Multi-objective optimization method and device for grid-connected microgrid

Abstract

The embodiment of the present invention discloses a multi-objective optimization method and device for a grid-connected microgrid. The method comprises: establishing a multi-objective microgrid optimization model for a photovoltaic and storage composite system; using a linear weighted summation method to convert the multi-objective problem of the multi-objective microgrid optimization model into a single-objective problem; solving the single-objective problem to obtain an optimal operation plan; establishing a microgrid grid-connected model for the photovoltaic and storage composite system based on a real-time simulation tool of MATLAB; and verifying the optimal operation plan based on the microgrid grid-connected model. By implementing the method of the embodiment of the present invention, a series of constraints such as the constraints of the energy storage system, the power balance constraints of the microgrid, the power constraints of photovoltaic and power grids, etc. can be fully considered. In addition, the impact of carbon emissions on the environment is also considered to improve the optimization effect.

Description

Technical Field

[0001] The present invention relates to a microgrid, and more specifically to a multi-objective optimization method and device for a grid-connected microgrid. Background Art

[0002] Photovoltaic power generation is an environmentally friendly and sustainable clean energy that uses solar energy to generate electricity.

[0003] In recent years, as electricity prices are charged at different time periods, energy storage systems can store electricity at valley prices and sell it at peak prices, which can increase commercial profits. Microgrids, as a new type of network structure, can interact with multiple distributed power sources, energy storage systems, control systems, information interaction systems, etc. In the current use of microgrids, there are specific impacts of energy storage systems on grid-connected microgrids, the impact of uncertainty on renewable energy sources such as photovoltaics, and high operating costs of microgrids. The existing microgrid optimization methods cannot fully consider a series of constraints such as energy storage system constraints, microgrid power balance constraints, photovoltaic and power grid power constraints, and the impact of carbon emissions on the environment, resulting in poor optimization results.

[0004] Therefore, it is necessary to design a new method that fully considers a series of constraints such as the constraints of the energy storage system, the power balance constraints of the microgrid, the power constraints of photovoltaics and the power grid. In addition, the impact of carbon emissions on the environment is also considered to improve the optimization effect. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the prior art and provide a multi-objective optimization method and device for a grid-connected microgrid.

[0006] To achieve the above object, the present invention adopts the following technical solution: a multi-objective optimization method for a grid-connected microgrid, comprising:

[0007] Establish a multi-objective microgrid optimization model for a photovoltaic and storage hybrid system;

[0008] The multi-objective problem of the multi-objective microgrid optimization model is transformed into a single-objective problem using the linear weighted summation method.

[0009] Solving the single objective problem to obtain an optimal operation plan;

[0010] A microgrid grid-connected model of the photovoltaic storage composite system is established based on MATLAB real-time simulation tools;

[0011] The optimal operation plan is verified according to the microgrid grid-connected model.

[0012] The further technical solution is: the multi-objective microgrid optimization model of the photovoltaic storage composite system is established, including:

[0013] Obtain the real-time electricity purchase cost, photovoltaic power generation income, energy storage peak-valley price difference income, microgrid total carbon emissions and average discharge times of the energy storage system in each period within the optimization time to obtain initial data;

[0014] Establishing a multi-objective microgrid optimization model for the photovoltaic and storage composite system based on the initial data;

[0015] The multi-objective microgrid optimization model of the photovoltaic storage composite system is a microgrid optimization model used to optimize microgrid costs, carbon emissions and average discharge times.

[0016] Its further technical solution is: the multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the microgrid cost when optimizing the microgrid cost. Among them, F1 is the total profit of the microgrid within the optimization time; T is the optimization time; P b (t) is the average power consumption of the microgrid during period t; P pv (t) is the average power of photovoltaic power supply during period t; P in (t) is the average charging power of the energy storage system during period t, P out (t) is the average discharge power of the energy storage system; C b (t) is the electricity cost coefficient of the microgrid during period t; w pv (t) is the photovoltaic operation cost coefficient during period t; w bt1 (t) is the operating cost coefficient of energy storage charging during period t; w bt2 (t) is the operating cost coefficient when the energy storage is discharged during period t; Δt is the duration of period t.

[0017] The further technical solution is: the constraint condition for maximizing the profit of the multi-objective microgrid optimization model of the photovoltaic storage composite system is: load (t)+P in (t) = P out (t)+P pv (t)+P grid (t); P b,MIN ≤P b (t)≤P b,MAX ;P pv,MIN ≤P pv (t)≤P pv,MAX ;P grid,MIN ≤P grid (t)≤P grid,MAX ; 0≤P in (t)≤P in,MAX ; 0≤P out (t)≤P out,MAX ;SOC min ≤SOC(t)≤SOCmax ; Among them, P b,MIN is the minimum average power sold by the microgrid, P b,MAX is the maximum average power sold by the microgrid, P pv,MIN The minimum average power for photovoltaic power supply, P pv,MAX The maximum average power for photovoltaic power supply, P grid is the power output of the power grid during period t, P grid,MIN is the minimum output of the power grid, P grid,MAX is the maximum output of the power grid, P in,MAX is the maximum average charging power of the energy storage system, P out,MAX is the maximum average discharge power of the energy storage system, SOC(t) is the state of charge of the energy storage system, SOC min SOC is the minimum state of charge of the energy storage system. max is the maximum state of charge of the energy storage system, SOC(t-1) is the state of charge at the previous moment of SOC(t), and I in (t) is the average charging current of the energy storage system during the time period t, I out is the average discharge current of the energy storage system during time period t, C bt is the maximum capacity of the energy storage system.

