Method for constructing integrated energy system optimal scheduling model considering virtual heat storage

Abstract

A method for constructing an integrated energy system optimal scheduling model considering virtual heat storage, relating to the technical field of power systems. The method comprises: establishing operational models of working equipment of an integrated energy system, establishing an integrated demand response model for integrated energy loads and an orderly charging model for electric vehicles, and establishing a heating network energy flow calculation model considering virtual heat storage; on the basis of a mathematical theory, processing a direct current power flow model of a power distribution network, and by using a second-order cone relaxation method, converting general power flow constraints into mixed integer quadratically constrained programming constraints; and according to power flow constraints of the power distribution network, a heating network and a gas network of the integrated energy system, by taking minimization of the total operating cost of an integrated energy operator as an objective function, constructing an integrated energy system optimal scheduling model considering virtual heat storage. The method achieves accurate description of operating characteristics of integrated energy systems, so as to implement coordinated optimal scheduling of multiple energy sources and improve the economy, environmental protection property and reliability of the systems, thus providing support for efficient operation of the integrated energy systems.

Description

A Method for Constructing an Optimal Scheduling Model for Integrated Energy Systems Considering Virtual Thermal Storage Technical Field

[0001] This invention relates to the field of power system technology, specifically to a method for constructing an integrated energy system optimization scheduling model that considers virtual thermal storage. Background Technology

[0002] The current scarcity of fossil fuels and increasingly serious environmental pollution pose potential threats to sustainable development, and the contradiction between energy supply and demand is becoming increasingly prominent. Therefore, not only does the power system need transformation, but also the development of low-carbon, safe, and efficient modern energy systems to improve energy utilization efficiency. Integrated energy systems, by utilizing their multi-energy complementarity, can effectively improve the system's economy, environmental friendliness, and reliability to a certain extent, saving operators' overall costs and contributing to the sustainable development of human society. Therefore, under the dual-carbon goal, researching operational optimization strategies for urban integrated energy systems is of great significance.

[0003] Given that various energy sources in an urban integrated energy system can be interconverted, the synergistic effect of these resources under a defined objective function inevitably possesses a large amount of adjustability, reflecting the resource flexibility of the integrated energy system. Optimal scheduling strategies can be derived through problem-solving, thereby better coordinating various flexible resources to determine the output or adjustment of different resources at different times. This allows for the full utilization of various flexible resources while saving costs and improving economic efficiency. The complexity of the energy conversion structure, the differences in time scales and energy flow among various energy sources, and uncertainties from the source and load sides are all key factors influencing decision-making and must be considered during the process. Therefore, research on the optimal scheduling of integrated energy systems is necessary to obtain the optimal scheduling scheme for urban integrated energy systems. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is:

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a method for constructing an optimized scheduling model for an integrated energy system considering virtual thermal storage, comprising: establishing an operational model for the working equipment of the integrated energy system; establishing an integrated energy load demand response model and an orderly charging model for electric vehicles; establishing a heat network energy flow calculation model considering virtual thermal storage; processing the DC power flow model of the distribution network based on mathematical theory, and using the second-order cone relaxation method to transform general power flow constraints into mixed integer quadratic constraint programming constraints; and constructing an optimized scheduling model for the integrated energy system considering virtual thermal storage based on the power flow constraints of the distribution network, heat network, and gas network of the integrated energy system, with the objective function of minimizing the total operating cost of the integrated energy operator.

[0007] As a preferred embodiment of the method for constructing an optimized scheduling model of an integrated energy system considering virtual thermal storage as described in this invention, the integrated energy system operating equipment model includes a wind turbine model, a photovoltaic generator model, a combined heat and power unit model, a gas boiler model, an electric boiler model, an electric-to-gas conversion equipment model, a battery model, and a thermal storage device model.

[0008] The wind turbine model is represented by the following formula:

[0009] In the formula, v is the wind speed of the wind turbine, S represents the swept area, and ρ is the air density; η max It is the maximum wind energy utilization rate, P max It is the maximum output power; η w It is the wind energy utilization rate; P r and P WTG (v) represents the rated power and actual power of the wind turbine, respectively. r These are the actual wind speed and the rated wind speed of the fan, v ci and v co These are the inflow velocity and outflow velocity of the fan, respectively. It is the total output power. This is the actual output power. It is the power of abandoned light; P WTG,climb It is the upper limit of the ramp constraint.

[0010] The photovoltaic generator model is represented by the following formula:

[0011] In the formula, It is the total output power of photovoltaics. This is the actual output power. It is the power of abandoned light; P PV,climb It is the upper limit of the photovoltaic ramp-up constraint.

[0012] The combined heat and power unit model:

[0013] In the formula, It is the electrical power generated by the combined heat and power unit. It is the heat power generated by the combined heat and power unit. It is the gas power consumed by the combined heat and power unit. It is the electrothermal proportionality coefficient. It is the gas-heat conversion coefficient; P CHP,min and P CHP,max These represent the lower and upper limits of the electrical power output of a combined heat and power (CHP) unit, respectively. This indicates the upper limit of the power ramp-up rate for combined heat and power (CHP) units; It is the total heat output of the combined heat and power unit. It is the power supplied to the heat load. It is the power stored in the thermal storage device.

[0014] The gas-fired boiler model is represented by the following formula:

[0015] In the formula, It is the heat power generated by the gas-fired boiler. η is the gas power consumed by the gas boiler. GB H is the gas-to-heat conversion coefficient of a gas-fired boiler; GB,min and H GB,max These represent the lower and upper limits of the thermal power output of the gas-fired boiler, respectively.

[0016] The gas-fired boiler model is represented by the following formula:

[0017] In the formula, It is the heat power generated by the gas-fired boiler. η is the gas power consumed by the gas boiler. GB H is the gas-to-heat conversion coefficient of a gas-fired boiler; GB,min and H GB,max These represent the lower and upper limits of the thermal power output of the gas-fired boiler, respectively.

[0018] The electric boiler model is represented by the following formula:

[0019] In the formula, It is the heat power generated by the electric boiler. It is the electrical power consumed by the electric boiler, η EH P is the electrothermal conversion coefficient of an electric boiler. EH,min and P EH,max These represent the lower and upper limits of the electrical power consumed by the electric boiler, respectively.

[0020] The model of the electro-gas conversion equipment is represented by the following formula:

[0021] In the formula, It is the gas power generated by P2G. This is the electrical power consumed by the P2G, η P2G P is the electrical conversion factor for an electric boiler. P2G,min and P P2G,max These represent the lower and upper limits of the electrical power consumed by the P2G, respectively.

