marginal battery aging cost.

综上，目标函数为：In summary, the objective function is:

Figure 252599DEST_PATH_IMAGE072
（5）
Figure 252599DEST_PATH_IMAGE072
(5)

其中，f表示混合储能系统的运营收益。where f represents the operating benefit of the hybrid energy storage system.

在另一实施例中，多种储能方式约束的约束条件，包括：移动储能系统约束、固定储能系统约束以及混合储能联合约束条件。In another embodiment, the constraints of multiple energy storage mode constraints include: mobile energy storage system constraints, fixed energy storage system constraints, and hybrid energy storage combined constraints.

其中，移动储能系统用于装载电池的移动储能车，移动储能系统是一种装载在车辆上的电池和电力转换系统，移动储能车辆在存在电价差的节点间行驶，在电价低的节点充电，在电价高的节点放电，以缓解电网拥堵,以实现时空套利；固定储能系统包括电池储能电站，可以为储能式电动汽车充电桩；混合储能联合是融合移动储能系统和固定储能系统，将混合储能系统应用于电网中。Among them, the mobile energy storage system is used for the mobile energy storage vehicle loaded with batteries. The mobile energy storage system is a battery and power conversion system loaded on the vehicle. The mobile energy storage vehicle travels between nodes where there is an electricity price difference. The fixed energy storage system includes battery energy storage power stations, which can be used as charging piles for energy storage electric vehicles; the hybrid energy storage system is a combination of mobile energy storage systems. systems and stationary energy storage systems, applying hybrid energy storage systems to the grid.

移动储能系统约束包括：路径约束函数、第一容量约束函数以及充放电约束函数；固定储能系统约束包括：第二容量约束函数；混合储能联合约束包括：第三容量约束函数。The mobile energy storage system constraints include: a path constraint function, a first capacity constraint function, and a charge-discharge constraint function; the fixed energy storage system constraints include: a second capacity constraint function; and the hybrid energy storage joint constraints include: a third capacity constraint function.

其中，移动储能系统的路径约束函数为：Among them, the path constraint function of the mobile energy storage system is:

Figure 510405DEST_PATH_IMAGE074
（6）
Figure 510405DEST_PATH_IMAGE074
(6)

其中，

Figure 409090DEST_PATH_IMAGE075
、
Figure 853978DEST_PATH_IMAGE076
分别表示移动储能车辆进、出节点的路径集合，n表示t时刻移动 储能车辆所在的节点位置，
Figure 863523DEST_PATH_IMAGE077
、
Figure 975835DEST_PATH_IMAGE078
分别表示移动储能车辆的起始节点和终止节点； in,
Figure 409090DEST_PATH_IMAGE075
,
Figure 853978DEST_PATH_IMAGE076
respectively represent the path set of the mobile energy storage vehicle entering and exiting the node, n represents the node position where the mobile energy storage vehicle is located at time t ,
Figure 863523DEST_PATH_IMAGE077
,
Figure 975835DEST_PATH_IMAGE078
respectively represent the start node and end node of the mobile energy storage vehicle;

具体地，除起始节点和终止节点外，移动储能车辆满足进出节点流量守恒。其中，进出节点流量守恒是指移动储能车辆分别进、出某节点，即为经过该节点。Specifically, except for the starting node and the ending node, the mobile energy storage vehicle satisfies the flow conservation in and out of the node. Among them, the conservation of flow in and out of the node means that the mobile energy storage vehicle enters and exits a node respectively, that is, it passes through the node.

具体地，移动储能系统的第一容量约束函数为：Specifically, the first capacity constraint function of the mobile energy storage system is:

Figure 45422DEST_PATH_IMAGE079
（7）
Figure 45422DEST_PATH_IMAGE079
(7)

Figure 243185DEST_PATH_IMAGE080
（8）
Figure 243185DEST_PATH_IMAGE080
(8)

Figure 784982DEST_PATH_IMAGE081
（9）
Figure 784982DEST_PATH_IMAGE081
(9)

