Electric heating cooperative operation optimization method and system responding to electricity price

Abstract

The invention provides an electricity price responding electric heating cooperative operation optimization method and system. The method comprises the steps of obtaining a heat load demand of a user, a thermal parameter of a heat supply network system in an electric heat compensation system and an electric power parameter of an electric power system in the electric heat compensation system; the electric power parameters comprise electricity price; based on the thermal load demand, the thermal parameters and the electric power parameters of the user, solving a pre-constructed electric heat compensation system optimization operation model by using a particle swarm algorithm to obtain an electric heat cooperative operation strategy responding to the electricity price; on the basis of an electricity price responding electric heating cooperative operation strategy, an electric heat supplementing system is used for providing heat energy for a user; wherein the optimized operation model of the electric heat compensation system is constructed by taking the minimum sum of the operation cost of the heat supply network system and the operation cost of the electric power system as an objective function. The operation cost of the electric heat compensation system is minimum, and electric heat connection with high energy utilization rate and economic benefit is fully combined, so that the electric heat cooperative operation strategy responding to the electricity price realizes electric heat coupling with optimized utilization.

Description

Technical Field

[0001] The present invention belongs to the technical field of control of electric heating equipment in heat exchange stations, and particularly relates to a method and system for optimizing the coordinated operation of electric and heat in response to electricity prices. Background Art

[0002] In the field of heating, municipal central heating is the main body, with huge stocks and demands. Compared with the rapid development of urban construction, the construction of municipal heating in the northern region lags behind relatively, and there are generally shortages in heat sources and heat networks. At the same time, due to the limitations of objective conditions such as site and energy supply in the construction of heat sources, and the heat loss of the large-scale primary and secondary heat pipelines is about 10%-20% (even up to 30%), therefore, any heat source supplement with economic and environmental protection value is of great significance for shaping a new heating format. Electric energy has the advantages of safety, convenience, high efficiency, etc. It advocates a new model of clean energy consumption, comprehensively implements the replacement of electric energy, accelerates the construction of "coal-to-electricity", and continuously increases the proportion of electric energy in terminal energy consumption, which plays an important role in achieving energy conservation and emission reduction, alleviating urban haze problems, improving air quality, and promoting the construction of ecological civilization.

[0003] After the large-scale implementation of the "coal-to-electricity" project in the northern region, a considerable number of electric heating devices have been connected to the distribution network during the heating season. Given the characteristics of the large number of types, different control methods, and scattered power consumption times of electric heating devices, their large-scale use has put forward higher requirements for the acceptance capacity, equipment facilities, and construction standards of the original low-voltage distribution network. By realizing the positive interaction between electric heating equipment and the power grid, while meeting the heat load requirements of users, it can fully respond to the regulation of the power grid, achieve the coordination of electricity and heat, reshape the form of centralized heat supply, promote the revolution of energy production and consumption, accelerate the increase of the proportion of clean heating, and build a clean heating system in the northern region that is green, economical, efficient, coordinated, and applicable, so as to contribute to the realization of the dual-carbon goal.

[0004] The existing electric-heat coupling modes and application scenarios are single, lacking a new way of electric-heat connection and mutual assistance with high energy utilization rate and economic benefits, which greatly restricts the flexible scheduling and optimal utilization of multiple energies. Summary of the Invention

[0005] To overcome the deficiencies of the above-mentioned prior art, in a first aspect, the present invention proposes a method for optimizing the coordinated operation of electric and heat in response to electricity prices, including: obtaining the heat load demand of users, the thermal parameters of the heat network system in the electric heat compensation system, and the power parameters of the power system in the electric heat compensation system; the power parameters include: electricity price;

[0006] Based on the heat load demand of the users, the thermal parameters, and the power parameters, using the particle swarm optimization algorithm to solve the pre-constructed optimal operation model of the electric heat compensation system, and obtaining a coordinated operation strategy of electric and heat in response to electricity prices;

[0007] Based on the response electricity price, an electric-heat collaborative operation strategy is adopted, and the electric heat compensation system is used to provide heat energy for the user;

[0008] Among them, the optimal operation model of the electric heat compensation system is constructed with the minimum sum of the operation cost of the heat network system and the operation cost of the power system as the objective function.

[0009] Preferably, the pre-construction process of the optimal operation model of the electric heat compensation system includes:

[0010] Based on the thermal price and heat extraction power of the heat network system in the electric heat compensation system, determine the operation cost function of the heat network system; based on the electricity price and electricity consumption power of the power system in the electric heat compensation system, determine the operation cost function of the power system;

[0011] Based on the operation cost function of the heat network system and the operation cost function of the power system, take the minimum sum of the operation cost of the heat network system and the operation cost of the power system as the objective function;

[0012] Based on the objective function, construct the constraint conditions of the objective function;

[0013] Based on the objective function and the constraint conditions of the objective function, construct the optimal operation model of the electric heat compensation system.

[0014] Preferably, the constraint conditions of the objective function include: the constraints of the power system, the heat extraction power constraint of the heat network system in the heat network system, and the heat load demand constraint of the heat network system;

[0015] The constraints of the power system include: the power grid power flow constraint of the power system, the branch capacity constraint of the power system, the node voltage constraint of the power system, and the equipment operation constraint of the power system;

[0016] The equipment of the power system includes: the electric heat compensation equipment and the circulation pump in the electric heat compensation equipment cluster; the electric heat compensation equipment in the electric heat compensation equipment cluster includes: an electric boiler, a heat storage device, and a heat pump.

[0017] Preferably, based on the heat load demand of the user, the thermal parameters, and the electrical parameters, using the particle swarm optimization algorithm to solve the pre-constructed optimal operation model of the electric heat compensation system, the electric-heat collaborative operation strategy of the response electricity price obtained includes:

[0018] Initialize the parameters in the particle swarm optimization algorithm, and each particle in the parameters represents a solution scheme of the electric-heat collaborative operation strategy of the response electricity price;

[0019] According to the heat load demand of the user, the thermal parameters and the electrical parameters, the fitness value of each particle is calculated using the fitness function, and the position and flight speed of the particle are updated to obtain a new particle; the position and flight speed of the particle are iteratively updated until the preset iteration termination condition is reached, and the particle with the minimum fitness value at this time is used as the electro-thermal coordinated operation strategy for the response electricity price.

