CN115456489B

CN115456489B - Method and device for planning inventory path of hybrid energy storage system and electronic equipment

Info

Publication number: CN115456489B
Application number: CN202211409098.7A
Authority: CN
Inventors: 陈新江; 何冠楠; 宋洁; 丁永康
Original assignee: Peking University
Current assignee: Peking University
Priority date: 2022-11-11
Filing date: 2022-11-11
Publication date: 2023-02-14
Anticipated expiration: 2042-11-11
Also published as: CN115456489A

Abstract

The application provides a method and a device for planning inventory paths of a hybrid energy storage system and electronic equipment, wherein the method comprises the following steps: establishing a spatiotemporal network diagram of the hybrid energy storage system; destroying the spatio-temporal network diagram to obtain a destroyed spatio-temporal path set and a deleted spatio-temporal path set; repairing the deleted space-time path set to obtain a repaired space-time path set; combining the damaged spatio-temporal path set and the repaired spatio-temporal path set to obtain a target spatio-temporal network diagram; and inputting the target space-time network diagram into the space-time network model to obtain an inventory path planning scheme of the hybrid energy storage system. By the method and the device, the problems that in the related technology, the solving difficulty is large, the time cost is large, and the scheduling and planning of the hybrid energy storage system under large-scale battery network nodes, multiple mobile energy storage vehicles and long-time scales are difficult are solved.

Description

Method and device for planning inventory path of hybrid energy storage system and electronic equipment

Technical Field

The invention relates to the technical field of energy storage, in particular to a method and a device for planning an inventory path of a hybrid energy storage system and electronic equipment.

Background

As the most practical and promising energy storage technology at present, an electrochemical energy storage battery (hereinafter referred to as battery energy storage) will be widely distributed in energy systems in the future, such as a fixed energy storage system (battery energy storage power station) and a mobile energy storage system (mobile energy storage vehicle loaded with batteries), and the fixed energy storage system and the mobile energy storage system can form a hybrid energy storage system considering coordination of charging and battery replacement, wherein the mobile energy storage system shuttles between power grid nodes with low power price difference, and discharges at high power price nodes through charging at low power price nodes, so as to realize profit set. The mobile energy storage system can also directly replace the battery at the fixed energy storage system, so that the charging and discharging process is quickly completed, and a better chance of arbitrage is found. The hybrid energy storage system can be applied to auxiliary services of an electric power system and an electric power market, and on one hand, the hybrid energy storage system can promote the consumption of renewable energy of a power grid, relieve the blockage of an output resistor and promote the low-carbon transformation of energy. On the other hand, the battery service system can effectively serve the transaction of the battery and the battery replacement of the rented electric vehicle, improve the utilization efficiency of the battery and enrich the business model of the battery.

The inventory path planning of the hybrid energy storage system can fully mobilize the flexibility of the hybrid energy storage system and maximize profit margin, and particularly, the inventory path planning problem of the hybrid energy storage system comprises battery charging and replacing planning of a fixed energy storage system, and battery charging and replacing and path planning of a mobile energy storage system.

The inventory path planning problem relates to high-dimensional decision variables and complex constraint conditions, and the inventory path planning problem is solved only by adopting a commercial solver such as a Gurobi in the prior art, so that the problems of high solving difficulty, high time overhead, difficulty in solving scheduling problems of a large-scale battery network node, multiple mobile energy storage vehicles and a long-time-scale hybrid energy storage system exist.

Disclosure of Invention

The application provides a method and a device for planning inventory paths of a hybrid energy storage system and electronic equipment, and aims to solve the problems that in the related art, the solving difficulty is high, the time cost is high, and the scheduling and planning of the hybrid energy storage system under the conditions of large-scale battery network nodes, multiple mobile energy storage vehicles and long time scale are difficult.

According to an aspect of the embodiments of the present application, there is provided a method for planning an inventory path of a hybrid energy storage system, the method including:

establishing a spatiotemporal network diagram of the hybrid energy storage system;

destroying the spatio-temporal network graph to obtain a destroyed spatio-temporal path set and a deleted spatio-temporal path set;

repairing the deleted spatio-temporal path set to obtain a repaired spatio-temporal path set;

combining the damaged spatiotemporal path set and the repaired spatiotemporal path set to obtain a target spatiotemporal network diagram;

and inputting the target space-time network diagram into a space-time network model to obtain an inventory path planning scheme of the hybrid energy storage system.

According to another aspect of the embodiments of the present application, there is also provided a hybrid energy storage system inventory path planning apparatus, including:

the establishing module is used for establishing a spatiotemporal network diagram of the hybrid energy storage system;

the destruction module is used for destroying the spatiotemporal network diagram to obtain a destroyed spatiotemporal path set and a deleted spatiotemporal path set;

the restoration module is used for restoring the deleted spatio-temporal path set to obtain a restored spatio-temporal path set;

the merging module is used for merging the damaged spatio-temporal path set and the repaired spatio-temporal path set to obtain a target spatio-temporal network diagram;

and the obtaining module is used for inputting the target space-time network diagram into a space-time network model to obtain an inventory path planning scheme of the hybrid energy storage system.

Optionally, the establishing module includes:

the first acquisition unit is used for acquiring the time-space data and the scheduling period of a battery network node in the hybrid energy storage system;

and the first obtaining unit is used for connecting the battery network nodes at all the moments in the scheduling period based on the spatiotemporal data to obtain the spatiotemporal network diagram.

Optionally, the destruction module comprises:

the second obtaining unit is used for obtaining a first preset number of space-time paths according to the space-time network diagram;

the third obtaining unit is used for obtaining the electricity prices of the battery network nodes at different moments according to the space-time data;

the first calculation unit is used for calculating the electricity price difference of the battery network nodes in the space-time path according to the electricity price and a first preset formula;

the judging unit is used for judging whether the electricity price difference of each space-time path is smaller than a first preset threshold value or not;

a deleting unit configured to delete the spatiotemporal path and store the spatiotemporal path in the deleted spatiotemporal path set, in a case where it is determined that the electricity price difference of the spatiotemporal path is smaller than the first preset threshold;

and a fourth obtaining unit, configured to obtain the damaged spatio-temporal path set under the condition that the electricity price difference of all remaining spatio-temporal paths is not less than the first preset threshold, where the remaining spatio-temporal paths are obtained after all spatio-temporal paths in the deleted spatio-temporal path set are removed from the spatio-temporal network map.