其中，

Figure 751801DEST_PATH_IMAGE082
表示t时刻移动储能车辆v的荷电状态，
Figure 992289DEST_PATH_IMAGE083
表示时刻移动储能车辆v的荷 电状态，
Figure 677349DEST_PATH_IMAGE084
表示自放电率，
Figure 28696DEST_PATH_IMAGE085
表示充电或放电效率；
Figure 850021DEST_PATH_IMAGE086
表示移动储能车辆的容量，
Figure 526990DEST_PATH_IMAGE087
表示t 时刻节点g的最大充电量或放电量，
Figure 433766DEST_PATH_IMAGE088
为第二决策变量，当t时刻移动储能车辆v在节点g处 充电、放电或更换电池时，
Figure 323225DEST_PATH_IMAGE089
，当t时刻移动储能车辆v在节点g处不充电、不放电且不更 换电池时，
Figure 264636DEST_PATH_IMAGE090
。 in,
Figure 751801DEST_PATH_IMAGE082
represents the state of charge of the mobile energy storage vehicle v at time t ,
Figure 992289DEST_PATH_IMAGE083
represents the state of charge of the mobile energy storage vehicle v at any time,
Figure 677349DEST_PATH_IMAGE084
represents the self-discharge rate,
Figure 28696DEST_PATH_IMAGE085
Indicates charging or discharging efficiency;
Figure 850021DEST_PATH_IMAGE086
represents the capacity of the mobile energy storage vehicle,
Figure 526990DEST_PATH_IMAGE087
represents the maximum charge or discharge capacity of node g at time t ,
Figure 433766DEST_PATH_IMAGE088
is the second decision variable, when the mobile energy storage vehicle v charges, discharges or replaces the battery at node g at time t ,
Figure 323225DEST_PATH_IMAGE089
, when the mobile energy storage vehicle v does not charge, discharge and replace the battery at node g at time t ,
Figure 264636DEST_PATH_IMAGE090
.

具体地，式（7）和（8）表示移动储能车辆的荷电状态不能超过其容量，式（9）表示移动储能车辆的充电量或放电量不能超过节点的最大充电量或放电量。Specifically, equations (7) and (8) indicate that the state of charge of the mobile energy storage vehicle cannot exceed its capacity, and equation (9) indicates that the charging or discharging amount of the mobile energy storage vehicle cannot exceed the maximum charging or discharging capacity of the node .

具体地，在调度过程中，移动储能系统约束的充放电约束函数为：Specifically, in the scheduling process, the charge-discharge constraint function constrained by the mobile energy storage system is:

Figure 581348DEST_PATH_IMAGE091
（10）
Figure 581348DEST_PATH_IMAGE091
(10)

Figure 240999DEST_PATH_IMAGE092
（11）
Figure 240999DEST_PATH_IMAGE092
(11)

其中，

Figure 934149DEST_PATH_IMAGE093
表示节点g处能同时进行充电、放电和更换电池的移动储能车辆的数量；in,
Figure 934149DEST_PATH_IMAGE093
Represents the number of mobile energy storage vehicles that can simultaneously charge, discharge and replace batteries at node g ;

式（10）表示同时在同一节点充电或放电的移动储能车辆不能超过该节点的充放电接口数，式（11）保证了移动储能车辆充放电或更换电池和路径规划的时空一致性。Equation (10) indicates that the number of mobile energy storage vehicles charging or discharging at the same node at the same time cannot exceed the number of charging and discharging interfaces of this node. Equation (11) ensures the spatial and temporal consistency of mobile energy storage vehicle charging and discharging or battery replacement and path planning.

具体地，固定储能系统的第二容量约束函数为：Specifically, the second capacity constraint function of the fixed energy storage system is:

Figure 730066DEST_PATH_IMAGE094
（12）
Figure 730066DEST_PATH_IMAGE094
(12)

Figure 483259DEST_PATH_IMAGE095
（13）
Figure 483259DEST_PATH_IMAGE095
(13)

Figure 630206DEST_PATH_IMAGE096
（14）
Figure 630206DEST_PATH_IMAGE096
(14)

其中，

Figure 861468DEST_PATH_IMAGE097
表示t时刻位于节点g处的固定储能系统的荷电状态，
Figure 511892DEST_PATH_IMAGE098
表示t-1时刻 位于节点g处的固定储能系统的荷电状态，
Figure 435985DEST_PATH_IMAGE099
表示固定储能系统的容量； in,
Figure 861468DEST_PATH_IMAGE097
represents the state of charge of the fixed energy storage system located at node g at time t ,
Figure 511892DEST_PATH_IMAGE098
represents the state of charge of the fixed energy storage system located at node g at time t -1,
Figure 435985DEST_PATH_IMAGE099
Indicates the capacity of the fixed energy storage system;

式（12）、（13）和（14）表示固定储能系统的荷电状态和充电量或放电量不能超过其容量。Equations (12), (13) and (14) indicate that the state of charge and the amount of charge or discharge of the fixed energy storage system cannot exceed its capacity.

