Multi-element heterogeneous optical storage system double-layer capacity optimization configuration method and system

Abstract

The invention discloses a multi-element heterogeneous optical storage system double-layer capacity optimization configuration method and system, and belongs to the technical field of optical storage system capacity configuration. The method comprises the following steps: constructing a multi-element heterogeneous light storage and charging system model which comprises a multi-element heterogeneous photovoltaic subsystem mathematical model and a multi-element heterogeneous energy storage subsystem mathematical model; based on a multi-element heterogeneous photovoltaic storage and charging system model, establishing an upper-layer optimization model by taking multi-element heterogeneous photovoltaic output as a decision variable, and establishing a lower-layer optimization model by taking energy storage system capacity as a decision variable; solving the objective functions of the upper-layer optimization model and the lower-layer optimization model to obtain an optimal configuration result of the multi-element heterogeneous optical storage and charging system; by establishing a multi-element heterogeneous photovoltaic subsystem mathematical model, capacity configuration is carried out on three types of photovoltaics, the problem of multi-element photovoltaic configuration is effectively solved, and the effects of reducing the operation cost of a charging station and improving the new energy conversion efficiency are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of capacity configuration of a photovoltaic storage system, and in particular relates to a double-layer capacity optimization configuration method and system for a multi-element heterogeneous photovoltaic storage system. Background Art

[0002] With the rapid development of the energy sector, the comprehensiveness of charging facilities has gradually improved. It has become a trend to configure photovoltaic and energy storage equipment in charging stations to promote low-carbon electricity consumption. Among them, considering the conversion efficiency, technical level, production process maturity, market share and other factors of photovoltaic cell components, for photovoltaic power stations, the commonly used component types can be roughly divided into crystalline silicon components and amorphous silicon components. Crystalline silicon components are further divided into monocrystalline silicon and polycrystalline silicon, and amorphous silicon components are mainly thin-film components. Energy storage equipment can be divided into power type, energy type, capacity type and backup type according to the application scenario. Power-type energy storage equipment types include supercapacitors, flywheel energy storage, etc., energy-type energy storage equipment types are mainly lithium batteries, and capacity-type energy storage equipment types include lead-carbon batteries, etc.

[0003] At present, photovoltaic charging stations generally use single photovoltaic equipment and energy storage equipment. This traditional operation mode leads to high cost and low energy utilization efficiency of charging stations. Therefore, a double-layer capacity optimization configuration method and system for multi-heterogeneous photovoltaic storage system is proposed. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method and system for optimizing the configuration of double-layer capacity of a multi-heterogeneous optical storage system, which solves the problems in the prior art.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for optimizing the configuration of double-layer capacity of a multi-element heterogeneous optical storage system comprises the following steps:

[0007] Construct a multi-dimensional heterogeneous photovoltaic storage and charging system model, including a multi-dimensional heterogeneous photovoltaic subsystem mathematical model and a multi-dimensional heterogeneous energy storage subsystem mathematical model;

[0008] Based on the multi-heterogeneous photovoltaic storage and charging system model, the upper optimization model is established with the multi-heterogeneous photovoltaic output as the decision variable, and the lower optimization model is established with the energy storage system capacity as the decision variable;

[0009] The objective functions of the upper optimization model and the lower optimization model are solved to obtain the optimal configuration result of the multi-element heterogeneous optical storage and charging system.

[0010] Further, the multi-element heterogeneous photovoltaic subsystem mathematical model includes: a multi-element heterogeneous photovoltaic subsystem output power mathematical model and an investment cost mathematical model;

[0011] The output power mathematical model of the multi-element heterogeneous photovoltaic subsystem is:

[0012] P TPV (t) = N PV ×P PV (t)

[0013] P PV (t) = I r η PV A PV

[0014] Where P PV (t) represents the photovoltaic power generation power of a single photovoltaic module at time t; P TPV (t) represents the photovoltaic power generation power of the multi-heterogeneous photovoltaic subsystem at time t, I r Represents the solar radiation on the photovoltaic template, η PV Represents the conversion efficiency of the photovoltaic template, A PV Represents the photovoltaic panel area, N PV Indicates the number of photovoltaic modules;

[0015] The mathematical model of investment cost of the multi-element heterogeneous photovoltaic subsystem is:

[0016] C T =C Mono +C Poly +C Thin

[0017] In the formula, C T represents the total investment cost of the multi-heterogeneous photovoltaic subsystem, C Mono represents the investment cost of the monocrystalline silicon photovoltaic subsystem, C Poly represents the investment cost of the polysilicon photovoltaic subsystem, C Thin Represents the investment cost of the thin-film photovoltaic subsystem.