[0022] The battery model is represented by the following formula:

[0023] In the formula, and Let represent the state of charge of the battery at time t and time t-1, respectively. and Let represent the charging and discharging power at time t, γ be the energy dissipation coefficient of the battery, λ be the charging and discharging efficiency coefficient of the battery, Δt be the unit scheduling time, and T be the scheduling period. and C represents the battery state of charge at the beginning and end of the scheduling cycle, respectively. SOC,min and C SOC,max P represents the lower and upper limits of the battery's state of charge. c,max It is the upper limit of the battery charging power, P dis,max It is the upper limit of the battery's discharge power.

[0024] The thermal storage device model is represented by the following formula:

[0025] In the formula, and These represent the amount of heat stored in the thermal storage device at time t and time t-1, respectively. and Let represent the charge and discharge heat power of the thermal storage device at time t, and β be the dissipation coefficient of the thermal storage device. and C represents the amount of heat stored in the thermal storage device at the beginning and end of the scheduling cycle, respectively. TSS,min and C TSS,max H represents the lower and upper limits of the heat storage capacity of a thermal storage device. c,max H is the upper limit of the thermal storage device's charging power. dis,max It is the upper limit of the heat release power of the thermal storage device.

[0026] As a preferred embodiment of the method for constructing an optimized scheduling model for an integrated energy system considering virtual thermal storage as described in this invention, the integrated energy load demand response is expressed by the following formula:

[0027] In the formula, These represent loads that can be reduced, loads that can be transferred, and alternative loads, respectively. It is the maximum participation ratio factor that can reduce electrical load. It is the total electrical load at time t; It is the maximum participation value of transferable electrical load; It is the maximum participation ratio coefficient for alternative electrical loads; It is the power of heat load converted from electrical substitute load. It is the electrothermal conversion coefficient of the alternative load; It is the power of the gas load converted from the electric load substitution. It is the electro-gas conversion coefficient;

[0028] The orderly charging model for electric vehicles is expressed by the following formula:

[0029] In the formula, k is the EV number. This represents the minimum percentage of battery capacity that is out of the grid, as specified by the EV user with the ID k. E represents the percentage of the battery capacity of the EV when it is connected to the grid. k It's the battery capacity. It represents the user's total charging demand; and The SOC of vehicle k at time periods t and t-1 are respectively; p k,t It is the average charging and discharging power of the EV in fast charging mode; α k,t It is a 01 variable, i.e., an identifier, representing charging or occupancy, taking the value 0 or 1, representing that the vehicle is in an occupied or charging state, respectively; T k N represents the total number of time periods during which vehicle k is connected to the power grid; EV This indicates the number of electric vehicles.

[0030] As a preferred embodiment of the method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described in this invention, the heat network energy flow calculation model considering virtual thermal storage includes,

[0031] The power exchange model between the heat exchange primary station and the heat exchange station is as follows:

[0032] In the formula, and These are the inlet temperature in the water supply pipe connected to the first heat exchange station and the outlet temperature in the return water pipe, respectively. It is the water flow rate through the first heat exchange station; c w It is the specific heat capacity of water; and These are the outlet temperature in the water supply pipe connected to the heat exchange station and the inlet temperature in the return water pipe, respectively. It is the flow rate of hot water passing through the heat exchange station; It is the heat load at time t.

[0033] The temperature mixing constraint and range constraint are as follows:

[0034] In the formula, and Let n and n represent the sets of pipes in the heating network that start and end at node n, respectively. q is the inlet temperature of water supply pipe k at time t; k and q j These are the flow rates of water supply pipes k and j, respectively, which remain constant. It is the outlet temperature of water supply pipe j at time t; and These are the upper and lower limits of the water supply pipe temperature, respectively. and These are the upper and lower limits of the return water pipe temperature, respectively.

[0035] The heat network model considering transmission delay and temperature loss is performed using the nodal method as follows:

[0036] In the formula, the superscript S represents only the water supply pipe, and the subscript p represents only the pipe p. The physical meaning of each symbol is consistent with the previous text. It is the transmission delay time constant of the entire pipeline. and ρ represents the length and cross-sectional area of ​​the water supply pipe p, respectively; w It is the density of water; It is the inlet temperature of hot water in water supply pipe p during the time period t-(K-1); It is the temperature loss coefficient of pipe p; It refers to the ambient temperature of the regional heating network.

[0037] As a preferred embodiment of the method for constructing an optimized scheduling model for a comprehensive energy system considering virtual thermal storage as described in this invention, the energy flow calculation model for the heating network considering virtual thermal storage further includes the following: the pipeline energy storage of the heating network is:

[0038] In the formula, This indicates the energy storage in the heating network's pipelines; the superscript R only indicates the return water pipeline of the heating network, and the physical meaning of each symbol remains consistent with the previous text. It is any set of pipes in the heating network.

[0039] The upper and lower limits and periodic recovery constraints for regional heating network pipeline energy storage are as follows:

[0040] In the formula, H PES,max and H PES,min These represent the upper and lower limits of energy storage in the heating network pipeline, respectively. and These refer to the pipeline energy storage at the beginning and end of the heating network's dispatch cycle.

[0041] As a preferred embodiment of the integrated energy system optimization scheduling model construction method considering virtual thermal storage described in this invention, the DC power flow model of the distribution network is processed based on mathematical theory, and the general power flow constraints are transformed into mixed integer quadratic constraint planning constraints using the second-order cone relaxation method.

[0042] In the formula, R represents the branch current at time t. line,l and X line,l This represents the resistance and reactance of branch l. and This represents the active and reactive power flowing through the branch. and These are the voltages at nodes i and j at time t, respectively; This represents the set of all branches of the distribution network; This represents the set of all nodes in the distribution network. and P represents the active and reactive power of the net outflow node n; line,max It is the maximum active power that the branch circuit can withstand. and These are the squares of the maximum allowable current and voltage, respectively.