[0018] Its further technical solution is: the multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the total carbon emissions when optimizing carbon emissions.

[0019] Among them, F2 is the total carbon emissions of the microgrid within the optimization time; T is the optimization time; α pv (t) is the carbon emission coefficient of photovoltaics; α out (t) is the carbon emission coefficient of energy storage during discharge; α grid (t) is the carbon emission coefficient of the power grid; α loat (t) is the user’s carbon emission coefficient; P grid (t) is the output of the power grid; P loat (t) is the power of the user-side load;

[0020] The constraint condition for the minimum total carbon emissions is P pv,MIN ≤P pv (t)≤P pv,MAX ; 0≤P in (t)≤P in,MAX ; 0≤P out (t)≤P out,MAX ;P grid,MIN ≤P b (t)≤P grid,MAX ; 0≤Pload (t)≤P load,MAX ;P load,MAX is the maximum load power on the user side.

[0021] The further technical solution is: the multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the average discharge times when optimizing the average discharge times. P out is the average power of the energy storage system during period t, W is the energy capacity of the energy storage system, and F3 is the average number of discharges of the energy storage system during the optimization period; the constraint condition for the minimum average number of discharges is: 0≤P out (t)≤P out,MAX .

[0022] The further technical solution is: the multi-objective problem of the multi-objective microgrid optimization model is converted into a single-objective problem by using the linear weighted summation method, including:

[0023] The multi-objective problem of the multi-objective microgrid optimization model is transformed into a single-objective problem using minF=αF1+βF2+ωF3, where α+β+ω=1, and α, β, and ω are weight coefficients.

[0024] The present invention also provides a multi-objective optimization device for a grid-connected microgrid, comprising:

[0025] An optimization model building unit is used to build a multi-objective microgrid optimization model for a photovoltaic and energy storage composite system;

[0026] A conversion unit, used for converting a multi-objective problem of a multi-objective microgrid optimization model into a single-objective problem by using a linear weighted summation method;

[0027] A solving unit, used for solving the single objective problem to obtain an optimal operation plan;

[0028] A grid-connected model building unit, used to build a microgrid grid-connected model of the photovoltaic storage composite system based on a real-time simulation tool of MATLAB;

[0029] A verification unit is used to verify the optimal operation plan according to the microgrid grid-connected model.

[0030] The beneficial effects of the present invention compared with the prior art are as follows: the present invention establishes a multi-objective microgrid optimization model for a photovoltaic storage composite system. The target microgrid optimization model can achieve minimum operating cost, minimum total carbon emissions, and minimum average discharge times of energy storage, and fully considers a series of constraints such as the constraints of the energy storage system, the power balance constraints of the microgrid, and the power constraints of photovoltaics and power grids. In addition, the impact of carbon emissions on the environment is also considered to improve the optimization effect.

[0031] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0033] Figure 1 A schematic diagram of an application scenario of a multi-objective optimization method for a grid-connected microgrid provided by an embodiment of the present invention;

[0034] Figure 2 A schematic diagram of a flow chart of a multi-objective optimization method for a grid-connected microgrid provided by an embodiment of the present invention;

[0035] Figure 3 A schematic diagram of a time-of-use electricity price table provided in an embodiment of the present invention;

[0036] Figure 4 A photovoltaic energy storage load power prediction diagram provided by an embodiment of the present invention;

[0037] Figure 5 The simulink simulation model diagram of MATLAB provided by the embodiment of the present invention;

[0038] Figure 6 A diagram showing the simulation results of the DC bus voltage of a microgrid provided in an embodiment of the present invention;

[0039] Figure 7 A schematic block diagram of a multi-objective optimization device for a grid-connected microgrid provided by an embodiment of the present invention;

[0040] Figure 8 A schematic block diagram of an optimization model building unit of a grid-connected microgrid multi-objective optimization device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] 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 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.

