Optimization control method and system for interaction between electric heating system cluster and power grid

Abstract

The invention discloses an optimization control method and system for interaction between an electric heating system cluster and a power grid. The optimization control method comprises the steps that equipment parameters, load data and electricity and heat purchasing prices in the electric heating and heat supplementing system cluster are obtained; based on the equipment parameters, the load data and the electricity and heat purchasing related parameters, solving a pre-constructed electric heating and heat supplementing system cluster optimization model by adopting a particle swarm algorithm to obtain an optimal output scheme of each electric heating and heat supplementing equipment in an electric heating and heat supplementing system cluster; based on the optimal output scheme of each electric heating and heat supplementing device, performing optimization control on an electric heating and heat supplementing system cluster; according to the invention, the problems that the interaction mode of the electric heating and heat compensation system and the power grid is not clear, safe and stable operation of the power grid is not facilitated and the operation cost is high are solved, and high-economy, safe and stable regulation and control of an electric heating and heat compensation system cluster is realized.

Description

Technical Field

[0001] This invention application belongs to the technical field of electric power, and particularly relates to an optimized control method and system for the interaction between an electric heating system cluster and the power grid. Background Art

[0002] In the heating field, municipal central heating is the main body, with huge stock and demand. Compared with the rapid development of urban construction, the construction of municipal heating in some areas with large heating demand is relatively lagging. At the same time, energy including coal is restricted by both increment and intensity, resulting in general shortages of heat sources and heat networks in municipal heating. However, the construction of heat sources is restricted by objective conditions such as site and energy supply. Coupled with the heat loss of about 10%-20% (even up to 30%) in the huge municipal primary and secondary heat pipelines, the problems of heat source and heat network shortages are becoming more and more serious, the heating effect at the end is difficult to be guaranteed, and the pressure to ensure people's livelihood during the heating period is increasing.

[0003] In recent years, the "coal-to-electricity" for clean heating has been vigorously promoted in the northern regions. Especially in the North China region, the "coal-to-electricity" project has been widely promoted. However, the rapid growth of various electric heating devices has also brought many problems, such as the increase in the peak-valley difference of the power grid due to the input of electric heating load, the too low voltage at the end of the distribution network during the peak electricity consumption period, the low utilization rate of electric heating devices, and the generally high operation cost. Under the background of the new power system, the existing construction equipment structure of the electric heating and heat supplement system cluster model is single, and there are also practical problems in the interaction between the urban heat exchange station electric heating and heat supplement system cluster and the power grid, such as unclear interaction modes between the source end and the load end and imperfect interaction mechanisms, which are not conducive to the safe and stable operation of the power grid. Summary of the Invention

[0004] To overcome the deficiencies of the above-mentioned prior art, this invention application proposes an optimized control method for the interaction between an electric heating system cluster and the power grid, including:

[0005] Obtain the parameters of each device, load data, and electricity and heat purchase prices in the electric heating and heat supplement system cluster;

[0006] Based on the parameters of each device, load data, and relevant parameters for electricity and heat purchase, use the particle swarm optimization algorithm to solve the pre-constructed optimization model of the electric heating and heat supplement system cluster, and obtain the optimal output power scheme of each electric heat supplement device in the electric heating and heat supplement system cluster;

[0007] Based on the optimal output power scheme of each electric heat supplement device, optimize and control the electric heating and heat supplement system cluster;

[0008] The cluster optimization model of the electric heating and heat compensation system is constructed according to the interaction mode between the electric heating and heat compensation system cluster and the power grid, with the goal of maximizing the overall operation benefit of the electric heating and heat compensation system cluster model participating in the peak shaving market of the power grid's auxiliary services. The electric heating and heat compensation system cluster model is constructed based on multiple cluster models formed by different combinations of electric heat compensation devices and the collaborative interaction relationship between the heat supply pipeline network and the electric heat compensation devices in the cluster.

[0009] Preferably, the construction of the electric heating and heat compensation system cluster model includes:

[0010] Construct a cluster model according to different combinations of electric heat compensation devices and the physical property constraints of each electric heat compensation device;

[0011] Construct a virtual heat storage model for the heat supply pipeline network based on the collaborative interaction relationship between the heat supply pipeline network and the electric heat compensation devices;

[0012] Construct an electric heating and heat compensation system cluster model based on the cluster model and the virtual heat storage model of the heat supply pipeline network;

[0013] Among them, the cluster model includes one or more of the following: direct heat pump cluster model, direct heat storage cluster model, and heat pump heat storage cluster model; the virtual heat storage model of the heat supply pipeline network includes one or more of the following: the thermal dynamic model of the heat supply pipeline, the charging and discharging power of the virtual heat storage tank, the heat storage capacity of the water supply pipeline network, and the virtual heat storage state index of the heat power pipeline network.

[0014] Preferably, the expression of the thermal dynamic model of the heat supply pipeline is as follows:

[0015]

[0016] In the formula: T t is the average water temperature of the heat storage tank in the water supply pipeline network at time t, where T t =(T t in +T t out ) / 2, T t in is the inlet water temperature of the heat storage tank in the water supply pipeline network at time t, T t out is the outlet water temperature of the heat storage tank in the water supply pipeline network at time t; T t-1 is the average water temperature of the heat storage tank in the water supply pipeline network at time t - 1; c p is the specific heat capacity of water; ρ is the density of water; A is the inner cross-sectional area of the pipeline; m is the mass flow rate; R is the total thermal resistance of the pipeline; T t a is the ambient temperature; Δt is the time interval, L is the length of the pipeline;

[0017] The expression for the heat storage and release power of the virtual heat storage tank is as follows:

[0018]

[0019] In the formula: is the heat storage and release power of the virtual heat storage tank of the heat supply pipe network at time t; is the heat source power at time t; is the heat load of the heat supply system at time t; is the heat loss at time t;

[0020] The expression for the heat storage capacity of the supply pipe network is as follows:

[0021]

[0022] In the formula: is the heat storage capacity of the supply pipe network at time t; ρ is the density of water; c p is the specific heat capacity of water; V S is the water capacity of the supply and return pipe networks; T S,min is the lower limit of the supply water temperature; T t S is the water temperature of the supply and return pipe networks at time t;

[0023] The expression for the virtual heat storage state index of the heat pipe network:

[0024]

[0025] In the formula: SOVTC(t) is the virtual heat storage state index of the primary pipe network at time t; T t S is the water temperature of the supply and return pipe networks at time t; T S,max is the upper limit of the supply water temperature of the supply and return pipe networks; T S,min is the lower limit of the supply water temperature of the supply and return pipe networks.

[0026] Preferably, the interaction modes between the electric heating and heat compensation system cluster and the power grid include: the cluster participating in the deep peak shaving interaction mode of the power grid and the new energy consumption interaction mode; the construction of the optimization model of the electric heating and heat compensation system cluster includes:

[0027] According to the cluster participating in the deep peak shaving interaction mode of the power grid and the new energy consumption interaction mode, with the goal of minimizing the comprehensive benefit of the electric heating and heat compensation system cluster model responding to the peak-valley-flat electricity price of the power grid and participating in the peak shaving market of the power grid's auxiliary services, a comprehensive benefit objective function is constructed;

[0028] According to the power grid operation constraints, heat network operation constraints after the electric heating and heat compensation system cluster is connected to the power grid, and the characteristics of each device in the electric heating and heat compensation system cluster, constraint conditions are set for the comprehensive benefit objective function;

[0029] The constraint conditions include one or more of the following: grid interaction constraint, heat network operation constraint, equipment operation constraint, and heat load demand constraint;

[0030] The grid interaction constraint includes one or more of the following: grid power flow constraint, branch capacity constraint, and node voltage constraint; the heat network operation constraint includes one or more of the following: upper and lower limits of supply and return water temperatures constraint, heat power balance constraint, heat medium flow rate constraint, relative opening of the primary regulating valve constraint, mass flow balance constraint, and temperature mixing constraint; the equipment operation constraint includes one or more of the following: electric boiler electric power constraint, heat storage device operation constraint, heat pump operation constraint, circulating pump power constraint, heat extraction power constraint from the municipal heat network.

[0031] Preferably, the expression of the comprehensive benefit objective function is as follows:

[0032] min F=C grid +C hpn +C maintain +C car -E peak -E allowance

[0033] In the formula: F is the comprehensive benefit objective function of the electric heat supplement system cluster of the heat exchange station; C grid is the electricity purchase cost; C hpn is the heat purchase cost of the municipal heat network; C maintain is the operation and maintenance cost; C car is the carbon emission cost; E peak is the income obtained from participating in the peak shaving ancillary service market; E allowance is the clean energy heating subsidy;

[0034] Among them, the expression of the electricity purchase cost C grid is as follows:

[0035]

[0036] In the formula: C grid is the electricity purchase cost; is the peak-valley-flat electricity price in the t period; N is the number of electric heat supplement system clusters; J is the number of electric heat supplement devices in a certain cluster; is the electric power consumed by the jth electric heat supplement device in the ith cluster at the t moment; is the power consumption of the circulating pump connected to the primary heat network side of the heat exchange station where the ith electric heat supplement cluster is located; Δt is the unit time period;

[0037] The expression of the heat purchase cost C hpn of the municipal heat network is as follows:

[0038]

[0039] Where: C hpn is the cost of purchasing heat from the municipal heat network; is the heat price of the municipal heat network within the unit time period t; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at time t, and Δt is the unit time period;

[0040] The operation and maintenance cost C maintain has the following expression:

[0041]

[0042] Where: k hpn is the operation cost coefficient of the heat pipeline; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at time t; k eth is the operation cost coefficient of the electric boiler; is the electric power of the electric boiler in the i-th cluster at time t; k ess is the operation cost coefficient of the heat storage device; is the electric power of heat storage of the heat storage device in the i-th cluster at time t; is the heat release power of the heat storage device in the i-th cluster at time t; k pump is the operation cost coefficient of the heat pump; is the electric power consumed by the heat pump system in the i-th cluster at time t; k wp is the operation cost coefficient of the circulation pump; is the power consumption of the circulation pump connected to the primary heat network side of the heat exchange station where the i-th electric heat supplementary heating cluster is located; Δt is the unit time period; N is the number of electric heating and supplementary heating system clusters;

[0043] The carbon emission cost C car has the following expression:

[0044]

[0045] Where: C car is the carbon emission cost; p car is the unit carbon emission cost; is the electric power consumed by the j-th electric heat supplementary heating device in the i-th cluster at time t; is the power consumption of the circulation pump in the i-th cluster at time t; δ ec is the carbon emission coefficient of purchasing electricity; δ hpn is the carbon emission coefficient of the municipal heat network for heating; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at time t; Δt is the unit time period; N is the number of electric heating and supplementary heating system clusters; J is the number of electric heat supplementary heating devices in a certain cluster;

[0046] The subsidy income E for participating in the peak shaving ancillary service market peak has the following expression:

[0047]

[0048] In the formula: E peak is the income obtained from participating in the peak shaving ancillary service market; is the subsidy electricity price for unit-time ancillary peak shaving; is the electric power consumed by the i-th cluster at time t; Δt is the unit time period; N is the number of clusters of the electric heating heat compensation system; k is the total number of time;

[0049] The clean energy heating subsidy E allowance has the following expression:

[0050]

[0051] In the formula: E allowance is the clean energy heating subsidy; c clean is the subsidy price; is the electric power consumed by the i-th cluster at time t; N is the number of clusters of the electric heating heat compensation system; Δt is the unit time period.

[0052] Preferably, the expression of the heat load demand constraint is as follows:

[0053]

[0054]

[0055]

[0056] In the formula: is the heat supply power from the i-th cluster to users at time t; is the heat supply power of the i-th cluster at time t; c p is the specific heat capacity of the heat medium; is the mass flow of the heat medium at the inlet of cluster i; is the supply water temperature at the inlet of cluster i at time t; is the return water temperature at the inlet of cluster i at time t; is the heat compensation power of cluster i at time t; is the heat release power of the electric boiler in the i-th cluster at time t; is the heat release power of the heat storage device in the i-th cluster at time t; are respectively the heat release powers of the heat pumps in the i-th cluster at time t; is the electric power of the electric boiler in the i-th cluster at time t; P ST-out is the heat release power of the heat storage tank; is the electric power consumed by the heat pump in the i-th cluster at time t; η EB is the electro-thermal conversion efficiency of the electric boiler; η ST-out is the heat release efficiency of the heat storage tank; COP is the performance coefficient of the heat pump.

