A Photovoltaic Energy Storage Collaborative Planning Method and System

Abstract

The embodiment of the present application discloses a photovoltaic energy storage collaborative planning method and system, the method comprising: based on the discretization of continuous time, collecting the normalized value of the annual photovoltaic output data, annual user load data and electricity market transaction data at the user's location; modeling the energy storage system; establishing a mixed integer linear programming model, the mixed integer linear programming model includes the annual comprehensive cost objective function of the planning model and the constraints of the planning model; solving the mixed integer linear programming model to obtain the planned capacity and scheduling method of photovoltaic energy storage. The embodiment of the present application can plan photovoltaic and energy storage capacity at the same time, and is suitable for users who are not equipped with photovoltaic systems. At the same time, it fully considers the diversity of user loads and the diversity of photovoltaic output, and also considers the differences between different types of electricity users, so that the planning results can better fit the actual situation of users.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of photovoltaic energy storage, and particularly to a photovoltaic energy storage collaborative planning method and system. Background Technique

[0002] As one of the effective solutions to address energy shortages and reduce carbon emissions, the photovoltaic energy storage system overcomes the adverse effects brought by the intermittency and randomness of photovoltaic power generation to system operation. Therefore, it has been vigorously developed in recent years. At the same time, more and more industrial and commercial users choose to install photovoltaic energy storage systems to reduce their electricity costs.

[0003] Based on this background, a set of photovoltaic energy storage collaborative planning methods for industrial and commercial distributed users is particularly important.

[0004] Currently, most of the planning methods on the market assume that the photovoltaic capacity has been determined and only plan the energy storage capacity. Such planning methods are not applicable to users without installed photovoltaic systems. At the same time, the data used in the planning methods on the market is too scarce, unable to fully consider the diversity of user loads and the diversity of photovoltaic power outputs, and also unable to ensure the safe and stable operation of the system. Summary of the Invention

[0005] Therefore, the embodiments of the present application provide a photovoltaic energy storage collaborative planning method and system, which can simultaneously plan the photovoltaic and energy storage capacities and are applicable to users without installed photovoltaic systems. At the same time, it fully considers the diversity of user loads and the diversity of photovoltaic power outputs, and also considers the differences between different types of electricity users, making the planning results better fit the actual situation of users.

[0006] To achieve the above object, the embodiments of the present application provide the following technical solutions:

[0007] According to the first aspect of the embodiments of the present application, a photovoltaic energy storage collaborative planning method is provided, and the method includes:

[0008] Based on the discretization of continuous time, collect the normalized annual photovoltaic power output data, annual user load data, and electricity market trading data at the user's location;

[0009] Model the energy storage system;

[0010] Establish a mixed-integer linear programming model, where the mixed-integer linear programming model includes the annual comprehensive cost objective function of the planning model and the constraint conditions of the planning model;

[0011] Use a solver to solve the mixed-integer linear programming model to obtain the planned capacities and scheduling methods of photovoltaic energy storage.

[0012] Optionally, the modeling of the energy storage system is carried out according to the following formula:

[0013] E k+1 = E k + P s·in · η in · Δt - P s·out · Δt / η out

[0014] Among them, E k represents the remaining power of energy storage at the k-th time node, P s·in and P s·out respectively represent the charging power and discharging power of energy storage, η in and η out respectively represent the charging efficiency and discharging efficiency of energy storage, and Δt represents the time interval.

[0015] Optionally, the annual comprehensive cost objective function of the planning model is as follows:

[0016] minC cost = C run + C invest

[0017] Among them, C cost represents the annual comprehensive cost; C run represents the annual electricity cost of the user; C invest represents the annual investment cost of the photovoltaic and energy storage system.

[0018] Optionally, the calculation method of the annual electricity cost of the user is as follows:

[0019]

[0020] Among them, N is the number of time nodes in a year, represents the electricity price when purchasing electricity from the power grid at the k-th time node, represents the electricity price when selling surplus photovoltaic electricity to the power grid at the k-th time node, represents the power of purchasing electricity from the power grid at the k-th time node, represents the power of selling electricity to the power grid at the k-th time node;

[0021] The calculation method of the annual investment cost is as follows:

[0022]

[0023] Among them, M s represents the investment cost of a unit-capacity energy storage device, M pv represents the investment cost of a unit-capacity photovoltaic device, E sN represents the energy storage planned capacity, P pvNRepresents the planned capacity of the photovoltaic, Y s Represents the lifespan of the energy storage device, Y pv Represents the lifespan of the photovoltaic device.

