Heat supply optimization method and device of electric heating comprehensive energy system, terminal equipment and storage medium

Abstract

The invention discloses a heat supply optimization method and device for an electric heating integrated energy system, terminal equipment and a storage medium, and the method comprises the steps: obtaining day-ahead scheduling energy data and intra-day scheduling energy data, and constructing a day-ahead scheduling optimization model and a corresponding first constraint condition; then under each first constraint condition, solving the day-ahead scheduling optimization model to obtain the output of each piece of optimized equipment, and constructing to obtain a heat supply output reference trajectory; then obtaining an intra-day scheduling optimization model and a corresponding second constraint condition which are constructed by taking the minimum difference between the heat supply output prediction trajectory and the heat supply output reference trajectory as a target; and finally, under each second constraint condition, solving the intra-day scheduling optimization model to obtain the optimized total heat supply amount, and then obtaining the optimized heat supply amount of each device. By implementing the method and the device, the cost of the energy system can be reduced while the heat supply amount of each piece of equipment in the next time period is optimized.

Description

Technical Field

[0001] The present invention relates to the field of energy system control, and in particular to a heating optimization method, device, terminal equipment and storage medium for an electric-thermal integrated energy system. Background Art

[0002] Electricity heat integrated energy system (HEIES) is an advanced energy system that integrates the production, conversion, storage, distribution and consumption of electricity and thermal energy. It achieves highly coordinated and optimized operation of multiple energy systems through advanced physical information technology and innovative management models.

[0003] In the prior art, when determining the optimal heating capacity of each device in an electric-thermal integrated energy system, the heating capacity of each device in the next period is usually optimized only by the current output of each device, without considering the cost of the entire electric-thermal integrated energy system. Summary of the invention

[0004] The present invention provides a method, device, terminal equipment and storage medium for optimizing the heat supply of an electric and thermal integrated energy system, which can optimize the heat supply of each device in the next period of time while reducing the cost of the entire electric and thermal integrated energy system as much as possible.

[0005] An embodiment of the present invention provides a method for optimizing heat supply of an electric-thermal integrated energy system, comprising:

[0006] Obtain the day-ahead dispatch energy data and the intraday dispatch energy data of the electric-heat integrated energy system in each time period within the current day-ahead dispatch time domain; wherein the day-ahead dispatch energy data includes: equipment input power and equipment heating power limit;

[0007] According to the above day-ahead dispatch energy data, with the goal of minimizing the sum of electricity purchase cost, natural gas purchase cost and operation cost, a day-ahead dispatch optimization model and a first constraint condition corresponding to the day-ahead dispatch optimization model are constructed; wherein the first constraint condition includes: a balance constraint and a first coupling device constraint;

[0008] Under the first constraint conditions, the day-ahead dispatch optimization model is solved to obtain the output of each device after optimization, and a reference trajectory of heat output is constructed based on the output of each device after optimization.

[0009] The actual output of each device at the beginning of the current intraday scheduling time is obtained, and based on the above intraday scheduling energy data, actual output, device input power, and device heating power limit, the intraday scheduling optimization model and the second constraint condition corresponding to the above intraday scheduling optimization model are constructed with the goal of minimizing the difference between the predicted trajectory of heat output and the above reference trajectory of heat output; wherein the above second constraint condition includes: heat storage constraint of the heat network, network constraint, user-side pressure and temperature constraint, and second coupling device constraint;

[0010] Under each second constraint condition, the above-mentioned intraday scheduling optimization model is solved to obtain the optimized total heating supply, and then the optimized heating supply of each device is calculated based on the above-mentioned total heating supply.

[0011] Furthermore, the above day-ahead scheduling optimization model is:

[0012]

[0013] minF=F e +F g +F p

[0014] In the formula, F e represents the electricity purchase cost, t represents the dispatching time domain M 1 In the tth period, M 1 represents the scheduling time domain, represents the electricity price in period t, represents the electricity purchase price during period t, Indicates the power purchased during period t, F g represents the cost of purchasing natural gas, represents the amount of natural gas purchased during period t, represents the unit price of natural gas in period t, F p represents the operating cost, d represents the dth device, D represents the total number of devices, C d represents the unit maintenance cost of equipment d, It represents the output of equipment d during period t, and F represents the sum of electricity purchase cost, natural gas purchase cost and operating cost.

