Energy capacity planning method and system based on charging, replacing and storing integrated power station

Abstract

The invention provides an energy capacity planning method and system based on a charging, replacing and storing integrated power station, and the method comprises the steps: selecting a target energy load type from an energy load type set of an integrated energy system according to the energy demands of a user; target equipment is selected from an equipment set of the comprehensive energy system according to the load capacity and the equipment parameters required by the user in combination with the target energy load type, and the equipment set of the comprehensive energy system comprises a charging, replacing and storing integrated power station; according to the target energy load type and the target equipment, solving a pre-constructed integrated energy system planning model by adopting a nonlinear programming solving method to obtain an energy capacity planning scheme of target equipment investment and construction; wherein the integrated energy system planning model is constructed according to the random output model of the photovoltaic equipment and the random output model of the wind power equipment by taking the minimum total operation cost of the integrated energy system as a target. And the energy capacity planning according to the diversified requirements of the electric energy supply mode of the user is met.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy technology, and specifically relates to an energy capacity planning method and system based on a charging, swapping and storage integrated power station. Background Art

[0002] In the integrated energy system, the proportion of distributed energy access is gradually increasing. At the same time, with the large-scale development of electric vehicles, the demand for the construction of charging and storage infrastructure has become extremely urgent. Traditional single charging and storage facilities cannot plan energy capacity according to the diversified needs of users' electricity replenishment methods. Summary of the invention

[0003] In order to overcome the above-mentioned deficiencies of the prior art, in the first aspect, the present invention proposes an energy capacity planning method based on an integrated charging, swapping and storage power station, comprising:

[0004] According to the energy demand of the user, a target energy load type is selected from the energy load type set of the integrated energy system;

[0005] According to the load amount and equipment parameters required by the user, combined with the target energy load type, select the target equipment from the equipment set of the comprehensive energy system, and the equipment set of the comprehensive energy system includes an integrated charging, swapping and storage power station;

[0006] According to the target energy load type and the target equipment, a pre-built integrated energy system planning model is solved by using a nonlinear programming method to obtain an energy capacity planning scheme for the target equipment;

[0007] The integrated energy system planning model is constructed based on the random output model of photovoltaic equipment and the random output model of wind power equipment with the goal of minimizing the total operating cost of the integrated energy system.

[0008] Preferably, the equipment set in the integrated energy system also includes: photovoltaic equipment and wind power equipment;

[0009] The construction process of the comprehensive energy system planning model includes:

[0010] According to the output characteristics of the photovoltaic equipment, uncertainty modeling is performed based on Beta distribution to obtain a random output model of the photovoltaic equipment;

[0011] According to the output characteristics of the wind power equipment, uncertainty modeling is performed based on Weibull distribution to obtain a random output model of the wind power equipment;

[0012] Based on the installed capacity of each device in the device set, a mathematical model, constraint conditions and a power balance model of the integrated energy system are established for each device;

[0013] According to the random output model of the photovoltaic equipment, the random output model of the wind power equipment, the mathematical model of each of the equipment, the constraints and the power balance model of the integrated energy system, a comprehensive energy system planning model is constructed with the goal of minimizing the total operating cost of the integrated energy system.

[0014] Preferably, the uncertainty modeling is performed based on the Beta distribution according to the output characteristics of the photovoltaic equipment to obtain a random output model of the photovoltaic equipment, including:

[0015] According to the output characteristics of photovoltaic equipment, the relationship between the output of photovoltaic equipment and light intensity is determined using probability density function;

[0016] Based on the relationship between the output of the photovoltaic device and the light intensity, a random output model of the photovoltaic device is constructed using Beta distribution.

[0017] Preferably, the uncertainty modeling is performed based on Weibull distribution according to the output characteristics of the wind power equipment to obtain a random output model of the wind power equipment, including:

[0018] According to the output characteristics of wind power equipment, the relationship between the output of wind power equipment and wind speed is determined using probability density function;

[0019] Based on the relationship between the output of the wind power equipment and the wind speed, a random output model of the wind power equipment is constructed using Weibull distribution.

[0020] Preferably, the random output model of the photovoltaic device is expressed as follows:

[0021]

[0022] In the above formula, f(I r ) is the probability density function of the actual value of the light intensity, α represents the degree to which the distribution form of the probability density curve shifts to the right; β represents the degree to which the distribution form of the probability density curve shifts to the left, Γ is the gamma function, Γ(p) is the solution value of the gamma function, I r is the actual value of light intensity, I rmax is the maximum light intensity, t is time t, P t,PV is the active power output of the photovoltaic device at time t; S is the total area of ​​the photovoltaic array in the photovoltaic device, η is the conversion efficiency of the photovoltaic array; p is a variable, t p-1 is the p-1 power of the illumination duration of the photovoltaic device, p is a positive integer greater than 1, φ is a natural constant, -t It is the natural constant raised to the power of -t.

[0023] Preferably, the expression of the random output model of the wind power equipment is as follows:

[0024]

[0025] In the above formula, f(v) is the probability density function of wind speed, k is the shape parameter of wind, c is the scale parameter of wind, v is wind speed, exp is the exponential function with natural constant as base, P t,WT P is the active power output of the wind turbine generator in the wind power equipment at time t; r is the rated power of the wind turbine in the wind power equipment; v ci is the cut-in wind speed, v co is the cut-off wind speed, v r is the rated wind speed.

[0026] Preferably, the total operating cost of the integrated energy system is expressed as follows:

[0027]