[0024] Optionally, the power balance constraint in the constraint conditions of the planning model is as follows:

[0025]

[0026] Wherein, Represents the charging power of the energy storage system at the k-th time node, Represents the discharging power of the energy storage system at the k-th time node, Represents the electricity load on the user side at the k-th time node, Represents the photovoltaic output at the k-th time node.

[0027] Optionally, the electricity quantity constraint of the energy storage system in the constraint conditions of the planning model is as follows:

[0028] E k+1 = E k + P s·in ·η in ·Δt - P s·out ·Δt / η out

[0029] (1 - U) ≤ E k ≤ 0.95E sN

[0030] Wherein, U represents the maximum discharge depth of the energy storage system;

[0031] The charge and discharge constraint of the energy storage system in the constraint conditions of the planning model is as follows:

[0032]

[0033]

[0034]

[0035]

[0036] P s·in·max = P s·out·max = C·E sN

[0037]

[0038] Wherein, And Are respectively the charging flag and discharging flag of the energy storage system. When charging, is 1, is 0; during discharging is 0, is 1; P s·in·max and P s·out·max are respectively the maximum charging power and the maximum discharging power of energy storage; C is the charge-discharge rate of energy storage.

[0039] Optionally, the photovoltaic constraint in the constraint conditions of the planning model follows the following formula:

[0040]

[0041] where is the normalized value of the photovoltaic output at time node k;

[0042] P pvN ≤ [S / S pv ·P ppv

[0043] where S is the maximum installable area of the photovoltaic panels; S pv is the area of a single photovoltaic panel; P ppv is the photovoltaic capacity of a single photovoltaic panel.

[0044] According to the second aspect of the embodiments of the present application, a photovoltaic energy storage collaborative planning system is provided, and the system includes:

[0045] A data collection module, configured to collect the normalized annual photovoltaic output data, the annual user load data, and the electricity market trading data of the user's location based on the discretization of continuous time;

[0046] An energy storage system modeling module, configured to model the energy storage system;

[0047] A mixed integer linear programming model modeling module, configured to model the mixed integer linear programming model, where the mixed integer linear programming model includes the annual comprehensive cost objective function of the planning model and the constraint conditions of the planning model;

[0048] A planning model solving module, configured to solve the mixed integer linear programming model by using a solver to obtain the planned capacity and scheduling method of the photovoltaic energy storage.

[0049] Optionally, the energy storage system modeling module is specifically configured to:

[0050] Model the energy storage system according to the following formula:

[0051] E k+1 =E k +P s·in ·η in ·Δt - P s·out·Δt / η out

[0052] Among them, E k represents the remaining power of the energy storage at the k-th time node, P s·in and P s·out represent the charging power and discharging power of the energy storage respectively, η in and η out represent the charging efficiency and discharging efficiency of the energy storage respectively, and Δt represents the time interval.

[0053] Optionally, the annual comprehensive cost objective function of the planning model is as follows:

[0054] minC cost = C run + C invest

[0055] Among them, C cost represents the annual comprehensive cost; C run represents the annual electricity cost of the user; C invest represents the annual investment cost of the photovoltaic and energy storage system.

[0056] Optionally, the calculation method of the annual electricity cost of the user is as follows:

[0057]

[0058] Among them, N is the number of time nodes in a year, represents the electricity price when purchasing electricity from the power grid at the k-th time node, represents the electricity price when selling the surplus photovoltaic electricity to the power grid at the k-th time node, represents the power of purchasing electricity from the power grid at the k-th time node, represents the power of selling electricity to the power grid at the k-th time node;

[0059] The calculation method of the annual investment cost is as follows:

[0060]

[0061] Among them, M s represents the investment cost of the energy storage device per unit capacity, M pv represents the investment cost of the photovoltaic device per unit capacity, E sN represents the planned capacity of the energy storage, P pvN represents the planned capacity of the photovoltaic, Y s represents the service life of the energy storage device, Y pv represents the service life of the photovoltaic device.