Optionally, the repair module comprises:

a fifth obtaining unit, configured to obtain a second preset number of spatio-temporal paths according to the deleted spatio-temporal path set;

a sixth obtaining unit, configured to obtain, according to the spatiotemporal data, a distance between the battery network nodes in the spatiotemporal path;

a seventh obtaining unit, configured to obtain, according to the electricity price difference, the distance, and a second preset formula, evaluation indexes of the second preset number of spatiotemporal paths;

the storage unit is used for storing the second preset number of spatiotemporal paths and the corresponding evaluation indexes into a spatiotemporal path set to be repaired;

and the processing unit is used for processing the space-time path set to be repaired by using a preset method to obtain the repaired space-time path set.

Optionally, the processing unit comprises:

the sequencing submodule is used for sequencing the spatio-temporal path set to be repaired according to a preset sequence based on the evaluation index to obtain a sequenced spatio-temporal path set;

and the repairing submodule is used for repairing the previous preset proportion of space-time paths in the sorted space-time path set to obtain the repaired space-time path set.

Optionally, the obtaining module includes:

the second acquisition unit is used for acquiring first state information related to a mobile energy storage system in the hybrid energy storage system;

the third acquisition unit is used for acquiring second state information related to a fixed energy storage system in the hybrid energy storage system;

the fourth acquiring unit is used for acquiring a first charging amount and a first discharging amount of the mobile energy storage system at different battery network nodes and at different moments;

the fifth obtaining unit is used for obtaining a second charging amount and a second discharging amount of the fixed energy storage system at different battery network nodes and at different moments;

an eighth obtaining unit, configured to obtain a comprehensive function and a constraint condition of the hybrid energy storage system according to the spatiotemporal data, the first state information, the second state information, the first charge amount, the first discharge amount, the second charge amount, and the second discharge amount;

and the establishing unit is used for establishing the space-time network model based on the comprehensive function and the constraint condition.

A ninth obtaining unit, configured to obtain a third preset number of candidate spatiotemporal paths according to the target spatiotemporal network map;

the second calculation unit is used for calculating the maximum benefits of the hybrid energy storage system under different candidate space-time paths respectively based on the comprehensive function and the constraint condition;

the unit is used for taking the candidate space-time path with the highest numerical value corresponding to the maximum profit as the optimal space-time path;

and the tenth obtaining unit is used for obtaining the inventory path planning scheme according to the optimal space-time path.

According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory communicate with each other through the communication bus; wherein the memory is used for storing the computer program; a processor for performing the method steps in any of the above embodiments by running the computer program stored on the memory.

According to a further aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to perform the method steps of any of the above embodiments when the computer program is executed.

In the embodiment of the application, a spatiotemporal network diagram of the hybrid energy storage system is established; destroying the spatio-temporal network diagram to obtain a destroyed spatio-temporal path set and a deleted spatio-temporal path set; repairing the deleted space-time path set to obtain a repaired space-time path set; combining the damaged spatiotemporal path set and the repaired spatiotemporal path set to obtain a target spatiotemporal network diagram; and inputting the target space-time network diagram into the space-time network model to obtain an inventory path planning scheme of the hybrid energy storage system. According to the method, a spatiotemporal network diagram containing all spatiotemporal paths is established, then the spatiotemporal network diagram is destroyed, the impracticable solutions and inferior solutions of the spatiotemporal paths are deleted, then the spatiotemporal paths with potential arbitrage opportunities are selectively repaired, and further a destroyed and repaired target spatiotemporal network diagram is obtained, and finally the spatiotemporal network model is used for solving the target spatiotemporal network diagram, and the inventory path planning scheme of the hybrid energy storage system is obtained. On one hand, the inventory path planning scheme of the hybrid energy storage system obtained by the method can maximize the operation income of the hybrid energy storage system and greatly reduce the time cost for obtaining the inventory path planning scheme, and on the other hand, the method simplifies a space-time network diagram and reduces the solving difficulty, so that the inventory path planning scheme of the hybrid energy storage system under large-scale battery network nodes, multiple mobile energy storage vehicles and long-time scales can be solved. The problems that in the related technology, the solving difficulty is high, the time cost is high, and the hybrid energy storage system is difficult to schedule and plan under the conditions of large-scale battery network nodes, multiple mobile energy storage vehicles and long time scale are solved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.

Fig. 1 is a schematic flow chart of an alternative method for planning an inventory path of a hybrid energy storage system according to an embodiment of the present application;

FIG. 2 is a spatiotemporal network diagram of an alternative hybrid energy storage system inventory path planning in accordance with embodiments of the present application;

FIG. 3 is a diagram of a target spatiotemporal network for an alternative hybrid energy storage system inventory path planning in accordance with an embodiment of the present application;

fig. 4 is a schematic flowchart of another alternative hybrid energy storage system inventory path planning method according to an embodiment of the application;

fig. 5 is a block diagram of an alternative hybrid energy storage system inventory path planning apparatus according to an embodiment of the present application;

fig. 6 is a block diagram of an alternative electronic device according to an embodiment of the present application.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The scheduling problem of the hybrid energy storage system is essentially the inventory path planning problem, and specifically, the inventory path planning relates to the charging and battery replacing planning of the fixed energy storage system, the charging and battery replacing of the mobile energy storage system and the path planning. The mobile energy storage system is a battery and a power conversion system loaded on a vehicle, the mobile energy storage vehicle runs among nodes with electricity price difference, charges the nodes with low electricity price and discharges the nodes with high electricity price, so that the congestion of a power grid is relieved, and the space-time profit is realized. The fixed energy storage system comprises a battery energy storage power station, and the mobile energy storage system can charge, discharge and replace batteries in the fixed energy storage system. The arbitrage of the hybrid energy storage system is mainly derived from the electricity price difference of each battery network node, namely the space-time path connecting two nodes with higher electricity price difference is higher, and the potential arbitrage opportunity is higher. Therefore, how to carry out scientific scheduling planning on the hybrid energy storage system under large-scale battery network nodes, multiple mobile energy storage vehicles and long-time scale is achieved, so that the operation benefit of the hybrid energy storage system is maximized, a large amount of planning time overhead can be saved, and the problem to be solved at present is solved.