具体地，混合储能系统的第三容量约束函数为：Specifically, the third capacity constraint function of the hybrid energy storage system is:

Figure 804650DEST_PATH_IMAGE100
（15）
Figure 804650DEST_PATH_IMAGE100
(15)

Figure 105181DEST_PATH_IMAGE101
（16）
Figure 105181DEST_PATH_IMAGE101
(16)

其中，

Figure 610112DEST_PATH_IMAGE102
表示t时刻节点g的最大充电量或放电量。 in,
Figure 610112DEST_PATH_IMAGE102
Represents the maximum charge or discharge amount of node g at time t .

式（15）表示固定储能系统和移动储能车辆的充放电量不能超过节点的最大充点亮或放电量，式（16）表示移动储能车辆更换电池产生的充放电量不能超过固定储能系统的容量。Equation (15) indicates that the charge and discharge capacity of the fixed energy storage system and mobile energy storage vehicle cannot exceed the maximum charge or discharge capacity of the node, and Equation (16) indicates that the charge and discharge capacity of the mobile energy storage vehicle from replacing the battery cannot exceed the fixed storage capacity. capacity of the energy system.

在一具体实施例中，以电动半挂式卡车和电池储能系统组成的移动储能系统，以电池储能电站为固定储能系统，由二者组成的混合储能系统为例，分析混合储能系统在电网中的最优运行策略和经济效益。其中，混合储能系统各项参数如表1所示：In a specific embodiment, taking a mobile energy storage system composed of an electric semi-trailer truck and a battery energy storage system, a battery energy storage power station as a fixed energy storage system, and a hybrid energy storage system composed of the two as an example, the hybrid energy storage system is analyzed. Optimal operation strategies and economic benefits of energy storage systems in power grids. Among them, the parameters of the hybrid energy storage system are shown in Table 1:

表1Table 1

Figure 705107DEST_PATH_IMAGE103
Figure 705107DEST_PATH_IMAGE103

本实施例将基于混合储能系统的时空决策模型运用于具有31个电网节点的案例中，其中固定储能系统安装在电网节点上，如表2所示，算例1~算例3分析了相对于仅移动储能和仅固定储能，混合储能系统具有更高的运营收益和更低的电池老化量。具体来说，混合储能系统的运营收益比仅移动储能和仅固定储能的运营收益之和增加了6.4%，电池老化量比仅移动储能和仅固定储能的电池老化量之和减少了19.6%。In this embodiment, the spatiotemporal decision-making model based on the hybrid energy storage system is applied to a case with 31 grid nodes, in which the fixed energy storage system is installed on the grid nodes, as shown in Table 2. Examples 1 to 3 analyze the Compared with mobile-only and stationary-only storage, hybrid energy storage systems have higher operating benefits and lower battery aging. Specifically, the operating benefit of the hybrid energy storage system is 6.4% higher than the sum of the operating benefits of mobile storage only and stationary storage only, and the battery aging amount is higher than the sum of the battery aging amount of mobile storage only and stationary storage only. A decrease of 19.6%.

表2Table 2

Figure 826647DEST_PATH_IMAGE104
Figure 826647DEST_PATH_IMAGE104

基于相同发明构思，本发明还提供一种基于混合储能系统的运行决策系统。Based on the same inventive concept, the present invention also provides an operation decision system based on a hybrid energy storage system.

图2是根据一示例性实施例提出的一种基于混合储能系统的运行决策系统的结构框图。如图2所示，该系统包括建立模块201、构建模块202以及决策模块203。Fig. 2 is a structural block diagram of an operation decision-making system based on a hybrid energy storage system proposed according to an exemplary embodiment. As shown in FIG. 2 , the system includes a building module 201 , a building module 202 and a decision module 203 .

建立模块201，用于建立以最大化运营收益的目标函数，以及多种储能方式约束的约束条件；The establishment module 201 is used to establish the objective function to maximize the operating income, and the constraint conditions constrained by various energy storage modes;

构建模块202，用于基于目标函数及约束条件构建混合储能系统的时空决策模型；A building module 202, used for building a spatiotemporal decision-making model of the hybrid energy storage system based on the objective function and constraints;

决策模块203，用于利用时空决策模型对混合储能系统的运行进行决策。The decision-making module 203 is used for making decisions on the operation of the hybrid energy storage system by using the spatiotemporal decision-making model.