[0057] Preferably, the load data includes one or more of the following: photovoltaic predicted power, wind power predicted power, power purchase from the superior power grid and load predicted power, heat supply load data of each cluster user; the power and heat purchase prices include one or more of the following: system power purchase price, heat purchase price, and heat sale price; each equipment parameter includes one or more of the following: each equipment parameter in the direct heating heat pump cluster model, each equipment parameter in the direct heating heat storage cluster model, each equipment parameter in the heat pump heat storage cluster model, the load of each node in the distribution network, the impedance of each branch, and the parameters of the heat network pipeline.

[0058] Based on the same inventive concept, the present application also provides an optimized control system for the interaction between an electric heating system cluster and the power grid, including: a data acquisition module, an optimal solution acquisition module, and an optimization control module;

[0059] Obtain the parameters of each device, load data, and power and heat purchase prices in the electric heating and heat compensation system cluster;

[0060] Based on the parameters of each device, load data, and power and heat purchase related parameters, use the particle swarm optimization algorithm to solve the pre-constructed optimization model of the electric heating and heat compensation system cluster, and obtain the optimal output scheme of each electric heat compensation device in the electric heating and heat compensation system cluster;

[0061] Based on the optimal output scheme of each electric heat compensation device, optimize and control the electric heating and heat compensation system cluster;

[0062] The optimization model of the electric heating and heat compensation system cluster is constructed according to the interaction mode between the electric heating and heat compensation system cluster and the power grid, with the goal of maximizing the overall operating efficiency of the electric heating and heat compensation system cluster model participating in the power grid auxiliary service peak shaving market. Among them, the electric heating and heat compensation system cluster model is constructed based on multiple cluster models formed by different combinations of electric heat compensation devices and the collaborative interaction relationship between the heat supply network and the electric heat compensation devices in the cluster.

[0063] Preferably, the construction of the electric heating and heat compensation system cluster model of the optimal solution acquisition module includes:

[0064] Construct a cluster model according to different combinations of electric heat compensation devices and the physical property constraints of each electric heat compensation device;

[0065] Construct a virtual heat storage model of the heat supply network based on the collaborative interaction relationship between the heat supply network and the electric heat compensation devices;

[0066] Based on the cluster model and the virtual heat storage model of the heat supply network, a cluster model of the electric heating and heat compensation system is constructed;

[0067] Among them, the cluster model includes one or more of the following: direct heat pump cluster model, direct heat storage cluster model, and heat pump heat storage cluster model; the virtual heat storage model of the heat supply network includes one or more of the following: thermal dynamic model of the heat supply pipeline, heat storage and release power of the virtual heat storage tank, heat storage amount of the water supply network, and virtual heat storage state index of the thermal pipeline network.

[0068] Preferably, the expression of the thermal dynamic model of the heat supply pipeline of the optimal solution acquisition module is as follows:

[0069]

[0070] In the formula: T t is the average water temperature of the heat storage tank of the water supply network at time t, where T t =(T t in +T t out ) / 2, T t in is the inlet water temperature of the heat storage tank of the water supply network at time t, T t out is the outlet water temperature of the heat storage tank of the water supply network at time t; T t-1 is the average water temperature of the heat storage tank of the water supply network at time t-1; c p is the specific heat capacity of water; ρ is the density of water; A is the inner cross-sectional area of the pipeline; m is the mass flow rate; R is the total thermal resistance of the pipeline; T t a is the ambient temperature; Δt is the interval time, and L is the length of the pipeline;

[0071] The expression of the heat storage and release power of the virtual heat storage tank is as follows:

[0072]

[0073] In the formula: is the heat storage and release power of the virtual heat storage tank of the heat supply network at time t; is the heat source power at time t; is the heat load of the heat supply system at time t; is the heat loss at time t;

[0074] The expression of the heat storage amount of the water supply network is as follows:

[0075]

[0076] In the formula: is the heat storage amount of the water supply network at time t; ρ is the density of water; cp is the specific heat capacity of water; V S is the water capacity of the supply and return water pipe network; T S,min is the lower limit of the supply water temperature; T t S is the water temperature of the supply and return water pipe network at time t;

[0077] Expression of the virtual heat storage state index of the heat pipe network:

[0078]

[0079] In the formula: SOVTC(t) is the virtual heat storage state index of the primary pipe network at time t; T t S is the water temperature of the supply and return water pipe network at time t; T S,max is the upper limit of the supply water temperature of the supply and return water pipe network; T S,min is the lower limit of the supply water temperature of the supply and return water pipe network.

[0080] Preferably, the interaction modes between the electric heating and heat compensation system cluster of the optimal solution acquisition module and the power grid include: the cluster participating in the deep peak shaving interaction mode of the power grid and the new energy consumption interaction mode; the construction of the optimization model of the electric heating and heat compensation system cluster includes:

[0081] According to the cluster participating in the deep peak shaving interaction mode of the power grid and the new energy consumption interaction mode, with the goal of minimizing the comprehensive benefit of the electric heating and heat compensation system cluster model responding to the peak-valley-flat electricity price of the power grid to participate in the peak shaving market of power grid auxiliary services, a comprehensive benefit objective function is constructed;

[0082] According to the power grid operation constraints, heat network operation constraints and the characteristics of each device in the electric heating and heat compensation system cluster after the electric heating and heat compensation system cluster is connected to the power grid, constraint conditions are set for the comprehensive benefit objective function;

[0083] The constraint conditions include one or more of the following: power grid interaction constraints, heat network operation constraints, device operation constraints and heat load demand constraints;

[0084] The power grid interaction constraints include one or more of the following: power grid power flow constraints, branch capacity constraints and node voltage constraints; the heat network operation constraints include one or more of the following: upper and lower limits of supply and return water temperature constraints, heat power balance constraints, heat medium flow constraints, relative opening degree constraints of the primary regulating valve, mass flow balance constraints and temperature mixing constraints; the device operation constraints include one or more of the following: electric boiler electric power constraints, heat storage device operation constraints, heat pump operation constraints, circulating pump power constraints, heat extraction power constraints of the municipal heat network.

[0085] Preferably, the expression of the comprehensive benefit objective function of the optimal solution acquisition module is as follows:

[0086] min F = C grid + C hpn + C maintain + C car - E peak - E allowance

[0087] Where: F is the comprehensive benefit objective function of the electric supplementary heating system cluster in the heat exchange station; C grid is the electricity purchase cost; C hpn is the heat purchase cost from the municipal heat network; C maintain is the operation and maintenance cost; C car is the carbon emission cost; E peak is the income obtained from participating in the peak shaving auxiliary service market; E allowance is the clean energy heating subsidy;

[0088] Among them, the electricity purchase cost C grid has the following expression:

[0089]

[0090] Where: C grid is the electricity purchase cost; is the peak-valley-flat electricity price within the t period; N is the number of electric heating supplementary heating system clusters; J is the number of electric supplementary heating devices in a certain cluster; is the electric power consumed by the jth electric supplementary heating device in the ith cluster at the tth moment; is the power consumption of the circulating pump connected to the primary heat network side of the heat exchange station where the ith electric supplementary heating cluster is located; Δt is the unit time period;

[0091] The heat purchase cost C from the municipal heat network hpn has the following expression:

[0092]

[0093] Where: C hpn is the heat purchase cost from the municipal heat network; is the thermal power price of the municipal heat network within the unit time period t; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at the tth moment, and Δt is the unit time period;

[0094] The operation and maintenance cost C maintain has the following expression:

[0095]

[0096] Where: k hpn is the operation cost coefficient of the heat pipeline; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at the tth moment; keth is the operating cost coefficient of the electric boiler; is the electric power of the electric boiler in the i-th cluster at time t; k ess is the operating cost coefficient of the heat storage device; is the electric power for heat storage of the heat storage device in the i-th cluster at time t; is the heat release power of the heat storage device in the i-th cluster at time t; k pump is the operating cost coefficient of the heat pump; is the electric power consumed by the heat pump system in the i-th cluster at time t; k wp is the operating cost coefficient of the circulation pump; is the power consumption of the circulation pump connected to the primary heat network side of the heat exchange station where the i-th electric heat supplementary cluster is located; Δt is the unit time period; N is the number of clusters of the electric heating and heat supplementary system;

[0097] The carbon emission cost C car has the following expression:

[0098]

[0099] In the formula: C car is the carbon emission cost; p car is the unit carbon emission cost; is the electric power consumed by the j-th electric heat supplementary device in the i-th cluster at time t; is the power consumption of the circulation pump in the i-th cluster at time t; δ ec is the carbon emission coefficient for purchasing electricity; δ hpn is the carbon emission coefficient for heating by the municipal heat network; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at time t; Δt is the unit time period; N is the number of clusters of the electric heating and heat supplementary system; J is the number of electric heat supplementary devices in a certain cluster;

[0100] The subsidy income E for participating in the peak shaving auxiliary service market peak has the following expression:

[0101]

[0102] In the formula: E peak is the income obtained from participating in the peak shaving auxiliary service market; is the subsidy electricity price for auxiliary peak shaving per unit time period; is the electric power consumed by the i-th cluster at time t; Δt is the unit time period; N is the number of clusters of the electric heating and heat supplementary system; k is the total number of time;

[0103] The clean energy heating subsidy E allowance has the following expression:

[0104]

[0105] Where: E allowance is the clean energy heating subsidy; c clean is the subsidy price; is the electric power consumed by the i-th cluster at time t; N is the number of clusters of the electric heating heat compensation system; Δt is the unit time period.

[0106] Preferably, the expression of the heat load demand constraint of the optimal solution acquisition module is as follows:

[0107]

[0108]

[0109]

[0110] Where: is the heat supply power from the i-th cluster to the user at time t; is the heat supply power of the i-th cluster at time t; c p is the specific heat capacity of the heat medium; is the mass flow of the heat medium at the inlet of cluster i; is the supply water temperature at the inlet of cluster i at time t; is the return water temperature at the inlet of cluster i at time t; is the heat compensation power of cluster i at time t; is the heat release power of the electric boiler in the i-th cluster at time t; is the heat release power of the heat storage device in the i-th cluster at time t; are respectively the heat release powers of the heat pumps in the i-th cluster at time t; is the electric power of the electric boiler in the i-th cluster at time t; P ST-out is the heat release power of the heat storage tank; is the electric power consumed by the heat pumps in the i-th cluster at time t; η EB is the electro-thermal conversion efficiency of the electric boiler; η ST-out is the heat release efficiency of the heat storage tank; COP is the performance coefficient of the heat pump.

[0111] Preferably, the load data of the data acquisition module includes one or more of the following: photovoltaic predicted power, wind power predicted power, power purchased from the superior power grid, load predicted power, and heat supply load data of each cluster user; the power and heat purchase prices include one or more of the following: system power purchase price, heat purchase price, and heat sale price; the parameters of each device include one or more of the following: parameters of each device in the direct heating heat pump cluster model, parameters of each device in the direct heating heat storage cluster model, parameters of each device in the heat pump heat storage cluster model, load of each node in the distribution network, impedance of each branch, and heat network pipeline parameters.