[0015] Furthermore, the above balance constraint is:

[0016]

[0017] In the formula, represents the amount of electricity generated by the combined heat and power generation during the period t, represents the electric power obtained from the grid during period t, represents the amount of electricity generated by the photovoltaic module during the period t, represents the total power of the demand side load during period t, represents the electrical power consumed by the device during period t, It represents the heat generated by the gas boiler during the period t. represents the heat generated by the waste heat boiler in the cogeneration during the period t, It represents the total heat load on the demand side during period t;

[0018] The first coupling device constraint is:

[0019]

[0020] In the formula, represents the input power of CHP in period t, η CHP represents the power generation efficiency of cogeneration, η loss represents the heat loss coefficient of cogeneration, Indicates the lower limit of the combined heat and power generation power, Indicates the upper limit of the combined heat and power generation power. Indicates the lower limit of the combined heat and power heating power. Indicates the upper limit of the combined heat and power heating power. Indicates the input power of the gas boiler, η GB Indicates the efficiency of gas boiler, Indicates the lower limit of the heating power of the gas boiler. Indicates the upper limit of the heating power of the gas boiler. Indicates the lower power limit for interaction with the grid, Indicates the upper limit of power for interaction with the grid.

[0021] Furthermore, the above-mentioned construction of a reference trajectory of heat output according to the output of each device after the above-mentioned optimization includes:

[0022] According to the output of each device after the above optimization, the optimized output ratio of each device is calculated;

[0023] The above-mentioned optimized output ratio is subjected to Taylor series expansion to obtain the above-mentioned heat supply output reference trajectory.

[0024] Furthermore, the above intraday scheduling optimization model is:

[0025]

[0026] r(t)=[r GB (t),r CHP (t),r TN (t)] T

[0027]

[0028] In the formula, represents the predicted trajectory of heat output under the predicted time, τ represents the interval length, y(t) represents the predicted trajectory of heat output at the scheduling time t, H(t) represents the total heat output at the scheduling time t, It represents the actual output ratio of all equipment at the dispatching time t, It represents the actual output ratio of the gas boiler at the dispatching time t, It represents the actual output ratio of the waste heat boiler of the combined heat and power at the scheduling time t, represents the actual output ratio of the heating network at the dispatching time t, represents the reference trajectory of heat output under the forecast time, r(t) represents the optimized output ratio of all equipment at the scheduling time t, r GB (t) represents the optimized output ratio of the gas boiler at the dispatching time t, r CHP (t) represents the optimized output ratio of the waste heat boiler in the cogeneration at the dispatching time t, r TN (t) represents the optimized output ratio of the heating network at the dispatching time t, It represents the difference between the predicted trajectory of heat output and the reference trajectory of heat output at the predicted time, T p represents the minimum error moment, J 1 Indicates that the scheduling time t tends to T p The error, Indicates the order.

[0029] Furthermore, the above heat network heat storage constraint is:

[0030]

[0031] In the formula, represents the heat storage capacity of the heat network at the predicted time, H(t+τ) represents the total heat supply at the predicted time, Indicates the actual output ratio of the heating network at the prediction time, Q i,c represents the maximum amount of heat that the pipe can store, Δτ delay Indicates the pipeline delay response time from the heat source point to each user;

[0032] The above network constraints are:

[0033]

[0034] The above user-side pressure and temperature constraints are:

[0035] P min ≤P k ≤P max

[0036] T min ≤Tk ≤T max

[0037] k∈N user

[0038] Where P min Indicates the minimum value of the node pressure at the user, P k represents the node pressure at user k, P max Indicates the maximum value of the node pressure at the user, T min Indicates the minimum value of the node temperature at the user, T k represents the node temperature at user k, T max Indicates the maximum value of the node temperature at the user, N user represents the number of users, k represents the kth user;

[0039] The above second coupling device constraint is:

[0040]

[0041] In the formula, Q CHP (t) represents the time function of the heating power of the waste heat boiler in the cogeneration, Q GB (t) represents the time function of the heating power of the gas boiler.

[0042] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0043] The present invention provides a heating optimization device for an electric-thermal integrated energy system, comprising:

[0044] Energy data acquisition module, day-ahead dispatch optimization model construction module, day-ahead dispatch optimization model solution module, intraday dispatch optimization model construction module, and intraday dispatch optimization model solution module;

[0045] The energy data acquisition module is used to obtain the day-ahead dispatch energy data and the intraday dispatch energy data of the electric-thermal integrated energy system in each time period within the current day-ahead dispatch time domain; wherein the day-ahead dispatch energy data includes: equipment input power and equipment heating power limit;

[0046] The day-ahead dispatch optimization model construction module is used to construct a day-ahead dispatch optimization model and a first constraint condition corresponding to the day-ahead dispatch optimization model based on the day-ahead dispatch energy data and with the goal of minimizing the sum of the electricity purchase cost, the natural gas purchase cost and the operating cost; wherein the first constraint condition includes: a balance constraint and a first coupling device constraint;