[0020] Preferably, the objective function satisfies the following formula:

[0021] minF = C grid + C hpn

[0022] In the above formula, F is the operating cost of the electric heat compensation system, C grid is the operating cost function of the power system, C hpn is the operating cost function of the heat network system, and minF is the minimum value of the operating cost of the electric heat compensation system.

[0023] Preferably, the expression of the operating cost function of the power system is as follows:

[0024]

[0025] In the above formula, C grid is the operating cost function of the power system, is the electricity price at time t, W is the number of electric heat compensation equipment clusters, σ is the σth electric heat compensation equipment cluster, is the electric power consumed by the σth electric heat compensation equipment cluster at time t, is the power consumption of the circulating pump connected to the primary heat network side of the heat exchange station where the σth electric heat compensation equipment cluster is located; Δt is the unit time period.

[0026] Preferably, the expression of the operating cost function of the heat network system is as follows:

[0027]

[0028] In the above formula, C hpn is the operating cost function of the heat network system, is the thermal price of the heat network system at time t; Q hpn (t) is the heat extraction power of the heat network system at time t.

[0029] Preferably, the power grid power flow constraint satisfies the following formula:

[0030]

[0031] In the above formula: i is the node in the power grid, j is a node different from node i, P E,iThe active power injected for node i, N is the set of nodes, U i is the voltage magnitude of node i, U j is the voltage magnitude of node j, G ij is the conductance of the branch between node i and node j in the power grid, θ ij is the phase angle difference of the branch between node i and node j, B ij is the susceptance of the branch between node i and node j, Q E,i is the reactive power injected for node i.

[0032] Preferably, the equipment operation constraints of the heat storage device satisfy the following formula:

[0033]

[0034] In the above formula: is the stored heat of the heat storage device at time t, is the stored heat of the heat storage device at time t - 1; S EB is the heat storage and heat release conversion flag of the heat storage device, S EB = 1 or 0, 1 means heat storage, 0 means heat release; P ST-in is the heat storage power of the heat storage device, 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, P ST-out is the heat release power of the heat storage device, Δt is the unit time period for the operation of the heat storage device, is the maximum stored heat of the heat storage device between the initial time and the end time of the scheduling period, is the maximum heat storage power of the heat storage device; is the maximum heat release power of the heat storage device, is the stored heat of the heat storage device at the end time of the scheduling period, is the stored heat of the heat storage device at the initial time.

[0035] In a second aspect, the present invention application also proposes a heat - electricity collaborative operation optimization system in response to electricity prices, including:

[0036] An acquisition module, configured to acquire the heat load demand of the user, the thermal parameters of the heat network system in the electric - heat complementary system, and the power parameters of the power system in the electric - heat complementary system; the power parameters include: electricity price;

[0037] A solving module, configured to solve the pre - constructed optimized operation model of the electric - heat complementary system by using the particle swarm algorithm based on the heat load demand of the user, the thermal parameters, and the power parameters, so as to obtain a heat - electricity collaborative operation strategy in response to electricity prices; wherein, the optimized operation model of the electric - heat complementary system is constructed with the sum of the operation cost of the heat network system and the operation cost of the power system being the objective function.

[0038] An energy supply module, configured to provide heat energy for the user by using the electric heat compensation system based on the electric-heat collaborative operation strategy of the response electricity price.

[0039] Preferably, the system further includes: an optimized operation model construction module for the electric heat compensation system, configured to:

[0040] Determine the operation cost function of the heat network system based on the thermal price and heat extraction power of the heat network system in the electric heat compensation system; determine the operation cost function of the power system based on the electricity price and power consumption of the power system in the electric heat compensation system;

[0041] Based on the operation cost function of the heat network system and the operation cost function of the power system, with the minimum sum of the operation cost of the heat network system and the operation cost of the power system as the objective function;

[0042] Based on the objective function, construct the constraint conditions of the objective function;

[0043] Based on the objective function and the constraint conditions of the objective function, construct an optimized operation model for the electric heat compensation system.

[0044] Preferably, the constraint conditions of the objective function include: the constraint of the power system, the heat extraction power constraint of the heat network system in the heat network system, and the heat load demand constraint of the heat network system;

[0045] The constraint of the power system includes: the grid power flow constraint of the power system, the branch capacity constraint of the power system, the node voltage constraint of the power system, and the equipment operation constraint of the power system;

[0046] The equipment of the power system includes: the electric heat compensation equipment and the circulation pump in the electric heat compensation equipment cluster; the electric heat compensation equipment in the electric heat compensation equipment cluster includes: electric boilers, heat storage devices, and heat pumps.

[0047] Preferably, the solving module is specifically configured to:

[0048] Initialize the parameters in the particle swarm optimization algorithm, where each particle represents a solution scheme of the electric-heat collaborative operation strategy of the response electricity price;

[0049] According to the heat load demand of the user, the thermal parameters, and the power parameters, calculate the fitness value of each particle by using the fitness function, update the position and flight speed of the particle to obtain a new particle; repeatedly iterate to update the position and flight speed of the particle until the preset iteration termination condition is reached, and take the particle with the minimum fitness value at this time as the electric-heat collaborative operation strategy of the response electricity price.

[0050] Preferably, the objective function satisfies the following formula:

[0051] minF = C grid + C hpn

[0052] In the above formula, F is the operating cost of the electric-assisted heating system, and C grid is the operating cost function of the power system, and C hpn is the operating cost function of the heat network system, and minF is the minimum value of the operating cost of the electric-assisted heating system.

[0053] Preferably, the expression of the operating cost function of the power system is as follows:

[0054]

[0055] In the above formula, C grid is the operating cost function of the power system, is the electricity price at time t, W is the number of electric-assisted heating equipment clusters, σ is the σth electric-assisted heating equipment cluster, is the electric power consumed by the σth electric-assisted heating equipment cluster at time t, is the power consumption of the circulating pump connected to the primary heat network side of the heat exchange station where the σth electric-assisted heating equipment cluster is located; Δt is the unit time period.

[0056] Preferably, the expression of the operating cost function of the heat network system is as follows:

[0057]

[0058] In the above formula, C hpn is the operating cost function of the heat network system, is the thermal price of the heat network system at time t; Q hpn (t) is the heat extraction power of the heat network system at time t.

