Multi-objective optimization multi-model integrated energy system configuration method

Abstract

The invention discloses a multi-target optimized multi-model comprehensive energy system configuration method. The method comprises the following steps: establishing a wind power generation mathematical model, a photovoltaic power generation mathematical model and an energy storage battery charging and discharging mathematical model; based on a load following strategy, a multi-target optimized multi-model integrated energy system operation model is established with the load missing rate as a first target, the minimum electricity cost as a second target and capacity limitation, power balance and energy storage battery output limitation as constraint conditions; and solving the model by adopting a network search algorithm to obtain an optimal configuration scheme of the integrated energy system, which meets the load missing rate and the lowest cost per kilowatt-hour. According to the method, on the premise of meeting the load power supply target, the load following control strategy and the network search algorithm are adopted, the optimal configuration scheme of the comprehensive energy system meeting the load missing rate and the minimum electricity cost is obtained, the power generation efficiency of the comprehensive energy system is improved, and the stability and economical efficiency of operation of the comprehensive energy system are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy application, and in particular relates to a multi-model integrated energy system configuration method with multi-objective optimization. Background Art

[0002] In recent years, with the increasing depletion of global fossil energy, people's demand for renewable energy has continued to increase. However, wind power generation and photovoltaic power generation have the characteristics of randomness, volatility, and uncertainty. The power fluctuations caused by the large-scale grid connection of renewable energy have brought a huge impact on the new power system. Since renewable energy generation is easily affected by the environment, the output power fluctuations are obvious and difficult to predict, which makes the power supply side of the power system fluctuate severely and cannot provide stable power supply. In order to alleviate the power fluctuations caused by the grid connection of renewable energy, the power consumption of the renewable integrated energy system is particularly important.

[0003] Energy storage plays a role in peak load regulation and frequency regulation in the integrated energy system. It can store or release energy in the system in a timely manner. When the power generation of renewable energy is insufficient, the energy of the energy storage battery can be released to ensure the stable power supply of the power system and reduce the adverse effects of power fluctuations caused by environmental changes on renewable energy. At the same time, different renewable energy output characteristics in the integrated energy system are different. For example, wind power generation is concentrated at night and early morning, while photovoltaic power generation is concentrated during the daytime. The two are complementary to a certain extent. Therefore, a reasonable configuration of energy storage capacity and renewable energy ratio is particularly important. A reasonable configuration of energy storage capacity and renewable energy ratio can improve the power consumption level of the integrated energy system, ensure stable power supply output, and improve the reliability of the integrated energy system.

[0004] However, the traditional configuration method of integrated energy system cannot promote the precise matching of supply and demand and ensure the reliable supply of power system. For example, the Chinese patent application with publication number CN118523306A proposes a method for calculating the renewable energy capacity based on the wind-solar complementary output characteristics. It obtains the wind-solar complementary output data throughout the year by calculating the first effective output value, and then calculates the minimum wind-solar installed capacity that can be absorbed in each hour of the typical summer day, the typical winter day and the heating small load day, which is the renewable energy capacity that can be absorbed; but it only considers the typical day load data for absorption calculation, lacks consideration of load uncertainty, and has certain limitations. For another example, the Chinese patent application with publication number CN118333474A proposes a method for solving the capacity planning problem of the island integrated energy system. It uses the energy system configuration optimization modeling, memory library classification and clone mutation strategy designed for different memory libraries to effectively balance the global search and local search capabilities, and improve the solution accuracy and efficiency; but it takes into account the economy, independence and energy utilization, but ignores the load fluctuation, which has certain limitations. Summary of the invention

[0005] In view of the above, the present invention provides a multi-model integrated energy system configuration method with multi-objective optimization, which adds a load fluctuation coefficient on the basis of load benchmark power data to reflect load volatility, and ultimately obtains the optimal configuration plan for the multi-model integrated energy system with the lowest cost per kilowatt-hour while satisfying the load missing rate.