[0028] In the above formula, C is the total cost of the integrated energy system, C inv is the investment cost of equipment in the integrated energy system; C op is the operating cost of the equipment; C fuel is the fuel cost in the comprehensive energy system; C grid is the electricity cost in the comprehensive energy system; C env is the environmental cost in the comprehensive energy system; s H is the equal annual value coefficient of the equipment set; s is the installation capacity construction cost of the equipment set, is the construction cost per unit capacity of the equipment set; is the operating cost per unit power of the equipment set, P t,s is the active power output by the device set at time t, where s represents the device set, which includes: photovoltaic device PV, wind power device WT, gas turbine GT, gas boiler GB, absorption chiller AC, electric chiller EC, heat pump HP, power-to-gas device P2G, charging system BCS, battery swap system BSS and energy storage device ES, and nES is the nth energy storage device; is the construction cost of the heat pump per heating unit capacity, is the construction cost per unit cooling capacity of the heat pump, δ fuel is the natural gas price; R g is the calorific value of natural gas combustion; δ t,buy represents the electricity purchase price at time t, δ t,sell represents the electricity price at time t; δ ctax E is the carbon tax; f is the amount of CO2 emitted from burning a unit of natural gas, E grid is the amount of CO2 emitted per unit of electricity; η gridis the transmission efficiency of the power grid, is the amount of fuel consumed by the gas turbine at time t, represents the amount of fuel consumed by the gas boiler at time t, h t,GT is the thermal power output of the gas turbine at time t, h t,GB is the output thermal power of the gas boiler at time t, r t,AC is the output cooling power of the absorption refrigerator at time t, r t,EC is the cooling power output of the electric refrigerator at time t, h t,HP is the output thermal power of the heat pump at time t, r t,HP is the cooling power output of the heat pump at time t, Q e-c,t is the hydrogen power generated by the electrolyzer device of the power-to-gas equipment at time t, u is the combination of the charging system and the battery replacement system, BCS is the charging system, BSS is the battery replacement system, P t,u,dis The discharge power of the charging system and the battery swapping system at time t, P t,u,ch is the charging power of the charging system and the battery swapping system at time t, The operating cost per unit power of the charging system and the battery replacement system; is the energy release power of the nth type of energy storage device at time t, is the charging power of the nth type of energy storage device at time t, P t,sell is the electricity sales power of the integrated energy system at time t, The operating cost per unit power of the nth type of energy storage equipment, where n is the energy type, including: electricity e, heat h, cold c, gas g, hydrogen q; P t,buy is the purchased power of the comprehensive energy system at time t, The operating cost per unit of electricity generated by the gas turbine GT, The operating cost of a gas turbine GT per unit of heat energy produced.

[0029] Preferably, the energy load type set includes: electric load, heat load, cooling load, gas load and hydrogen load;

[0030] The power balance model of the comprehensive energy system includes: a mathematical model of electric power balance, a mathematical model of thermal power balance, a mathematical model of cold power balance, a mathematical model of gas power balance, and a mathematical model of hydrogen power balance.

[0031] In the second aspect, the present invention application also proposes an energy system planning system based on an integrated charging, swapping and storage power station, comprising:

[0032] A target energy load type selection module is used to select a target energy load type from the energy load type set of the integrated energy system according to the energy demand of the user;

[0033] A target device selection module, for selecting a target device from a device set of the integrated energy system according to the load amount and device parameters required by the user and in combination with the target energy load type, wherein the device set of the integrated energy system includes an integrated charging, swapping and storage power station;

[0034] An energy capacity planning scheme acquisition module is used to solve the pre-built comprehensive energy system planning model using a nonlinear planning solution method according to the target energy load type and the target equipment, and obtain an energy capacity planning scheme for the target equipment;

[0035] The integrated energy system planning model is constructed based on the random output model of photovoltaic equipment and the random output model of wind power equipment with the goal of minimizing the total operating cost of the integrated energy system.

[0036] Preferably, the equipment set in the integrated energy system also includes: photovoltaic equipment and wind power equipment;

[0037] The system also includes: a comprehensive energy system planning model building module, which is used to:

[0038] According to the output characteristics of the photovoltaic equipment, uncertainty modeling is performed based on Beta distribution to obtain a random output model of the photovoltaic equipment;

[0039] According to the output characteristics of the wind power equipment, uncertainty modeling is performed based on Weibull distribution to obtain a random output model of the wind power equipment;

[0040] Based on the installed capacity of each device in the device set, a mathematical model, constraint conditions and a power balance model of the integrated energy system are established for each device;

[0041] According to the random output model of the photovoltaic equipment, the random output model of the wind power equipment, the mathematical model of each of the equipment, the constraints and the power balance model of the integrated energy system, a comprehensive energy system planning model is constructed with the goal of minimizing the total operating cost of the integrated energy system.

[0042] Preferably, the random output model of the photovoltaic device includes:

[0043] The output and light intensity relationship determination unit is used to determine the relationship between the output and light intensity of the photovoltaic device using a probability density function according to the output characteristics of the photovoltaic device;

[0044] The random output model building unit of the photovoltaic device is used to build the random output model of the photovoltaic device using Beta distribution based on the relationship between the output of the photovoltaic device and the light intensity.

[0045] Preferably, the random output model of the wind power equipment includes:

[0046] The output and wind speed relationship determination unit is used to determine the relationship between the output and wind speed of the wind power equipment using a probability density function according to the output characteristics of the wind power equipment;

[0047] The random output model unit of the wind power equipment is used to build a random output model of the wind power equipment using Weibull distribution based on the relationship between the output of the wind power equipment and the wind speed.

[0048] Preferably, the random output model of the photovoltaic device is expressed as follows:

[0049]

[0050] In the above formula, f(I r ) is the probability density function of the actual value of the light intensity, α represents the degree to which the distribution form of the probability density curve shifts to the right; β represents the degree to which the distribution form of the probability density curve shifts to the left, Γ is the gamma function, Γ(p) is the solution value of the gamma function, I r is the actual value of light intensity, I rmax is the maximum light intensity, t is time t, P t,PV is the active power output of the photovoltaic device at time t; S is the total area of ​​the photovoltaic array in the photovoltaic device, η is the conversion efficiency of the photovoltaic array; p is a variable, t p-1 is the p-1 power of the illumination duration of the photovoltaic device, p is a positive integer greater than 1, φ is a natural constant, -t It is the natural constant raised to the power of -t.

[0051] Preferably, the expression of the random output model of the wind power equipment is as follows:

[0052]

[0053] In the above formula, f(v) is the probability density function of wind speed, k is the shape parameter of wind, c is the scale parameter of wind, v is wind speed, exp is the exponential function with natural constant as base, P t,WT P is the active power output of the wind turbine generator in the wind power equipment at time t; r is the rated power of the wind turbine in the wind power equipment; v ci is the cut-in wind speed, v co is the cut-off wind speed, v r is the rated wind speed.

[0054] Preferably, the total operating cost of the integrated energy system is expressed as follows:

[0055]