[0043] As a preferred embodiment of the method for constructing an optimized scheduling model for a comprehensive energy system considering virtual thermal storage as described in this invention, the objective function is: minf = f MTESD,TSS +f buy +f ECD +f DG +f pun -f env +f IDR -f EV ;

[0044] In the formula, f is the total scheduling cost, f MTESD,TSS It is the operation and maintenance cost of physical thermal storage devices among various types of energy storage equipment, f buy This refers to the cost of purchasing electricity and gas from the upstream power grid and natural gas network. ECD It is the operating cost of energy conversion equipment, fDG It is the operation and maintenance cost of distributed power generation, f pun It is the penalty fee for abandoning wind and solar power, f env It is the environmental benefits of renewable energy, f IDR It is the comprehensive demand response compensation cost, f EV It is the profit obtained by integrated energy operators from selling electricity to EV operators; c TSS This refers to the unit power operation and maintenance cost of the thermal storage device; It involves purchasing electricity from the higher-level power grid. It refers to the amount of gas purchased from the natural gas network, c E and c G These are the unit electricity price and gas price; c EH c P2G and c GB These are the unit power operation and maintenance costs of energy conversion equipment such as EH, P2G, and GB; c WTG c CHP and c PV It refers to the unit power operation and maintenance cost of distributed sources such as wind turbines, combined heat and power units, and photovoltaic power generation units; c pun is the penalty coefficient for wind and solar power curtailment; e is the environmental benefit coefficient for renewable energy sources such as wind and solar power. These are the participating power of reduceable and transferable loads in the integrated heat and gas demand response, respectively. IDR It is the cost coefficient for unit power compensation in comprehensive demand response; c EV Revenue per unit power generated by integrated energy operators when selling electricity to EV operators.

[0045] To further address the aforementioned technical problems, this invention provides the following technical solution: a system for constructing an optimized scheduling model for an integrated energy system considering virtual thermal storage, comprising: a model building module for establishing an operational model of the integrated energy system's working equipment, an integrated energy load demand response model, an orderly charging model for electric vehicles, and a heat network energy flow calculation model considering virtual thermal storage; a power flow processing module for processing the DC power flow model of the distribution network based on mathematical theory, and using the second-order cone relaxation method to transform general power flow constraints into mixed integer quadratic constraint programming constraints; and an optimized scheduling module for constructing an optimized scheduling model for the integrated energy system considering virtual thermal storage, based on the power flow constraints of the integrated energy system's distribution network, heat network, and gas network, with the objective function of minimizing the total operating cost of the integrated energy operator.

[0046] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described above.

[0047] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described above.

[0048] The beneficial effects of this invention are as follows: By establishing system operating equipment models, load demand response models, electric vehicle charging models, and heat network energy flow calculation models considering virtual thermal storage, and employing mathematical methods to handle power flow constraints in the distribution network, this invention constructs an optimized scheduling model with the objective of minimizing total operating costs. This method comprehensively considers the characteristics of various equipment in the system, demand response, electric vehicle charging, and virtual energy storage in the heat network, enabling a more accurate description of the operating characteristics of the integrated energy system. This achieves coordinated and optimized scheduling of multiple energy sources, improves the system's economy, environmental friendliness, and reliability, and provides strong support for the efficient operation of integrated energy systems. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 shows the node voltage vector and network structure diagram of the method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage according to an embodiment of the present invention.

[0051] Figure 2 is a flowchart illustrating a method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage, provided by an exemplary embodiment of the present invention.

[0052] Figure 3 is a schematic diagram of the urban integrated energy network topology provided by an exemplary embodiment of the present invention;

[0053] Figure 4 is a schematic diagram of electrical load, heat load, gas load, photovoltaic and wind power curves, and electricity price curve under a typical operating scenario provided by an exemplary embodiment of the present invention.

[0054] Figure 5 is a schematic diagram of an optimized scheduling strategy for electricity, heat, and gas in an integrated energy system provided by an exemplary embodiment of the present invention. Detailed Implementation

[0055] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0056] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0057] Example 1

[0058] Referring to Figures 1 to 5, an embodiment of the present invention provides a method for constructing an optimized scheduling model for a comprehensive energy system considering virtual thermal storage, comprising:

[0059] S1: Establish an operational model for the working equipment of the integrated energy system, an integrated energy load demand response model and an orderly charging model for electric vehicles, and an energy flow calculation model for the heat network that considers virtual thermal storage.

[0060] S1.1: Establish an operational model for the working equipment of the integrated energy system.

[0061] The integrated energy system operating equipment model includes wind turbine model, photovoltaic generator set model, combined heat and power unit model, gas boiler model, electric boiler model, electric-to-gas equipment model, battery model, and thermal storage device model.

[0062] Wind turbines are a clean and renewable energy source, playing a vital role in China's energy structure. Wind power drives a turbine, which then converts mechanical energy into electrical energy through a generator. In this process, a speed increaser is responsible for increasing the turbine speed, enabling the generator to reach operating conditions. The electrical energy is then connected to the grid via a transformer and power electronic devices. A control system monitors and controls the entire wind turbine's operation to ensure safe and stable power generation. The wind turbine model is represented by the following formula:

[0063] In the formula, v is the wind speed of the wind turbine, S represents the swept area, and ρ is the air density; η max It is the maximum wind energy utilization rate, P max It is the maximum output power; η w It is the wind energy utilization rate; P r and P WTG (v) represents the rated power and actual power of the wind turbine, respectively.r These are the actual wind speed and the rated wind speed of the fan, v ci and v co These are the inflow velocity and outflow velocity of the fan, respectively. It is the total output power. This is the actual output power. It is the power of abandoned light; P WTG,climb It's a climbing constraint.

[0064] Photovoltaic power generation is a multifunctional solar energy technology that converts solar energy into electrical energy through solar panels. It requires no fuel and has significant environmental benefits. The control system monitors and regulates the energy conversion and output of photovoltaic power generation to ensure stable operation. Power conversion equipment such as distribution cabinets and inverters convert direct current (DC) into alternating current (AC), which is then stepped up by a transformer before being connected to the grid. The photovoltaic generator set model is represented by the following formula:

[0065] In the formula, It is the total output power of photovoltaics. This is the actual output power. It is the power of abandoned light; P PV,climb This is the upper limit of the photovoltaic ramp-up constraint;

[0066] As a distributed power generation system, CHP units consume natural gas power while simultaneously generating electricity and heat. Compared to traditional distributed power generation systems, CHP units have high energy efficiency and low pollution emissions, and are widely used in integrated energy systems (IES). Because CHP units generate heat and electricity simultaneously, their operating modes are considered to be either "heat-driven power generation" or "electricity-driven heat generation," resulting in relatively poor adjustability during integrated energy system dispatching. The large output of CHP units can crowd out renewable energy sources, leading to frequent instances of wind and solar power curtailment. The combined heat and power (CHP) unit model is represented by the following formula:

[0067] In the formula, It is the electrical power generated by the combined heat and power unit. It is the heat power generated by the combined heat and power unit. It is the gas power consumed by the combined heat and power unit. It is the electrothermal proportionality coefficient. It is the gas-heat conversion coefficient; P CHP,min and P CHP,max These represent the lower and upper limits of the electrical power output of a combined heat and power (CHP) unit, respectively. This indicates the upper limit of the power ramp-up rate for combined heat and power (CHP) units; It is the total heat output of the combined heat and power unit. It is the power supplied to the heat load. It is the power stored in the thermal storage device.

