Comprehensive energy system optimization scheduling model construction method considering virtual heat storage

Abstract

The invention discloses an integrated energy system optimization scheduling model construction method considering virtual heat storage, and relates to the technical field of power systems, and the method comprises the steps: building an integrated energy system working equipment operation model, building an integrated energy load comprehensive demand response model and an electric vehicle ordered charging model, establishing a heat supply network energy flow calculation model considering virtual heat storage; the method comprises the following steps: processing a power distribution network direct current power flow model based on a mathematical theory, and converting a general power flow constraint condition into a mixed integer quadratic constraint programming constraint by adopting a second-order cone relaxation method; and according to the power flow constraint conditions of a power distribution network, a heat supply network and a gas network of the integrated energy system, taking minimization of the total operation cost of an integrated energy operator as a target function, and constructing an integrated energy system optimization scheduling model considering virtual heat storage. According to the method, the operation characteristics of the comprehensive energy system are accurately described, coordinated optimization scheduling of various energy sources is realized, the economy, environmental protection and reliability of the system are improved, and support is provided for efficient operation of the comprehensive energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for constructing an optimization scheduling model for an integrated energy system considering virtual heat storage. Background Art

[0002] At present, the shortage of fossil fuels and the increasingly serious environmental pollution problems have brought potential threats to sustainable development, and the contradiction between energy supply and demand has become increasingly prominent. Therefore, not only the power system needs to be transformed, but also a low-carbon, safe and efficient modern energy system needs to be developed to improve energy utilization efficiency. By utilizing its multi-energy complementary characteristics, the integrated energy system can effectively improve the economy, environmental protection and reliability of the system to a certain extent, save the comprehensive cost of operators, and contribute to the sustainable development of human society. Therefore, it is of great significance to study the operation optimization strategy of the urban integrated energy system.

[0003] Given that various types of energy in the urban integrated energy system can be converted into each other, the synergy of various resources under a certain objective function must have a large amount of adjustable space, which is a reflection of the flexibility of the resources of the integrated energy system. The optimal scheduling strategy can be obtained by solving, so as to better coordinate various flexible resources, determine the output or adjustment amount of different resources at different times, and save costs and improve economic benefits while making full use of various flexible resources. Among them, the complexity of the energy conversion structure, the differences in the time scales of various energies, the differences in energy flows, and the uncertainties from the source and load sides will all become key factors affecting decision-making and need to be taken into consideration in the process. Therefore, in order to obtain the optimal scheduling plan for the urban integrated energy system, it is necessary to carry out research on the optimal scheduling of the integrated energy system. Summary of the invention

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

[0005] Therefore, the technical problems solved by the present invention are:

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for constructing an optimal scheduling model of an integrated energy system considering virtual heat storage, comprising: establishing an operation model of working equipment of the integrated energy system, establishing an integrated energy load comprehensive demand response model and an electric vehicle orderly charging model, and establishing a heat network energy flow calculation model considering virtual heat storage; processing the DC power flow model of the distribution network based on mathematical theory, and using the second-order cone relaxation method to convert general power flow constraints into mixed integer quadratic constraint programming constraints; according to the power flow constraints of the distribution network, heat network, and gas network of the integrated energy system, with minimizing the total operating cost of the integrated energy operator as the objective function, constructing an optimal scheduling model of the integrated energy system considering virtual heat storage.

[0007] As a preferred scheme of the method for constructing an optimization scheduling model of an integrated energy system considering virtual heat storage described in the present invention, the integrated energy system working equipment operation model includes a wind turbine model, a photovoltaic generator set model, a cogeneration unit model, a gas boiler model, an electric boiler model, a power-to-gas equipment model, a battery model, and a heat storage device model.

[0008] The fan model is expressed by the following formula:

[0009]

[0010] Where v is the wind speed of the wind turbine, S is the swept area, ρ is the air density; η max is the maximum wind energy utilization factor, P max is the maximum output power; η w is the wind energy utilization rate; P r and P WTG (v) are the rated power and actual power of the fan, v and v r are the actual wind speed and rated wind speed of the fan, v ci and v co are the inflow wind speed and outflow wind speed of the fan respectively; is the total output power, is the actual output power, is the abandoned optical power; P WTG,climb is the upper limit of the climbing constraint.

[0011] The photovoltaic generator model is expressed by the following formula:

[0012]

[0013] In the formula, is the total photovoltaic output power, is the actual output power, is the abandoned optical power; P PV,climb It is the upper limit of photovoltaic ramp constraint.

[0014] The combined heat and power unit model:

[0015]

[0016] In the formula, is the electrical power generated by the cogeneration unit, is the thermal power generated by the cogeneration unit, is the gas power consumed by the cogeneration unit, is the electrothermal proportionality coefficient, is the gas heat transfer coefficient; P CHP,min and P CHP,maxThey represent the lower and upper limits of the electric power output of the cogeneration unit respectively; Indicates the upper limit of the electric power ramp rate of the cogeneration unit; is the total heat output of the cogeneration unit, is the power supplied to the heat load, is the power stored in the heat storage device.

[0017] The gas boiler model is expressed by the following formula:

[0018]

[0019] In the formula, is the thermal power generated by the gas boiler, is the gas power consumed by the gas boiler, η GB is the gas heat conversion coefficient of the gas boiler; H GB,min and H GB,max They respectively represent the lower and upper limits of the thermal power output of the gas boiler.

[0020] The gas boiler model is expressed by the following formula:

[0021]

[0022] In the formula, is the thermal power generated by the gas boiler, is the gas power consumed by the gas boiler, η GB is the gas heat conversion coefficient of the gas boiler; H GB,min and H GB,max They respectively represent the lower and upper limits of the thermal power output of the gas boiler.

[0023] The electric boiler model is expressed by the following formula:

[0024]

[0025] In the formula, is the thermal power generated by the electric boiler, is the electric power consumed by the electric boiler, η EH is the electric-heat conversion coefficient of the electric boiler; P EH,min and P EH,max They respectively represent the lower and upper limits of the electric power consumed by the electric boiler.

[0026] The power-to-gas plant model is expressed by the following formula:

[0027]

[0028] In the formula, is the gas power generated by P2G, is the electrical power consumed by P2G, η P2Gis the electrical conversion coefficient of the electric boiler; P P2G,min and P P2G,max They represent the lower and upper limits of P2G power consumption respectively.