上述基于混合储能系统的运行决策系统的具体限定以及有益效果可以参见上文中对于基于混合储能系统的运行决策方法的限定，在此不再赘述。上述各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。For the specific limitations and beneficial effects of the above-mentioned operation decision-making system based on the hybrid energy storage system, reference may be made to the above definition of the operation decision-making method based on the hybrid energy storage system, which will not be repeated here. The above-mentioned modules can be implemented in whole or in part by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

图3是根据一示例性实施例提出的一种计算机设备的硬件结构示意图。如图3所示，该设备包括一个或多个处理器310以及存储器320，存储器320包括持久内存、易失内存和硬盘，图3中以一个处理器310为例。该设备还可以包括：输入装置330和输出装置340。FIG. 3 is a schematic diagram of a hardware structure of a computer device according to an exemplary embodiment. As shown in FIG. 3 , the device includes one or more processors 310 and a memory 320 . The memory 320 includes persistent memory, volatile memory, and a hard disk. In FIG. 3 , one processor 310 is used as an example. The apparatus may further include: an input device 330 and an output device 340 .

处理器310、存储器320、输入装置330和输出装置340可以通过总线或者其他方式连接，图3中以通过总线连接为例。The processor 310, the memory 320, the input device 330, and the output device 340 may be connected by a bus or in other ways, and the connection by a bus is taken as an example in FIG. 3 .

处理器310可以为中央处理器（Central Processing Unit，CPU）。处理器310还可以为其他通用处理器、数字信号处理器（Digital Signal Processor，DSP）、专用集成电路（Application Specific Integrated Circuit，ASIC）、现场可编程门阵列（Field-Programmable Gate Array，FPGA）或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等芯片，或者上述各类芯片的组合。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。The processor 310 may be a central processing unit (Central Processing Unit, CPU). The processor 310 may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or Other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components and other chips, or a combination of the above types of chips. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

存储器320作为一种非暂态计算机可读存储介质，包括持久内存、易失内存和硬盘，可用于存储非暂态软件程序、非暂态计算机可执行程序以及模块，如本申请实施例中的业务管理方法对应的程序指令/模块。处理器310通过运行存储在存储器320中的非暂态软件程序、指令以及模块，从而执行服务器的各种功能应用以及数据处理，即实现上述任意一种基于混合储能系统的运行决策方法。The memory 320 is a non-transitory computer-readable storage medium, including persistent memory, volatile memory, and hard disk, and can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as in the embodiments of the present application. The program instruction/module corresponding to the business management method. The processor 310 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions and modules stored in the memory 320 , that is, implementing any of the above-mentioned operation decision methods based on the hybrid energy storage system.

存储器320可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储根据、需要使用的数据等。此外，存储器320可以包括高速随机存取存储器，还可以包括非暂态存储器，例如至少一个磁盘存储器件、闪存器件、或其他非暂态固态存储器件。在一些实施例中，存储器320可选包括相对于处理器310远程设置的存储器，这些远程存储器可以通过网络连接至数据处理装置。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。The memory 320 may include a storage program area and a storage data area, wherein the storage program area may store an operating system and an application program required by at least one function; the storage data area may store data used according to, and need, and the like. Additionally, memory 320 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 320 may optionally include memory located remotely from processor 310, which may be connected to the data processing apparatus via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

输入装置330可接收输入的数字或字符信息，以及产生与用户设置以及功能控制有关的键信号输入。输出装置340可包括显示屏等显示设备。The input device 330 may receive inputted numerical or character information, and generate key signal input related to user setting and function control. The output device 340 may include a display device such as a display screen.

一个或者多个模块存储在存储器320中，当被一个或者多个处理器310执行时，执行如图1所示的基于混合储能系统的运行决策方法。One or more modules are stored in the memory 320, and when executed by one or more processors 310, perform the operation decision method based on the hybrid energy storage system as shown in FIG. 1 .

上述产品可执行本发明实施例所提供的方法，具备执行方法相应的功能模块和有益效果。未在本实施例中详尽描述的技术细节，具体可参见如图1所示的实施例中的相关描述。The above product can execute the method provided by the embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. For technical details that are not described in detail in this embodiment, reference may be made to the relevant description in the embodiment shown in FIG. 1 for details.