[0112] Compared with the closest prior art, the beneficial effects of the present invention application are as follows:

[0113] The present invention patent application is an optimized control method and system for the interaction between an electric heating system cluster and the power grid, including: obtaining the parameters of each device, load data, and electricity and heat purchase prices in the electric heating and heat compensation system cluster; based on the parameters of each device, load data, and electricity and heat purchase-related parameters, using the particle swarm optimization algorithm to solve the pre-constructed optimization model of the electric heating and heat compensation system cluster, and obtaining the optimal output scheme of each electric heat compensation device in the electric heating and heat compensation system cluster; based on the optimal output scheme of each electric heat compensation device, optimizing the control of the electric heating and heat compensation system cluster; the optimization model of the electric heating and heat compensation system cluster is constructed with the goal of maximizing the comprehensive operation benefit of the electric heating and heat compensation system cluster model participating in the power grid auxiliary service peak regulation market according to the interaction mode between the electric heating and heat compensation system cluster and the power grid, where the electric heating and heat compensation system cluster model is constructed based on multiple cluster models formed by different combinations of electric heat compensation devices and the collaborative interaction relationship between the heat supply network and the electric heat compensation devices in the cluster; the present invention patent application constructs multiple cluster models based on different combinations of electric heat compensation devices, and constructs the electric heating and heat compensation system cluster model based on the collaborative interaction relationship between the heat supply network and the electric heat compensation devices in the cluster, solves the problems of single equipment in the electric heating and heat compensation system cluster model and the lack of consideration of the interaction between equipment and heat network pipelines, and realizes more accurate modeling of the electric heating and heat compensation system. Description of the Drawings

[0114] Figure 1 It is a flowchart of an optimized control method for the interaction between an electric heating system cluster and the power grid provided by the present invention application;

[0115] Figure 2 It is a flowchart of an ancillary service transaction provided by the present invention application;

[0116] Figure 3 It is a schematic diagram of the interaction structure between the electric heating and heat compensation system cluster of an urban heat exchange station and the power grid provided by the present invention application;

[0117] Figure 4 It is a flowchart of the particle swarm optimization algorithm provided by the present invention application;

[0118] Figure 5 It is a topology diagram of the electric heat compensation system cluster of an urban heat exchange station provided by the present invention application;

[0119] Figure 6 It is a distribution diagram of the power grid nodes connected to the electric heat compensation system cluster provided by the present invention application;

[0120] Figure 7 It is a predicted power diagram of photovoltaic, wind power, the superior power grid, and electric load provided by the present invention application;

[0121] Figure 8 Output diagram of heat exchange station 1 provided for this invention application

[0122] Figure 9 Output diagram of heat exchange station 2 provided for this invention application

[0123] Figure 10 Output diagram of heat exchange station 3 provided for this invention application

[0124] Figure 11 Change diagram of virtual electric heat storage tank capacity and heat storage and release power of heat exchange station 1 provided for this invention application

[0125] Figure 12 Change diagram of virtual electric heat storage tank capacity and heat storage and release power of heat exchange station 2 provided for this invention application

[0126] Figure 13 Change diagram of virtual electric heat storage tank capacity and heat storage and release power of heat exchange station 3 provided for this invention application

[0127] Figure 14 Change diagram of heat storage quantification index and water supply temperature of pipeline 1 provided for this invention application

[0128] Figure 15 Change diagram of heat storage quantification index and water supply temperature of pipeline 2 provided for this invention application

[0129] Figure 16 Change diagram of heat storage quantification index and water supply temperature of pipeline 3 provided for this invention application

[0130] Figure 17 Schematic diagram of an optimized control system for the interaction between an electric heating system cluster and the power grid provided for this invention application Specific implementation manner

[0131] The following further elaborates on the specific implementation manner of this invention application in conjunction with the attached drawings

[0132] Example 1

[0133] An optimized control method for the interaction between an electric heating system cluster and the power grid provided in this application is as Figure 1 shown and includes

[0134] Step 1: Obtain the parameters of each device, load data, and electricity and heat purchase prices in the electric heating and heat compensation system cluster

[0135] Step 2: Based on the parameters of each device, load data, and relevant parameters for electricity and heat purchase, use the particle swarm optimization algorithm to solve the pre-constructed optimization model of the electric heating and heat compensation system cluster to obtain the optimal output scheme of each electric heat compensation device in the electric heating and heat compensation system cluster

[0136] Step 3: Based on the optimal output schemes of the electric heat supplementary devices, optimize the control of the electric heating and heat supplementary system cluster;

[0137] The optimization model of the electric heating and heat supplementary system cluster is constructed according to the interaction mode between the electric heating and heat supplementary system cluster and the power grid, with the goal of maximizing the overall operation benefit of the electric heating and heat supplementary system cluster model participating in the power grid auxiliary service peak shaving market. The electric heating and heat supplementary system cluster model is constructed based on multiple cluster models formed by different combinations of electric heat supplementary devices and the collaborative interaction relationship between the heat supply network and the electric heat supplementary devices in the cluster.

[0138] Specifically, Step 1 includes:

[0139] Obtain the parameters of each device, load data, and electricity and heat purchase prices in the electric heating and heat supplementary system cluster; the load data includes one or more of the following: photovoltaic predicted power, wind power predicted power, power purchased from the superior power grid, load predicted power, and heat supply load data of each cluster user. The electricity and heat purchase prices include one or more of the following: system electricity purchase price, heat purchase price, and heat sale price; the parameters of each device include one or more of the following: parameters of each device in the direct heat pump cluster model, parameters of each device in the direct heat energy storage cluster model, parameters of each device in the heat pump energy storage cluster model, load of each node in the distribution network, impedance of each branch, and parameters of the heat network pipeline; the present invention patent application obtains multiple data of the electric heating and heat supplementary system cluster, providing data support for obtaining the optimal output schemes of the electric heat supplementary devices in the electric heating and heat supplementary system cluster.

[0140] Specifically, Step 2 includes:

[0141] According to different combinations of electric heat supplementary devices and physical characteristic constraints of each electric heat supplementary device, construct a cluster model, which includes: direct heat + heat storage, direct heat + heat pump, and heat pump + heat storage cluster output models and a virtual heat storage model of the heat supply network. It is characterized by including the following steps:

[0142] (1) "Direct heat + heat pump" cluster model

[0143]

[0144] In the formula: is the heat supplementary power at time t of Cluster 1; m is the number of electric boilers in Cluster 1; F is the number of heat pumps in Cluster 1; is the heat release power of the e-th electric boiler at time t of Cluster 1; is the heat release power of the f-th heat pump at time t of Cluster 1, and Cluster 1 is the "direct heat + heat pump" cluster;

[0145] Among them, the heat release power of the electric boiler The expression of is as follows:

[0146]

[0147] Wherein, is the heat release power of the e-th electric boiler at the t-th moment of Cluster 1; is the power consumption of the e-th electric boiler at the t-th moment of Cluster 1; η EB is the electro-thermal conversion efficiency of the electric boiler, and the same type of electric heat supplement equipment in one cluster has the same model, so η EB is the same;

[0148] The heat release power of the heat pump The expression is as follows:

[0149]

[0150] Wherein: The heat release power of the f-th heat pump at the t-th moment of Cluster 1; is the power consumption of the f-th heat pump at the t-th moment of Cluster 1, and COP is the performance coefficient;

[0151] Among them, the electric boiler equipment and the heat pump equipment should meet the following constraints of their own physical characteristics:

[0152]

[0153]

[0154] Wherein: P EB1 (t) is the electric power consumed by the electric boiler at the t-th moment of Cluster 1; is the maximum allowable electric power consumption of the electric boiler, and this formula is the physical constraint of the electric boiler equipment; P pump1 (t) is the power consumption of the heat pump operation at the t-th moment of Cluster 1; is the rated power of the heat pump operation, and this formula is the physical constraint of the heat pump equipment affected by the operation characteristics of the compressor and the water pump;

[0155] In addition, the "direct heating + heat pump" cluster and the heat network jointly supply heat to users, then

[0156]

[0157] Wherein: is the heat supplement power of the "direct heating + heat pump" Cluster 1 at the t-th moment; is the heat supply power of the primary heat network of the heat exchange station at the t-th moment of Cluster 1; is the heat load demand of the users supplied by the heat exchange station where Cluster 1 is located at the t-th moment; this formula is the heat supply ratio constraint of the "direct heating + heat pump" cluster and the heat network;

[0158] (2) "Direct heating + heat storage" cluster model

[0159]

[0160] Wherein: is the supplementary heating power of "direct heating + heat storage" at time t in Cluster 2; Z is the number of heat storage tanks in Cluster 2; M is the number of electric boilers in Cluster 2; is the heat release power of the e'-th electric boiler at time t in Cluster 2; is the heat release power of the l-th heat storage tank at time t in Cluster 2; η ST-out is the heat release efficiency of the heat storage tank, and the "direct heating + heat storage" cluster is Cluster 2;

[0161] The specific expressions for the heat storage capacity and power consumption of the heat storage tank are as follows:

[0162]

[0163] Wherein: is the heat storage capacity of the l-th heat storage tank in Cluster 2 at time t; is the heat storage capacity of the l-th heat storage tank in Cluster 2 at time t-1; is the heat storage and heat release conversion flag of the l-th heat storage tank in Cluster 2, 1 for heat storage and 0 for heat release; is the electric power consumed for heat storage of the l-th heat storage tank in Cluster 2; η ST-in is the heat storage efficiency of the heat storage tank; η ST-out is the heat release efficiency of the heat storage tank; is the heat release power of the l-th heat storage tank in Cluster 2 at time t; Δt is the unit time period;

[0164] In view of the limitations of the physical characteristics of the heat storage tank itself, the following formula should be satisfied during operation:

[0165]

[0166] Wherein: is the heat storage capacity of the heat storage tank at time t; is the maximum heat storage capacity of the heat storage tank; is the heat storage capacity of the heat storage tank at the end of the scheduling period; is the heat storage capacity of the heat storage tank at the initial moment; Q ST-out is the heat release power of the heat storage tank; is the maximum heat release power of the heat storage tank, Q ST-in is the heat storage power of the heat storage tank, is the maximum heat storage power of the heat storage tank.

[0167] This formula is the physical characteristic constraint of the heat storage tank during operation;

[0168] In addition, the "direct heating + heat storage" cluster and the heat network jointly supply heat to users, then

[0169]

[0170] In the formula: is the heat supplement power of "direct heating + heat storage" at time t in cluster 2; is the heat supply power of the primary heat network of the heat exchange station where cluster 2 is located at time t; is the heat load demand of the users supplied by the primary heat network of the heat exchange station where cluster 2 is located at time t. This formula is the heating ratio constraint between the "direct heating + heat storage" cluster and the heat network;

[0171] (3) "Heat pump + heat storage" cluster model

[0172] According to the above, the cluster model of the system can be directly obtained as follows:

[0173]

[0174] In the formula: is the heat supplement power of "heat pump + heat storage" at time t in cluster 3; is the heat release power of the f'-th heat pump at time t in cluster 3; is the heat release power of the l'-th heat storage tank at time t in cluster 3; η ST-out is the heat release efficiency of the heat storage tank; h is the total number of heat pumps in cluster 3; Z' is the total number of heat storage tanks in cluster 3. Cluster 3 is the "heat pump + heat storage" cluster;

[0175] Among them, the "heat pump + heat storage" cluster and the heat network jointly supply heat to users, and its expression is as follows:

[0176]

[0177] In the formula: is the heat supplement power of "heat pump + heat storage" at time t in cluster 3; is the heat supply power of the primary heat network of the heat exchange station where cluster 3 is located; is the heat supply power of the primary heat network of the heat exchange station where cluster 3 is located; This formula is the heating ratio constraint between the "heat pump + heat storage" cluster and the heat network;

[0178] (4) Virtual heat storage model of the heat supply network

[0179] The thermal dynamic characteristics of the heat supply pipeline are reflected in the dynamic change of the temperature of the heat medium in the pipeline. A one-dimensional temperature field is established along the axial direction of the heat supply pipeline, and the heat supply network is regarded as a virtual heat storage tank. According to the law of conservation of energy, a thermal dynamic model of the heat supply pipeline is established:

[0180]

[0181] In the formula: T tis the average water temperature of the heat storage tank in the water supply network at time t, where T t =(T t in +T t out ) / 2, T t in is the inlet water temperature of the heat storage tank in the water supply network at time t, T t out is the outlet water temperature of the heat storage tank in the water supply network at time t; T t-1 is the average water temperature of the heat storage tank in the water supply network at time t-1; c p is the specific heat capacity of water; ρ is the density of water; A is the inner cross-sectional area of the pipeline; m is the mass flow rate; R is the total thermal resistance of the pipeline; T t a is the ambient temperature; Δt is the time interval, L is the length of the pipeline;

[0182] The heat source exchanges heat with the heat exchange station. According to the conservation of heat power, the heat source power at time t can be determined from the supply and return water temperatures

[0183]

[0184] In the formula: is the heat source power at time t; c p is the specific heat capacity of water; m is the mass flow rate; T t S,in is the supply water temperature; T t S ,out is the return water temperature;

[0185] As the link for heat exchange between the primary heat network and the secondary heat network, the secondary heat network and the buildings connected to the heat exchange station can be equivalent to a heat load, and the heat load of the heating system at time t and the heat load of the heat exchange station

[0186]

[0187] In the formula: is the heat load of the heating system at time t; is the heat load of the heat exchange station at time t; c p is the specific heat capacity of water; m is the mass flow rate; T t R,in is the inlet temperature of the return water pipeline at time t; T t S,out is the return water temperature;

[0188] Taking into account the heat loss of the heating pipeline network, the linear expression of the heat loss can be obtained:

[0189]

