A modeling method of integrated energy system considering device variable working condition characteristics

Abstract

The application discloses a kind of considering equipment variable working condition characteristics comprehensive energy system's modeling method, comprising: 1, the dynamic energy hub model considering that energy conversion efficiency changes with load rate is established;2, the heat and power output mechanism of hydrogen fuel cell and cogeneration unit is analyzed, considering different factors influencing its heat and power output power, the heat and power output model of hydrogen fuel cell and cogeneration unit is established.3, considering the influence of equipment load rate on equipment operating efficiency, the functional relationship of equipment efficiency and load rate is obtained by polynomial fitting, and then the equipment model considering equipment variable working condition characteristics is established;4, based on the variable working condition equipment model established, the double-layer optimization model of comprehensive energy system is established.The application considers the influencing factors of equipment efficiency, can effectively reduce model error, improve wind power consumption degree, to promote comprehensive energy efficient use.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system optimization and control, and specifically to an integrated energy system modeling method that considers the variable operating conditions of equipment. Background Technology

[0002] As research into energy technologies deepens, multi-energy coordinated and complementary energy utilization methods are becoming increasingly widespread. The energy internet has become a key to integrating multiple energy sources and realizing energy reform, and the integrated energy system is an important carrier of the energy internet, enabling the coupling and complementarity of traditional electricity, heat, and natural gas networks to achieve multi-energy coupling and complementarity and tiered energy utilization.

[0003] Integrated energy systems often involve various energy devices operating under complex conditions, and the system's performance demands high accuracy from the device models. Current research, to reduce complexity, often assumes the operating efficiency of energy devices is constant, thus lowering the accuracy of the solutions. However, in actual operation, the efficiency of these devices often exhibits significant nonlinear characteristics due to variations in operating conditions. The neglect of these variable operating characteristics in traditional models reduces the accuracy of the device models, consequently affecting the final outcome of the decision-making process. Summary of the Invention

[0004] To overcome the shortcomings of the existing methods, this invention proposes a modeling method for integrated energy systems that considers the variable operating conditions of equipment, aiming to take into account the factors affecting equipment efficiency, improve the degree of wind power absorption, and thus promote the efficient utilization of integrated energy.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] The present invention provides a modeling method for an integrated energy system considering the variable operating conditions of equipment. The integrated energy system comprises: a proton exchange membrane electrolyzer, a methane reactor, a hydrogen fuel cell, a hydrogen storage tank, a combined heat and power (CHP) unit, and a gas-fired boiler. Wind power, the upstream power grid, and the gas grid supply the integrated energy system. Part of the electrical energy is directly supplied to the electrical load via a transformer, and the remaining electrical energy is converted into hydrogen by the proton exchange membrane electrolyzer and stored in the hydrogen storage tank for use by the hydrogen fuel cell and the methane reactor. The CHP unit and the gas-fired boiler are powered by natural gas produced by the methane reactor and the upstream gas grid. The modeling method is performed according to the following steps:

[0007] Step 1: Use equation (1) to establish a dynamic energy hub model that considers the change in energy conversion efficiency with load rate:

[0008]

[0009] In equation (1), Le,t L h,t These represent the electrical load demand and thermal load demand of the integrated energy system at time t, respectively; P vt,t P s,t These represent the wind power absorption capacity and the power purchased from the upstream power grid by the integrated energy system at time t, respectively; P g,t P o,t Let Q be the natural gas supply power and fuel oil supply power of the integrated energy system at time t, respectively; out,t and Q in,t It represents the energy released and stored by the hydrogen storage tank at time t; α e,t α h,t β and t are the energy coupling coefficients between electrical energy and electrical load and thermal load at time t, respectively; e,t β h,t γ represents the energy coupling coefficients of natural gas with electrical load and thermal load at time t, respectively; e,t γ h,t These are the energy coupling coefficients between the hydrogen energy in the hydrogen storage tank and the electrical load and thermal load at time t, respectively.

[0010] The energy coupling coefficient of the integrated energy system is obtained using equations (2), (3), and (4):

[0011]

[0012]

[0013]

[0014] In equations (2), (3), and (4), ν 1,t It is the energy distribution ratio at time t; ν 2,t It is the hydrogen energy distribution ratio at time t; ν 3,t η is the natural gas distribution ratio at time t; T It is the transformer efficiency; η EC,t η is the conversion efficiency of the electrolyzer at time t; MR,t η is the conversion efficiency of the methane reactor at time t; GB,t η is the conversion efficiency of the gas-fired boiler at time t; HFCe,t and η HFCh,t η is the conversion efficiency of hydrogen into electricity and heat via a hydrogen fuel cell at time t; CHPe,t and η CHPh,t It is the conversion efficiency of natural gas into electricity and heat through a combined heat and power unit at time t;

[0015] Step 2: Analyze the thermoelectric output mechanism of hydrogen fuel cells and cogeneration units, consider the different factors affecting their thermoelectric output power, and establish a thermoelectric output model for hydrogen fuel cells and cogeneration units.

[0016] Step 2.1: Construct a thermoelectric output model for hydrogen fuel cells:

[0017] Step 2.1.1: Determine the voltage V generated by a single hydrogen fuel cell using equation (5). cell :

[0018] V cell =E Nernst -η act -η ohm -η con (5)

[0019] In equation (5); E Nernst η is the Nernst voltage of a single cell, obtained from equation (6); act The activation polarization overvoltage loss of a single cell is given by equation (7); η ohm The ohmic overvoltage loss of a single cell is given by equation (8); η con The concentration overvoltage loss of a single cell is given by equation (9);

[0020]

[0021] In equation (6), λ n T is the standard coefficient; T is the stack temperature of the fuel cell; T b Standard ambient temperature; The standard entropy change is given by: F = Faraday constant; R = gas constant. These are the partial pressures of hydrogen, oxygen, and water vapor, respectively.