本发明实施例还提供了一种非暂态计算机存储介质，计算机存储介质存储有计算机可执行指令，该计算机可执行指令可执行上述任意方法实施例中的认证方法。其中，存储介质可为磁碟、光盘、只读存储记忆体（Read-Only Memory，ROM）、随机存储记忆体（RandomAccess Memory，RAM）、快闪存储器（Flash Memory）、硬盘（Hard Disk Drive，缩写：HDD）或固态硬盘（Solid-State Drive，SSD)等；存储介质还可以包括上述种类的存储器的组合。Embodiments of the present invention further provide a non-transitory computer storage medium, where the computer storage medium stores computer-executable instructions, where the computer-executable instructions can execute the authentication method in any of the foregoing method embodiments. The storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, Abbreviation: HDD) or solid-state drive (Solid-State Drive, SSD), etc.; the storage medium may also include a combination of the above-mentioned types of memories.

显然，上述实施例仅仅是为清楚地说明所作的举例，而并非对实施方式的限定。对于所属领域的普通技术人员来说，在上述说明的基础上还可以做出其它不同形式的变化或变动。这里无需也无法对所有的实施方式予以穷举。而由此所引伸出的显而易见的变化或变动仍处于本发明创造的保护范围之中。Obviously, the above-mentioned embodiments are only examples for clear description, and are not intended to limit the implementation manner. For those of ordinary skill in the art, changes or modifications in other different forms can also be made on the basis of the above description. There is no need and cannot be exhaustive of all implementations here. And the obvious changes or changes derived from this are still within the protection scope of the present invention.

Claims (10)