[0056] In the above formula, C is the total cost of the integrated energy system, C inv is the investment cost of equipment in the integrated energy system; C op is the operating cost of the equipment; C fuel is the fuel cost in the comprehensive energy system; C grid is the electricity cost in the comprehensive energy system; C env is the environmental cost in the comprehensive energy system; s H is the equal annual value coefficient of the equipment set; s is the installation capacity construction cost of the equipment set, is the construction cost per unit capacity of the equipment set; is the operating cost per unit power of the equipment set, P t,s is the active power output by the device set at time t, where s represents the device set, which includes: photovoltaic device PV, wind power device WT, gas turbine GT, gas boiler GB, absorption chiller AC, electric chiller EC, heat pump HP, power-to-gas device P2G, charging system BCS, battery swap system BSS and energy storage device ES, and nES is the nth energy storage device; is the construction cost of the heat pump per heating unit capacity, is the construction cost per unit cooling capacity of the heat pump, δ fuel is the natural gas price; R g is the calorific value of natural gas combustion; δ t,buy represents the electricity purchase price at time t, δ t,sell represents the electricity price at time t; δ ctax E is the carbon tax; f is the amount of CO2 emitted from burning a unit of natural gas, E grid is the amount of CO2 emitted per unit of electricity; η grid is the transmission efficiency of the power grid, is the amount of fuel consumed by the gas turbine at time t, represents the amount of fuel consumed by the gas boiler at time t, h t,GT is the thermal power output of the gas turbine at time t, h t,GB is the output thermal power of the gas boiler at time t, r t,AC is the output cooling power of the absorption refrigerator at time t, r t,EC is the cooling power output of the electric refrigerator at time t, h t,HP is the output thermal power of the heat pump at time t, r t,HP is the cooling power output of the heat pump at time t, Q e-c,t is the hydrogen power generated by the electrolyzer device of the power-to-gas equipment at time t, u is the combination of the charging system and the battery replacement system, BCS is the charging system, BSS is the battery replacement system, P t,u,disThe discharge power of the charging system and the battery swapping system at time t, P t,u,ch is the charging power of the charging system and the battery swapping system at time t, The operating cost per unit power of the charging system and the battery replacement system; is the energy release power of the nth type of energy storage device at time t, is the charging power of the nth type of energy storage device at time t, The operating cost per unit power of the nth type of energy storage equipment, where n is the energy type, including: electricity e, heat h, cold c, gas g, hydrogen q; P t,buy is the purchased power of the comprehensive energy system at time t.

[0057] Preferably, the energy load type set includes: electric load, heat load, cooling load, gas load and hydrogen load;

[0058] The power balance model of the comprehensive energy system includes: a mathematical model of electric power balance, a mathematical model of thermal power balance, a mathematical model of cold power balance, a mathematical model of gas power balance, and a mathematical model of hydrogen power balance.

[0059] In a third aspect, the present invention application further proposes an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0060] The memory is used to store one or more programs;

[0061] When the one or more programs are executed by the at least one processor, the energy capacity planning method based on the integrated charging, swapping and storage power station is implemented.

[0062] In a fourth aspect, the present invention application further proposes a readable storage medium on which an execution program is stored. When the execution program is executed, the energy capacity planning method based on the integrated charging, swapping and storage power station is implemented.

[0063] Compared with the closest prior art, the present invention has the following beneficial effects:

[0064] The energy capacity planning method and system based on the integrated power station of charging, swapping and storage of the present invention include: selecting a target energy load type from the energy load type set of the integrated energy system according to the energy demand of the user; selecting a target device from the device set of the integrated energy system according to the load amount and device parameters required by the user, in combination with the target energy load type, wherein the device set of the integrated energy system includes the integrated power station of charging, swapping and storage; solving the pre-constructed integrated energy system planning model according to the target energy load type and the target device by a nonlinear programming solution method to obtain an energy capacity planning scheme for the target device; wherein the integrated energy system planning model is constructed based on the random output model of photovoltaic equipment and the random output model of wind power equipment with the goal of minimizing the total operating cost of the integrated energy system. According to the energy demand of the user, the pre-constructed integrated energy system planning model is solved by the integrated energy system planning model to obtain an energy capacity planning scheme for the target device, thereby obtaining the optimal installation capacity of the integrated power station of charging, swapping and storage, so as to meet the energy capacity planning according to the diversified needs of solving the user's electric energy replenishment method. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic flow chart of an energy capacity planning method based on an integrated charging, swapping and storage power station provided in the present invention application;

[0066] Figure 2 A flowchart of a specific use case of an energy capacity planning method based on an integrated charging, swapping and storage power station provided by the present invention;

[0067] Figure 3 A diagram of the energy conversion process for a certain area provided for the present invention application;

[0068] Figure 4 An architecture diagram of an energy capacity planning system based on an integrated charging, swapping and storage power station provided by the present invention;

[0069] Figure 5 A schematic diagram of the operation of an electronic device provided in the present invention application. DETAILED DESCRIPTION

[0070] The specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings.

[0071] Embodiment 1:

[0072] like Figure 1 As shown, the present invention application provides an energy capacity planning method based on an integrated charging, swapping and storage power station, which may include:

[0073] Step 1: According to the user's energy demand, select the target energy load type from the energy load type set of the integrated energy system;

[0074] Step 2: According to the load amount and equipment parameters required by the user and in combination with the target energy load type, a target device is selected from the equipment set of the integrated energy system, wherein the equipment set of the integrated energy system includes an integrated charging, swapping and storage power station;

[0075] Step 3: According to the target energy load type and the target equipment, a nonlinear programming solution method is used to solve the pre-constructed integrated energy system planning model to obtain the energy capacity planning scheme for the target equipment; wherein the integrated energy system planning model is constructed based on the random output model of photovoltaic equipment and the random output model of wind power equipment with the goal of minimizing the total operating cost of the integrated energy system.

[0076] In the above step 1, the energy load type set may include: electric load, thermal load, cooling load, gas load and hydrogen load.

[0077] In the above step 2, the equipment set may also include: gas turbine GT (Gas Turbine), gas boiler GB (Gas fired boiler), electric refrigeration machine EC (Electric refrigeration machine), absorption chiller AC (Absorption chiller), heat pump HP (heat pump), power to gas equipment P2G (Power to Gas), energy storage equipment ES (Energy storage equipment) whose energy types are electricity e (electric), heat h (heat), cold c (cold), gas g (gas), and hydrogen q (Hydrogen);

[0078] In the above step 2, the equipment set in the integrated energy system may also include: photovoltaic equipment and wind power equipment;

[0079] In the above step 3, the construction process of the integrated energy system planning model may include:

[0080] Step 3.1. According to the output characteristics of the photovoltaic equipment, uncertainty modeling is performed based on Beta distribution to obtain a random output model of the photovoltaic equipment;

[0081] Step 3.2. According to the output characteristics of the wind power equipment, uncertainty modeling is performed based on Weibull distribution to obtain a random output model of the wind power equipment;

[0082] Step 3.3. Based on the installed capacity of each device in the device set, establish a mathematical model, constraint conditions and a power balance model of the integrated energy system for each device;

[0083] Step 3.4. Based on the random output model of the photovoltaic equipment, the random output model of the wind power equipment, the mathematical model of each of the equipment, the constraints and the power balance model of the integrated energy system, a comprehensive energy system planning model is constructed with the goal of minimizing the total operating cost of the integrated energy system.

[0084] In the above step 3.1, the uncertainty modeling is performed based on the Beta distribution according to the output characteristics of the photovoltaic device to obtain a random output model of the photovoltaic device, which may include:

[0085] According to the output characteristics of photovoltaic equipment, the relationship between the output of photovoltaic equipment and light intensity is determined using probability density function;

[0086] Based on the relationship between the output of the photovoltaic device and the light intensity, a random output model of the photovoltaic device is constructed using Beta distribution.

