Optimized scheduling method and device for adjustable capability of integrated energy system, equipment and medium

Abstract

The invention relates to the technical field of power system optimal scheduling, in particular to an optimal scheduling method, device and equipment for the adjustable capacity of an integrated energy system and a medium, and the method comprises the steps: building a mathematical model for evaluating the adjustable capacity of the integrated energy system based on chance constraints; establishing operation constraint conditions and upper and lower limit constraints of power of each device in the integrated energy system; establishing an opportunity constraint planning model, considering the uncertainty of wind power photovoltaic output based on an opportunity constraint planning method, and converting the uncertainty constraint into a deterministic constraint condition; establishing an adjustable capability evaluation model of the integrated energy system based on the chance-constrained programming model; the comprehensive energy system adjustable capacity evaluation model is solved, an optimization result is obtained, the adjustable capacity range of the comprehensive energy system is drawn, a superior system can better dispatch the comprehensive energy system, complete and stable operation of a power grid is facilitated, the operation stability of the comprehensive energy system is improved, and the reliability of power supply is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and dispatching, and in particular to a method, device, equipment and medium for optimizing and dispatching the adjustable capacity of an integrated energy system. Background Art

[0002] With the rapid economic growth and the improvement of productivity, people's demand for primary energy such as coal, oil and natural gas is also increasing. However, the combustion of primary energy and its secondary processed products produce a large amount of harmful gases and greenhouse gases. Vigorously developing renewable energy generation such as hydropower, wind power and photovoltaic power generation and improving the absorption rate of renewable energy have become issues of widespread concern in countries around the world. The output of renewable energy is greatly affected by the weather and has uncertainty and volatility, which brings challenges to the safe and stable operation of the power system. In order to absorb more renewable energy and realize the safe and stable operation of the power system, many scholars at home and abroad have proposed the concept of integrated energy system (IES).

[0003] Multi-energy coupling can effectively improve energy utilization, and the development of various energy conversion equipment also provides a means for the interconnection of different energy systems. Through the scientific scheduling of multiple energy sources, it is possible to achieve efficient energy utilization, improve system economy, meet the needs of users for cascade utilization of multiple energy sources, absorb renewable energy, improve the utilization efficiency of various energy equipment, effectively promote energy conservation and emission reduction, and promote the low-carbon development of multi-energy systems. Therefore, a comprehensive, in-depth and systematic study of the optimization planning and operation issues of the integrated energy system is of great guiding significance for improving the energy utilization efficiency of the system, realizing the cascade utilization of energy, and enhancing the flexibility and reliability of system operation.

[0004] The integrated energy system can include both clean energy generation forms such as wind power and photovoltaic power generation, as well as traditional power generation forms such as coal and natural gas. The two complement each other and take advantage of each other, while taking into account the stability of power supply and the advantages of energy conservation and environmental protection. The integrated energy system cluster interconnection system can provide an effective way to achieve the comprehensive utilization of multiple energy sources such as electricity, gas, heat, and cold, reduce the overall energy supply and operation costs of the integrated energy system, and improve the overall optimization and regulation level and regulation economy of the integrated energy system cluster system by unified coordinated optimization and dispatching of the integrated energy system energy supply network, large power grid, and natural gas network including renewable energy supply, and become one of the key research directions to meet the current green and clean energy supply and sustainable development of energy utilization.

[0005] However, many studies on the optimal operation and scheduling of the integrated energy system (IES) have found it difficult to obtain the support capability of the integrated energy system (IES) for the operation of the power grid. In the context of grid-load interaction, the complex coupling between multiple energy flows within the integrated energy system (IES) and how to accurately evaluate the overall superior power grid regulation capability of the integrated energy system (IES) are important issues in realizing the participation of the integrated energy system (IES) in power grid regulation.

[0006] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0007] The present invention provides a method, device, equipment and medium for optimizing the scheduling of the adjustable capacity of an integrated energy system, thereby effectively solving the problems in the background technology.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is: a method for optimizing the adjustable capacity of a comprehensive energy system, comprising the following steps:

[0009] S10: Establish a mathematical model for evaluating the adjustable capacity of the integrated energy system based on opportunity constraints, including at least establishing demand response models for new energy supply equipment, energy storage equipment, and demand side;

[0010] S20: Establishing operation constraints and upper and lower power constraints of each device in the integrated energy system, so as to limit each device to operate within a rated operation area, wherein the operation constraints include device operation constraints, electrical and thermal power balance constraints, and economic constraints;

[0011] S30: establishing a chance constraint programming model, taking into account the uncertainty of wind power and photovoltaic power output based on the chance constraint programming method, and converting the uncertainty constraint into a deterministic constraint condition;

[0012] S40: Establishing an adjustable capacity evaluation model for the integrated energy system based on the chance-constrained programming model;

[0013] S50: Solve the integrated energy system adjustable capacity evaluation model, obtain optimization results, and draw the adjustable capacity range of the integrated energy system.

[0014] Furthermore, in step S10, the demand response model includes a translatable load model, and the constraints of the translatable load model include:

[0015]

[0016] In the formula, α trepresents the translation state of the translatable load within a certain period of time t, α t =1 indicates load translation, α t =0 means the load does not translate; t st Indicates the starting time; t con It represents the continuous operation time of the shiftable load; T represents the number of scheduling periods.

[0017] Further, in step S10, the demand response model includes a transferable load model, and the transferable load model and the constraint model include:

[0018] -P tran,min ≤P tran,t ≤P tran,max ;

[0019]

[0020] Where P tran,t represents the transfer amount of transferable load at time t; P tran,min and P tran,max Respectively represent the minimum and maximum values ​​of the transferable load power.

[0021] Further, in step S10, the demand response model includes a curtailable load model, and the curtailable load model and the constraint model include:

[0022] P cut,t =(1-ε t δ t )P′ cut,t ;

[0023]

[0024] In the formula, δ t is a 0 / 1 variable, indicating the reduction status of the reducible load within a certain period of time t. δ = 1 indicates that the reducible load is reduced, and δ = 0 indicates that it is not reduced; ε t is the load reduction factor in period t, ε t ∈(0,1);P′ cut,t N is the power of the load that can be reduced before it participates in the dispatch; max is the maximum number of reductions P cut,t Indicates the power that can be reduced after the load participates in scheduling.