[0068] The gas-fired boiler (GB) model is represented by the following formula:

[0069] In the formula, It is the heat power generated by the gas-fired boiler. η is the gas power consumed by the gas boiler. GB H is the gas-to-heat conversion coefficient of a gas-fired boiler; GB,min and H GB,max These represent the lower and upper limits of the thermal power output of the gas-fired boiler, respectively.

[0070] Ideally, GB uses natural gas as fuel, consuming gas power to generate heat power. The working principle of GB is to heat water by burning gas, raising the water temperature and converting it into heat energy; that is, the heat energy generated by the combustion of gas is transferred to the water, raising the water temperature.

[0071] The electric boiler (EH) model is represented by the following formula:

[0072] In the formula, It is the heat power generated by the electric boiler. It is the electrical power consumed by the electric boiler, η EH P is the electrothermal conversion coefficient of an electric boiler. EH,min and P EH,max These represent the lower and upper limits of the electrical power consumed by the electric boiler, respectively.

[0073] Ideally, the EH (Electromagnetic Heater) utilizes the principle of electromagnetic induction to convert electrical energy into heat energy, making it an energy conversion device. Its characteristics include small size, variety of types, simple structure, and ease of assembly. Compared to traditional boilers, EHs offer advantages such as high heating efficiency and clean, environmentally friendly operation.

[0074] The power-to-gas (P2G) equipment model is represented by the following formula:

[0075] In the formula, It is the gas power generated by P2G. This is the electrical power consumed by the P2G, η P2G P is the electrical conversion factor for an electric boiler. P2G,min and P P2G,max These represent the lower and upper limits of the electrical power consumed by the P2G, respectively.

[0076] Ideally, P2G (Power-to-Gas) equipment is a key component in the interconnection of electricity and gas systems, converting electrical power into natural gas power. The flexibility of P2G makes it an important energy coupling unit, capable of adjusting its operation according to load demand and gas supply. Common P2G technology involves the reaction of carbon dioxide and hydrogen to produce methane, which is then transported to gas pipelines, achieving energy conversion between the power system and the gas grid. This allows for closer integration between the power and natural gas systems, resulting in efficient energy conversion and utilization. In practical IES (Environmentally Integrated Gas Systems), P2G equipment must participate to a certain extent, but due to limitations in output capacity, its conversion power is generally not large, playing only a minor regulatory role.

[0077] The storage battery (ESS) model is represented by the following formula:

[0078] In the formula, and Let represent the state of charge of the battery at time t and time t-1, respectively. and Let represent the charging and discharging power at time t, γ be the energy dissipation coefficient of the battery, λ be the charging and discharging efficiency coefficient of the battery, Δt be the unit scheduling time, and T be the scheduling period. and C represents the battery state of charge at the beginning and end of the scheduling cycle, respectively. SOC,min and C SOC,max P represents the lower and upper limits of the battery's state of charge. c,max It is the upper limit of the battery charging power, P dis,max It is the upper limit of the battery's discharge power.

[0079] Since most energy conversion can be achieved through thermal energy, thermal energy storage technology is considered one of the simplest energy storage methods. It plays a crucial role in addressing the increasingly severe energy problem and has become an indispensable component of integrated energy systems. The thermal energy storage device (TSS) model is represented by the following formula:

[0080] In the formula, and These represent the amount of heat stored in the thermal storage device at time t and time t-1, respectively. and Let represent the charge and discharge heat power of the thermal storage device at time t, and β be the dissipation coefficient of the thermal storage device. and C represents the amount of heat stored in the thermal storage device at the beginning and end of the scheduling cycle, respectively. TSS,min and C TSS,max H represents the lower and upper limits of the heat storage capacity of a thermal storage device.c,max H is the upper limit of the thermal storage device's charging power. dis,max It is the upper limit of the heat release power of the thermal storage device.

[0081] S1.2: Establish a comprehensive energy load demand response model and an orderly charging model for electric vehicles.

[0082] Specifically, the Integrated Demand Response (IDR) model for integrated energy loads: On the demand side, integrated demand response refers to extending the demand response of general electrical loads to other types of loads in an integrated energy system, such as electricity, gas, and heat. It fully utilizes the complementary nature of multiple energy sources within the integrated energy system, breaking down barriers between different energy types, thereby achieving a higher energy utilization rate for demand-side resources than traditional electrical load demand response. There are many ways to classify integrated demand response. One approach is to categorize electricity, heat, and gas loads into conventional loads, reducible loads, and transferable loads, respectively. Reducible loads refer to the portion of integrated energy load that can be directly eliminated when the load is high, with cost compensation; transferable loads refer to the portion of load that can be transferred to other time periods when the load is high, with cost compensation. Another classification method is to categorize IES loads into fixed loads, price-based loads, and substitute loads. For price-based loads, time-of-use pricing can be changed to encourage users to adjust their energy consumption behavior; for substitute loads, different forms of energy can be selected to provide users with the same energy needs. In this invention, the load demand response (IDR) primarily considers three categories: reducible load, transferable load, and alternative load. Transferable load is only allowed to participate in regulation during a portion of the 24-hour period. The integrated energy load demand response (IDR) model is expressed by the following formula:

[0083] In the formula, These represent loads that can be reduced, loads that can be transferred, and alternative loads, respectively. It is the maximum participation ratio factor that can reduce electrical load. It is the total electrical load at time t; It is the maximum participation value of transferable electrical load; It is the maximum participation ratio coefficient for alternative electrical loads; It is the power of heat load converted from electrical substitute load. It is the electrothermal conversion coefficient of the alternative load; It is the power of the gas load converted from the electric load substitution. It is the electro-gas conversion coefficient.

[0084] The orderly charging model for electric vehicles (EVs) is expressed by the following formula:

[0085] In the formula, k is the EV number. This represents the minimum percentage of battery capacity that is out of the grid, as specified by the EV user with the ID k. E represents the percentage of the battery capacity of the EV when it is connected to the grid. k It's the battery capacity. It represents the user's total charging demand; and The SOC of vehicle k at time periods t and t-1 are respectively; p k,t This is the average charging and discharging power of the EV in fast charging mode (the charging piles in the complex are fast charging by default); α k,t It is a 01 variable, i.e., an identifier, representing charging or occupancy, taking the value 0 or 1, representing that the vehicle is in an occupied or charging state, respectively; T k N represents the total number of time periods during which vehicle k is connected to the power grid; EV This indicates the number of electric vehicles.

[0086] It should be noted that the EVs considered in this invention are all located in urban complex charging stations. The range of net charging amount required by an EV after arriving at an urban complex charging station is determined by the grid-connected state of charge, the minimum off-grid state of charge specified by the user, and the battery capacity.