[0006] A multi-model integrated energy system configuration method for multi-objective optimization includes the following steps:

[0007] (1) Collect necessary information based on different user needs and usage scenarios;

[0008] (2) Considering load fluctuation, the load fluctuation coefficient is added to the load benchmark power data to establish a comprehensive load mathematical model;

[0009] (3) Considering the uncertainty of wind and solar power generation, mathematical models of wind power generation, photovoltaic power generation and energy storage battery charging and discharging are established;

[0010] (4) Determine the economic and physical parameters of the equipment based on the price range and physical parameters of different equipment in the system;

[0011] (5) Based on the above mathematical model and economic and physical parameters, a multi-model integrated energy system operation model is established based on a load following strategy with a multi-objective optimization strategy to meet the load loss rate as the first goal and the lowest electricity cost as the second goal;

[0012] (6) Optimizing and solving the multi-model integrated energy system operation model under the constraints of capacity limitation, power balance, and energy storage battery output limitation to obtain the optimal equipment configuration plan for the integrated energy system.

[0013] Furthermore, the information collected in step (1) includes load benchmark power data, geographical location information of the integrated energy system installation site, and meteorological data; the economic parameters in step (4) include investment cost, replacement cost, depreciation residual value, and operation and maintenance cost, and the physical parameters include rated power and service life.

[0014] Furthermore, the expression of the comprehensive load mathematical model in step (2) is as follows:

[0015] P L_demand (t) = P L_ref (1+α day +α hour )

[0016] Where: P L_demand (t) is the expected load power at time t, P L_refis the load reference power, α day is the daily load fluctuation coefficient, α hour is the hourly load fluctuation coefficient, and t represents time.

[0017] Furthermore, the expression of the wind power generation mathematical model in step (3) is as follows:

[0018]

[0019] Where: P W (t) is the wind power generation power of the system at time t, P r is the rated output power of the wind turbine in the system, v(t) is the wind speed at the location of the system at time t, and v in is the cut-in wind speed, v r is the rated wind speed, v out is the cut-out wind speed, ρ 0 is the actual air density, ρ is the air density at standard temperature and atmospheric pressure (1.225 kg / m 3 );

[0020] The rated output power P r The expression is as follows:

[0021]

[0022] Where: A is the swept area of ​​the wind turbine, C is the p is the wind energy utilization coefficient of the wind turbine.

[0023] Furthermore, the expression of the photovoltaic power generation mathematical model in step (3) is as follows:

[0024]

[0025] Where: P PV (t) is the photovoltaic power generation of the system at time t, C PV is the photovoltaic installed capacity of the system, G PV is the photovoltaic attenuation coefficient, I T is the solar radiation power density incident on the photovoltaic module in the current time step, I T,STC is the solar radiation power density incident on the photovoltaic module under standard test conditions, β P is the power temperature coefficient, T c (t) is the temperature of the photovoltaic module at time t, T c,STC is the temperature of the PV module under standard test conditions.

[0026] Furthermore, the expression of the energy storage battery charging and discharging mathematical model in step (3) is as follows:

[0027]

[0028] Where: P B (t) is the power of the energy storage battery at time t, P B,c (t) and P B,dc (t) are the charging power and discharging power of the energy storage battery at time t, P es is the maximum charge and discharge power of the energy storage battery, SOC(t-1) is the state of charge of the energy storage battery at time t-1, SOC B,c (t) represents the state of charge of the energy storage battery at the time t, SOC B,dc (t) represents the state of charge of the energy storage battery at the time of discharge, ξ and η b They are the self-discharge conversion efficiency and charge-discharge conversion efficiency of the energy storage battery respectively.

[0029] Furthermore, the expression of the multi-model integrated energy system operation model in step (5) is as follows:

[0030]

[0031] Where: LCOE is the system's electricity cost, C cap is the initial investment cost of system equipment, C O&M is the system equipment operation and maintenance cost, C rep is the cost of replacing system equipment, C sal is the residual value of system equipment, E unmet is the unmet load for the whole year, E demand is the annual load demand, f unmet is the load loss rate, f unmet_r Optimize the load miss rate for the target, E t It is the total power generation consumed by the system load throughout the year.