1. An operation decision method based on a hybrid energy storage system is characterized by comprising the following steps:
establishing an objective function for maximizing operation income and constraint conditions constrained by multiple energy storage modes;
constructing a space-time decision model of the hybrid energy storage system based on the objective function and the constraint condition;
and utilizing the space-time decision model to make a decision on the operation of the hybrid energy storage system.
2. The method of claim 1,
the objective function includes a market revenue function and a cost function, the objective function is a difference between the market revenue function and the cost function, and the cost function includes a transportation cost function, a battery replacement cost function, and a battery aging cost function.
3. The method of claim 2,
the market gain function is:
Figure 554496DEST_PATH_IMAGE001
，
wherein,REVthe revenue of the market is expressed and,
Figure 241829DEST_PATH_IMAGE002
representing a set of battery network node-time pairs，
Figure 570042DEST_PATH_IMAGE003
Representing nodesgIn thattThe node at the time of day is marginal in electricity prices,
Figure 710036DEST_PATH_IMAGE004
、
Figure 414687DEST_PATH_IMAGE005
respectively representtIs located at a node at a timegThe amount of charge and the amount of discharge of the fixed energy storage system,
Figure 956527DEST_PATH_IMAGE006
a collection of mobile energy storage vehicles is represented,
Figure 461501DEST_PATH_IMAGE007
、
Figure 88791DEST_PATH_IMAGE008
respectively representtConstantly-moving energy-storage vehiclevAt a nodegThe amount of charge and discharge at the point;
the cost function is:
Figure 65974DEST_PATH_IMAGE009
，
wherein,
Figure 727900DEST_PATH_IMAGE010
which represents a cost of transportation and,
Figure 397916DEST_PATH_IMAGE011
indicating the cost of replacing the battery,
Figure 512502DEST_PATH_IMAGE012
representing the cost of battery aging.
4. The method of claim 3,
the transportation cost function is:
Figure 293376DEST_PATH_IMAGE013
，
wherein,
Figure 809808DEST_PATH_IMAGE014
representing a set of paths consisting of nodes of the battery network,
Figure 650725DEST_PATH_IMAGE015
indicating a mobile energy storage vehiclevOn the way
Figure 252608DEST_PATH_IMAGE016
The transportation cost of the above;
Figure 571594DEST_PATH_IMAGE017
as a first decision variable, when the mobile energy storage vehicle passes through the path
Figure 208112DEST_PATH_IMAGE016
When the utility model is used, the water is discharged,
Figure 219930DEST_PATH_IMAGE018
when the mobile energy storage vehicle does not pass through the path
Figure 43530DEST_PATH_IMAGE016
When the temperature of the water is higher than the set temperature,
Figure 166206DEST_PATH_IMAGE019
；
the replacement battery cost function is:
Figure 391651DEST_PATH_IMAGE020
，
wherein,
Figure 574371DEST_PATH_IMAGE021
the cost of unit battery replacement is shown,
Figure 150846DEST_PATH_IMAGE022
、
Figure 77214DEST_PATH_IMAGE023
respectively representtConstantly-moving energy storage vehiclevAt a nodegThe amount of charge and discharge generated after the battery is replaced;
the battery aging cost function is:
Figure 157165DEST_PATH_IMAGE024
，
wherein,
Figure 776365DEST_PATH_IMAGE025
representing the battery marginal aging cost.
5. The method of claim 1, wherein the constraints of the plurality of energy storage mode constraints comprise: the method comprises the following steps of mobile energy storage system constraint, fixed energy storage system constraint and hybrid energy storage combined constraint conditions.
6. The method of claim 5,
the mobile energy storage system constraints include: a path constraint function, a first capacity constraint function and a charge-discharge constraint function;
the fixed energy storage system restraint comprises: a second capacity constraint function;
the hybrid energy storage joint constraint comprises: a third capacity constraint function.
7. The method of claim 6,
the path constraint function of the mobile energy storage system is as follows:
Figure 308978DEST_PATH_IMAGE027
，
wherein,
Figure 39036DEST_PATH_IMAGE028
、
Figure 973494DEST_PATH_IMAGE029
respectively represents the path sets of the mobile energy storage vehicle entering and exiting nodes,
Figure 763596DEST_PATH_IMAGE030
、
Figure 783504DEST_PATH_IMAGE031
respectively representing the starting node and the terminating node of a mobile energy storage vehicle,nto representtMoving the node position of the energy storage vehicle at all times;
the first capacity constraint function of the mobile energy storage system is as follows:
Figure 317254DEST_PATH_IMAGE032
，
wherein,
Figure 371798DEST_PATH_IMAGE033
to representtConstantly-moving energy-storage vehiclevThe state of charge of (a) is,
Figure 67221DEST_PATH_IMAGE034
represent
Figure 840005DEST_PATH_IMAGE035
Constantly-moving energy-storage vehiclevThe state of charge of (a) is,
Figure 911866DEST_PATH_IMAGE036
it is shown that the self-discharge rate,
Figure 555337DEST_PATH_IMAGE037
represents charge or discharge efficiency;
the charge and discharge constraint function constrained by the mobile energy storage system is as follows:
Figure 687241DEST_PATH_IMAGE038
，
wherein,
Figure 675883DEST_PATH_IMAGE039
is a second decision variable whentConstantly-moving energy-storage vehiclevAt a nodegWhen it is time to charge or discharge or replace the battery,
Figure 551435DEST_PATH_IMAGE040
when is coming into contact withtConstantly-moving energy-storage vehiclevAt a nodegWhen the battery is not charged, not discharged and not replaced,
Figure 580571DEST_PATH_IMAGE041
，
Figure 617797DEST_PATH_IMAGE042
representing nodesgThe number of mobile energy storage vehicles capable of simultaneously performing charging, discharging and battery replacement;
the second capacity constraint function of the fixed energy storage system is:
Figure 365173DEST_PATH_IMAGE043
，
wherein,
Figure 513258DEST_PATH_IMAGE044
to representtIs located at a node at a momentgThe state of charge of the stationary energy storage system,
Figure 662479DEST_PATH_IMAGE045
to represent
Figure 605027DEST_PATH_IMAGE046
Is located at a node at a timegThe state of charge of the stationary energy storage system;
the third capacity constraint function of the hybrid energy storage system is:
Figure 574120DEST_PATH_IMAGE047
，
wherein,
Figure 791475DEST_PATH_IMAGE048
to representtTime nodegThe maximum charge or discharge amount of.
8. An operation decision system based on a hybrid energy storage system, characterized in that the system comprises:
the establishing module is used for establishing an objective function for maximizing operation income and constraint conditions constrained by multiple energy storage modes;
the building module is used for building a space-time decision model of the hybrid energy storage system based on the objective function and the constraint condition;
and the decision module is used for making a decision on the operation of the hybrid energy storage system by using the space-time decision model.
9. A computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the hybrid energy storage system-based operation decision method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing computer instructions for causing a computer to perform the hybrid energy storage system based operation decision method according to any one of claims 1 to 7.
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