[0062] Optionally, the power balance constraint in the constraint conditions of the planning model follows the following formula:

[0063]

[0064] where, represents the charging power of the energy storage system at the k-th time node, represents the discharging power of the energy storage system at the k-th time node, represents the user-side electricity load at the k-th time node, represents the PV output at the k-th time node.

[0065] Optionally, the electricity quantity constraint of the energy storage system in the constraint conditions of the planning model follows the following formula:

[0066] E k+1 = E k + P s·in · η in · Δt - P s·out · Δt / η out

[0067] (1 - U) ≤ E k ≤ 0.95E sN

[0068] where U represents the maximum discharge depth of the energy storage system;

[0069] The charge and discharge constraint of the energy storage system in the constraint conditions of the planning model follows the following formula:

[0070]

[0071]

[0072]

[0073]

[0074] P s·in·max = P s·out·max = C · E sN

[0075]

[0076] where, and are respectively the charging flag and discharging flag of the energy storage system. During charging, is 1, is 0; during discharging is 0, is 1; P s·in·max and Ps·out·max They are the maximum charging power and the maximum discharging power of energy storage respectively; C is the charge-discharge rate of energy storage.

[0077] Optionally, the photovoltaic constraint in the constraint conditions of the planning model follows the following formula:

[0078]

[0079] Wherein, is the normalized value of the photovoltaic output at time node k;

[0080] P pvN ≤ [S / S pv ·P ppv

[0081] Wherein, S is the maximum installable area of the photovoltaic panel; S pv is the area of a single photovoltaic panel; P ppv is the photovoltaic capacity of a single photovoltaic panel.

[0082] According to the third aspect of the embodiments of the present application, there is provided a device, the device includes: a data acquisition device, a processor and a memory; the data acquisition device is used to acquire data; the memory is used to store one or more program instructions; the processor is used to execute one or more program instructions to execute the method according to any one of the first aspect.

[0083] According to the fourth aspect of the embodiments of the present application, there is provided a computer-readable storage medium, the computer storage medium contains one or more program instructions, and the one or more program instructions are used to execute the method according to any one of the first aspect.

[0084] In summary, the embodiments of the present application provide a photovoltaic energy storage collaborative planning method and system. First, based on the discretization of continuous time, collect the normalized annual photovoltaic output data, annual user load data and electricity market trading data of the user's location; model the energy storage system; model the mixed-integer linear programming model, and the mixed-integer linear programming model includes the annual comprehensive cost objective function of the planning model and the constraint conditions of the planning model; use a solver to solve the mixed-integer linear programming model to obtain the planned capacity and scheduling method of photovoltaic energy storage. It can plan the capacities of photovoltaic and energy storage simultaneously and is applicable to users without installed photovoltaic systems. At the same time, it fully considers the diversity of user loads and the diversity of photovoltaic outputs, and also considers the differences between different types of electricity users, so that the planning results can better fit the actual situation of users. Description of the Drawings

[0085] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are merely exemplary. For those of ordinary skill in the art, without creative efforts, other implementation drawings can be obtained according to the provided drawings.

[0086] The structures, ratios, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention. Therefore, they do not have substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0087] Figure 1 It is a schematic flow chart of a photovoltaic energy storage collaborative planning method provided by an embodiment of the present application;

[0088] Figure 2 It is a schematic flow chart of a photovoltaic energy storage collaborative planning method provided by another embodiment of the present application;

[0089] Figure 3 It is a block diagram of a photovoltaic energy storage collaborative planning system provided by an embodiment of the present application. Specific Embodiments

[0090] The following specific embodiments illustrate the embodiments of the present invention. Those familiar with this technology can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0091] Figure 1 A photovoltaic energy storage collaborative planning method provided by an embodiment of the present application is shown. It is mainly applicable to the photovoltaic energy storage planning of industrial and commercial distributed users, and the planning goal is to minimize the annual comprehensive cost. In the process of formulating the constraint conditions, factors such as the characteristics of multiple electricity users, the randomness and diversity of photovoltaic power output, and the trading policies of the electricity market are considered. At the same time, the installed capacities of photovoltaic and energy storage and the dispatching method of the user-side photovoltaic energy storage system are planned. The method includes the following steps:

[0092] Step 101: Based on the discretization of continuous time, collect the normalized annual photovoltaic power output data, annual user load data, and electricity market trading data at the user's location;

[0093] Step 102: Model the energy storage system;

[0094] Step 103: Establish a mixed-integer linear programming model, where the mixed-integer linear programming model includes the annual comprehensive cost objective function of the planning model and the constraint conditions of the planning model;

[0095] Step 104: Use a solver to solve the mixed-integer linear programming model to obtain the planned capacity and scheduling method of the photovoltaic energy storage.