[0042] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0043] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0044] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0045] See also Figure 1 and Figure 2 , Figure 1 A schematic diagram of an application scenario of the multi-objective optimization method for a grid-connected microgrid provided in an embodiment of the present invention. Figure 2 A schematic flow chart of a multi-objective optimization method for a grid-connected microgrid provided in an embodiment of the present invention. The multi-objective optimization method for a grid-connected microgrid is applied in a server. The server 20 exchanges data with a terminal, establishes a multi-objective microgrid optimization model for a photovoltaic and storage composite system, sets the maximum profit of the microgrid, i.e., the minimum cost, the minimum total carbon emissions of the microgrid, and the minimum average charge and discharge times of the energy storage battery as constraints, solves the parameters of the optimization model, and converts the multi-objective problem into a single-objective problem, further solves the optimal operation plan, and establishes a microgrid grid-connected model of the photovoltaic and storage composite system based on the real-time simulation tool of MATLAB, verifies the optimal operation plan, and presents the optimal operation plan on the terminal.

[0046] In this embodiment, the energy storage system used is a lithium-ion battery, that is, the photovoltaic storage composite system is a lithium-ion battery, and the lithium-ion battery has the advantages of high voltage, large specific energy, long cycle life, and good safety performance.

[0047] Figure 2 Schematic diagram of the multi-objective optimization method for a grid-connected microgrid provided by an embodiment of the present invention. Figure 2 As shown, the method includes the following steps S110 to S150.

[0048] S110. Establish a multi-objective microgrid optimization model for a photovoltaic and energy storage hybrid system.

[0049] In this embodiment, the multi-objective microgrid optimization model refers to an optimization model for optimizing the microgrid cost, the total carbon emissions of the microgrid, and the average discharge times of the energy storage system. Specifically, the multi-objective microgrid optimization model of the photovoltaic storage composite system is a microgrid optimization model for optimizing the microgrid cost, carbon emissions, and the average discharge times.

[0050] In one embodiment, the above-mentioned step S110 may include steps S111 - S112 .

[0051] S111. Obtain the real-time electricity purchase cost, photovoltaic power generation income, energy storage peak-valley price difference income, microgrid total carbon emissions and average discharge times of the energy storage system in each period within the optimization time to obtain initial data.

[0052] In this embodiment, the initial data refers to the real-time electricity purchase cost, photovoltaic power generation income, energy storage peak-valley price difference income, etc., the total carbon emissions of the microgrid, and the average discharge times of the energy storage system in each period within the optimization time.

[0053] S112, establishing a multi-objective microgrid optimization model for the photovoltaic and energy storage composite system according to the initial data;

[0054] In this embodiment, a photovoltaic-storage composite microgrid grid-connected model is established with the objective functions of maximizing the profit of the photovoltaic-storage coordinated microgrid, minimizing the total carbon emissions of the microgrid, and minimizing the number of charge and discharge times of the energy storage battery; and the decision variables and constraints of the model are determined.

[0055] Specifically, the multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the microgrid cost when optimizing the microgrid cost. Among them, F1 is the total profit of the microgrid within the optimization time; T is the optimization time; P b (t) is the average power consumption of the microgrid during period t; P pv (t) is the average power of photovoltaic power supply during period t; P in (t) is the average charging power of the energy storage system during period t, P out (t) is the average discharge power of the energy storage system; C b (t) is the electricity cost coefficient of the microgrid during period t; w pv (t) is the photovoltaic operation cost coefficient during period t; w bt1 (t) is the operating cost coefficient of energy storage charging during period t; w bt2 (t) is the operating cost coefficient when the energy storage is discharged during period t; Δt is the duration of period t.

[0056] Microgrids have the lowest cost and the highest profit.

[0057] The constraint condition for maximizing profit of the multi-objective microgrid optimization model of the photovoltaic storage system is: load (t)+P in (t) = P out (t)+P pv (t)+P grid (t); P b,MIN ≤P b (t)≤P b,MAX ;P pv,MIN ≤P pv (t)≤P pv,MAX ;P grid,MIN ≤P grid (t)≤P grid,MAX ; 0≤P in (t)≤P in,MAX ; 0≤P out (t)≤P out,MAX ;SOC min ≤SOC(t)≤SOC max ; Among them, P b,MIN is the minimum average power sold by the microgrid, P b,MAX is the maximum average power sold by the microgrid, P pv,MIN The minimum average power for photovoltaic power supply, P pv,MAX The maximum average power for photovoltaic power supply, P grid is the power output of the power grid during period t, P grid,MIN is the minimum output of the power grid, P grid,MAX is the maximum output of the power grid, P in,MAX is the maximum average charging power of the energy storage system, P out,MAX is the maximum average discharge power of the energy storage system, SOC(t) is the state of charge of the energy storage system, SOC min SOC is the minimum state of charge of the energy storage system. max is the maximum state of charge of the energy storage system, SOC(t-1) is the state of charge at the previous moment of SOC(t), and I in (t) is the average charging current of the energy storage system during the time period t, I out is the average discharge current of the energy storage system during time period t, C bt is the maximum capacity of the energy storage system.

