Microgrid system planning and design method and device based on double-layer optimization

Abstract

The invention discloses a micro-grid system planning and design method and device based on double-layer optimization, and relates to the field of micro-grid system planning and design, and the method comprises the following steps: S1, collecting the operation data of wind power, photovoltaic, hydropower, an energy storage power station, an electrolytic cell and a hydrogen storage tank in a micro-grid system, and the load data of the micro-grid system; s2, constructing a micro-grid system double-layer optimization model based on a double-layer optimization method; the upper-layer model is a capacity optimization layer, and the maximum total income within the operating age limit is used as a target function; the lower-layer model is a scheduling optimization layer, the maximum operation net income in a scheduling period is taken as an objective function, and system power balance, equipment operation and capacity are taken as constraint conditions; and S3, solving the double-layer optimization model by using an improved grey wolf algorithm and a solver. The microgrid system is planned and designed through a double-layer optimization method, a clean energy power generation and energy storage device capacity configuration scheme is reasonably configured, and an energy scheduling strategy is optimized.

Description

Technical Field

[0001] The present invention relates to the field of microgrid system planning and design, and in particular to a microgrid system planning and design method and device based on double-layer optimization. Background Art

[0002] As the global demand for renewable energy continues to grow, distributed energy technology has rapidly developed into an important trend in the current power sector. Microgrids, as a solution that effectively integrates distributed energy, can achieve localized energy production and consumption, thereby effectively reducing losses during power transmission and significantly enhancing the resilience and reliability of the power system.

[0003] At present, the research of domestic and foreign scholars on microgrid systems mainly focuses on capacity optimization configuration or optimization scheduling mode, and directly adopts traditional intelligent bionic algorithms. However, in the planning and design process of microgrid systems, single-level optimization methods are often difficult to fully meet the diverse needs and complexity of the system. At the same time, traditional intelligent bionic algorithms have shortcomings such as poor population diversity and easy to fall into local optimality, which makes it impossible to obtain the optimal solution. Therefore, it is necessary to propose a microgrid system planning and design method and device based on double-layer optimization.

[0004] Through this microgrid system planning and design method and device based on double-layer optimization, the microgrid system can formulate the optimal capacity configuration and operation strategy in the face of uncertainty and complexity, thereby maximizing economic benefits and improving the stability and reliability of the system. Summary of the invention

[0005] The present invention proposes a microgrid system planning and design method and device based on double-layer optimization to solve the problems raised in the above background technology.

[0006] The purpose of the present invention can be achieved by the following technical solution: A microgrid system planning and design method based on double-layer optimization, specifically comprising:

[0007] S1. Collect the operation data of wind power, photovoltaic power, hydropower, energy storage power station, electrolyzer, hydrogen storage tank in the microgrid system and the load data of the microgrid system;

[0008] S2. Construct a two-layer optimization model for microgrid system based on the two-layer optimization method;

[0009] S3. Solve the two-level optimization model using the improved grey wolf algorithm and solver.

[0010] Furthermore, the step S2 of constructing a two-layer optimization model of a microgrid system based on a two-layer optimization method specifically includes:

[0011] S21. Constructing upper layer capacity optimization model;

[0012] S22. Constructing a lower-level scheduling optimization model;

[0013] Furthermore, the construction of the upper layer capacity optimization model in step S21 specifically includes:

[0014] S211: Objective function:

[0015]

[0016] Where: is the total revenue during the operating life, is the net operating income during the system dispatch period, C in is the total investment cost of each equipment;

[0017]

[0018] Where: P wN , P pvN , P hyN , P bsN , P eleN , Q gN are the installed capacities of wind turbines, photovoltaic units, hydroelectric generators, energy storage power stations, electrolyzers, and hydrogen storage tanks, respectively. w , C pv , C hy , C bs , C ele , C g They are the unit investment costs of wind turbines, photovoltaic units, hydroelectric units, energy storage power stations, electrolyzers, and hydrogen storage tanks;

[0019] S212: Constraints:

[0020]