[0025] Furthermore, in step S20, the operation constraint conditions of each device in the integrated energy system are established, and the device is an electric boiler. The operation constraint condition model of the electric boiler includes:

[0026] H EB,t =η EB PEB,t ;

[0027] 0≤P EB,t ≤P EB,max ;

[0028] In the formula, H EB,t is the heat generation power of the electric boiler in period t; η EB is the heat generation efficiency of the electric boiler; P EB,t and P EB,max is the electric power and maximum electric power consumption of the electric boiler during period t.

[0029] Furthermore, in step S20, the operation constraint conditions of each device in the integrated energy system are established, and the device is a gas boiler. The operation constraint condition model of the gas boiler includes:

[0030] H GB,t =G GB,t η GB ;

[0031] 0≤H GB,t ≤H GBmax ;

[0032] In the formula, H GB,t is the heating power of the gas boiler in period t; G GB,t is the power consumed by the gas boiler during the period t; η GB is the heating efficiency; H GBmax It is the maximum heat generating power of the boiler itself.

[0033] Furthermore, in step S20, the operation constraint conditions of each device in the integrated energy system are established, and the device is a power-to-gas device. The operation constraint condition model of the power-to-gas device includes:

[0034] 0≤P P2G,t ≤P P2G,max ;

[0035] Where P P2G,t P is the electric power consumed by the power-to-gas device during the period t; P2G,max The maximum electrical power consumed by the power-to-gas device.

[0036] Furthermore, in step S20, the operation constraint conditions of each of the devices in the integrated energy system are established, and the devices are gas turbines and waste heat boilers. The operation constraint condition models of the gas turbines and waste heat boilers include:

[0037] P MT,t =G MT,t η MT ·LHV;

[0038]

[0039] Where P MT,t is the electrical energy generated by the gas turbine at time t; G MT,t is the amount of natural gas consumed by the gas turbine at time t; η MT is the conversion efficiency of the gas turbine; LHV is the calorific value of natural gas; H MT,t is the heat generated after conversion; η L is the combustion heat loss coefficient.

[0040] Furthermore, in step S20, the operation constraint conditions of each device in the integrated energy system are established, and the device is a battery. The operation constraint condition model of the battery includes:

[0041]

[0042] 0≤P esc,t ≤B c,t P esc,max ;

[0043] 0≤P esd,t ≤B d,t P esd,max ;

[0044] B c,t +B d,t ≤1;

[0045] W es,min ≤W es,t ≤W es,max ;

[0046] W es,0 =W es,T ;

[0047] Where W es,t , W es,max , W es,min are the amount of electricity stored in the battery during period t, the maximum storage capacity and the minimum storage capacity; γ c , γ d are the charging efficiency and discharging efficiency of the battery respectively; P esc,t , P esd,t are the charging power and discharging power of the battery in time period t respectively; P esc,max , P esd,max are the maximum charging power and maximum discharging power allowed by the battery respectively; B c,t , B d,t They represent the two working states of the battery, namely the charging state and the discharging state. c,t =1 means the battery is in charging state, B d,t=1 means the battery is in the discharge state and the energy storage device can only work in one state; W es,0 , W es,T are the storage capacities at the beginning and end of the dispatching period respectively; W es,t-1 It represents the amount of electricity stored in the battery during the period t-1; Δt represents the length of a scheduling period.

[0048] Furthermore, in step S20, the operation constraint conditions of each device in the integrated energy system are established, and the device is a gas storage device. The operation constraint condition model of the gas storage device includes:

[0049]

[0050] 0≤G gc,t ≤C c,t G gc,max ;

[0051] 0≤G gd,t ≤C d,t G gd,max ;

[0052] C c,t +C d,t ≤1;

[0053] 0≤G g,t ≤G g,max ;

[0054] G g,0 =G g,T ;

[0055] In the formula, G g,t is the amount of natural gas stored in the gas storage device at time t; G g,max Indicates the maximum amount of natural gas that can be stored in the gas storage device; G gc,t and G gd,t are the gas storage and gas injection powers of the gas storage device at time t, respectively; η gc and η gd Represent the efficiency of gas storage and gas injection respectively; C c,t and C d,t They represent the two working states of the gas storage device at time t, namely, gas storage and gas injection. c,t =1 means the gas storage device is working in the gas storage state, C d,t =1 means the gas storage device is working in the gas injection state; G gc,max and G gd,max Respectively represent the maximum gas storage and gas injection power of the gas storage device; G g,0 and G g,T They respectively represent the amount of natural gas stored in the gas storage device at the beginning and end of the scheduling period.

[0056] Furthermore, in step S20, the operation constraint conditions of each device in the integrated energy system are established, and the device is a heat storage device. The operation constraint condition model of the heat storage device includes:

[0057]

[0058] 0≤H rc,t ≤A c,t H rc,max ;

[0059] 0≤H rd,t ≤A c,t H rd,max ;

[0060] A c,t +A d,t ≤1;

[0061] 0≤S r,t ≤S r,max ;

[0062] S r,0 =S r,T ;

[0063] In the formula, S r,t represents the heat storage capacity of the heat storage device in time period t; S r,max Indicates the maximum heat storage capacity of the heat storage device; H rc,t and H rd,t are the heat storage power and heat release power of the heat storage device in period t respectively; η rc , η rd Respectively represent the heat storage and heat release efficiency of the heat storage device; H rc,max and H rd,max are the maximum heat storage power and heat release power of the heat storage device respectively; A c,t and A d,t They represent the two working states of the heat storage device, namely the heat storage state and the heat release state. c,t =1 means the heat storage device is working in the heat storage state, A d,t =1 means the heat storage device is working in the heat release state; S r,0 , S r,T They respectively represent the heat storage capacity of the heat storage device at the beginning and end of the scheduling period.

[0064] Furthermore, in step S20, the economic constraint model includes:

[0065] C total ≤C;

[0066] C total =C buy +C ab +C sto+C car +C DR ;

[0067] In the formula, C total is the total cost; C is the artificially preset cost; C buy is the total energy purchase cost of IES; C ab C is the cost of curtailing wind and solar power; sto is the depreciation cost of energy storage; C car is the carbon emission cost; C DR is the demand response cost.

[0068] Furthermore, in step S30, a chance-constrained programming model is established, based on a chance-constrained programming method, taking into account the uncertainty of wind power and photovoltaic output, and the chance-constrained programming model includes:

[0069]

[0070] Where Ef(x,ζ) is the expected value of the objective function under specific decision variables and random variables; x is the decision variable; ζ is the random variable; P r {·} is the probability of event {·} occurring; g i (x,ζ)≤0 is the inequality constraint in the system; β i is the confidence level of the probability of the event occurring, β i ∈(0,1); D is the deterministic constraint set.