[0018] Further, the multi-element heterogeneous energy storage subsystem mathematical model includes: a charge and discharge mathematical model and an investment cost mathematical model of each energy storage subsystem;

[0019] The charging and discharging mathematical model of each energy storage subsystem is:

[0020]

[0021] E(t)=E(t-Δt)(1-δ)+P ch (t)η ch Δt

[0022]

[0023] Where E(t) represents the capacity of a single energy storage system at time t, δ represents the self-discharge rate of a single energy storage system, and P ch (t), P dis (t) respectively represent the charging and discharging power of a single energy storage system at time t, Δt represents the scheduling interval of a single energy storage subsystem, η ch represents the charge-discharge conversion efficiency of a single energy storage subsystem, SOC(t) represents the state of charge of a single energy storage subsystem at time t, SOC0 represents the initial state of a single energy storage system, and E ba is the rated capacity of a single energy storage system;

[0024] The energy storage subsystem investment cost mathematical model includes: a supercapacitor energy storage subsystem investment cost mathematical model and a lithium battery energy storage subsystem investment cost mathematical model;

[0025] The investment cost mathematical model of the supercapacitor energy storage subsystem is:

[0026]

[0027] In the formula, C sp represents the investment cost of the supercapacitor energy storage subsystem, represents the unit power cost of the supercapacitor energy storage subsystem, represents the installed power of the supercapacitor energy storage subsystem, Represents the unit capacity cost of the supercapacitor energy storage subsystem, Represents the installed capacity of the supercapacitor energy storage subsystem;

[0028] The mathematical model of the investment cost of the lithium battery energy storage subsystem is:

[0029]

[0030] In the formula, C Li represents the investment cost of the lithium battery energy storage subsystem, Represents the unit power cost of the lithium battery energy storage subsystem, represents the installed power of the lithium battery energy storage subsystem, μ represents the cell replacement rate of the lithium battery energy storage subsystem, Represents the unit capacity cost of the lithium battery energy storage subsystem, Represents the installed capacity of the lithium battery energy storage subsystem.

[0031] Furthermore, the objective function of the upper optimization model is:

[0032] min F1=λ c ·T c +λ P ·|PTMono (t)+P TPoly (t)+P TThin (t)-P Load (t)|

[0033] Where F1 represents the objective function of the upper optimization model, λ c represents the weight coefficient of photovoltaic system investment cost, λ P The weight coefficient representing the absolute value of the difference between photovoltaic output power and electricity demand, P TMono (t) represents the photovoltaic power generation power of the single crystal silicon photovoltaic subsystem at time t, P TPoly (t) represents the photovoltaic power generation power of the polycrystalline silicon photovoltaic subsystem at time t, P TThin (t) represents the photovoltaic power generation power of the thin-film photovoltaic subsystem at time t, P Load (t) represents the system load power demand at time t;

[0034] The constraints of the upper optimization model include:

[0035] 1) Photovoltaic system capacity constraints:

[0036]

[0037] Where, t Horizon Indicates the last scheduling instruction time;

[0038] 2) Constraints on the number of photovoltaic installed capacity:

[0039] N Mono +N Poly +N Thin ≤N PVmax

[0040] Among them, N Mono Represents the number of monocrystalline silicon photovoltaic templates, N Poly Indicates the number of polysilicon photovoltaic modules, N Thin Indicates the number of thin-film photovoltaic modules, N PVmax Indicates the maximum number of photovoltaic modules that the charging station can accommodate.

[0041] 5. A dual-layer capacity optimization configuration method for a multi-element heterogeneous photovoltaic storage system according to claim 4, characterized in that the objective function of the lower layer optimization model is:

[0042] min F2=C Li +C sp

[0043] Where F2 represents the objective function of the lower optimization model, C Li represents the investment cost of the lithium battery energy storage subsystem, C sprepresents the investment cost of the supercapacitor energy storage subsystem;

[0044] The constraints of the lower optimization model include:

[0045] 1) Power balance constraints:

[0046] P Load (t) = P Ba (t)+P TPV (t)+P G (t)

[0047] Where P Load (t) represents the charging load at time t, P Ba (t) represents the energy storage power of the multi-element energy storage subsystem at time t, P G (t) represents the power exchanged by the power grid at time t;

[0048] 2) Energy storage installed capacity constraints:

[0049] E sp +E Li ≤E Bamax

[0050] 3) Single energy storage subsystem charging and discharging power constraints:

[0051] 0≤P ch ≤P chmax

[0052] 0≤P dis ≤P dismax

[0053] Where P chmax , P dismax Respectively represent the limit values ​​of charging power and discharging power of the energy storage system;

[0054] 4) Charge state constraints:

[0055] SOC min ≤SOC(t)≤SOC max

[0056] In the formula, SOC min , SOC max They are the upper and lower limits of the state of charge respectively.

