Integrated energy system scheduling optimization method considering equipment time-sharing output constraint

Abstract

The invention provides an integrated energy system scheduling optimization method considering equipment time-sharing output constraint, which relates to the technical field of energy systems and comprises the following steps: S1, acquiring an objective function based on an annual operation cost saving rate, a primary energy saving rate and a carbon dioxide emission reduction rate of equipment, obtaining a first constraint condition based on an energy balance constraint, an energy storage device constraint and an area constraint, and performing iterative optimization on the target function based on the first constraint condition to obtain an optimal capacity; s2, obtaining a variable efficiency model based on a load efficiency function of the production type equipment and a temperature efficiency function of the conversion type equipment, and updating the first constraint condition based on the variable efficiency model and a time-sharing output constraint to obtain a second constraint condition, and performing iterative optimization on the target function based on the second constraint condition to obtain an optimal time-sharing output constraint. According to the method, the time-sharing output constraint is applied to the variable-working-condition integrated energy system, and the scheduling accuracy of the integrated energy system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy systems, and in particular to a method for optimizing the scheduling of an integrated energy system taking into account equipment time-sharing output constraints. Background Art

[0002] In a variable operating condition integrated energy system, there are obvious differences in the efficiency of the equipment in the integrated energy system under different environments and load rates. For example, the efficiency of equipment such as gas turbines and generator sets will change with changes in operating conditions. According to the load rate-efficiency characteristics of the equipment, when the load of the equipment is very low, the actual efficiency of the equipment is also very small, and some equipment will generate more energy consumption and carbon emissions when operating at low power, which will reduce the efficiency of the integrated energy system.

[0003] Therefore, it is urgent to provide a solution to improve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a comprehensive energy system scheduling optimization method taking into account the time-sharing output constraints of equipment, so as to improve the problems existing in the above-mentioned background technology.

[0005] The present invention provides a comprehensive energy system scheduling optimization method considering equipment time-sharing output constraints, which adopts the following technical solutions:

[0006] The objective function is obtained based on the annual operation cost saving rate, primary energy saving rate and carbon dioxide emission reduction rate of the equipment, the first constraint condition is obtained based on the energy balance constraint, the energy storage device constraint and the area constraint, and the objective function is iteratively optimized based on the first constraint condition to obtain the optimal capacity, wherein the equipment is composed of production equipment and conversion equipment;

[0007] A variable efficiency model is obtained based on the load efficiency function of the production equipment and the temperature efficiency function of the conversion equipment. The first constraint condition is updated based on the variable efficiency model and the time-sharing output constraint to obtain the second constraint condition. Based on the second constraint condition, the objective function is iteratively optimized to obtain the optimal time-sharing output constraint.

[0008] The beneficial effects of the integrated energy system scheduling optimization method proposed by the present invention that takes into account the time-sharing output constraints of equipment are: first, it takes into account the impact of temperature and load rate on equipment efficiency, effectively reduces the situation where the equipment operates at a low load rate, and is more in line with real life; second, time-sharing output constraints are set according to different seasons, and the scheduling of equipment is more accurate.

[0009] Optionally, when obtaining the objective function based on the annual computing cost saving rate, primary energy saving rate and carbon dioxide emission reduction rate of the equipment,

[0010] The expression of the annual operation cost saving rate PECSR is:

[0011]

[0012] Among them, F b(SP) is the fuel consumed by the SP system boiler, F b(CCHP) is the fuel consumed by the CCHP system boiler, E grid(SP) The power purchased from the SP system, E grid(CCHP) is the power grid purchase amount of CCHP system, η grid(SP) is the transmission and distribution efficiency of the SP system considering grid losses, η grid(CCHP) is the transmission and distribution efficiency of CCHP system considering grid loss, F gt(CCHP) It is the fuel consumed by the gas turbine of the CCHP system.

[0013] The expression of the primary energy saving rate ATCSR is:

[0014]

[0015] in, I is the total number of devices, I i is the capacity of device i, A i is the unit capacity cost, r is the annual interest rate, l is the number of years, OM i is the proportion of equipment i-year maintenance cost to equipment investment, η1 is the natural gas price, η2 is the power purchase price of the power grid, E loss Q is the power wasted by the system. loss is the amount of heat wasted by the system, P1 is the penalty coefficient for wasted electricity, and P2 is the penalty coefficient for wasted heat.