[0029] The battery model is expressed by the following formula:

[0030]

[0031] In the formula, and They represent the state of charge of the battery at time t and time t-1 respectively. and They represent the charge and discharge power at time t, γ is the energy dissipation coefficient of the battery, λ is the charge and discharge efficiency coefficient of the battery, Δt is the unit scheduling time; T represents the scheduling period, and They represent the battery charge state at the beginning and end of the scheduling period, respectively. SOC,min and C SOC,max Indicates the lower and upper limits of the battery state of charge, P c,max is the upper limit of the battery charging power, P dis,max It is the upper limit of battery discharge power.

[0032] The heat storage device model is expressed by the following formula:

[0033]

[0034] In the formula, and They represent the heat storage capacity of the heat storage device at time t and time t-1 respectively, and They represent the charging and discharging power of the heat storage device at time t, and β is the dissipation coefficient of the heat storage device; and represents the heat storage capacity of the heat storage device at the beginning and end of the scheduling period, C TSS,min and C TSS,max Indicates the lower and upper limits of the heat storage capacity of the heat storage device, H c,max is the upper limit of the charging power of the heat storage device, H dis,max It is the upper limit of the heat release power of the heat storage device.

[0035] As a preferred solution of the method for constructing an optimal scheduling model of an integrated energy system considering virtual heat storage described in the present invention, the integrated energy load integrated demand response is expressed by the following formula:

[0036]

[0037] In the formula, They represent curtailable load, transferable load and alternative load respectively; is the maximum participation ratio coefficient that can reduce the electric load, is the total electrical load at time t; is the maximum participation value of the transferable electrical load; is the maximum participation ratio coefficient of alternative electric load; It is the heat load power converted from the electrical replacement load. is the electric-to-heat conversion coefficient of the alternative load; It is the gas load power converted from the electricity replacement load. is the electricity-gas conversion coefficient;

[0038] The electric vehicle orderly charging model is expressed by the following formula:

[0039]

[0040] Where k is the number of EV, It represents the percentage of the battery capacity of the minimum off-grid state of charge specified by the EV user numbered k. Indicates the percentage of the EV's grid-connected charge state to the battery capacity, E k is the battery capacity, is the total charging demand of the user; and are the SOC of vehicle k at time t and t-1 respectively; p k,t is the average charging and discharging power of EV in fast charging mode; α k,t is a 01 variable representing charging or occupying, i.e., an identifier, with a value of 0 or 1, representing that the vehicle is in the 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 Represents the number of electric vehicles.

[0041] As a preferred solution of the method for constructing an optimal scheduling model of an integrated energy system considering virtual heat storage described in the present invention, wherein: the heat network energy flow calculation model considering virtual heat storage includes:

[0042] The power exchange model of the heat exchange first station and the heat exchange station is:

[0043]

[0044] In the formula, and are the inlet temperature in the water supply pipe connected to the first heat exchange station and the outlet temperature in the return pipe respectively; is the water flow through the first heat exchange station; c w is the specific heat capacity of water; and are the outlet temperature in the water supply pipe connected to the heat exchange station and the inlet temperature in the water return pipe, respectively; is the hot water flow rate through the heat exchange station; is the heat load at time t.

[0045] The temperature mix constraints and range constraints are:

[0046]

[0047] In the formula, and They represent the set of pipes in the heating network with node n as the starting point and the end point respectively; is the inlet temperature of the water supply pipe k at time t; q k and q j are the flow rates of water supply pipes k and j, respectively, which remain constant; is the outlet temperature of water supply pipe j at time t; and They are the upper and lower limits of the water supply pipe temperature; and They are the upper and lower limits of the return pipe temperature respectively.

[0048] The node method is used to model the heat network considering transmission delay and temperature loss as follows:

[0049]

[0050] In the formula, the superscript S only represents the water supply pipe, the subscript p only represents the pipe p, and the physical meaning of each symbol remains consistent with the previous text. is the transmission delay time constant of the entire pipeline, and Respectively represent the length and cross-sectional area of ​​the water supply pipe p; ρ w is the density of water; is the inlet temperature of hot water in the water supply pipe p during the time period t-(K-1); is the temperature loss coefficient of pipe p; is the ambient temperature of the district heating network.

[0051] As a preferred solution of the method for constructing an optimal scheduling model of an integrated energy system considering virtual heat storage described in the present invention, wherein: the heat network energy flow calculation model considering virtual heat storage also includes that the pipeline energy storage of the heat network is:

[0052]

[0053] In the formula, Indicates the pipeline energy storage of the heating network; the superscript R only indicates the return pipe of the heating network, and the physical meaning of each symbol is consistent with the previous text; It is any set of pipes in the heat network.

[0054] The upper and lower limit constraints and period recovery constraints of the regional heating network pipeline energy storage are:

[0055]

[0056] In the formula, H PES,max and H PES,min They represent the upper and lower limits of the heat network pipeline energy storage respectively; and They are the pipeline energy storage of the heat network at the beginning and end of the dispatch period, respectively.

[0057] As a preferred solution of the method for constructing an optimal dispatching model of an integrated energy system considering virtual heat storage described in the present invention, the DC power flow model of the distribution network is processed based on mathematical theory, and the second-order cone relaxation method is used to convert the general power flow constraints into mixed integer quadratic constraint programming constraints:

[0058]

[0059] In the formula, represents the branch current at time t, R line,l and X line,l represents the resistance and reactance of branch l, and represents the active power and reactive power flowing through the branch, and are the voltages of nodes i and j at time t respectively; Represents the collection of all branches of the distribution network; Represents the set of all nodes in the distribution network. and is the net active and reactive power flowing out of node n; P line,max is the maximum active power that the branch can withstand, and They are the squares of the maximum values ​​allowed for current and voltage, respectively.