[0032] Furthermore, the initial investment cost of the system equipment C cap The expression is as follows:

[0033]

[0034] Where: N i is the number of devices i in the system, C i is the initial investment cost of equipment i, i = W, PV, BAT correspond to wind turbine, photovoltaic module, and energy storage battery respectively;

[0035] The system equipment operation and maintenance cost C O&M The expression is as follows:

[0036]

[0037] Where: Y is the system operation life, C i_O&M is the annual operation and maintenance cost of equipment i;

[0038] The system equipment replacement cost C rep The expression is as follows:

[0039]

[0040] Where: C i_rep is the replacement cost of equipment i;

[0041] The residual value of the system equipment C sal The expression is as follows:

[0042]

[0043] Where: Y remain_life_i is the remaining useful life of equipment i, Y life_i is the useful life of equipment i.

[0044] Furthermore, the constraint expressions of capacity limitation, power balance, and energy storage battery output limitation in step (6) are as follows:

[0045] P L (t)≤P system_max

[0046] P PV (t)≤P system_max

[0047] P W (t)≤P system_max

[0048] P B (t)≤P system_max

[0049] P PV (t)+P W (t)+P B (t) = P L (t)≤P L_demand (t)

[0050] SOC min ≤SOC(t)≤SOC max

[0051] Where: P W (t) is the wind power generation power of the system at time t, P PV (t) is the photovoltaic power generation of the system at time t, P B (t) is the power of the energy storage battery at time t, P L (t) is the load power of the system at time t, Psystem_max is the maximum output power of the system, P L_demand (t) is the load expected power at time t, SOC(t) is the state of charge of the energy storage battery at time t, SOC min SOC is the end-of-discharge state of the energy storage battery. max It is the cut-off charging state of the energy storage battery.

[0052] Furthermore, in step (6), a network search algorithm is used to optimize and solve the multi-model integrated energy system operation model.

[0053] Based on the above technical solution, the present invention has the following beneficial technical effects:

[0054] 1. The present invention combines basic load information with load power fluctuation, adds load fluctuation coefficient to load data, proposes a comprehensive load mathematical model, and refines the load information on the demand side of the power grid.

[0055] 2. The present invention establishes a mathematical model of wind, light, storage, and combines the mathematical models of wind, light, storage, and load to establish an operation model, thereby achieving a comprehensive understanding and accurate analysis of the operation of the integrated energy system, and providing a theoretical basis for the power consumption analysis and power supply stability analysis of the integrated energy system.

[0056] 3. The present invention obtains the global optimal solution of the multi-model combined energy system ratio scheme through a network search algorithm, providing a reference for the comprehensive energy system ratio design. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic flow chart of the multi-model integrated energy system configuration method for multi-objective optimization of the present invention.

[0058] Figure 2 To increase the schematic diagram of the comprehensive load power curve under different load fluctuation coefficients.

[0059] Figure 3 The present invention is a flow chart of using a network search algorithm to solve a multi-model combined energy system operation model.

[0060] Figure 4 Schematic diagram of load benchmark data in an embodiment of the present invention.

[0061] Figure 5 It is a schematic diagram of annual load expected power data in an embodiment of the present invention.

[0062] Figure 6 Schematic diagram of annual wind speed data in an embodiment of the present invention.

[0063] Figure 7 Schematic diagram of annual sunlight intensity data in an embodiment of the present invention.

[0064] Figure 8 Schematic diagram of the organizational structure of an independent integrated energy system in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to describe the present invention more specifically, the technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0066] like Figure 1 As shown, the multi-model integrated energy system configuration method for multi-objective optimization of the present invention comprises the following steps:

[0067] (1) Collect necessary information based on different user requirements and usage scenarios.

[0068] The usage scenario of this implementation is Figure 8 Taking the independent integrated energy system shown as an example, it includes photovoltaic power generation equipment 201, photovoltaic inverter 202, wind power generation equipment 203, wind turbine converter 204, energy storage battery equipment 205, energy storage bidirectional converter 206, load 207, and AC bus 208, wherein the photovoltaic power generation equipment 201 and the wind power generation equipment 203 are connected to the AC bus 208 through the photovoltaic inverter 202 and the wind turbine converter 204 respectively to supply power to the load 207; the energy storage battery equipment 205 is connected to the AC bus 208 through the energy storage bidirectional converter 206, and the charging and discharging state of the energy storage battery equipment 205 is determined according to its own charge state and the power generation conditions of the photovoltaic power generation equipment 201 and the wind power generation equipment 203, so as to stabilize the AC bus 208 and realize stable power supply to the load 207.