[0016] The expression of the carbon dioxide emission reduction rate CDESR is:

[0017]

[0018] Among them, μ1 is the CO2 emission coefficient of burning natural gas, and μ2 is the CO2 emission coefficient of purchasing electricity from the power grid.

[0019] The expression of the objective function is:

[0020] Objective=ω1PECSR+ω2ATCSR+ω3CDESR;

[0021] Among them, ω1, ω2 and ω3 are the weights of annual operating cost saving rate, primary energy saving rate and carbon dioxide emission reduction rate respectively, and Objective is the objective function.

[0022] Optionally, the production equipment includes: a gas turbine GT, a photovoltaic panel PV, a solar collector ST, and a supplementary combustion boiler GB; the conversion equipment includes: an electric refrigerator EC, an absorption refrigerator AC, a heat storage tank TST, a battery BA, and a heat exchanger HE.

[0023] Optionally, the second constraint condition consists of an energy balance constraint, an energy storage device constraint, an area constraint and a time-sharing output constraint.

[0024] Optionally, the energy storage device constraint consists of an electric energy constraint and an energy constraint, wherein:

[0025] The expression of the electric energy constraint is:

[0026]

[0027] Among them, E bat (t) is the amount of electricity in the battery at time t; and is the charging and discharging power of the battery at time t; and Battery charging and discharging efficiency; and To charge and discharge binary variables;

[0028] The expression of the energy constraint is:

[0029]

[0030] Among them, Q tst (t) is the amount of heat already in the heat storage tank at time t; and is the charging and discharging power of the heat storage tank at time t; and is the charging and discharging efficiency of the heat storage tank; and To charge and discharge binary variables.

[0031] Optionally, the expression of the area constraint is:

[0032] A pv +A st ≤A total ;

[0033] Among them, A pv is the photovoltaic panel laying area, A st is the area for laying solar thermal collectors, A total The total area that can provide photovoltaic and solar thermal collectors for the park.

[0034] Optionally, the expression of the time-sharing output constraint is:

[0035]

[0036] Among them, η gt,e is the electrical efficiency of the gas turbine, η gt,r is the thermal efficiency of the gas turbine, COP ec is the efficiency of the electric refrigerator, f ec is the load factor of the electric refrigerator, f gt is the load factor of the gas turbine, x gt is the output constraint of the gas turbine, x ec is the output constraint of the electric refrigerator, a1, b1, c1, d1, a2, b2, c2, d2, a4, b4, c4 are fitting coefficients, T out is the ambient temperature.

[0037] Optionally, when the gas turbine load factor f gt Greater than the gas turbine output constraint x gt The gas turbine can only work when the load rate of the electric refrigerator is f ec Greater than the output constraint x of the electric refrigerator ec The electric refrigerator can only work when

[0038] Optionally, when the objective function is iteratively optimized based on the second constraint condition to obtain the optimal time-sharing output constraint, the Cplex solver is used for solving the optimal time-sharing output constraint, and the optimal time-sharing output constraint is the optimal time-sharing output constraint of the gas turbine and the electric refrigerator in the transition season, winter, and summer, and the expression is:

[0039]

[0040] Among them, x gt * represents the optimal time-sharing output constraint of the gas turbine in the transition season, winter, and summer, x ec * represents the optimal time-sharing output constraint of the electric refrigerator in the transition season, winter and summer, and St(2) represents the second constraint. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of a comprehensive energy system scheduling optimization method considering equipment time-sharing output constraints provided by the present invention;

[0042] Figure 2 It is a flow chart of a comprehensive energy system scheduling optimization model considering equipment time-sharing output constraints provided by the present invention;

[0043] Figure 3 It is a structural block diagram of the comprehensive energy system provided by the present invention;

[0044] Figure 4 It is a structural diagram of the SP system provided by the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be understood by people with general skills in the field to which the present invention belongs. "Including" and similar words used in this article mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0046] The embodiment of the present invention provides a method for optimizing the scheduling of an integrated energy system taking into account the time-sharing output constraints of equipment. Figure 1 ,include:

[0047] S1. Obtaining an objective function based on the annual computing cost saving rate, primary energy saving rate and carbon dioxide emission reduction rate of the equipment, obtaining a first constraint condition based on an energy balance constraint, an energy storage device constraint and an area constraint, and iteratively optimizing the objective function based on the first constraint condition to obtain an optimal capacity, wherein the equipment is composed of production equipment and conversion equipment;

[0048] S2. Obtain a variable efficiency model based on the load efficiency function of the production equipment and the temperature efficiency function of the conversion equipment. Update the first constraint condition based on the variable efficiency model and the time-sharing output constraint to obtain the second constraint condition. And iteratively optimize the objective function based on the second constraint condition to obtain the optimal time-sharing output constraint.