[0022]

[0023] In equation (7), ξ1, ξ2, ξ3, and ξ4 are four parameter coefficients; The oxygen concentration in a hydrogen fuel cell; i PEMFC The current density of a single cell in the stack;

[0024] η ohm =-i PEMFC ×(R electronic +R proton (8)

[0025] In equation (8), R electronic For electron flow resistance; R proton This represents the resistance to proton flow.

[0026] η con =mexp(ni PEMFC (9)

[0027] In equation (9), m is the conductivity of the electrolyte; n is the porosity of the gas diffusion layer;

[0028] Step 2.1.2: If N1 batteries are connected in series, then the output voltage V of the series-connected batteries can be obtained using equation (10). st :

[0029] V st =N1V cell (10)

[0030] If N2 batteries are connected in parallel, the current I of the parallel battery stack can be obtained using equation (11):

[0031]

[0032] In equation (11), Let t be the hydrogen input rate of the hydrogen fuel cell at time t; This refers to the molar mass of hydrogen gas.

[0033] The electrical power P of a hydrogen fuel cell stack consisting of multiple hydrogen fuel cells connected in series and parallel at time t is obtained using equation (12). HFCe,t :

[0034]

[0035] Step 2.1.4: Use equation (13) to obtain the thermal power P of the hydrogen fuel cell stack composed of multiple hydrogen fuel cells connected in series and parallel at time t. HFCh,t :

[0036] P HFCh,t =P HFCh,EX,t +P HFCh,AIR,t (13)

[0037] In equation (13): P HFCh,EX,t P represents the heat carried away by the cooling water from the hydrogen fuel cell stack at time t, obtained from equation (14); HFCh,AIR,t The heat dissipated into the air by the hydrogen fuel cell stack at time t is given by equation (15);

[0038]

[0039]

[0040] In equations (14) and (15), K and W are the heat transfer coefficient and rated flow rate of the heat exchanger, respectively; W L,t Let t be the flow rate of cooling water returning to the heating network; Let S be the cooling water flow rate at time t; A and B be the flow coefficients of the heat exchanger, respectively; S and S' are the flow rates of the cooling water at time t. AIR These are the heat exchanger area and the air contact area, respectively; K AIRT is the air heat dissipation coefficient; L T AIR These are the low-temperature water temperature and air temperature measured in the heat exchanger, respectively.

[0041] Using equation (16), the conversion efficiency η of hydrogen gas into electricity via a hydrogen fuel cell at time t is obtained. HFCe,t and the conversion efficiency η of converting to heat HFCh,t :

[0042]

[0043] In equation (17), P HFCin,t The dimensionless power conversion value of the hydrogen energy input to the hydrogen fuel cell stack at time t;

[0044] Step 2.2: Establish a thermal power output model for a combined heat and power (CHP) unit;

[0045] Step 2.2.1: When the cogeneration unit does not undergo supplementary combustion, the variable operating condition characteristics of the cogeneration unit are obtained using equation (17):

[0046]

[0047] In equation (17), P CHPin,t P represents the input natural gas power of the combined heat and power unit at time t; CHPe,t P CHPh,t These represent the output electrical and thermal power of the cogeneration unit at time t; P HFCN The rated electrical output power of the combined heat and power unit; n CHPe,i n CHPh,i These are the electrical efficiency function coefficient and the heat-to-power ratio function coefficient of a combined heat and power (CHP) unit, respectively; α CHPh,t,0 P represents the heat-to-power ratio of a combined heat and power (CHP) unit at time t under non-combustion conditions. CHPin,max P CHPin,min These represent the upper and lower limits of the input power of the combined heat and power unit, respectively; ΔP CHPmax ΔP CHPmin These are the upper and lower limits of the ramp power for combined heat and power units, respectively.

[0048] Step 2.2.2: Use equation (18) to obtain the control model of the afterburning on the thermoelectric ratio:

[0049]

[0050] In equation (18), α CHPh,t The heat-to-electric ratio after combustion at time t; k1 is the combustion coefficient; A is the combustion rate; P CHP,in The natural gas power input to the combined heat and power unit;

[0051] Step 3: Consider the impact of equipment load rate on equipment operating efficiency, obtain the functional relationship between equipment efficiency and load rate through polynomial fitting, and thus establish an equipment model that considers the variable operating conditions of the equipment.

[0052] Step 3.1: Construct a proton exchange membrane electrolyzer model using equation (19):

[0053]

[0054] In equation (19), P ECin,t P ECout,t η represents the dimensional conversion values ​​of the input electrical power and output hydrogen energy of the proton exchange membrane electrolyzer at time t, respectively; EC,t Let a be the energy conversion efficiency of the proton exchange membrane electrolyzer at time t; EC,i P is the efficiency conversion coefficient of the proton exchange membrane electrolyzer; ECN P is the rated power of the proton exchange membrane electrolyzer; ECin,max P ECin,min These represent the maximum and minimum input power of the proton exchange membrane electrolyzer, respectively; ΔP ECmax ΔP ECmin These represent the maximum and minimum ramp power of the proton exchange membrane electrolyzer, respectively.

[0055] Step 3.2: Construct a gas-fired boiler model using equation (20):

[0056]

[0057] In equation (20), P GBin,t P GBout,t η represents the dimensionless power of the natural gas input to the gas-fired boiler and the output thermal power at time t, respectively; GB,t Let a be the energy conversion efficiency of the gas-fired boiler at time t; GB,i P is the efficiency conversion factor for a gas-fired boiler. GBN P is the rated power of the gas-fired boiler. GBin,max P GBin,min These represent the maximum and minimum input power of the gas-fired boiler, respectively; ΔP GBmax ΔP GBmin These represent the maximum and minimum ramp power of the gas-fired boiler, respectively.