[0096] In a possible implementation manner, in step 101, regarding the discretization of continuous time: 96 time nodes can be taken in a day, and the time interval between two adjacent time nodes is 15 minutes. Therefore, a total of 35040 time nodes are obtained in a year.

[0097] In a possible implementation manner, in step 101, data collection: Collect the normalized annual photovoltaic output data, annual user load data, and data related to the electricity market trading policy at the user's location, including time-of-use electricity price data and feed-in tariff data for surplus photovoltaic electricity.

[0098] In a possible implementation manner, the modeling of the energy storage system is carried out according to the following formula (1):

[0099] E k+1 =E k +P s·in ·η in ·Δt - P s·out ·Δt / η out Formula (1)

[0100] where E k represents the remaining power of the energy storage at the kth time node, P s·in and P s·out represent the charging power and discharging power of the energy storage respectively, η in and η out represent the charging efficiency and discharging efficiency of the energy storage respectively, and Δt represents the time interval.

[0101] In a possible implementation manner, the annual comprehensive cost objective function of the planning model is as follows according to formula (2):

[0102] min C cost =C run +C invest Formula (2)

[0103] where C cost represents the annual comprehensive cost; C run represents the annual electricity cost of the user; C invest represents the annual investment cost of the photovoltaic and energy storage systems.

[0104] In a possible implementation, the calculation method of the annual electricity cost of the user is as follows according to formula (3):

[0105]

[0106] Where N is the number of time nodes in a year, represents the electricity price when purchasing electricity from the power grid at the k-th time node, represents the electricity price when selling surplus photovoltaic electricity to the power grid at the k-th time node, represents the power of purchasing electricity from the power grid at the k-th time node, represents the power of selling electricity to the power grid at the k-th time node;

[0107] In a possible implementation, the calculation method of the annual investment cost is as follows according to formula (4):

[0108]

[0109] Where M s represents the investment cost of the energy storage device per unit capacity, M pv represents the investment cost of the photovoltaic device per unit capacity, E sN represents the energy storage planned capacity, P pvN represents the photovoltaic planned capacity, Y s represents the lifespan of the energy storage device, Y pv represents the lifespan of the photovoltaic device.

[0110] In a possible implementation, the power balance constraint in the constraint conditions of the planning model is as follows according to formula (5):

[0111]

[0112] Where, represents the charging power of the energy storage system at the k-th time node, represents the discharging power of the energy storage system at the k-th time node, represents the user-side electricity load at the k-th time node, represents the photovoltaic output at the k-th time node.

[0113] In a possible implementation, the electricity quantity constraint of the energy storage system in the constraint conditions of the planning model is as follows according to formulas (6) and (7):

[0114] E k+1 = E k + P s·in · η in · Δt - P s·out · Δt / ηout Formula (6)

[0115] (1 - U) ≤ E k ≤ 0.95E sN Formula (7)

[0116] Wherein, U represents the maximum discharge depth of the energy storage system;

[0117] In a possible implementation, the charge - discharge constraint of the energy storage system in the constraint conditions of the planning model is as follows in Formulas (8) - (13):

[0118]

[0119]

[0120]

[0121]

[0122] P s·in·max =P s·out·max =C·E sN Formula (12)

[0123]

[0124] Wherein, and are respectively the charge flag and discharge flag of the energy storage system. During charging, is 1, is 0; during discharging is 0, is 1; P s·in·max and P s·out·max are respectively the maximum charge power and maximum discharge power of the energy storage; C is the charge - discharge rate of the energy storage.