[0057] Further, solving the objective functions of the upper optimization model and the lower optimization model includes the following steps:

[0058] S31, based on the objective function and constraints of the upper optimization model, optimize the configuration capacity of monocrystalline silicon, polycrystalline silicon and thin-film photovoltaic subsystems, and solve to obtain the optimal solution for the capacity configuration of the multi-heterogeneous photovoltaic subsystem, that is, the installed capacity of photovoltaics in the multi-heterogeneous photovoltaic storage charging station;

[0059] S32, based on typical photovoltaic conditions and the mathematical model of photovoltaic subsystem output power, obtain the actual photovoltaic output of the multi-heterogeneous photovoltaic subsystem throughout the year;

[0060] S33, according to the photovoltaic output and load charging demand, based on the objective function and constraint conditions of the lower optimization model, solving to obtain the optimal solution for the capacity optimization configuration of the supercapacitor-lithium-ion battery hybrid energy storage subsystem;

[0061] S34, combining the optimal solution of the capacity configuration of the multi-element photovoltaic subsystem and the optimal solution of the capacity optimization configuration of the energy storage subsystem, conducts a comprehensive analysis and evaluation of the capacity optimization configuration scheme of the multi-element heterogeneous photovoltaic storage and charging system to obtain the capacity optimization configuration result of the multi-element heterogeneous photovoltaic storage and charging system.

[0062] A dual-layer capacity optimization configuration system for a multi-element heterogeneous optical storage system, comprising:

[0063] Multivariate heterogeneous photovoltaic storage and charging system model construction module: construct a multivariate heterogeneous photovoltaic storage and charging system model, including a multivariate heterogeneous photovoltaic subsystem mathematical model and a multivariate heterogeneous energy storage subsystem mathematical model;

[0064] Upper and lower optimization model construction module: Based on the multi-heterogeneous photovoltaic storage and charging system model, the upper optimization model is established with multi-heterogeneous photovoltaic output as the decision variable, and the lower optimization model is established with the energy storage system capacity as the decision variable;

[0065] And, a model solving module: solves the objective functions of the upper optimization model and the lower optimization model to obtain the optimal configuration result of the multi-element heterogeneous optical storage and charging system.

[0066] A computer storage medium stores a readable program, which can execute the above-mentioned method for optimizing the double-layer capacity configuration of a multi-element heterogeneous optical storage system when the program is running.

[0067] An electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0068] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for optimizing the configuration of double-layer capacity of a multi-heterogeneous optical storage system.

[0069] A computer program product includes computer instructions, wherein the computer instructions instruct a computing device to execute operations corresponding to the above-mentioned method for optimizing the configuration of double-layer capacity of a multi-heterogeneous optical storage system.

[0070] Beneficial effects of the present invention:

[0071] 1. The present invention establishes a mathematical model of a multi-heterogeneous photovoltaic subsystem and configures the capacity of three types of photovoltaics, effectively solving the problem of multi-photovoltaic configuration, thereby reducing the operating cost of charging stations and improving the efficiency of new energy conversion.

[0072] 2. The present invention can obtain the corresponding capacity configuration method according to the various parameter characteristics of the photovoltaic system and the energy storage system; on the basis of meeting the load demand response, it can achieve the dual carbon goals of high penetration of photovoltaic energy and enhance the ability to consume photovoltaic energy locally. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0074] Figure 1 It is a flow chart of the optimization configuration method of the present invention;

[0075] Figure 2 It is a schematic diagram of the structure of the multi-element heterogeneous photovoltaic storage charging station of the present invention;

[0076] Figure 3 It is a detailed flow chart of the optimization configuration method of the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] Example 1

[0079] like Figure 1 and Figure 3 As shown, a method for optimizing the configuration of double-layer capacity of a multi-element heterogeneous optical storage system comprises the following steps:

[0080] S1, construct a multi-heterogeneous photovoltaic storage and charging system model, including a multi-heterogeneous photovoltaic subsystem mathematical model and a multi-heterogeneous energy storage subsystem mathematical model;

[0081] The multi-element heterogeneous photovoltaic storage and charging system includes a multi-element photovoltaic subsystem and a multi-element energy storage subsystem;

[0082] The system load of the multi-heterogeneous photovoltaic storage charging station is the typical electric vehicle charging load and other loads; in order to ensure the stability of electric vehicle charging load power consumption and the maximum utilization of energy, the system is configured with a single crystal silicon photovoltaic subsystem, a polycrystalline silicon photovoltaic subsystem and a thin film photovoltaic subsystem (i.e., a multi-photovoltaic subsystem), as well as a supercapacitor energy storage subsystem and a lithium battery energy storage subsystem (i.e., a multi-energy storage subsystem).