[0060] As a preferred solution of the method for constructing an optimal scheduling model of an integrated energy system considering virtual heat storage described in the present invention, the objective function is:

[0061] minf=f MTESD,TSS +f buy +f ECD +f DG +f pun -f env +f IDR -f EV ;

[0062]

[0063]

[0064] Where f is the total scheduling cost, f MTESD,TSS is the operation and maintenance cost of the physical heat storage device in the multi-type energy storage equipment, f buy is the cost of purchasing electricity and gas from the upper power grid and natural gas grid, ECD is the operating cost of the energy conversion equipment, f DG is the operation and maintenance cost of distributed generation, f pun is the penalty fee for abandoning wind and solar power, env is the environmental benefit of renewable energy, f IDR is the comprehensive demand response compensation cost, f EV is the profit obtained by the integrated energy operator from selling electricity to the EV operator; c TSS is the unit power operation and maintenance cost of the thermal storage device; It is to purchase electricity from the upper power grid. is the amount of gas purchased from the natural gas grid, c E and c G are the unit electricity price and gas price respectively; c EH 、c P2G and c GB 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 is the unit power operation and maintenance cost of distributed sources such as wind turbines, cogeneration units, and photovoltaic generators; c pun is the penalty coefficient for abandoning wind and solar power; e is the environmental benefit coefficient of renewable energy such as wind power and photovoltaic power; are the participating powers of curtailable load and transferable load in comprehensive demand response of heat and gas, respectively, and c IDR is the unit power compensation cost coefficient for comprehensive demand response; c EV The unit power revenue of the integrated energy operator selling electricity to the EV operator.

[0065] To further solve the above technical problems, the present invention provides the following technical solutions: a system for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage, comprising: a model building module, used to establish an operation model of working equipment of the integrated energy system, an integrated energy load comprehensive demand response model, an electric vehicle orderly charging model, and a heat network energy flow calculation model considering virtual heat storage; a flow processing module, used to process the DC flow model of the distribution network based on mathematical theory, and use the second-order cone relaxation method to convert general flow constraints into mixed integer quadratic constraint programming constraints; an optimization scheduling module, used to construct an optimal scheduling model for the integrated energy system considering virtual heat storage according to the flow constraints of the distribution network, heat network, and gas network of the integrated energy system, with minimizing the total operating cost of the integrated energy operator as the objective function.

[0066] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method for constructing an optimization scheduling model of an integrated energy system considering virtual heat storage as described above are implemented.

[0067] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method for constructing an optimization scheduling model of an integrated energy system considering virtual heat storage as described above are implemented.

[0068] Beneficial effects of the present invention: The present invention constructs an optimization scheduling model with the goal of minimizing the total operating cost by establishing a system working equipment operation model, a load demand response model, an electric vehicle charging model, and a heat network energy flow calculation model considering virtual heat storage, and using mathematical methods to process the power flow constraints of the distribution network. This method comprehensively considers factors such as the characteristics of various types of equipment in the system, demand response, electric vehicle charging, and virtual energy storage in the heat network, and can more accurately describe the operating characteristics of the integrated energy system, thereby achieving coordinated and optimized scheduling of multiple energy sources, improving the economy, environmental protection, and reliability of the system, and providing strong support for the efficient operation of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0070] Figure 1 A node voltage vector and network structure diagram of a method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage provided by an embodiment of the present invention.

[0071] Figure 2 It is a flow chart of a method for constructing an optimization scheduling model of an integrated energy system considering virtual heat storage provided by an exemplary embodiment of the present invention;

[0072] Figure 3 is a schematic diagram of the topology of an urban integrated energy network provided by an exemplary embodiment of the present invention;

[0073] Figure 4 It is a schematic diagram of electric load, thermal load, gas load, photovoltaic and wind power curves, electricity price curve, etc. in a typical operation scenario provided by an exemplary embodiment of the present invention;

[0074] Figure 5 It 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 DESCRIPTION

[0075] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0076] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0077] Example 1

[0078] Reference Figure 1 to Figure 5 , which is an embodiment of the present invention, provides a method for constructing an optimal scheduling model of an integrated energy system considering virtual heat storage, comprising:

[0079] S1: Establish an integrated energy system working equipment operation model, establish an integrated energy load integrated demand response model and an electric vehicle orderly charging model, and establish a heat network energy flow calculation model considering virtual heat storage.

[0080] S1.1: Establish an operation model of working equipment of the integrated energy system.

[0081] The operating model of the working equipment of the integrated energy system includes a wind turbine model, a photovoltaic generator set model, a cogeneration unit model, a gas boiler model, an electric boiler model, a power-to-gas equipment model, a battery model, and a heat storage device model.

[0082] Among them, wind turbines are one of the clean and renewable energy sources and occupy an important position in China's energy structure. Wind drives the turbine and converts mechanical energy into electrical energy through the generator. In this process, the speed increaser is responsible for increasing the turbine speed so that the generator reaches the operating conditions. The electrical energy is connected to the grid through the transformer and power electronics. The control system monitors and controls the operation of the entire wind turbine to ensure safe and stable power generation. The wind turbine model is expressed by the following formula:

[0083]

[0084]

[0085] Where v is the wind speed of the wind turbine, S is the swept area, ρ is the air density; η max is the maximum wind energy utilization factor, P max is the maximum output power; η w is the wind energy utilization rate; P r and P WTG (v) are the rated power and actual power of the fan, v and v r are the actual wind speed and rated wind speed of the fan, v ci and v co are the inflow wind speed and outflow wind speed of the fan respectively; is the total output power, is the actual output power, is the abandoned optical power; P WTG,climb It is subject to hill climbing constraints.

[0086] Photovoltaic power generation is a multifunctional solar technology that converts solar energy into electrical energy through solar panels. It does not require fuel and has good environmental benefits. The control system monitors and regulates the energy conversion and output of photovoltaic power generation to ensure its stable operation. Power conversion equipment such as distribution cabinets and inverters convert direct current into alternating current, and then connects to the grid after boosting the voltage through a step-up transformer. The photovoltaic generator model is expressed by the following formula:

[0087]

[0088] In the formula, is the total photovoltaic output power, is the actual output power, is the abandoned optical power; P PV,climb is the upper limit of photovoltaic ramp constraint;

[0089] As a distributed output device, the operation of CHP units consumes natural gas power and generates electrical power and thermal power at the same time. Compared with traditional distributed output equipment, CHP units have the characteristics of high energy utilization and low pollution emissions, and are widely used in IES. Since the thermal energy and electrical energy of the CHP unit are generated at the same time, it is believed that its operation mode has two modes: "heat determines electricity" or "electricity determines heat". In the scheduling process of the integrated energy system, the adjustability is relatively poor. The large output of CHP units will squeeze out the output of new energy, resulting in frequent "wind abandonment" and "light abandonment" phenomena. The combined heat and power unit (CHP) model is expressed by the following formula:

[0090]

[0091] In the formula, is the electrical power generated by the cogeneration unit, is the thermal power generated by the cogeneration unit, is the gas power consumed by the cogeneration unit, is the electrothermal proportionality coefficient, is the gas heat transfer coefficient; P CHP,min and P CHP,max They represent the lower and upper limits of the electric power output of the cogeneration unit respectively; Indicates the upper limit of the electric power ramp rate of the cogeneration unit; is the total heat output of the cogeneration unit, is the power supplied to the heat load, is the power stored in the heat storage device.