[0049] In some embodiments, when executing step S1 to obtain the objective function based on the annual computing cost saving rate, primary energy saving rate and carbon dioxide emission reduction rate of the equipment, wherein:

[0050] The expression of the annual operation cost saving rate PECSR is:

[0051]

[0052] Among them, F b(SP) is the fuel consumed by the SP system boiler, F b(CCHP) is the fuel consumed by the CCHP system boiler, E grid(SP) The power purchased from the SP system, E grid(CCHP) is the power grid purchase amount of CCHP system, ηgrid(SP) is the transmission and distribution efficiency of the SP system considering grid losses, η grid(CCHP) is the transmission and distribution efficiency of CCHP system considering grid loss, F gt(CCHP) It is the fuel consumed by the gas turbine of the CCHP system.

[0053] The expression of the primary energy saving rate ATCSR is:

[0054]

[0055] in, I is the total number of devices, I i is the capacity of device i, A i is the unit capacity cost, r is the annual interest rate, l is the number of years, OM i is the proportion of equipment i-year maintenance cost to equipment investment, η1 is the natural gas price, η2 is the power purchase price of the power grid, E loss Q is the power wasted by the system. loss is the amount of heat wasted by the system, P1 is the penalty coefficient for wasted electricity, and P2 is the penalty coefficient for wasted heat.

[0056] The expression of the carbon dioxide emission reduction rate CDESR is:

[0057]

[0058] Among them, μ1 is the CO2 emission coefficient of burning natural gas, and μ2 is the CO2 emission coefficient of purchasing electricity from the power grid.

[0059] The expression of the objective function is:

[0060] Objective=ω1PECSR+ω2ATCSR+ω3CDESR;

[0061] Among them, ω1, ω2 and ω3 are the weights of annual operating cost saving rate, primary energy saving rate and carbon dioxide emission reduction rate respectively, and Objective is the objective function.

[0062] In some embodiments, when executing step S1, the optimal capacity is determined by the optimal capacity N of the gas turbine. gt , the optimal capacity of the boiler N gb , the optimal capacity N of the electric refrigerator ec and the optimal capacity N of the absorption chiller ac , the optimal capacity of battery energy storage N bat and the optimal capacity of the heat storage tank N ts t composition.

[0063] in, St(1) represents the first constraint condition.

[0064] Furthermore, the process of iteratively optimizing the objective function in step S1 includes:

[0065] S1-1, input weather data, load data and the value range of decision variables, where the search range of decision variables is shown in Table 1;

[0066] S1-2, set the relevant variables of particle swarm optimization and the number of particles in the particle swarm;

[0067] S1-3. Initialize the particle position x with a uniformly distributed random vector i and speed v i ;

[0068] S1-4, using CCHP simulation model to calculate the objective function of each particle;

[0069] S1-5. For each particle, if its objective function is consistent with the best position p it has passed best For comparison, it is taken as the current best position p best ;

[0070] S1-6, determine whether the end condition is met (the number of iterations reaches the maximum value or the objective function between particles reaches the minimum error). If the end condition is met, the optimal value of the objective function and the local optimal value of each particle are output. If the end condition is not met, the particle velocity and particle position need to be updated, and steps S1-4, S1-5, and S1-6 are repeated.

[0071] Table 1 Decision variable search range

[0072]

[0073] Furthermore, when executing step S1, the production equipment includes: a gas turbine GT, a photovoltaic panel PV, a solar collector ST, and a supplementary combustion boiler GB, and the conversion equipment includes: an electric refrigerator EC, an absorption refrigerator AC, a heat storage tank TST, a battery BA, and a heat exchanger HE.

[0074] In fact, the basic working process of the integrated energy system is as follows: first, the energy generated by the new energy equipment is consumed. If there is excess energy, it will be stored in the energy storage device; if the energy generated by the new energy equipment does not meet the requirements of the system, the energy in the energy storage device will be used; if it still cannot meet the user's requirements, internal combustion engine power generation, power purchase from the power grid or supplementary combustion from boilers will be considered.