[0059] Preferably, the power grid power flow constraint satisfies the following formula:

[0060]

[0061] In the above formula: i is the node in the power grid, j is the node different from node i, P E,i is the active power injected by node i, N is the set of nodes, U i is the voltage amplitude of node i, U j is the voltage amplitude of node j, G ij is the conductance of the branch between node i and node j in the power grid, θ ij is the phase angle difference of the branch between node i and node j, B ij is the susceptance of the branch between node i and node j, Q E,iThe reactive power injected into node i.

[0062] Preferably, the equipment operation constraints of the heat storage device satisfy the following formula:

[0063]

[0064] In the above formula: is the stored heat of the heat storage device at time t, is the stored heat of the heat storage device at time t-1; S EB is the heat storage and heat release conversion flag of the heat storage device, S EB = 1 or 0, 1 means heat storage, 0 means heat release; P ST-in is the heat storage power of the heat storage device, 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, P ST-out is the heat release power of the heat storage device, Δt is the unit time period of the operation of the heat storage device, is the maximum stored heat of the heat storage device between the initial time and the end time of the scheduling period, is the maximum heat storage power of the heat storage device; is the maximum heat release power of the heat storage device, is the stored heat of the heat storage device at the end time of the scheduling period, is the stored heat of the heat storage device at the initial time.

[0065] In a third aspect, the present invention application also proposes an electronic device, including: at least one processor and a memory; the memory and the processor are connected by a bus;

[0066] The memory is used to store one or more programs;

[0067] When the one or more programs are executed by the at least one processor, the described method for optimizing the collaborative operation of electric heating in response to electricity prices is implemented.

[0068] In a fourth aspect, the present invention application also proposes a readable storage medium, on which an execution program is stored, and when the execution program is executed, the described method for optimizing the collaborative operation of electric heating in response to electricity prices is implemented.

[0069] Compared with the closest prior art, the beneficial effects of the present invention application are as follows:

[0070] An electro-thermal collaborative operation optimization method and system according to the present invention include: obtaining the heat load demand of a user, the thermodynamic parameters of a heat network system in an electric-to-heat compensation system, and the power parameters of a power system in the electric-to-heat compensation system; the power parameters include: electricity price; based on the heat load demand of the user, the thermodynamic parameters, and the power parameters, using a particle swarm algorithm to solve a pre-constructed optimized operation model of the electric-to-heat compensation system to obtain an electro-thermal collaborative operation strategy in response to the electricity price; based on the electro-thermal collaborative operation strategy in response to the electricity price, using the electric-to-heat compensation system to provide heat energy for the user; wherein, the optimized operation model of the electric-to-heat compensation system is constructed with the minimum sum of the operation cost of the heat network system and the operation cost of the power system as the objective function. With the minimum sum of the operation cost of the heat network system and the operation cost of the power system as the objective function, an optimized operation model of the electric-to-heat compensation system is constructed, and a particle swarm algorithm is introduced for solution to obtain an electro-thermal collaborative operation strategy in response to the electricity price. The minimum operation cost of the electric-to-heat compensation system fully combines the electro-thermal connection of high energy utilization rate and economic benefits, so that the electro-thermal collaborative operation strategy in response to the electricity price realizes the electro-thermal coupling of flexible scheduling and optimized utilization of multiple energies. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a flowchart of an electro-thermal collaborative operation optimization method in response to the electricity price provided by the present invention application;

[0072] Figure 2 It is a basic structure diagram of an electric-to-heat compensation system in an electro-thermal collaborative operation optimization method provided by the present invention application;

[0073] Figure 3 It is a flowchart of a particle swarm algorithm in an electro-thermal collaborative operation optimization method provided by the present invention application;

[0074] Figure 4 It is a framework diagram of a heat pump heat compensation system in an electro-thermal collaborative operation optimization method provided by the present invention application;

[0075] Figure 5 It is an operation scheduling diagram of an electric-to-heat compensation device in an electro-thermal collaborative operation optimization method provided by the present invention application;

[0076] Figure 6 It is an architecture diagram of an electro-thermal collaborative operation optimization system provided by the present invention application;

[0077] Figure 7 It is an operation schematic diagram of an electronic device provided by the present invention application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0078] The following further elaborates on the specific embodiments of the present invention application with reference to the accompanying drawings.

[0079] Example 1:

[0080] As Figure 1 shown, the present invention application proposes an optimized method for coordinated operation of electric heating in response to electricity price, which may include:

[0081] Step 1: Obtain the heat load demand of the user, the thermal parameters of the heat network system in the electric heat compensation system, and the power parameters of the power system in the electric heat compensation system; the power parameters include: electricity price;

[0082] Step 2: Based on the heat load demand of the user, the thermal parameters, and the power parameters, use the particle swarm optimization algorithm to solve the pre-constructed optimized operation model of the electric heat compensation system to obtain a coordinated operation strategy of electric heating in response to electricity price; wherein, the optimized operation model of the electric heat compensation system is constructed with the sum of the operation cost of the heat network system and the operation cost of the power system being the minimum as the objective function;

[0083] Step 3: Based on the coordinated operation strategy of electric heating in response to electricity price, use the electric heat compensation system to provide heat energy for the user.

[0084] In the above, in the above Step 2, the process of pre-constructing the optimized operation model of the electric heat compensation system may include:

[0085] Step 2.1: Based on the thermal price and heat extraction power of the heat network system in the electric heat compensation system, determine the operation cost function of the heat network system; based on the electricity price and power consumption of the power system in the electric heat compensation system, determine the operation cost function of the power system;

[0086] Step 2.2: Based on the operation cost function of the heat network system and the operation cost function of the power system, take the sum of the operation cost of the heat network system and the operation cost of the power system being the minimum as the objective function;

[0087] Step 2.3: Based on the objective function, construct the constraint conditions of the objective function;

[0088] Step 2.4: Based on the objective function and the constraint conditions of the objective function, construct the optimized operation model of the electric heat compensation system.