[0058] The multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the total carbon emissions when optimizing carbon emissions. Among them, F2 is the total carbon emissions of the microgrid within the optimization time; T is the optimization time; α pv (t) is the carbon emission coefficient of photovoltaics; α out(t) is the carbon emission coefficient of energy storage during discharge; α grid (t) is the carbon emission coefficient of the power grid; α loat (t) is the user’s carbon emission coefficient; P grid (t) is the output of the power grid; P loat (t) is the power of the user-side load;

[0059] The constraint condition for the minimum total carbon emissions is P pv,MIN ≤P pv (t)≤P pv,MAX ; 0≤P in (t)≤P in,MAX ; 0≤P out (t)≤P out,MAX ;P grid,MIN ≤P b (t)≤P grid,MAX ; 0≤P load (t)≤P load,MAX ;P load,MAX is the maximum load power on the user side.

[0060] The multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the average discharge times when optimizing the average discharge times. The model formula is: P out is the average power of the energy storage system during period t, W is the energy capacity of the energy storage system, and F3 is the average number of discharges of the energy storage system during the optimization period; the constraint condition for the minimum average number of discharges is: 0≤P out (t)≤P out,MAX .

[0061] S120. Using a linear weighted summation method, the multi-objective problem of the multi-objective microgrid optimization model is converted into a single-objective problem.

[0062] In this embodiment, the single-objective problem refers to a problem formed by weighing the multi-objective problem of the multi-objective microgrid optimization model.

[0063] Specifically, minF=αF1+βF2+ωF3 is used to transform the multi-objective problem of the multi-objective microgrid optimization model into a single-objective problem, where α+β+ω=1, and α, β, and ω are weight coefficients.

[0064] S130: Solve the single objective problem to obtain an optimal operation plan.

[0065] In this embodiment, the optimal operation plan refers to a plan formed by comprehensively considering the constraints of the multi-objective problem to optimally solve the linear single-objective problem.

[0066] S140. Establish a microgrid grid-connected model of the photovoltaic and energy storage composite system based on MATLAB real-time simulation tools.

[0067] In this embodiment, the microgrid grid-connected model of the photovoltaic and storage composite system refers to a photovoltaic and storage coordinated microgrid grid-connected model established based on MATLAB's real-time simulation tool Simulink, which corresponds one-to-one to the optimization model.

[0068] S150: Verify the optimal operation plan according to the microgrid grid-connected model.

[0069] The optimal operation plan is run on the microgrid grid-connected model to ensure the operation effect of the optimal operation plan.

[0070] For example: a microgrid includes a photovoltaic cell with a capacity of 70 kW, an energy storage system with a capacity of 40 kW, and a random simulated capacity uncertain load; see Figure 3 and Figure 4 ,Depend on Figure 3 Time-of-use electricity price list and Figure 4 Photovoltaic energy storage load power prediction diagram and corresponding It is calculated that: minF1 = 3002.87. In this embodiment, the carbon emission coefficient is shown in Table 1.

[0071] Table 1. Carbon emission coefficient table

[0072]

[0073]

[0074] From Table 1 and Figure 4 The photovoltaic energy storage load power prediction diagram and the above formula show that minF2 = 775.84. Figure 4 as well as It turns out that minF3=8.6.

[0075] The multi-objective problem is transformed into a single-objective problem using the linear weighted summation method. The optimization model after transformation into a single-objective problem is shown in Table 2.

[0076] Table 2. Objective function solution table

[0077]

[0078] minF=αF1+βF2+ωF3, α+β+ω=1, α, β, and ω are weight coefficients; α, β, and ω are 0.6, 0.3, and 0.1 respectively.

[0079] In this embodiment, a photovoltaic-storage composite microgrid grid-connected model is established with the objective functions of maximizing the profit of the photovoltaic-storage coordinated microgrid, minimizing the total carbon emissions of the microgrid, and minimizing the number of charge and discharge times of the energy storage battery; the decision variables and constraints of the model are determined; the multi-objective problem is converted into a single-objective problem according to the linear weighted summation method; the single-objective optimization problem is solved to obtain the optimal solution; based on the optimal solution, the optimal operation plan of the system is finally determined; Figure 5 and Figure 6 As shown, the photovoltaic storage cooperative microgrid grid-connected model is established based on MATLAB's real-time simulation tool simulink; the model can also be used to further study the impact of the microgrid on the power quality of the large power grid under the optimal operation plan, such as frequency regulation and voltage regulation. It not only has the microgrid operation status that considers the impact of the photovoltaic storage composite system, but also improves the profit of the microgrid system, reduces the total carbon emissions of the microgrid, and reduces the number of charge and discharge times of the energy storage system. Moreover, the photovoltaic storage cooperative microgrid grid-connected model is established based on MATLAB's real-time simulation tool simulink, which can further study the impact of the microgrid on the power quality of the large power grid. The method of this embodiment takes into account the specific impact of the energy storage system on the grid-connected microgrid; takes into account the impact of the uncertainty of renewable energy sources such as photovoltaics; not only takes into account the minimization of the microgrid operating cost under multiple constraints, but also takes into account the impact on the environment. The present invention minimizes the number of discharges of the energy storage system as much as possible to extend the life of the energy storage system, thereby saving materials for manufacturing the energy storage system and achieving the purpose of protecting the environment; taking into account the minimization of the carbon emissions of the entire system under the premise of increasing the profit of the microgrid as much as possible, it not only takes into account environmental protection, but also responds to the "dual carbon" strategy.