[0087] S1.3: Establish a thermal network energy flow calculation model that considers virtual thermal storage.

[0088] Specifically, the power exchange model between the heat exchange primary station and the heat exchange station is as follows:

[0089] In the formula, and These are the inlet temperature in the water supply pipe connected to the first heat exchange station and the outlet temperature in the return water pipe, respectively. It is the water flow rate through the first heat exchange station; c w It is the specific heat capacity of water; and These are the outlet temperature in the water supply pipe connected to the heat exchange station and the inlet temperature in the return water pipe, respectively. It is the flow rate of hot water passing through the heat exchange station; It is the heat load at time t.

[0090] The temperature mixing constraint and range constraint are as follows:

[0091] In the formula, and Let n and n represent the sets of pipes in the heating network that start and end at node n, respectively. q is the inlet temperature of water supply pipe k at time t; k and q jThese are the flow rates of water supply pipes k and j, respectively, which remain constant. It is the outlet temperature of water supply pipe j at time t; and These are the upper and lower limits of the water supply pipe temperature, respectively. and These are the upper and lower limits of the return water pipe temperature, respectively.

[0092] Alternatively, the nodal method can be used to model the heat network considering transmission delay and temperature loss as follows:

[0093] In the formula, the superscript S represents only the water supply pipe, and the subscript p represents only the pipe p. The physical meaning of each symbol is consistent with the previous text. It is the transmission delay time constant of the entire pipeline. and ρ represents the length and cross-sectional area of ​​the water supply pipe p, respectively; w It is the density of water; It is the inlet temperature of hot water in water supply pipe p during the time period t-(K-1); It is the temperature loss coefficient of pipe p; It refers to the ambient temperature of the regional heating network.

[0094] After sorting, we get:

[0095] The energy storage capacity of the heating network's pipelines is:

[0096] In the formula, This indicates the energy storage in the heating network's pipelines; the superscript R only indicates the return water pipeline of the heating network, and the physical meaning of each symbol remains consistent with the previous text. It is any set of pipes in the heating network.

[0097] The upper and lower limits and periodic recovery constraints for regional heating network pipeline energy storage are as follows:

[0098] In the formula, H PES,max and H PES,min These represent the upper and lower limits of energy storage in the heating network pipeline, respectively. and These refer to the pipeline energy storage at the beginning and end of the heating network's dispatch cycle.

[0099] S2: Based on mathematical theory, the DC power flow model of the distribution network is processed, and the second-order cone relaxation method is used to transform the general power flow constraints into mixed integer quadratic constraints for programming.

[0100] In one embodiment, the DC power flow model of the distribution network constructed in S2 is specifically as follows:

[0101] Optionally, relevant expressions can be written based on the node voltage vectors and network structure diagram shown in Figure 1.

[0102] In the formula, ΔU represents the line voltage drop, and I line R represents the branch current. line and X line P represents the branch resistance and reactance. line and Q line U represents the active and reactive power flowing through the branch. i and U j ΔU1 represents the voltage at nodes i and j, respectively, and ΔU2 represents the horizontal component of the voltage drop.

[0103] The branch power flow calculation constraints can be obtained as follows:

[0104] In the formula, This represents the set of all branches of the distribution network.

[0105] The power balance equation for active and reactive power can be expressed as:

[0106] In the formula, This represents the set of all nodes in the distribution network. and Let P represent the sets of lines with node n as the starting and ending points, respectively. n,out and Q n,out It represents the active and reactive power of the net outflow node n.

[0107] Because the constraints related to power flow are nonlinear, they are difficult to solve using a solver. Therefore, this invention employs a second-order cone relaxation method to transform general power flow constraints into mixed-integer quadratic constraint programming (MIQCP) constraints. Verification through actual solutions shows that this method is effective and reasonable, successfully transforming a non-convex problem into a convex one, and the optimal solution point remains unchanged after relaxation, thus allowing for solver-based solutions. The constraints are expressed as follows: The power flow constraints in the integrated energy system of this invention adopt DC power flow constraints, completely ignoring reactive power in the distribution network. For the parts of the decision variables containing square terms, the variable substitution method is used to treat the entire square term as a whole and list the constraints. The superscripts of the relevant variables only indicate the scheduling time interval, while the rest of the physical meaning remains consistent.

[0108] In the formula, R represents the branch current at time t. line,l and Xline,l This represents the resistance and reactance of branch l. and This represents the active and reactive power flowing through the branch. and These are the voltages at nodes i and j at time t, respectively; This represents the set of all branches of the distribution network; This represents the set of all nodes in the distribution network. and P represents the active and reactive power of the net outflow node n; line,max It is the maximum active power that the branch circuit can withstand. and These are the squares of the maximum allowable current and voltage, respectively.

[0109] S3: Based on the power flow constraints of the distribution network, heating network, and gas network of the integrated energy system, and with the objective function of minimizing the total operating cost of the integrated energy operator, an optimized scheduling model for the integrated energy system considering virtual thermal storage is constructed.

[0110] Specifically, the objective function is: minf = f MTESD,TSS +f buy +f ECD +f DG +f pun -f env +f IDR -f EV ;

[0111] In the formula, f is the total scheduling cost, f MTESD,TSS It is the operation and maintenance cost of physical thermal storage devices among various types of energy storage equipment, f buy This refers to the cost of purchasing electricity and gas from the upstream power grid and natural gas network. ECD It is the operating cost of energy conversion equipment, f DG It is the operation and maintenance cost of distributed power generation, f pun It is the penalty fee for abandoning wind and solar power, f env It is the environmental benefits of renewable energy, f IDR It is the comprehensive demand response compensation cost, f EV It is the profit obtained by integrated energy operators from selling electricity to EV operators; c TSS This refers to the unit power operation and maintenance cost of the thermal storage device; It involves purchasing electricity from the higher-level power grid. It refers to the amount of gas purchased from the natural gas network, c E and c G These are the unit electricity price and gas price; c EH c P2G and c GBThese are the unit power operation and maintenance costs of energy conversion equipment such as EH, P2G, and GB; c WTG c CHP and c PV It refers to the unit power operation and maintenance cost of distributed sources such as wind turbines, combined heat and power units, and photovoltaic power generation units; c pun is the penalty coefficient for wind and solar power curtailment; e is the environmental benefit coefficient for renewable energy sources such as wind and solar power. These are the participating power of reduceable and transferable loads in the integrated heat and gas demand response, respectively. IDR It is the cost coefficient for unit power compensation in comprehensive demand response; c EV Revenue per unit power generated by integrated energy operators when selling electricity to EV operators.