[0083] like Figure 2 As shown in the figure, the multi-heterogeneous photovoltaic storage system is connected to the DC power grid and interacts with the power grid; the photovoltaic system is composed of monocrystalline silicon, polycrystalline silicon and thin-film solar panels connected in series and parallel, and is mainly installed in idle spaces such as the roof of the charging station; the photovoltaic array absorbs solar energy to generate direct current, and the generated electricity is connected to the charging station through the bus to supply the charging load and the charging station’s own needs, which is the main power source in the photovoltaic charging station; if the photovoltaic system has surplus electricity, it can be sent to the energy storage system in the station or sold to the power grid; other loads in the charging station mainly refer to the power loads of other devices such as lighting equipment and relay protection in the charging station; the charging pile uses the electricity on the power supply bus to meet the regular charging and fast charging of vehicles in the station.

[0084] S11, based on the solar energy resources and photovoltaic material characteristics under typical conditions, construct a mathematical model of a multi-dimensional heterogeneous photovoltaic subsystem, including: a mathematical model of the output power of the multi-dimensional heterogeneous photovoltaic subsystem and a mathematical model of the investment cost;

[0085] Among them, the output power of the multi-heterogeneous photovoltaic subsystem is affected by radiation, the operating temperature of different photovoltaic materials, conversion efficiency, photovoltaic panel area, and the number of photovoltaic templates;

[0086] The output power mathematical model of the multi-element heterogeneous photovoltaic subsystem is:

[0087] P TPV (t) = N PV ×N PV (t)

[0088] P PV (t) = I r η PV A PV

[0089] Where P PV (t) represents the photovoltaic power generation power of a single photovoltaic module at time t; P TPV (t) represents the photovoltaic power generation power of the multi-heterogeneous photovoltaic subsystem at time t, I r Represents the solar radiation on the photovoltaic template, η PVRepresents the conversion efficiency of the photovoltaic template, A PV Represents the photovoltaic panel area, N PV Indicates the number of photovoltaic modules;

[0090] The mathematical model of investment cost of the multi-element heterogeneous photovoltaic subsystem is:

[0091] C T =C Mono +C Poly +C Thin

[0092] In the formula, C T represents the total investment cost of the multi-heterogeneous photovoltaic subsystem, C Mono represents the investment cost of the monocrystalline silicon photovoltaic subsystem, C Poly represents the investment cost of the polysilicon photovoltaic subsystem, C Thin represents the investment cost of the thin-film photovoltaic subsystem;

[0093] Among them, the investment cost mathematical models of the monocrystalline silicon photovoltaic subsystem, the polycrystalline silicon photovoltaic subsystem and the thin-film photovoltaic subsystem are the same, and the investment cost mathematical model of each photovoltaic subsystem is expressed by the following formula:

[0094] C PV =CRF(i,N)×(C I +C R )+OM

[0095] In the formula, C PV represents the total investment cost of a single photovoltaic subsystem, CRF(i,N) represents the capital recovery factor, i represents the interest rate per cycle of a single photovoltaic subsystem, N is the project period, and C I Indicates the construction cost, C R represents replacement cost, OM represents operation and maintenance cost;

[0096] S12, constructing a mathematical model of a multi-element heterogeneous energy storage subsystem, including: a charging and discharging mathematical model and an investment cost mathematical model of each energy storage subsystem;

[0097] The multi-heterogeneous energy storage subsystem includes a supercapacitor energy storage subsystem and a lithium battery energy storage subsystem. By configuring the multi-heterogeneous energy storage capacity, it can effectively absorb photovoltaic energy, reduce the dependence of charging stations on the power grid, and effectively reduce the energy storage configuration cost. Among them, the lithium battery energy storage subsystem processes the low-frequency current component on the DC bus side, and the supercapacitor energy storage subsystem processes the high-frequency current component on the DC bus side.