[0190] In the formula: is the heat loss at time t; λ is the thermal conductivity of water; A is the inner cross-sectional area of the pipeline; ρ is the density of water; L is the length of the pipeline, T t R,in is the inlet temperature of the return water pipeline at time t; T t S,in is the supply water temperature, T a is the ambient temperature;

[0191] Then, according to the conservation of the thermal power of the thermal system, the heat storage and release power of the virtual heat storage tank of the heat supply network can be obtained as:

[0192]

[0193] In the formula: is the heat storage and release power of the virtual heat storage tank of the heat supply network at time t; is the heat source power at time t; is the heat load of the heat supply system at time t; is the heat loss at time t;

[0194] When , it means that the heat source output is higher than the heat load, and the heat supply network stores heat; when , it means that the heat source output is lower than the heat load, and the heat supply network releases heat to meet the heat load demand of users;

[0195] In order to describe the heat storage level of the heat supply network, a virtual heat storage state (SOVTS) index is established based on the virtual heat storage tank model. As can be seen from the previous section, the virtual heat storage quantity of the heat supply network can be macroscopically determined by the difference between the heat supply quantity and the heat consumption quantity. Its essence is affected by the pipeline temperature and has a certain limit value. The heat storage of the heat supply system is mainly reflected in the increase of the supply water temperature. Therefore, the heat storage quantity of the water supply network is defined as:

[0196]

[0197] In the formula: is the heat storage quantity of the water supply network at time t; ρ is the density of water; c p is the specific heat capacity of water; V S is the water capacity of the supply and return water pipelines; T S,min is the lower limit of the supply water temperature; T t S is the water temperature of the supply and return water pipelines at time t;

[0198] To ensure the safe and stable operation of the heating system, it is necessary to set the upper and lower limits of the supply water temperature according to the design specifications. Therefore, the maximum heat storage capacity and the maximum heat storage and release power of the pipe network can be expressed as:

[0199] H S,max =c p ρV k (T S,max -T S,min )

[0200] Q S,max =c p m k (T S,max -T S,min )

[0201] Where: H S,max is the maximum heat storage capacity of the pipe network; Q S,max is the maximum heat storage and release power of the pipe network; T S,max is the upper limit of the supply water temperature of the supply and return pipe network; T S,min is the lower limit of the supply water temperature of the supply and return pipe network; ρ is the density of water; c p is the specific heat capacity of water; m k is the mass flow rate of the supply pipe k; V k is the volume of the supply pipe k;

[0202] Furthermore, the virtual heat storage state index of the heat pipe network is obtained:

[0203]

[0204] Where: SOVTC(t) is the virtual heat storage state index of the primary pipe network at time t. Further simplifying the formula: H S,max is the maximum heat storage capacity of the pipe network; is the heat storage capacity of the supply pipe network at time t;

[0205]

[0206] Where: SOVTC(t) is the normalized index of the virtual heat storage capacity of the heat pipe network at time t; T t S is the water temperature of the supply and return pipe network at time t; T S,max is the upper limit of the supply water temperature of the supply and return pipe network; T S,min is the lower limit of the supply water temperature of the supply and return pipe network;

[0207] SOVTC(t) can be regarded as the normalized index of the virtual heat storage capacity of the heat pipe network, which is proportional to the average temperature of the supply and return pipes. Theoretically, the higher the temperature of the heat pipe network, the stronger its heat storage capacity and the higher the corresponding heat loss. Therefore, when adjusting the heat pipe network, while making full use of the heat storage of the heat network, it is also necessary to ensure that the heat loss is within a reasonable range;

[0208] The services that can be provided to the power grid include deep peak shaving and new energy accommodation. The market participants include peak shaving resource consumers, power trading centers, peak shaving resource providers, and power dispatching agencies. The electric heating and heat supplement system cluster of urban heat exchange stations, as a centralized control type flexible load, participates in the auxiliary service peak shaving market and interacts with the power grid. Through optimizing the control strategy, the flexible adjustment and economy of electric heat supplement equipment are realized, and the safety and stability of the power system and the accommodation capacity of photovoltaic and wind power are improved;

[0209] The electric heat supplement system cluster is represented by the market entities of heat exchange stations and participates in the power auxiliary service market. Through the day-ahead bidding method, the real-time peak shaving capacity (flexible adjustment power) that can be provided and the auxiliary service price are determined. After the capacity is called, the auxiliary service income corresponding to the capacity is obtained; the electricity consumed by the electric heat supplement system cluster is purchased from the power grid by the new heat exchange station at the time-of-use electricity price;

[0210] The process of auxiliary service trading is as follows Figure 2 as shown.

[0211] 1) Before a certain time point on the bidding day, the power dispatching agency issues the opening information of the deep peak shaving market, including but not limited to:

[0212] ① New heat exchange stations that can participate in the deep peak shaving market transaction;

[0213] ② The predicted power generation capacity of wind power and photovoltaic power on the next day, the expected curtailment of wind and light, etc.;

[0214] ③ The lower limit of the output of the electric heat supplement system participating in the deep peak shaving market;

[0215] ④ The start and end times of market declaration;

[0216] ⑤ The market trading process.

[0217] 2) During the bidding day period, each new heat exchange station conducts centralized bidding declarations for the deep peak shaving market.

[0218] 3) After the bidding day, the power dispatching agency forms a day-ahead power generation plan considering the deep peak shaving market transaction based on the power grid operation needs, the predicted output of new energy, the total market supply and demand, and after safety verification.

[0219] 4) During operation on the same day, the electric heating and heat compensation system consumes electricity according to the daily electricity consumption plan released in advance. Based on the ultra-short-term load forecast and the predicted output of new energy generating units, when it is expected that wind and light curtailment will occur, the power dispatching agency starts the deep peak shaving market, and calls new type heat exchange stations for deep regulation in ascending order according to the total supply curve of the deep peak shaving market, and starts the electric heating and heat compensation equipment. The new energy generating units are arranged to increase power generation in proportion according to the total demand of the deep peak shaving market, and the clearing is rolled out in a 15-minute cycle. Based on passing the security check, the daily power generation plan considering the results of the deep peak shaving transaction is formed.

[0220] The interactive structure between the electric heating and heat compensation system cluster of urban heat exchange stations and the power grid is constructed as Figure 3 shown;

[0221] The interactive conditions between the electric heating and heat compensation cluster and the power grid include the traditional optimal power flow constraints of all nodes in the region, the differentiated equipment characteristics and working state constraints corresponding to the nodes where the urban heat exchange stations are located, and are characterized by the following steps:

[0222] (1) Grid interaction constraints

[0223] The constraints of the power system mainly include grid power flow constraints, branch capacity constraints and node voltage constraints.

[0224] ① Grid power flow constraints

[0225] After the electric heating and heat compensation equipment is connected, the original network architecture of the urban distribution network remains basically unchanged. What changes is the load node power where the electric heating and heat compensation equipment is located. With the connection of the electric heating and heat compensation cluster, the load power of this node increases significantly, resulting in changes in the power flow of the entire distribution network and then reaching a new power flow balance. The AC power flow is used as the analysis method for the power system.

[0226]

[0227] In the formula: is the set of nodes in the power system; P E,a represents the injected active power of node a; Q E,a represents the injected reactive power of node a; U a represents the voltage amplitude of node a; U b represents the voltage amplitude of node b; G ab represents the conductance of branch ab; B ab represents the susceptance of branch ab; θ ab represents the phase angle difference of branch ab;

[0228] ② Branch capacity constraints

[0229] To ensure the stability of the power system, the apparent capacity of each branch of the power network at any time cannot exceed its maximum value;

[0230]

[0231] Where: P E,ab (t) represents the active power of branch ab at time t; Q E,ab (t) represents the reactive power of branch ab at time t; U a (t) represents the voltage amplitude of node a at time t; U b (t) represents the voltage amplitude of node b at time t; G ab represents the conductance of branch ab; B ab represents the susceptance of branch ab; θ ab (t) represents the phase angle difference of branch ab at time t; S E,ab (t) represents the apparent power of branch ab at time t; S E,ab,max represents the upper limit of the transmission capacity of branch ab;

[0232] ③ Node voltage constraint

[0233] After connecting the electric heat compensation equipment, it is necessary to impose upper and lower limits on the voltage of the load nodes in the power system to ensure the safety and stability of the power system;

[0234] U a.min ≤U a (t)≤U a.max

[0235] Where: U a.min represents the allowable lower voltage limit of node a; U a (t) represents the voltage amplitude of node a at time t; U a.max represents the allowable upper voltage limit of node a;

[0236] (2) Heat network operation constraint

[0237] The network constraints of the heat network mainly include the heat inertia constraint of the heat network related to the dynamic characteristics of the heat medium, the upper and lower limits of the supply and return water temperatures, the heat power balance constraint, the heat medium flow constraint, the relative opening degree constraint of the primary regulating valve, the mass flow balance constraint, and the temperature mixing constraint.

[0238] ① Upper and lower limits of supply and return water temperatures constraint

[0239] There are temperature constraints on both the supply and return water temperatures in the heat supply pipe network. The upper and lower limits of the temperature mainly come from the requirements of the heat supply company for the supply water temperature and return water temperature according to the heating standard. The supply water temperature and return water temperature should be restricted within a certain range:

[0240]

[0241] Where: is the supply water temperature at time t; is the return water temperature at time t; is the lower limit of the supply water temperature; is the lower limit of the return water temperature; is the upper limit of the supply water temperature; represents the upper limit of the return water temperature;

[0242] ② Heat power balance constraint

[0243] According to the law of conservation of energy, there are only two directions for the energy flowing into a node, namely flowing out of the node and exchanging with the outside world. The relationship between the heat power consumed by each node and the temperature and flow rate can be expressed by the following formula:

[0244]

[0245] In the formula: Φ(t) represents the heat power consumed by the node at time t; c p is the specific heat capacity of the heat medium; m q is the mass flow rate of the heat medium injected into the heat node; is the supply water temperature at time t; is the return water temperature at time t.

[0246] ③ Heat medium flow rate constraint

[0247] The heat generated at the heat source is transmitted to the heat exchange station through the heat medium. To avoid regulation imbalance caused by too small or too large heat medium flow rate in the pipeline, it is necessary to control the heat medium flow rate within a reasonable range.

[0248]

[0249] In the formula: m q (t) is the mass flow rate of the heat medium injected into the heat node at time t; V(t) represents the heat medium flow rate in the pipeline at time t; ρ represents the density of the heat medium; V min represents the minimum heat medium flow rate allowed to pass through the pipeline; V max represents the maximum heat medium flow rate allowed to pass through the pipeline.

[0250] ④ Relative opening degree constraint of the primary regulating valve

[0251] The primary regulating valve plays a very important role in the regulation of the thermal system. By controlling the relative opening degree of the valve, the flow rate on the primary side of the heat exchanger is adjusted, and then the heat supply provided to the heat user is changed. The expression is:

[0252]

[0253] In the formula: K V is the relative opening degree of the primary valve; l V is the spool stroke at a certain opening degree of the regulating valve; l V.maxIt is the valve core stroke when the regulating valve is fully open.

[0254] According to different flow characteristics, valves are divided into linear flow characteristics, equal percentage flow characteristics, quick opening flow characteristics, and parabolic characteristic regulating valves. Since the equal percentage flow characteristic regulating valve has a small absolute flow change at small openings and a large absolute flow change at large openings, the regulation sensitivity of the heat exchange equipment's heat transfer amount is improved, and its regulation characteristics are superior to the other three types. Therefore, this paper selects the equal percentage flow characteristic valve, and the mathematical model is:

[0255]

[0256] In the formula: K V (t) represents the relative opening of the primary regulating valve at time t; V(t) represents the heat medium flow rate in the pipeline at time t; R V is the adjustable ratio of the regulating valve; V max represents the maximum heat medium flow rate allowed to pass through the pipeline.

[0257] R V The calculation formula of is as follows:

[0258]

[0259] R V is the adjustable ratio of the regulating valve; V max represents the maximum heat medium flow rate allowed to pass through the pipeline; V min represents the minimum heat medium flow rate allowed to pass through the pipeline.