Based on the above, according to an aspect of the embodiments of the present application, there is provided a method for planning an inventory path of a hybrid energy storage system, as shown in fig. 1, a process of the method may include the following steps:

and S101, establishing a spatiotemporal network diagram of the hybrid energy storage system.

Optionally, all possible schemes (including feasible schemes and infeasible schemes) of the hybrid energy storage system inventory path planning are taken as the space-time path of the space-time network, and the space-time network is essentially a forward full-connection diagram. Therefore, the spatiotemporal network is represented by a spatiotemporal network graph, the spatiotemporal network is a solution space of the inventory path planning problem of the hybrid energy storage system, and the inventory path planning of the hybrid energy storage system finds the optimal spatiotemporal path combination in the spatiotemporal network, so that the operation benefit of the hybrid energy storage system is maximized.

And S102, destroying the spatio-temporal network diagram to obtain a destroyed spatio-temporal path set and a deleted spatio-temporal path set.

Optionally, in a destruction stage of the spatio-temporal network map, the electricity price difference of each spatio-temporal path in the spatio-temporal network map is calculated, and the spatio-temporal paths with the point electricity price difference lower than a threshold value are deleted by setting an electricity price difference threshold value. The remaining spatio-temporal paths constitute a set of destroyed spatio-temporal paths

The deleted spatio-temporal paths constitute a deleted set of spatio-temporal paths

。

And S103, repairing the deleted space-time path set to obtain a repaired space-time path set.

Optionally, in the repair phase of the spatio-temporal network diagram, a spatio-temporal path arbitrage opportunity evaluation index is defined for calculating and evaluating potential arbitrage opportunities of the deleted spatio-temporal paths. On the basis, a space-time path repairing threshold value is defined, and the deleted space-time path set is repaired

The potential arbitrage value of the spatiotemporal path is greater than the repair threshold. The repaired space-time path forms a repaired space-time path set

。

And step S104, combining the damaged spatiotemporal path set and the repaired spatiotemporal path set to obtain a target spatiotemporal network diagram.

Optionally, aggregating the destroyed spatiotemporal paths

And the set of repaired spatio-temporal paths

And (4) merging to obtain a damaged and repaired spatio-temporal network diagram, namely a target spatio-temporal network diagram, as shown in a formula (1).

(1)

Wherein,

a target spatiotemporal network graph is represented.

And S105, inputting the target space-time network diagram into the space-time network model to obtain an inventory path planning scheme of the hybrid energy storage system.

Optionally, the damaged and repaired target spatiotemporal network diagram is input into a spatiotemporal network model for solving the inventory path planning of the hybrid energy storage system, then a commercial solver such as a Gurobi is used for solving the spatiotemporal network model, and finally an inventory path planning scheme of the hybrid energy storage system is output. The inventory path planning of the hybrid energy storage system aims at a dispatching periodTAnd planning a charging and battery replacing scheme of the fixed energy storage system, a charging and battery replacing scheme of the mobile energy storage system and a path planning scheme.

In the embodiment of the application, a spatiotemporal network diagram of the hybrid energy storage system is established; destroying the spatio-temporal network diagram to obtain a destroyed spatio-temporal path set and a deleted spatio-temporal path set; repairing the deleted space-time path set to obtain a repaired space-time path set; combining the damaged spatiotemporal path set and the repaired spatiotemporal path set to obtain a target spatiotemporal network diagram; and inputting the target space-time network diagram into the space-time network model to obtain an inventory path planning scheme of the hybrid energy storage system. According to the method, a spatiotemporal network diagram containing all spatiotemporal paths is established, then the spatiotemporal network diagram is destroyed, the infeasible solutions and inferior solutions of the spatiotemporal paths are deleted, then the spatiotemporal paths with potential arbitrage opportunities are selectively repaired, the destroyed and repaired target spatiotemporal network diagram is obtained, and finally the target spatiotemporal network diagram is solved by utilizing a spatiotemporal network model, and the inventory path planning scheme of the hybrid energy storage system is obtained. On one hand, the obtained inventory path planning scheme of the hybrid energy storage system can maximize the operation income of the hybrid energy storage system and greatly reduce the time cost for obtaining the inventory path planning scheme, and on the other hand, the inventory path planning scheme of the hybrid energy storage system under large-scale battery network nodes, multiple mobile energy storage vehicles and long time scales can be solved. The problems that in the related technology, the solving difficulty is high, the time cost is high, and the hybrid energy storage system is difficult to schedule and plan under the conditions of large-scale battery network nodes, multiple mobile energy storage vehicles and long time scale are solved.

As an alternative embodiment, the method for establishing the spatiotemporal network diagram of the hybrid energy storage system comprises the following steps:

acquiring time-space data and a scheduling period of a battery network node in a hybrid energy storage system;

and connecting the battery network nodes at all the moments in the scheduling period based on the spatiotemporal data to obtain a spatiotemporal network diagram.

Optionally, obtaining a scheduling period of the hybrid energy storage system

And spatiotemporal data, the spatiotemporal data comprising: the number of the grid nodes, the electricity prices, the geographical positions and the like at different moments, the number of the grid nodes of the fixed energy storage system, the electricity prices, the geographical positions and the like at different moments, the number of the mobile energy storage system and the dispatching period

Starting at an initial time

End point of the end time, etc., wherein the grid node and the stationary energy storage systemAll belong to battery network nodes.

Based on the spatiotemporal data, battery network nodes at different moments in a scheduling period are listed and connected to obtain a spatiotemporal network diagram.

Optionally, a spatiotemporal network diagram of the hybrid energy storage system is constructed by taking a hybrid energy storage system comprising a grid node, a fixed energy storage system and a mobile energy storage system as an example, as shown in fig. 2. In FIG. 2

Respectively representing the start and end of the scheduling period at the initial and end times,

it is indicated that the scheduling period is,trepresents a certain moment of the scheduling period, an

。

Respectively representtTime of day grid nodeiAnd a stationary energy storage systemjThe two nodes form a battery network together, and the two nodes form a battery network node set togetherGI.e. by

。

Within a connection scheduling period

And obtaining all candidate paths by the battery network nodes at all the moments to form a spatiotemporal network diagram of the hybrid energy storage system, as shown in fig. 2. Definition of

Is a nodeiIn thatpTime andjin thatqA space-time path formed by connecting the time,Nfor the set of all spatio-temporal paths contained in the spatio-temporal network diagram, i.e.