[0112] The static model of the air network is represented as follows:

[0113] In the formula, L is the natural gas pipeline number; It is a collection of natural gas pipelines; m L,max It is the upper limit of the flow rate.

[0114] The overall energy power balance constraint is:

[0115] In the formula, This represents the distribution network load at time t after considering IDR. This takes into account the heat load at time t after IDR. This takes into account the gas load of the natural gas network at time t after IDR.

[0116] The constraints on electricity and gas purchase volumes are as follows:

[0117] In the formula, P grid,max and G grid,max These are the upper limits for the amount of electricity and gas that can be purchased from the upper-level power grid or natural gas grid.

[0118] In summary, the present invention proposes a method for constructing an optimal scheduling model for an integrated energy system that considers virtual thermal storage. By establishing operating models for the distribution network, heating network, and gas network, as well as mathematical models for the operation of each device, and considering the power flow constraints of the integrated energy system's distribution network, heating network, and gas network, the optimal scheduling strategy is obtained by minimizing the total operating cost of the integrated energy operator.

[0119] Example 2

[0120] As an embodiment of the present invention, a method system for constructing an optimized scheduling model of an integrated energy system considering virtual thermal storage is provided, comprising: a model building module for establishing an operational model of the integrated energy system's working equipment, an integrated energy load demand response model, an orderly charging model for electric vehicles, and a heat network energy flow calculation model considering virtual thermal storage; a power flow processing module for processing the DC power flow model of the distribution network based on mathematical theory, and using the second-order cone relaxation method to transform general power flow constraints into mixed integer quadratic constraint programming constraints; and an optimized scheduling module for constructing an optimized scheduling model of the integrated energy system considering virtual thermal storage based on the power flow constraints of the integrated energy system's distribution network, heat network, and gas network, with the objective function of minimizing the total operating cost of the integrated energy operator.

[0121] Example 3

[0122] This is one embodiment of the present invention, which differs from the previous embodiment in that:

[0123] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0124] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0125] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0126] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0127] Example 4

[0128] As an embodiment of the present invention, a method for constructing an optimized scheduling model of a comprehensive energy system considering virtual thermal storage is provided. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculations and simulation experiments.

[0129] To verify the effectiveness of the proposed scheduling method, a city integrated energy system as shown in Figure 3 was used for testing, consisting of an IEEE 33-node distribution network, a 20-node Belgian natural gas network, and a 44-node heating network. Figure 4 shows the electrical load, heat load, gas load, photovoltaic and wind power curves, and electricity price curves under typical operating scenarios.

[0130] Figure 5 shows the optimized scheduling results of the integrated energy system, Table 1 shows the input parameter values ​​of the resources, and Table 2 shows the cost results of the urban integrated energy system.

[0131] Table 1. Model-related constant parameters

[0132] Table 2 Operating Costs

[0133] The power system can meet the electrical load demand within the system. The difference between the sum of the output of each device (including the equivalent "negative power" converted by the device) and the current load at each moment in the diagram is due to demand response. From 1:00 to 6:00, the electricity price is at its lowest, at 335 yuan / MWh, and the electrical load demand is relatively small, less than 0.5MW. Since this is nighttime, wind power output is relatively high, around 0.3MW, resulting in a large purchase of electricity from the upstream grid, leading to excess power. This excess power is transferred or consumed through P2G, EH, and ESS, with a transfer amount of approximately 0.5MW. This reduces wind curtailment and lowers the operator's operating costs. From 7:00 to 11:00, the electricity price is at its peak, reaching 1250 yuan / MWh. Combined with the load generated by electric vehicles, the total electrical load demand is high, exceeding 0.7MW. The peak price limits the amount of electricity purchased from the upstream grid during dispatch, resulting in a purchase power of less than 0.1MW. During this period, the ESS (Electric Power Supply) releases power to meet real-time power balance, and demand response takes effect (approximately 0.01MW), effectively reducing actual power demand. This demonstrates that the ESS often purchases more electricity from the grid during off-peak hours, storing the excess and releasing it during peak hours. After 7:00 AM, the CHP (Consumer Power Supply) output remains relatively constant, essentially reaching its upper limit of 0.22MW. This is because electricity, heat, and gas loads are all relatively high during this period, and the CHP can enter a "heat-driven power" or "electricity-driven heat" output mode, converting gas power into electricity and heat power simultaneously at a certain ratio. Compared to the penalties and gas purchase costs associated with curtailing wind and solar power, incorporating the CHP into the overall dispatch is more economical, thus reducing operating costs. Between 11:00 and 16:00, photovoltaic output reaches its peak, around 0.5MW, with the electricity price at grid parity of 780 yuan / MWh. Electricity purchases from the grid are moderate (0.1MW-0.3MW), with only a small amount of power stored through ESS (Energy Storage System) or converted through EH (Energy Heater) (below 0.2MW). Between 16:00 and 20:00, the orderly charging of EVs results in a relatively high total load even after considering EVs. As the electricity price returns to peak levels, grid purchases are again limited (below 0.2MW), and ESS continues to release a small amount of power (less than 0.1MW) to maintain power balance. At this time, almost no power is converted or stored. Between 22:00 and 24:00, the electricity price returns to off-peak levels, and grid purchases increase again, reaching the upper limit of 0.8MW. Excess energy is either stored through ESS, converted into heat through EH, or converted into gas power through P2G. This portion of power exceeds 0.5MW, effectively maintaining the economic efficiency of the integrated energy system dispatch.

[0134] The heating network can meet the heat load demand within the system. The difference between the sum of the outputs of all equipment (including the equivalent "negative power" converted from actual equipment) and the current load is due to demand response and virtual heat storage in the heating network. By comparing the curves of the actual heat load and the equivalent heat load considering virtual heat storage, it can be seen that the virtual heat storage characteristics of the heating network have smoothed out the original actual heat load considerably, reducing the peak heat load from 0.372MW to 0.315MW and the valley value from 0.167MW to 0.203MW, truly playing the role of "peak shaving and valley filling" in heat load. Due to the significant effect of the virtual heat storage characteristics of the pipeline, the actual heat storage TSS plays a more significant role in scheduling only when the heat load is very high, i.e., from 17:00 to 19:00 (exceeding 0.3MW) (between 0.05MW and 0.1MW). During this period, the TSS releases heat power, playing an auxiliary role in regulating virtual heat storage. The heat storage of the TSS mainly comes from the conversion of EH, which generally occurs during the electrical power redundancy period mentioned above. Specifically, during off-peak electricity hours (1:00-6:00 and 22:00-24:00), when electricity prices are low, operators will choose to purchase a large amount of electricity from the grid because the cost of purchasing electricity from the grid is relatively low. At this time, there is a surplus of electricity, and EH will participate in a large amount to supply the heat load. With the participation of CHP to a certain extent, the heat load will become redundant. The excess heat power will be stored in the pipe in the form of pipe temperature or temperature difference, i.e., virtual heat storage power. Between 7:00 and 21:00, the heat load is relatively high. During this period, the electricity price is generally at parity or peak price, and the EH conversion is relatively low. The heat power undertaken by the CHP unit exceeds 70%. Since the gas price of 660 yuan / MWh is between the parity and off-peak electricity price, the most economical dispatch strategy during this period is for the operator to purchase more gas power from the natural gas network and convert it. Therefore, the gas boiler works during this period, converting gas power into heat power, with the conversion power not exceeding 0.1MW. The equivalent effect of virtual thermal storage between 7:00 and 21:00 is to release the thermal storage power of other periods to meet the high heat load demand during this period.