[0047] The day-ahead scheduling optimization model solving module is used to solve the day-ahead scheduling optimization model under the first constraint conditions to obtain the output of each device after optimization, and to construct a reference trajectory of heat output according to the output of each device after optimization;

[0048] The above-mentioned intraday scheduling optimization model construction module is used to obtain the actual output of each device at the beginning of the current intraday scheduling time, and according to the above-mentioned intraday scheduling energy data, actual output, device input power, and equipment heating power limit, with the goal of minimizing the difference between the predicted trajectory of heat output and the above-mentioned reference trajectory of heat output, to construct the intraday scheduling optimization model and the second constraint condition corresponding to the above-mentioned intraday scheduling optimization model; wherein the above-mentioned second constraint condition includes: heat storage constraint of the heat network, network constraint, user-side pressure and temperature constraint, and second coupling device constraint;

[0049] The above-mentioned intraday scheduling optimization model solving module is used to solve the above-mentioned intraday scheduling optimization model under each second constraint condition to obtain the optimized total heating supply, and then calculate the optimized heating supply of each equipment based on the above-mentioned total heating supply.

[0050] Furthermore, the above-mentioned day-ahead scheduling optimization model solving module includes:

[0051] Equipment output ratio calculation unit and heat output reference trajectory calculation unit;

[0052] The equipment output ratio calculation unit is used to calculate the optimized output ratio of each device according to the output of each device after the optimization;

[0053] The above-mentioned heating output reference trajectory calculation unit is used to perform Taylor series expansion on the above-mentioned optimized output ratio to obtain the above-mentioned heating output reference trajectory.

[0054] Based on the above method embodiment, the present invention provides a terminal device embodiment;

[0055] The present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the heating optimization method of an electric-thermal integrated energy system described in any embodiment of the present invention.

[0056] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0057] The present invention provides a storage medium, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a heating optimization method for an electric-thermal integrated energy system according to any embodiment of the present invention.

[0058] The embodiments of the present invention have the following beneficial effects:

[0059] The present invention provides a method, device, terminal device and storage medium for optimizing the heating of an electric-thermal integrated energy system. The above method includes: obtaining the day-ahead scheduling energy data and the intraday scheduling energy data of the electric-thermal integrated energy system in each time period within the current day-ahead scheduling time domain; wherein the day-ahead scheduling energy data includes: equipment input power and equipment heating power limit; then, based on the day-ahead scheduling energy data, with the goal of minimizing the sum of electricity purchase cost, natural gas purchase cost and operating cost, construct a day-ahead scheduling optimization model, and the first constraint condition corresponding to the day-ahead scheduling optimization model; wherein the first constraint condition includes: balance constraint and first coupling device constraint; then, under each first constraint condition, solve the day-ahead scheduling optimization model to obtain the output of each device after optimization, and according to the output of each device after optimization, Construct a reference trajectory of heat output; then obtain the actual output of each device at the beginning of the current intraday scheduling time, and according to the above intraday scheduling energy data, actual output, device input power, and device heating power limit, with the heat output prediction trajectory, the difference between the heat output reference trajectory and the heat output is minimized as the goal, construct an intraday scheduling optimization model and the second constraint corresponding to the above intraday scheduling optimization model; wherein the above second constraint includes: heat storage constraint of the heat network, network constraint, user side pressure and temperature constraint, and second coupling device constraint; finally, under each second constraint, solve the above intraday scheduling optimization model to obtain the optimized total heat supply, and then calculate the heat supply of each device after optimization according to the above total heat supply. Therefore, the present invention is based on the optimal output of each device obtained by the day-ahead scheduling, obtains the heat output reference trajectory, and then solves the heat supply of each device under the minimum difference between the heat output prediction trajectory and the heat output reference trajectory, that is, the heat output prediction trajectory is closer to the heat output reference trajectory obtained under the optimization of the minimum cost of the day-ahead scheduling model, so that the solution result also has the advantage of minimum cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of a method for optimizing heat supply in an electric-thermal integrated energy system provided by one embodiment of the present invention.

[0061] Figure 2It is a structural schematic diagram of a heating optimization device for an electric-thermal integrated energy system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0062] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing heat supply of an electric-thermal integrated energy system, comprising:

[0064] Step S101: obtaining the day-ahead dispatch energy data and the intraday dispatch energy data of the electric-heat integrated energy system in each time period within the current day-ahead dispatch time domain; wherein the day-ahead dispatch energy data includes: equipment input power and equipment heating power limit;

[0065] Specifically, the above-mentioned day-ahead energy dispatched data also includes: electricity sales price, electricity purchase price, electricity purchase power, natural gas purchase volume, natural gas unit price, unit maintenance cost of equipment, total number of equipment, electricity generated by cogeneration, electricity obtained from the power grid, electricity generated by photovoltaic panels, total demand side load power, equipment power consumption, heat generated by gas boilers, heat generated by waste heat boilers in cogeneration, total demand side heat load, cogeneration power generation efficiency, cogeneration heat loss coefficient, cogeneration power generation lower limit, cogeneration power generation upper limit, power interaction with the power grid and power interaction with the power grid upper limit.