[0071] Furthermore, in step S30, a chance constraint programming model is established. Based on the chance constraint programming method, the uncertainty of wind power and photovoltaic power output is considered, and the uncertainty constraint is converted into a deterministic constraint condition. The conversion into a deterministic constraint condition model includes:

[0072]

[0073] Where P grid,t represents the interaction power between IES and the upper power grid at time t, kW; P PV,t,c and P w,t,c They represent the photovoltaic and wind power actually absorbed by IES at time t; P esd,t and P esc,t are the discharge power and charge power of the battery at time t respectively; P P2G,t P is the power consumed by P2G (electric converter) at time t; load,t is the electrical load at time t; F -1 () represents the inverse cumulative distribution function of a variable that satisfies the normal distribution. and Respectively represent the variance of the normal distribution satisfied by wind power and photovoltaic power.

[0074] Furthermore, in step S40, an adjustable capacity evaluation model of the integrated energy system is established based on the opportunity constraint, and the model includes:

[0075]

[0076] Where: P grid,t It represents the interaction power between IES and the upper power grid at time t, in kW; E grid,t Indicates the accumulated amount of electricity exchanged between IES and the upper-level power grid up to time t, in kWh; minP grid,t Indicates the minimum value of the interaction power between IES and the upper power grid at time t; maxP grid,t Indicates the maximum value of the interaction power between IES and the upper power grid at time t; minE grid,t Indicates the minimum value of the accumulated interactive electricity between IES and the upper-level power grid at time t; maxE grid,t It indicates the maximum value of the accumulated interactive electricity between IES and the upper-level power grid up to time t.

[0077] Furthermore, in step S50, solving the integrated energy system adjustable capacity evaluation model to obtain an optimization result includes:

[0078] The mixed integer linear programming method is used to solve the adjustable capacity evaluation model of the integrated energy system based on MATLAB+Yalmip+Gurobi solver, the upper and lower limits of the integrated energy system's demand for electricity at each moment are drawn, and the adjustable capacity range of the integrated energy system is obtained.

[0079] The present invention also includes an optimization scheduling device for the adjustable capacity of a comprehensive energy system, using the above method, including:

[0080] Establish a mathematical model module for establishing a mathematical model for evaluating the adjustable capacity of the integrated energy system based on opportunity constraints, including at least establishing demand response models for new energy supply equipment, energy storage equipment and demand side respectively;

[0081] Establish a constraint module, which is used to establish the operation constraint conditions and upper and lower limit constraints of power of each device in the comprehensive energy system, so as to limit each device to operate within the rated operation area. The operation constraint conditions include equipment operation constraint, electrical and thermal power balance constraint and economic constraint;

[0082] Establishing a chance constraint module for establishing a chance constraint programming model, based on the chance constraint programming method, taking into account the uncertainty of wind power and photovoltaic power output, and converting the uncertainty constraint into a deterministic constraint condition;

[0083] Establishing an evaluation model module for establishing an adjustable capacity evaluation model of a comprehensive energy system based on the chance-constrained programming model;

[0084] The evaluation model solving module is used to solve the evaluation model of the adjustable capacity of the integrated energy system, obtain the optimization result, and draw the adjustable capacity range of the integrated energy system.

[0085] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0086] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0087] The beneficial effects of the present invention are:

[0088] By establishing a model for evaluating the adjustable capacity of the integrated energy system based on opportunity constraints, the integrated energy system model is connected to the superior power grid and natural gas pipeline network, and its interior includes new energy supply equipment, such as wind power and photovoltaics; energy coupling equipment, such as gas boilers, electric boilers, power-to-gas, gas turbines and waste heat boilers; energy storage equipment, such as batteries, heat storage tanks and gas storage tanks. The demand side includes electric load, heat load and gas load that take demand response into consideration, while considering economic constraints; based on the opportunity constraint method, considering the uncertainty of wind power and photovoltaic output, two groups of objective functions are established. Objective function group 1 is the maximum and minimum values ​​of the interactive electric power on the interconnection line between the integrated energy system and the superior power grid at each moment; objective function group 2 is the maximum and minimum values ​​of the accumulated interactive electric power between the integrated energy system and the superior power grid interconnection line from time zero to each moment. The constraints include the operation constraints of each device in the integrated energy system and the upper and lower limits of power, the electric power balance constraints, natural gas power constraints, thermal power balance constraints and economic constraints considering the opportunity constraints. Based on the above, an evaluation model for the adjustable capacity range of the integrated energy system based on the opportunity constraint method is established. Based on the MATLAB platform, the gurobi solver of the Yalmip optimization toolkit is called to solve and draw the upper and lower limits of the integrated energy system's demand for electricity, and then describe the adjustable capacity range of the integrated energy system. This method enables the superior system to better dispatch the integrated energy system, which is conducive to the completely stable operation of the power grid, improves the stability of the operation of the integrated energy system, and ensures the reliability of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0090] Figure 1 A flow chart of an optimal dispatching method for adjustable capacity of an integrated energy system;

[0091] Figure 2 It is a schematic diagram of the system structure of the integrated energy system;

[0092] Figure 3 It is a schematic diagram of the structure of an optimization dispatching device for the adjustable capacity of a comprehensive energy system;

[0093] Figure 4 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0094] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0095] like Figure 1 , Figure 2 As shown: A method for optimizing the adjustable capacity of a comprehensive energy system comprises the following steps:

[0096] S10: Establish a mathematical model for evaluating the adjustable capacity of the integrated energy system based on opportunity constraints, including at least establishing new energy supply equipment, such as wind power and photovoltaic power, energy coupling equipment, such as gas boilers, electric boilers, power-to-gas and gas boilers, etc., energy storage equipment, such as electricity storage, heat storage and gas storage, and a demand response model on the demand side; the demand side includes the electricity load, heat load and gas load that take into account the demand response model;

[0097] S20: Establish operating constraints and upper and lower limits of power for each device in the integrated energy system to limit the operation of each device within the rated operating area, including at least the output constraints of wind power and photovoltaic power, the constraints of power storage, heat storage and gas storage equipment, and the power balance constraints and output upper and lower limits of electric boilers, power-to-gas, and gas boiler coupling equipment during operation; operating constraints include equipment operation constraints, electrical and thermal power balance constraints, and economic constraints; establish economic constraints, including economic constraints such as energy purchase costs, wind and solar power abandonment penalties, carbon emissions, and demand response compensation costs;

[0098] S30: Establish a chance-constrained programming model, based on the chance-constrained programming method, taking into account the uncertainty of wind power and photovoltaic power output, and transforming the uncertainty constraints into deterministic constraints;

[0099] S40: Establish an adjustable capacity evaluation model for integrated energy systems based on the chance-constrained programming model; set two sets of objective functions;

[0100] Objective function group 1 is the maximum and minimum values ​​of the interactive electric power on the interconnection line between the integrated energy system and the upper power grid at each moment;

[0101] Objective function group 2 is the maximum and minimum values ​​of the accumulated interactive power between the integrated energy system and the upper power grid tie line starting from time zero and ending at each time;

[0102] S50: Solve the integrated energy system adjustable capacity evaluation model, obtain the optimization result, and draw the adjustable capacity range of the integrated energy system.