[0075] In some embodiments, when executing step S2, the second constraint condition consists of an energy balance constraint, an energy storage device constraint, an area constraint, and a time-sharing output constraint.

[0076] Specifically, the energy storage device constraint consists of an electric energy constraint and an energy constraint, wherein the electric energy constraint is expressed as:

[0077]

[0078] Among them, E bat (t) is the amount of electricity in the battery at time t; and is the charging and discharging power of the battery at time t; and Battery charging and discharging efficiency; and To charge and discharge binary variables;

[0079] The expression of the energy constraint is:

[0080]

[0081] Among them, Q tst (t) is the amount of heat already in the heat storage tank at time t; and is the charging and discharging power of the heat storage tank at time t; and is the charging and discharging efficiency of the heat storage tank; and To charge and discharge binary variables.

[0082] Furthermore, the expression of the area constraint is:

[0083] A pv +A st ≤A total ;

[0084] Among them, A pv is the photovoltaic panel laying area, A st is the solar collector laying area, A total The total area that can provide photovoltaic and solar thermal collectors for the park.

[0085] Specifically, when executing step S2, the variable efficiency model includes: an electrical efficiency and thermal efficiency model of an internal combustion engine, a variable efficiency model of a boiler, a variable efficiency model of an electric refrigerator, and a variable efficiency model of an absorption refrigerator.

[0086] Furthermore, the electrical efficiency and thermal efficiency of the internal combustion engine will change with the load rate of the equipment; when the load rate increases, the electrical efficiency will increase and the thermal efficiency will decrease, wherein the electrical efficiency and thermal efficiency of the internal combustion engine are expressed as:

[0087]

[0088] Among them, η gt,e is the electrical efficiency of the internal combustion engine, η gt,r is the thermal efficiency of the internal combustion engine, E gt The amount of electricity required by the internal combustion engine, GT rated is the rated power of the internal combustion engine; the value of a1 is 0.1283, the value of b1 is 0.6592, the value of c1 is 0.7945, and the value of d1 is 0.003; the value of a2 is -0.7098, the value of b2 is 1.5206, the value of c2 is -1.1191, and the value of d2 is 0.835.

[0089] Furthermore, the efficiency of the boiler will change with the load rate of the equipment. When the load rate increases, the boiler efficiency will increase accordingly. The expression of the variable efficiency model of the stove is:

[0090]

[0091] Among them, η gb is the efficiency of the boiler, Q gb The heat required by the boiler, GB rated is the rated capacity of the boiler, the value of a3 is -0.49992, the value of b3 is 1.22, and the value of d3 is 0.07608.

[0092] Furthermore, the efficiency of the electric refrigerator will change with the change of the ambient temperature. When the temperature rises, the efficiency of the electric refrigerator will first decrease and then increase. The expression of the variable efficiency model of the electric refrigerator is:

[0093]

[0094] Among them, COP ec is the efficiency of the electric refrigerator, T out is the ambient temperature, where a4 is 0.0038, b4 is -0.2606, and c4 is 7.0016.

[0095] Furthermore, the efficiency of the absorption chiller will change with the load rate. When the load rate increases, the efficiency of the absorption chiller will also increase. The expression of the variable efficiency model of the absorption chiller is:

[0096]

[0097] Among them, COP ac is the variable efficiency model of the absorption chiller, Q ac is the heat required by the absorption chiller, AC ratedis the rated capacity of the absorption chiller, where a5 is 1.24, b5 is 0.66, c5 is -0.915, d5 is 1.24, and e5 is 0.015.

[0098] Furthermore, the expression of the time-sharing output constraint is:

[0099]

[0100] Among them, η gt,e is the electrical efficiency of the gas turbine, η gt,r is the thermal efficiency of the gas turbine, COP ec is the efficiency of the electric refrigerator, f ec is the load factor of the electric refrigerator, f gt is the load factor of the gas turbine, x gt is the output constraint of the gas turbine, x ec is the output constraint of the electric refrigerator, a1, b1, c1, d1, a2, b2, c2, d2, a4, b4, c4 are fitting coefficients, T out is the ambient temperature.