[0058] Step 3.3: Construct a methane reactor model using equation (21):

[0059]

[0060] In equation (21), P MRin,t P MRout,tη represents the dimensional equivalent power of hydrogen input to the methane reactor at time t, and the dimensional equivalent power of natural gas output, respectively; MR,t Let a be the energy conversion efficiency of the methane reactor at time t; MR,i P is the efficiency conversion coefficient of the methane reactor. MRN P is the rated power of the methane reactor. MRin,max P MRin,min These represent the maximum and minimum input power of the methane reactor, respectively; ΔP MRmax ΔP MRmin These represent the maximum and minimum ramp-up power of the methane reactor, respectively.

[0061] Step 3.4: Define the charging power / discharging power of the hydrogen storage tank as a constant value within the time interval Δt. Then, using equation (22), the energy storage relationship of the hydrogen storage tank at adjacent times can be obtained:

[0062] E t+1 =E t (1-λ ES )+P ES,t Δt (22)

[0063] In equation (22), E t+1 E t λ represents the energy stored in the hydrogen storage tank at adjacent times t and t+1. ES P represents the energy release rate of the hydrogen storage tank per unit time. ES,t Let P be the charging and discharging power of the hydrogen storage tank at time t, and let P be the power when charging the hydrogen storage tank. ES,t It is positive; Δt is the length of a unit time.

[0064] Energy storage constraints for constructing hydrogen storage tanks using equation (23):

[0065] E min ≤E t ≤E max (twenty three)

[0066] In equation (23), E max E min The maximum and minimum energy that the hydrogen storage tank is allowed to store;

[0067] The charging and discharging power constraints of the hydrogen storage tank are constructed using equation (24):

[0068]

[0069] In equation (24), P ES,C,max P ES,D,max These are the upper limits for charging power and discharging power of the hydrogen storage tank, respectively.

[0070] The ramp-up power limit for the hydrogen storage tank is constructed using equation (25):

[0071]

[0072] In equation (25), ΔP ES,C,max ΔP ES,C,min These represent the upper and lower limits of the ramp power during hydrogen storage tank charging, respectively; ΔP ES,D,max ΔP ES,D,min These are the upper and lower limits of the ramp power during hydrogen storage tank energy release, respectively;

[0073] Step 4: Based on the established variable operating condition equipment model, a two-layer optimization model for the integrated energy system is established, with wind power absorption level and environmental pollution level as the upper-level optimization objectives and energy efficiency as the lower-level optimization objective.

[0074] Step 4.1: Using equation (26), set the upper-level optimization objective F1 as the maximum difference between the wind power consumption level M and the environmental pollution level P, and the decision variables are the electricity allocation ratio, hydrogen energy allocation ratio, and natural gas allocation ratio:

[0075] maxF1=MP (26)

[0076] In equation (26), M represents the wind power absorption level, which is obtained from equation (27), and P represents the environmental pollution level, which is obtained from equation (28).

[0077]

[0078] In equation (27), α vt These are the weighting coefficients;

[0079]

[0080] In equation (28), C g C o These are the CO2 emission coefficients for the purchased natural gas and the purchased supplementary fuel oil, respectively; Q gas Q o The lower calorific values ​​are those of natural gas and supplementary fuel oil, respectively.

[0081] Step 4.2: Using equation (29), set the optimization objective F2 of the lower-level optimization model to the maximum energy efficiency, and the decision variables are the cooling water flow rate of the hydrogen fuel cell and the combustion rate of the cogeneration unit.

[0082]

[0083] Step 4.3: Constraints for constructing the two-level optimization model of the integrated energy system:

[0084] Step 4.3.1: Construct wind power output constraints using equation (30):

[0085] P VT,t (t)-P vt,t (t)≥0 (30)

[0086] In equation (30), P VT,t (t) represents the predicted wind power value at time t;

[0087] Step 4.3.2: Construct start-stop constraints for the hydrogen storage tank using equation (31):

[0088] U out,t +U in,t ≤1 (31)

[0089] In equation (31), U out,t Let U be a flag indicating when the hydrogen storage tank releases energy at time t. When the hydrogen storage tank releases energy at time t, let U... out,t Set U to 1; otherwise, set U to 1. out,t =0; U in,t Let U be a flag indicating the energy stored in the hydrogen storage tank at time t. When the hydrogen storage tank stores energy at time t, let U... in,t Set U to 1; otherwise, set U to 1. in,t =0;

[0090] Step 4.3.3: Construct natural gas constraints using equation (32):

[0091] P GBin,t +P CHPin,t ≥P MRout,t (32)

[0092] Step 4.4: Using equations (26) and (29) as the objective functions of the bi-level optimization model, under the constraints of the bi-level optimization model satisfying equations (1)-(4), (12), (14), (16)-(25), and (30)-(32), the bi-level optimization model is solved using a solver. The resulting decision includes: wind power consumption, purchased electricity, natural gas and fuel oil consumption, and the thermal power output of each energy device.

[0093] The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0094] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program is executed by a processor to perform the steps of the above-described method.

[0095] Compared with existing technologies, the beneficial effects of this invention are reflected in:

[0096] 1. This invention takes into account the change in energy conversion efficiency with load rate and establishes a dynamic energy hub model, making the energy hub model more accurate and better able to reflect the actual situation.

[0097] 2. This invention analyzes the thermoelectric output mechanism of hydrogen fuel cells and cogeneration units. The established thermoelectric power output model not only more accurately reflects the influencing factors of output power, but also clarifies its controllable quantity. By controlling the cooling water flow rate of hydrogen fuel cells and the combustion rate of cogeneration units, the thermoelectric output of the two can be controlled to a certain extent, thereby achieving the effect of optimizing thermoelectric coupling.

[0098] 3. The equipment model established by this invention, which considers the characteristics of variable operating conditions, reduces the error caused by constant efficiency and improves the accuracy of the model. This is of great significance for ensuring the reasonable operation of the system and better meeting actual needs.