[0098] Among them, the charging and discharging mathematical models of supercapacitors and lithium batteries are the same, and the charging and discharging mathematical model of each energy storage subsystem is expressed by the following formula:

[0099]

[0100] E(t)=E(t-Δt)(1-δ)+P ch (t)η ch Δt

[0101]

[0102] Where E(t) represents the capacity of a single energy storage system at time t, δ represents the self-discharge rate of a single energy storage system, and P ch (t), P dis (t) respectively represent the charging and discharging power of a single energy storage system at time t, Δt represents the scheduling interval of a single energy storage subsystem, η ch represents the charge-discharge conversion efficiency of a single energy storage subsystem, SOC(t) represents the state of charge of a single energy storage subsystem at time t, SOC0 represents the initial state of a single energy storage system, and E ba is the rated capacity of a single energy storage system;

[0103] It should be noted that although the charging and discharging model of each energy storage system is the same, the energy storage materials are different, and the parameters in the corresponding mathematical models are different.

[0104] The mathematical model of the energy storage subsystem investment cost includes: the mathematical model of the supercapacitor energy storage subsystem investment cost and the mathematical model of the lithium battery energy storage subsystem investment cost;

[0105] The mathematical model of investment cost of supercapacitor energy storage subsystem is:

[0106]

[0107] In the formula, C sp represents the investment cost of the supercapacitor energy storage subsystem, represents the unit power cost of the supercapacitor energy storage subsystem, represents the installed power of the supercapacitor energy storage subsystem, Represents the unit capacity cost of the supercapacitor energy storage subsystem, Represents the installed capacity of the supercapacitor energy storage subsystem;

[0108] The mathematical model of the investment cost of the lithium battery energy storage subsystem is:

[0109]

[0110] In the formula, C Li represents the investment cost of the lithium battery energy storage subsystem, Represents the unit power cost of the lithium battery energy storage subsystem, represents the installed power of the lithium battery energy storage subsystem, μ represents the cell replacement rate of the lithium battery energy storage subsystem, Represents the unit capacity cost of the lithium battery energy storage subsystem, Represents the installed capacity of the lithium battery energy storage subsystem.

[0111] S2, based on the multivariate heterogeneous photovoltaic storage and charging system model, the upper optimization model is established with the multivariate heterogeneous photovoltaic output as the decision variable, and the lower optimization model is established with the energy storage system capacity as the decision variable;

[0112] The goal of the upper optimization model is to minimize the sum of the total investment cost of the multi-heterogeneous photovoltaic system and the absolute value of the difference between the output power and the load demand. The objective function of the upper optimization model is:

[0113] min F1=λ c ·T c +λ P ·|P TMono (t)+P TPoly (t)+P TThin (t)-P Load (t)|

[0114] Where F1 represents the objective function of the upper optimization model, λ c represents the weight coefficient of photovoltaic system investment cost, λ P The weight coefficient representing the absolute value of the difference between photovoltaic output power and electricity demand; P TMono (t) represents the photovoltaic power generation power of the single crystal silicon photovoltaic subsystem at time t, P TPoly (t) represents the photovoltaic power generation power of the polycrystalline silicon photovoltaic subsystem at time t, P TThin (t) represents the photovoltaic power generation power of the thin-film photovoltaic subsystem at time t, P Load (t) represents the system load power demand at time t;

[0115] The goal of the upper optimization model is to comprehensively consider the investment cost of the photovoltaic system and the imbalance between output power and load demand. The objective function is obtained by normalization processing and linear weighted summation, and finally the optimal configuration of the capacity of the multi-element photovoltaic subsystem is achieved.

[0116] The constraints of the upper optimization model include:

[0117] 1) Photovoltaic system capacity constraints:

[0118]

[0119] Where, t Horizon Indicates the last scheduling instruction time;

[0120] 2) Constraints on the number of photovoltaic installed capacity:

[0121] N Mono +N Poly +N Thin ≤N PVmax

[0122] Among them, N Mono Represents the number of monocrystalline silicon photovoltaic templates, N Poly Indicates the number of polysilicon photovoltaic modules, N Thin Indicates the number of thin-film photovoltaic modules, N PVmax Indicates the maximum number of photovoltaic modules that the charging station can accommodate;

[0123] The goal of the lower optimization model is to minimize the investment cost of the multi-energy storage subsystem as much as possible on the basis of satisfying the goal of the upper optimization model, that is, obtaining the photovoltaic output of the charging station throughout the year. The objective function of the lower optimization model is:

[0124] min F2=C Li +C sp

[0125] Where F2 represents the objective function of the lower optimization model, C Li represents the investment cost of the lithium battery energy storage subsystem, C sp represents the investment cost of the supercapacitor energy storage subsystem;

[0126] The constraints of the lower optimization model include:

[0127] 1) Power balance constraints:

[0128] P Load (t) = P Ba (t)+P TPV (t)+P G (t)

[0129] Where P Load (t) represents the charging load at time t, P Ba (t) represents the energy storage power of the multi-element energy storage subsystem at time t, P G (t) represents the power exchanged by the power grid at time t;

[0130] 2) Energy storage installed capacity constraints:

[0131] E sp +E Li ≤E Bamax

[0132] 3) Single energy storage subsystem charging and discharging power constraints:

[0133] 0≤P ch ≤P chmax

[0134] 0≤P dis ≤P dismax

[0135] Where P chmax , P dismax Respectively represent the limit values ​​of charging power and discharging power of the energy storage system;

[0136] 4) Charge state constraints:

[0137] SOC min ≤SOC(t)≤SOC max

[0138] In the formula, SOC min , SOC max They are the upper and lower limits of the state of charge respectively.

[0139] S3, solving the objective functions of the upper optimization model and the lower optimization model to obtain the optimal configuration result of the multi-element heterogeneous optical storage and charging system;

[0140] Solving the objective functions of the upper optimization model and the lower optimization model includes the following steps:

[0141] S31, based on the objective function and constraints of the upper optimization model, optimize the configuration capacity of monocrystalline silicon, polycrystalline silicon and thin-film photovoltaic subsystems, and solve to obtain the optimal solution for the capacity configuration of the multi-heterogeneous photovoltaic subsystem, that is, the installed capacity of photovoltaics in the multi-heterogeneous photovoltaic storage charging station;

[0142] S32, based on typical photovoltaic conditions and the mathematical model of photovoltaic subsystem output power, obtain the actual photovoltaic output of the multi-heterogeneous photovoltaic subsystem throughout the year;

[0143] S33, according to the photovoltaic output and load charging demand, based on the objective function and constraint conditions of the lower optimization model, solving to obtain the optimal solution for the capacity optimization configuration of the supercapacitor-lithium-ion battery hybrid energy storage subsystem;

[0144] S34, combining the optimal solution of the capacity configuration of the multi-element photovoltaic subsystem and the optimal solution of the capacity optimization configuration of the energy storage subsystem, conducts a comprehensive analysis and evaluation of the capacity optimization configuration scheme of the multi-element heterogeneous photovoltaic storage and charging system to obtain the capacity optimization configuration result of the multi-element heterogeneous photovoltaic storage and charging system.

[0145] Based on similar inventive concepts, an embodiment of the present invention further provides a computer storage medium storing a readable program, which can execute the above-mentioned method for optimizing the dual-layer capacity configuration of a multi-heterogeneous optical storage system when the program is running.

[0146] Based on similar inventive concepts, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0147] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for optimizing the configuration of double-layer capacity of a multi-heterogeneous optical storage system.

[0148] Based on similar inventive concepts, an embodiment of the present invention further provides a computer program product, including computer instructions, which instruct a computing device to execute operations corresponding to the above-mentioned method for optimizing the configuration of double-layer capacity of a multi-heterogeneous optical storage system.

[0149] Example 2

[0150] In this embodiment, a dual-layer capacity optimization configuration system for a multi-element heterogeneous optical storage system is proposed, specifically comprising:

[0151] Multivariate heterogeneous photovoltaic storage and charging system model construction module: construct a multivariate heterogeneous photovoltaic storage and charging system model, including a multivariate heterogeneous photovoltaic subsystem mathematical model and a multivariate heterogeneous energy storage subsystem mathematical model;

[0152] Upper and lower optimization model construction module: Based on the multi-heterogeneous photovoltaic storage and charging system model, the upper optimization model is established with multi-heterogeneous photovoltaic output as the decision variable, and the lower optimization model is established with the energy storage system capacity as the decision variable;

[0153] Model solving module: solves the objective functions of the upper optimization model and the lower optimization model to obtain the optimal configuration result of the multi-element heterogeneous optical storage and charging system.