[0103] In an embodiment of the present invention, under the premise of satisfying the constraints established in the above steps, the objective function of the adjustable capacity range evaluation model of the integrated energy system is solved, and finally the result of the adjustable capacity range evaluation of the integrated energy system can be obtained.

[0104] By establishing a chance constraint model, the scheme transforms the uncertainty of wind power and photovoltaic output into controllable deterministic constraints; this transformation transforms random factors into linear or nonlinear deterministic constraints at a high confidence level through statistical probability methods, so that the system can still optimize scheduling in the face of uncertainty; by setting economic constraints and multi-objective functions, the system can balance between different objectives. The objective function in the scheme comprehensively considers economy and environmental protection to improve the overall operating efficiency of the system; through the flexible scheduling of electricity, heat and gas storage equipment, the system's response ability to load fluctuations is improved, and the system's flexibility is increased; through the coordinated optimization of multi-energy coupling equipment and new energy supply equipment, the complementarity of various energy forms is achieved, promoting the low-carbon development of the system.

[0105] As a preference of the above embodiment, in step S10, the demand response model includes a translatable load model, and the constraints of the translatable load model include:

[0106]

[0107]

[0108] In the formula, α t represents the translation state of the translatable load within a certain period of time t, α t =1 indicates load translation, α t =0 means the load does not translate; tst Indicates the starting time; t con It represents the continuous operation time of the shiftable load; T represents the number of scheduling periods.

[0109] By shifting part of the load during the power off-peak period, the power demand during the peak period is reduced, achieving the purpose of peak shaving and valley filling and improving the stability of the system.

[0110] In this embodiment, in step S10, the demand response model includes a transferable load model, and the transferable load model and the constraint model include:

[0111] -P tran,min ≤P tran,t ≤P tran,max ;

[0112]

[0113] Where P tran,t represents the transfer amount of transferable load at time t; P tran,min and P tran,max Respectively represent the minimum and maximum values ​​of the transferable load power.

[0114] Through flexible load transfer, the system can choose to transfer energy consumption during low-price periods and reduce the total energy consumption cost.

[0115] Wherein, in step S10, the demand response model includes a curtailable load model, and the curtailable load model and the constraint model include:

[0116] P cut,t =(1-ε t δ t )P′ cut,t ;

[0117]

[0118] In the formula, δ t is a 0 / 1 variable, indicating the reduction status of the reducible load within a certain period of time t. δ = 1 indicates that the reducible load is reduced, and δ = 0 indicates that it is not reduced; ε t is the load reduction factor in period t, ε t ∈(0,1);P′ cut,t N is the power of the load that can be reduced before it participates in the dispatch; max is the maximum number of reductions; P cut,t Indicates the power that can be reduced after the load participates in scheduling.

[0119] By setting a load reduction strategy, some controllable loads can be reduced when power generation is insufficient or demand surges, ensuring stable system operation and improving system reliability and safety. During peak electricity price periods, appropriate load reduction can reduce expensive electricity purchases, reduce operating costs, and increase the economic benefits of the system.

[0120] In this embodiment, in step S20, the model of the power consumption constraint of wind power and photovoltaic new energy power generation devices includes:

[0121] 0≤P w,t ≤P w,t,max

[0122] 0≤P PV,t ≤P PV,t,max

[0123] Where P w,t is the wind power consumed by the integrated energy system at time t, P w,t,max is the maximum power of the wind turbine, P PV,t is the photovoltaic power consumed by the integrated energy system at time t, P PV,t,max It is the maximum output power of photovoltaic operation under MPPT.

[0124] As a preferred embodiment of the above, in step S20, the operation constraint conditions of each device in the integrated energy system are established, the device is an electric boiler, and the operation constraint condition model of the electric boiler includes:

[0125] H EB,t =η EB P EB,t ;

[0126] 0≤P EB,t ≤P EB,max ;

[0127] In the formula, H EB,t is the heat generation power of the electric boiler in period t; η EB is the heat generation efficiency of the electric boiler; P EB,t and P EB,max is the electric power and maximum electric power consumption of the electric boiler during period t.

[0128] By setting the heat production efficiency of the electric boiler, the efficiency of converting electrical energy into thermal energy can be maximized, energy losses can be reduced, and the overall energy conversion efficiency can be improved.

[0129] In this embodiment, in step S20, the operation constraint conditions of each device in the integrated energy system are established. The device is a gas boiler. The operation constraint condition model of the gas boiler includes:

[0130] H GB,t =G GB,t ηGB ;

[0131] 0≤H GB,t ≤H GBmax ;

[0132] In the formula, H GB,t is the heating power of the gas boiler in period t; G GB,t is the power consumed by the gas boiler during the period t; η GB is the heating efficiency; H GBmax It is the maximum heat generating power of the boiler itself.

[0133] By optimizing the operation of gas boilers, the combustion efficiency of natural gas can be improved, thermal energy output can be maximized, and energy waste can be reduced.

[0134] In step S20, the operation constraint conditions of each device in the integrated energy system are established. The device is a power-to-gas device. The operation constraint condition model of the power-to-gas device includes:

[0135] 0≤P P2G,t ≤P P2G,max ;

[0136] Where P P2G,t P is the electric power consumed by the power-to-gas device during the period t; P2G,max The maximum electrical power consumed by the power-to-gas device.

[0137] Power-to-gas devices can operate when electricity supply is abundant and cheap, converting electricity into gas for storage, increasing the flexibility and regulation capabilities of the system.