[0260] When the primary regulating valve is working, to prevent the valve diameter selection from being too large, which affects its regulation performance and economy, its maximum opening generally does not exceed 90%; to prevent the serious erosion of the valve core and valve seat by the fluid at small openings, which damages the valve core and makes the flow characteristics of the valve worse or even fails, its minimum opening generally is not less than 10%, that is: 0.1 ≤ K V (t) ≤ 0.9, where K V (t) represents the relative opening of the primary regulating valve at time t;

[0261] ⑤ Mass flow balance constraint

[0262] The flow rates of each pipeline at each node should satisfy the flow continuity equation: the mass flow rate of the heat medium flowing into a node is equal to the sum of the mass flow rates of the heat medium flowing out of the node and the heat medium injected into the node. The calculation formula for the mass flow rate of the heat medium flowing into a node is as follows:

[0263] ∑m in -∑m out =m q

[0264] In the formula: min is the mass flow rate of the heat medium in each pipe flowing into the node; m out is the mass flow rate of the heat medium in each pipe flowing out of the node; m q is the mass flow rate of the heat medium injected into the thermal node.

[0265] ⑥ Temperature mixing constraint

[0266] For a radial heating network, there is only a node where a single pipe flows into multiple pipes, and the temperature of the heat medium at the node can be considered constant. A regenerative heating network with the same topological structure but opposite heat medium flow directions has a node where the heat medium in multiple pipes converges into one pipe. Since the temperatures of the pipes before convergence are not necessarily the same, the temperature of the heat medium after convergence satisfies the following equation:

[0267] (∑m out )T out =∑(m in T in )(∑m out )T out =Σ(m in T in )

[0268] In the formula: m in is the mass flow rate of the heat medium in each pipe flowing into the node; m out is the mass flow rate of the heat medium in each pipe flowing out of the node; T out is the mixing temperature of the heat medium flowing into the node; T in is the temperature at the end of each pipe flowing into the node.

[0269] (3) Equipment operation constraint

[0270] ① Electric boiler electric power constraint

[0271]

[0272] In the formula: is the maximum electric power that the electric boiler is allowed to consume at time t; P EB (t) is the electric power consumed by the electric boiler at time t.

[0273] ③ Thermal energy storage device operation constraint

[0274] As an electricity-transferable flexible load, the thermal energy storage device stores heat at night using low-cost electricity and provides heating during the day when electricity prices are high by dissipating heat. During operation, the stored heat, heat storage power, and heat release power of the thermal energy storage tank cannot exceed their limits. To enable the thermal energy storage tank to participate in scheduling normally in the next scheduling period, it is assumed in the model that the stored heat of the thermal energy storage device at the end of the scheduling period is equal to its initial stored heat. The operation constraints of the thermal energy storage device are as follows:

[0275]

[0276] Wherein: is the heat storage amount of the heat storage tank at time t; is the heat storage amount of the heat storage tank at time t-1; S EB is the heat storage and heat release conversion flag of the heat storage tank, 1 means heat storage, 0 means heat release; P ST-in is the heat storage power of the heat storage tank; P ST-out is the heat release power of the heat storage tank; η ST-in is the heat storage efficiency of the heat storage device; η ST-out is the heat release efficiency of the heat storage device; is the maximum heat storage amount; is the heat storage amount of the heat storage tank at the end of the scheduling period; is the heat storage amount of the heat storage tank at the initial moment; is the maximum heat storage power of the heat storage device; is the maximum heat release power of the heat storage device, and Δt is the unit time period.

[0277] ④ Heat pump operation constraint

[0278] The performance of the heat pump unit is closely related to the power of the compressor and water pump during operation. If the operating power of the heat pump unit exceeds the rated power, the compressor and water pump will operate overloaded, and in severe cases, the compression pump machinery and drive motor will be damaged; when the operating power is too low, the service life of the unit will be reduced and energy will be wasted. Therefore, in order to achieve better operating results, it is necessary to constrain the performance of the heat pump unit. The actual operating power of each heat pump unit cannot be less than 0.25 times the rated power, which can be expressed as:

[0279]

[0280] Wherein, is the rated power of heat pump operation; P pump (t) is the heat release power of the heat pump at time t.

[0281] ④ Circulating pump power constraint

[0282] As an important device in the central heating pipe network, the circulating pump consumes a part of electrical energy, adjusts its own speed, generates a pressure difference in the supply and return pipe networks respectively, and promotes the heat medium to circulate in the heating system. The mathematical model is:

[0283]

[0284] Wherein: P wp (t) is the power of the circulating pump at time t; V(t) represents the heat medium flow in the pipeline at time t; g is the gravitational acceleration constant; H wp is the head of the circulating pump; γ wpis the efficiency of the circulation pump; ρ is the density of water.

[0285] To prevent the problem of excessive pump flow, the circulation pump should meet the maximum output limit during operation. The expression is

[0286] 0 ≤ P wp (t) ≤ P wp.max

[0287] In the formula: P wp (t) is the power of the circulation pump at time t; P wp.max is the maximum power consumption of the circulation pump.

[0288] ⑤ Heat extraction power constraint of the municipal heat network

[0289] The heat exchange station is connected to the municipal heat network through a heat pipeline. Due to pipeline constraints, there are upper and lower limits on the heat power transmitted from the municipal heat network to the heat exchange station. The heat purchase power constraint can be expressed as

[0290] Q hpn.min ≤ Q hpn (t) ≤ Q hpn.max

[0291] In the formula: Q hpn (t) is the heat purchase power of the heat exchange station from the municipal heat network at time t; Q hpn.min is the lower limit of the heat purchase power of the heat exchange station from the municipal heat network; Q hpn.max is the upper limit of the heat purchase power of the heat exchange station from the municipal heat network.

[0292] (4) Heat load demand constraint

[0293] The municipal heat network and the electric supplementary heat system jointly supply heat to users to meet the heat load demand of users. The building thermal inertia constraint involved is shown in the following formula. The heat power balance expression for heating users is:

[0294]

[0295] In the formula: is the heat supply power from the i-th cluster to users at time t; is the heat supply power of the i-th cluster at time t; is the heat supply power of the i-th cluster at time t.

[0296] Among them, the part of the heat network supplying heat to users is mainly that the municipal heat network distributes heat to each heat exchange station through the primary pipeline network, and then the heat exchange station plate heat exchanger exchanges heat and supplies heat to users through the secondary pipeline network. Its heat supply power can be expressed as:

[0297]

[0298] In the formula: is the heating power of the i-th cluster after one-time heat exchange in the heat network at time t; c p is the specific heat capacity of the heat medium; is the mass flow of the heat medium at the inlet of cluster i; is the supply water temperature at the inlet of cluster i at time t; is the return water temperature at the inlet of cluster i at time t.

[0299] The heating power expression of the electric supplementary heating cluster is:

[0300]

[0301] In the formula: is the supplementary heating power of cluster i at time t; is the heat release power of the electric boiler in the i-th cluster at time t; is the heat release power of the heat storage device in the i-th cluster at time t; are respectively the heat release powers of the heat pumps in the i-th cluster at time t; is the electric power consumed by the electric boiler in the i-th cluster at time t; P ST-out is the heat release power of the heat storage tank; is the heat release power of the heat pump of cluster i at time t; η EB is the electro-thermal conversion efficiency of the electric boiler; η ST-out is the heat release efficiency of the heat storage tank; COP is the performance coefficient of the heat pump.

[0302] As a flexible demand-side resource inside the heat exchange station, this section proposes an optimal control strategy for the electric heating supplementary heating system cluster, that is, by controlling the response of the electric supplementary heating equipment to the peak-valley-flat electricity price of the power grid and participating in the auxiliary service peaking market, interacting with the power grid while meeting the most basic demand of user heat load, and meeting other various constraint conditions, so as to achieve the optimal economic operation of the electric supplementary heating equipment.

[0303] From the perspective of the heating company, a heat supplement cluster function aiming at economy and energy efficiency is established. On the one hand, consider the benefits obtained by the electric heat supplement group in responding to the peak-valley-flat electricity price of the power grid and participating in the peak shaving of the auxiliary service market. On the other hand, consider the operating costs of the electric heat supplement group, the cost of purchasing heat from the municipal heat network, the operation and maintenance costs, and the carbon emission costs, such as the operation costs of the heat network. Taking the minimum system operation cost as the objective function, with heat load balance, the power of the electric heat supplement equipment, the heat supply of the municipal heat network, and the temperature of the heat network pipeline as constraints, through the optimization algorithm, adjust the output of the electric heat supplement cluster and the municipal heat network, so as to achieve the optimal operating state. According to the interactive mode of the cluster participating in the deep peak shaving of the power grid and the interactive mode of consuming new energy, with the minimum comprehensive benefit of the electric heating heat supplement system cluster model responding to the peak-valley-flat electricity price of the power grid and participating in the peak shaving market of the power grid auxiliary service as the goal, construct a comprehensive benefit objective function; according to the power grid operation constraints, heat network operation constraints and the characteristics of each device in the electric heating heat supplement system cluster after it is connected to the power grid, set constraint conditions for the comprehensive benefit objective function; the constraint conditions include one or more of the following: power grid interaction constraints, heat network operation constraints, equipment operation constraints and heat load demand constraints; the power grid interaction constraints include one or more of the following: power grid power flow constraints, branch capacity constraints and node voltage constraints; the heat network operation constraints include one or more of the following: upper and lower limits of the supply and return water temperature constraints, heat power balance constraints, heat medium flow constraints, relative opening degree constraints of the primary regulating valve, mass flow balance constraints and temperature mixing constraints; the equipment operation constraints include one or more of the following: electric boiler electric power constraints, heat storage device operation constraints, heat pump operation constraints, circulating pump power constraints, heat extraction power constraints of the municipal heat network.

[0304] The comprehensive benefit objective function can be expressed as follows:

[0305] min F=C grid +C hpn +C maintain +C car -E peak -E allowance

[0306] In the formula: F is the comprehensive benefit of the electric heat supplement system cluster of the heat exchange station; C grid is the electricity purchase cost, mainly including the electricity purchase of the circulating pump and the electric heat supplement equipment; C hpn is the cost of purchasing heat from the municipal heat network; C maintain is the operation and maintenance cost, mainly including the maintenance of the heat pipeline, the electric heat supplement cluster, the circulating pump and the heat network; C car is the carbon emission cost, mainly including the carbon emissions corresponding to the electricity purchase and heat purchase; E peak is the income obtained from participating in the peak shaving auxiliary service market; E allowance is the clean energy heating subsidy.

[0307] (1) The calculation formula for the electricity purchase cost is as follows:

[0308]

[0309] Where: C grid is the electricity purchase cost; is the electricity price during the t period; N is the number of clusters of the electric heating and heat compensation system; J is the number of electric heat compensation devices in a certain cluster; is the electric power consumed by the j-th electric heat compensation device in the i-th cluster at the t moment, mainly including electric boilers, heat storage devices and heat pumps; is the power consumption of the circulation pump connected to the primary heat network side of the heat exchange station where the i-th electric heat compensation cluster is located; Δt is the unit time period.

[0310] The specific expression of the electric heat compensation device is as follows:

[0311]

[0312] Where: is the electric power consumed by the electric heat compensation device in the i-th cluster at the t moment; is the electric power of the electric boiler in the i-th cluster at the t moment; is the electric power for heat storage of the heat storage device in the i-th cluster at the t moment; is the electric power consumed by the heat pump system in the i-th cluster at the t moment.

[0313] (2) The heat purchase cost of the municipal heat network

[0314] The heat exchange station obtains heat through the municipal heat network, and its heat purchase cost model can be expressed as:

[0315]

[0316] Where: C hpn is the heat purchase cost of the municipal heat network; is the thermal price of the municipal heat network per unit time period; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at the t moment, and Δt is the unit time period.

[0317] (3) The operation and maintenance cost

[0318] When electric boilers, heat pumps and heat storage devices are in operation, additional operation costs will be generated. The operation costs mainly include personnel costs, repair and maintenance costs, etc. Its cost can be estimated by multiplying the power consumption or heat release power by the operation cost coefficient. The calculation formula is:

[0319]

[0320] Where: k hpn is the operation cost coefficient of the heat pipeline; Qhpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at time t; k eth is the operating cost coefficient of the electric boiler; is the electric power of the electric boiler in the i-th cluster at time t; k ess is the operating cost coefficient of the heat storage device; is the electric power for heat storage of the heat storage device in the i-th cluster at time t; is the heat release power of the heat storage device in the i-th cluster at time t; k pump is the operating cost coefficient of the heat pump; is the electric power consumed by the heat pump system in the i-th cluster at time t; k wp is the operating cost coefficient of the circulation pump; is the power consumption of the circulation pump connected to the primary heat network side of the heat exchange station where the i-th electric supplementary heating cluster is located; Δt is the unit time period.