[0101] Specifically, when the gas turbine load factor f gt Greater than the gas turbine output constraint x gt The gas turbine can only work when the load rate of the electric refrigerator is f ec Greater than the output constraint x of the electric refrigerator ec The electric refrigerator can only work when

[0102] In fact, the capacity of the gas turbine is the key to determining the capacity of other equipment including the waste heat recovery system and the auxiliary gas boiler. The electric refrigerator also plays an important role in the integrated energy system. During the high temperature period in summer, the use of absorption refrigerators will increase the investment and operating costs of the integrated energy system for a large amount of cooling load demand. By introducing electric refrigerators into the integrated energy system, centralized distribution and optimized control of cooling load can be achieved, thereby improving the efficiency of the entire integrated energy system. Therefore, it is necessary for the present invention to set time-sharing output constraints in the gas turbine and the electric refrigerator.

[0103] In some embodiments, when executing step S2 to iteratively optimize the objective function based on the second constraint condition to obtain the optimal time-sharing output constraint, the Cplex solver is used to solve the optimal time-sharing output constraint, which is the optimal time-sharing output constraint of the gas turbine and the electric refrigerator in the transition season, winter, and summer, and the expression is:

[0104]

[0105] Among them, x gt* represents the optimal time-sharing output constraint of the gas turbine in the transition season, winter, and summer, x ec * represents the optimal time-sharing output constraint of the electric refrigerator in the transition season, winter and summer, and St(2) represents the second constraint.

[0106] In some embodiments, see Figure 2 The present invention provides an integrated energy system scheduling optimization model that considers the equipment time-sharing output constraints and is divided into a first-stage optimization model and a second-stage optimization model. In the first-stage optimization model, the objective function is composed of the annual operating cost saving rate, primary energy saving rate and carbon dioxide emission reduction rate of the equipment. The output result is the equipment capacity, and the input data is energy demand, weather data, energy price and equipment parameters. The first constraint condition is energy balance constraint, energy storage equipment constraint and area constraint.

[0107] In the second-stage optimization model, the objective function is also composed of the equipment's annual computing cost savings rate, primary energy savings rate and carbon dioxide emission reduction rate. The input data is also energy demand, weather data, energy prices and equipment parameters. The second constraint is energy balance constraint, energy storage equipment constraint, area constraint and time-sharing output constraint.

[0108] In some embodiments, see Figure 3 The electrical load is generated by photovoltaic panels PV, gas turbines, power grids, battery energy storage and electric refrigerators. The cooling load is generated by electric refrigerators and absorption refrigerators. The heat load is generated by gas turbines, boilers, photovoltaic panels PV, solar collectors ST, heat storage tanks, absorption refrigerators and heat exchangers.

[0109] In some embodiments, see Figure 4 The working principle of the SP system is that the electrical load is fully met by the power grid, the cold load is provided by the electric refrigerator, and the boiler consumes fuel to generate heating steam to provide heat to users through the heat exchange equipment to meet the heat load demand.

[0110] Although the embodiments of the present invention are described in detail above, it is obvious to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein may have other embodiments and may be implemented or realized in a variety of ways.

Claims

1. A method for optimizing the scheduling of an integrated energy system considering the time-sharing output constraints of equipment, characterized in that: include: The objective function is obtained based on the annual operation cost saving rate, primary energy saving rate and carbon dioxide emission reduction rate of the equipment, the first constraint condition is obtained based on the energy balance constraint, the energy storage device constraint and the area constraint, and the objective function is iteratively optimized based on the first constraint condition to obtain the optimal capacity, wherein the equipment is composed of production equipment and conversion equipment; A variable efficiency model is obtained based on the load efficiency function of the production equipment and the temperature efficiency function of the conversion equipment. The first constraint condition is updated based on the variable efficiency model and the time-sharing output constraint to obtain the second constraint condition. Based on the second constraint condition, the objective function is iteratively optimized to obtain the optimal time-sharing output constraint.