[0092] The gas boiler (GB) model is expressed by the following formula:

[0093]

[0094] In the formula, is the thermal power generated by the gas boiler, is the gas power consumed by the gas boiler, η GB is the gas heat conversion coefficient of the gas boiler; H GB,min and H GB,max They respectively represent the lower and upper limits of the thermal power output of the gas boiler.

[0095] Preferably, GB uses natural gas as fuel, consumes gas power to generate thermal power. The working principle of GB is to heat water by burning gas, so that the water temperature is increased and converted into thermal energy, that is, the thermal energy generated by the combustion of gas is transferred to water, increasing the temperature of the water.

[0096] The electric boiler (EH) model is expressed by the following formula:

[0097]

[0098] In the formula, is the thermal power generated by the electric boiler, is the electric power consumed by the electric boiler, η EH is the electric-heat conversion coefficient of the electric boiler; P EH,min and P EH,max They respectively represent the lower and upper limits of the electric power consumed by the electric boiler.

[0099] Preferably, EH uses the principle of electromagnetic induction to convert electrical energy into heat energy and is an energy conversion device. Its characteristics include small size, many types, simple structure, easy assembly, etc. Compared with traditional boilers, EH has the advantages of high heating efficiency, clean and environmentally friendly.

[0100] The power-to-gas (P2G) plant model is represented by the following formula:

[0101]

[0102] In the formula, is the gas power generated by P2G, is the electrical power consumed by P2G, η P2G is the electrical conversion coefficient of the electric boiler; P P2G,min and P P2G,max They represent the lower and upper limits of P2G power consumption respectively.

[0103] Preferably, P2G equipment is a key device for the interconnection of electricity and gas systems. P2G can convert electric power into natural gas power. The flexible adjustability of P2G makes it an important energy coupling unit, which can flexibly adjust the operation mode according to load demand and gas supply. The common P2G technology is that carbon dioxide and hydrogen react to produce methane, which is transported to the gas pipeline to achieve energy conversion between the power system and the gas grid, so that the power system and the natural gas system can be more closely connected, thereby achieving effective conversion and utilization of energy. In the actual IES, P2G equipment must have a certain amount of participation, but due to the upper and lower limits of the output, the conversion power it participates in is generally not very large, and it only plays a small role in regulation.

[0104] The storage battery (ESS) model is expressed by the following formula:

[0105]

[0106] In the formula, and They represent the state of charge of the battery at time t and time t-1 respectively. and They represent the charge and discharge power at time t, γ is the energy dissipation coefficient of the battery, λ is the charge and discharge efficiency coefficient of the battery, Δt is the unit scheduling time; T represents the scheduling period, and They represent the battery charge state at the beginning and end of the scheduling period, respectively. SOC,min and C SOC,max Indicates the lower and upper limits of the battery state of charge, P c,max is the upper limit of the battery charging power, P dis,max It is the upper limit of battery discharge power.

[0107] Since most energy conversion can be achieved in the form of thermal energy, thermal storage technology is considered to be the simplest way of energy storage. It plays an extremely important role in the current situation where energy problems are becoming increasingly severe, and has become one of the indispensable equipment in the integrated energy system. The thermal storage device (TSS) model is expressed by the following formula:

[0108]

[0109] In the formula, and They represent the heat storage capacity of the heat storage device at time t and time t-1 respectively, and They represent the charging and discharging power of the heat storage device at time t, and β is the dissipation coefficient of the heat storage device; and represents the heat storage capacity of the heat storage device at the beginning and end of the scheduling period, C TSS,min and C TSS,max Indicates the lower and upper limits of the heat storage capacity of the heat storage device, H c,max is the upper limit of the charging power of the heat storage device, H dis,max It is the upper limit of the heat release power of the heat storage device.

[0110] S1.2: Establish a comprehensive energy load demand response model and an orderly charging model for electric vehicles.

[0111] Specifically, the integrated energy load integrated demand response (IDR) model: The integrated demand response on the demand side refers to extending the demand response of general electric loads to other types of loads in the integrated energy system such as electricity, gas, and heat, making full use of the characteristics of multiple energy complementarity in the integrated energy system, breaking down the barriers between different types of energy, and achieving a higher energy utilization rate of demand-side resources than the traditional electric load demand response. There are many ways to classify integrated demand response. One way is to divide the three loads of electricity, heat, and gas into conventional loads, curtailable loads, and transferable loads. Curtailable loads refer to the part of the integrated energy load that can be directly cut off on the basis of cost compensation when the load is high; transferable loads refer to the part of the load that can be transferred to the rest of the scheduling period on the basis of cost compensation when the load is high. Another classification method is to divide the IES load into fixed loads, price-type loads, and alternative loads. For price-type loads, users can be prompted to adjust their energy consumption behavior by changing the time-of-use energy price; for alternative loads, users can be provided with the same energy demand by selecting different forms of energy. The load IDR in this invention focuses on three types of loads: curtailable loads, transferable loads and alternative loads, among which transferable loads are only allowed to participate in regulation in part of the 24-hour period. The comprehensive energy load integrated demand response (IDR) model is expressed by the following formula:

[0112]

[0113] In the formula, They represent curtailable load, transferable load and alternative load respectively; is the maximum participation ratio coefficient that can reduce the electric load, is the total electrical load at time t; is the maximum participation value of the transferable electrical load; is the maximum participation ratio coefficient of alternative electric load; It is the heat load power converted from the electrical replacement load. is the electric-to-heat conversion coefficient of the alternative load; It is the gas load power converted from the electricity replacement load. It is the electricity-gas conversion coefficient.

[0114] The orderly charging model of electric vehicles (EV) is expressed by the following formula:

[0115]

[0116] Where k is the number of EV, It represents the percentage of the battery capacity of the minimum off-grid state of charge specified by the EV user numbered k. Indicates the percentage of the EV's grid-connected charge state to the battery capacity, E kis the battery capacity, is the total charging demand of the user; and are the SOC of vehicle k at time t and t-1 respectively; p k,t is the average charging and discharging power of EV in fast charging mode (the charging piles at the complex station are fast charging by default); α k,t is a 01 variable representing charging or occupying, i.e., an identifier, with a value of 0 or 1, representing that the vehicle is in the 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 Represents the number of electric vehicles.