[0138] As a preferred embodiment of the above, in step S20, the operation constraint conditions of each device in the integrated energy system are established, and the devices are gas turbines and waste heat boilers. The operation constraint condition model of the gas turbine and the waste heat boiler includes:

[0139] P MT,t =G MT,t η MT ·LHV;

[0140]

[0141] Where P MT,t is the electrical energy generated by the gas turbine at time t; G MT,t is the amount of natural gas consumed by the gas turbine at time t; η MT is the conversion efficiency of the gas turbine; LHV is the calorific value of natural gas; H MT,t is the heat generated after conversion; η L is the combustion heat loss coefficient.

[0142] Gas turbines can achieve efficient energy utilization and improve the energy conversion efficiency of the overall system by converting natural gas into electricity and heat.

[0143] In this embodiment, in step S20, the operation constraint conditions of each device in the integrated energy system are established. The device is a battery. The operation constraint condition model of the battery includes:

[0144]

[0145] 0≤P esc,t ≤B c,t P esc,max ;

[0146] 0≤P esd,t ≤B d,t P esd,max ;

[0147] B c,t +B d,t ≤1;

[0148] W es,min ≤W es,t ≤W es,max ;

[0149] W es,0 =W es,T ;

[0150] Where W es,t , W es,max , W es,min are the amount of electricity stored in the battery during period t, the maximum storage capacity and the minimum storage capacity; γ c , γ d are the charging efficiency and discharging efficiency of the battery respectively; P esc,t , P esd,t are the charging power and discharging power of the battery in time period t respectively; P esc,max , P esd,max are the maximum charging power and maximum discharging power allowed by the battery respectively; B c,t , B d,t They represent the two working states of the battery, namely the charging state and the discharging state. c,t =1 means the battery is in charging state, B d,t =1 means the battery is in the discharge state and the energy storage device can only work in one state; W es,0 , W es,T are the storage capacities at the beginning and end of the dispatching period respectively; W es,t-1 It represents the amount of electricity stored in the battery during the period t-1; Δt represents the length of a scheduling period.

[0151] Batteries can be charged and discharged at different time periods to achieve flexible regulation of electricity and enhance the system's responsiveness to load fluctuations. Through reasonable charging and discharging scheduling, the efficiency of electricity utilization can be optimized, wind and solar power abandonment can be reduced, and the absorption rate of renewable energy can be increased.

[0152] In step S20, the operation constraint conditions of each device in the integrated energy system are established. The device is a gas storage device. The operation constraint condition model of the gas storage device includes:

[0153]

[0154] 0≤G gc,t ≤C c,t G gc,max ;

[0155] 0≤G gd,t ≤C d,t G gd,max ;

[0156] C c,t +C d,t ≤1;

[0157] 0≤G g,t ≤G g,max ;

[0158] G g,0 =G g,T ;

[0159] In the formula, G g,t is the amount of natural gas stored in the gas storage device at time t; G g,max Indicates the maximum amount of natural gas that can be stored in the gas storage device; G gc,t and G gd,t are the gas storage and gas injection powers of the gas storage device at time t, respectively; η gc and η gd Represent the efficiency of gas storage and gas injection respectively; C c,t and C d,t They represent the two working states of the gas storage device at time t, namely, gas storage and gas injection. c,t =1 means the gas storage device is working in the gas storage state, C d,t =1 means the gas storage device is working in the gas injection state; G gc,max and G gd,max Respectively represent the maximum gas storage and gas injection power of the gas storage device; G g,0 and G g,T They respectively represent the amount of natural gas stored in the gas storage device at the beginning and end of the scheduling period.

[0160] Gas storage equipment can adjust the gas supply when energy demand changes, enhance the flexibility of the system, and maintain stable gas network pressure; gas storage equipment can adjust the gas supply when energy demand changes, enhance the flexibility of the system, and maintain stable gas network pressure.

[0161] As a preference of the above embodiment, in step S20, operation constraints of various devices in the integrated energy system are established, the devices are heat storage devices, and the operation constraint model of the heat storage devices includes:

[0162]

[0163] 0≤H rc,t ≤A c,t H rc,max ;

[0164] 0≤H rd,t ≤A c,t H rd,max ;

[0165] A c,t +A d,t ≤1;

[0166] 0≤S r,t ≤S r,max ;

[0167] S r,0 =S r,T ;

[0168] In the formula, S r,t represents the heat storage capacity of the heat storage device in time period t; S r,max Indicates the maximum heat storage capacity of the heat storage device; H rc,t and H rd,t are the heat storage power and heat release power of the heat storage device in period t respectively; η rc , η rd Respectively represent the heat storage and heat release efficiency of the heat storage device; H rc,max and H rd,max are the maximum heat storage power and heat release power of the heat storage device respectively; A c,t and A d,t They represent the two working states of the heat storage device, namely the heat storage state and the heat release state. c,t =1 means the heat storage device is working in the heat storage state, A d,t =1 means the heat storage device is working in the heat release state; S r,0 , S r,T They respectively represent the heat storage capacity of the heat storage device at the beginning and end of the scheduling period.

[0169] Thermal storage equipment can adjust the heat supply when energy demand changes, improve the flexibility and stability of the system, and ensure the temperature of the heating network is stable.

[0170] In this embodiment, in step S20, the economic constraint model includes:

[0171] C total ≤C;

[0172] C total =C buy +C ab +C sto +C car +C DR ;

[0173] In the formula, C total is the total cost; C is the artificially preset cost; C buy is the total energy purchase cost of IES; C ab C is the cost of curtailing wind and solar power; sto is the depreciation cost of energy storage; C car is the carbon emission cost; C DR is the demand response cost.

[0174] The economic constraint model takes into account a variety of cost factors, comprehensively reflects the operating costs of the integrated energy system, and helps to identify the potential for cost savings.

[0175] In step S30, a chance-constrained programming model is established. Based on the chance-constrained programming method, the uncertainty of wind power and photovoltaic power output is considered. The chance-constrained programming model includes:

[0176]

[0177] Where Ef(x,ζ) is the expected value of the objective function under specific decision variables and random variables; x is the decision variable; ζ is the random variable; P r {·} is the probability of event {·} occurring; g i (x,ζ)≤0 is the inequality constraint in the system; β i is the confidence level of the probability of the event occurring, β i ∈(0,1); D is the deterministic constraint set.

[0178] By introducing random variables and chance constraints, the model can better handle the uncertainty of output from renewable energy sources such as wind power and photovoltaics, and improve the robustness of the scheduling scheme.