[0089] In the above, the constraint conditions of the objective function include: constraints of the power system, heat extraction power constraints of the heat network system in the heat network system, and heat load demand constraints of the heat network system in the heat network system;

[0090] The constraints of the power system mainly include: grid power flow constraints of the power system, branch capacity constraints of the power system, node voltage constraints of the power system, and equipment operation constraints of the power system;

[0091] The equipment of the power system mainly includes: the electric heat compensation equipment and the circulation pump in the electric heat compensation equipment cluster; the electric heat compensation equipment in the electric heat compensation equipment cluster mainly includes: the electric boiler, the heat storage device and the heat pump.

[0092] In the above steps 2.1 - 2.4, the operating cost of the heat network system consists of the power system cost and the heat network system cost. Among them, the power system cost includes the electricity consumption cost of the electric heat compensation equipment and the electricity consumption cost of the circulation pump, and the heat network cost refers to the heat extraction cost from the heat network system. Among them, the electric heat compensation system can be the electric heat compensation system cluster of the heat exchange station.

[0093] In the above, the objective function satisfies the following formula:

[0094] minF = C grid + C hpn

[0095] In the above formula, F is the operating cost of the electric heat compensation system, C grid is the operating cost function of the power system, C hpn is the operating cost function of the heat network system, and minF is the minimum value of the operating cost of the electric heat compensation system.

[0096] In the above, the expression of the operating cost function of the power system is as follows:

[0097]

[0098] In the above formula, C grid is the operating cost function of the power system, is the electricity price at time t, W is the number of electric heat compensation equipment clusters, σ is the σth electric heat compensation equipment cluster, is the electric power consumed by the σth electric heat compensation equipment 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 σth electric heat compensation equipment cluster is located; Δt is the unit time period.

[0099] In the above, the expression of the operating cost function of the heat network system is as follows:

[0100]

[0101] In the above formula, C hpn is the operating cost function of the heat network system, is the thermal price of the heat network system at time t; Q hpn (t) is the heat extraction power of the heat network system at time t.

[0102] In the above, the power grid power flow constraint satisfies the following formula:

[0103]

[0104] In the above formula: i is a node in the power grid, j is a node different from node i, and P E,i is the active power injected at node i, N is the set of nodes, and U i is the voltage amplitude of node i, and U j is the voltage amplitude of node j, and G ij is the conductance of the branch between node i and node j in the power grid, and θ ij is the phase angle difference of the branch between node i and node j, and B ij is the susceptance of the branch between node i and node j, and Q E,i is the reactive power injected at node i.

[0105] As described above, the generation of power grid power flow is mainly due to the fact that after the electric heating compensation equipment is connected to the distribution network, the original distribution network architecture remains basically unchanged, and what changes is the load node power where the electric heating compensation equipment is located. With the access of the electric heating compensation cluster, the load power of this node increases significantly, resulting in the change of the power flow of the entire distribution network, and then reaching a new power flow balance. The present invention uses the AC power flow as the analysis method of the power system.

[0106] As described above, to ensure the stability of the power system, the apparent capacity of each branch of the power grid at any moment cannot exceed its maximum value; the branch capacity constraint satisfies the following formula:

[0107]

[0108] S E,ij (t) ≤ S E,ij.max

[0109] In the above formula: t is the moment, and P E,ij (t) is the active power of the branch between node i and node j at moment t; U i (t) is the voltage of node i at moment t, and U j (t) is the voltage of node j at moment t, and G ij is the conductance of the branch between node i and node j in the power grid, and B ij is the susceptance of the branch between node i and node j, and θ ij (t) is the phase angle difference of the branch between node i and node j at moment t, and Q E,ij (t) is the reactive power of the branch between node i and node j at moment t; S E,ij (t) is the apparent power of the branch between node i and node j at moment t, and S E,ij.max is the maximum value of the apparent power of the branch between node i and node j.

[0110] After connecting the electric supplementary heating device, it is necessary to impose upper and lower limits on the voltage of the load nodes of the power system to ensure the safety and stability of the power system. The node voltage constraint satisfies the following formula:

[0111] U i.min ≤U i (t)≤U i.max

[0112] In the above formula: U i (t) is the voltage of node i at time t, U i.min is the lower limit of the voltage allowed for node i; U i.max is the upper limit of the voltage allowed for node i.

[0113] The above-mentioned electric boiler electric power constraint satisfies the following formula:

[0114]

[0115] In the formula, P EB (t) is the electric power consumed by the electric boiler at time t, is the maximum electric power allowed to be consumed by the electric boiler at time t.

[0116] The above-mentioned equipment operation constraint of the heat storage device satisfies the following formula:

[0117]

[0118] In the above formula: is the heat storage of the heat storage device at time t, is the heat storage of the heat storage device at time t-1; S EB is the heat storage and heat release conversion flag of the heat storage device, S EB =1 or 0, 1 means heat storage, 0 means heat release; P ST-in is the heat storage power of the heat storage device, 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, P ST-out is the heat release power of the heat storage device, Δt is the unit time period of the heat storage device operation, is the maximum heat storage of the heat storage device between the initial time and the end time of the scheduling period, is the maximum heat storage power of the heat storage device; is the maximum heat release power of the heat storage device, is the heat storage of the heat storage device at the end time of the scheduling period, is the heat storage of the heat storage device at the initial time.

[0119] As described above, when the heat pump unit is in operation, its performance is closely related to the power of the compressor and water pump of the attached circulation pump. 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 machinery and drive motor of the compressor will be damaged; when the operating power is too low, the service life of the compressor will be reduced and energy will be wasted. Therefore, in order to achieve better operating results, it is necessary to restrict the performance of the heat pump unit. The actual operating power of each heat pump cannot be less than 0.25 times the rated power, which can be expressed by the following formula:

[0120]

[0121] In the above formula: is the rated power of the heat pump operation, and P pump (t) is the power of the heat pump operation at time t.

[0122] As described above, the circulation pump is an important device in the central heating pipe network. By consuming a part of the electric energy and adjusting its own speed, it generates a pressure difference in the supply and return water pipe networks respectively, and promotes the heat medium to carry out heat circulation in the heating system. The operation constraint of the circulation pump satisfies the following formula:

[0123]

[0124] In the above formula: P wp (t) is the power of the circulation pump at time t, g is the gravitational acceleration constant, and H wp is the head of the circulation pump, and γ wp is the efficiency of the circulation pump, V(t) is the velocity of the fluid in the liquid where the circulation pump is located at time t, and ρ is the liquid density.