[0125] In a possible implementation, the PV constraint in the constraint conditions of the planning model is as follows:

[0126]

[0127] Wherein, is the normalized value of the PV output at time node k;

[0128] P pvN ≤ [S / S pv · P ppv Formula (15)

[0129] Wherein, S is the maximum installable area of the PV panels; S pv is the area of a single PV panel; Pppv is the photovoltaic capacity of a single photovoltaic panel.

[0130] It can be seen that the method provided by the embodiments of the present application can simultaneously plan the optimal installation capacities of photovoltaic and energy storage and also plan the scheduling method of the distributed user-side photovoltaic-storage system. By taking into account the differences in electricity loads among different users and the differences in the maximum installable area of photovoltaic panels, the characteristics of multiple power users are fully considered, making the planning results more in line with the actual situation of users. And the planning is based on one-year data, fully considering the diversity of user electricity loads, the seasonality and diversity of photovoltaic power generation.

[0131] Figure 2 The flow diagram of the photovoltaic-storage collaborative planning method provided by another embodiment of the present application is shown, which is applied to the industrial and commercial distributed user photovoltaic-storage collaborative planning method. This method mainly consists of 6 steps: discretization of continuous time, data collection, modeling of the energy storage system, establishment of the annual comprehensive cost objective function of the planning model, establishment of the constraint conditions of the planning model, and solution of the planning model. By fully considering various factors such as the characteristics of multiple power users, the randomness and diversity of photovoltaic power generation, and the electricity market trading policies, the planning results can minimize the annual comprehensive cost to the greatest extent on the basis of ensuring the safe and stable operation of the system, improve the enthusiasm of industrial and commercial users to install photovoltaic and energy storage devices, and play a promoting role in the realization of China's "carbon peak and carbon neutrality" goals.

[0132] In summary, the embodiments of the present application provide a photovoltaic-storage collaborative planning method. First, based on the discretization of continuous time, collect the normalized annual photovoltaic power generation data, annual user load data, and electricity market trading data at the user's location; model the energy storage system; model the mixed-integer linear programming model, which includes the annual comprehensive cost objective function of the planning model and the constraint conditions of the planning model; use a solver to solve the mixed-integer linear programming model to obtain the planned capacities and scheduling methods of photovoltaic and energy storage. It can plan the capacities of photovoltaic and energy storage simultaneously and is applicable to users without installed photovoltaic systems. At the same time, it fully considers the diversity of user loads and the diversity of photovoltaic power generation, and also considers the differences among different types of power users, making the planning results better conform to the actual situation of users.

[0133] Based on the same technical concept, the embodiments of the present application also provide a photovoltaic-storage collaborative planning system, as Figure 3 shown, the system includes:

[0134] A data collection module 301, configured to collect the normalized annual photovoltaic power generation data, annual user load data, and electricity market trading data at the user's location based on the discretization of continuous time;

[0135] The energy storage system modeling module 302 is used to model the energy storage system;

[0136] The mixed-integer linear programming model modeling module 303 is used to model the mixed-integer linear programming model, and the mixed-integer linear programming model includes the annual comprehensive cost objective function of the planning model and the constraint conditions of the planning model;

[0137] The planning model solving module 304 is used to solve the mixed-integer linear programming model by using a solver to obtain the planned capacity and scheduling method of the photovoltaic energy storage.

[0138] Based on the same technical concept, an embodiment of the present application further provides a device, and the device includes: a data acquisition device, a processor, and a memory; the data acquisition device is used to acquire data; the memory is used to store one or more program instructions; the processor is used to execute one or more program instructions to execute the method described above.

[0139] Based on the same technical concept, an embodiment of the present application further provides a computer-readable storage medium, and the computer storage medium contains one or more program instructions, and the one or more program instructions are used to execute the method described above.

[0140] In the present specification, the various embodiments of the above methods are all described in a progressive manner. For the same or similar parts among the various embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0141] It should be noted that although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that these operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution.

[0142] Although the present application provides method operation steps such as in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way among the execution orders of numerous steps and does not represent the only execution order. When the actual device or client product is executed, it may be executed in the method order shown in the embodiments or the drawings or executed in parallel (for example, in a parallel processor or multi-threaded processing environment, or even in a distributed data processing environment). The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, product or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, product or device. Without further limitation, there is no exclusion of additional identical or equivalent elements in the process, method, product or device comprising the said elements.