[0179] As a preferred embodiment of the above, in step S30, a chance constraint programming model is established. Based on the chance constraint programming method, the uncertainty of wind power and photovoltaic power output is considered, and the uncertainty constraints are converted into deterministic constraint conditions, and then converted into a deterministic constraint condition model, including:

[0180] The electric power balance without considering the opportunity constraint is:

[0181] P grid,t +P PV,t,c +P w,t,c +P esd,t +P MT,t =P P2G,t +P esc,t +P EB,t +P load,t ;

[0182] Where: P grid,t represents the interaction power between IES and the upper power grid at time t, kW; P PV,t,c and P w,t,c They represent the photovoltaic and wind power actually absorbed by IES at time t; P esd,t and P esc,t are the discharge power and charge power of the battery at time t respectively; P MT,t is the electric power generated by the gas turbine at time t; P P2G,t P is the power consumed by P2G (electric converter) at time t; load,t is the electrical load at time t;

[0183] Considering the influence of the prediction error of wind and solar power output, in order to maintain the balance of system power, the error of wind power and photovoltaic output is introduced, and it is assumed that the error of both meets the expectation of 0 and the variance is σ 2 , change the above power balance constraint to:

[0184] P grid,t +(P PV,t,c +δ pv,t )+(P w,t,c +δ w,t )+P esd,t +P CHP,t =P P2G,t +P esc,t +P EB,t +P load,t ;

[0185] In the formula, δ w,t and δ pv,t They respectively represent the errors in the predicted values ​​of wind power and photovoltaic output power at time t.

[0186] Applying the theory of chance constrained programming, the confidence level of the above equation is set to be no less than α, and the power balance constraint of the above equation can be transformed into the following:

[0187] Pr{P grid,t +(P PV,t,c +δ pv,t )+(P w,t,c +δ w,t )+Pesd,t +P CHP,t ≥P P2G,t +P esc,t +P EB,t +P load,t}≥α;

[0188] Since the above formula contains the random variable δ pv,t and δ w,t , which cannot be solved directly, so it is converted into a deterministic constraint.

[0189]

[0190] Where P grid,t represents the interaction power between IES and the upper power grid at time t, kW; P PV,t,c and P w,t,c They represent the photovoltaic and wind power actually absorbed by IES at time t; P esd,t and P esc,t are the discharge power and charge power of the battery at time t respectively; P P2G,t P is the power consumed by P2G (electric converter) at time t; load,t is the electrical load at time t; F -1 () represents the inverse cumulative distribution function of a variable that satisfies the normal distribution. and Respectively represent the variance of the normal distribution satisfied by wind power and photovoltaic power.

[0191] By introducing error terms and confidence levels, uncertainty management can be converted into deterministic constraints, thereby improving the solvability and practicality of the model. After being converted into deterministic constraints, the output errors of wind power and photovoltaics can be effectively handled during the optimization process, thereby improving the robustness and stability of the integrated energy system.

[0192] The gas bus power balance constraint model considering the uncertainty of wind power and photovoltaic output includes:

[0193] G G,t +G P2G,t +G gd,t =G MT,t +G GB,t +G gc,t +G load,t ;

[0194] The thermal bus power balance constraint model considering the uncertainty of wind power and photovoltaic output includes:

[0195] H eb,t +H rd,t +H GB,t +H MT,t =H load,t +H rc,t ;

[0196] In the formula, G load,t is the gas load at time t; H load,t is the heat load at time t.

[0197] In this embodiment, in step S40, an adjustable capacity evaluation model of the integrated energy system is established based on the opportunity constraint, and the model includes:

[0198]

[0199] Where: P grid,t It represents the interaction power between IES and the upper power grid at time t, in kW; E grid,t Indicates the accumulated amount of electricity exchanged between IES and the upper-level power grid up to time t, in kWh; minP grid,t Indicates the minimum value of the interaction power between IES and the upper power grid at time t; maxP grid,t Indicates the maximum value of the interaction power between IES and the upper power grid at time t; minE grid,t Indicates the minimum value of the accumulated interactive electricity between IES and the upper-level power grid at time t; maxE grid,t It indicates the maximum value of the accumulated interactive electricity between IES and the upper-level power grid up to time t.

[0200] In the embodiment of the present invention, the upper and lower limits of the power constraints of the wind and solar power output absorbed by the comprehensive energy system, the operation constraints of batteries, gas storage tanks and heat storage tanks, the operation constraints and power upper and lower limits of P2G (electricity converter), gas boilers, electric boilers, gas turbines and waste heat boilers, the demand response model constraints and the total cost constraints considering carbon emissions are used as the constraints of the model to establish a comprehensive energy system adjustable capacity evaluation model.

[0201] In step S50, the integrated energy system adjustable capacity evaluation model is solved to obtain an optimization result, including:

[0202] The mixed integer linear programming method is used to solve the adjustable capacity evaluation model of the integrated energy system based on MATLAB+Yalmip+Gurobi solver. The upper and lower limits of the integrated energy system's demand for electricity at each moment are drawn to obtain the adjustable capacity range of the integrated energy system.

[0203] From the above, it can be seen that the embodiment of the present invention provides an evaluation method for the adjustable capacity range of an integrated energy system based on opportunity constraints. By establishing a model for evaluating the adjustable capacity of an integrated energy system based on opportunity constraints, the integrated energy system model is externally connected to the upper power grid and the natural gas pipeline network, and its internal includes new energy supply equipment, such as wind power and photovoltaics; energy coupling equipment, such as gas boilers, electric boilers, power-to-gas, gas turbines and waste heat boilers; energy storage equipment, such as batteries, heat storage tanks and gas storage tanks, and the demand side includes electric loads, thermal loads and gas loads that take demand response into consideration, while considering economic constraints; based on the opportunity constraint method, considering the uncertainty of wind power and photovoltaic output, two groups of objective functions are established, objective function group 1 is the maximum and minimum values ​​of the interactive electric power on the interconnection line between the integrated energy system and the upper power grid at each moment; objective function group 2 is the maximum and minimum values ​​of the accumulated interactive electric power between the integrated energy system and the upper power grid interconnection line starting from time zero to each moment. The constraints include the operation constraints of each device in the integrated energy system and the upper and lower limits of power, the electric power balance constraints, natural gas power constraints, thermal power balance constraints and economic constraints considering the opportunity constraints. Based on the above, an evaluation model for the adjustable capacity range of the integrated energy system based on the opportunity constraint method is established. Based on the MATLAB platform, the gurobi solver of the Yalmip optimization toolkit is called to solve and draw the upper and lower limits of the integrated energy system's demand for electricity, and then describe the adjustable capacity range of the integrated energy system. This method enables the superior system to better dispatch the integrated energy system, which is conducive to the completely stable operation of the power grid, improves the stability of the operation of the integrated energy system, and ensures the reliability of power supply.