[0080] Based on MATLAB's real-time simulation tool Simulink, a grid-connected model of the photovoltaic-storage collaborative microgrid was established, and the impact of the photovoltaic-storage collaborative microgrid on the power quality of the grid can be further studied under the optimal goal.

[0081] The above-mentioned multi-objective optimization method for the grid-connected microgrid establishes a multi-objective microgrid optimization model for the photovoltaic storage composite system. This target microgrid optimization model can achieve the minimum operating cost, the minimum total carbon emissions, and the minimum average discharge times of energy storage. It fully considers a series of constraints such as the constraints of the energy storage system, the power balance constraints of the microgrid, and the power constraints of photovoltaics and the power grid. In addition, it also considers the impact of carbon emissions on the environment to improve the optimization effect.

[0082] Figure 7 is a schematic block diagram of a multi-objective optimization device 300 for a grid-connected microgrid provided in an embodiment of the present invention. Figure 7As shown, corresponding to the above-mentioned multi-objective optimization method for a grid-connected microgrid, the present invention further provides a multi-objective optimization device 300 for a grid-connected microgrid. The multi-objective optimization device 300 for a grid-connected microgrid includes a unit for executing the above-mentioned multi-objective optimization method for a grid-connected microgrid, and the device can be configured in a server. Specifically, please refer to Figure 7 The grid-connected microgrid multi-objective optimization device 300 includes an optimization model building unit 301, a conversion unit 302, a solution unit 303, a grid-connected model building unit 304 and a verification unit 305.

[0083] The optimization model establishment unit 301 is used to establish a multi-objective microgrid optimization model of the photovoltaic and storage hybrid system; the conversion unit 302 is used to transform the multi-objective problem of the multi-objective microgrid optimization model into a single-objective problem by using a linear weighted summation method; the solution unit 303 is used to solve the single-objective problem to obtain an optimal operation plan; the grid-connected model establishment unit 304 is used to establish a microgrid grid-connected model of the photovoltaic and storage hybrid system based on MATLAB's real-time simulation tool; the verification unit 305 is used to verify the optimal operation plan according to the microgrid grid-connected model.

[0084] In one embodiment, if Figure 8 As shown, the optimization model building unit 301 includes a data acquisition subunit 3011 and a model building subunit 3012 .

[0085] The data acquisition subunit 3011 is used to obtain the real-time electricity purchase cost, photovoltaic power generation income, energy storage peak-valley price difference income, total carbon emissions of the microgrid and the average discharge times of the energy storage system in each period within the optimization time to obtain initial data; the model establishment subunit 3012 is used to establish a multi-objective microgrid optimization model of the photovoltaic storage composite system based on the initial data.

[0086] In one embodiment, the conversion unit 302 is used to convert the multi-objective problem of the multi-objective microgrid optimization model into a single-objective problem by using minF=αF1+βF2+ωF3, wherein α+β+ω=1, and α, β, ω are weight coefficients.

[0087] It should be noted that technicians in the relevant field can clearly understand that the specific implementation process of the above-mentioned grid-connected microgrid multi-objective optimization device 300 and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and conciseness of the description, it will not be repeated here.

[0088] The above-mentioned grid-connected microgrid multi-objective optimization device 300 can be implemented in the form of a computer program, and the computer program can be run on a computer device. The computer device can be a server, and the server can be an independent server or a server cluster composed of multiple servers.

[0089] The computer device includes a processor, a memory and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0090] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute a multi-objective optimization method for a grid-connected microgrid.

[0091] The processor is used to provide computing and control capabilities to support the operation of the entire computer equipment.

[0092] The internal memory provides an environment for the operation of a computer program in a non-volatile storage medium, and when the computer program is executed by a processor, the processor can execute a multi-objective optimization method for a grid-connected microgrid. The network interface is used for network communication with other devices.

[0093] The processor is used to run a computer program stored in the memory to implement the following steps:

[0094] A multi-objective microgrid optimization model for a photovoltaic-storage hybrid system is established; the multi-objective problem of the multi-objective microgrid optimization model is converted into a single-objective problem using a linear weighted sum method; the single-objective problem is solved to obtain an optimal operation plan; a microgrid grid-connected model of the photovoltaic-storage hybrid system is established based on a real-time simulation tool such as MATLAB; and the optimal operation plan is verified based on the microgrid grid-connected model.