[0066] Specifically, the above-mentioned intraday energy scheduling data include: the actual output ratio of the heating network, the maximum heat that can be stored in the pipeline, the pipeline delay response time from the heat source point to each user, the minimum node pressure at the user, the node pressure at the user, the maximum node pressure at the user, the minimum node temperature at the user, the node temperature at the user, and the maximum node temperature at the user.

[0067] Step S102: Based on the day-ahead dispatch energy data, with the goal of minimizing the sum of the electricity purchase cost, the natural gas purchase cost and the operating cost, a day-ahead dispatch optimization model and a first constraint condition corresponding to the day-ahead dispatch optimization model are constructed; wherein the first constraint condition includes: a balance constraint and a first coupling device constraint;

[0068] In a preferred embodiment, the above day-ahead scheduling optimization model is:

[0069]

[0070]

[0071] minF=F e +F g +F p

[0072] In the formula, F e represents the electricity purchase cost, t represents the dispatching time domain M 1 In the tth period, M 1 represents the scheduling time domain, It represents the electricity price in period t, in units of $ / kWh. It represents the electricity price in period t, in units of $ / kWh. Indicates the power purchased in period t, in kW, F g represents the cost of purchasing natural gas, represents the amount of natural gas purchased during period t, in cubic meters. Indicates the natural gas unit price in period t, in $ / m 3 , F p represents the operating cost, d represents the dth device, D represents the total number of devices, C d represents the unit maintenance cost of equipment d, in $ / kW, It represents the output of equipment d during period t, in kW, and F represents the sum of electricity purchase cost, natural gas purchase cost and operating cost.

[0073] Specifically, the above-mentioned equipment d is specifically cogeneration, gas boiler and heating network.

[0074] Specifically, the day-ahead scheduling stage is a slow time scale scheduling with a sampling time of 1 hour, and the entire time domain length is 24 hours. The main goal of day-ahead scheduling is to ensure that the units of the entire system can meet the load demand at the lowest cost, and to determine the approximate operating status of the operating units from a long-term perspective. At this control level, the slow and fast dynamic characteristics in the system can be ignored. At this stage, the model can be expressed as a large-scale linear programming problem. At this time, the day-ahead scheduling optimization model takes the minimum operating cost as the objective function to ensure the energy balance of the system.

[0075] In this preferred embodiment, a day-ahead scheduling optimization model is constructed by scheduling energy data on the day-ahead.

[0076] In another preferred embodiment, the above balance constraint is:

[0077]

[0078] In the formula, It represents the amount of electricity generated by the combined heat and power generation during the period t, in kW. It represents the electric power obtained from the power grid during the period t, in kW. It represents the amount of electricity generated by the photovoltaic module during the period t, in kW. It represents the total power of the demand side load in the period t, in kW. It represents the electric power consumed by the equipment in the period t, in kW. It represents the heat generated by the gas boiler during the period t, in kW. It represents the heat generated by the waste heat boiler in the cogeneration during the period t, in kW. It represents the total heat load on the demand side during period t, in kW;

[0079] The first coupling device constraint is:

[0080]

[0081] In the formula, represents the input power of CHP in period t, η CHP represents the power generation efficiency of cogeneration, η loss represents the heat loss coefficient of cogeneration, Indicates the lower limit of the combined heat and power generation power, Indicates the upper limit of the combined heat and power generation power. Indicates the lower limit of the combined heat and power heating power. Indicates the upper limit of the combined heat and power heating power. Indicates the input power of the gas boiler, η GB Indicates the efficiency of gas boiler, Indicates the lower limit of the heating power of the gas boiler. Indicates the upper limit of the heating power of the gas boiler. Indicates the lower power limit for interacting with the grid, in kW. Indicates the upper limit of power for interacting with the grid, in kW.

[0082] Preferably, the above balance constraint ensures the safe and stable operation of HEIES by ensuring the balance between supply and demand of electrical energy and thermal energy in the HEIES system.

[0083] In this preferred embodiment, the constraint conditions of the day-ahead dispatch optimization model are constructed based on the day-ahead dispatch energy data.