[0143] The units, devices or modules etc. illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. For the convenience of description, when describing the above devices, they are divided into various modules according to functions and described separately. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware, or the modules implementing the same function can be realized by the combination of multiple sub-modules or sub-units, etc. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0144] Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, the method steps can be logically programmed to enable the controller to be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. to achieve the same function. Therefore, such a controller can be regarded as a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0145] The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0146] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present application.

[0147] The various embodiments in this specification are described in a progressive manner. For the same or similar parts between the various embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on.

[0148] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present application. It should be understood that the above are only specific embodiments of the present application and are not used to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A photovoltaic energy storage collaborative planning method, characterized in that, the method includes: Based on the discretization of continuous time, collect the normalized annual photovoltaic output data, annual user load data, and electricity market transaction data at the user's location; Model the energy storage system according to the following formula: E k+1 = E k + P s·in · η in · Δt - P s·out · Δt / η out Among them, E k represents the remaining power of the energy storage at the k-th time node, P s·in and P s·out represent the charging power and discharging power of the energy storage respectively, η in and η out represent the charging efficiency and discharging efficiency of the energy storage respectively, and Δt represents the time interval; Establish a mixed-integer linear programming model, which includes the annual comprehensive cost objective function of the planning model and the constraint conditions of the planning model; The power balance constraint in the constraint conditions of the planning model is as follows: Among them, represents the charging power of the energy storage system at the k-th time node, represents the discharging power of the energy storage system at the k-th time node, represents the user-side electricity load at the k-th time node, represents the PV output at the k-th time node, represents the power of selling electricity to the grid at the k-th time node, represents the power of purchasing electricity from the grid at the k-th time node; The electricity quantity constraint of the energy storage system in the constraint conditions of the planning model is as follows: E k+1 = E k + P s·in · η in · Δt - P s·out · Δt / η out (1 - U) ≤ E k ≤ 0.95E sN Among them, U represents the maximum discharge depth of the energy storage system, and E sN represents the energy storage planned capacity; The charge and discharge constraint of the energy storage system in the constraint conditions of the planning model is as follows: P s·in·max = P s·out·max = C·E sN Among them, and are the charging flag and discharging flag of the energy storage system respectively. During charging, is 1, is 0; during discharging is 0, is 1; P s·in·max and P s·out·max are the maximum charging power and maximum discharging power of the energy storage respectively; C is the charge-discharge ratio of the energy storage; represents the user-side power consumption load at the k-th time node, represents the discharging power of the energy storage system at the k-th time node; The photovoltaic constraint in the constraint conditions of the planning model is as follows: Among them, is the normalized value of the photovoltaic output at time node k; P pvN represents the planned capacity of the photovoltaic; represents the photovoltaic output at the k-th time node; P pvN ≤ [S / S pv ·P ppv Among them, S is the maximum installable area of the photovoltaic panel; S pv is the area of a single photovoltaic panel; P ppv is the photovoltaic capacity of a single photovoltaic panel; Solve the mixed-integer linear programming model to obtain the planned capacity and scheduling method of photovoltaic energy storage.

2. The method according to claim 1, characterized in that, the annual comprehensive cost objective function of the planning model is as follows: minC cost = C run + C invest Among them, C cost represents the annual comprehensive cost; C run represents the annual electricity cost of the user; C invest represents the annual investment cost of the photovoltaic and energy storage system.

3. The method according to claim 2, characterized in that, the calculation method of the annual electricity cost of the user is as follows: where N is the number of time nodes in a year, represents the electricity price when purchasing electricity from the power grid at the k-th time node, represents the electricity price when selling surplus PV electricity to the power grid at the k-th time node, represents the power of purchasing electricity from the power grid at the k-th time node, represents the power of selling electricity to the power grid at the k-th time node.

4. The method according to claim 2, characterized in that, the calculation method of the annual investment cost is as follows: Among them, M s represents the investment cost of the energy storage device per unit capacity, M pv represents the investment cost of the photovoltaic device per unit capacity, E sN represents the energy storage planned capacity, P pvN represents the photovoltaic planned capacity, Y s represents the lifespan of the energy storage device, Y pv represents the lifespan of the photovoltaic device.