[0154] The method of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0155] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for optimizing the dual-layer capacity configuration of a multi-element heterogeneous optical storage system, characterized in that: The following steps are involved: Construct a multi-dimensional heterogeneous photovoltaic storage and charging system model, including a multi-dimensional heterogeneous photovoltaic subsystem mathematical model and a multi-dimensional heterogeneous energy storage subsystem mathematical model; Based on the multi-heterogeneous photovoltaic storage and charging system model, the upper optimization model is established with the multi-heterogeneous photovoltaic output as the decision variable, and the lower optimization model is established with the energy storage system capacity as the decision variable; The objective functions of the upper optimization model and the lower optimization model are solved to obtain the optimal configuration result of the multi-element heterogeneous optical storage and charging system.

2. The method for optimizing the dual-layer capacity of a multi-element heterogeneous optical storage system according to claim 1, characterized in that: The multi-element heterogeneous photovoltaic subsystem mathematical model includes: a multi-element heterogeneous photovoltaic subsystem output power mathematical model and an investment cost mathematical model; The output power mathematical model of the multi-element heterogeneous photovoltaic subsystem is: P TPV (t)=N PV ×P PV (t) P PV (t)=I r η PV A PV Where P PV (t) represents the photovoltaic power generation power of a single photovoltaic module at time t; P TPV (t) represents the photovoltaic power generation power of the multi-heterogeneous photovoltaic subsystem at time t, I r Represents the solar radiation on the photovoltaic template, η PV Represents the conversion efficiency of the photovoltaic template, A PV Represents the photovoltaic panel area, N PV Indicates the number of photovoltaic modules; The mathematical model of investment cost of the multi-element heterogeneous photovoltaic subsystem is: C T =C Mono +C Poly +C Thin In the formula, C T represents the total investment cost of the multi-heterogeneous photovoltaic subsystem, C Mono represents the investment cost of the monocrystalline silicon photovoltaic subsystem, C Poly represents the investment cost of the polysilicon photovoltaic subsystem, C Thin Represents the investment cost of the thin-film photovoltaic subsystem.

3. The method for optimizing the dual-layer capacity of a multi-element heterogeneous optical storage system according to claim 2, characterized in that: The mathematical model of the multi-element heterogeneous energy storage subsystem includes: a charging and discharging mathematical model and an investment cost mathematical model of each energy storage subsystem; The charging and discharging mathematical model of each energy storage subsystem is: E(t)=E(t-Δt)(1-δ)+P ch (t)h ch Δt Where E(t) represents the capacity of a single energy storage system at time t, δ represents the self-discharge rate of a single energy storage system, and P ch (t), P dis (t) respectively represent the charging and discharging power of a single energy storage system at time t, Δt represents the scheduling interval of a single energy storage subsystem, η ch represents the charge-discharge conversion efficiency of a single energy storage subsystem, SOC(t) represents the state of charge of a single energy storage subsystem at time t, SOC0 represents the initial state of a single energy storage system, and E ba is the rated capacity of a single energy storage system; The energy storage subsystem investment cost mathematical model includes: a supercapacitor energy storage subsystem investment cost mathematical model and a lithium battery energy storage subsystem investment cost mathematical model; The investment cost mathematical model of the supercapacitor energy storage subsystem is: In the formula, C sp represents the investment cost of the supercapacitor energy storage subsystem, represents the unit power cost of the supercapacitor energy storage subsystem, represents the installed power of the supercapacitor energy storage subsystem, Represents the unit capacity cost of the supercapacitor energy storage subsystem, Represents the installed capacity of the supercapacitor energy storage subsystem; The mathematical model of the investment cost of the lithium battery energy storage subsystem is: In the formula, C Li represents the investment cost of the lithium battery energy storage subsystem, Represents the unit power cost of the lithium battery energy storage subsystem, represents the installed power of the lithium battery energy storage subsystem, μ represents the cell replacement rate of the lithium battery energy storage subsystem, Represents the unit capacity cost of the lithium battery energy storage subsystem, Represents the installed capacity of the lithium battery energy storage subsystem.

4. The method for optimizing the dual-layer capacity configuration of a multi-element heterogeneous optical storage system according to claim 3, characterized in that: The objective function of the upper optimization model is: min F1=λ c ·T c +λ P ·|P TMono (t)+P TPoly (t)+P TThin (t)-P Load (t)| Where F1 represents the objective function of the upper optimization model, λ c represents the weight coefficient of photovoltaic system investment cost, λ P The weight coefficient representing the absolute value of the difference between photovoltaic output power and electricity demand, P TMono (t) represents the photovoltaic power generation power of the single crystal silicon photovoltaic subsystem at time t, P TPoly (t) represents the photovoltaic power generation power of the polycrystalline silicon photovoltaic subsystem at time t, P TThin (t) represents the photovoltaic power generation power of the thin-film photovoltaic subsystem at time t, P Load (t) represents the system load power demand at time t; The constraints of the upper optimization model include: 1) Photovoltaic system capacity constraints: In the formula, t Horizon Indicates the last scheduling instruction time; 2) Constraints on the number of photovoltaic installed capacity: N Mono +N Poly +N Thin ≤N PVmax Among them, N Mono Represents the number of monocrystalline silicon photovoltaic templates, N Poly Indicates the number of polycrystalline silicon photovoltaic modules, N Thin Indicates the number of thin-film photovoltaic modules, N PVmax Indicates the maximum number of photovoltaic modules that the charging station can accommodate.