. The inventory path planning of the hybrid energy storage system aims at a dispatching periodTIn the interior, a charging and battery replacing scheme of a fixed energy storage system and a charging and battery replacing and path planning scheme of a mobile energy storage system are planned, namely in a space-time networkNThe optimal spatio-temporal path, namely the combination of the optimal paths in fig. 2, is found, so that the operation benefit of the hybrid energy storage system is maximized.

In the embodiment of the application, all candidate space-time paths are obtained by constructing the space-time network diagram, a solution space of the inventory path planning problem of the hybrid energy storage system is formed, and a basis is provided for obtaining the inventory path planning scheme of the hybrid energy storage system through subsequent solution.

As an alternative embodiment, the method for destroying the spatiotemporal network graph to obtain the destroyed spatiotemporal path set and the deleted spatiotemporal path set comprises the following steps:

obtaining a first preset number of space-time paths according to the space-time network diagram;

according to the time-space data, the electricity prices of the battery network nodes at different moments are obtained;

calculating the electricity price difference of the battery network nodes in the time-space path according to the electricity price and a first preset formula;

judging whether the electricity price difference of each space-time path is smaller than a first preset threshold value or not;

deleting the space-time path and storing the space-time path into a deleted space-time path set under the condition that the electricity price difference of the space-time path is smaller than a first preset threshold value;

and under the condition that the electricity price difference of all the remaining spatio-temporal paths is not less than a first preset threshold, obtaining a destroyed spatio-temporal path set, wherein the remaining spatio-temporal paths are obtained after the spatio-temporal network diagram removes all spatio-temporal paths in the deleted spatio-temporal path set.

Optionally, the space-time network diagram shown in fig. 2 has a high complexity, and if a solver Gurobi is used for solving the problem of planning the inventory path of a single mobile energy storage system on a short time scale (e.g., one day). But it is difficult to solve the problem of hybrid energy storage path planning on a multi-vehicle and long-time scale (for multiple days, such as one year).

The arbitrage of the hybrid energy storage system mainly comes from the electricity price difference of each battery network node, namely the potential arbitrage chance of the spatiotemporal path is higher when the electricity price difference is higher, and the potential arbitrage chance of the spatiotemporal path is lower when the electricity price difference is lower. Therefore, all the space-time paths with low electricity price difference in the space-time network diagram can be deleted, the space-time network is simplified, and complexity is reduced.

Obtaining a set of spatio-temporal paths from a spatio-temporal network mapNAll of the spatio-temporal paths in (1), including

The first predetermined number represents a plurality, and no specific number is limited herein.

Obtaining electricity prices at different times of a battery network node, e.g.

And

are respectively represented byiIn thatpTime of day andjin thatqElectricity prices at the time of day.

By spatio-temporal paths

For example, according to the above-mentioned electricity rates

、

And equation (2), a first predetermined equation, calculating the spatio-temporal path

The electricity price difference between the medium battery network nodes i and j.

(2)

Wherein,

representing battery network nodesiIn thatpTime and battery network nodejIn thatqSpatio-temporal paths formed by moments

The electrovalence of (2). Using the same method, the electricity price difference of all spatio-temporal paths is calculated.

Defining a threshold value of the electric valence differencePDNamely, the first preset threshold, all spatio-temporal paths in the spatio-temporal network diagram shown in fig. 2 whose electricity price difference is lower than the threshold are deleted, and the spatio-temporal network is simplified. Arbitrary spatiotemporal paths

And if so:

(3)

the selected node is deletediAndjin thatp、qTime-space path formed by connecting time

As shown in equation (4):

(4)

wherein,

is composed ofNDeleting spatiotemporal paths

The post-spatio-temporal network diagram is a destroyed spatio-temporal path set and is timed at the same timeEmpty path

Adding into deleted spatio-temporal path sets

I.e., the set of deleted spatio-temporal paths, as shown in equation (5):

(5)

repeatedly executing the formulas (3), (4) and (5) until the electricity price differences of the remaining space-time paths in the original space-time network diagram are all larger than the electricity price difference threshold valuePD. Finally, the deleted space-time path set is obtained

And a set of destroyed spatio-temporal paths

。

Figure 3 is a spatiotemporal network diagram optionally obtained from the spatiotemporal network diagram of figure 2 through the above-described destruction steps, as shown in figure 3, which is more sparse and more compact than that of figure 2, i.e., it is easier to find an optimal inventory path in the network than that of figure 2.

In the embodiment of the application, potential arbitrage opportunities are reflected by using the electricity price difference between the battery network nodes in the spatiotemporal path, the spatiotemporal path with low arbitrage opportunities is deleted, the spatiotemporal network diagram is simplified, the subsequent calculation difficulty and calculation cost are reduced, and then the inventory path planning scheme can be obtained under the complex condition of the hybrid energy storage system. The problems that in the related technology, the solving difficulty is high, the time cost is high, and the hybrid energy storage system is difficult to schedule and plan under the conditions of large-scale battery network nodes, multiple mobile energy storage vehicles and long time scale are solved.

As an alternative embodiment, repairing the deleted spatio-temporal path set to obtain a repaired spatio-temporal path set, including:

obtaining a second preset number of space-time paths according to the deleted space-time path set;

obtaining the distance between battery network nodes in a space-time path according to the space-time data;

obtaining evaluation indexes of a second preset number of space-time paths according to the electricity price difference, the distance and a second preset formula;

storing a second preset number of spatiotemporal paths and corresponding evaluation indexes into a spatiotemporal path set to be repaired;

and processing the space-time path set to be repaired by using a preset method to obtain a repaired space-time path set.

Alternatively, a method of deleting spatiotemporal paths using valence differences may delete spatiotemporal paths with potential arbitrage opportunities. This is because: on the one hand, the arbitrage opportunities of the hybrid energy storage system depend on the difference in electricity prices between the nodes. On the other hand, the spatial distance between different nodes reflects the opportunistic cost of the hybrid energy storage system, i.e. the longer the distance between nodes, the more travel time is required. In the spatio-temporal network repairing stage, an evaluation index of the spatio-temporal path potential arbitrage is defined, and the evaluation index is used for reflecting the potential arbitrage opportunity of the deleted spatio-temporal path. By deleted spatio-temporal path sets

Spatio-temporal paths in

For example, the evaluation index is defined as shown in formula (6), and formula (6) is a second preset formula.