[0069] In this implementation method, the necessary information collected includes: load benchmark data, longitude and latitude of the integrated energy system installation site, and meteorological data of the integrated energy system installation site.

[0070] (2) Considering load fluctuation, the load fluctuation coefficient is added to the load benchmark data to establish a comprehensive load mathematical model.

[0071] The comprehensive load mathematical model in this implementation is:

[0072] P L_demand (t) = P L_ref (1+α day +α hour )

[0073] Where: P L_demand (t) is the expected load power at time t (kW), P L_ref is the load benchmark data (kW), α day is the daily load fluctuation coefficient, α houris the hourly load fluctuation coefficient, the load benchmark data P L_ref It is 0 to 24 hours, and is the load reference power per hour.

[0074] like Figure 2 The figure shows the load fluctuation coefficient of the comprehensive load curve. 201 is the load power curve of the first week of the year. It does not add any fluctuation coefficient on the basis of the load benchmark data. The daily load curve is repeated day after day. However, in fact, the size and shape of the load profile change every day, so adding the load fluctuation coefficient can make the load data more realistic. 202 is to add the daily load fluctuation coefficient on the basis of 201, and set α day =20% forms a new weekly load power curve, the daily load power curve maintains the same profile, and changes the daily power consumption within a 20% expansion ratio; 203 is the hourly load fluctuation coefficient α added on the basis of 201 hour =15% load power curve, so that the hourly load power curve remains within 15% fluctuation, so as to change the profile of the load power curve; 204 is 201 plus α day =20%,α hour =15% load power curve, combined with the daily load fluctuation coefficient and the hourly load fluctuation coefficient, can make the load curve more realistic.

[0075] (3) Considering the uncertainty of wind and solar power generation, mathematical models of wind power generation and photovoltaic power generation are established; mathematical models of energy storage battery charging and discharging are established.

[0076] The expression of the wind power generation mathematical model in this implementation is as follows:

[0077]

[0078] Where: P W (t) is the wind power generation power of the system at time t (kW), P r is the rated output power of the wind turbine in the system (kW), v(t) is the wind speed at the system location at time t (m / s), v in is the cut-in wind speed (m / s), v r is the rated wind speed (m / s), v out is the cut-out wind speed (m / s), ρ 0 is the actual air density (kg / m 3 ), ρ is the air density at standard temperature and atmospheric pressure (1.225 kg / m 3 ).

[0079] Among them, the rated output power P r The expression is as follows:

[0080]

[0081] Where: A is the swept area of ​​the wind turbine (m 2 ), C p is the wind energy utilization coefficient of the wind turbine.

[0082] The expression of the mathematical model of photovoltaic power generation is as follows:

[0083]

[0084] Where: P PV (t) is the photovoltaic power generation of the system at time t (kW), C PV is the photovoltaic installed capacity of the system (kW), G PV is the photovoltaic attenuation coefficient (%), I T is the solar radiation power density incident on the photovoltaic module in the current time step (kW / m 2 ), I T,STC is the solar radiation power density incident on the photovoltaic module under standard test conditions (kW / m 2 ), β P is the power temperature coefficient (% / ℃), T c (t) is the temperature of the photovoltaic module at time t (℃), T c,STC is the temperature of the photovoltaic module under standard test conditions (℃).

[0085] The expression of the mathematical model of energy storage battery charging and discharging is as follows:

[0086]

[0087] Where: P B (t) is the power of the energy storage battery at time t (kW), P B,c (t) and P B,dc (t) are the charging power and discharging power of the energy storage battery at time t (kW), P es is the maximum charge and discharge power of the energy storage battery (kW), SOC(t-1) is the state of charge of the energy storage battery at time t-1, SOC B,c (t) represents the state of charge of the energy storage battery at the time t, SOC B,dc (t) represents the state of charge of the energy storage battery at the time of discharge, ξ and η b They are the self-discharge conversion efficiency and charge-discharge conversion efficiency of the energy storage battery respectively.

[0088] (4) Based on the different price ranges and physical parameters of the equipment, establish an equipment economic parameter database and a physical parameter database.

[0089] In this embodiment, the economic parameters include the investment cost, replacement cost, depreciation residual value, and operation and maintenance cost of the equipment, and the physical parameters include the rated power and service life of the equipment.