5. The method for optimizing the double-layer capacity of a multi-element heterogeneous optical storage system according to claim 4, characterized in that: The objective function of the lower optimization model is: my F2=C Li +C sp Where F2 represents the objective function of the lower optimization model, C Li represents the investment cost of the lithium battery energy storage subsystem, C sp represents the investment cost of the supercapacitor energy storage subsystem; The constraints of the lower optimization model include: 1) Power balance constraints: P Load (t)=P Ba (t)+P TPV (t)+P G (t) Where P Load (t) represents the charging load at time t, P Ba (t) represents the energy storage power of the multi-element energy storage subsystem at time t, P G (t) represents the power exchanged by the power grid at time t; 2) Energy storage installed capacity constraints: AND sp +E Li ≤E Bamax 3) Single energy storage subsystem charging and discharging power constraints: 0≤P ch ≤P chmax 0≤P dis ≤P dismax Where P chmax , P dismax Respectively represent the limit values ​​of charging power and discharging power of the energy storage system; 4) Charge state constraints: SOC min ≤SOC(t)≤SOC max In the formula, SOC min , SOC max They are the upper and lower limits of the state of charge respectively.

6. The method for optimizing the dual-layer capacity configuration of a multi-element heterogeneous optical storage system according to claim 1, characterized in that: Solving the objective functions of the upper optimization model and the lower optimization model includes the following steps: S31, based on the objective function and constraints of the upper optimization model, optimize the configuration capacity of monocrystalline silicon, polycrystalline silicon and thin-film photovoltaic subsystems, and solve to obtain the optimal solution for the capacity configuration of the multi-heterogeneous photovoltaic subsystem, that is, the installed capacity of photovoltaics in the multi-heterogeneous photovoltaic storage charging station; S32, based on typical photovoltaic conditions and the mathematical model of photovoltaic subsystem output power, obtain the actual photovoltaic output of the multi-heterogeneous photovoltaic subsystem throughout the year; S33, according to the photovoltaic output and load charging demand, based on the objective function and constraint conditions of the lower optimization model, solving to obtain the optimal solution for the capacity optimization configuration of the supercapacitor-lithium-ion battery hybrid energy storage subsystem; S34, combining the optimal solution of the capacity configuration of the multi-element photovoltaic subsystem and the optimal solution of the capacity optimization configuration of the energy storage subsystem, conducts a comprehensive analysis and evaluation of the capacity optimization configuration scheme of the multi-element heterogeneous photovoltaic storage and charging system to obtain the capacity optimization configuration result of the multi-element heterogeneous photovoltaic storage and charging system.

7. A dual-layer capacity optimization configuration system for a multi-element heterogeneous optical storage system, characterized in that: include: Multivariate heterogeneous photovoltaic storage and charging system model construction module: construct a multivariate heterogeneous photovoltaic storage and charging system model, including a multivariate heterogeneous photovoltaic subsystem mathematical model and a multivariate heterogeneous energy storage subsystem mathematical model; Upper and lower optimization model construction module: Based on the multi-heterogeneous photovoltaic storage and charging system model, the upper optimization model is established with multi-heterogeneous photovoltaic output as the decision variable, and the lower optimization model is established with the energy storage system capacity as the decision variable; And, a model solving module: solves the objective functions of the upper optimization model and the lower optimization model to obtain the optimal configuration result of the multi-element heterogeneous optical storage and charging system.

8. A computer storage medium storing a readable program, characterized in that: When the program is running, it can execute the method for optimizing the double-layer capacity configuration of a multi-heterogeneous optical storage system as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to a method for optimizing the configuration of double-layer capacity of a multi-heterogeneous optical storage system as described in any one of claims 1-6.

10. A computer program product comprising computer instructions, characterized in that: The computer instructions instruct the computing device to execute operations corresponding to the method for optimizing the configuration of double-layer capacity of a multi-heterogeneous optical storage system as described in any one of claims 1-6.

Images