[0095] In one embodiment, when the processor implements the step of establishing a multi-objective microgrid optimization model for a photovoltaic and energy storage hybrid system, the processor specifically implements the following steps:

[0096] Obtain the real-time electricity purchase cost, photovoltaic power generation income, energy storage peak-valley price difference income, microgrid total carbon emissions and average discharge times of the energy storage system in each period within the optimization time to obtain initial data; establish a multi-objective microgrid optimization model of the photovoltaic storage composite system based on the initial data;

[0097] The multi-objective microgrid optimization model of the photovoltaic storage composite system is a microgrid optimization model used to optimize microgrid costs, carbon emissions and average discharge times.

[0098] The multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the microgrid cost when optimizing the microgrid cost. Among them, F1 is the total profit of the microgrid within the optimization time; T is the optimization time; P b (t) is the average power consumption of the microgrid during period t; P pv(t) is the average power of photovoltaic power supply during period t; P in (t) is the average charging power of the energy storage system during period t, P out (t) is the average discharge power of the energy storage system; C b (t) is the electricity cost coefficient of the microgrid during period t; w pv (t) is the photovoltaic operation cost coefficient during period t; w bt1 (t) is the operating cost coefficient of energy storage charging during period t; w bt2 (t) is the operating cost coefficient when the energy storage is discharged during period t; Δt is the duration of period t.

[0099] The constraint condition for maximizing profit of the multi-objective microgrid optimization model of the photovoltaic storage system is: load (t)+P in (t) = P out (t)+P pv (t)+P grid (t); P b,MIN ≤P b (t)≤P b,MAX ;P pv,MIN ≤P pv (t)≤P pv,MAX ;P grid,MIN ≤P grid (t)≤P grid,MAX ; 0≤P in (t)≤P in,MAX ; 0≤P out (t)≤P out,MAX ;SOC min ≤SOC(t)≤SOC max ; Among them, P b,MIN is the minimum average power sold by the microgrid, P b,MAX is the maximum average power sold by the microgrid, P pv,MIN The minimum average power for photovoltaic power supply, P pv,MAX The maximum average power for photovoltaic power supply, P grid is the power output of the power grid during period t, P grid,MIN is the minimum output of the power grid, P grid,MAX is the maximum output of the power grid, P in,MAX is the maximum average charging power of the energy storage system, P out,MAX is the maximum average discharge power of the energy storage system, SOC(t) is the state of charge of the energy storage system, SOC min SOC is the minimum state of charge of the energy storage system. max is the maximum state of charge of the energy storage system, SOC(t-1) is the state of charge at the previous moment of SOC(t), and I in(t) is the average charging current of the energy storage system during the time period t, I out is the average discharge current of the energy storage system during time period t, C bt is the maximum capacity of the energy storage system.

[0100] The multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the total carbon emissions when optimizing carbon emissions. Among them, F2 is the total carbon emissions of the microgrid within the optimization time; T is the optimization time; α pv (t) is the carbon emission coefficient of photovoltaics; α out (t) is the carbon emission coefficient of energy storage during discharge; α grid (t) is the carbon emission coefficient of the power grid; α loat (t) is the user’s carbon emission coefficient; P grid (t) is the output of the power grid; P loat (t) is the power of the user-side load;

[0101] The constraint condition for the minimum total carbon emissions is P pv,MIN ≤P pv (t)≤P pv,MAX ; 0≤P in (t)≤P in,MAX ; 0≤P out (t)≤P out,MAX ;P grid,MIN ≤P b (t)≤P grid,MAX ; 0≤P load (t)≤P load,MAX ;P load,MAX is the maximum load power on the user side.

[0102] The multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the average discharge times when optimizing the average discharge times. The model formula is: P out is the average power of the energy storage system during period t, W is the energy capacity of the energy storage system, and F3 is the average number of discharges of the energy storage system during the optimization period; the constraint condition for the minimum average number of discharges is: 0≤P out (t)≤P out,MAX .

[0103] In one embodiment, when the processor implements the step of converting the multi-objective problem of the multi-objective microgrid optimization model into a single-objective problem by using the linear weighted summation method, the processor specifically implements the following steps:

[0104] The multi-objective problem of the multi-objective microgrid optimization model is transformed into a single-objective problem using minF=αF1+βF2+ωF3, where α+β+ω=1, and α, β, and ω are weight coefficients.

[0105] It should be understood that in the embodiments of the present application, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0106] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0107] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps:

[0108] A multi-objective microgrid optimization model for a photovoltaic-storage hybrid system is established; the multi-objective problem of the multi-objective microgrid optimization model is converted into a single-objective problem using a linear weighted sum method; the single-objective problem is solved to obtain an optimal operation plan; a microgrid grid-connected model of the photovoltaic-storage hybrid system is established based on a real-time simulation tool such as MATLAB; and the optimal operation plan is verified based on the microgrid grid-connected model.