[0135] The natural gas pipeline network can meet the gas load demand within the system. The difference between the sum of the outputs of all equipment (including the equivalent "negative power" converted by the equipment) and the current load is due to demand response. Between 1:00-6:00 and 22:00-24:00, when electricity prices are lower than gas prices, distribution network operators will purchase a large amount of electricity from the upstream grid. A portion of this electricity will be converted into gas power through P2G equipment to supply the gas load, with the converted gas power reaching a maximum limit of 0.15MW. Between 7:00-21:00, because gas prices are lower than electricity prices, integrated energy operators will purchase sufficient natural gas (reaching the maximum gas power limit of 0.624MW) to fully supply the gas load. The surplus natural gas will either become fuel for CHP units or be converted into thermal power through GB, with a conversion power of 0.3MW-0.4MW, thereby achieving economic efficiency in the integrated energy system dispatch.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for constructing an optimized scheduling model for a comprehensive energy system considering virtual thermal storage, characterized in that, include: Establish an operational model for the working equipment of the integrated energy system, an integrated energy load demand response model and an orderly charging model for electric vehicles, and a heat network energy flow calculation model that considers virtual thermal storage. Based on mathematical theory, the DC power flow model of the distribution network is processed, and the second-order cone relaxation method is used to transform the general power flow constraints into mixed integer quadratic constraint programming constraints. Based on the power flow constraints of the distribution network, heating network, and gas network of the integrated energy system, and with the objective function of minimizing the total operating cost of the integrated energy operator, an optimal scheduling model for the integrated energy system considering virtual thermal storage is constructed.

2. The method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described in claim 1, characterized in that: The integrated energy system operating equipment model includes a wind turbine model, a photovoltaic generator model, a combined heat and power unit model, a gas boiler model, an electric boiler model, an electric-to-gas conversion equipment model, a battery model, and a thermal storage device model. The wind turbine model is represented by the following formula: In the formula, v is the wind speed of the wind turbine, S represents the swept area, and ρ is the air density; η max It is the maximum wind energy utilization rate, P max This is the maximum output power; η w It is the wind energy utilization rate; P r and P WTG (v) represents the rated power and actual power of the wind turbine, respectively. r These are the actual wind speed and the rated wind speed of the fan, v ci and v co These are the inflow velocity and outflow velocity of the fan, respectively. It is the total output power. This is the actual output power. It is the power of abandoned light; P WTG,climb It is the upper limit of the ramp constraint; The photovoltaic generator model is represented by the following formula: In the formula, It is the total output power of photovoltaics. This is the actual output power. It is the power of abandoned light; P PV,climb This is the upper limit of the photovoltaic ramp-up constraint; The combined heat and power unit model: In the formula, It is the electrical power generated by the combined heat and power unit. It is the heat power generated by the combined heat and power unit. It is the gas power consumed by the combined heat and power unit. It is the electrothermal proportionality coefficient. It is the gas-heat conversion coefficient; P CHP,min and P CHP,max These represent the lower and upper limits of the electrical power output of a combined heat and power (CHP) unit, respectively. This indicates the upper limit of the power ramp-up rate for combined heat and power (CHP) units; It is the total heat output of the combined heat and power unit. It is the power supplied to the heat load. It is the power stored in the thermal storage device; The gas-fired boiler model is represented by the following formula: In the formula, It is the heat power generated by the gas-fired boiler. η is the gas power consumed by the gas boiler. GB H is the gas-to-heat conversion coefficient of a gas-fired boiler; GB,min and H GB,max These represent the lower and upper limits of the thermal power output of the gas-fired boiler, respectively. The gas-fired boiler model is represented by the following formula: In the formula, It is the heat power generated by the gas-fired boiler. η is the gas power consumed by the gas boiler. GB H is the gas-to-heat conversion coefficient of a gas-fired boiler; GB,min and H GB,max These represent the lower and upper limits of the thermal power output of the gas-fired boiler, respectively. The electric boiler model is represented by the following formula: In the formula, It is the heat power generated by the electric boiler. It is the electrical power consumed by the electric boiler, η EH P is the electrothermal conversion coefficient of an electric boiler. EH,min and P EH,max These represent the lower and upper limits of the electrical power consumed by the electric boiler, respectively. The model of the electro-gas conversion equipment is represented by the following formula: In the formula, It is the gas power generated by P2G. This is the electrical power consumed by the P2G, η P2G P is the electrical conversion factor for an electric boiler. P2G,min and P P2G,max These represent the lower and upper limits of the electrical power consumed by P2G, respectively. The battery model is represented by the following formula: In the formula, and Let represent the state of charge of the battery at time t and time t-1, respectively. and Let represent the charging and discharging power at time t, γ be the energy dissipation coefficient of the battery, λ be the charging and discharging efficiency coefficient of the battery, Δt be the unit scheduling time, and T be the scheduling period. and C represents the battery state of charge at the beginning and end of the scheduling cycle, respectively. SOC,min and C SOC,max P represents the lower and upper limits of the battery's state of charge. c,max It is the upper limit of the battery charging power, P dis,max It is the upper limit of the battery's discharge power; The thermal storage device model is represented by the following formula: In the formula, and These represent the amount of heat stored in the thermal storage device at time t and time t-1, respectively. and Let represent the charge and discharge heat power of the thermal storage device at time t, and β be the dissipation coefficient of the thermal storage device. and C represents the amount of heat stored in the thermal storage device at the beginning and end of the scheduling cycle, respectively. TSS,min and C TSS,max H represents the lower and upper limits of the heat storage capacity of a thermal storage device. c,max H is the upper limit of the thermal storage device's charging power. dis,max It is the upper limit of the heat release power of the thermal storage device.