[0099] 4. This invention uses a device model that considers the characteristics of varying operating conditions to establish an optimization model for the integrated energy system, which reduces model errors, improves the degree of wind power absorption, and promotes the efficient utilization of integrated energy. Attached Figure Description

[0100] Figure 1 This is a schematic diagram of the integrated energy system of the present invention;

[0101] Figure 2 This is a flowchart illustrating the modeling process of the integrated energy system based on the variable operating conditions of equipment according to the present invention. Detailed Implementation

[0102] The technical solution of the present invention will now be described more clearly and completely with reference to the embodiments and accompanying drawings.

[0103] In this embodiment, a modeling method for an integrated energy system considering the variable operating conditions of equipment is described, such as... Figure 1 As shown, the system includes: a proton exchange membrane electrolyzer, a methane reactor, a hydrogen fuel cell, a hydrogen storage tank, a combined heat and power (CHP) unit, and a gas-fired boiler. It utilizes wind power, the upstream power grid, and the gas grid as a comprehensive energy system. Part of the electricity is directly supplied to the electrical load via transformers, while the remaining electricity is converted into hydrogen by the proton exchange membrane electrolyzer and stored in the hydrogen storage tank for use by the hydrogen fuel cell and the methane reactor. The CHP unit and the gas-fired boiler are powered by natural gas produced by the methane reactor and the upstream gas grid. This modeling method is as follows: Figure 2 As shown, the specific steps are as follows:

[0104] Step 1: Use equation (1) to establish a dynamic energy hub model that considers the change in energy conversion efficiency with load rate:

[0105]

[0106] In equation (1), L e,t L h,t These represent the electrical load demand and thermal load demand of the integrated energy system at time t, respectively; P vt,t P s,t These represent the wind power absorption capacity and the power purchased from the upstream power grid by the integrated energy system at time t, respectively; P g,t P o,t Let Q be the natural gas supply power and fuel oil supply power of the integrated energy system at time t, respectively; out,t and Q in,t It represents the energy released and stored by the hydrogen storage tank at time t; α e,t α h,t β and t are the energy coupling coefficients between electrical energy and electrical load and thermal load at time t, respectively; e,t β h,t γ represents the energy coupling coefficients of natural gas with electrical load and thermal load at time t, respectively; e,t γ h,t These are the energy coupling coefficients between the hydrogen energy in the hydrogen storage tank and the electrical load and thermal load at time t, respectively.

[0107] The energy coupling coefficient of the integrated energy system is obtained using equations (2), (3), and (4):

[0108]

[0109]

[0110]

[0111] In equations (2), (3), and (4), ν 1,t It is the energy distribution ratio at time t; ν 2,t It is the hydrogen energy distribution ratio at time t; ν 3,t η is the natural gas distribution ratio at time t; T It is the transformer efficiency; η EC,t η is the conversion efficiency of the electrolyzer at time t; MR,t η is the conversion efficiency of the methane reactor at time t; GB,t η is the conversion efficiency of the gas-fired boiler at time t; HFCe,t and η HFCh,t η is the conversion efficiency of hydrogen into electricity and heat via a hydrogen fuel cell at time t; CHPe,t and η CHPh,t It is the conversion efficiency of natural gas into electricity and heat through a combined heat and power unit at time t;

[0112] This dynamic energy hub model is more closely aligned with the actual process, and its accuracy is significantly improved compared to traditional energy hubs.

[0113] Step 2: Analyze the thermoelectric output mechanism of hydrogen fuel cells and cogeneration units, consider the different factors affecting their thermoelectric output power, and establish thermoelectric output models for hydrogen fuel cells and cogeneration units.

[0114] Step 2.1: Analyze the internal operating mechanism of the hydrogen fuel cell and construct a thermoelectric output model of the hydrogen fuel cell. It can be found that its electrothermal output power is not only related to the input power, but also affected by the cooling water flow rate.

[0115] Step 2.1.1: Determine the voltage V generated by a single hydrogen fuel cell using equation (5). cell :

[0116] V cell =E Nernst -η act -η ohm -η con (5)

[0117] In equation (5); E Nernst η is the Nernst voltage of a single cell, obtained from equation (6); act The activation polarization overvoltage loss of a single cell is given by equation (7); η ohm The ohmic overvoltage loss of a single cell is given by equation (9); η con The concentration overvoltage loss of a single cell is given by equation (12);

[0118]

[0119] In equation (6), λ n T is the standard coefficient; T is the stack temperature of the fuel cell; T b Standard temperature; The standard entropy change is given by: F = Faraday constant; R = gas constant. These are the partial pressures of hydrogen, oxygen, and water vapor, respectively.

[0120]

[0121] In equation (7), ξ1, ξ2, ξ3, and ξ4 are four parameter coefficients; The oxygen concentration for the hydrogen fuel cell is given by equation (8); i PEMFC The current density of a single cell in the stack;

[0122]

[0123] η ohm =-i PEMFC ×(R electronic +R proton(9)

[0124] In equation (9), R electronic For electron flow resistance; R proton The resistance to proton flow is given by equation (10).

[0125]

[0126] In equation (10), r m is the membrane resistivity, obtained from equation (11); A is the effective area of ​​the unit cell; L is the thickness of the fuel cell polymer membrane.

[0127]

[0128] η con =mexp(ni PEMFC (12)

[0129] In equation (12), m is the conductivity of the electrolyte; n is the porosity of the gas diffusion layer;

[0130] Step 2.1.2: If N1 batteries are connected in series, then the output voltage V of the series-connected batteries can be obtained using equation (13). st :

[0131] V st =N1V cell (13)

[0132] If N2 batteries are connected in parallel, the stack current I after parallel connection can be obtained using equation (14):

[0133]

[0134] In equation (14), Let t be the hydrogen input rate of the hydrogen fuel cell at time t; This refers to the molar mass of hydrogen gas.