[0087] The above is mainly based on the output characteristics of photovoltaic equipment, using probability density function to represent the relationship between photovoltaic equipment output and light intensity, and performing uncertainty modeling based on Beta distribution to obtain a random output model of photovoltaic equipment (PV);

[0088] The above-mentioned random output model of the photovoltaic device can be expressed as follows:

[0089]

[0090] In the above formula, f(I r ) is the probability density function of the actual value of the light intensity, α represents the degree to which the distribution form of the probability density curve shifts to the right; β represents the degree to which the distribution form of the probability density curve shifts to the left, Γ is the gamma function, Γ(p) is the solution value of the gamma function, I r is the actual value of light intensity, I rmax is the maximum light intensity, t is time t, P t,PV is the active power output of the photovoltaic device at time t; S is the total area of ​​the photovoltaic array in the photovoltaic device, η is the conversion efficiency of the photovoltaic array; p is a variable, t p-1 is the p-1 power of the illumination duration of the photovoltaic device, p is a positive integer greater than 1, φ is a natural constant, -t It is the natural constant raised to the power of -t.

[0091]

[0092] In the above formula: μ1 is the mean of the light intensity, σ1 is the variance of the mean of the light intensity.

[0093] After the random output model of the photovoltaic device is built, the random output model of the photovoltaic device can be used to calculate the output power of the photovoltaic device.

[0094] In the above step 3.2, performing uncertainty modeling based on Weibull distribution according to the output characteristics of the wind power equipment to obtain a random output model of the wind power equipment may include:

[0095] According to the output characteristics of wind power equipment, the relationship between the output of wind power equipment and wind speed is determined using probability density function;

[0096] Based on the relationship between the output of the wind power equipment and the wind speed, a random output model of the wind power equipment is constructed using Weibull distribution.

[0097] The above is mainly based on the output characteristics of wind power equipment, uses probability density function to represent the relationship between the output of wind power equipment and wind speed, adopts Weibull distribution to perform uncertainty modeling, and obtains the random output model of wind power equipment (WT).

[0098] The above-mentioned random output model of the wind power equipment can be expressed as follows:

[0099]

[0100] In the above formula, f(v) is the probability density function of wind speed, k is the shape parameter of wind, c is the scale parameter of wind, v is wind speed, exp is the exponential function with natural constant as base, P t,WT P is the active power output of the wind turbine generator in the wind power equipment at time t; r is the rated power of the wind turbine in the wind power equipment; v ci is the cut-in wind speed, v co is the cut-off wind speed, v r is the rated wind speed.

[0101] After the random output model of the wind power equipment is built, the output power of the wind power equipment can be calculated using the random output model of the wind power equipment.

[0102] In step 3.4, after establishing the mathematical model and constraints of the equipment set, a mathematical model of the power balance of the energy types of the integrated energy system is established.

[0103] Among them, the mathematical models of the above-mentioned equipment sets are as follows:

[0104] The mathematical model of photovoltaic equipment PV is as follows:

[0105] HPV =max[P t,PV ]

[0106] In the above formula, H PV Represents the installed capacity of photovoltaic equipment, P t,PV represents the output power of the photovoltaic device at time t;

[0107] The mathematical model of wind power equipment WT is as follows:

[0108] H WT =max[P t,WT ]

[0109] In the above formula, H WT Represents the installed capacity of wind power equipment, P t,WT It represents the active power output by the wind turbine generator in the wind power equipment at time t;

[0110] The mathematical model of the gas turbine GT is as follows:

[0111]

[0112] In the above formula, P t,GT is the electrical power output of the gas turbine GT at time t, is the amount of fuel consumed by the gas turbine at time t, η GT is the combustion efficiency of the gas turbine; h t,GT is the thermal power output of the gas turbine at time t, m e is the thermal-electric ratio of the gas turbine; GT Indicates whether the gas turbine is installed. is the lower limit of gas turbine installation capacity, H GT is the installed capacity of the gas turbine, The upper limit of gas turbine installed capacity; x t,GT Indicates whether the gas turbine is running at time t, is the lower limit of the gas turbine output power, is the upper limit of the gas turbine output power; ρ GT,min is the minimum load rate of the gas turbine; ρ GT,max is the maximum load rate of the gas turbine.

[0113] The mathematical model of the energy storage device ES is as follows:

[0114]

[0115] In the above formula, is the initial energy of the nth type of energy storage device, ε0 is the ratio of the initial capacity of the energy storage device to the energy storage capacity, H nES is the installed capacity of the nth type of energy storage equipment; S t＝1,nis the storage capacity of the nth type of energy storage device at time t=1, S t-1,n is the storage capacity of the nth type of energy storage device at time t-1, σ n is the self-damage rate of the nth type of energy storage equipment, is the initial energy of the nth type of energy storage device, is the charging power of the nth type of energy storage device at time t, is the charging efficiency of the nth type of energy storage device, is the energy release efficiency of the nth type of energy storage device; S t,n is the storage capacity of the nth type of energy storage device at time t, ρ n,min is the minimum load rate of the nth type of energy storage equipment, ρ n,max is the maximum load rate of the nth type of energy storage equipment; is the maximum charging rate of the nth type of energy storage device, is the maximum energy discharge rate of the nth type of energy storage device; is the charging installation factor of the nth type of energy storage device at time t; It is the charging installation factor and discharging installation factor of the nth type of energy storage equipment at time t; n is the energy type, including: electricity e, heat h, cold c, gas g, hydrogen q.

[0116] The mathematical model of the electric refrigerator EC and the absorption refrigerator AC is as follows:

[0117]

[0118] In the above formula, r t,AC is the output cooling power of the absorption refrigerator at time t, λ AC is the efficiency of the absorption chiller, h t,AC is the input power of the absorption refrigerator at time t; r t,EC is the output cooling power of the electric refrigerator at time t, λ EC is the efficiency of the electric refrigerator, p t,EC is the input power of the electric refrigerator at time t; y Z Indicates whether the Z-type refrigeration equipment is installed, H Z Indicates the installed capacity of the Z-type refrigeration equipment, The lower limit of installed capacity for Class Z refrigeration equipment, Upper limit of installed capacity for Class Z refrigeration equipment; x t,Z Indicates whether the Z-type refrigeration equipment is running. is the upper limit of the output power of Class Z refrigeration equipment, r t,Z is the output power of the Z-type refrigeration equipment at time t, It is the lower limit of the output power of Class Z refrigeration equipment; Z is the type of refrigeration equipment, including: electric refrigerator and absorption refrigerator.