[0109] In one embodiment, when the processor executes the computer program to implement the step of establishing a multi-objective microgrid optimization model for a photovoltaic and energy storage hybrid system, the processor specifically implements the following steps:

[0110] Obtain the real-time electricity purchase cost, photovoltaic power generation income, energy storage peak-valley price difference income, microgrid total carbon emissions and average discharge times of the energy storage system in each period within the optimization time to obtain initial data; establish a multi-objective microgrid optimization model of the photovoltaic storage composite system based on the initial data;

[0111] The multi-objective microgrid optimization model of the photovoltaic storage composite system is a microgrid optimization model used to optimize microgrid costs, carbon emissions and average discharge times.

[0112] The multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the microgrid cost when optimizing the microgrid cost. Among them, F1 is the total profit of the microgrid within the optimization time; T is the optimization time; P b (t) is the average power consumption of the microgrid during period t; P pv (t) is the average power of photovoltaic power supply during period t; P in (t) is the average charging power of the energy storage system during period t, P out (t) is the average discharge power of the energy storage system; C b (t) is the electricity cost coefficient of the microgrid during period t; w pv (t) is the photovoltaic operation cost coefficient during period t; w bt1 (t) is the operating cost coefficient of energy storage charging during period t; w bt2 (t) is the operating cost coefficient when the energy storage is discharged during period t; Δt is the duration of period t.

[0113] The constraint condition for maximizing profit of the multi-objective microgrid optimization model of the photovoltaic storage system is: load (t)+P in (t) = P out (t)+P pv (t)+P grid (t); P b,MIN ≤P b (t)≤P b,MAX ;P pv,MIN ≤P pv (t)≤P pv,MAX ;P grid,MIN ≤P grid (t)≤P grid,MAX ; 0≤P in (t)≤P in,MAX ; 0≤P out (t)≤P out,MAX ;SOC min ≤SOC(t)≤SOC max ; Among them, P b,MIN is the minimum average power sold by the microgrid, P b,MAX is the maximum average power sold by the microgrid, P pv,MIN The minimum average power for photovoltaic power supply, P pv,MAXThe maximum average power for photovoltaic power supply, P grid is the power output of the power grid during period t, P grid,MIN is the minimum output of the power grid, P grid,MAX is the maximum output of the power grid, P in,MAX is the maximum average charging power of the energy storage system, P out,MAX is the maximum average discharge power of the energy storage system, SOC(t) is the state of charge of the energy storage system, SOC min SOC is the minimum state of charge of the energy storage system. max is the maximum state of charge of the energy storage system, SOC(t-1) is the state of charge at the previous moment of SOC(t), and I in (t) is the average charging current of the energy storage system during the time period t, I out is the average discharge current of the energy storage system during time period t, C bt is the maximum capacity of the energy storage system.

[0114] The multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the total carbon emissions when optimizing carbon emissions. Among them, F2 is the total carbon emissions of the microgrid within the optimization time; T is the optimization time; α pv (t) is the carbon emission coefficient of photovoltaics; α out (t) is the carbon emission coefficient of energy storage during discharge; α grid (t) is the carbon emission coefficient of the power grid; α loat (t) is the user’s carbon emission coefficient; P grid (t) is the output of the power grid; P loat (t) is the power of the user-side load;

[0115] The constraint condition for the minimum total carbon emissions is P pv,MIN ≤P pv (t)≤P pv,MAX ; 0≤P in (t)≤P in,MAX ; 0≤P out (t)≤P out,MAX ;P grid,MIN ≤P b (t)≤P grid,MAX ; 0≤P load (t)≤P load,MAX ;P load,MAX is the maximum load power on the user side.

[0116] The multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the average discharge times when optimizing the average discharge times. The model formula is: P outis the average power of the energy storage system during period t, W is the energy capacity of the energy storage system, and F3 is the average number of discharges of the energy storage system during the optimization period; the constraint condition for the minimum average number of discharges is: 0≤P out (t)≤P out,MAX .

[0117] In one embodiment, when the processor executes the computer program to implement the step of converting the multi-objective problem of the multi-objective microgrid optimization model into a single-objective problem by using a linear weighted summation method, the processor specifically implements the following steps:

[0118] The multi-objective problem of the multi-objective microgrid optimization model is transformed into a single-objective problem using minF=αF1+βF2+ωF3, where α+β+ω=1, and α, β, and ω are weight coefficients.

[0119] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0120] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0121] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit 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.