[0135] The electrical power P of a hydrogen fuel cell stack consisting of multiple hydrogen fuel cells connected in series and parallel at time t is obtained using equation (15). HFCe,t :

[0136]

[0137] Step 2.1.3: Considering that the faster the cooling water flow rate in the circulating heat dissipation system of a hydrogen fuel cell, the more obvious the heat dissipation effect, then under the condition that other factors remain unchanged, the cooling water flow rate will affect the stack temperature. The heat energy generated by the reaction cannot be fully utilized; some of the heat energy will be lost to the air. Using equation (16), the thermal power P of the hydrogen fuel cell stack composed of multiple hydrogen fuel cells connected in series and parallel at time t can be obtained. HFCh,t :

[0138] P HFCh,t =P HFCh,EX,t +P HFCh,AIR,t (16)

[0139] In equation (16): P HFCh,EX,t P represents the heat carried away by the cooling water from the hydrogen fuel cell stack at time t, obtained from equation (17); HFCh,AIR,t The heat dissipated into the air by the hydrogen fuel cell stack at time t is given by equation (18);

[0140]

[0141]

[0142] In equations (17) and (18), K and W are the heat transfer coefficient and rated flow rate of the heat exchanger, respectively; W L,t Let t be the flow rate of cooling water returning to the heating network; Let S be the cooling water flow rate at time t; A and B be the flow coefficients of the heat exchanger, respectively; S and S' are the flow rates of the cooling water at time t. AIR These are the heat exchanger area and the air contact area, respectively; K AIR T is the air heat dissipation coefficient; L T AIR These are the low-temperature water temperature and air temperature measured in the heat exchanger, respectively.

[0143] Using equation (19), the conversion efficiency η of hydrogen gas into electricity via a hydrogen fuel cell at time t is obtained. HFCe,t and the conversion efficiency η of converting to heat HFCh,t :

[0144]

[0145] In equation (19), P HFCin,t The dimensionless power conversion value of the hydrogen energy input to the hydrogen fuel cell stack at time t;

[0146] From equations (15) and (17), it can be seen that by controlling the hydrogen flow rate... and cooling water flow rate It is possible to control the thermoelectric output power of hydrogen fuel cells, thereby optimizing thermoelectric coupling and improving the energy utilization efficiency of the integrated energy system.

[0147] Step 2.2: Define the operating efficiency of a combined heat and power (CHP) unit as related to its input natural gas power. A CHP unit with a supplementary combustion device can control its thermal output to some extent by controlling the amount of supplementary combustion fuel. Establish a CHP unit thermal output model:

[0148] Step 2.2.1: Without supplementary combustion, the operating efficiency of the cogeneration unit is affected by the load rate. Its power generation efficiency is fitted as a fourth-order function of the electrical load rate, and its heat-to-power ratio is fitted as a quadratic function of the electrical load rate. Using equation (20), the variable operating condition characteristics of the cogeneration unit are obtained:

[0149]

[0150] In equation (20), P CHPin,t P represents the input natural gas power of the combined heat and power unit at time t; CHPe,t P CHPh,t These represent the output electrical and thermal power of the cogeneration unit at time t; P HFCN The rated electrical output power of the combined heat and power unit; n CHPe,i n CHPh,i These are the electrical efficiency function coefficient and the heat-to-power ratio function coefficient of a combined heat and power (CHP) unit, respectively; α CHPh,t,0 P represents the heat-to-power ratio of a combined heat and power (CHP) unit at time t under non-combustion conditions. CHPin,max P CHPin,min These represent the upper and lower limits of the input power of the combined heat and power unit, respectively; ΔP CHPmax ΔP CHPmin These are the upper and lower limits of the ramp power for combined heat and power units, respectively.

[0151] Step 2.2.2: By adding supplementary combustion fuel oil, the heat-to-power ratio of the cogeneration unit can be controlled, thereby controlling its heat output within a certain range and improving the overall energy utilization efficiency of the system. The control model of the supplementary combustion on the heat-to-power ratio is obtained using equation (21):

[0152]

[0153] In equation (21), α CHPh,t The heat-to-electricity ratio after combustion is given; k1 is the combustion coefficient; A is the combustion rate; P CHP,in It provides natural gas power to the combined heat and power (CHP) unit.

[0154] As can be seen from equation (21), by controlling the combustion rate, the heat output power of the cogeneration unit can be controlled, thereby optimizing the thermoelectric coupling and improving the energy utilization efficiency of the integrated energy system.

[0155] Step 3: Consider the impact of equipment load rate on equipment operating efficiency. Obtain the functional relationship between equipment efficiency and load rate through polynomial fitting, and then establish an equipment model that considers the variable operating conditions of the equipment.

[0156] Step 3.1: Define the working efficiency of the proton exchange membrane electrolyzer as related to its input electrical power. Fit its working efficiency as a quadratic function of the input electrical power, and unify the dimensions of its input electrical energy and output hydrogen energy into power. Construct a proton exchange membrane electrolyzer model using equation (22):

[0157]

[0158] In equation (22), P ECin,t P ECout,t η represents the dimensional conversion values ​​of the input electrical power and output hydrogen energy of the proton exchange membrane electrolyzer at time t, respectively; EC,t Let a be the energy conversion efficiency of the proton exchange membrane electrolyzer at time t; EC,i P is the efficiency conversion coefficient of the proton exchange membrane electrolyzer; ECN P is the rated power of the proton exchange membrane electrolyzer; ECin,max P ECin,min These represent the maximum and minimum input power of the proton exchange membrane electrolyzer, respectively; ΔP ECmax ΔP ECmin These represent the maximum and minimum ramp power of the proton exchange membrane electrolyzer, respectively.