[0125] As described above, in order to prevent the problem of over-flow of the circulation pump, the circulation pump should meet the maximum output limit during operation, and the expression is:

[0126] 0 ≤ P wp (t) ≤ P wp.max

[0127] In the above formula, P wp.max is the maximum power consumption of the circulation pump.

[0128] As described above, the heat exchange station is connected to the heat network through a thermal pipeline. Due to pipeline constraints, there are upper and lower limits for the heat power transmitted from the heat network to the heat exchange station. The heat extraction power constraint of the heat network can be expressed as:

[0129] Q hpn.min ≤ Q hpn (t) ≤ Q hpn.max

[0130] In the above formula, Q hpn (t) is the heat purchase power at time t, and Qhpn.min is the lower limit of the heat purchase thermal power of the heat online shopping, Q hpn.max is the upper limit of the heat purchase thermal power of the heat online shopping.

[0131] As described above, such as Figure 2 shown, the electric supplementary heat system supplies heat to users to meet the heat load demand of users. The heat power balance expression for supplying heat to users is:

[0132]

[0133] In the formula, is the heat supply power from the δ-th electric supplementary heat system to the user at time t; is the heat supply power of the primary heat network heat exchange in the δ-th electric supplementary heat system at time t; is the heat supply power of the electric supplementary heat cluster in the δ-th electric supplementary heat system at time t.

[0134] Among them, the heat supply power of the electric supplementary heat system can be expressed as:

[0135]

[0136] In the formula, is the heat supply power of the electric supplementary heat system at time t, is the mass flow rate of the heat medium at the inlet of the -th heat exchange station; is the supply water temperature at the inlet of the -th heat exchange station at time t; is the return water temperature at the inlet of the -th heat exchange station at time t, c p is the specific heat capacity of water.

[0137] As described above, the heat supply power expression of the electric supplementary heat cluster is:

[0138]

[0139] In the above formula, is the heat supply power of the δ-th electric supplementary heat cluster, is the heat release power of the electric boiler in the δ-th electric supplementary heat cluster at time t, is the heat release power of the heat storage device in the δ-th electric supplementary heat cluster at time t, is the heat release power of the heat pump in the δ-th electric supplementary heat cluster at time t; is the electric power consumed by the electric boiler at time t, η EB is the electro-thermal conversion efficiency of the electric boiler; η ST-out is the electro-thermal conversion efficiency of the heat storage device, P ST-out is the heat release efficiency of the heat storage device, COP is the performance coefficient of the heat pump, P pump (t) is the rated power of the heat pump operation.

[0140] As described above, the heat storage device, as a flexible electricity - transferable load, stores heat using low - cost electricity at night and provides heating during the day when the electricity price is high by dissipating the stored heat. During operation, the stored heat quantity, heat storage power, and heat release power of the heat storage device cannot exceed their limits. And to enable the heat storage device to participate in scheduling normally in the next scheduling period, it is assumed that at the end of the scheduling period, the stored heat quantity of the heat storage device is equal to its initial stored heat quantity.

[0141] As described above, based on the user's heat load demand, the thermal parameters, and the electrical parameters, the particle swarm algorithm is used to solve the pre - constructed optimal operation model of the electricity - supplemented heat system, and an electric - heat collaborative operation strategy in response to the electricity price is obtained, including:

[0142] Initialize the parameters in the particle swarm algorithm, where each particle represents a solution scheme of the electric - heat collaborative operation strategy in response to the electricity price;

[0143] According to the user's heat load demand, the thermal parameters, and the electrical parameters, use the fitness function to calculate the fitness value of each particle, update the position and flying speed of the particle, and obtain new particles; repeatedly iterate to update the position and flying speed of the particle until the preset iteration termination condition is reached, and then use the particle with the minimum fitness value at this time as the electric - heat collaborative operation strategy in response to the electricity price.

[0144] As described above, the optimal operation model of the electricity - supplemented heat system is a non - linear optimization problem, and 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 the non - linear constraint conditions as penalty terms for the optimization objectives. While retaining the non - linear characteristics of the model, the optimal solution is achieved. Although the computational efficiency is lower than that of traditional mathematical optimization algorithms, it has stronger adaptability and the particle swarm algorithm is used for solving.

[0145] As Figure 3 shown, the process of the particle swarm algorithm (Particle Swarm Optimization, PSO) can be specifically as follows:

[0146] (1) Randomly initialize the positions and velocities of the particle swarm: Each particle represents a solution scheme of the electric - heat collaborative operation strategy in response to the electricity price. The dimension of the particle corresponds to the decision variables, including the power of the electricity - supplemented heat equipment (such as the power of the electric boiler, heat pump, and heat storage device). The velocity of the particle represents the change range of each decision variable;

[0147] (2) Calculate the fitness value of each particle: The fitness value of each particle is calculated through the fitness function, that is, the total operating cost of the electricity - supplemented heat system under specific regulation, which consists of the power system cost and the heat network system cost;

[0148] (3) For each particle, compare its fitness value with the individual extreme value. If it is better, update the current individual extreme value. That is, if the current electro-thermal collaborative operation strategy in response to the electricity price enables the lowest operating cost of the electricity-supplemented heat system, update the position of the particle to this optimal solution;

[0149] (4) For each particle, compare its fitness value with the global extreme value. If it is better, update the current global extreme value. This step indicates that the overall regulation strategy of the electricity-supplemented heat system has reached the optimal solution, and the regulation scheme of the entire system has been updated to the global best.

[0150] (5) Update the position and flight speed of each particle.

[0151] (6) If the preset iteration termination condition (usually set as the maximum number of iterations) is not reached, return to step (2). If it is reached, stop the calculation. Finally, the particle swarm algorithm outputs an optimal regulation strategy, that is, the electro-thermal collaborative operation strategy in response to the electricity price, determines the optimal power of the electricity-supplemented heat equipment in each period, minimizes the operating cost of the system to the greatest extent, and fully responds to the fluctuations of the electricity price to ensure the positive interaction between the power grid system and the electricity-supplemented heat system.