[0117] It should be noted that the EVs considered in the present invention are all considered to be in urban complex stations. The range of net charging required by EVs after arriving at the charging piles at urban complex stations is determined by the grid-connected state of charge, the minimum off-grid state of charge specified by the user, and the battery capacity.

[0118] S1.3: Establish a heat network energy flow calculation model considering virtual heat storage.

[0119] Specifically, the power exchange model of the heat exchange first station and the heat exchange station is:

[0120]

[0121] In the formula, and are the inlet temperature in the water supply pipe connected to the first heat exchange station and the outlet temperature in the return pipe respectively; is the water flow through the first heat exchange station; c w is the specific heat capacity of water; and are the outlet temperature in the water supply pipe connected to the heat exchange station and the inlet temperature in the water return pipe, respectively; is the hot water flow rate through the heat exchange station; is the heat load at time t.

[0122] The temperature mix constraints and range constraints are:

[0123]

[0124] In the formula, and They represent the set of pipes in the heating network with node n as the starting point and the end point respectively; is the inlet temperature of the water supply pipe k at time t; q k and q j are the flow rates of water supply pipes k and j, respectively, which remain constant; is the outlet temperature of water supply pipe j at time t; and They are the upper and lower limits of the water supply pipe temperature; and They are the upper and lower limits of the return pipe temperature respectively.

[0125] Alternatively, the node method is used to model the heat network taking into account transmission delay and temperature loss as follows:

[0126]

[0127] In the formula, the superscript S only represents the water supply pipe, the subscript p only represents the pipe p, and the physical meaning of each symbol remains consistent with the previous text. is the transmission delay time constant of the entire pipeline, and Respectively represent the length and cross-sectional area of ​​the water supply pipe p; ρ w is the density of water; is the inlet temperature of hot water in the water supply pipe p during the time period t-(K-1); is the temperature loss coefficient of pipe p; is the ambient temperature of the district heating network.

[0128] After sorting, we can get:

[0129]

[0130] The pipeline energy storage of the heat network is:

[0131]

[0132] In the formula, Indicates the pipeline energy storage of the heating network; the superscript R only indicates the return pipe of the heating network, and the physical meaning of each symbol is consistent with the previous text; It is any set of pipes in the heat network.

[0133] The upper and lower limit constraints and period recovery constraints of the regional heating network pipeline energy storage are:

[0134]

[0135] In the formula, H PES,max and H PES,min They represent the upper and lower limits of the heat network pipeline energy storage respectively; and They are the pipeline energy storage of the heat network at the beginning and end of the dispatch period, respectively.

[0136] 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 constraint programming constraints.

[0137] In one embodiment, in S2, the constructed DC power flow model of the distribution network is specifically:

[0138] Optionally, according to the attached Figure 1 The node voltage vector and network structure diagram are shown, and the relevant expressions are listed.

[0139]

[0140] In the formula, ΔU represents the line voltage drop, I line Represents branch current, R line and X line Represents branch resistance and reactance, P line and Q line Indicates the active power and reactive power flowing through the branch, U i and U j are the voltages at nodes i and j respectively, ΔU1 represents the longitudinal component of the voltage drop, and ΔU2 represents the transverse component of the voltage drop.

[0141] The branch power flow calculation constraint formula can be obtained:

[0142] In the formula, Represents the collection of all branches of the distribution network.

[0143] The active and reactive power balance can be expressed as:

[0144]

[0145] In the formula, Represents the set of all nodes in the distribution network. and They represent the set of lines with n nodes as the starting and ending points. n,out and Q n,out is the net active and reactive power flowing out of node n.

[0146] Since the constraints related to the line flow are nonlinear, it is difficult to use a solver to solve. Therefore, the present invention uses a second-order cone relaxation method to convert general flow constraints into mixed integer quadratic constraint programming (MIQCP) constraints. After actual solution verification, this method is effective and reasonable, successfully converting non-convex problems into convex problems, and the optimal solution point remains unchanged after relaxation, so that the solver can be used for solving. The constraints are expressed as:

[0147] The distribution network flow constraint in the integrated energy system of the present invention adopts DC flow constraint, and completely ignores the reactive power in the distribution network. For the part of the decision variable containing square terms, the variable replacement method is directly used to treat the entire square term as a whole to write the constraint conditions. The superscripts of the relevant variables only represent the scheduling time interval, and the rest of the physical meanings remain consistent.

[0148]

[0149] In the formula, represents the branch current at time t, R line,l and X line,l represents the resistance and reactance of branch l, and represents the active power and reactive power flowing through the branch, and are the voltages of nodes i and j at time t respectively; Represents the collection of all branches of the distribution network; Represents the set of all nodes in the distribution network. and is the net active and reactive power flowing out of node n; P line,max is the maximum active power that the branch can withstand, and They are the squares of the maximum values ​​allowed for current and voltage, respectively.

[0150] S3: According to the flow constraints of the distribution network, heat network and gas network of the integrated energy system, and with minimizing the total operating cost of the integrated energy operator as the objective function, an optimal scheduling model of the integrated energy system considering virtual heat storage is constructed.

[0151] Specifically, the objective function is:

[0152] min f=f MTESD,TSS +f buy +f ECD +f DG +f pun -f env +f IDR -f EV ;

[0153]

[0154] Where f is the total scheduling cost, f MTESD,TSS is the operation and maintenance cost of the physical heat storage device in the multi-type energy storage equipment, f buy is the cost of purchasing electricity and gas from the upper power grid and natural gas grid, ECD is the operating cost of the energy conversion equipment, f DG is the operation and maintenance cost of distributed generation, fpun is the penalty fee for abandoning wind and solar power, env is the environmental benefit of renewable energy, f IDR is the comprehensive demand response compensation cost, f EV is the profit obtained by the integrated energy operator from selling electricity to the EV operator; c TSS is the unit power operation and maintenance cost of the thermal storage device; It is to purchase electricity from the upper power grid. is the amount of gas purchased from the natural gas grid, c E and c G are the unit electricity price and gas price respectively; c EH 、c P2G and c GB 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 is the unit power operation and maintenance cost of distributed sources such as wind turbines, cogeneration units, and photovoltaic generators; c pun is the penalty coefficient for abandoning wind and solar power; e is the environmental benefit coefficient of renewable energy such as wind power and photovoltaic power; are the participating powers of curtailable load and transferable load in comprehensive demand response of heat and gas, respectively, and c IDR is the unit power compensation cost coefficient of comprehensive demand response; c EV The unit power revenue of the integrated energy operator from selling electricity to the EV operator.