[0119] The mathematical model of the heat pump HP is as follows:

[0120]

[0121] In the above formula, r t,HP is the cooling power output of the heat pump at time t, λ HP,r is the cooling efficiency of the heat pump, P t,HP is the input power of the heat pump at time t; h t,HP is the output thermal power of the heat pump at time t, λ HP,h is the heating efficiency of the heat pump; H HP Indicates the installed capacity of the heat pump, is the lower limit of the heat pump installation capacity, The upper limit of the heat pump installation capacity; x t,HP Indicates whether the heat pump HP is running at time t, is the lower limit of the cooling power output of the heat pump, The upper limit of the cooling power output of the heat pump; is the upper limit of the heat pump output thermal power, The lower limit of the heat pump output thermal power; HP Indicates whether the heat pump HP is installed.

[0122] The mathematical model of the gas boiler GB is as follows:

[0123]

[0124] In the above formula, h t,GB is the output thermal power of the gas boiler at time t, represents the amount of fuel consumed by the gas boiler at time t, η GB Indicates the combustion efficiency of the gas boiler; GB Indicates whether the gas boiler is installed. is the lower limit of gas boiler installation capacity, H GB is the installed capacity of the gas boiler, The upper limit of gas boiler installation capacity; x t,GB Indicates whether the gas boiler is running at time t. is the lower limit of the thermal power output of the gas boiler, It is the upper limit of the thermal power output of the gas boiler.

[0125] The mathematical model of the power-to-gas plant P2G is as follows:

[0126]

[0127] In the above formula, Q e-c,t is the hydrogen power generated by the electrolyzer of the power-to-gas equipment at time t, η e-c is the electricity-to-hydrogen conversion efficiency, P e-c,tThe electric power consumed by the electrolyzer device of the power-to-gas equipment at time t; is the amount of CO2 consumed by the power-to-gas equipment at time t, is the density of CO2, is the volume of CO2 consumed by the power-to-gas equipment at time t, is the volume of gas generated by the power-to-gas device at time t; is the hydrogen consumption of the power-to-gas equipment at time t, is the calorific value of CH4 from the power-to-gas equipment; is the power of natural gas generated by the power-to-gas device at time t, η P2G is the efficiency of the power-to-gas equipment; H P2G is the capacity of the power-to-gas equipment, The upper limit of the installed capacity of the power-to-gas equipment is The lower limit of installed capacity for power-to-gas equipment;

[0128] Upper limit for natural gas power generation for power-to-gas plants, The lower limit of natural gas power generation for power-to-gas equipment; P2G Indicates whether the power-to-gas device is installed; x t,P2G Indicates whether the power-to-gas device is running at time t.

[0129] Secondly, the mathematical model of the charging system BCS of the integrated charging, swapping and storage power station is as follows:

[0130]

[0131] In the above formula, P t,BCS,ch is the charging power of the charging system at time t, n PB,ch Indicates the number of power batteries PB being charged, P t,PB,ch represents the charging power of the power battery PB at time t, η ch Indicates charging efficiency; P t,BCS,dis is the discharge power of the charging system at time t, n PB,dis Indicates the number of power batteries PB being discharged, P t,PB,dis represents the discharge power of the power battery PB at time t, η dis Indicates discharge efficiency; P t,BCS is the total power of the charging system at time t; y BCS Indicates whether the charging system is installed, H BCS is the capacity of the charging system, Indicates the lower limit of the installed capacity of the charging system. Indicates the upper limit of the installed capacity of the charging system; is the upper limit of the output power of the charging system. is the lower limit of the charging system output power, x t,BCSIndicates whether the charging system is running at time t.

[0132] Secondly, the mathematical model of the battery swap system BSS of the integrated charging, swapping and storage power station is as follows:

[0133]

[0134] In the above formula, N t is the number of batteries in the battery swap system at time t, N t-1 is the number of batteries in the battery swap system at time t-1, N need represents the demand for battery replacement by electric vehicle (EV) users, t represents time t, and t tr Indicates the transportation time of the battery; P t,BSS,ch represents the charging power of the battery swap system at time t, x n,t is the grid-connected state variable of battery n in period t, 1 indicates that it is in the grid-connected state, and 0 indicates that it is in the off-grid state; represents the charging power of the battery at time t; P t,BSS,dis represents the discharge power of the battery swap system at time t, represents the discharge power of the battery at time t, B att is the collection of batteries in the battery replacement system; P t,BSS is the total power of the battery swap system at time t; BSS Indicates whether the battery replacement system is installed, H BSS is the capacity of the battery swap system; Indicates the upper limit of the installed capacity of the battery swap system. Indicates the lower limit of the installed capacity of the battery swap system; is the upper limit of the output power of the battery swap system. is the lower limit of the output power of the battery swap system; x t,BSS Indicates whether the battery swap system is running at time t.

[0135] The mathematical model for purchasing and selling electricity in the integrated energy system is as follows:

[0136]

[0137] In the above formula, P t,buy is the power purchased by the comprehensive energy system at time t, P t,sell is the electricity sales power of the integrated energy system at time t; is the upper limit of the power purchased by the integrated energy system, is the upper limit of the power sold by the integrated energy system. is the electricity purchasing factor of the comprehensive energy system at time t, is the electricity sales factor of the comprehensive energy system at time t.

[0138] In steps 3.3 and 3.4, the power balance model of the integrated energy system may include: a mathematical model of electric power balance, a mathematical model of thermal power balance, a mathematical model of cold power balance, a mathematical model of gas power balance, and a mathematical model of hydrogen power balance.

[0139] Among them, the mathematical model of electric power balance is as follows:

[0140]

[0141] In the above formula: P L,t is the load power at time t, is the discharge power of the energy storage device at time t, is the charging power of the energy storage device at time t;

[0142] Among them, the mathematical model of thermal power balance is as follows:

[0143]

[0144] In the above formula: h L,t is the heat load power at time t; is the heat release power in the energy storage device at time t; is the charging power in the energy storage device at time t;

[0145] The mathematical model of cooling power balance is as follows:

[0146]

[0147] In the above formula, r L,t is the cooling load power at time t; is the cooling power in the energy storage device at time t; is the charging and cooling power in the energy storage device at time t;

[0148] The mathematical model of gas power balance is as follows:

[0149]

[0150] In the above formula, g L,t is the gas load power at time t; is the deflation power of the energy storage device at time t; is the charging power of the energy storage device at time t;

[0151] The mathematical model of hydrogen power balance is as follows:

[0152]

[0153] In the above formula, q L,t is the hydrogen load power at time t; is the hydrogen release power of the hydrogen storage device at time t; is the hydrogen charging power of the hydrogen storage equipment at time t.