(6)

Wherein,

representing deleted spatiotemporal paths

An evaluation index of the potential arbitrage opportunity,

representing nodesi、jThe geographical distance of (a) is determined,

the time-space data of the battery network nodes can be used for calculation.

Computing a set of deleted spatio-temporal paths

Evaluating potential arbitrage opportunity evaluation values of all the spatio-temporal paths, and adding the spatio-temporal paths corresponding to the estimated arbitrage opportunity evaluation values into a spatio-temporal path set to be repaired

As shown in formula (7):

(7)

on the basis of a potential arbitrage opportunity evaluation method for defining a spatiotemporal path, a preset method is adopted to repair deleted spatiotemporal paths with remarkable arbitrage opportunities in a certain proportion, and the preset method comprises but is not limited to the following steps: defining a time-space path repairing threshold value, and then repairing a time-space path set to be repaired

And the spatiotemporal path with the medium evaluation index larger than the repair threshold value. Or, first, firstly

Sequencing the medium space-time paths according to corresponding evaluation indexes, and repairing the space-time paths in a certain proportion, wherein the sequencing mode can be ascending, descending or self-defined sequence sequencing according to the evaluation indexes, and the proportion can be selectedTo select the sorted ones according to the sorting mode

The space-time paths with a certain proportion in the front or the back can also be extracted in a user-defined extraction mode to be repaired.

In the embodiment of the application, an evaluation index of the potential arbitrage of the spatiotemporal path is defined, the evaluation index is used for reflecting the potential arbitrage of the deleted spatiotemporal path, and then the spatiotemporal path with a larger arbitrage is repaired, so that the potential arbitrage of the hybrid energy storage system is prevented from being lost.

As an optional embodiment, processing the spatio-temporal path set to be repaired by using a preset method to obtain a repaired spatio-temporal path set, including:

sequencing the spatiotemporal path set to be repaired according to a preset sequence based on the evaluation index to obtain a sequenced spatiotemporal path set;

and repairing the previous preset proportion of space-time paths in the sorted space-time path set to obtain a repaired space-time path set.

Optionally, the sets are aggregated according to evaluation index pairs

Sorting in descending order and defining repair thresholdTHI.e. preset proportion, repairing the space-time path set

Middle frontTH% spatio-temporal path. Namely, it is

(8)

Wherein,

representing the set of spatiotemporal paths after the repair,

representing a screening function acting as a set of screens

Middle evaluation index is in frontTH% space-time path. Equation (8) represents the filtered set

Middle arbitrage opportunity assessment frontTH% of spatio-temporal paths and adding these spatio-temporal paths into the set of repaired spatio-temporal paths

In (1).

In the embodiment of the application, the spatio-temporal path set to be repaired is sorted in a descending order according to the evaluation index, and the spatio-temporal path set before the spatio-temporal path set is repairedTH% space-time path, avoids the potential arbitrage opportunity loss of the hybrid energy storage system, and the method is easy to realize and has small time overhead.

As an alternative embodiment, prior to inputting the target spatio-temporal network map into the spatio-temporal network model, the method further comprises:

acquiring first state information related to a mobile energy storage system in a hybrid energy storage system;

acquiring second state information related to a fixed energy storage system in the hybrid energy storage system;

the method comprises the steps that first charging quantity and first discharging quantity of a mobile energy storage system at different battery network nodes and at different moments are obtained;

acquiring a second charging amount and a second discharging amount of the fixed energy storage system at different battery network nodes and at different moments;

obtaining a comprehensive function and a constraint condition of the hybrid energy storage system according to the spatio-temporal data, the first state information, the second state information, the first charging amount, the first discharging amount, the second charging amount and the second discharging amount;

and establishing a space-time network model based on the comprehensive function and the constraint condition.

Optionally, the obtaining first state information related to the mobile energy storage system specifically includes: the number of vehicles in the mobile energy storage system, the transportation cost of the vehicles, the battery replacement cost of the vehicles, the aging cost of the batteries of the vehicles, the conditions of the vehicles entering and exiting the battery network nodes, the capacity and the maximum discharge capacity of the batteries of the vehicles, the charge states of the vehicles at different moments, the maximum charge and discharge capacity of the battery network nodes and the number of charge and discharge interfaces.

Acquiring second state information related to the fixed energy storage system, specifically including: the state of charge of the energy storage system at different moments, the capacity of the energy storage system and the aging cost of the battery are fixed.

The synthesis function includes a market revenue function and a cost function, the synthesis function being the difference between the market revenue function and the cost function.

Because the mobile energy storage system and the fixed energy storage system are charged at a low electricity price and discharged at a high electricity price to realize arbitrage, the market benefit of the hybrid energy storage system can be calculated according to the electricity prices of the battery network nodes at different moments in the time-space data, the first charging amount and the first discharging amount of the mobile energy storage system, the number of vehicles and the second charging amount and the second discharging amount of the fixed energy storage system, and a market benefit function is constructed on the basis of the market benefit function.

The cost function includes vehicle transportation costs, vehicle battery replacement costs, and respective battery aging costs for the vehicle and the stationary energy storage system.

Obviously, the market revenue minus cost is the final profit, so the synthesis function is the market revenue function minus the cost function.

The constraint conditions comprise a path constraint function of the mobile energy storage system, a capacity constraint function of the mobile energy storage system, a charge and discharge constraint function and a capacity constraint function of the fixed energy storage system.

The vehicle receives the limit of the space-time path, and the space-time path of the vehicle is restricted according to the conditions that the vehicle enters and exits the battery network node, so that a path restriction function is generated.

The state of charge of the mobile energy storage vehicle cannot exceed the capacity of the battery of the mobile energy storage vehicle, and the charging amount or the discharging amount of the vehicle cannot exceed the maximum charging amount or the maximum discharging amount of the battery network node, so that a capacity constraint function of the mobile energy storage system is generated.