[0084] Step S103: Under the first constraint conditions, the day-ahead scheduling optimization model is solved to obtain the output of each device after optimization, and a reference trajectory of heat output is constructed according to the output of each device after optimization;

[0085] In this preferred embodiment, the heat output reference trajectory is constructed based on the output of each device after the above optimization, including:

[0086] According to the output of each device after the above optimization, the optimized output ratio of each device is calculated;

[0087] The above-mentioned optimized output ratio is subjected to Taylor series expansion to obtain the above-mentioned heat supply output reference trajectory.

[0088] Specifically, the reference trajectory of heat supply output is obtained based on the day-ahead dispatch optimization model.

[0089] In this preferred embodiment, the reference trajectory of the heat output is obtained by calculating the Taylor series expansion of the output ratio of each device after optimization.

[0090] Step S104: obtaining the actual output of each device at the beginning of the current intraday scheduling time, and constructing an intraday scheduling optimization model and a second constraint condition corresponding to the intraday scheduling optimization model based on the intraday scheduling energy data, actual output, device input power, and device heating power limit, with the goal of minimizing the difference between the predicted trajectory of heat output and the reference trajectory of heat output; wherein the second constraint condition includes: heat storage constraint of the heat network, network constraint, user-side pressure and temperature constraint, and second coupling device constraint;

[0091] Specifically, the priority of the operation of each device in the thermal system during the intraday scheduling stage is: combined heat and power (CHP)> gas boiler (GB)> heating network (TN). Considering the dynamic delay characteristics and heat storage properties of the thermal network in the thermal system, the output of the thermal system is corrected on the basis of the day-ahead scheduling, that is, the above-mentioned second coupling device constraint is constructed. At this stage, the main goal is to track the reference trajectory of the heat supply output and ensure that the thermal output parameters are within the acceptable range for users. At the same time, the heat storage characteristics of the thermal network are used to achieve the effect of load peak shifting and valley filling, thereby improving energy efficiency.

[0092] In a preferred embodiment, the above intraday scheduling optimization model is:

[0093]

[0094] r(t)=[r GB (t),r CHP (t),r TN (t)] T

[0095]

[0096] In the formula, represents the predicted trajectory of heat output under the predicted time, τ represents the interval length, y(t) represents the predicted trajectory of heat output at the scheduling time t, H(t) represents the total heat output at the scheduling time t, It represents the actual output ratio of all equipment at the dispatching time t, It represents the actual output ratio of the gas boiler at the dispatching time t, It represents the actual output ratio of the waste heat boiler of the combined heat and power at the scheduling time t, represents the actual output ratio of the heating network at the dispatching time t, represents the reference trajectory of heat output under the forecast time, r(t) represents the optimized output ratio of all equipment at the scheduling time t, r GB (t) represents the optimized output ratio of the gas boiler at the dispatching time t, r CHP (t) represents the optimized output ratio of the waste heat boiler in the cogeneration at the dispatching time t, r TN (t) represents the optimized output ratio of the heating network at the dispatching time t, It represents the difference between the predicted trajectory of heat output and the reference trajectory of heat output at the predicted time, T p represents the error moment, J 1 Indicates that the scheduling time t tends to T p The error, Indicates the order.

[0097] Specifically, the Taylor series expansion expression of the heat output prediction trajectory is:

[0098]

[0099] In the formula, represents high-order truncation, so when high-order truncation is ignored, the above expression becomes:

[0100]

[0101] Specifically, the formula are functions that vary with time.

[0102] Specifically, similar to the above-mentioned heat supply output prediction trajectory, the above-mentioned heat supply output reference trajectory is:

[0103]

[0104] r(t)=[r GB (t),r CHP (t),r TN (t)] T

[0105] Specifically, define Λ(τ), Y(t), and R(t) as the vectors of time, output, and reference rules, respectively:

[0106]

[0107] Specifically, the expression for the difference between the predicted trajectory of the heat supply output under the above-mentioned prediction time and the reference trajectory of the heat supply output is specifically:

[0108]

[0109] E(t)=Y(t)-R(t)

[0110] Specifically, substitute the above difference expression into get:

[0111]

[0112] In this preferred embodiment, an intraday scheduling optimization model is constructed.

[0113] In another preferred embodiment, the above heat network heat storage constraint is:

[0114]

[0115] In the formula, represents the heat storage capacity of the heat network at the predicted time, H(t+τ) represents the total heat supply at the predicted time, Indicates the actual output ratio of the heating network at the prediction time, Q i,c represents the maximum amount of heat that the pipe can store, Δτ delay Indicates the pipeline delay response time from the heat source point to each user;

[0116] Specifically, when the pipeline delay response time is less than the interval time, it indicates that the fluid in the pipeline network is in a transient flow process during this process. At this time, there is a heat storage or heat release process in the pipeline network; when the pipeline delay response time is greater than the interval time, it indicates that the fluid in the pipeline network is in a steady-state flow process, and the heat storage or heat release process is stable, and the heat storage or heat release change is 0.