[0155] The static model of the gas network is expressed as:

[0156]

[0157] Where, L is the natural gas network pipeline number; It is the collection of natural gas network pipelines; m L,max is the upper limit of flow rate.

[0158] The comprehensive energy power balance constraint is:

[0159]

[0160] In the formula, It represents the distribution network load at time t after considering IDR. is the heat load at time t after considering IDR. It is the gas load of the natural gas grid at time t after considering IDR.

[0161] The constraints on the amount of electricity and gas purchased are:

[0162]

[0163] Where Pgrid,max and G grid,max They are the upper limits of the amount of electricity and gas purchased from the superior power grid or natural gas grid.

[0164] In summary, the present invention proposes a method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage. By establishing operation models of the distribution network, heat network, and gas network and the operation mathematical models of each device, on this basis, the flow constraints of the distribution network, heat network, and gas network of the integrated energy system are considered. The objective function is to minimize the total operating cost of the integrated energy operator, and an optimal scheduling model for the integrated energy system considering virtual heat storage is constructed to obtain the optimal scheduling strategy.

[0165] Example 2

[0166] As an embodiment of the present invention, a method system for constructing an optimal scheduling model of an integrated energy system considering virtual heat storage is provided, comprising: a model building module, used to establish an operation model of working equipment of the integrated energy system, an integrated energy load integrated demand response model, an electric vehicle orderly charging model, and a heat network energy flow calculation model considering virtual heat storage; a power flow processing module, used to process a DC power flow model of a distribution network based on mathematical theory, and to convert general power flow constraints into mixed integer quadratic constraint programming constraints using a second-order cone relaxation method; an optimization scheduling module, used to construct an optimal scheduling model of an integrated energy system considering virtual heat storage according to the power flow constraints of the distribution network, heat network, and gas network of the integrated energy system, with minimizing the total operating cost of the integrated energy operator as the objective function.

[0167] Example 3

[0168] It is an embodiment of the present invention, which is different from the previous embodiment in that:

[0169] If the functions are implemented in the form of 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 the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0170] 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 conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "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 conjunction with such instruction execution systems, devices or apparatuses.

[0171] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a 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, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0172] 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.

[0173] Example 4

[0174] As an embodiment of the present invention, a method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0175] In order to verify the effectiveness of the proposed scheduling method, Figure 3The urban integrated energy system shown in the figure is tested, including IEEE 33-node distribution network, 20-node Belgian natural gas network and 44-node heating network. The power load, heat load, gas load, photovoltaic and wind power curves, electricity price curves, etc. in typical operation scenarios are shown in the figure. Figure 4 shown.

[0176] Figure 5 The optimization scheduling results of the integrated energy system are shown. Table 1 shows the input parameter values ​​of the resources, and Table 2 shows the cost results of the urban integrated energy system.

[0177] Table 1 Model related constant parameters

[0178]

[0179]

[0180]

[0181] Table 2 Operating costs

[0182]

[0183] The power system can meet the demand for electric loads within the system. The difference between the sum of the outputs of each device at each moment in the figure (including the equivalent "negative power" converted by the device) and the current load is due to demand response. From 1:00 to 6:00, the electricity price is at the valley price of 335 yuan / MWh, the demand for electric load is small, and the required power is less than 0.5MW. At this time, it is at night, and the wind power output is large, about 0.3MW, so a large amount of electricity will be purchased from the superior power grid, and excess electric power will be brought in. This excess electric power will be transferred or consumed through P2G, EH, and ESS, and the amount of electric power transferred is about 0.5MW. This can reduce the amount of wind abandonment and reduce the operating costs of operators. From 7:00 to 11:00, the electricity price is at its peak, reaching 1,250 yuan / MWh. After adding the load generated by electric vehicles, the total demand for electric load is high, exceeding 0.7MW; the peak price limits the amount of electricity purchased from the superior power grid during dispatch, and the purchased power is less than 0.1MW. During this period, ESS releases electric power to meet the real-time balance of electric power, and demand response takes effect (about 0.01MW), which is equivalent to weakening the actual electric power demand. It can be seen that ESS often purchases more electricity from the upper power grid when the electricity price is valley, stores the excess electricity, and releases electric power when the price is peak. After 7:00, the output of CHP is relatively constant, basically reaching the upper limit of 0.22MW of electric power output. This is because the electricity, heat, and gas loads are relatively high during this period, and CHP can enter the "heat-to-electricity" or "electricity-to-heat" output mode, converting gas power into electricity and heat power at the same time according to a certain proportional coefficient. Compared with the penalty cost and gas purchase cost of certain wind and solar abandonment, it will be more economical to include CHP in the global dispatch, thereby reducing operating costs. From 11:00 to 16:00, the photovoltaic output is extremely large, reaching about 0.5MW, the electricity price is at parity 780 yuan / MWh, the amount of electricity purchased from the upper power grid is moderate (0.1MW-0.3MW), and only a small amount of electricity is stored through ESS or converted through EH (less than 0.2MW). From 16:00 to 20:00, the orderly charging behavior of EVs will make the total power load during this period relatively high after considering EVs; the electricity price returns to the peak price, and the purchase of electricity from the upper power grid is restricted again (less than 0.2MW). ESS continues to release a small amount of electricity (less than 0.1MW) to maintain the balance of electricity. At this time, almost no electricity is converted and stored. From 22:00 to 24:00, the electricity price returns to the valley price, and the amount of electricity purchased from the grid is increased again, reaching the upper limit of 0.8MW. The excess electricity is either stored through ESS, converted into heat energy through EH, or converted into gas power through P2G. This part of electricity exceeds 0.5MW, which well maintains the economy of the integrated energy system dispatch.