[0090] (5) Establish a multi-model integrated energy system operation model based on a load following strategy, with the load loss rate as the first goal, the minimum cost per kilowatt-hour as the second goal, and capacity limitation, power balance, and energy storage battery output limitation as constraints for multi-objective optimization.

[0091] In this implementation, the expression of the multi-model integrated energy system operation model is as follows:

[0092]

[0093] Where: LCOE is the system's electricity cost, C cap is the initial investment cost of system equipment (￥), C O&M is the system equipment operation and maintenance cost (￥), C rep is the cost of replacing system equipment (￥), C sal is the residual value of system equipment (￥), E unmet is the unmet load for the whole year (kWh), E demand is the annual load demand (kWh), f unmet is the load loss rate, f unmet_r Optimize the load miss rate for the target, E t It is the total power generation (kWh) consumed by the system load throughout the year.

[0094] Among them, the initial investment cost of equipment is C cap The expression is as follows:

[0095]

[0096] Where: N i is the number of devices i in the system, C i is the initial investment cost of equipment i (￥ / unit), i=W, PV, BAT correspond to wind turbine, photovoltaic module, and energy storage battery respectively.

[0097] Equipment operation and maintenance costs C O&M The expression is as follows:

[0098]

[0099] Where: Y is the system operation life (years), C i_O&M is the annual operation and maintenance cost of device i (￥ / unit / year).

[0100] Equipment replacement cost C rep The expression is as follows:

[0101]

[0102] Where: C i_rep is the replacement cost of device i (￥ / unit).

[0103] Equipment residual value C sal The expression is as follows:

[0104]

[0105] Where: Y remain_life_i is the remaining useful life of equipment i (years), Y life_i is the useful life of equipment i (years).

[0106] In this implementation, the constraints include capacity limitation constraints, power balance constraints, and energy storage battery output constraints; wherein the mathematical expression of the capacity constraint is:

[0107] P L (t)≤P system_max

[0108] P PV (t)≤P system_max

[0109] P W (t)≤P system_max

[0110] P B (t)≤P system_max

[0111] Where: P system_max is the maximum output power of the system;

[0112] The mathematical expression of the power balance constraint is:

[0113] P PV (t)+P W (t)+P B (t) = P L (t)≤P L_demand (t)

[0114] Where: P L (t) is the load power of the system at time t;

[0115] The mathematical expression of the energy storage battery output constraint is:

[0116] SOC min ≤SOC(t)≤SOC max

[0117] Among them: SOC minSOC is the end-of-discharge state of the energy storage battery. max It is the cut-off charging state of the energy storage battery.

[0118] (6) A network search algorithm is used to solve the multivariable nonlinear global optimal solution of the multi-model integrated energy system operation model and obtain the optimal ratio scheme of the integrated energy system.

[0119] In this implementation, a network search algorithm is used to solve the global optimal solution of the multi-model combined energy system operation model, such as Figure 3 As shown:

[0120] 6a. Update the network search algorithm parameters to generate a multi-model integrated energy system configuration model;

[0121] 6b. Perform parameter calculation to obtain the output power of each device, the expected load power, and the load loss rate f unmet , if f unmet Meet the target optimization load missing rate f unmet_r , then go to step 6c, otherwise go to step 6a;

[0122] 6c. Calculate the parameters to obtain the LCOE. If the LCOE is lower than the result of the previous iteration, proceed to step 6d; otherwise, proceed to step 6e.

[0123] 6d. Update the lowest LCOE and the best configuration parameters;

[0124] 6e. After reaching the upper limit of system iterations, the operation results and related parameters are directly output, and the results are the global optimal configuration scheme of the multi-model combined energy system operation model.

[0125] Below we provide a set of system load benchmark data, daily power consumption is 165kWh / day, the system is used off-grid, and the load loss rate is kept within 5%; load benchmark data such as Figure 4 As shown, according to α day =20%,α hour =15% Expand the load benchmark data to generate the expected annual load power for 8760 hours throughout the year, such as Figure 5 The figure shows the expected load power data for one year in this embodiment, with an annual cumulative power demand of 60,000 kWh and a peak power consumption of 20 kW. Figure 6 and Figure 7 They are respectively the annual wind speed data and annual sunlight intensity data with a one-hour resolution in the area where the system of this embodiment is located.