[0117] The above network constraints are:

[0118]

[0119] The above user-side pressure and temperature constraints are:

[0120] P min ≤P k ≤P max

[0121] T min ≤Tk ≤T max

[0122] k∈N user

[0123] Where P min Indicates the minimum value of the node pressure at the user, P k represents the node pressure at user k, P max Indicates the maximum value of the node pressure at the user, T min Indicates the minimum value of the node temperature at the user, T k represents the node temperature at user k, T max Indicates the maximum value of the node temperature at the user, N user represents the number of users, k represents the kth user;

[0124] The above second coupling device constraint is:

[0125]

[0126] In the formula, Q CHP (t) represents the time function of the heating power of the waste heat boiler in the cogeneration, Q GB (t) represents the time function of the heating power of the gas boiler.

[0127] In this preferred embodiment, the constraints corresponding to the intraday scheduling optimization model are constructed.

[0128] Step S105: Under each second constraint condition, the intraday scheduling optimization model is solved to obtain the optimized total heating amount, and then the heating amount of each device after optimization is calculated based on the total heating amount.

[0129] Specifically, after obtaining the optimized total heat supply, the product of the heat supply ratio of each device and the total heat supply is calculated according to the heat supply ratio of each device to obtain the optimized heat supply of each device. The heat supply ratio of each device can be set according to actual conditions.

[0130] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0131] like Figure 2 As shown, an embodiment of the present invention provides a heating optimization device for an electric-thermal integrated energy system, comprising:

[0132] Energy data acquisition module, day-ahead dispatch optimization model construction module, day-ahead dispatch optimization model solution module, intraday dispatch optimization model construction module, and intraday dispatch optimization model solution module;

[0133] The energy data acquisition module is used to obtain the day-ahead dispatch energy data and the intraday dispatch energy data of the electric-thermal integrated energy system in each time period within the current day-ahead dispatch time domain; wherein the day-ahead dispatch energy data includes: equipment input power and equipment heating power limit;

[0134] The day-ahead dispatch optimization model construction module is used to construct a day-ahead dispatch optimization model and a first constraint condition corresponding to the day-ahead dispatch optimization model based on the day-ahead dispatch energy data and with the goal of minimizing the sum of the electricity purchase cost, the natural gas purchase cost and the operating cost; wherein the first constraint condition includes: a balance constraint and a first coupling device constraint;

[0135] The day-ahead scheduling optimization model solving module is used to solve the day-ahead scheduling optimization model under the first constraint conditions to obtain the output of each device after optimization, and to construct a reference trajectory of heat output according to the output of each device after optimization;

[0136] The above-mentioned intraday scheduling optimization model construction module is used to obtain the actual output of each device at the beginning of the current intraday scheduling time, and according to the above-mentioned intraday scheduling energy data, actual output, device input power, and equipment heating power limit, with the goal of minimizing the difference between the predicted trajectory of heat output and the above-mentioned reference trajectory of heat output, to construct the intraday scheduling optimization model and the second constraint condition corresponding to the above-mentioned intraday scheduling optimization model; wherein the above-mentioned second constraint condition includes: heat storage constraint of the heat network, network constraint, user-side pressure and temperature constraint, and second coupling device constraint;

[0137] The above-mentioned intraday scheduling optimization model solving module is used to solve the above-mentioned intraday scheduling optimization model under each second constraint condition to obtain the optimized total heating supply, and then calculate the optimized heating supply of each equipment based on the above-mentioned total heating supply.

[0138] In a preferred embodiment, the above-mentioned day-ahead scheduling optimization model solving module includes:

[0139] Equipment output ratio calculation unit and heat output reference trajectory calculation unit;

[0140] The equipment output ratio calculation unit is used to calculate the optimized output ratio of each device according to the output of each device after the optimization;

[0141] The above-mentioned heating output reference trajectory calculation unit is used to perform Taylor series expansion on the above-mentioned optimized output ratio to obtain the above-mentioned heating output reference trajectory.

[0142] It should be noted that the device embodiments described above are merely schematic, wherein the modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without creative work. The above schematic diagram is only an example of a heat supply optimization device for an electric and thermal integrated energy system, and does not constitute a limitation on a heat supply optimization device for an electric and thermal integrated energy system, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

[0143] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0144] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a heating optimization method for an electric-thermal integrated energy system described in any embodiment of the present invention.