2. According to claim 1, a method for optimizing the scheduling of an integrated energy system taking into account the time-sharing output constraints of equipment is characterized in that: When obtaining the objective function based on the annual computing cost saving rate, primary energy saving rate and carbon dioxide emission reduction rate of the equipment, The expression of the annual operation cost saving rate PECSR is: Among them, F b(SP) is the fuel consumed by the SP system boiler, F b(CCHP) is the fuel consumed by the CCHP system boiler, E grid(SP) The power purchased from the SP system, E grid(CCHP) is the power grid purchase amount of CCHP system, η grid(SP) is the transmission and distribution efficiency of the SP system considering grid losses, η grid(CCHP) is the transmission and distribution efficiency of CCHP system considering grid loss, F gt(CCHP) It is the fuel consumed by the gas turbine of the CCHP system. The expression of the primary energy saving rate ATCSR is: in, I is the total number of devices, I i is the capacity of device i, A i is the unit capacity cost, r is the annual interest rate, l is the number of years, OM i is the proportion of equipment i-year maintenance cost to equipment investment, η1 is the natural gas price, η2 is the power purchase price of the power grid, E loss Q is the power wasted by the system. loss is the amount of heat wasted by the system, P1 is the penalty coefficient for wasted electricity, and P2 is the penalty coefficient for wasted heat. The expression of the carbon dioxide emission reduction rate CDESR is: Among them, μ1 is the CO2 emission coefficient of burning natural gas, and μ2 is the CO2 emission coefficient of purchasing electricity from the power grid. The expression of the objective function is: Objective=ω1PECSR+ω2ATCSR+ω3CDESR; Among them, ω1, ω2 and ω3 are the weights of annual operating cost saving rate, primary energy saving rate and carbon dioxide emission reduction rate respectively, and Objective is the objective function.

3. The method for optimizing the scheduling of an integrated energy system considering the time-sharing output constraints of equipment according to claim 1, characterized in that: The production equipment includes: gas turbine GT, photovoltaic panel PV, solar collector ST, supplementary combustion boiler GB; the conversion equipment includes: electric refrigerator EC, absorption refrigerator AC, heat storage tank TST, battery BA, heat exchanger HE.

4. The method for optimizing the scheduling of an integrated energy system considering the time-sharing output constraints of equipment according to claim 1, characterized in that: The second constraint condition consists of an energy balance constraint, an energy storage device constraint, an area constraint and a time-sharing output constraint.

5. The method for optimizing the scheduling of an integrated energy system considering the time-sharing output constraints of equipment according to claim 4 is characterized in that: The energy storage device constraints consist of electrical energy constraints and energy constraints, where: The expression of the electric energy constraint is: Among them, E bat (t) is the amount of electricity in the battery at time t; and is the charging and discharging power of the battery at time t; and Battery charging and discharging efficiency; and To charge and discharge binary variables; The expression of the energy constraint is: Among them, Q tst (t) is the amount of heat already in the heat storage tank at time t; and is the charging and discharging power of the heat storage tank at time t; and is the charging and discharging efficiency of the heat storage tank; and To charge and discharge binary variables.

6. The method for optimizing the scheduling of an integrated energy system considering the time-sharing output constraints of equipment according to claim 4 is characterized in that: The expression of the area constraint is: A pv +A st ≤A total ; Among them, A pv is the photovoltaic panel laying area, A st is the area for laying solar thermal collectors, A total The total area that can provide photovoltaic and solar thermal collectors for the park.

7. The method for optimizing the scheduling of an integrated energy system considering the time-sharing output constraints of equipment according to claim 4, characterized in that: The expression of the time-sharing output constraint is: Among them, η gt,e is the electrical efficiency of the gas turbine, η gt,r is the thermal efficiency of the gas turbine, COP ec is the efficiency of the electric refrigerator, f ec is the load factor of the electric refrigerator, f gt is the load factor of the gas turbine, x gt is the output constraint of the gas turbine, x ec is the output constraint of the electric refrigerator, a1, b1, c1, d1, a2, b2, c2, d2, a4, b4, c4 are fitting coefficients, T out is the ambient temperature.

8. The method for optimizing the scheduling of an integrated energy system considering the time-sharing output constraints of equipment according to claim 7, characterized in that: When the gas turbine load factor f gt Greater than the gas turbine output constraint x gt The gas turbine can only work when the load rate of the electric refrigerator is f ec Greater than the output constraint x of the electric refrigerator ec The electric refrigerator can only work when 9. The method for optimizing the scheduling of a comprehensive energy system considering the time-sharing output constraints of equipment according to claim 1, characterized in that: When the objective function is iteratively optimized based on the second constraint condition to obtain the optimal time-sharing output constraint, the Cplex solver is used to solve the optimal time-sharing output constraint, which is the optimal time-sharing output constraint of the gas turbine and the electric refrigerator in the transition season, winter, and summer, and the expression is: Among them, x gt * represents the optimal time-sharing output constraint of the gas turbine in the transition season, winter, and summer, x ec * represents the optimal time-sharing output constraint of the electric refrigerator in the transition season, winter and summer, and St(2) represents the second constraint.

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