[0204] The present invention also includes an optimized dispatching device for the adjustable capacity of a comprehensive energy system, such as Figure 3 As shown, using the method as described above, including:

[0205] Establish a mathematical model module for establishing a mathematical model for evaluating the adjustable capacity of the integrated energy system based on opportunity constraints, including at least establishing demand response models for new energy supply equipment, energy storage equipment and demand side respectively;

[0206] Establish a constraint module to establish the operating constraints and upper and lower power constraints of each device in the integrated energy system, which is used to limit the operation of each device within the rated operating area. The operating constraints include equipment operation constraints, electrical and thermal power balance constraints, and economic constraints.

[0207] Establish an opportunity constraint module to establish an opportunity constraint programming model. Based on the opportunity constraint programming method, the uncertainty of wind power and photovoltaic power output is considered and the uncertainty constraints are converted into deterministic constraints.

[0208] Establish an evaluation model module to establish an adjustable capacity evaluation model for the integrated energy system based on the chance-constrained programming model;

[0209] The evaluation model solving module is used to solve the evaluation model of the adjustable capacity of the integrated energy system, obtain the optimization results, and draw the adjustable capacity range of the integrated energy system.

[0210] See also Figure 4 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0211] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0212] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0213] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0214] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0215] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0216] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0217] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0218] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0219] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0220] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An optimization scheduling method for the adjustable capacity of an integrated energy system, characterized in that: The steps include: S10: Establish a mathematical model for evaluating the adjustable capacity of the integrated energy system based on opportunity constraints, including at least establishing a demand response model for new energy supply equipment, energy storage equipment, and demand side; S20: Establishing operation constraints and upper and lower power constraints of each device in the integrated energy system, so as to limit each device to operate within a rated operation area, wherein the operation constraints include device operation constraints, electrical and thermal power balance constraints, and economic constraints; S30: establishing a chance constraint programming model, taking into account the uncertainty of wind power and photovoltaic power output based on the chance constraint programming method, and converting the uncertainty constraint into a deterministic constraint condition; S40: Establishing an adjustable capacity evaluation model for the integrated energy system based on the chance-constrained programming model; S50: Solve the integrated energy system adjustable capacity evaluation model, obtain optimization results, and draw the adjustable capacity range of the integrated energy system.

2. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S10, the demand response model includes a translatable load model, and the constraints of the translatable load model include: In the formula, α t represents the translation state of the translatable load within a certain period of time t, α t =1 indicates load translation, α t =0 means the load does not translate; t st Indicates the starting time; t con It represents the continuous operation time of the shiftable load; T represents the number of scheduling periods.

3. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S10, the demand response model includes a transferable load model, and the transferable load model and the constraint model include: -P tran,min ≤P tran,t ≤P tran,max ; Where P tran,t represents the transfer amount of transferable load at time t; P tran,min and P tran,max Respectively represent the minimum and maximum values ​​of the transferable load power.

4. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S10, the demand response model includes a curtailable load model, and the curtailable load model and the constraint model include: P cut,t =(1-e t d t )P′ cut,t ; In the formula, δ t is a 0 / 1 variable, indicating the reduction status of the reducible load within a certain period of time t. δ = 1 indicates that the reducible load is reduced, and δ = 0 indicates that it is not reduced; ε t is the load reduction factor in period t, ε t ∈(0,1);P′ cut,t N is the power of the load that can be reduced before it participates in the dispatch; max is the maximum number of reductions; P cut,t Indicates the power that can be reduced after the load participates in scheduling.

5. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S20, the operation constraint conditions of each device in the integrated energy system are established. The device is an electric boiler. The operation constraint condition model of the electric boiler includes: H EB,t =the EB P EB,t ; 0≤P EB,t ≤P EB,max ; In the formula, H EB,t is the heat generation power of the electric boiler in the period t; η EB is the heat generation efficiency of the electric boiler; P EB,t and P EB,max is the electric power and maximum electric power consumption of the electric boiler during period t.

6. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S20, the operation constraint conditions of each device in the integrated energy system are established. The device is a gas boiler. The operation constraint condition model of the gas boiler includes: H GB,t =G GB,t or GB ; 0≤H GB,t ≤H GBmax ; In the formula, H GB,t is the heating power of the gas boiler in period t; G GB,t is the power consumed by the gas boiler during the period t; η GB is the heating efficiency; H GBmax It is the maximum heat generating power of the boiler itself.

7. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S20, the operation constraint conditions of each device in the integrated energy system are established. The device is a power-to-gas device. The operation constraint condition model of the power-to-gas device includes: 0≤P P2G,t ≤P P2G,max ; Where P P2G,t P is the electric power consumed by the power-to-gas device during the period t; P2G,max The maximum electrical power consumed by the power-to-gas device.

8. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S20, the operation constraint conditions of each device in the integrated energy system are established. The devices are gas turbines and waste heat boilers. The operation constraint condition models of the gas turbines and waste heat boilers include: P MT,t =G MT,t η MT ·LHV; Where P MT,t is the electrical energy generated by the gas turbine at time t; G MT,t is the amount of natural gas consumed by the gas turbine at time t; η MT is the conversion efficiency of the gas turbine; LHV is the calorific value of natural gas; H MT,t is the heat generated after conversion; η L is the combustion heat loss coefficient.

9. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S20, the operation constraint conditions of each device in the integrated energy system are established, and the device is a battery. The operation constraint condition model of the battery includes: 0≤P esc,t ≤B c,t P esc,max ; 0≤P esd,t ≤B d,t P esd,max ; B c,t +B d,t ≤1; IN es,min ≤In es,t ≤In es,max ; IN es,0 =In es,T ; Where W es,t , W es,max , W es,min are the amount of electricity stored in the battery during period t, the maximum storage capacity and the minimum storage capacity; γ c , γ d are the charging efficiency and discharging efficiency of the battery respectively; P esc,t , P esd,t are the charging power and discharging power of the battery in time period t respectively; P esc,max , P esd,max are the maximum charging power and maximum discharging power allowed by the battery respectively; B c,t , B d,t They represent the two working states of the battery, namely the charging state and the discharging state. c,t =1 means the battery is in charging state, B d,t =1 means the battery is in the discharge state and the energy storage device can only work in one state; W es,0 , W es,T are the storage capacities at the beginning and end of the dispatching period respectively; W es,t-1 It represents the amount of electricity stored in the battery during the period t-1; Δt represents the length of a scheduling period.

10. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S20, the operation constraint conditions of each device in the integrated energy system are established, and the device is a gas storage device. The operation constraint condition model of the gas storage device includes: 0≤G gc,t ≤C c,t G gc,max ; 0≤G gd,t ≤C d,t G gd,max ; C c,t +C d,t ≤1; 0≤G g,t ≤G g,max ; G g,0 =G g,T ; In the formula, G g,t is the amount of natural gas stored in the gas storage device at time t; G g,max Indicates the maximum amount of natural gas that can be stored in the gas storage device; G gc,t and G gd,t are the gas storage and gas injection powers of the gas storage device at time t, respectively; η gc and η gd Represent the efficiency of gas storage and gas injection respectively; C c,t and C d,t They represent the two working states of the gas storage device at time t, namely, gas storage and gas injection. c,t =1 means the gas storage device is working in the gas storage state, C d,t =1 means the gas storage device is working in the gas injection state; G gc,max and G gd,max Respectively represent the maximum gas storage and gas injection power of the gas storage device; G g,0 and G g,T They respectively represent the amount of natural gas stored in the gas storage device at the beginning and end of the scheduling period.

11. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S20, the operation constraint conditions of each device in the integrated energy system are established, and the device is a heat storage device. The operation constraint condition model of the heat storage device includes: 0≤H rc,t ≤A c,t H rc,max ; 0≤H rd,t ≤A c,t H rd,max ; A c,t +A d,t ≤1; 0≤S r,t ≤S r,max ; S r,0 =S r,T ; In the formula, S r,t represents the heat storage capacity of the heat storage device in time period t; S r,max Indicates the maximum heat storage capacity of the heat storage device; H rc,t and H rd,t are the heat storage power and heat release power of the heat storage device in period t respectively; η rc , η rd Respectively represent the heat storage and heat release efficiency of the heat storage device; H rc,max and H rd,max are the maximum heat storage power and heat release power of the heat storage device respectively; A c,t and A d,t They represent the two working states of the heat storage device, namely the heat storage state and the heat release state. c,t =1 means the heat storage device is working in the heat storage state, A d,t =1 means the heat storage device is working in the heat release state; S r,0 , S r,T They respectively represent the heat storage capacity of the heat storage device at the beginning and end of the scheduling period.

12. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S20, the economic constraint model includes: C total ≤C; C total =C buy +C ab +C sto +C car +C DR ; In the formula, C total is the total cost; C is the artificially preset cost; C buy is the total energy purchase cost of IES; C ab C is the cost of curtailing wind and solar power; sto is the depreciation cost of energy storage; C car is the carbon emission cost; C DR is the demand response cost.

13. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S30, a chance-constrained programming model is established based on a chance-constrained programming method, taking into account the uncertainty of wind power and photovoltaic output. The chance-constrained programming model includes: Where Ef(x,ζ) is the expected value of the objective function under specific decision variables and random variables; x is the decision variable; ζ is the random variable; P r {·} is the probability of event {·} occurring; g i (x,ζ)≤0 is the inequality constraint in the system; β i is the confidence level of the probability of the event occurring, β i ∈(0,1); D is the deterministic constraint set.

14. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S30, a chance constraint programming model is established. Based on the chance constraint programming method, the uncertainty of wind power and photovoltaic power output is considered, and the uncertainty constraint is converted into a deterministic constraint condition. The conversion into a deterministic constraint condition model includes: Where P grid,t represents the interaction power between IES and the upper power grid at time t, kW; P PV,t,c and P w,t,c They represent the photovoltaic and wind power actually absorbed by IES at time t; P esd,t and P esc,t are the discharge power and charge power of the battery at time t respectively; P P2G,t P is the power consumed by P2G (electric converter) at time t; load,t is the electrical load at time t; F -1 () represents the inverse cumulative distribution function of a variable that satisfies the normal distribution. and Respectively represent the variance of the normal distribution satisfied by wind power and photovoltaic power.

15. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S40, an adjustable capacity evaluation model of the integrated energy system is established based on the opportunity constraint, and the model includes: Where: P grid,t It represents the interaction power between IES and the upper power grid at time t, in kW; E grid,t Indicates the accumulated amount of electricity exchanged between IES and the upper-level power grid up to time t, in kWh; minP grid,t Indicates the minimum value of the interaction power between IES and the upper power grid at time t; maxP grid,t Indicates the maximum value of the interaction power between IES and the upper power grid at time t; minE grid,t Indicates the minimum value of the accumulated interactive electricity between IES and the upper-level power grid at time t; maxE grid,t It indicates the maximum value of the accumulated interactive electricity between IES and the upper-level power grid up to time t.

16. The method for optimizing the adjustable capacity of a comprehensive energy system according to claim 1, characterized in that: In step S50, solving the integrated energy system adjustable capacity evaluation model to obtain an optimization result includes: The mixed integer linear programming method is used to solve the adjustable capacity evaluation model of the integrated energy system based on MATLAB+Yalmip+Gurobi solver, the upper and lower limits of the integrated energy system's demand for electricity at each moment are drawn, and the adjustable capacity range of the integrated energy system is obtained.

17. An optimization and scheduling device for the adjustable capacity of a comprehensive energy system, characterized in that: Use of the method according to any one of claims 1 to 16, comprising: Establish a mathematical model module for establishing a mathematical model for evaluating the adjustable capacity of the integrated energy system based on opportunity constraints, including at least establishing demand response models for new energy supply equipment, energy storage equipment and demand side respectively; Establish a constraint module, which is used to establish the operation constraint conditions and upper and lower limit constraints of power of each device in the comprehensive energy system, so as to limit each device to operate within the rated operation area. The operation constraint conditions include equipment operation constraint, electrical and thermal power balance constraint and economic constraint; Establishing a chance constraint module for establishing a chance constraint programming model, based on the chance constraint programming method, taking into account the uncertainty of wind power and photovoltaic power output, and converting the uncertainty constraint into a deterministic constraint condition; Establishing an evaluation model module for establishing an adjustable capacity evaluation model of a comprehensive energy system based on the chance-constrained programming model; The evaluation model solving module is used to solve the evaluation model of the adjustable capacity of the integrated energy system, obtain the optimization result, and draw the adjustable capacity range of the integrated energy system.

18. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 16 is implemented.

19. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 16 is implemented.

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