[0145] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the device;

[0146] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The device may include, but is not limited to, a processor, a memory;

[0147] The processor referred to herein may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned device, and uses various interfaces and lines to connect various parts of the entire device;

[0148] The above-mentioned memory can be used to store the above-mentioned computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned device by running or executing the computer programs and / or modules stored in the above-mentioned memory, and calling the data stored in the memory. The above-mentioned memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; in addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0149] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0150] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a heating optimization method for an electric-thermal integrated energy system described in any embodiment of the present invention.

[0151] In this embodiment, the storage medium is a computer-readable storage medium, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0152] Compared with the prior art, by implementing the above-mentioned embodiments of the present invention, it is possible to optimize the heating amount of each device in the next period while reducing the cost of the entire electric and thermal integrated energy system as much as possible.

[0153] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A heating optimization method for an electric-thermal integrated energy system, characterized in that: include: Obtain the day-ahead dispatch energy data and the intraday dispatch energy data of the electric-heat integrated energy system in each time period within the current day-ahead dispatch time domain; wherein the day-ahead dispatch energy data includes: equipment input power and equipment heating power limit; According to the day-ahead dispatch energy data, with the goal of minimizing the sum of the electricity purchase cost, the natural gas purchase cost and the operating cost, a day-ahead dispatch optimization model and a first constraint condition corresponding to the day-ahead dispatch optimization model are constructed; wherein the first constraint condition includes: a balance constraint and a first coupling device constraint; Under each first constraint condition, the day-ahead scheduling optimization model is solved to obtain the output of each device after optimization, and a reference trajectory of heat output is constructed according to the output of each device after optimization; The actual output of each device at the beginning of the current intraday scheduling time is obtained, and according to the intraday scheduling energy data, actual output, device input power, and device heating power limit, the intraday scheduling optimization model and the second constraint condition corresponding to the intraday scheduling optimization model are constructed with the goal of minimizing the difference between the predicted trajectory of heat supply output and the reference trajectory of heat supply output; wherein the second constraint condition includes: heat storage constraint of the heat network, network constraint, user-side pressure and temperature constraint, and second coupling device constraint; Under each second constraint condition, the intraday scheduling optimization model is solved to obtain the optimized total heating amount, and then the optimized heating amount of each device is calculated based on the total heating amount.

2. The method for optimizing heating supply of an electric-thermal integrated energy system according to claim 1, characterized in that: The day-ahead scheduling optimization model is: minF=F e +F g +F p In the formula, F e represents the electricity purchase cost, t represents the tth period in the dispatching time domain M1, M1 represents the dispatching time domain, represents the electricity price in period t, represents the electricity purchase price during period t, Indicates the power purchased during period t, F g represents the cost of purchasing natural gas, represents the amount of natural gas purchased during period t, represents the unit price of natural gas in period t, F p represents the operating cost, d represents the dth device, D represents the total number of devices, C d represents the unit maintenance cost of equipment d, It represents the output of equipment d during period t, and F represents the sum of electricity purchase cost, natural gas purchase cost and operating cost.

3. The method for optimizing heating supply of an electric-thermal integrated energy system according to claim 2, characterized in that: The balance constraint is: In the formula, represents the amount of electricity generated by the combined heat and power generation during the period t, represents the electric power obtained from the grid during period t, It represents the amount of electricity generated by the photovoltaic module during the period t. represents the total power of the demand side load during period t, represents the electrical power consumed by the device during period t, It represents the heat generated by the gas boiler during the period t. represents the heat generated by the waste heat boiler in the cogeneration during the period t, It represents the total heat load on the demand side during period t; The first coupling device constraint is: In the formula, represents the input power of CHP in period t, η CHP represents the power generation efficiency of cogeneration, η loss represents the heat loss coefficient of cogeneration, Indicates the lower limit of the combined heat and power generation power, Indicates the upper limit of the combined heat and power generation power. Indicates the lower limit of the combined heat and power heating power. Indicates the upper limit of the combined heat and power heating power. Indicates the input power of the gas boiler, η GB Indicates the efficiency of gas boiler, Indicates the lower limit of the heating power of the gas boiler. Indicates the upper limit of the heating power of the gas boiler. Indicates the lower power limit for interaction with the grid, Indicates the upper limit of power for interaction with the grid.

4. The method for optimizing heating supply of an electric-thermal integrated energy system according to claim 3, characterized in that: The step of constructing a heat output reference trajectory according to the output of each device after optimization includes: According to the output of each device after optimization, the optimized output ratio of each device is calculated; The optimized output ratio is subjected to Taylor series expansion to obtain the heat supply output reference trajectory.