[0184] The heating network can meet the heat load demand in the system. The difference between the sum of the output of each device (including the equivalent "negative power" converted by the actual device) and the current load is due to demand response and virtual heat storage of the heat network. By comparing the two curves of actual heat load and equivalent heat load after considering virtual heat storage, it can be seen that the virtual heat storage characteristics of the heat network have smoothed the original actual heat load adjustment, adjusting the peak value of the heat load from 0.372MW to 0.315MW, and the valley value from 0.167MW to 0.203MW, which really plays the role of "peak shaving and valley filling" of the heat load. Since the virtual heat storage characteristics of the pipeline are very significant, only when the heat load is very high, that is, 17:00-19:00 (exceeding 0.3MW), the physical heat storage TSS participates in the scheduling more obviously (between 0.05MW and 0.1MW). During this period, TSS releases thermal power and plays an auxiliary role in the regulation of virtual heat storage; TSS heat storage mainly comes from the conversion of EH, which is generally carried out during the electric power redundancy period mentioned above. Specifically, during the hours of 1:00-6:00 and 22:00-24:00 when electricity prices are at valley prices, since the cost of purchasing electricity from the superior power grid is relatively low, operators will choose to buy a large amount of electricity from the power grid. At this time, the electricity is redundant, and EH will participate in large quantities to supply the heat load. With a certain amount of participation from CHP, the heat load will become redundant, and the excess thermal power will be stored in the pipeline in the form of pipeline temperature or temperature difference, that is, virtual heat storage power. During 7:00-21:00, the thermal load is relatively high. At this time, the overall electricity price is at parity or peak price, the EH conversion is relatively small, and the thermal power borne by the CHP unit exceeds 70%; since the gas price of 660 yuan / MWh is between the parity and valley prices of electricity, the most economical scheduling strategy during this period is for the operator to buy more gas power from the natural gas grid and convert the gas power at the same time. Therefore, the gas boiler works during this period to convert the gas power into thermal power, and the conversion power does not exceed 0.1MW; the equivalent effect of virtual heat storage during 7:00-21:00 is to release the thermal storage power in the remaining time periods to meet the high demand for thermal load during this period.

[0185] The natural gas pipeline network can meet the gas load demand in 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 is due to demand response. At 1:00-6:00 and 22:00-24:00, the electricity price is lower than the gas price. The distribution network operator will choose to buy a large amount of electric power from the upper power grid. Part of this electric power will be converted into gas power through P2G equipment to supply the gas load, and the converted gas power reaches a maximum limit of 0.15MW. At 7:00-21:00, since the gas price is lower than the electricity price, the integrated energy operator will choose to buy enough natural gas (reaching the maximum gas power limit of 0.624MW) to fully supply the gas load. The redundant natural gas will either become the raw material of the CHP unit or be converted into thermal power through GB. The conversion power is 0.3MW-0.4MW, so as to achieve the economy of integrated energy system scheduling.

[0186] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage, characterized in that: include: Establish an integrated energy system working equipment operation model, establish an integrated energy load integrated demand response model and an electric vehicle orderly charging model, and establish a heat network energy flow calculation model considering virtual heat storage; Based on mathematical theory, the DC power flow model of distribution network is processed and the general power flow constraints are transformed into mixed integer quadratic constraint programming constraints by using second-order cone relaxation method. According to the flow constraints of the distribution network, heat network and gas network of the integrated energy system, an optimal scheduling model of the integrated energy system considering virtual heat storage is constructed with minimizing the total operating cost of the integrated energy operator as the objective function.

2. The method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage according to claim 1, characterized in that: The working equipment operation model of the comprehensive energy system includes a wind turbine model, a photovoltaic generator model, a cogeneration unit model, a gas boiler model, an electric boiler model, a power-to-gas equipment model, a battery model, and a heat storage device model; The fan model is expressed by the following formula: Where v is the wind speed of the wind turbine, S represents the swept area, and ρ is the air density; η max is the maximum wind energy utilization rate, P max is the maximum output power; η w is the wind energy utilization rate; P r and P WTG (v) are the rated power and actual power of the fan, v and v r are the actual wind speed and rated wind speed of the fan, v ci and v co are the inflow wind speed and outflow wind speed of the fan respectively; is the total output power, is the actual output power, is the abandoned optical power; P WTG,climb is the upper limit of the climbing constraint; The photovoltaic generator model is expressed by the following formula: In the formula, is the total photovoltaic output power, is the actual output power, is the abandoned optical power; P PV,climb is the upper limit of photovoltaic ramp constraint; The combined heat and power unit model: In the formula, is the electrical power generated by the cogeneration unit, is the thermal power generated by the cogeneration unit, is the gas power consumed by the cogeneration unit, is the electrothermal proportionality coefficient, is the gas-heat transfer coefficient; P CHP,min and P CHP,max They represent the lower and upper limits of the electric power output of the cogeneration unit respectively; Indicates the upper limit of the electric power ramp rate of the cogeneration unit; is the total heat output of the cogeneration unit, is the power supplied to the heat load, is the power stored in the heat storage device; The gas boiler model is expressed by the following formula: In the formula, is the thermal power generated by the gas boiler, is the gas power consumed by the gas boiler, η GB is the gas heat conversion coefficient of the gas boiler; H GB,min and H GB,max They represent the lower and upper limits of the thermal power output of the gas boiler respectively; The gas boiler model is expressed by the following formula: In the formula, is the thermal power generated by the gas boiler, is the gas power consumed by the gas boiler, η GB is the gas heat conversion coefficient of the gas boiler; H GB,min and H GB,max They represent the lower and upper limits of the thermal power output of the gas boiler respectively; The electric boiler model is expressed by the following formula: In the formula, is the thermal power generated by the electric boiler, is the electric power consumed by the electric boiler, η EH is the electric-heat conversion coefficient of the electric boiler; P EH,min and P EH,max They represent the lower and upper limits of the electric power consumed by the electric boiler respectively; The power-to-gas plant model is expressed by the following formula: In the formula, is the gas power generated by P2G, is the electrical power consumed by P2G, η P2G is the electrical conversion coefficient of the electric boiler; P P2G,min and P P2G,max They represent the lower and upper limits of the P2G power consumption respectively; The battery model is expressed by the following formula: In the formula, and They represent the state of charge of the battery at time t and time t-1 respectively. and They represent the charge and discharge power at time t, γ is the energy dissipation coefficient of the battery, λ is the charge and discharge efficiency coefficient of the battery, and Δt is the unit scheduling time; T represents the scheduling period, and They represent the battery charge state at the beginning and end of the scheduling period, respectively. SOC,min and C SOC,max Indicates the lower and upper limits of the battery state of charge, P c,max is the upper limit of the battery charging power, P dis,max It is the upper limit of battery discharge power; The heat storage device model is expressed by the following formula: In the formula, and They represent the heat storage capacity of the heat storage device at time t and time t-1 respectively, and They represent the charging and discharging power of the heat storage device at time t, and β is the dissipation coefficient of the heat storage device; and represents the heat storage capacity of the heat storage device at the beginning and end of the scheduling period, C TSS,min and C TSS,max Indicates the lower and upper limits of the heat storage capacity of the heat storage device, H c,max is the upper limit of the heat storage device charging power, H dis,max It is the upper limit of the heat release power of the heat storage device.