[0152] The present invention makes full use of the characteristics of the electricity-supplemented heat system. While meeting the heat load demand of users, through its good response characteristics and regulation ability and by utilizing the flat peak-valley electricity price of the power grid. On the one hand, it can stimulate the enthusiasm of the electricity-supplemented heat system to participate in the power market, promote the wide application of electric energy substitution, and improve the energy utilization rate and economic benefits of the electricity-supplemented heat system; on the other hand, the flexible role of the electricity-supplemented heat system can flexibly respond to the dispatching of the power grid, achieving the functions of peak shaving and valley filling and promoting the consumption of new energy, and improving the stability of the power system through electro-thermal coupling and coordination.

[0153] The present invention proposes an electricity price incentive regulation for the electricity-supplemented heat system facing the power grid and an electro-thermal collaborative operation optimization method with economy as the goal. An optimized operation model of the electricity-supplemented heat system corresponding to the electricity price is established, and electro-thermal collaborative optimization is carried out on the system from the system operation regulation level to obtain the optimal system operation strategy.

[0154] The present invention provides an electricity price incentive regulation for the electricity-supplemented heat system cluster facing the power grid and an electro-thermal collaborative operation optimization method with economy as the goal. Taking the lowest system operation cost as the objective function, with heat load balance, the power of the electricity-supplemented heat equipment, and also with heat network heating and heat network pipeline temperature as constraints, the particle swarm algorithm is used to solve the optimized operation model of the electricity-supplemented heat system. Finally, the effectiveness of the electro-thermal collaborative operation strategy in response to the electricity price proposed by the present invention can be verified through examples, thereby forming a regulation mechanism for the electricity-supplemented heat system to respond to the power grid as a controllable load.

[0155] By understanding the current relevant policies such as heat price and electricity price, combining relevant historical data, and based on the particle swarm algorithm and the constraint conditions of the objective function, this invention studies the electricity price incentive regulation for the power grid and the cluster operation optimization strategy of the electric heat compensation system in urban areas with economy as the goal. It not only further improves the economy of heating but also flexibly responds to the electricity price to participate in the dispatching of the power grid, which helps to promote the electric heat compensation technology.

[0156] The electric heat compensation technology for the coupling of power grid and heat network is a new model and direction for heating construction. This invention helps to increase the electricity sales volume of power grid enterprises, improve the operation efficiency of the power grid, realize "increasing sales and income" of power grid enterprises, and improve the economy of electric heating by responding to the electricity price to participate in the power grid dispatching. At the same time, it alleviates the pressure of the heating company to ensure people's livelihood during the heating period, provides a reference and basis for the government, power grid enterprises, and heating enterprises to jointly improve the clean heating level in the region in winter and achieve the carbon reduction goal, and realizes a win-win situation for power grid enterprises, heating enterprises, and users.

[0157] Based on the method of this invention, a specific use case is provided: as Figure 4 shown, an electric heat compensation system composed of a plate heat exchanger + a solid heat storage tank (heat - heat) + an air source heat pump + a heating pump + a circulation pump and installing a constant temperature mixing valve at the heat pipeline can be used to supply heat to users. In the optimization calculation process, the power costs and heat costs of each device are selected according to Table 1. The heat cost refers to the heat purchase price of the Beijing heating station from the municipal heat network, and the time - of - use electricity price refers to the price level provided by the Beijing Development and Reform Commission. Among them, the division of peak - valley periods of electricity price is as follows: peak period (10:00 - 15:00; 18:00 - 21:00), flat period (7:00 - 10:00; 15:00 - 18:00; 21:00 - 23:00), valley period (23:00 - 7:00). For the convenience of calculation, the prices of various devices are respectively converted into unit power or unit capacity prices.

[0158] Table 1 Parameter values for the optimized operation model calculation of the electric heat compensation system

[0159]

[0160] The verification of the optimization results is as follows:

[0161] For the heating system, in addition to meeting the basic requirement of the user's heat load demand, making a timely adjustment to the change of the heat load to avoid too high a delay in heating to demand is also an important indicator reflecting the operation effect of the heating system. For the heat pump heat compensation system, when the heat load demand changes, it is necessary to timely adjust the heat supply quantity and load distribution ratio of the plate heat exchanger, heat pump unit, and heat storage device. Due to the influence of the time - of - use electricity price, the heat storage scheme of the water pump and heat storage device also needs to be adjusted accordingly. The optimized operation results of the electric heat compensation equipment are shown in Figure 5 .

[0162] It can be seen from Figure 5 that the heat load demand increases significantly at night, which corresponds to the time when the electricity price is relatively low. Therefore, the start priority of the air source heat pump unit is higher than that of the plate heat exchanger and the heat storage device, and the heat storage device releases heat prior to the plate heat exchanger. Therefore, from early morning to 9 am, it is always in the mode of "air source heat pump unit running at full load + heat storage device releasing heat" or "air source heat pump unit running at full load + plate heat exchanger". The heat storage device performs rapid heat storage at the next moment after the heat release is completed, and the plate heat exchanger has not reached the heat exchange capacity limit, and releases it at the next moment. During the day, the heat load demand is small, and this usually corresponds to the time when the electricity price is high. Therefore, the start priority of the plate heat exchanger in the system equipment is higher than that of the heat pump unit. Therefore, heat exchange through the plate heat exchanger can meet the requirements. From the results of system scheduling, the air source heat pump heat supplementary system enables the heat pump and the heat storage device to participate in system scheduling more, can accurately cover the heat load, and minimize the waste caused by the redundant supply of heat energy.

[0163] Embodiment 2:

[0164] As Figure 6 shown, the present invention also provides an optimized electro-thermal collaborative operation system in response to electricity prices, including:

[0165] An acquisition module, configured to acquire the heat load demand of the user, the thermal parameters of the heat network system in the electric heat supplementary system, and the power parameters of the power system in the electric heat supplementary system; the power parameters include: electricity price;

[0166] A solution module, configured to solve the pre-constructed optimized operation model of the electric heat supplementary system by using the particle swarm algorithm based on the heat load demand of the user, the thermal parameters, and the power parameters, to obtain an electro-thermal collaborative operation strategy in response to electricity prices; wherein, the optimized operation model of the electric heat supplementary system is constructed with the sum of the operation costs of the heat network system and the power system being minimized as the objective function.