[0126] For wind power, this embodiment adopts a 3kW wind turbine generator set with an installation height of 20m, an initial construction and installation cost of 6,000 yuan / kW, a service life of 20 years, an operation and maintenance cost of 10 yuan / kw / year, and a replacement cost of 5,000 yuan / kW. The wind turbine power generation is calculated based on the wind speed per hour with reference to the above-mentioned wind power generation mathematical model.

[0127] For photovoltaics, this embodiment uses 0.6kW photovoltaic modules, with an initial construction and installation cost of 2,000 yuan / kW, a service life of 20 years, an operation and maintenance cost of 10 yuan / kw / year, and a replacement cost of 800 yuan / kW. The photovoltaic power generation power is calculated based on the wind speed per hour with reference to the above-mentioned photovoltaic power generation mathematical model.

[0128] For the energy storage battery, this embodiment adopts a 10kWh chemical lithium battery energy storage system, with an initial construction and installation cost of 900 yuan / kWh, a service life of 10 years, an operation and maintenance cost of 0 yuan / kW / year, and a replacement cost of 900 yuan / kWh. The energy storage charging and discharging power is calculated based on the wind speed per hour with reference to the energy storage mathematical model.

[0129] For the converter, the initial installation cost of this embodiment is 1,000 yuan / kW, the service life is 20 years, the operation and maintenance cost is 0 yuan / kW / year, and the replacement cost is 1,000 yuan / kW.

[0130] After obtaining the above comprehensive energy equipment parameter information, set the load missing rate <5% and the objective function of the lowest electricity cost. According to the equipment parameter data, set the objective function and build the comprehensive energy system operation model; use the network search algorithm to seek the global optimal solution of the multi-model energy system with a load missing rate of <5% and the lowest electricity cost within the set system iteration number range, and get the best ratio scheme: configure 24kW wind power generation, that is, 8 3kW wind turbines, configure 25kW photovoltaic power generation, configure 170kWh energy storage battery, the annual cumulative missing load is 1685kWh, the load missing rate is 2.79%, and the electricity cost is 0.735.

[0131] The above description of the embodiments is to facilitate the understanding and application of the present invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments. Improvements and modifications made by those skilled in the art to the present invention based on the disclosure of the present invention should be within the protection scope of the present invention.

Claims

1. A multi-model integrated energy system configuration method for multi-objective optimization, comprising the following steps: (1) Collect necessary information based on different user needs and usage scenarios; (2) Considering load fluctuation, the load fluctuation coefficient is added to the load benchmark power data to establish a comprehensive load mathematical model; (3) Considering the uncertainty of wind and solar power generation, mathematical models of wind power generation, photovoltaic power generation and energy storage battery charging and discharging are established; (4) Determine the economic and physical parameters of the equipment based on the price range and physical parameters of different equipment in the system; (5) Based on the above mathematical model and economic and physical parameters, a multi-model integrated energy system operation model is established based on a load following strategy with a multi-objective optimization strategy to meet the load loss rate as the first goal and the lowest electricity cost as the second goal; (6) Optimizing and solving the multi-model integrated energy system operation model under the constraints of capacity limitation, power balance, and energy storage battery output limitation to obtain the optimal equipment configuration plan for the integrated energy system.

2. The multi-model integrated energy system configuration method according to claim 1, characterized in that: The information collected in step (1) includes load benchmark power data, geographical location information of the integrated energy system installation site, and meteorological data; the economic parameters in step (4) include investment cost, replacement cost, depreciation residual value, and operation and maintenance cost, and the physical parameters include rated power and service life.

3. The multi-model integrated energy system configuration method according to claim 1, characterized in that: The expression of the comprehensive load mathematical model in step (2) is as follows: P L_demand (t)=P L_ref (1+a day +a hour ) Where: P L_demand (t) is the expected load power at time t, P L_ref is the load reference power, α day is the daily load fluctuation coefficient, α hour is the hourly load fluctuation coefficient, and t represents time.