[0021] Where: P , P , P , P , P , Q are the lower limits of installed capacity of wind turbines, photovoltaic units, hydroelectric generators, energy storage power stations, electrolyzers, and hydrogen storage tanks, respectively. , P , P , P , P , Q They are the upper limits of installed capacity for wind turbines, photovoltaic units, hydroelectric units, energy storage power stations, electrolyzers, and hydrogen storage tanks;

[0022] Furthermore, the construction of the lower-layer scheduling optimization model in step S22 specifically includes:

[0023] S221: Objective function:

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] Where: F np is the profit during the scheduling period, F sale is the revenue from electricity and hydrogen sales, F env For environmental benefits, F ope is the operation and maintenance cost, F curt F is the penalty cost for wind and solar curtailment. buy is the electricity purchase cost; T is the dispatch period, σ w , σ pv , σ hy , σ bs are the environmental benefit coefficients of wind power, photovoltaic power, hydropower and energy storage, C sale , C g , C buy are the unit prices of electricity sales, hydrogen sales, and electricity purchase, respectively. w (t), P pv (t), P hy (t), P bs_ch (t), P sale (t), Q g_out (t), P buy (t) are wind power operation power, photovoltaic operation power, hydropower operation power, energy storage charging power, electricity sales power, hydrogen sales power, and electricity purchase power at time t, N is the number of various types of equipment, R is the unit operation and maintenance cost of device i, P i (t) is the operating power of device i at time t, ρ w , pv are the penalty coefficients for wind power abandonment and solar power abandonment, Pw curt(t) and Ppv curt(t) are the wind power abandonment and solar power abandonment at time t, respectively;

[0031] S222: Constraints:

[0032] Power balance constraints:

[0033]

[0034] Wind power, photovoltaic and hydropower operation constraints:

[0035]

[0036] Where: U w (t), U pv (t), U hy (t) are wind power, photovoltaic and hydropower operation state variables, and U w (t), U pv (t), U hy (t)∈{0,1};

[0037] Energy storage power station operation and capacity constraints:

[0038]

[0039] Where: U bs_ch (t), U bs_dis (t) is the working state variable of the energy storage power station at time t, and U bs_ch (t), U bs_dis (t)∈{0,1}, Pmax bs_ch(t), Pmax bs_dis(t) are the maximum charging power and maximum discharging power of energy storage at time t, S(t), S(t-1) are the capacity of energy storage equipment at time t and time t-1, η ch , η dis are the energy storage charging and discharging efficiency coefficients respectively;

[0040] Electrolyzer and hydrogen storage tank operation and capacity constraints:

[0041]

[0042] Where: P e (t) is the electrolytic cell power at time t, PN e is the rated power of the electrolytic cell, Q g_in (t), Q g_out (t) is the amount of hydrogen stored and released at time t, U g_in (t), U g_out (t) is the working state variable of the hydrogen storage tank at time t, and U g_in (t), U g_out (t)∈{0,1}, Qmax g_in(t) and Qmax g_out(t) are the maximum allowable hydrogen storage capacity and maximum allowable hydrogen discharge capacity of the hydrogen storage tank at time t, respectively. g (t), Q g (t-1) is the capacity of the hydrogen storage tank at time t and time t-1, η eis the electrolysis efficiency coefficient.

[0043] Furthermore, the specific steps of solving the two-layer optimization model using the improved grey wolf algorithm and solver described in step S3 are as follows:

[0044] S31: input the collected operation data of wind power, photovoltaic power, hydropower, energy storage power station, electrolyzer, hydrogen storage tank and load data of microgrid system into the double-layer optimization model;

[0045] S32: Use the improved grey wolf algorithm to solve the upper configuration optimization model; initialize the grey wolf population, and bring the configuration capacities of wind power, photovoltaic power, hydropower, energy storage power stations, electrolyzers, and hydrogen storage tanks randomly generated in the upper layer into the lower scheduling optimization model as constraints for the lower scheduling optimization;

[0046] S33: The lower scheduling optimization layer uses a solver to solve the output strategies of wind power, photovoltaic power, hydropower, energy storage power stations, electrolyzers, and hydrogen storage tanks on a typical day based on the upper configuration plan, and feeds the results back to the upper layer;

[0047] S34: The upper configuration optimization layer updates the convergence factor and the gray wolf population of the configuration optimization layer based on the feedback data of the scheduling optimization layer, and updates the configuration plan;

[0048] S35: Determine whether the maximum number of iterations has been reached. If so, output the configuration plan and the output strategies of wind power, photovoltaic power, hydropower, energy storage power station, electrolyzer, and hydrogen storage tank. Otherwise, continue with steps S32-34 until the maximum number of iterations has been reached.