And meanwhile, the number of charging and discharging interfaces of the vehicle charged or discharged at the same battery network node cannot exceed that of the charging and discharging interfaces of the node, and in addition, the charging and discharging constraint function is generated to ensure the space-time consistency of charging and discharging of the mobile energy storage vehicle or battery replacement and path planning.

The charging and discharging amount of the fixed energy storage system and the mobile energy storage vehicle cannot exceed the maximum charging amount or the discharging amount of the node, the charging and discharging amount generated by replacing the battery of the mobile energy storage vehicle cannot exceed the capacity of the fixed energy storage system, and a capacity constraint function of the fixed energy storage system is generated.

And establishing a space-time network model based on the comprehensive functions and the constraint conditions.

In the embodiment of the application, after the profit modes and the state information of the mobile energy storage system and the fixed energy storage system are analyzed, the comprehensive function and the constraint condition are generated, the spatiotemporal network model is established, the real profit overlap condition of the hybrid energy storage system can be accurately calculated through the spatiotemporal network model, the battery energy storage efficiency is improved, the operation income is improved, and a foundation is provided for selecting the optimal spatiotemporal path and finally obtaining the inventory path planning scheme of the hybrid energy storage system.

As an alternative embodiment, the obtaining of the inventory path planning scheme of the hybrid energy storage system includes:

obtaining a third preset number of candidate space-time paths according to the target space-time network diagram;

respectively calculating the maximum benefits of the hybrid energy storage system under different candidate space-time paths based on the comprehensive function and the constraint condition;

taking the candidate space-time path with the highest numerical value corresponding to the maximum profit as the optimal space-time path;

and obtaining an inventory path planning scheme according to the optimal space-time path.

Optionally, a plurality of candidate spatio-temporal paths are obtained according to the target spatio-temporal network diagram, that is, the destroyed and repaired spatio-temporal network diagram, and the third preset number represents a plurality, and the number is not limited herein.

By utilizing the space-time network model, the maximum benefit of the hybrid energy storage system under the conditions of different candidate space-time paths, different charging amounts, different discharging amounts and whether a battery needs to be replaced is solved through Gurobi, and the maximum benefit is the maximum numerical value of a comprehensive function calculated under the condition that constraint conditions are met.

And taking the candidate space-time path with the maximum numerical value corresponding to the maximum profit as the optimal space-time path.

The inventory path planning scheme of the hybrid energy storage system is obtained by combining the optimal space-time path, the respective charging amount and discharging amount of the fixed energy storage system and the mobile energy storage system in the optimal space-time path and whether the battery needs to be replaced, and the inventory path planning scheme comprises the following steps: and the method comprises the steps of charging and battery replacing planning of a fixed energy storage system, charging and battery replacing of a mobile energy storage system and path planning.

In the embodiment of the application, the maximum profit of the candidate space-time path is calculated, then the candidate path with the highest numerical value corresponding to the maximum profit in different candidate paths is used as the optimal path, and then the inventory path planning scheme is obtained.

As an alternative embodiment, fig. 4 is a schematic flowchart of another alternative hybrid energy storage system inventory path planning method according to an embodiment of the present application, where the method includes:

constructing a space-time network diagram for planning the inventory path of the hybrid energy storage system; calculating the electricity price difference of the space-time network path; defining a spatio-temporal network path electricity price difference threshold; deleting the space-time network path with the price difference lower than a threshold value; defining a spatio-temporal path arbitrage opportunity evaluation index; calculating a arbitrage opportunity value of the deleted spatiotemporal path; defining a time-space path restoration threshold value, and restoring the time-space path with the arbitrage value larger than the restoration threshold value; and solving the damaged and repaired space-time network, and outputting a hybrid energy storage system scheduling scheme.

Optionally, firstly, a spatiotemporal network diagram for the inventory path planning of the hybrid energy storage system is constructed according to the power grid nodes and the scheduling period, the spatiotemporal network diagram is essentially a forward fully-connected diagram and contains all possible inventory path planning schemes of the hybrid energy storage system in the scheduling period, namely, the spatiotemporal network forms a solution space of the scheduling problem of the hybrid energy storage system. And secondly, calculating the electricity price difference of each space-time network path in a space-time network destruction stage, and deleting the space-time paths with point electricity price differences lower than a threshold value by setting an electricity price difference threshold value. Then, in the spatio-temporal network repairing stage, a spatio-temporal path arbitrage opportunity evaluation index is defined for calculating and evaluating potential arbitrage opportunities of the deleted spatio-temporal paths. On the basis, a spatio-temporal path restoration threshold value is defined, and the spatio-temporal path with the restoration arbitrage value larger than the restoration threshold value is restored. And finally, solving the damaged and repaired space-time network, and outputting a hybrid energy storage system scheduling scheme.

In the embodiment of the application, most of infeasible solutions and inferior solutions in a solution space are removed by deleting a space-time path with a small electricity price difference in the space-time network, and then the space-time path with a potential profit overlap chance is selectively repaired, so that the complexity of the space-time network is reduced, the method can solve the more complex system inventory path planning problem of the hybrid energy storage system more quickly, the planning efficiency is obviously improved, and technical support can be provided for intelligent decision and efficient management of the hybrid energy storage system.

Based on the above embodiments, in order to test the effectiveness of the above method, three groups of examples are set, and the method (algorithm) of the present application is used in combination with the commercial solver Gurobi to solve the above examples, and compared with the result of the solution only using the commercial solver Gurobi.

Alternatively, the examples and algorithm parameter settings are shown in Table 1,PDthe experimental results for monthly and annual plans for the electricity price difference threshold are shown in tables 2 and 3, respectively.

TABLE 1 examples and Algorithm parameter settings

TABLE 2 monthly planning experiment results

TABLE 3 annual planning test results

Wherein, the optimal solution obtained by using Gurobi only is used as a contrast, and the difference between the result of the algorithm and Gurobi combined solution provided by the application and the optimal solution and the saved time are analyzed. As can be seen from tables 2 and 3, in all the test examples, compared with using Gurobi alone, the algorithm + Gurobi joint solution method provided by the present application can reduce the time overhead by more than 90% at the cost of accuracy loss lower than 1%, and significantly improve the planning efficiency of the hybrid energy storage system. The method has remarkable benefits in solving the problem of planning the inventory path of the hybrid energy storage system with large-scale power grid nodes and multiple mobile energy storage systems, and can provide technical support for intelligent decision and efficient management of the hybrid energy storage system.