4. The multi-model integrated energy system configuration method according to claim 1, characterized in that: The expression of the wind power generation mathematical model in step (3) is as follows: Where: P W (t) is the wind power generation power of the system at time t, P r is the rated output power of the wind turbine in the system, v(t) is the wind speed at the location of the system at time t, and v in is the cut-in wind speed, v r is the rated wind speed, v out is the cut-out wind speed, ρ0 is the actual air density, and ρ is the air density at standard temperature and atmospheric pressure; The rated output power P r The expression is as follows: Where: A is the swept area of ​​the wind turbine, C is the p is the wind energy utilization coefficient of the wind turbine.

5. The multi-model integrated energy system configuration method according to claim 1, characterized in that: The expression of the photovoltaic power generation mathematical model in step (3) is as follows: Where: P PV (t) is the photovoltaic power generation of the system at time t, C PV is the photovoltaic installed capacity of the system, G PV is the photovoltaic attenuation coefficient, I T is the solar radiation power density incident on the photovoltaic module in the current time step, I T,STC is the solar radiation power density incident on the photovoltaic module under standard test conditions, β P is the power temperature coefficient, T c (t) is the temperature of the photovoltaic module at time t, T c,STC is the temperature of the PV module under standard test conditions.

6. The multi-model integrated energy system configuration method according to claim 1, characterized in that: The expression of the energy storage battery charge and discharge mathematical model in step (3) is as follows: Where: P B (t) is the power of the energy storage battery at time t, P B,c (t) and P B,dc (t) are the charging power and discharging power of the energy storage battery at time t, P es is the maximum charge and discharge power of the energy storage battery, SOC(t-1) is the state of charge of the energy storage battery at time t-1, SOC B,c (t) represents the state of charge of the energy storage battery at the time t, SOC B,dc (t) represents the state of charge of the energy storage battery at the time of discharge, ξ and η b They are the self-discharge conversion efficiency and charge-discharge conversion efficiency of the energy storage battery respectively.

7. The multi-model integrated energy system configuration method according to claim 1, characterized in that: The expression of the multi-model integrated energy system operation model in step (5) is as follows: Where: LCOE is the system's electricity cost, C cap is the initial investment cost of system equipment, C O&M is the system equipment operation and maintenance cost, C rep is the cost of replacing system equipment, C sal is the residual value of system equipment, E unmet is the unmet load for the whole year, E demand is the annual load demand, f unmet is the load loss rate, f unmet_r Optimize the load miss rate for the target, E t It is the total power generation consumed by the system load throughout the year.

8. The multi-model integrated energy system configuration method according to claim 7, characterized in that: The initial investment cost of the system equipment is C cap The expression is as follows: Where: N i is the number of devices i in the system, C i is the initial investment cost of equipment i, i = W, PV, BAT correspond to wind turbine, photovoltaic module, and energy storage battery respectively; The system equipment operation and maintenance cost C O&M The expression is as follows: Where: Y is the system operation life, C i_O&M is the annual operation and maintenance cost of equipment i; The system equipment replacement cost C rep The expression is as follows: Where: C i_rep is the replacement cost of equipment i; The residual value of the system equipment C sal The expression is as follows: Where: Y remain_life_i is the remaining useful life of equipment i, Y life_i is the useful life of equipment i.

9. The multi-model integrated energy system configuration method according to claim 1, characterized in that: The constraint expressions of capacity limitation, power balance, and energy storage battery output limitation in step (6) are as follows: P L (t)≤P system_max P PV (t)≤P system_max P W (t)≤P system_max P B (t)≤P system_max P PV (t)+P W (t)+P B (t)=P L (t)≤P L_demand (t) SOC min ≤SOC(t)≤SOC max Where: P W (t) is the wind power generation power of the system at time t, P PV (t) is the photovoltaic power generation of the system at time t, P B (t) is the power of the energy storage battery at time t, P L (t) is the load power of the system at time t, P system_max is the maximum output power of the system, P L_demand (t) is the load expected power at time t, SOC(t) is the state of charge of the energy storage battery at time t, SOC min SOC is the end-of-discharge state of the energy storage battery. max It is the cut-off charging state of the energy storage battery.

10. The multi-model integrated energy system configuration method according to claim 1, characterized in that: In the step (6), a network search algorithm is used to optimize and solve the multi-model integrated energy system operation model.

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