Park integrated energy system data processing method, device and equipment and storage medium

Abstract

The invention provides a park integrated energy system data processing method and device, equipment and a storage medium. The method comprises the following steps: acquiring initial data of the park integrated energy system; and inputting the initial data into a pre-constructed upper-layer planning model and a pre-constructed lower-layer optimization model to obtain optimization result data of the park integrated energy system. According to the technical scheme, the upper-layer model determines the equipment model, capacity and number with the lowest planning cost as the target, investment can be accurately planned in the initial stage of park integrated energy system construction, and fund waste caused by improper equipment model selection or unreasonable capacity is avoided. The lower-layer optimization model enables the operation cost and the subsidy cost to be the lowest, the equipment operation can be adjusted according to the real-time energy price and demand by optimizing the equipment output scheme, the energy purchase cost and the equipment operation energy consumption cost are reduced, and the economic benefits are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy utilization, and particularly relates to a method, device, equipment and storage medium for processing data of an integrated energy system in a park. Background Art

[0002] Integrated energy systems (IES) break the physical isolation barriers between traditional energy systems, conform to the development trend of renewable energy, and are increasingly valued by the academic and industrial circles. Integrated demand response (IDR) makes full use of the advantages of mutual coupling and substitution between energies, changes the energy consumption method among different energy flows, and further improves the operation flexibility and energy utilization efficiency of the system. However, due to factors such as the user's cognitive level and emergencies, the uncertainty characteristics of IDR significantly hinder the efficient and economic utilization of IDR, make the regulation of energy supply and demand balance in IES more difficult, and bring severe challenges to the operation and management of IES. For example, the planning of the integrated energy system in a park is blind, and equipment is selected based on experience or simple estimation, resulting in a mismatch between the equipment and the actual demand. The output of the equipment is only based on a fixed mode or simple rules, and cannot be flexibly adjusted according to factors such as real-time energy prices and load changes, resulting in high operating costs and low energy utilization efficiency. Summary of the Invention

[0003] Aiming at the problems existing in the prior art, the present invention provides a method, device, equipment and storage medium for processing data of an integrated energy system in a park. To achieve the above object, the technical solution adopted by the present invention is as follows: In a first aspect, the present application proposes a method for processing data of an integrated energy system in a park, including: Obtaining initial data of the integrated energy system in the park; Inputting the initial data into a pre-constructed upper-layer planning model and a lower-layer optimization model to obtain optimized result data of the integrated energy system in the park; The optimized result data includes: the equipment model, capacity and quantity of each device in the integrated energy system in the park; and the output scheme of each device. The upper-layer planning model is used to determine the equipment model, capacity and quantity of each device in the integrated energy system in the park with the lowest planning cost of the integrated energy system as the optimization goal. The lower-layer optimization model is used to minimize the operating cost and subsidy cost of the integrated energy system in the park and formulate the output scheme of each device in the integrated energy system in the park.

[0004] In one embodiment, the objective function of the upper-layer planning model includes: the annual value of the allocation of the total investment in the equipment of the park integrated energy system, the annual operating cost of the park integrated energy system, and the reduced revenue due to the implementation of integrated demand response; The total investment mainly includes the initial investment cost of the equipment, the operation and maintenance cost, and the salvage value; The total investment also includes the number of equipment units, the operation status variables, and the capital recovery factor.

[0005] In one embodiment, the constraints of the upper-layer planning model include: peak-valley price constraint and capacity constraint; The capacity constraint includes the maximum heat load constraint and the maximum cold load constraint; In the maximum heat load constraint, the capacity of a single device is calculated, and the product with the capacity auxiliary parameter is used to obtain the capacity reference parameter value of a single device; For the capacity reference parameter values of each single device, a summation operation is performed to obtain the total first capacity sum value; The total first capacity sum value is greater than the maximum heat load; In the maximum cold load constraint, the capacity of a single device is calculated, and the product with the capacity auxiliary parameter is used to obtain the capacity reference parameter value of a single device; For the capacity reference parameter values of each single device, a summation operation is performed to obtain the total second capacity sum value; the total second capacity sum value is greater than the maximum cold load.