[0167] An energy supply module, configured to supply heat energy to the user by using the electric heat supplementary system based on the electro-thermal collaborative operation strategy in response to electricity prices.

[0168] Further, the system further includes: an optimized operation model construction module of the electric heat supplementary system, configured to:

[0169] Determine the operation cost function of the heat network system based on the thermal price and heat extraction power of the heat network system in the electric heat supplementary system; determine the operation cost function of the power system based on the electricity price and power consumption of the power system in the electric heat supplementary system;

[0170] Based on the operating cost function of the heat network system and the operating cost function of the power system, with the sum of the operating cost of the heat network system and the operating cost of the power system being minimized as the objective function;

[0171] Based on the objective function, construct the constraint conditions of the objective function;

[0172] Based on the objective function and the constraint conditions of the objective function, construct an optimized operation model for the electric heat compensation system.

[0173] Furthermore, the constraint conditions of the objective function include: the constraints of the power system, the heat extraction power constraint in the heat network system, and the heat load demand constraint in the heat network system;

[0174] The constraints of the power system include: the power grid power flow constraint of the power system, the branch capacity constraint of the power system, the node voltage constraint of the power system, and the equipment operation constraint of the power system;

[0175] The equipment of the power system includes: the electric heat compensation equipment and the circulating pump in the electric heat compensation equipment cluster; the electric heat compensation equipment in the electric heat compensation equipment cluster includes: electric boilers, heat storage devices, and heat pumps.

[0176] Furthermore, the solving module is specifically used for:

[0177] Initialize the parameters in the particle swarm optimization algorithm, where each particle represents a co - operation strategy plan for electric and heat in response to electricity prices;

[0178] According to the heat load demand of the user, the thermal parameters, and the power parameters, use the fitness function to calculate the fitness value of each particle, update the position and flight speed of the particle to obtain new particles; repeatedly iterate to update the position and flight speed of the particle until the preset iteration termination condition is reached, and take the particle with the minimum fitness value at this time as the co - operation strategy for electric and heat in response to electricity prices.

[0179] Furthermore, the objective function satisfies the following formula:

[0180] minF = C grid +C hpn

[0181] In the above formula, F is the operating cost of the electric heat compensation system, C grid is the operating cost function of the power system, C hpn is the operating cost function of the heat network system, and minF is the minimum value of the operating cost of the electric heat compensation system.

[0182] Furthermore, the expression of the operating cost function of the power system is as follows:

[0183]

[0184] In the above formula, C grid is the operating cost function of the power system, is the electricity price at time t, W is the number of electric heat-supplemented equipment clusters, and σ is the σth electric heat-supplemented equipment cluster, is the electric power consumed by the σth electric heat-supplemented equipment cluster at time t, is the power consumption of the circulating pump connected to the primary heat network side of the heat exchange station where the σth electric heat-supplemented equipment cluster is located; Δt is the unit time period.

[0185] Furthermore, the operating cost function of the heat network system is expressed as follows:

[0186]

[0187] In the above formula, C hpn is the operating cost function of the heat network system, is the thermal price of the heat network system at time t; Q hpn (t) is the heat extraction power of the heat network system at time t.

[0188] Furthermore, the power grid power flow constraint satisfies the following formula:

[0189]

[0190] In the above formula: i is a node in the power grid, j is a node different from node i, P E,i is the active power injected at node i, N is the set of nodes, U i is the voltage amplitude of node i, U j is the voltage amplitude of node j, G ij is the conductance of the branch between node i and node j in the power grid, θ ij is the phase angle difference of the branch between node i and node j, B ij is the susceptance of the branch between node i and node j, Q E,i is the reactive power injected at node i.

[0191] Furthermore, the equipment operation constraint of the heat storage device satisfies the following formula:

[0192]

[0193] In the above formula: is the stored heat of the heat storage device at time t, is the stored heat of the heat storage device at time t - 1; S EB is the heat storage and heat release conversion flag of the heat storage device, S EB = 1 or 0, 1 for heat storage, 0 for heat release; PST-in is the heat storage power of the heat storage device, 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, P ST-out is the heat release power of the heat storage device, and Δt is the unit time period during which the heat storage device operates. is the maximum heat storage of the heat storage device between the initial moment and the end moment of the scheduling period. is the maximum heat storage power of the heat storage device; is the maximum heat release power of the heat storage device. is the heat storage of the heat storage device at the end moment of the scheduling period. is the heat storage of the heat storage device at the initial moment.

[0194] The system of the present invention has the following advantages:

[0195] 1. By comprehensively considering the flexible load characteristics of the electric heat compensation equipment and combining the current heating and power supply policies, the present invention proposes an operation optimization method for the electric heat compensation system based on electricity price response, realizes the environmental protection and economic advantages of the electric heat compensation project, and provides a strong reference for the demonstration application and actual operation of the technology.

[0196] 2. An operation optimization objective for the electric heat compensation system with economy as the goal is constructed. Combining the constraints of interaction with the power grid, the constraints of the equipment itself, the operation constraints of the heat network, and the heat load demand constraints, an optimization operation model of the electric heat compensation system as a controllable load is established. The particle swarm algorithm is used to solve the optimization operation model of the electric heat compensation system, and finally, the effectiveness of the scheduling strategy proposed by the present invention is verified through an example.

[0197] Example 3:

[0198] As Figure 7 shown, the present invention also provides an electronic device, which may be a computer device, a single-chip microcomputer device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected by a bus; the memory can be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, and this data can be called and / or modified when the instructions are executed.

[0199] The processor may be a Central Processing Unit (CPU), or it 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 storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a method for optimizing the coordinated operation of electric heating in response to electricity prices in the above embodiments.

[0200] Embodiment 4:

[0201] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device-readable storage medium (Memory). The electronic device-readable storage medium is a memory device in the electronic device, used to store programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and, of course, the extended storage medium supported by the electronic device. The storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, in this storage space, there are also stored one or more instructions suitable for being loaded and executed by the processor. These instructions can be one or more executable programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. By the processor loading and executing one or more instructions stored in the storage medium, the steps of a method for optimizing the coordinated operation of electric heating in response to electricity prices in the above embodiments can be implemented.