5. A photovoltaic energy storage collaborative planning system, characterized in that, the system includes: A data collection module, which is used to collect the normalized annual photovoltaic output data, annual user load data, and electricity market transaction data at the user's location based on the discretization of continuous time; An energy storage system modeling module, which is used to model the energy storage system according to the following formula: E k+1 = E k + P s·in · η in · Δt - P s·out · Δt / η out Among them, E k represents the remaining power of the energy storage at the k-th time node, P s·in and P s·out respectively represent the charging power and discharging power of the energy storage, η in and η out respectively represent the charging efficiency and discharging efficiency of the energy storage, and Δt represents the time interval; A mixed-integer linear programming model modeling module, which is used to establish a mixed-integer linear programming model, and the mixed-integer linear programming model includes the annual comprehensive cost objective function of the planning model and the constraint conditions of the planning model; The power balance constraint in the constraint conditions of the planning model is as follows: Among them, represents the charging power of the energy storage system at the k-th time node, represents the discharging power of the energy storage system at the k-th time node, represents the user-side electricity load at the k-th time node, represents the photovoltaic output at the k-th time node, represents the power of selling electricity to the power grid at the k-th time node, represents the power of purchasing electricity from the power grid at the k-th time node; The electricity quantity constraint of the energy storage system in the constraint conditions of the planning model is as follows: E k+1 = E k + P s·in · η in · Δt - P s·out · Δt / η out (1 - U) ≤ E k ≤ 0.95E sN where U represents the maximum depth of discharge of the energy storage system, and E sN represents the energy storage planned capacity; The charge and discharge constraint of the energy storage system in the constraint conditions of the planning model is as follows: P s·in·max = P s·out·max = C·E sN Among them, and are the charging flag and discharging flag of the energy storage system respectively. During charging, is 1, is 0; during discharging is 0, is 1; P s·in·max and P s·out·max are the maximum charging power and maximum discharging power of the energy storage respectively; C is the charge-discharge rate of the energy storage; represents the user-side power consumption load at the k-th time node, represents the discharging power of the energy storage system at the k-th time node; The photovoltaic constraint in the constraint conditions of the planning model is as follows: Among them, is the normalized value of the PV output at time node k; P pvN represents the planned capacity of the PV; represents the PV output at the k-th time node; P pvN ≤ [S / S pv ·P ppv Among them, S is the maximum installable area of the photovoltaic panel; S pv is the area of a single photovoltaic panel; P ppv is the photovoltaic capacity of a single photovoltaic panel; A planning model solving module, which is used to solve the mixed-integer linear programming model to obtain the planned capacity and scheduling method of photovoltaic energy storage.

6. The system according to claim 5, characterized in that, the annual comprehensive cost objective function of the planning model is as follows: minC cost = C run + C invest Among them, C cost represents the annual comprehensive cost; C run represents the annual electricity cost of the user; C invest represents the annual investment cost of the photovoltaic and energy storage system.

7. The system according to claim 6, characterized in that, the calculation method of the annual electricity cost of the user is as follows: where N is the number of time nodes in a year, represents the electricity price when purchasing electricity from the power grid at the k-th time node, represents the electricity price when selling surplus PV electricity to the power grid at the k-th time node, represents the power of purchasing electricity from the power grid at the k-th time node, represents the power of selling electricity to the power grid at the k-th time node.

8. The system according to claim 6, characterized in that, the calculation method of the annual investment cost is as follows: Among them, M s represents the investment cost of the energy storage device per unit capacity, M pv represents the investment cost of the photovoltaic device per unit capacity, E sN represents the energy storage planned capacity, P pvN represents the photovoltaic planned capacity, Y s represents the lifespan of the energy storage device, Y pv represents the lifespan of the photovoltaic device.

9. A device, the device includes: A data acquisition device, a processor, and a memory; The data acquisition device is used to acquire data; The memory is used to store one or more program instructions; The processor is used to execute one or more program instructions to execute a photovoltaic energy storage collaborative planning method according to any one of claims 1 to 4.

10. A computer-readable storage medium, wherein the computer-readable storage medium contains one or more program instructions for executing a photovoltaic energy storage collaborative planning method according to any one of claims 1 to 4.

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