[0154] The integrated energy system planning model of the present invention is constructed based on the random output model of photovoltaic equipment and the random output model of wind power equipment with the goal of minimizing the total operating cost of the integrated energy system, wherein the total operating cost of the integrated energy system is minimized, including: the installation capacity of the target equipment in the integrated energy system is optimal and the total cost is minimized, and the economic cost and environmental cost are minimized; that is:

[0155] min C

[0156] Where C is the total cost of the integrated energy system;

[0157] In the above step 3.4, the total operating cost of the integrated energy system can be expressed as follows:

[0158]

[0159] In the above formula, C is the total cost of the integrated energy system, C inv is the investment cost of equipment in the integrated energy system; C op is the operating cost of the equipment; C fuel is the fuel cost in the comprehensive energy system; C grid is the electricity cost in the comprehensive energy system; C env is the environmental cost in the comprehensive energy system; s H is the equal annual value coefficient of the equipment set; s is the installation capacity construction cost of the equipment set, is the construction cost per unit capacity of the equipment set; is the operating cost per unit power of the equipment set, P t,s is the active power output by the device set at time t, where s represents the device set, which includes: photovoltaic device PV, wind power device WT, gas turbine GT, gas boiler GB, absorption chiller AC, electric chiller EC, heat pump HP, power-to-gas device P2G, charging system BCS, battery swap system BSS and energy storage device ES, and nES is the nth energy storage device; is the construction cost of the heat pump per heating unit capacity, is the construction cost per unit cooling capacity of the heat pump, δ fuel is the natural gas price; R g is the calorific value of natural gas combustion; δ t,buy represents the electricity purchase price at time t, δ t,sell represents the electricity price at time t; δ ctax E is the carbon tax; f is the amount of CO2 emitted from burning a unit of natural gas, Egrid is the amount of CO2 emitted per unit of electricity; η grid is the transmission efficiency of the power grid, is the amount of fuel consumed by the gas turbine at time t, Indicates the amount of fuel consumed by the gas boiler at time t.

[0160] The above step 4 is solved by using a mixed integer nonlinear model and outputs a planning result, which includes: the optimal capacity investment and the minimum total cost of the equipment.

[0161] like Figure 2 As shown, a flowchart of a specific use case of the energy capacity planning method based on an integrated charging, swapping and storage power station disclosed in the present invention, first, a random output model of photovoltaic equipment PV is built using Beta distribution to complete the uncertainty modeling of photovoltaic equipment; a random output model of wind power equipment WT is built using Weibull distribution to complete the uncertainty modeling of wind power equipment; then, based on the established comprehensive energy system planning model, and based on the known parameters of the target equipment, the target energy load and the energy demand of the user, a nonlinear programming method is used to solve the comprehensive energy system planning model, and the capacity configuration, optimal economic cost and carbon emissions of the target equipment are output; thereby, the optimal installation capacity of the target equipment under the minimum total cost is obtained.

[0162] For example: the area of ​​a certain area is 10km*33km, and its energy conversion process is as follows: Figure 3 The energy load in this area is electricity, heat, cold, gas and hydrogen. The energy conversion equipment is used to realize the mutual conversion between different energy sources, thus meeting the user's load demand.

[0163] First, according to the energy consumption statistics of the region, the annual energy consumption of electricity, heat, cooling, gas and hydrogen are 139997.546MWh, 56581.356MWh, 35016.165MWh, 5262.9Mm 3 h and 30729.1kg. Then, taking this area as an example, while meeting the energy demand of users, the total operating cost of the integrated energy system is minimized as the objective function, and nonlinear programming is used to solve the problem, and finally the planning results are output. This includes: the optimal capacity investment and construction of equipment and the minimum total cost. The minimum total cost is 126.4822 million yuan, and the optimal capacity of the equipment is shown in Table 1 below.

[0164] Table 1 Equipment capacity planning results (unit: kW)

[0165] equipment PV GT GB HP EC AC P2G BCS BSS eES HkD capacity 1000 636 500 600 300 280 200 460 500 800 600

[0166] The method of the present invention is applicable to the integrated energy system, rather than the "traditional single charging / swapping facility" in the prior art; the present invention fully considers the uncertainty of renewable energy generation, and uses Beta distribution and Weibull distribution to model the probability distribution of photovoltaic and wind power output, thereby smoothing the volatility of renewable energy, improving the wind and solar power consumption level and energy utilization rate, reducing the phenomenon of wind and solar power abandonment, ensuring that the system has sufficient regulation capacity and regulation space, and improving the system's operating efficiency and reliability.

[0167] Embodiment 2:

[0168] like Figure 4 As shown, the present invention application provides an energy system planning system based on an integrated charging, swapping and storage power station, including:

[0169] A target energy load type selection module is used to select a target energy load type from the energy load type set of the integrated energy system according to the energy demand of the user;

[0170] A target device selection module, for selecting a target device from a device set of the integrated energy system according to the load amount and device parameters required by the user and in combination with the target energy load type, wherein the device set of the integrated energy system includes an integrated charging, swapping and storage power station;

[0171] An energy capacity planning scheme acquisition module is used to solve the pre-built comprehensive energy system planning model using a nonlinear planning solution method according to the target energy load type and the target equipment, and obtain an energy capacity planning scheme for the target equipment;

[0172] The integrated energy system planning model is constructed based on the random output model of photovoltaic equipment and the random output model of wind power equipment with the goal of minimizing the total operating cost of the integrated energy system.

[0173] Furthermore, the equipment set in the integrated energy system also includes: photovoltaic equipment and wind power equipment;

[0174] The system also includes: a comprehensive energy system planning model building module, which is used to:

[0175] According to the output characteristics of the photovoltaic equipment, uncertainty modeling is performed based on Beta distribution to obtain a random output model of the photovoltaic equipment;

[0176] According to the output characteristics of the wind power equipment, uncertainty modeling is performed based on Weibull distribution to obtain a random output model of the wind power equipment;

[0177] Based on the installed capacity of each device in the device set, a mathematical model, constraint conditions and a power balance model of the integrated energy system are established for each device;

[0178] According to the random output model of the photovoltaic equipment, the random output model of the wind power equipment, the mathematical model of each of the equipment, the constraints and the power balance model of the integrated energy system, a comprehensive energy system planning model is constructed with the goal of minimizing the total operating cost of the integrated energy system.

[0179] Furthermore, the random output model of the photovoltaic device includes:

[0180] The output and light intensity relationship determination unit is used to determine the relationship between the output and light intensity of the photovoltaic device using a probability density function according to the output characteristics of the photovoltaic device;

[0181] The random output model building unit of the photovoltaic device is used to build the random output model of the photovoltaic device using Beta distribution based on the relationship between the output of the photovoltaic device and the light intensity.

[0182] Furthermore, the random output model of the wind power equipment includes:

[0183] The output and wind speed relationship determination unit is used to determine the relationship between the output and wind speed of the wind power equipment using a probability density function according to the output characteristics of the wind power equipment;

[0184] The random output model unit of the wind power equipment is used to build a random output model of the wind power equipment using Weibull distribution based on the relationship between the output of the wind power equipment and the wind speed.