[0321] (4) Carbon emission cost

[0322] The carbon emission cost comes from system power purchase and municipal heat purchase, and the calculation formula is:

[0323]

[0324] In the formula: C car is the carbon emission cost; p car is the unit carbon emission cost; is the electric power consumed by the j-th electric supplementary heating device in the i-th cluster at time t; is the power consumption of the circulation pump in the i-th electric supplementary heating cluster at time t; δ ec is the carbon emission coefficient of power purchase; δ hpn is the carbon emission coefficient of municipal heat network heating; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at time t; Δt is the unit time period; N is the number of electric heating and supplementary heating system clusters; J is the number of electric supplementary heating devices in a certain cluster.

[0325] (5) Subsidy income from participating in the peak shaving auxiliary service market

[0326]

[0327] In the formula: E peak is the income obtained from participating in the peak shaving auxiliary service market; k is the auxiliary peak shaving time period specified by the power grid; is the subsidy electricity price for unit time period of auxiliary peak shaving; is the electric power consumed by the i-th cluster at time t; Δt is the unit time period; N is the number of electric heating and supplementary heating system clusters; k is the total number of time.

[0328] (6) Clean energy heating subsidy

[0329]

[0330] Where: E allowance is the clean energy heating subsidy; c clean is the subsidy price; is the electric power consumed by the i-th cluster at time t; N is the number of clusters of the electric heating heat compensation system; Δt is the unit time period.

[0331] The optimal operation model of the heat pump heat compensation system in the urban heat exchange station is a non-linear optimization problem. Both the constraint conditions and the objective function include non-linear equations. The artificial intelligence algorithm can effectively handle non-linear models, equivalent multiple objective functions to the optimization objectives, and use non-linear constraint conditions as the penalty terms of the optimization objectives. While retaining the non-linear characteristics of the model, the optimization solution is achieved. Although the computational efficiency is reduced compared with the traditional mathematical optimization algorithm, the adaptability is stronger. The particle swarm algorithm is used for solving, and the flow chart is as Figure 4 shown.

[0332] The process of the standard PSO algorithm is as follows:

[0333] (1) Randomly initialize the positions and velocities of the particle swarm; each particle usually represents a solution in the search space, and this solution represents an n-dimensional vector composed of decision variables, including and Q hpn (t);

[0334] (2) Calculate the fitness value of each particle; the fitness value refers to the objective function value;

[0335] (3) For each particle, compare its fitness value with the individual extreme value. If it is better, update the current individual extreme value; the individual extreme value is the value of the optimal solution reached by each particle during the search process in the particle swarm algorithm. Each particle will record the optimal solution in its own search process, that is, the corresponding fitness value. The individual extreme value represents the best state that the particle has reached during the search process, that is, the optimal solution for the particle itself. The particle updates its position and moves according to its individual extreme value.

[0336] (4) For each particle, compare its fitness value with the global extreme value. If it is better, update the current global extreme value; the global extreme value is the value of the optimal solution reached by all particles during the entire search process of the particle swarm algorithm. The global extreme value represents the optimal solution found by the entire particle swarm algorithm, that is, the best state reached by all particles together. The goal of the particle swarm algorithm is to find the global extreme value to find the optimal solution.

[0337] (5) Update the positions and flying speeds of each particle. The position of a particle represents a solution in the search space, and the speed controls the moving direction and speed of the particle in the search space. Specifically, the position of a particle represents the parameter values of the current solution, and the speed represents the change speed of each parameter.

[0338] (6) If the pre-set stopping criterion (usually set as the maximum number of iterations) is not reached, return to step 2); if it is reached, stop the calculation.

[0339] The personal best and the global best play important roles in the particle swarm optimization algorithm. The personal best helps a particle move and update its position individually, enabling the particle to better explore and search the solution space. The global best, on the other hand, can guide the particle swarm to search in the direction of the overall optimal solution, promoting the convergence of the algorithm.

[0340] In each iteration of the particle swarm optimization algorithm, the personal best and the global best are updated and adjusted to reflect the optimal solution in the search process. Through continuous updating and iteration, the particle swarm optimization algorithm can gradually converge to the global optimal solution or a region close to the global optimal solution. This invention patent application constructs multiple cluster models based on different combinations of electric heat compensation devices, constructs a virtual heat storage model for the heat supply network based on the collaborative interaction relationship between the heat supply network and the electric heat compensation devices in the cluster, and constructs a cluster model for the electric heating and heat compensation system based on the above models, solving the problems of single equipment in the cluster model of the electric heating and heat compensation system and the lack of consideration of the interaction between the equipment and the heat network pipeline, and realizing a more accurate modeling of the electric heating and heat compensation system. This invention patent application constructs an objective function for the comprehensive benefit in the cluster optimization model of the electric heating and heat compensation system by having each electric heat compensation device in the cluster model of the electric heating and heat compensation system respond to the peak-valley-flat electricity price and participate in the operation of the power grid auxiliary service peaking market, and conducts regulation after solving, solving the problems of unclear interaction mode between the electric heating and heat compensation system and the power grid, which is not conducive to the safe and stable operation of the power grid and has high operating costs, and realizing an economic, safe and stable regulation of the cluster of the electric heating and heat compensation system. This invention patent application utilizes the inertia of the heat network to assist in the consumption of clean energy, increase the electricity sales volume of the power grid, and relieve the pressure of the heating company to ensure people's livelihood during the heating period.

[0341] Specifically, step 3 includes:

[0342] Based on the optimal output power scheme of each electric heat compensation device, optimize the control of the cluster of the electric heating and heat compensation system. The optimal output power scheme of this invention patent application takes the heating and heat compensation device of the heat exchange station as a controllable load to respond to the peak-valley-flat electricity price and participate in the transaction of the power auxiliary service market, achieving a win-win situation for the power company and the heating company.

[0343] Embodiment 2:

[0344] Specifically, the proposed model is verified. Taking the electric heat supplement system cluster of small - scale urban heat exchange stations as an example, the overall topology is as follows Figure 5 As shown, the municipal heat network serves as the main heat source for heat users in this area. The heat exchange station, as a link connecting the municipal heat network and heat users, supplies heat to heat users. There are 3 heat exchange stations in this area, and each heat exchange station is equipped with corresponding electric heat supplement equipment due to various constraints. The following assumptions are made in this section:

[0345] (1) There is an open outdoor space at Heat Exchange Station 1, which is far from users and has a large heating load.

[0346] (2) Both the indoor and outdoor spaces at Heat Exchange Station 2 are large, and the heating load is large.

[0347] (3) The indoor space at Heat Exchange Station 3 is large, and the heating load is small.

[0348] Therefore, 2 air - source heat pumps with a capacity of 128.64 kW are configured at Heat Exchange Station 1; 3 air - source heat pumps with a capacity of 128.64 kW and 1 electric heat storage tank with a capacity of 360 kWh are configured at Heat Exchange Station 2; 3 electric boilers with a capacity of 30 kW are configured at Heat Exchange Station 3. Since there is more than one heat - power pipeline and the hydraulic conditions have affected each other, the quality - regulation method is adopted in this section. Photovoltaic power, wind power, and the superior power grid serve as the source end of the power system in this area, and the electric heat supplement system cluster and other electric loads serve as the energy - consuming end. The distribution map of the power grid nodes connected to the electric heat supplement system cluster is as follows Figure 6 As shown, a 10 - node 0.4 kV distribution network is selected for the power grid. Heat Exchange Station 1 is connected to Node 1, Heat Exchange Station 2 is connected to Node 2, and Heat Exchange Station 3 is connected to Node 4.

[0349] Input data such as the parameters of each device, the loads of each node in the distribution network, the impedances of each branch, and the heating loads of users. The parameters of the heat - power pipeline are shown in Table 1:

[0350] Table 1 Heat - power pipeline parameters

[0351]

[0352] The parameters of the heating equipment are shown in Table 2:

[0353] Table 2 Heating equipment parameters

[0354]

[0355]

[0356] The predicted powers of photovoltaic power, wind power, the superior power grid, and electric loads are as follows Figure 7 As shown, the system's electricity purchase, heat purchase, and heat sale prices are shown in Table 3:

[0357] Table 3 Electricity price, heat purchase, and heat sale price list

[0358]

[0359] The load values of each node in the distribution network are shown in Table 4:

[0360] Table 4 Load of Each Node in the Distribution Network

[0361]

[0362] The impedance values of each branch are shown in Table 5:

[0363] Table 5 Impedance of Each Branch in the Distribution Network

[0364]

[0365]

[0366] The heating load data of 3 heat exchange stations are shown in Table 6:

[0367] Table 6 Heating Load of Users

[0368]

[0369]

[0370] From Figure 7 it can be seen that at 11:00 and 12:00, the PV is in a large power generation state, but due to the low power consumption of the load, the phenomenon of PV curtailment occurs; similarly, in the period from 19:00 to 23:00, the wind power is in a large power generation state, but the load power is low, resulting in the phenomenon of wind curtailment. Since the electric heat compensation equipment is adjustable, the heat exchange station can participate in the auxiliary service peak shaving market and jointly declare the response capacity and price with the new energy power generation enterprise. Through the MCP clearing method and considering the distribution network grid constraint, the response period, capacity and price of the final transaction are shown in Table 7:

[0371] Table 7 Transaction Situation Table

[0372]

[0373] According to the established electric heating and heat compensation system cluster model, according to the interaction structure with the power grid, establish the objective function with the lowest system operation cost, take each constraint condition as the constraint, and solve the optimal control model of the electric heating and heat compensation system cluster in MATLAB according to the implementation steps of the particle swarm optimization algorithm, adjust the output of the electric heat compensation cluster, and finally the output of the electric heat compensation cluster and the operation results of the heat load provided to the user side are as Figure 8 、 9 and Figure 10 show, the capacity and storage and release power of the virtual and electric heat storage tanks change as Figure 11 、 12As shown in Figures 13, the heat storage quantification indexes and the changes in the supply water temperature of pipelines 1, 2, and 3 are as follows Figure 14 , 15 and 16.

[0374] It can be seen that the heat pump system of heat exchange station 1 operates at the rated power during both the normal and valley periods of the power grid, without adjustable potential; during the peak period of the power grid, the heat pump system operates at a low power, with certain adjustable potential. Therefore, heat exchange station 1 can participate in the auxiliary service peak shaving market during the peak period, and consume the excess new energy by increasing the power consumption of the heat pump system.

[0375] The heat pump output of heat exchange station 2 is the same as that of heat exchange station 1. The electric heat storage tank of heat exchange station 2 makes full use of the peak-valley-flat electricity price of the power grid, stores heat during the valley period to reach its rated capacity. If the heat load demand of users is high, it will be in a state of alternating heat storage and heat release; during the normal and peak periods, the heat storage tank releases heat to the low limit to minimize the energy purchase cost. If it participates in the peak shaving market, the electric heat storage tank will store heat during the peak electricity price period, increasing the power consumption, thereby changing the operating state within a day, ensuring that the initial capacity of the electric heat storage tank is the same, and its own capacity will always be in dynamic change, and it can better play its transferable energy characteristics.

[0376] When not participating in the auxiliary service market, on the one hand, the electric boiler system of heat exchange station 3 only outputs power during the valley period. On the other hand, due to the constraint of the power grid power flow, on the premise of giving priority to operating the heat pump, the output of the electric boiler is not near the rated value to meet the requirements of the power grid voltage. Even when participating in the auxiliary service market, its responsive capacity is also restricted by the distribution network architecture and cannot fully respond.

[0377] Comparing with the traditional municipal heat network heating, the cost values, profits, and the changes in the new energy consumption rate of the electric heat compensation system cluster of heat exchange stations under the three scenarios of only responding to the peak-valley electricity price and participating in the auxiliary service market are shown in Table 8:

[0378] Table 8 Changes in cost, profit, and new energy consumption rate

[0379]

[0380] As can be seen from the above table, after the heat exchange station system cluster participates in the auxiliary service market, although the energy purchase cost is higher than that of only responding to the peak-valley-flat electricity price. The reason is that in order to consume more photovoltaic and wind power, while increasing the heating power, the electric heat compensation equipment needs to bear the additional energy purchase cost during the peak period of the power grid. However, due to obtaining the peak shaving compensation, its peak shaving income can offset part of the energy purchase cost. Under the condition of ensuring that the heating income remains unchanged and the supply water temperature of the heat supply network changes within an acceptable range, the total profit is increased compared with the two scenarios of traditional municipal heat network heating and only responding to the electricity price, and the new energy consumption rate is also increased.