[0122] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0123] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0124] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A multi-objective optimization method for a grid-connected microgrid, characterized in that: include: Establish a multi-objective microgrid optimization model for a photovoltaic and storage hybrid system; The multi-objective problem of the multi-objective microgrid optimization model is transformed into a single-objective problem using the linear weighted summation method. Solving the single objective problem to obtain an optimal operation plan; A microgrid grid-connected model of the photovoltaic storage composite system is established based on MATLAB real-time simulation tools; Verifying the optimal operation scheme according to the microgrid grid-connected model; The multi-objective microgrid optimization model of the photovoltaic storage composite system is established, including: Obtain the real-time electricity purchase cost, photovoltaic power generation income, energy storage peak-valley price difference income, microgrid total carbon emissions and average discharge times of the energy storage system in each period within the optimization time to obtain initial data; Establishing a multi-objective microgrid optimization model for the photovoltaic and storage composite system based on the initial data; The multi-objective microgrid optimization model of the photovoltaic storage composite system is a microgrid optimization model for optimizing microgrid costs, carbon emissions and average discharge times; The multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the microgrid cost when optimizing the microgrid cost. Among them, F1 is the total profit of the microgrid within the optimization time; T is the optimization time; P b (t) is the average power consumption of the microgrid during period t; P pv (t) is the average power of photovoltaic power supply during period t; P in (t) is the average charging power of the energy storage system during period t, P out (t) is the average discharge power of the energy storage system; C b (t) is the electricity cost coefficient of the microgrid during period t; w pv (t) is the photovoltaic operation cost coefficient during period t; w bt1 (t) is the operating cost coefficient of energy storage charging during period t; w bt2 (t) is the operating cost coefficient when the energy storage is discharged during period t; Δt is the duration of period t; The multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the total carbon emissions when optimizing carbon emissions. Among them, F2 is the total carbon emissions of the microgrid within the optimization time; T is the optimization time; α pv (t) is the carbon emission coefficient of photovoltaics; α out (t) is the carbon emission coefficient of energy storage during discharge; α grid (t) is the carbon emission coefficient of the power grid; α loat (t) is the user’s carbon emission coefficient; P grid (t) is the output of the power grid; P loat (t) is the power of the user-side load; The constraint condition for the minimum total carbon emissions is P pv,MIN ≤P pv (t)≤P pv,MAX ; 0≤P in (t)≤P in,MAX ; 0≤P out (t)≤P out,MAX ;P grid,MIN ≤P b (t)≤P grid,MAX ; 0≤P load (t)≤P load,MAX ;P load,MAX is the maximum load power on the user side; The multi-objective microgrid optimization model of the photovoltaic storage composite system minimizes the average discharge times when optimizing the average discharge times. The model formula is: P out is the average power of the energy storage system during period t, W is the energy capacity of the energy storage system, and F3 is the average number of discharges of the energy storage system during the optimization period; the constraint condition for the minimum average number of discharges is: 0≤P out (t)≤P out,MAX ; The linear weighted summation method is used to transform the multi-objective problem of the multi-objective microgrid optimization model into a single-objective problem, including: The multi-objective problem of the multi-objective microgrid optimization model is transformed into a single-objective problem using minF=αF1+βF2+ωF3, where α+β+ω=1, and α, β, and ω are weight coefficients.

2. The multi-objective optimization method for a grid-connected microgrid according to claim 1, characterized in that: The constraint condition for maximizing profit of the multi-objective microgrid optimization model of the photovoltaic storage system is: load (t)+P in (t) = P out (t)+P pv (t)+P grid (t); P b,MIN ≤P b (t)≤P b,MAX ;P pv,MIN ≤P pv (t)≤P pv,MAX ;P grid,MIN ≤P grid (t)≤P grid,MAX ; 0≤P in (t)≤P in,MAX ; 0≤P out (t)≤P out,MAX ;SOC min ≤SOC(t)≤SOC max ; Among them, P b,MIN is the minimum average power sold by the microgrid, P b,MAX is the maximum average power sold by the microgrid, P pv,MIN The minimum average power for photovoltaic power supply, P pv,MAX The maximum average power for photovoltaic power supply, P grid is the power output of the power grid during period t, P grid,MIN is the minimum output of the power grid, P grid,MAX is the maximum output of the power grid, P in,MAX is the maximum average charging power of the energy storage system, P out,MAX is the maximum average discharge power of the energy storage system, SOC(t) is the state of charge of the energy storage system, SOC min SOC is the minimum state of charge of the energy storage system. max is the maximum state of charge of the energy storage system, SOC(t-1) is the state of charge at the previous moment of SOC(t), and I in (t) is the average charging current of the energy storage system during the time period t, I out is the average discharge current of the energy storage system during time period t, C bt is the maximum capacity of the energy storage system.

3. A multi-objective optimization device for a grid-connected microgrid, using the multi-objective optimization method for a grid-connected microgrid as claimed in claim 1, characterized in that: include: An optimization model building unit is used to build a multi-objective microgrid optimization model for a photovoltaic and energy storage composite system; A conversion unit, used for converting a multi-objective problem of a multi-objective microgrid optimization model into a single-objective problem by using a linear weighted summation method; A solving unit, used for solving the single objective problem to obtain an optimal operation plan; A grid-connected model building unit, used to build a microgrid grid-connected model of the photovoltaic storage composite system based on a real-time simulation tool of MATLAB; A verification unit is used to verify the optimal operation plan according to the microgrid grid-connected model.

Images