[0202] 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 memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0203] This application for invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application for invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and 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 means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0204] 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 article including instruction means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0205] 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, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application for invention and not to limit its protection scope. Although this application for invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading this application for invention, various changes, modifications, or equivalent replacements can still be made to the specific implementation manners of the application. However, these changes, modifications, or equivalent replacements are all within the protection scope of the pending claims of the application.

Claims

1. A method for optimizing the coordinated operation of electricity and heat in response to electricity prices, characterized in that: include: Obtain the user's heat load demand, the thermal parameters of the heat network system in the electric heating system, and the power parameters of the power system in the electric heating system; The electricity parameters include: electricity price; Based on the heat load demand of the user, the thermal parameters and the power parameters, a pre-built electric heating system optimization operation model is solved by using a particle swarm algorithm to obtain an electric-heat collaborative operation strategy that responds to the electricity price; Based on the electricity-heat coordinated operation strategy in response to the electricity price, the electric heating system is used to provide heat energy to the user; The optimization operation model of the electric heating system is constructed with the minimum sum of the operation cost of the heating network system and the operation cost of the power system as the objective function.

2. The method according to claim 1, characterized in that The pre-construction process of the electric heating system optimization operation model includes: Based on the heat price and heat extraction power of the heat network system in the electric heating system, the operation cost function of the heat network system is determined; based on the electricity price and electricity consumption of the power system in the electric heating system, the operation cost function of the power system is determined; Based on the operation cost function of the heating network system and the operation cost function of the power system, the objective function is to minimize the sum of the operation cost of the heating network system and the operation cost of the power system; Based on the objective function, construct constraint conditions of the objective function; Based on the objective function and the constraints of the objective function, an optimization operation model of the electric heating system is constructed.

3. The method according to claim 2, characterized in that The constraint conditions of the objective function include: the constraint of the power system, the constraint of the heat network heat extraction power in the heat network system, and the constraint of the heat load demand in the heat network system; The constraints of the power system include: power grid flow constraints of the power system, branch capacity constraints of the power system, node voltage constraints of the power system, and equipment operation constraints of the power system; The equipment of the electric power system includes: the electric heating equipment and the circulation pump in the electric heating equipment cluster; the electric heating equipment in the electric heating equipment cluster includes: an electric boiler, a heat storage device and a heat pump.

4. The method according to claim 1, characterized in that: The method uses a particle swarm algorithm to solve the pre-built electric heating system optimization operation model based on the user's heat load demand, the thermal parameters and the power parameters, and obtains an electric-heat collaborative operation strategy that responds to the electricity price, including: Initializing parameters in a particle swarm algorithm, wherein each particle in the parameters represents an electric-heat coordinated operation strategy scheme that responds to an electricity price; According to the user's heat load demand, the thermal parameters and the power parameters, the fitness value of each particle is calculated using a fitness function, the position and flight speed of the particle are updated, and a new particle is obtained; the position and flight speed of the particle are updated repeatedly until a preset iteration termination condition is reached, and the particle with the smallest fitness value at this time is used as the electric-heat collaborative operation strategy that responds to the electricity price.

5. The method according to claim 2, characterized in that: The objective function satisfies the following formula: minF=C grid +C hpn In the above formula, F is the operating cost of the electric heating system, C grid is the operating cost function of the power system, C hpn is the operating cost function of the heating network system, and minF is the minimum operating cost of the electric heating system.

6. The method according to claim 2, characterized in that The operating cost function of the power system is expressed as follows: In the above formula, C grid is the operating cost function of the power system, is the electricity price at time t, W is the number of electric heating equipment clusters, σ is the σth electric heating equipment cluster, is the electric power consumed by the σth electric heating equipment cluster at time t, is the power consumption of the circulating pump connected to the primary heating network side of the heat exchange station where the σth electric heating equipment cluster is located; Δt is the unit time period.

7. The method according to claim 2, characterized in that: The operating cost function of the heating network system is expressed as follows: In the above formula, C hpn is the operating cost function of the heating network system, is the heat price of the heating network system at time t; Q hpn (t) is the heating power of the heating network system at time t, and Δt is the unit time period.

8. The method according to claim 3, characterized in that The power grid flow constraint satisfies the following formula: In the above formula: i is a node in the power grid, j is a node different from node i, P E,i is the active power injected by node i, N is the set of nodes, U i is the voltage amplitude at node i, U j is the voltage amplitude at node j, G ij is the conductance of the branch between node i and node j in the power grid, θ ij is the phase angle difference between the branch from node i to node j, B ij is the susceptance of the branch between node i and node j, Q E,i The reactive power injected into node i.

9. The method according to claim 3, characterized in that: The equipment operation constraints of the thermal storage device satisfy the following formula: In the above formula: is the heat storage capacity of the heat storage device at time t, S is the heat storage of the heat storage device at time t-1; EB It is the heat storage and heat release conversion mark of the heat storage device. EB =1 or 0, 1 for heat storage, 0 for heat release; P ST-in is the heat storage power of the heat storage device, 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, P ST-out is the heat release power of the heat storage device, Δt is the unit time period of the heat storage device operation, is the maximum heat storage capacity of the heat storage device between the initial time and the end of the scheduling cycle, is the maximum heat storage power of the heat storage device; is the maximum heat release power of the heat storage device, is the heat storage capacity of the heat storage device at the end of the scheduling cycle, is the heat storage capacity of the heat storage device at the initial moment.

10. An electricity-heat coordinated operation optimization system responsive to electricity prices, characterized in that: include: An acquisition module is used to obtain the user's heat load demand, the thermal parameters of the heat network system in the electric heating system, and the power parameters of the power system in the electric heating system; The electricity parameters include: electricity price; A solution module is used to solve the pre-built electric heating system optimization operation model based on the user's heat load demand, the thermal parameters and the power parameters by using a particle swarm algorithm to obtain an electric-heat collaborative operation strategy that responds to the electricity price; wherein the electric heating system optimization operation model is constructed with the minimum sum of the operation cost of the heat network system and the operation cost of the power system as the objective function; An energy supply module is used to provide thermal energy to the user by using the electric heating system based on the electric-heat cooperative operation strategy that responds to the electricity price.

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