[0159] Step 3.2: Define the working efficiency of a gas-fired boiler as related to its input natural gas power. Fit its working efficiency as a linear function of the input natural gas power, and unify the dimensions of its input natural gas energy and output heat energy into power. Construct a gas-fired boiler model using equation (23):

[0160]

[0161] In equation (23), P GBin,t P GBout,t η represents the dimensionless power of the natural gas input to the gas-fired boiler and the output thermal power at time t, respectively; GB,t Let a be the energy conversion efficiency of the gas-fired boiler at time t; GB,i P is the efficiency conversion factor for a gas-fired boiler. GBN P is the rated power of the gas-fired boiler. GBin,max P GBin,min These represent the maximum and minimum input power of the gas-fired boiler, respectively; ΔP GBmax ΔP GBmin These represent the maximum and minimum ramp power of the gas-fired boiler, respectively.

[0162] Step 3.3: Define the working efficiency of the methane reactor as related to its input hydrogen power, fit its working efficiency as a linear function of the input hydrogen power, and unify the dimensions of the input hydrogen energy and output natural gas energy into power. Construct a methane reactor model using equation (24):

[0163]

[0164] In equation (24), P MRin,t P MRout,t η represents the dimensional equivalent power of hydrogen input to the methane reactor at time t, and the dimensional equivalent power of natural gas output, respectively; MR,t Let a be the energy conversion efficiency of the methane reactor at time t; MR,i P is the efficiency conversion coefficient of the methane reactor. MRN P is the rated power of the methane reactor. MRin,max P MRin,min These represent the maximum and minimum input power of the methane reactor, respectively; ΔP MRmax ΔP MRmin These represent the maximum and minimum ramp-up power of the methane reactor, respectively.

[0165] Step 3.4: Define the charging power / discharging power of the hydrogen storage tank as a constant value within the time interval Δt. Then, using equation (25), the energy storage relationship of the hydrogen storage tank at adjacent times can be obtained:

[0166] E t+1 =E t (1-λ ES )+P ES,t Δt (25)

[0167] In equation (25), E t+1 E t λ represents the energy stored in the hydrogen storage tank at adjacent times t and t+1. ES P represents the energy release rate of the hydrogen storage tank per unit time. ES,t Let P be the charging and discharging power of the hydrogen storage tank at time t, and let P be the power when charging the hydrogen storage tank. ES,t It is positive; Δt is the length of a unit time.

[0168] Energy storage constraints for constructing hydrogen storage tanks using equation (26):

[0169] E min ≤E t ≤E max (26)

[0170] In equation (26), E max E min The maximum and minimum energy that the hydrogen storage tank is allowed to store;

[0171] The charging and discharging power constraints of the hydrogen storage tank are constructed using equation (27):

[0172]

[0173] In equation (27), PES,C,max P ES,D,max These are the upper limits for charging power and discharging power of the hydrogen storage tank, respectively.

[0174] The ramp-up power limit for the hydrogen storage tank is constructed using equation (28):

[0175]

[0176] In equation (28), ΔP ES,C,max ΔP ES,C,min These represent the upper and lower limits of the ramp power during hydrogen storage tank charging, respectively; ΔP ES,D,max ΔP ES,D,min These are the upper and lower limits of the ramp power during hydrogen storage tank energy release, respectively;

[0177] Step 4: Based on the established variable operating condition equipment model, with wind power absorption level and environmental pollution level as the upper-level optimization objectives and energy efficiency as the lower-level optimization objective, a two-level optimization model of the integrated energy system is established, and the gurobi solver is called to solve it. The upper-level strategy is continuously updated and iterated according to the results of the lower-level optimization until the optimal solution is reached.

[0178] Step 4.1: Using equation (29), set the upper-level optimization objective F1 as the maximum difference between the wind power consumption level M and the environmental pollution level P, and the decision variables are the electricity allocation ratio, hydrogen energy allocation ratio, and natural gas allocation ratio:

[0179] maxF1=MP (29)

[0180] In equation (29), M represents the wind power absorption level, which is obtained from equation (30), and P represents the environmental pollution level, which is obtained from equation (31).

[0181] The wind power absorption level is shown in equation (30).

[0182]

[0183] In equation (30), α vt These are the weighting coefficients;

[0184]

[0185] In equation (31), C g C o These are the CO2 emission coefficients for the purchased natural gas and the purchased supplementary fuel oil, respectively; Q gas Q o The lower calorific values ​​are those of natural gas and supplementary fuel oil, respectively.

[0186] Step 4.2: Using equation (32), set the optimization objective F2 of the lower-level optimization model to the maximum energy efficiency, and the decision variables are the cooling water flow rate of the hydrogen fuel cell and the combustion rate of the cogeneration unit.

[0187]

[0188] Step 4.3: Constraints for constructing the two-level optimization model of the integrated energy system:

[0189] Step 4.3.1: Construct wind power output constraints using equation (33):

[0190] P VT,t (t)-P vt,t (t)≥0 (33)

[0191] In equation (33), P VT,t (t) represents the predicted wind power value at time t;

[0192] Step 4.3.2: Construct start-stop constraints for the hydrogen storage tank using equation (34):

[0193] U out,t +U in,t ≤1 (34)

[0194] In equation (34), U out,t Let U be a flag indicating when the hydrogen storage tank releases energy at time t. When the hydrogen storage tank releases energy at time t, let U... out,t Set U to 1; otherwise, set U to 1. out,t =0; U in,t Let U be a flag indicating the energy stored in the hydrogen storage tank at time t. When the hydrogen storage tank stores energy at time t, let U... in,t Set U to 1; otherwise, set U to 1. in,t =0;

[0195] Step 4.3.3: Construct natural gas constraints using equation (35):

[0196] P GBin,t +P CHPin,t ≥P MRout,t (35)

[0197] Step 4.4: Using equations (29) and (32) as the objective functions of the bi-level optimization model, under the constraints of the bi-level optimization model satisfying equations (1)-(4), (15), (17), (19)-(28), and (33)-(35), the bi-level optimization model is solved using a solver. The resulting decision includes: wind power consumption, purchased electricity, natural gas and fuel oil consumption, and the thermal power output of each energy device.