3. The method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described in claim 2, characterized in that: The integrated energy load demand response is expressed by the following formula: In the formula, These represent loads that can be reduced, loads that can be transferred, and alternative loads, respectively. It is the maximum participation ratio factor that can reduce electrical load. It is the total electrical load at time t; It is the maximum participation value of transferable electrical load; It is the maximum participation ratio coefficient for alternative electrical loads; It is the power of heat load converted from electrical substitute load. It is the electrothermal conversion coefficient of the alternative load; It is the power of the gas load converted from the electric load substitution. It is the electro-gas conversion coefficient; The orderly charging model for electric vehicles is expressed by the following formula: In the formula, k is the EV number. This represents the minimum percentage of battery capacity that is out of the grid, as specified by the EV user with the ID k. E represents the percentage of the battery capacity of the EV when it is connected to the grid. k It's the battery capacity. It represents the user's total charging demand; and The SOC of vehicle k at time periods t and t-1 are respectively; p k,t It is the average charging and discharging power of the EV in fast charging mode; α k,t It is a 01 variable, i.e., an identifier, representing charging or occupancy, with a value of 0 or 1, representing that the vehicle is in an occupied or charging state, respectively; T k N represents the total number of time periods during which vehicle k is connected to the power grid; EV This indicates the number of electric vehicles.

4. The method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described in claim 3, characterized in that: The heat network energy flow calculation model considering virtual thermal storage includes: The power exchange model between the heat exchange primary station and the heat exchange station is as follows: In the formula, and These are the inlet temperature in the water supply pipe connected to the first heat exchange station and the outlet temperature in the return water pipe, respectively. It is the water flow rate through the first heat exchange station; c w It is the specific heat capacity of water; and These are the outlet temperature in the water supply pipe connected to the heat exchange station and the inlet temperature in the return water pipe, respectively. It is the flow rate of hot water passing through the heat exchange station; It is the heat load at time t; The temperature mixing constraint and range constraint are as follows: In the formula, and Let n and n represent the sets of pipes in the heating network that start and end at node n, respectively. q is the inlet temperature of water supply pipe k at time t; k and q j These are the flow rates of water supply pipes k and j, respectively, which remain constant. It is the outlet temperature of water supply pipe j at time t; and These are the upper and lower limits of the water supply pipe temperature, respectively. and These are the upper and lower limits of the return water pipe temperature, respectively. The heat network model considering transmission delay and temperature loss is performed using the nodal method as follows: In the formula, the superscript S represents only the water supply pipe, and the subscript p represents only the pipe p. The physical meaning of each symbol is consistent with the previous text. It is the transmission delay time constant of the entire pipeline. and ρ represents the length and cross-sectional area of ​​the water supply pipe p, respectively; w It is the density of water; It is the inlet temperature of hot water in water supply pipe p during the time period t-(K-1); It is the temperature loss coefficient of pipe p; It refers to the ambient temperature of the regional heating network.

5. The method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described in claim 4, characterized in that: The heat network energy flow calculation model that considers virtual thermal storage also includes The energy storage capacity of the heating network's pipelines is: In the formula, This indicates the energy storage in the heating network's pipelines; the superscript R only indicates the return water pipeline of the heating network, and the physical meaning of each symbol remains consistent with the previous text. It is any set of pipes in the heating network; The upper and lower limits and periodic recovery constraints for regional heating network pipeline energy storage are as follows: In the formula, H PES,max and H PES,min These represent the upper and lower limits of energy storage in the heating network pipeline, respectively. and These refer to the pipeline energy storage at the beginning and end of the heating network's dispatch cycle.

6. The method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described in claim 5, characterized in that: Based on mathematical theory, the DC power flow model of the distribution network is processed, and the second-order cone relaxation method is used to transform the general power flow constraints into mixed-integer quadratic constraint programming constraints: In the formula, R represents the branch current at time t. line,l and X line,l This represents the resistance and reactance of branch l. and This represents the active and reactive power flowing through the branch. and These are the voltages at nodes i and j at time t, respectively; This represents the set of all branches of the distribution network; This represents the set of all nodes in the distribution network. and P represents the active and reactive power of the net outflow node n; line,max It is the maximum active power that the branch circuit can withstand. and These are the squares of the maximum allowable current and voltage, respectively.

7. The method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described in claim 6, characterized in that: The objective function is: min f = f MTESD,TSS +f buy +f ECD +f DG +f pun -f env +f IDR -f EV ; In the formula, f is the total scheduling cost, f MTESD,TSS It is the operation and maintenance cost of physical thermal storage devices among various types of energy storage equipment, f buy This refers to the cost of purchasing electricity and gas from the upstream power grid and natural gas network. ECD It is the operating cost of energy conversion equipment, f DG It is the operation and maintenance cost of distributed power generation, f pun It is the penalty fee for abandoning wind and solar power, f env It is the environmental benefits of renewable energy, f IDR It is the comprehensive demand response compensation cost, f EV It is the profit obtained by integrated energy operators from selling electricity to EV operators; c TSS This refers to the unit power operation and maintenance cost of the thermal storage device; It involves purchasing electricity from the higher-level power grid. It refers to the amount of gas purchased from the natural gas network, c E and c G These are the unit electricity price and gas price; c EH c P2G and c GB These are the unit power operation and maintenance costs of energy conversion equipment such as EH, P2G, and GB; c WTG c CHP and c PV It refers to the unit power operation and maintenance cost of distributed sources such as wind turbines, combined heat and power units, and photovoltaic power generation units; c pun is the penalty coefficient for wind and solar power curtailment; e is the environmental benefit coefficient for renewable energy sources such as wind and solar power. These are the participating power of reduceable and transferable loads in the integrated heat and gas demand response, respectively. IDR It is the cost coefficient for unit power compensation in comprehensive demand response; c EV Revenue per unit power generated by integrated energy operators when selling electricity to EV operators.

8. A system employing the method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described in any one of claims 1 to 7, characterized in that, include: The model building module is used to build an operational model of the integrated energy system's working equipment, an integrated energy load demand response model, an orderly charging model for electric vehicles, and a heat network energy flow calculation model that considers virtual thermal storage. The power flow processing module is used to process the DC power flow model of the distribution network based on mathematical theory. It uses the second-order cone relaxation method to transform the general power flow constraints into mixed integer quadratic constraint programming constraints. The optimization scheduling module is used to construct an optimization scheduling model for the integrated energy system that considers virtual thermal storage, based on the power flow constraints of the integrated energy system's distribution network, heating network, and gas network, with the objective function of minimizing the total operating cost of the integrated energy operator.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for constructing an integrated energy system optimization scheduling model considering virtual thermal storage as described in any one of claims 1 to 7.

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