[0381] Example 3:

[0382] Based on the same inventive concept, the present invention application also provides an optimized control system for the interaction between an electric heating system cluster and the power grid. The system structure is as Figure 17 shown, including:

[0383] A data acquisition module, an optimal solution acquisition module, and an optimization control module;

[0384] Obtain the parameters of each device, load data, and electricity and heat purchase prices in the electric heating and heat compensation system cluster;

[0385] Based on the parameters of each device, load data, and relevant parameters of electricity and heat purchase, use the particle swarm optimization algorithm to solve the pre-constructed optimization model of the electric heating and heat compensation system cluster, and obtain the optimal output scheme of each electric heat compensation device in the electric heating and heat compensation system cluster;

[0386] Based on the optimal output scheme of each electric heat compensation device, optimize and control the electric heating and heat compensation system cluster;

[0387] The optimization model of the electric heating and heat compensation system cluster is constructed according to the interaction mode between the electric heating and heat compensation system cluster and the power grid, with the goal of maximizing the overall operating efficiency of the electric heating and heat compensation system cluster model participating in the power grid auxiliary service peaking market. Among them, the electric heating and heat compensation system cluster model is constructed based on multiple cluster models formed by different combinations of electric heat compensation devices, and the collaborative interaction relationship between the heat supply network and the electric heat compensation devices in the cluster.

[0388] Preferably, the construction of the electric heating and heat compensation system cluster model of the optimal solution acquisition module includes:

[0389] Construct a cluster model according to different combinations of electric heat compensation devices and the physical property constraints of each electric heat compensation device;

[0390] Construct a virtual heat storage model of the heat supply network based on the collaborative interaction relationship between the heat supply network and the electric heat compensation devices;

[0391] Based on the cluster model and the virtual heat storage model of the heat supply network, construct an electric heating and heat compensation system cluster model;

[0392] Among them, the cluster model includes one or more of the following: direct heat pump cluster model, direct heat storage cluster model, and heat pump heat storage cluster model; the virtual heat storage model of the heat supply network includes one or more of the following: thermal dynamic model of the heat supply pipeline, charging and discharging power of the virtual heat storage tank, heat storage capacity of the water supply network, and virtual heat storage state index of the heat power network.

[0393] Preferably, the expression of the thermal dynamic model of the heat supply pipeline of the optimal solution acquisition module is as follows:

[0394]

[0395] In the formula: T t is the average water temperature of the heat storage tank in the water supply network at time t, where T t =(T t in +T t out ) / 2, T t in is the inlet water temperature of the heat storage tank in the water supply network at time t, T t out is the outlet water temperature of the heat storage tank in the water supply network at time t; T t-1 is the average water temperature of the heat storage tank in the water supply network at time t-1; c p is the specific heat capacity of water; ρ is the density of water; A is the inner cross-sectional area of the pipe; m is the mass flow rate; R is the total thermal resistance of the pipe; T t a is the ambient temperature; Δt is the time interval, L is the length of the pipe;

[0396] The expression for the heat storage and release power of the virtual heat storage tank is as follows:

[0397]

[0398] In the formula: is the heat storage and release power of the virtual heat storage tank in the heat supply network at time t; is the heat source power at time t; is the heat load of the heat supply system at time t; is the heat loss at time t;

[0399] The expression for the heat storage capacity of the water supply network is as follows:

[0400]

[0401] In the formula: is the heat storage capacity of the water supply network at time t; ρ is the density of water; c p is the specific heat capacity of water; V S is the water capacity of the supply and return water networks; T S,min is the lower limit of the supply water temperature; T t S is the water temperature of the supply and return water networks at time t;

[0402] The expression for the virtual heat storage state index of the heat power network:

[0403]

[0404] In the formula: SOVTC(t) is the virtual heat storage state index of the primary pipe network at time t; T tS is the water temperature of the supply and return water pipe network at time t; T S,max is the upper limit of the supply water temperature of the supply and return water pipe network; T S,min is the lower limit of the supply water temperature of the supply and return water pipe network.

[0405] Preferably, the interaction modes between the electric heating and heat compensation system cluster of the optimal solution acquisition module and the power grid include: the cluster participating in the deep peak shaving interaction mode of the power grid and the new energy consumption interaction mode; the construction of the optimization model of the electric heating and heat compensation system cluster includes:

[0406] According to the cluster participating in the deep peak shaving interaction mode of the power grid and the new energy consumption interaction mode, with the minimum comprehensive benefit of the electric heating and heat compensation system cluster model responding to the peak-valley-flat electricity price of the power grid and participating in the peak shaving market of the power grid's ancillary services as the goal, a comprehensive benefit objective function is constructed;

[0407] According to the power grid operation constraints, heat network operation constraints and the characteristics of each device in the electric heating and heat compensation system cluster after the electric heating and heat compensation system cluster is connected to the power grid, constraint conditions are set for the comprehensive benefit objective function;

[0408] The constraint conditions include one or more of the following: power grid interaction constraints, heat network operation constraints, equipment operation constraints and heat load demand constraints;

[0409] The power grid interaction constraints include one or more of the following: power grid power flow constraints, branch capacity constraints and node voltage constraints; the heat network operation constraints include one or more of the following: upper and lower limits of supply and return water temperature constraints, heat power balance constraints, heat medium flow constraints, relative opening degree constraints of the primary regulating valve, mass flow balance constraints and temperature mixing constraints; the equipment operation constraints include one or more of the following: electric boiler electric power constraints, heat storage device operation constraints, heat pump operation constraints, circulating pump power constraints, heat extraction power constraints of the municipal heat network.

[0410] Preferably, the expression of the comprehensive benefit objective function of the optimal solution acquisition module is as follows:

[0411] min F = C grid + C hpn + C maintain + C car - E peak - E allowance

[0412] In the formula: F is the comprehensive benefit objective function of the heat exchange station's electric heat compensation system cluster; C grid is the electricity purchase cost; C hpn is the heat purchase cost of the municipal heat network; C maintain is the operation and maintenance cost; C car is the carbon emission cost; E peakRevenue obtained from participating in the peak shaving ancillary service market; E allowance Subsidy for clean energy heating;

[0413] Among them, the electricity purchase cost C grid The expression is as follows:

[0414]

[0415] In the formula: C grid Is the electricity purchase cost; Is the peak-valley-flat electricity price within the t period; N is the number of clusters of the electric heating and heat supplement system; J is the number of electric heat supplement devices in a certain cluster; Is the electric power consumed by the jth electric heat supplement device in the ith cluster at the tth moment; Is the power consumption of the circulating pump connected to the primary heat network side of the heat exchange station where the ith electric heat supplement cluster is located; Δt is the unit time period;

[0416] The heat purchase cost C of the municipal heat network hpn The expression is as follows:

[0417]

[0418] In the formula: C hpn Is the heat purchase cost of the municipal heat network; Is the heat price of the municipal heat network within the unit time period t; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at the tth moment, and Δt is the unit time period;

[0419] The operation and maintenance cost C maintain The expression is as follows:

[0420]

[0421] In the formula: k hpn Is the operation cost coefficient of the heat pipeline; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at the tth moment; k eth Is the operation cost coefficient of the electric boiler; Is the electric power of the electric boiler in the ith cluster at the tth moment; k ess Is the operation cost coefficient of the heat storage device; Is the electric power for heat storage of the heat storage device in the ith cluster at the tth moment; Is the heat release power of the heat storage device in the ith cluster at the tth moment; k pump Is the operation cost coefficient of the heat pump; Is the electric power consumed by the heat pump system in the ith cluster at the tth moment; k wp Is the operation cost coefficient of the circulating pump; is the power consumption of the circulation pump connected to the primary heat network side of the heat exchange station where the i-th electric heat supplement cluster is located; Δt is the unit time period; N is the number of electric heating heat supplement system clusters;

[0422] The carbon emission cost C car has the following expression:

[0423]

[0424] In the formula: C car is the carbon emission cost; p car is the unit carbon emission cost; is the electric power consumed by the j-th electric heat supplement device in the i-th cluster at time t; is the power consumption of the circulation pump in the i-th cluster at time t; δ ec is the carbon emission coefficient of electricity purchase; δ hpn is the carbon emission coefficient of municipal heat network heating; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at time t; Δt is the unit time period; N is the number of electric heating heat supplement system clusters; J is the number of electric heat supplement devices in a certain cluster;

[0425] The subsidy income E for participating in the peak shaving auxiliary service market peak has the following expression:

[0426]

[0427] In the formula: E peak is the income obtained from participating in the peak shaving auxiliary service market; is the subsidy electricity price for unit time period peak shaving assistance; is the electric power consumed by the i-th cluster at time t; Δt is the unit time period; N is the number of electric heating heat supplement system clusters; k is the total number of time;

[0428] The clean energy heating subsidy E allowance has the following expression:

[0429]

[0430] In the formula: E allowance is the clean energy heating subsidy; c clean is the subsidy price; is the electric power consumed by the i-th cluster at time t; N is the number of electric heating heat supplement system clusters; Δt is the unit time period.

[0431] Preferably, the expression of the heat load demand constraint of the optimal solution acquisition module is as follows:

[0432]

[0433]

[0434]

[0435] Wherein: is the heating power supplied by the i-th cluster to the user at time t; is the heating power of the i-th cluster at time t; c p is the specific heat capacity of the heat medium; is the mass flow of the heat medium at the inlet of cluster i; is the supply water temperature at the inlet of cluster i at time t; is the return water temperature at the inlet of cluster i at time t; is the supplementary heating power of cluster i at time t; is the heat release power of the electric boiler in the i-th cluster at time t; is the heat release power of the heat storage device in the i-th cluster at time t; are respectively the heat release powers of the heat pumps in the i-th cluster at time t; is the electric power of the electric boiler in the i-th cluster at time t; P ST-out is the heat release power of the heat storage tank; is the electric power consumed by the heat pumps in the i-th cluster at time t; η EB is the electro-thermal conversion efficiency of the electric boiler; η ST-out is the heat release efficiency of the heat storage tank; COP is the performance coefficient of the heat pump.

[0436] Preferably, the load data of the data acquisition module includes one or more of the following: photovoltaic predicted power, wind power predicted power, power purchase power from the superior power grid, load predicted power, and heating load data of each cluster user; the power purchase and heat purchase prices include one or more of the following: system power purchase price, heat purchase price, and heat sale price; the parameters of each device include one or more of the following: parameters of each device in the direct heating heat pump cluster model, parameters of each device in the direct heating heat storage cluster model, parameters of each device in the heat pump heat storage cluster model, load of each node in the distribution network, impedance of each branch, and parameters of the heat network pipeline.

[0437] Embodiment 4:

[0438] Based on the same inventive concept, the present invention application also provides a computer device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of an optimized control method for the interaction between an electric heating system cluster and the power grid in the above embodiments.

[0439] Embodiment 5:

[0440] Based on the same inventive concept, the present invention application also provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The one or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the steps of an optimized control method for the interaction between an electric heating system cluster and the power grid in the above embodiments.

[0441] Those skilled in the art should understand that the embodiments of the present invention application can be provided as a method, a system, or a computer program product. Therefore, the present invention application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0442] The present invention application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0443] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device that implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0444] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0445] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention application and not to limit its protection scope. Although the present invention application has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present invention application, various changes, modifications, or equivalent replacements can still be made to the specific implementation manners of the application, but these changes, modifications, or equivalent replacements are all within the protection scope of the pending claims of the application.

Claims

1. An optimization control method for the interaction between an electric heating system cluster and the power grid, characterized in that, it includes: Obtain the parameters of each device, load data, and power and heat purchase prices in the electric heating and heat compensation system cluster; Based on the parameters of each device, load data, and power and heat purchase-related parameters, use the particle swarm optimization algorithm to solve the pre-constructed optimization model of the electric heating and heat compensation system cluster, and obtain the optimal output plan of each electric heat compensation device in the electric heating and heat compensation system cluster; Based on the optimal output plan of each electric heat compensation device, optimize the control of the electric heating and heat compensation system cluster; The optimization model of the electric heating and heat compensation system cluster is constructed according to the interaction mode between the electric heating and heat compensation system cluster and the power grid, with the goal of maximizing the overall operating efficiency of the electric heating and heat compensation system cluster model participating in the power grid auxiliary service peak shaving market. The electric heating and heat compensation system cluster model is constructed based on multiple cluster models formed by different combinations of electric heat compensation devices and the collaborative interaction relationship between the heat supply network and the electric heat compensation devices in the cluster.