According to another aspect of the embodiment of the present application, there is also provided a hybrid energy storage system inventory path planning apparatus for implementing the hybrid energy storage system inventory path planning method. Fig. 5 is a block diagram of an alternative hybrid energy storage system inventory path planning apparatus according to an embodiment of the present application, and as shown in fig. 5, the apparatus may include:

the establishing module 501 is used for establishing a spatiotemporal network diagram of the hybrid energy storage system;

a destroying module 502 for destroying the spatio-temporal network graph to obtain a destroyed spatio-temporal path set and a deleted spatio-temporal path set;

a repairing module 503, configured to repair the deleted spatio-temporal path set to obtain a repaired spatio-temporal path set;

a merging module 504, configured to merge the damaged spatio-temporal path set and the repaired spatio-temporal path set to obtain a target spatio-temporal network map;

and an obtaining module 505, configured to input the target spatiotemporal network map into the spatiotemporal network model, so as to obtain an inventory path planning scheme of the hybrid energy storage system.

It should be noted that the establishing module 501 in this embodiment may be configured to execute the step S101, the destroying module 502 in this embodiment may be configured to execute the step S102, the repairing module 503 in this embodiment may be configured to execute the step S103, the combining module 504 in this embodiment may be configured to execute the step S104, and the obtaining module 505 in this embodiment may be configured to execute the step S105.

Through the modules, a spatiotemporal network diagram containing all spatiotemporal paths is established, then the spatiotemporal network diagram is destroyed, the impracticable solutions and inferior solutions of the spatiotemporal paths are deleted, then the spatiotemporal paths with potential arbitrage opportunities are selectively repaired, a destroyed and repaired target spatiotemporal network diagram is obtained, and finally, the spatiotemporal network model is used for solving the target spatiotemporal network diagram, and the inventory path planning scheme of the hybrid energy storage system is obtained. On one hand, the obtained inventory path planning scheme of the hybrid energy storage system can maximize the operation benefits of the hybrid energy storage system and greatly reduce the time cost for obtaining the inventory path planning scheme, and on the other hand, the inventory path planning scheme of the hybrid energy storage system under large-scale battery network nodes, multiple mobile energy storage vehicles and long time scales can be solved. The problems that in the related technology, the solving difficulty is high, the time cost is high, and the hybrid energy storage system is difficult to schedule and plan under the conditions of large-scale battery network nodes, multiple mobile energy storage vehicles and long time scale are solved.

As an alternative embodiment, the establishing module comprises:

the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring the time-space data and the scheduling period of a battery network node in the hybrid energy storage system;

and the first obtaining unit is used for connecting the battery network nodes at all the moments in the scheduling period based on the spatiotemporal data to obtain a spatiotemporal network diagram.

As an alternative embodiment, the destruction module comprises:

the deleting unit is used for deleting the space-time path and storing the space-time path into the deleted space-time path set under the condition that the electricity price difference of the space-time path is determined to be smaller than a first preset threshold value;

and a fourth obtaining unit, configured to obtain a damaged spatio-temporal path set under the condition that the electricity price difference of all remaining spatio-temporal paths is not less than the first preset threshold, where the remaining spatio-temporal paths are obtained after removing all spatio-temporal paths in the deleted spatio-temporal path set.

As an alternative embodiment, the repair module comprises:

a seventh obtaining unit, configured to obtain evaluation indexes of a second preset number of spatiotemporal paths according to the electrovalence difference, the distance, and a second preset formula;

the storage unit is used for storing a second preset number of spatiotemporal paths and corresponding evaluation indexes into a spatiotemporal path set to be repaired;

As an alternative embodiment, the processing unit comprises:

As an alternative embodiment, the obtaining module includes:

the eighth obtaining unit is used for obtaining a comprehensive function and a constraint condition of the hybrid energy storage system according to the spatio-temporal data, the first state information, the second state information, the first charging amount, the first discharging amount, the second charging amount and the second discharging amount;

and the establishing unit is used for establishing a space-time network model based on the comprehensive function and the constraint condition.

A ninth obtaining unit, configured to obtain a third preset number of candidate spatio-temporal paths according to the target spatio-temporal network map;

It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments.

According to another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the method for planning an inventory path of a hybrid energy storage system, where the electronic device may be a server, a terminal, or a combination thereof.

Fig. 6 is a block diagram of an alternative electronic device according to an embodiment of the present application, as shown in fig. 6, including a processor 601, a communication interface 602, a memory 603, and a communication bus 604, where the processor 601, the communication interface 602, and the memory 603 complete communications with each other through the communication bus 604, where,

a memory 603 for storing a computer program;

the processor 601, when executing the computer program stored in the memory 603, implements the following steps:

destroying the spatio-temporal network diagram to obtain a destroyed spatio-temporal path set and a deleted spatio-temporal path set;

repairing the deleted space-time path set to obtain a repaired space-time path set;

and inputting the target space-time network diagram into the space-time network model to obtain an inventory path planning scheme of the hybrid energy storage system.

Alternatively, in this embodiment, the communication bus may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.

The communication interface is used for communication between the electronic equipment and other equipment.

The memory may include RAM, and may also include non-volatile memory, such as at least one disk memory. Alternatively, the memory may be at least one memory device located remotely from the processor.

As an example, as shown in fig. 6, the memory 603 may include, but is not limited to, an establishing module 501, a destroying module 502, a repairing module 503, a merging module 504, and a obtaining module 505 in the hybrid energy storage system inventory path planning apparatus. In addition, the inventory path planning apparatus may further include, but is not limited to, other module units in the hybrid energy storage system inventory path planning apparatus, which is not described in this example again.

The processor may be a general-purpose processor, and may include but is not limited to: a CPU (Central Processing Unit), an NP (Network Processor), and the like; but also a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.

Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.

It can be understood by those skilled in the art that the structure shown in fig. 6 is only an illustration, and the device implementing the method for planning the inventory path of the hybrid energy storage system may be a terminal device, and the terminal device may be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 6 does not limit the structure of the electronic device. For example, the terminal device may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 6, or have a different configuration than shown in FIG. 6.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.