5. The method for optimizing heat supply of an electric-thermal integrated energy system according to claim 4, characterized in that: The intraday scheduling optimization model is: r(t)=[r GB (t),r CHP (t),r TN (t)] T In the formula, represents the predicted trajectory of heat output under the predicted time, τ represents the interval length, y(t) represents the predicted trajectory of heat output at the scheduling time t, H(t) represents the total heat output at the scheduling time t, It represents the actual output ratio of all equipment at the dispatching time t, It represents the actual output ratio of the gas boiler at the dispatching time t, It represents the actual output ratio of the waste heat boiler of the combined heat and power at the scheduling time t, represents the actual output ratio of the heating network at the dispatching time t, represents the reference trajectory of heat output under the forecast time, r(t) represents the optimized output ratio of all equipment at the scheduling time t, r GB (t) represents the optimized output ratio of the gas boiler at the dispatching time t, r CHP (t) represents the optimized output ratio of the waste heat boiler in the cogeneration at the dispatching time t, r TN (t) represents the optimized output ratio of the heating network at the dispatching time t, It represents the difference between the predicted trajectory of heat output and the reference trajectory of heat output at the predicted time, T p represents the minimum error time, J1 represents the scheduling time t tends to T p The error, Indicates the order.

6. The method for optimizing heat supply of an electric-thermal integrated energy system according to claim 5, characterized in that: The heat storage constraint of the heat network is: In the formula, represents the heat storage capacity of the heat network at the predicted time, H(t+τ) represents the total heat supply at the predicted time, Indicates the actual output ratio of the heating network at the prediction time, Q i,c represents the maximum amount of heat that the pipe can store, Δτ delay Indicates the pipeline delay response time from the heat source point to each user; The network constraints are: The user-side pressure and temperature constraints are: P min ≤P k ≤P max T min ≤T k ≤T max k∈N user Where P min Indicates the minimum value of the node pressure at the user, P k represents the node pressure at user k, P max Indicates the maximum value of the node pressure at the user, T min Indicates the minimum value of the node temperature at the user, T k represents the node temperature at user k, T max Indicates the maximum value of the node temperature at the user, N user represents the number of users, k represents the kth user; The second coupling device constraint is: In the formula, Q CHP (t) represents the time function of the heating power of the waste heat boiler in the cogeneration, Q GB (t) represents the time function of the heating power of the gas boiler.

7. A heating optimization device for an electric and thermal integrated energy system, characterized in that: include: Energy data acquisition module, day-ahead dispatch optimization model construction module, day-ahead dispatch optimization model solution module, intraday dispatch optimization model construction module, and intraday dispatch optimization model solution module; The energy data acquisition module is used to obtain the day-ahead dispatch energy data and the intraday dispatch energy data of the electric-thermal integrated energy system in each time period within the current day-ahead dispatch time domain; wherein the day-ahead dispatch energy data includes: equipment input power and equipment heating power limit; The day-ahead dispatch optimization model building module is used to build a day-ahead dispatch optimization model and a first constraint condition corresponding to the day-ahead dispatch optimization model according to the day-ahead dispatch energy data with the goal of minimizing the sum of the electricity purchase cost, the natural gas purchase cost and the operating cost; wherein the first constraint condition includes: a balance constraint and a first coupling device constraint; The day-ahead scheduling optimization model solving module is used to solve the day-ahead scheduling optimization model under each first constraint condition to obtain the output of each device after optimization, and to construct a heat output reference trajectory according to the output of each device after optimization; The intraday scheduling optimization model construction module is used to obtain the actual output of each device at the beginning of the current intraday scheduling time, and to construct the intraday scheduling optimization model and the second constraint condition corresponding to the intraday scheduling optimization model based on the intraday scheduling energy data, actual output, device input power, and device heating power limit, with the goal of minimizing the difference between the predicted trajectory of heat supply output and the reference trajectory of heat supply output; wherein the second constraint condition includes: heat storage constraint of the heat network, network constraint, user-side pressure and temperature constraint, and second coupling device constraint; The intraday scheduling optimization model solving module is used to solve the intraday scheduling optimization model under each second constraint condition to obtain the optimized total heating supply, and then calculate the optimized heating supply of each device based on the total heating supply.

8. The heating optimization device for an electric-thermal integrated energy system according to claim 7, characterized in that: The day-ahead scheduling optimization model solving module includes: Equipment output ratio calculation unit and heat output reference trajectory calculation unit; The device output ratio calculation unit is used to calculate the optimized output ratio of each device according to the output of each device after optimization; The heat supply output reference trajectory calculation unit is used to perform Taylor series expansion on the optimized output ratio to obtain the heat supply output reference trajectory.

9. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a heating optimization method for an electric-thermal integrated energy system as described in any one of claims 1 to 6.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a heating optimization method for an electric-thermal integrated energy system as described in any one of claims 1 to 6.

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