[0006] In one embodiment, the objective function of the lower-layer optimization model is to minimize the intra-day operating cost and subsidy cost of the park integrated energy system as the objective function.

[0007] In one embodiment, the constraint conditions of the lower-layer optimization model include power balance constraint, equipment operation constraint, and power purchase and sale constraint; the power purchase and sale constraint is that the park integrated energy system cannot purchase and sell electricity simultaneously.

[0008] In one embodiment, the method further includes: classifying the load types in the integrated energy system into base load, shiftable load, transferable load, and curtailable load; The curtailable load is used to change the user's energy consumption characteristics and curtail the corresponding load according to the dispatching requirements.

[0009] In one embodiment, the constraint of the curtailable load is that the binary variable of the curtailable electrical load curtailment state is between the upper limit and the lower limit of the continuous electrical load curtailment time; Determine the power upper limit of the curtailable heat load; Determine the lower limit of the power of the heat load that can be reduced according to the specific heat capacity of water and the mass of the injected water in the user-side heat network within the time interval; Determine the subsidy cost of the load that can be reduced according to the electrical load power of the load that can be reduced and the subsidy coefficient of the load that can be reduced.

[0010] In a second aspect, the present application proposes a data processing device for a park integrated energy system, including: An acquisition module, configured to acquire initial data of the park integrated energy system; A processing module, configured to input the initial data into a pre-constructed upper-layer planning model and a lower-layer optimization model to obtain the optimized result data of the park integrated energy system; The optimized result data includes: the equipment models, capacities and quantities of various devices in the park integrated energy system; and the output power schemes of each device; The upper-layer planning model is used to determine the equipment models, capacities and quantities of various devices in the park integrated energy system with the lowest planning cost of the integrated energy system as the optimization goal; The lower-layer optimization model is used to minimize the operating cost and subsidy cost of the park integrated energy system and formulate the output power schemes of each device in the park integrated energy system.

[0011] In a third aspect, the present application proposes an electronic device, including: a processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to execute the steps of implementing the method described in any one of the above.

[0012] In a fourth aspect, the present application proposes a computer-readable storage medium, on which computer program instructions are stored, and when the program instructions are executed by a processor, the steps of implementing the method described in any one of the above are realized.

[0013] The present invention has the following beneficial effects: By using the pre-constructed upper-layer planning model and lower-layer optimization model, the initial data is input into the pre-constructed upper-layer planning model and lower-layer optimization model to obtain the optimized result data. The cost control is remarkable. The upper-layer model determines the equipment models, capacities and quantities with the lowest planning cost as the goal, and can accurately plan the investment at the initial stage of the construction of the park integrated energy system, avoiding waste of funds caused by improper equipment selection or unreasonable capacity. The lower-layer optimization model minimizes the operating cost and subsidy cost. By optimizing the output power schemes of the devices, the device operation can be adjusted according to the real-time energy price and demand, reducing the energy procurement and equipment operation energy consumption costs and improving the economic benefits. Description of the Drawings

[0014] The accompanying drawings are used to provide a further understanding of the present disclosure and form a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure, but do not constitute a limitation to the present disclosure. In the accompanying drawings: Figure 1 It is a flowchart of a method for processing data of a campus integrated energy system in an embodiment of the present application; Figure 2 It is a structural block diagram of a device for processing data of a campus integrated energy system in an embodiment of the present application; Figure 3 It is a structural block diagram of an electronic device in an embodiment of the present application. Specific Embodiments

[0015] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention. It should be noted that the following detailed descriptions are all exemplary and are intended to provide further explanations for the present application.

[0016] A method for processing data of a campus integrated energy system, referring to the flowchart of a method for processing data of a campus integrated energy system shown in the attached Figure 1 The method includes the following steps: In step S102, initial data of the campus integrated energy system is acquired.

[0017] Among them, the initial data may include but is not limited to the following data: 1. Energy demand data: covering historical consumption data and future prediction data of electricity, heat, cold, gas and other energies of various users (such as industrial, commercial, residential, etc.) in the campus. Including energy load curves in different seasons and different time periods, which are used to understand the change rules of energy demand.