[0198] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0199] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

Claims

1. A modeling method for a comprehensive energy system considering the variable operating conditions of equipment, characterized in that, The integrated energy system includes: a proton exchange membrane electrolyzer, a methane reactor, a hydrogen fuel cell, a hydrogen storage tank, a combined heat and power (CHP) unit, and a gas-fired boiler. It utilizes wind power, the upstream power grid, and the gas grid to power the integrated energy system. Part of the electricity is directly supplied to the electrical load via a transformer, and the remaining electricity is converted into hydrogen by the proton exchange membrane electrolyzer and stored in the hydrogen storage tank for use by the hydrogen fuel cell and the methane reactor. The CHP unit and the gas-fired boiler are powered by natural gas produced by the methane reactor and the upstream gas grid. The modeling method is performed according to the following steps: Step 1: Use equation (1) to establish a dynamic energy hub model that considers the change in energy conversion efficiency with load rate: (1) In equation (1), , These represent the electrical load demand and thermal load demand of the integrated energy system at time t, respectively. , These represent the wind power absorption capacity and the power purchased from the upper-level power grid by the integrated energy system at time t, respectively. , These represent the natural gas supply power and fuel oil supply power of the integrated energy system at time t, respectively. and It represents the energy release and storage power of the hydrogen storage tank at time t; , These are the energy coupling coefficients between electrical energy and electrical load and thermal load at time t, respectively. , These are the energy coupling coefficients of natural gas with electrical load and thermal load at time t, respectively. , These are the energy coupling coefficients between the hydrogen energy in the hydrogen storage tank and the electrical load and thermal load at time t, respectively. The energy coupling coefficient of the integrated energy system is obtained using equations (2), (3), and (4): (2) (3) (4) In equations (2), (3), and (4), It is the energy distribution ratio at time t; It represents the hydrogen energy distribution ratio at time t; It is the natural gas distribution ratio at time t; It refers to transformer efficiency; It is the conversion efficiency of the electrolytic cell at time t; It is the conversion efficiency of the methane reactor at time t; It represents the conversion efficiency of the gas-fired boiler at time t; and It is the conversion efficiency of hydrogen into electricity and heat through a hydrogen fuel cell at time t; and It is the conversion efficiency of natural gas into electricity and heat through a combined heat and power unit at time t; Step 2: Analyze the thermoelectric output mechanism of hydrogen fuel cells and cogeneration units, consider the different factors affecting their thermoelectric output power, and establish a thermoelectric output model for hydrogen fuel cells and cogeneration units. Step 2.1: Construct a thermoelectric output model for hydrogen fuel cells: Step 2.1.1: Determine the voltage generated by a single hydrogen fuel cell using equation (5). : (5) In equation (5); The Nernst voltage of a single cell is given by equation (6); The activation polarization overvoltage loss of a single cell is given by equation (7); The ohmic overvoltage loss of a single cell is given by equation (8); The concentration overvoltage loss of a single cell is given by equation (9); (6) In equation (6), is the standard coefficient; T is the stack temperature of the fuel cell; Standard ambient temperature; The standard entropy change is the value under standard atmospheric pressure; F is the Faraday constant. It is the gas constant; , , These are the partial pressures of hydrogen, oxygen, and water vapor, respectively. (7) In equation (7), , , , There are 4 parameter coefficients; The oxygen concentration in the hydrogen fuel cell; The current density of a single cell in the stack; (8) In equation (8), This represents resistance to electron flow. This represents the resistance to proton flow. (9) In equation (9), m is the conductivity of the electrolyte; n is the porosity of the gas diffusion layer; Step 2.1.2: If N1 batteries are connected in series, then the output voltage of the series-connected batteries can be obtained using equation (10). : (10) If N2 batteries are connected in parallel, the current of the parallel stack can be obtained using equation (11). : (11) In equation (11), Let t be the hydrogen input rate of the hydrogen fuel cell at time t; This refers to the molar mass of hydrogen gas. The electrical power of a hydrogen fuel cell stack consisting of multiple hydrogen fuel cells connected in series and parallel at time t is obtained using equation (12). : (12) Step 2.1.4: Use equation (13) to obtain the thermal power of the hydrogen fuel cell stack, which consists of multiple hydrogen fuel cells connected in series and parallel, at time t. : (13) In equation (13): The heat carried away by the cooling water from the hydrogen fuel cell stack at time t is given by equation (14). The heat dissipated into the air by the hydrogen fuel cell stack at time t is given by equation (15); (14) (15) In equations (14) and (15), K and W are the heat transfer coefficient and rated flow rate of the heat exchanger, respectively. Let t be the flow rate of cooling water returning to the heating network; Let be the cooling water flow rate at time t; A and B are the flow coefficients of the heat exchanger, respectively. , These are the heat exchanger area and the air contact area, respectively. The air heat dissipation coefficient; , These are the low-temperature water temperature and air temperature measured in the heat exchanger, respectively. Equation (16) is used to obtain the conversion efficiency of hydrogen gas into electricity via a hydrogen fuel cell at time t. and the conversion efficiency of heat : (16) In equation (17), The dimensionless power conversion value of the hydrogen energy input to the hydrogen fuel cell stack at time t; Step 2.2: Establish a thermal power output model for a combined heat and power (CHP) unit; Step 2.2.1: When the cogeneration unit does not undergo supplementary combustion, the variable operating condition characteristics of the cogeneration unit are obtained using equation (17): (17) In equation (17), The input natural gas power of the combined heat and power unit at time t; , These represent the output electrical and thermal power of the cogeneration unit at time t, respectively. This refers to the rated electrical output power of the combined heat and power unit. , These are the electrical efficiency function coefficient and the heat-to-power ratio function coefficient of the combined heat and power unit, respectively. The heat-to-power ratio of a combined heat and power unit at time t under non-combustion conditions; , These are the upper and lower limits of the input