[0049] Furthermore, the improved grey wolf algorithm described in step S32 specifically includes:

[0050] S321. Introduce a convergence factor based on the improved cosine function to make it nonlinear and decrease, thereby increasing the initial exploration capability of the algorithm. The details are as follows:

[0051]

[0052] Where: a is the attenuation factor, t is the current number of iterations, and T is the maximum number of iterations;

[0053] S322. Introduce random dispersion strategy and proportional weight based on step length Euclidean distance, introduce random individuals and improve search mechanism to enhance population diversity and increase individual exploration ability, as follows:

[0054]

[0055]

[0056]

[0057] Where: X1, X2, X3 represent the position changes caused by α, β and δ respectively, W1, W2, W3 represent the learning rates of ω for α, β and δ respectively, λ1, λ2 are weight factors, θ1, θ2 are random numbers, and θ∈{0,1}, X a , X b are two individuals randomly selected from the gray wolf population, and a≠b≠i.

[0058] Furthermore, a microgrid system planning and design device based on double-layer optimization includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0059] Compared with the prior art, the advantages of the present invention are:

[0060] (1) The present invention plans and designs the microgrid system through a two-layer optimization method, so that the microgrid system can formulate the optimal capacity configuration and operation strategy in the face of uncertainty and complexity, thereby maximizing economic benefits and improving the stability and reliability of the microgrid system.

[0061] (2) The present invention enhances the diversity of the gray wolf population, increases the individual exploration ability, improves the convergence speed, and solves the problem of premature convergence caused by falling into local optimality by improving the gray wolf algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 This is the overall flow chart of this example;

[0064] Figure 2 This is the structure diagram of the microgrid system in this example;

[0065] Figure 3 Solve the flow chart for this example;

[0066] Figure 4 This is the flowchart for solving the two-layer optimization model based on the improved grey wolf algorithm in this example. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0068] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0069] See also Figures 1 to 4 As shown, a microgrid system planning and design method based on double-layer optimization specifically includes:

[0070] S1. Collect the operation data and load data of wind power, photovoltaic power, hydropower, energy storage power station, electrolyzer, and hydrogen storage tank in the microgrid system.

[0071] S2. Construct a two-layer optimization model for microgrid system based on the two-layer optimization method;

[0072] Construct an upper-layer capacity optimization model, including:

[0073] (1) Objective function:

[0074]

[0075] Where: is the total revenue during the operating life, is the net operating income during the system dispatch period, C in is the total investment cost of each equipment;

[0076]

[0077] Where: P wN , P pvN , P hyN , P bsN , P eleN , Q gN are the installed capacities of wind turbines, photovoltaic units, hydroelectric generators, energy storage power stations, electrolyzers, and hydrogen storage tanks, respectively. w , C pv , C hy , C bs , C ele , C g They are the unit investment costs of wind turbines, photovoltaic units, hydroelectric units, energy storage power stations, electrolyzers, and hydrogen storage tanks;

[0078] (2) Constraints:

[0079]

[0080] Where: P , P , P , P , P , Q are the lower limits of installed capacity of wind turbines, photovoltaic units, hydroelectric generators, energy storage power stations, electrolyzers, and hydrogen storage tanks, respectively. , P , P , P , P , Q They are the upper limits of installed capacity for wind turbines, photovoltaic units, hydroelectric units, energy storage power stations, electrolyzers, and hydrogen storage tanks;

[0081] Construct the lower-level scheduling optimization model, including:

[0082] (1) Objective function:

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] Where: F np is the profit during the scheduling period, F sale is the revenue from electricity and hydrogen sales, F env For environmental benefits, F ope is the operation and maintenance cost, F curt F is the penalty cost for wind and solar curtailment. buy is the electricity purchase cost; T is the dispatch period, σ w , σ pv , σ hy , σ bs are the environmental benefit coefficients of wind power, photovoltaic power, hydropower and energy storage, C sale , C g , C buy are the unit prices of electricity sales, hydrogen sales, and electricity purchase, respectively. w (t), P pv (t), P hy (t), P bs_ch (t), P sale(t), Q g_out (t), P buy (t) are wind power operation power, photovoltaic operation power, hydropower operation power, energy storage charging power, electricity sales power, hydrogen sales power, and electricity purchase power at time t, N is the number of various types of equipment, R is the unit operation and maintenance cost of device i, P i (t) is the operating power of device i at time t, ρ w , pv are the penalty coefficients for wind power abandonment and solar power abandonment, Pw curt(t) and Ppv curt(t) are the wind power abandonment and solar power abandonment at time t, respectively;

[0090] (2) Constraints:

[0091] Power balance constraints:

[0092]

[0093] Wind power, photovoltaic and hydropower operation constraints:

[0094]

[0095] Where: U w (t), U pv (t), U hy (t) are wind power, photovoltaic and hydropower operation state variables, and U w (t), U pv (t), U hy (t)∈{0,1};

[0096] Energy storage power station operation and capacity constraints:

[0097]

[0098] Where: U bs_ch (t), U bs_dis (t) is the working state variable of the energy storage power station at time t, and U bs_ch (t), U bs_dis (t)∈{0,1}, Pmax bs_ch(t), Pmax bs_dis(t) are the maximum charging power and maximum discharging power of energy storage at time t, S(t), S(t-1) are the capacity of energy storage equipment at time t and time t-1, η ch , η dis are the energy storage charging and discharging efficiency coefficients respectively;

[0099] Electrolyzer and hydrogen storage tank operation and capacity constraints:

[0100]

[0101] Where: P e (t) is the electrolytic cell power at time t, PN e is the rated power of the electrolytic cell, Q g_in (t), Q g_out (t) is the amount of hydrogen stored and released at time t, U g_in (t), U g_out (t) is the working state variable of the hydrogen storage tank at time t, and U g_in (t), U g_out (t)∈{0,1}, Qmax g_in(t) and Qmax g_out(t) are the maximum allowable hydrogen storage capacity and maximum allowable hydrogen discharge capacity of the hydrogen storage tank at time t, respectively. g (t), Q g (t-1) is the capacity of the hydrogen storage tank at time t and time t-1, η e is the electrolysis efficiency coefficient.

[0102] S3. Solve the two-level optimization model using the improved grey wolf algorithm and solver;

[0103] The collected operating data of wind power, photovoltaic power, hydropower, energy storage power station, electrolyzer, hydrogen storage tank and load data of microgrid system are input into the two-layer optimization model;

[0104] The improved grey wolf algorithm is used to solve the upper configuration optimization model; the grey wolf population is initialized, and the configuration capacities of wind power, photovoltaic power, hydropower, energy storage power stations, electrolyzers, and hydrogen storage tanks randomly generated in the upper layer are brought into the lower scheduling optimization model as constraints for the lower scheduling optimization;

[0105] The lower scheduling optimization layer uses a solver to solve the output strategies of wind power, photovoltaic power, hydropower, energy storage power stations, electrolyzers, and hydrogen storage tanks on a typical day based on the upper configuration plan, and feeds the results back to the upper layer;

[0106] The upper configuration optimization layer updates the convergence factor and gray wolf population of the configuration optimization layer and updates the configuration plan based on the feedback data of the scheduling optimization layer;

[0107] Determine whether the maximum number of iterations has been reached. If so, output the configuration plan and the output strategies of wind power, photovoltaic power, hydropower, energy storage power station, electrolyzer, and hydrogen storage tank. Otherwise, continue with steps S32-34 until the maximum number of iterations has been reached.