3. The method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage according to claim 2, characterized in that: The comprehensive energy load comprehensive demand response is expressed by the following formula: In the formula, They represent curtailable load, transferable load and alternative load respectively; is the maximum participation ratio coefficient that can reduce the electric load, is the total electrical load at time t; is the maximum participation value of the transferable electrical load; is the maximum participation ratio coefficient of alternative electric load; It is the heat load power converted from the electrical replacement load. is the electric-to-heat conversion coefficient of the alternative load; It is the gas load power converted from the electricity replacement load. is the electricity-gas conversion coefficient; The electric vehicle orderly charging model is expressed by the following formula: Where k is the number of EV, It represents the percentage of the battery capacity of the minimum off-grid state of charge specified by the EV user numbered k. Indicates the percentage of the EV's grid-connected charge state to the battery capacity, E k is the battery capacity, is the total charging demand of the user; and are the SOC of vehicle k at time t and t-1 respectively; p k,t is the average charging and discharging power of EV in fast charging mode; α k,t It is a 01 variable representing charging or occupying, i.e., an identifier, which takes the value 0 or 1, indicating that the vehicle is in the 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 Represents the number of electric vehicles.

4. The method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage according to claim 3, characterized in that: The heat network energy flow calculation model considering virtual heat storage includes: The power exchange model of the heat exchange first station and the heat exchange station is: In the formula, and are the inlet temperature in the water supply pipe connected to the first heat exchange station and the outlet temperature in the return pipe respectively; is the water flow through the first heat exchange station; c w is the specific heat capacity of water; and are the outlet temperature in the water supply pipe connected to the heat exchange station and the inlet temperature in the water return pipe, respectively; is the hot water flow rate through the heat exchange station; is the heat load at time t; The temperature mix constraints and range constraints are: In the formula, and They represent the set of pipes in the heating network with node n as the starting point and the end point respectively; is the inlet temperature of the water supply pipe k at time t; q k and q j are the flow rates of water supply pipes k and j, respectively, which remain constant; is the outlet temperature of water supply pipe j at time t; and They are the upper and lower limits of the water supply pipe temperature; and They are the upper and lower limits of the return pipe temperature respectively; The node method is used to model the heat network considering transmission delay and temperature loss as follows: In the formula, the superscript S only represents the water supply pipe, the subscript p only represents the pipe p, and the physical meaning of each symbol remains consistent with the previous text. is the transmission delay time constant of the entire pipeline, and Respectively represent the length and cross-sectional area of ​​the water supply pipe p; ρ w is the density of water; is the inlet temperature of hot water in the water supply pipe p during the time period t-(K-1); is the temperature loss coefficient of pipe p; is the ambient temperature of the district heating network.

5. The method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage according to claim 4, characterized in that: The heat network energy flow calculation model considering virtual heat storage also includes: The pipeline energy storage of the heat network is: In the formula, Indicates the pipeline energy storage of the heating network; the superscript R only indicates the return water pipeline of the heating network, and the physical meaning of each symbol is consistent with the previous text; is any set of pipes in the heat network; The upper and lower limit constraints and period recovery constraints of the regional heating network pipeline energy storage are: In the formula, H PES,max and H PES,min They represent the upper and lower limits of the heat network pipeline energy storage respectively; and They are the pipeline energy storage of the heat network at the beginning and end of the dispatch period, respectively.

6. The method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage according to 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, represents the branch current at time t, R line,l and X line,l represents the resistance and reactance of branch l, and represents the active power and reactive power flowing through the branch, and are the voltages of nodes i and j at time t respectively; Represents the collection of all branches of the distribution network; Represents the set of all nodes in the distribution network. and is the net active and reactive power flowing out of node n; P line,max is the maximum active power that the branch can withstand, and They are the squares of the maximum values ​​allowed for current and voltage, respectively.

7. The method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage according to claim 6, characterized in that: The objective function is: minf=f MTESD,TSS +f buy +f ECD +f DG +f pun -f env +f IDR -f EV ; Where f is the total scheduling cost, f MTESD,TSS is the operation and maintenance cost of the physical heat storage device in the multi-type energy storage equipment, f buy is the cost of purchasing electricity and gas from the upper power grid and natural gas grid, ECD is the operating cost of the energy conversion equipment, f DG is the operation and maintenance cost of distributed generation, f pun is the penalty fee for abandoning wind and solar power, env is the environmental benefit of renewable energy, f IDR is the comprehensive demand response compensation cost, f EV is the profit obtained by the integrated energy operator from selling electricity to the EV operator; c TSS is the unit power operation and maintenance cost of the thermal storage device; It is to purchase electricity from the upper power grid. is the amount of gas purchased from the natural gas grid, c E and c G are the unit electricity price and gas price respectively; c EH 、c P2G and c GB 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 is the unit power operation and maintenance cost of distributed sources such as wind turbines, cogeneration units, and photovoltaic generators; c pun is the penalty coefficient for abandoning wind and solar power; e is the environmental benefit coefficient of renewable energy such as wind power and photovoltaic power; are the participating powers of curtailable load and transferable load in comprehensive demand response of heat and gas, respectively, and c IDR is the unit power compensation cost coefficient for comprehensive demand response; c EV The unit power revenue of the integrated energy operator from selling electricity to the EV operator.

8. A system using the method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage as described in any one of claims 1 to 7, characterized in that: include: Model building module, used to establish the operation model of working equipment of the integrated energy system, the integrated energy load integrated demand response model, the orderly charging model of electric vehicles, and the heat network energy flow calculation model considering virtual heat storage; The power flow processing module is used to process the DC power flow model of the distribution network based on mathematical theory, and uses the second-order cone relaxation method to convert 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 considering virtual heat storage based on the 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.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage are implemented 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 a processor, the steps of the method for constructing an optimal scheduling model for an integrated energy system considering virtual heat storage are implemented as described in any one of claims 1 to 7.

Images