[0185] Furthermore, the expression of the random output model of the photovoltaic device is as follows:

[0186]

[0187] In the above formula, f(I r ) is the probability density function of the actual value of the light intensity, α represents the degree to which the distribution form of the probability density curve shifts to the right; β represents the degree to which the distribution form of the probability density curve shifts to the left, Γ is the gamma function, Γ(p) is the solution value of the gamma function, I r is the actual value of light intensity, I rmax is the maximum light intensity, t is time t, P t,PV is the active power output of the photovoltaic device at time t; S is the total area of ​​the photovoltaic array in the photovoltaic device, η is the conversion efficiency of the photovoltaic array; p is a variable, t p-1 is the p-1 power of the illumination duration of the photovoltaic device, p is a positive integer greater than 1, φ is a natural constant, -t It is the natural constant raised to the power of -t.

[0188] Furthermore, the expression of the random output model of the wind power equipment is as follows:

[0189]

[0190] In the above formula, f(v) is the probability density function of wind speed, k is the shape parameter of wind, c is the scale parameter of wind, v is wind speed, exp is the exponential function with natural constant as base, P t,WT P is the active power output of the wind turbine generator in the wind power equipment at time t; r is the rated power of the wind turbine in the wind power equipment; v ci is the cut-in wind speed, v co is the cut-off wind speed, v r is the rated wind speed.

[0191] Furthermore, the total operating cost of the integrated energy system is expressed as follows:

[0192]

[0193] In the above formula, C is the total cost of the integrated energy system, C inv is the investment cost of equipment in the integrated energy system; C op is the operating cost of the equipment; C fuel is the fuel cost in the comprehensive energy system; C grid is the electricity cost in the comprehensive energy system; C env is the environmental cost in the comprehensive energy system; s H is the equal annual value coefficient of the equipment set; s is the installation capacity construction cost of the equipment set, is the construction cost per unit capacity of the equipment set; is the operating cost per unit power of the equipment set, P t,s is the active power output by the device set at time t, where s represents the device set, which includes: photovoltaic device PV, wind power device WT, gas turbine GT, gas boiler GB, absorption chiller AC, electric chiller EC, heat pump HP, power-to-gas device P2G, charging system BCS, battery swap system BSS and energy storage device ES, and nES is the nth energy storage device; is the construction cost of the heat pump per heating unit capacity, is the construction cost per unit cooling capacity of the heat pump, δ fuel is the natural gas price; R g is the calorific value of natural gas combustion; δ t,buy represents the electricity purchase price at time t, δ t,sell represents the electricity price at time t; δ ctax E is the carbon tax; f is the amount of CO2 emitted from burning a unit of natural gas, Egrid is the amount of CO2 emitted per unit of electricity; η grid is the transmission efficiency of the power grid, is the amount of fuel consumed by the gas turbine at time t, Indicates the amount of fuel consumed by the gas boiler at time t.

[0194] Furthermore, the energy load type set includes: electric load, heat load, cooling load, gas load and hydrogen load;

[0195] The power balance model of the comprehensive energy system includes: a mathematical model of electric power balance, a mathematical model of thermal power balance, a mathematical model of cold power balance, a mathematical model of gas power balance, and a mathematical model of hydrogen power balance.

[0196] Those skilled in the art should know that: most of the current research on the problem of characterizing the volatility of new energy power generation mainly focuses on scenario analysis and probability analysis methods to construct uncertainty models, and has not yet achieved a balance between the economy and power supply reliability of the integrated energy system. The system of the present invention is used to provide convenient charging services for electric vehicle users, and an integrated charging, swapping and storage power station is added to the integrated energy system. Compared with electric vehicle charging stations, the integrated charging, swapping and storage power station of the present invention can achieve decoupled calculations of power battery clusters and electric vehicle clusters in time, space and assets by adding battery swapping facilities, and can achieve a balance in the power supply reliability of the integrated energy system. By participating in the electric energy market and the frequency regulation market, carbon emissions and the occurrence of wind and light abandonment are reduced, which helps the peak and frequency regulation of the power grid; by adding energy storage facilities, distributed power sources can be absorbed, and emergency power supply support is provided. The use of integrated charging, swapping and storage power stations to achieve peak-to-valley shifting and improve the utilization rate of renewable energy, and the integration of charging, swapping and storage power stations with integrated energy systems can effectively achieve the economy of integrated energy systems.

[0197] Embodiment 3:

[0198] like Figure 5 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0199] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of an energy capacity planning method based on an integrated charging, swapping and storage power station in the above embodiment.

[0200] Embodiment 4:

[0201] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of an energy capacity planning method based on an integrated charging, swapping and storage power station in the above embodiment.

[0202] Those skilled in the art will appreciate that the embodiments of the present invention application can be provided as methods, systems, or computer program products. Therefore, the present invention application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present invention application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0203] The present invention application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present invention application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0204] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0205] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention application rather than to limit its protection scope. Although the present invention application is described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present invention application, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. An energy capacity planning method based on an integrated charging, swapping and storage power station, characterized in that: include: According to the energy demand of the user, a target energy load type is selected from the energy load type set of the integrated energy system; According to the load amount and equipment parameters required by the user, combined with the target energy load type, select the target equipment from the equipment set of the comprehensive energy system, and the equipment set of the comprehensive energy system includes an integrated charging, swapping and storage power station; According to the target energy load type and the target equipment, a pre-built integrated energy system planning model is solved by using a nonlinear programming method to obtain an energy capacity planning scheme for the target equipment; The integrated energy system planning model is constructed based on the random output model of photovoltaic equipment and the random output model of wind power equipment with the goal of minimizing the total operating cost of the integrated energy system.

2. The method according to claim 1, characterized in that The equipment set in the integrated energy system also includes: photovoltaic equipment and wind power equipment; The construction process of the comprehensive energy system planning model includes: According to the output characteristics of the photovoltaic equipment, uncertainty modeling is performed based on Beta distribution to obtain a random output model of the photovoltaic equipment; According to the output characteristics of the wind power equipment, uncertainty modeling is performed based on Weibull distribution to obtain a random output model of the wind power equipment; Based on the installed capacity of each device in the device set, a mathematical model, constraint conditions and a power balance model of the integrated energy system are established for each device; According to the random output model of the photovoltaic equipment, the random output model of the wind power equipment, the mathematical model of each of the equipment, the constraints and the power balance model of the integrated energy system, a comprehensive energy system planning model is constructed with the goal of minimizing the total operating cost of the integrated energy system.