[0108] The improved grey wolf algorithm specifically includes:

[0109] The convergence factor based on the improved cosine function is introduced to make it nonlinear and decreasing, thus increasing the initial exploration ability of the algorithm, as follows:

[0110]

[0111] Where: a is the attenuation factor, t is the current number of iterations, and T is the maximum number of iterations;

[0112] The random dispersion strategy and the proportional weight based on the step length Euclidean distance are introduced to enhance the population diversity and increase the individual exploration ability by introducing random individuals and improving the search mechanism. The details are as follows:

[0113]

[0114]

[0115]

[0116] Where: X1, X2, X3 represent the position changes caused by α, β and δ respectively, W1, W2, W3 represent the learning rates of ω for α, β and δ respectively, λ1, λ2 are weight factors, θ1, θ2 are random numbers, and θ∈{0,1}, X a , X b are two individuals randomly selected from the gray wolf population, and a≠b≠i.

[0117] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A microgrid system planning and design method based on double-layer optimization, characterized in that: The method specifically comprises the following steps: S1. Collect the operation data of wind power, photovoltaic power, hydropower, energy storage power station, electrolyzer, hydrogen storage tank in the microgrid system and the load data of the microgrid system; S2. Construct a two-layer optimization model for microgrid system based on the two-layer optimization method; S3. Solve the two-level optimization model using the improved grey wolf algorithm and solver.

2. According to a microgrid system planning and design method based on double-layer optimization according to claim 1, it is characterized in that: The two-layer optimization model described in step S2 includes: S21: The upper model is the capacity optimization layer, which takes the maximum total revenue within the operating life of the microgrid system as the objective function, the capacity range allowed by the equipment as the constraint condition, and the configuration capacity of wind power, photovoltaic, hydropower, energy storage power station, electrolyzer, and hydrogen storage tank as variables; S22: The lower model is the dispatch optimization layer, which takes the maximization of the net operating profit of the microgrid system during the dispatch period as the objective function, the system power balance, equipment operation and capacity as constraints, and the output of wind power, photovoltaic power, hydropower, electrolyzer, hydrogen storage tank and the charging and discharging power of the energy storage power station as variables.

3. The microgrid system planning and design method based on double-layer optimization according to claim 1 is characterized in that: The specific steps of solving the two-layer optimization model described in step S3 are as follows: S31: input the collected operation data of wind power, photovoltaic power, hydropower, energy storage power station, electrolyzer, hydrogen storage tank and load data of microgrid system into the double-layer optimization model; S32: Use the improved grey wolf algorithm to solve the upper configuration optimization model; initialize the grey wolf population, and bring the configuration capacities of wind power, photovoltaic power, hydropower, energy storage power stations, electrolyzers, and hydrogen storage tanks randomly generated in the upper layer into the lower scheduling optimization model as constraints for the lower scheduling optimization; S33: The lower scheduling optimization layer uses a solver to solve the output strategies of wind power, photovoltaic power, hydropower, energy storage power stations, electrolyzers, and hydrogen storage tanks on a typical day based on the upper configuration plan, and feeds the results back to the upper layer; S34: The upper configuration optimization layer updates the convergence factor and the gray wolf population of the configuration optimization layer based on the feedback data of the scheduling optimization layer, and updates the configuration plan; S35: Determine whether the maximum number of iterations has been reached. If so, output the configuration plan and the output strategies of wind power, photovoltaic power, hydropower, energy storage power station, electrolyzer, and hydrogen storage tank. Otherwise, continue with steps S32-34 until the maximum number of iterations has been reached.

4. The microgrid system planning and design method based on double-layer optimization according to claim 1 is characterized in that: The improved grey wolf algorithm described in step S32 specifically includes: S321. Introduce a convergence factor based on the improved cosine function to make it nonlinear and decrease, thereby increasing the initial exploration capability of the algorithm. The details are as follows: Where: a is the attenuation factor, t is the current number of iterations, and T is the maximum number of iterations; S322. Introduce random dispersion strategy and proportional weight based on step length Euclidean distance, introduce random individuals and improve search mechanism to enhance population diversity and increase individual exploration ability, as follows: Where: X1, X2, X3 represent the position changes caused by α, β and δ respectively, W1, W2, W3 represent the learning rates of ω for α, β and δ respectively, λ1, λ2 are weight factors, θ1, θ2 are random numbers, and θ∈{0,1}, X a , X b are two individuals randomly selected from the gray wolf population, and a≠b≠i.

5. A microgrid system planning and design device based on double-layer optimization, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: The processor implements the steps of the method of claim 1 when executing the computer program.

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