2. The method according to claim 1, characterized in that, The construction of the electric heating and heat compensation system cluster model includes: Construct a cluster model according to different combinations of electric heat compensation devices and the physical property constraints of each electric heat compensation device; Construct a virtual heat storage model of the heat supply network based on the collaborative interaction relationship between the heat supply network and the electric heat compensation devices; Based on the cluster model and the virtual heat storage model of the heat supply network, construct an electric heating and heat compensation system cluster model; Among them, the cluster model includes one or more of the following: direct heat pump cluster model, direct heat storage cluster model, and heat pump heat storage cluster model; the virtual heat storage model of the heat supply network includes one or more of the following: the thermal dynamic model of the heat supply pipeline, the heat release and absorption power of the virtual heat storage tank, the heat storage capacity of the water supply network, and the virtual heat storage state index of the heat power network.

3. The method according to claim 2, characterized in that, The expression of the thermal dynamic model of the heat supply pipeline is as follows: Where: T t is the average water temperature of the heat storage tank in the water supply network at time t, where T t =(T t in +T t out ) / 2, T t in is the inlet water temperature of the heat storage tank in the water supply network at time t, T t out is the outlet water temperature of the heat storage tank in the water supply network at time t; T t-1 is the average water temperature of the heat storage tank in the water supply network at time t-1; c p is the specific heat capacity of water; ρ is the density of water; A is the inner cross-sectional area of the pipe; m is the mass flow rate; R is the total thermal resistance of the pipe; T t a is the ambient temperature; Δt is the time interval, L is the length of the pipe; The expression of the heat release and absorption power of the virtual heat storage tank is as follows: In the formula: is the heat storage and release power of the virtual heat storage tank in the heat supply network at time t; is the heat source power at time t; is the heat load of the heat supply system at time t; is the heat loss at time t; The expression of the heat storage capacity of the water supply network is as follows: Where: is the heat storage of the water supply network at time t; ρ is the density of water; c p is the specific heat capacity of water; V S is the water volume of the supply and return water networks; T S,min is the lower limit of the water supply temperature; T t S is the water temperature of the supply and return water networks at time t; The expression of the virtual heat storage state index of the heat power network: Where: SOVTC(t) is the virtual heat storage state index of the primary pipe network at time t; T t S is the water temperature of the supply and return pipe networks at time t; T S,max is the upper limit of the supply water temperature of the supply and return pipe networks; T S,min is the lower limit of the supply water temperature of the supply and return pipe networks.

4. The method according to claim 2, characterized in that, The interaction mode between the electric heating and heat compensation system cluster and the power grid includes: the cluster participates in the power grid deep peak shaving interaction mode and the new energy consumption interaction mode; the construction of the optimization model of the electric heating and heat compensation system cluster includes: According to the cluster's participation in the power grid deep peak shaving interaction mode and the new energy consumption interaction mode, with the goal of minimizing the overall efficiency of the electric heating and heat compensation system cluster model responding to the peak-valley-flat electricity price of the power grid and participating in the power grid auxiliary service peak shaving market, construct an overall efficiency objective function; According to the power grid operation constraints, heat network operation constraints, and the characteristics of each device in the electric heating and heat compensation system cluster after the electric heating and heat compensation system cluster is connected to the power grid, set constraint conditions for the overall efficiency objective function; The constraint conditions include one or more of the following: power grid interaction constraints, heat network operation constraints, device operation constraints, and heat load demand constraints; The grid interaction constraints include one or more of the following: grid power flow constraints, branch capacity constraints, and node voltage constraints; the heat network operation constraints include one or more of the following: upper and lower limits of supply and return water temperatures, heat power balance constraints, heat medium flow constraints, relative opening degree constraints of primary regulating valves, mass flow balance constraints, and temperature mixing constraints; the equipment operation constraints include one or more of the following: electric boiler electric power constraints, thermal energy storage device operation constraints, heat pump operation constraints, circulating pump power constraints, and heat extraction power constraints of the municipal heat network.

5. The method according to claim 4, wherein, the expression of the comprehensive benefit objective function is as follows: minF = C grid + C hpn + C maintain + C car - E peak - E allowance In the formula: F is the comprehensive benefit objective function of the electric supplementary heating system cluster in the heat exchange station; C grid is the electricity purchase cost; C hpn is the heat purchase cost from the municipal heat network; C maintain is the operation and maintenance cost; C car is the carbon emission cost; E peak is the income obtained from participating in the peak shaving ancillary service market; E allowance is the clean energy heating subsidy; Among them, the electricity purchase cost C grid has the following expression: Where: C grid is the electricity purchase cost; is the peak-valley-flat electricity price during period t; N is the number of electric heating and heat compensation system clusters; J is the number of electric heat compensation devices in a certain cluster; is the electric power consumed by the j-th electric heat compensation device in the i-th cluster at time t; is the power consumption of the circulation pump connected to the primary heat network side of the heat exchange station where the i-th electric heat compensation cluster is located; Δt is the unit time period; The heat purchase cost C of the municipal heat network hpn has the following expression: Where: C hpn is the heat purchase cost of the municipal heat network; is the heat price of the municipal heat network within the unit time period t; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at time t, and Δt is the unit time period; The operation and maintenance cost C maintain has the following expression: where: k hpn is the operating cost coefficient of the heating pipeline; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at time t; k eth is the operating cost coefficient of the electric boiler; is the electric power of the electric boiler in the i-th cluster at time t; k ess is the operating cost coefficient of the heat storage device; is the electric power for heat storage of the heat storage device in the i-th cluster at time t; is the heat release power of the heat storage device in the i-th cluster at time t; k pump is the operating cost coefficient of the heat pump; is the electric power consumed by the heat pump system in the i-th cluster at time t; k wp is the operating cost coefficient of the circulation pump; is the power consumption of the circulation pump connected to the primary heat network side of the heat exchange station where the i-th electric heat supplementary heating cluster is located; Δt is the unit time period; N is the number of electric heating and supplementary heating system clusters; The carbon emission cost C car has the following expression: Where: C car is the carbon emission cost; p car is the unit carbon emission cost; is the electric power consumed by the j-th electric heat compensation device in the i-th cluster at time t; is the power consumption of the circulating pump in the i-th cluster at time t; δ ec is the carbon emission coefficient for purchasing electricity; δ hpn is the carbon emission coefficient for heating by the municipal heat network; Q hpn (t) is the heat extraction power of the heat exchange station from the municipal heat network at time t; Δt is the unit time period; N is the number of clusters of the electric heating heat compensation system; J is the number of electric heat compensation devices in a certain cluster; The subsidy income E from participating in the peaking auxiliary service market peak has the following expression: Where: E peak is the revenue obtained from participating in the peaking auxiliary service market; is the peaking auxiliary subsidy electricity price per unit time period; is the electric power consumed by the i-th cluster at time t; Δt is the unit time period; N is the number of clusters of the electric heating and heat compensation system; k is the total number of time periods; The clean energy heating subsidy E allowance has the following expression: Where: E allowance is the clean energy heating subsidy; c clean is the subsidy price; is the electric power consumed by the i-th cluster at time t; N is the number of clusters of the electric heating and heat compensation system; Δt is the unit time period.

6. The method according to claim 4, wherein, the expression of the heat load demand constraint is as follows: Wherein: is the heating power supplied by the i-th cluster to the user at time t; is the heating power of the i-th cluster at time t; c p is the specific heat capacity of the heat medium; is the mass flow of the heat medium at the inlet of cluster i; is the supply water temperature at the inlet of cluster i at time t; is the return water temperature at the inlet of cluster i at time t; is the supplementary heating power of cluster i at time t; is the heat release power of the electric boiler in the i-th cluster at time t; is the heat release power of the heat storage device in the i-th cluster at time t; are respectively the heat release powers of the heat pumps in the i-th cluster at time t; is the electric power of the electric boiler in the i-th cluster at time t; P ST-out is the heat release power of the heat storage tank; is the electric power consumed by the heat pumps in the i-th cluster at time t; η EB is the electro-thermal conversion efficiency of the electric boiler; η ST-out is the heat release efficiency of the heat storage tank; COP is the performance coefficient of the heat pump.

7. The method according to claim 1, wherein, the load data includes one or more of the following: photovoltaic predicted power, wind power predicted power, power purchase from the superior grid, load predicted power, and heating load data of each cluster user; the power purchase and heat purchase prices include one or more of the following: system power purchase price, heat purchase price, and heat selling price; each equipment parameter includes one or more of the following: parameters of each equipment in the direct heat pump cluster model, parameters of each equipment in the direct heat energy storage cluster model, parameters of each equipment in the heat pump and heat energy storage cluster model, load of each node in the distribution network, impedance of each branch, and heat network pipeline parameters.

8. An optimized control system for the interaction between an electric heating system cluster and the grid, wherein, it includes: a data acquisition module, an optimal solution acquisition module, and an optimized control module; acquire parameters of each equipment, load data, and power purchase and heat purchase prices in the electric heating and heat supplement system cluster; based on the parameters of each equipment, load data, and power purchase and heat purchase related parameters, use the particle swarm algorithm to solve the pre-constructed optimization model of the electric heating and heat supplement system cluster, and obtain the optimal output scheme of each electric heat supplement equipment in the electric heating and heat supplement system cluster; based on the optimal output scheme of each electric heat supplement equipment, perform optimized control on the electric heating and heat supplement system cluster; the optimization model of the electric heating and heat supplement system cluster is constructed with the goal of maximizing the comprehensive operation benefit of the electric heating and heat supplement system cluster model participating in the grid auxiliary service peaking market according to the interaction mode between the electric heating and heat supplement system cluster and the grid, wherein the electric heating and heat supplement system cluster model is constructed based on multiple cluster models formed by different combinations of electric heat supplement equipment and the collaborative interaction relationship between the heat supply network and the electric heat supplement equipment in the cluster.

9. The system according to claim 8, wherein, the construction of the electric heating and heat supplement system cluster model of the optimal solution acquisition module includes: construct a cluster model according to different combinations of electric heat supplement equipment and the physical property constraints of each electric heat supplement equipment; construct a virtual heat storage model of the heat supply network based on the collaborative interaction relationship between the heat supply network and the electric heat supplement equipment; construct an electric heating and heat supplement system cluster model based on the cluster model and the virtual heat storage model of the heat supply network; Among them, the cluster model includes one or more of the following: direct heating heat pump cluster model, direct heating heat storage cluster model, and heat pump heat storage cluster model; the virtual heat storage model of the heating pipe network includes one or more of the following: thermal dynamic model of the heating pipeline, heat storage and release power of the virtual heat storage tank, heat storage capacity of the water supply pipe network, and virtual heat storage state index of the thermal pipeline network.

10. The system according to claim 9, characterized in that the expression of the thermal dynamic model of the heating pipeline of the optimal solution acquisition module is as follows: Where: T t is the average water temperature of the heat storage tank in the water supply network at time t, where T t =(T t in +T t out ) / 2, T t in is the inlet water temperature of the heat storage tank in the water supply network at time t, T t out is the outlet water temperature of the heat storage tank in the water supply network at time t; T t-1 is the average water temperature of the heat storage tank in the water supply network at time t-1; c p is the specific heat capacity of water; ρ is the density of water; A is the inner cross-sectional area of the pipe; m is the mass flow rate; R is the total thermal resistance of the pipe; T t a is the ambient temperature; Δt is the time interval, and L is the length of the pipe; the expression of the heat storage and release power of the virtual heat storage tank is as follows: Where: is the heat storage and release power of the virtual heat storage tank in the heat supply network at time t; is the heat source power at time t; is the heat load of the heat supply system at time t; is the heat loss at time t; the expression of the heat storage capacity of the water supply pipe network is as follows: In the formula: is the heat storage of the water supply network at time t; ρ is the density of water; c p is the specific heat capacity of water; V S is the water volume of the supply and return water networks; T S,min is the lower limit of the water supply temperature; T t S is the water temperature of the supply and return water networks at time t; the expression of the virtual heat storage state index of the thermal pipeline network: Where: SOVTC(t) is the virtual heat storage state index of the primary pipe network at time t; T t S is the water temperature of the supply and return pipe network at time t; T S,max is the upper limit of the supply water temperature of the supply and return pipe network; T S,min is the lower limit of the supply water temperature of the supply and return pipe network.

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