power of the combined heat and power unit, respectively. , These are the upper and lower limits of the ramp power for combined heat and power units, respectively. Step 2.2.2: Use equation (18) to obtain the control model of the afterburning on the thermoelectric ratio: (18) In equation (18), The heat-to-electric ratio after reburning at time t; This refers to the afterburning coefficient; For afterburner rate; Step 3: Consider the impact of equipment load rate on equipment operating efficiency, obtain the functional relationship between equipment efficiency and load rate through polynomial fitting, and thus establish an equipment model that considers the variable operating conditions of the equipment. Step 3.1: Construct a proton exchange membrane electrolyzer model using equation (19): (19) In equation (19), , These are the power dimension-converted values ​​of the input electrical power and output hydrogen energy of the proton exchange membrane electrolyzer at time t, respectively. Let be the energy conversion efficiency of the proton exchange membrane electrolyzer at time t; The efficiency conversion coefficient of the proton exchange membrane electrolyzer; This refers to the rated power of the proton exchange membrane electrolyzer. , These represent the maximum and minimum input power of the proton exchange membrane electrolyzer, respectively. , These represent the maximum and minimum ramp power of the proton exchange membrane electrolyzer, respectively. Step 3.2: Construct a gas-fired boiler model using equation (20): (20) In equation (20), , These represent the dimensionless power input to the gas-fired boiler and the output thermal power at time t, respectively. Let be the energy conversion efficiency of the gas-fired boiler at time t; This is the efficiency conversion coefficient for a gas-fired boiler. This refers to the rated power of the gas-fired boiler. , These represent the maximum and minimum input power of the gas-fired boiler, respectively. , These represent the maximum and minimum ramp power of the gas-fired boiler, respectively. Step 3.3: Construct a methane reactor model using equation (21): (21) In equation (21), , These are the dimensionless power conversion values ​​of the hydrogen energy input and the natural gas output of the methane reactor at time t, respectively. Let be the energy conversion efficiency of the methane reactor at time t; The efficiency conversion coefficient of the methane reactor; This refers to the rated power of the methane reactor; , These represent the maximum and minimum input power of the methane reactor, respectively. , These represent the maximum and minimum ramp-up power of the methane reactor, respectively. Step 3.4: Define the hydrogen storage tank. If the charging power / discharging power is a constant value within a time period, then the energy storage relationship of the hydrogen storage tank at adjacent times can be obtained using equation (22): (22) In equation (22), , The energy stored in the hydrogen storage tank at adjacent times t and t+1; This represents the energy release rate of the hydrogen storage tank per unit time. Let be the charging and discharging power of the hydrogen storage tank at time t, and let be the charging and discharging power of the hydrogen storage tank at time t. It is positive; The unit of time length; Energy storage constraints for constructing hydrogen storage tanks using equation (23): (23) In equation (23), , The maximum and minimum energy that the hydrogen storage tank is allowed to store; The charging and discharging power constraints of the hydrogen storage tank are constructed using equation (24): (24) In equation (24), , These are the upper limits for charging power and discharging power of the hydrogen storage tank, respectively. The ramp-up power limit for the hydrogen storage tank is constructed using equation (25): (25) In equation (25), , These are the upper and lower limits of the ramp power during hydrogen storage tank charging, respectively. , These are the upper and lower limits of the ramp power during hydrogen storage tank energy release, respectively; Step 4: Based on the established variable operating condition equipment model, a two-layer optimization model for the integrated energy system is established, with wind power absorption level and environmental pollution level as the upper-level optimization objectives and energy efficiency as the lower-level optimization objective. Step 4.1: Use equation (26) to set the upper-level optimization objective. The decision variables are the maximum difference between the wind power consumption level M and the environmental pollution level P, and the decision variables are the electricity allocation ratio, the hydrogen energy allocation ratio, and the natural gas allocation ratio. (26) In equation (26), M represents the wind power absorption level, which is obtained from equation (27), and P represents the environmental pollution level, which is obtained from equation (28). (27) In equation (27), These are the weighting coefficients; (28) In equation (28), , These are the purchased natural gas and the purchased supplementary fuel oil, respectively. Emission coefficient; , The lower calorific values ​​are those of natural gas and supplementary fuel oil, respectively. Step 4.2: Use equation (29) to set the optimization objective of the lower-level optimization model. To maximize energy efficiency, the decision variables are the cooling water flow rate of the hydrogen fuel cell and the afterburning rate of the cogeneration unit. (29) Step 4.3: Constraints for constructing the two-level optimization model of the integrated energy system: Step 4.3.1: Construct wind power output constraints using equation (30): (30) In equation (30), Let be the predicted wind power value at time t; Step 4.3.2: Construct start-stop constraints for the hydrogen storage tank using equation (31): (31) In equation (31), Let be a flag indicating when the hydrogen storage tank releases energy at time t. When the hydrogen storage tank releases energy at time t, let Set it to 1; otherwise, let it be 1. =0; Let be a flag indicating the energy stored in the hydrogen storage tank at time t. When the hydrogen storage tank stores energy at time t, let... Set it to 1; otherwise, let it be 1. =0; Step 4.3.3: Construct natural gas constraints using equation (32): (32) Step 4.4: Using equations (26) and (29) as the objective functions of the bi-level optimization model, under the constraints of the bi-level optimization model satisfying equations (1)-(4), (12), (14), (16)-(25), and (30)-(32), the bi-level optimization model is solved using a solver. The resulting decision includes: wind power consumption, purchased electricity, natural gas and fuel oil consumption, and the thermal power output of each energy device.

2. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports a processor in executing the method of claim 1, the processor being configured to execute the program stored in the memory.

3. A computer-readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to perform the steps of the method of claim 1.

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