According to still another aspect of an embodiment of the present application, there is also provided a storage medium. Optionally, in this embodiment, the storage medium may be configured to store a program code for executing the method for planning the inventory path of the hybrid energy storage system.

Optionally, in this embodiment, the storage medium may be located on at least one of a plurality of network devices in a network shown in the above embodiment.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:

combining the damaged spatio-temporal path set and the repaired spatio-temporal path set to obtain a target spatio-temporal network diagram;

Optionally, the specific example in this embodiment may refer to the example described in the above embodiment, which is not described again in this embodiment.

Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a U disk, a ROM, a RAM, a removable hard disk, a magnetic disk, or an optical disk.

In the description herein, reference to the description of the terms "this embodiment," "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.

It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. This need not be, nor should it be exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.

Claims

1. A method for planning an inventory path of a hybrid energy storage system, the method comprising:

establishing a spatiotemporal network diagram of a hybrid energy storage system, wherein the establishing of the spatiotemporal network diagram of the hybrid energy storage system comprises: acquiring space-time data of battery network nodes in the hybrid energy storage system;

destroying the spatiotemporal network graph to obtain a destroyed spatiotemporal path set and a deleted spatiotemporal path set, wherein the destroying the spatiotemporal network graph to obtain the destroyed spatiotemporal path set and the deleted spatiotemporal path set comprises: obtaining a first preset number of space-time paths according to the space-time network diagram; obtaining the electricity prices of the battery network nodes at different moments according to the time-space data; calculating the electricity price difference of the battery network nodes in the space-time path according to the electricity price and a first preset formula; judging whether the electricity price difference of each space-time path is smaller than a first preset threshold value or not; deleting the spatiotemporal path and storing the spatiotemporal path into the deleted spatiotemporal path set if it is determined that the electricity price difference of the spatiotemporal path is less than the first preset threshold; under the condition that the electricity price difference of all the remaining spatio-temporal paths is not less than the first preset threshold, obtaining the destroyed spatio-temporal path set, wherein the remaining spatio-temporal paths are obtained after all the spatio-temporal paths in the deleted spatio-temporal path set are removed from the spatio-temporal network diagram;

merging the damaged spatio-temporal path set and the repaired spatio-temporal path set to obtain a target spatio-temporal network diagram;

acquiring first state information related to a mobile energy storage system in the hybrid energy storage system;

acquiring a first charging amount and a first discharging amount of the mobile energy storage system at different battery network nodes and at different moments;

obtaining a comprehensive function and a constraint condition of the hybrid energy storage system according to the spatiotemporal data, the first state information, the second state information, the first charging amount, the first discharging amount, the second charging amount and the second discharging amount;

establishing a spatio-temporal network model based on the comprehensive function and the constraint condition;

and inputting the target spatiotemporal network diagram into the spatiotemporal network model to obtain an inventory path planning scheme of the hybrid energy storage system.

2. The method of claim 1, wherein establishing the spatiotemporal network map of the hybrid energy storage system comprises:

acquiring a scheduling cycle of a battery network node in the hybrid energy storage system;

and connecting the battery network nodes at all the moments in the scheduling period based on the spatiotemporal data to obtain the spatiotemporal network diagram.

3. The method of claim 2, wherein said repairing said deleted spatio-temporal path set resulting in a repaired spatio-temporal path set comprises:

obtaining the distance between the battery network nodes in the space-time path according to the space-time data;

obtaining evaluation indexes of the second preset number of space-time paths according to the electricity price difference, the distance and a second preset formula;

storing the second preset number of spatiotemporal paths and the corresponding evaluation indexes into a spatiotemporal path set to be repaired;

and processing the space-time path set to be repaired by using a preset method to obtain the repaired space-time path set.

4. The method according to claim 3, wherein the processing the spatio-temporal path set to be repaired by using a preset method to obtain the repaired spatio-temporal path set comprises:

and repairing the previous preset proportion of space-time paths in the sorted space-time path set to obtain the repaired space-time path set.

5. The method according to claim 1, wherein the obtaining an inventory path planning scheme for the hybrid energy storage system comprises:

taking the candidate space-time path with the highest numerical value corresponding to the maximum profit as an optimal space-time path;

and obtaining the inventory path planning scheme according to the optimal space-time path.

6. A hybrid energy storage system inventory path planning device, comprising:

an establishing module for establishing a spatiotemporal network diagram of a hybrid energy storage system, wherein the establishing module comprises: the first acquisition unit is used for acquiring the spatiotemporal data of the battery network nodes in the hybrid energy storage system;

a destruction module for destroying the spatio-temporal network graph to obtain a destroyed spatio-temporal path set and a deleted spatio-temporal path set, wherein the destruction module comprises: the second obtaining unit is used for obtaining a first preset number of space-time paths according to the space-time network diagram; the third obtaining unit is used for obtaining the electricity prices of the battery network nodes at different moments according to the space-time data; the first calculation unit is used for calculating the electricity price difference of the battery network nodes in the space-time path according to the electricity price and a first preset formula; the judging unit is used for judging whether the electricity price difference of each space-time path is smaller than a first preset threshold value or not; a deleting unit, configured to delete the spatiotemporal path and store the spatiotemporal path into the deleted spatiotemporal path set if it is determined that the power price difference of the spatiotemporal path is smaller than the first preset threshold; a fourth obtaining unit, configured to obtain the damaged spatio-temporal path set when the electricity price difference of all remaining spatio-temporal paths is not smaller than the first preset threshold, where the remaining spatio-temporal paths are obtained by removing all spatio-temporal paths in the deleted spatio-temporal path set from the spatio-temporal network map;

the repair module is used for repairing the deleted space-time path set to obtain a repaired space-time path set;

the fourth obtaining unit is used for obtaining first charging quantity and first discharging quantity of the mobile energy storage system at different battery network nodes and at different moments;

the establishing unit is used for establishing a space-time network model based on the comprehensive function and the constraint condition;

and the obtaining module is used for inputting the target spatiotemporal network diagram into the spatiotemporal network model to obtain an inventory path planning scheme of the hybrid energy storage system.

7. An electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein said processor, said communication interface and said memory communicate with each other via said communication bus,

the memory for storing a computer program;

the processor for performing the method steps of any one of claims 1 to 5 by running the computer program stored on the memory.

8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method steps of any one of claims 1 to 5.