3. The method according to claim 2, characterized in that According to the output characteristics of the photovoltaic device, uncertainty modeling is performed based on Beta distribution to obtain a random output model of the photovoltaic device, including: According to the output characteristics of photovoltaic equipment, the relationship between the output of photovoltaic equipment and light intensity is determined using probability density function; Based on the relationship between the output of the photovoltaic device and the light intensity, a random output model of the photovoltaic device is constructed using Beta distribution.

4. The method according to claim 2, characterized in that: According to the output characteristics of the wind power equipment, uncertainty modeling is performed based on Weibull distribution to obtain a random output model of the wind power equipment, including: According to the output characteristics of wind power equipment, the relationship between the output of wind power equipment and wind speed is determined using probability density function; Based on the relationship between the output of the wind power equipment and the wind speed, a random output model of the wind power equipment is constructed using Weibull distribution.

5. The method according to claim 3, characterized in that: The expression of the random output model of the photovoltaic equipment is as follows: In the above formula, f(I r ) is the probability density function of the actual value of the light intensity, α represents the degree to which the distribution form of the probability density curve shifts to the right; β represents the degree to which the distribution form of the probability density curve shifts to the left, Γ is the gamma function, Γ(p) is the solution value of the gamma function, I r is the actual value of light intensity, I rmax is the maximum light intensity, t is time t, P t,PV is the active power output of the photovoltaic device at time t; S is the total area of ​​the photovoltaic array in the photovoltaic device, η is the conversion efficiency of the photovoltaic array; p is a variable, t p-1 is the p-1 power of the illumination duration of the photovoltaic device, p is a positive integer greater than 1, φ is a natural constant, -t It is the natural constant raised to the power of -t.

6. The method according to claim 4, characterized in that The expression of the random output model of the wind power equipment is as follows: In the above formula, f(v) is the probability density function of wind speed, k is the shape parameter of wind, c is the scale parameter of wind, v is wind speed, exp is the exponential function with natural constant as base, P t,WT P is the active power output of the wind turbine generator in the wind power equipment at time t; r is the rated power of the wind turbine in the wind power equipment; v ci is the cut-in wind speed, v co is the cut-off wind speed, v r is the rated wind speed.

7. The method according to any one of claims 1 to 5, characterized in that: The total operating cost of the integrated energy system is expressed as follows: In the above formula, C is the total cost of the integrated energy system, C inv is the investment cost of equipment in the integrated energy system; C op is the operating cost of the equipment; C fuel is the fuel cost in the comprehensive energy system; C grid is the electricity cost in the comprehensive energy system; C env is the environmental cost in the comprehensive energy system; s H is the equal annual value coefficient of the equipment set; s is the installation capacity construction cost of the equipment set, is the construction cost per unit capacity of the equipment set; is the operating cost per unit power of the equipment set, P t,s is the active power output by the device set at time t, where s represents the device set, which includes: photovoltaic device PV, wind power device WT, gas turbine GT, gas boiler GB, absorption chiller AC, electric chiller EC, heat pump HP, power-to-gas device P2G, charging system BCS, battery swap system BSS and energy storage device ES, and nES is the nth energy storage device; is the construction cost of the heat pump per heating unit capacity, is the construction cost per unit cooling capacity of the heat pump, δ fuel is the natural gas price; R g is the calorific value of natural gas combustion; δ t,buy represents the electricity purchase price at time t, δ t,sell represents the electricity price at time t; δ ctax E is the carbon tax; f is the amount of CO2 emitted from burning a unit of natural gas, E grid is the amount of CO2 emitted per unit of electricity; η grid is the transmission efficiency of the power grid, is the amount of fuel consumed by the gas turbine at time t, represents the amount of fuel consumed by the gas boiler at time t, h t,GT is the thermal power output of the gas turbine at time t, h t,GB is the output thermal power of the gas boiler at time t, r t,AC is the output cooling power of the absorption refrigerator at time t, r t,EC is the cooling power output of the electric refrigerator at time t, h t,HP is the output thermal power of the heat pump at time t, r t,HP is the cooling power output of the heat pump at time t, Q e-c,t is the hydrogen power generated by the electrolyzer device of the power-to-gas equipment at time t, u is the combination of the charging system and the battery replacement system, BCS is the charging system, BSS is the battery replacement system, P t,u,dis The discharge power of the charging system and the battery swapping system at time t, P t,u,ch is the charging power of the charging system and the battery swapping system at time t, The operating cost per unit power of the charging system and the battery replacement system; is the energy release power of the nth type of energy storage device at time t, is the charging power of the nth type of energy storage device at time t, P t,sell is the electricity sales power of the integrated energy system at time t, The operating cost per unit power of the nth type of energy storage equipment, where n is the energy type, including: electricity e, heat h, cold c, gas g, hydrogen q; P t,buy is the purchased power of the comprehensive energy system at time t, The operating cost per unit of electricity generated by the gas turbine GT, The operating cost of a gas turbine GT per unit of heat energy produced.

8. The method according to claim 2, characterized in that: The energy load type set includes: electric load, heat load, cooling load, gas load and hydrogen load; The power balance model of the comprehensive energy system includes: a mathematical model of electric power balance, a mathematical model of thermal power balance, a mathematical model of cold power balance, a mathematical model of gas power balance, and a mathematical model of hydrogen power balance.

9. An energy capacity planning system based on an integrated charging, swapping and storage power station, characterized in that: include: A target energy load type selection module is used to select a target energy load type from the energy load type set of the integrated energy system according to the energy demand of the user; A target device selection module, for selecting a target device from a device set of the integrated energy system according to the load amount and device parameters required by the user and in combination with the target energy load type, wherein the device set of the integrated energy system includes an integrated charging, swapping and storage power station; An energy capacity planning scheme acquisition module is used to solve the pre-built comprehensive energy system planning model using a nonlinear planning solution method according to the target energy load type and the target equipment, and obtain an energy capacity planning scheme for the target equipment; The integrated energy system planning model is constructed based on the random output model of photovoltaic equipment and the random output model of wind power equipment with the goal of minimizing the total operating cost of the integrated energy system.

10. The system according to claim 9, characterized in that The equipment set in the integrated energy system also includes: photovoltaic equipment and wind power equipment; The system also includes: a comprehensive energy system planning model building module, which is used to: According to the output characteristics of the photovoltaic equipment, uncertainty modeling is performed based on Beta distribution to obtain a random output model of the photovoltaic equipment; According to the output characteristics of the wind power equipment, uncertainty modeling is performed based on Weibull distribution to obtain a random output model of the wind power equipment; Based on the installed capacity of each device in the device set, a mathematical model, constraint conditions and a power balance model of the integrated energy system are established for each device; According to the random output model of the photovoltaic equipment, the random output model of the wind power equipment, the mathematical model of each of the equipment, the constraints and the power balance model of the integrated energy system, a comprehensive energy system planning model is constructed with the goal of minimizing the total operating cost of the integrated energy system.

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