[0018] 2. Energy supply data: involving relevant information of various energy supply equipment in the campus, such as the rated power, efficiency, fuel type and price of generator sets; the heating capacity and thermal efficiency of heating boilers; the conveying capacity and gas source price of natural gas supply pipelines, etc.

[0019] 3. Equipment performance data: including technical parameters of energy conversion equipment (such as cogeneration units, heat pumps, etc.) and energy storage equipment (such as battery energy storage, heat storage and cold storage equipment, etc.), such as conversion efficiency, charge and discharge efficiency, energy storage capacity, service life, etc. These data are used to evaluate the operating performance of the equipment under different working conditions.

[0020] 4. Network topology data: Describes the layout and connection relationships of the energy network within the park, such as the line parameters of the power network, the capacity and connection method of transformers; the pipe length, pipe diameter, and heat insulation performance of the thermal network; the pipe layout and node information of the gas network, etc., which are used to analyze the transmission and distribution of energy in the network.

[0021] 5. Meteorological data: Includes meteorological information such as local temperature, humidity, sunshine duration, wind speed, etc., because meteorological conditions have a significant impact on energy demand (such as air conditioning cooling, heating demand) and the power generation capacity of renewable energy sources (such as solar energy, wind energy).

[0022] 6. Economic data: Such as price fluctuation data of various energy sources, investment costs of equipment, operation and maintenance costs, government energy subsidy policies, etc., which are important bases for cost analysis and optimization.

[0023] 7. Environmental data: Includes local environmental quality standards, pollutant emission limits, etc., which are of great significance for evaluating the impact of the energy system on the environment and meeting environmental protection requirements.

[0024] 8. Policy and regulation data: Involves energy-related policies and regulations, such as energy management regulations, energy conservation and emission reduction targets, etc., which will affect the planning and operation strategies of the energy system.

[0025] In step S104, the above initial data is input into the pre-constructed upper-layer planning model and lower-layer optimization model to obtain the optimized result data of the above park integrated energy system.

[0026] The above optimized result data includes: the equipment models, capacities, and quantities of each device in the above park integrated energy system; and the output power plan of each device.

[0027] Among them, the above upper-layer planning model is used to determine the equipment models, capacities, and quantities of each device in the above park integrated energy system with the lowest planning cost of the integrated energy system as the optimization goal.

[0028] The above lower-layer optimization model is used to minimize the operation cost and subsidy cost of the above park integrated energy system and formulate the output power plan of each device in the above park integrated energy system.

[0029] The technical solution of this application uses a pre - constructed upper - layer planning model and a lower - layer optimization model. The above - mentioned initial data is input into the pre - constructed upper - layer planning model and lower - layer optimization model to obtain the optimized result data of the above - mentioned park integrated energy system. The cost control is remarkable. The upper - layer model determines the equipment model, capacity and quantity with the goal of minimizing the planning cost, which can accurately plan the investment in the initial stage of the park integrated energy system construction and avoid the waste of funds caused by improper equipment selection or unreasonable capacity. The lower - layer optimization model minimizes the operation cost and subsidy cost. By optimizing the equipment output plan, it can adjust the equipment operation according to the real - time energy price and demand, reduce the energy procurement and equipment operation energy consumption cost, and at the same time make full use of the subsidy policy to improve the economic benefit.

[0030] From the perspective of the overall system planning of the upper - layer model, it reasonably selects the equipment combination, enables different energy equipment to cooperate with each other, and gives full play to their respective advantages. The lower - layer optimization model, at the operation level, dynamically adjusts the equipment output according to real - time data, realizes the reasonable distribution of energy among different equipment, improves the energy utilization efficiency, and reduces energy waste.

[0031] Through the collaborative optimization of the upper - and lower - layer optimization models, the determined equipment configuration and output plan can better meet the diversity and volatility of the park's energy demand. The reasonable equipment selection and capacity configuration provide sufficient energy supply capacity, while the optimized output plan can be flexibly adjusted according to the load change to ensure the stable operation of the system and reduce problems such as power outages caused by insufficient energy supply or equipment failures.

[0032] In some embodiments, the objective function of the above - mentioned upper - layer planning model includes: the annual value of the total investment of the park integrated energy system equipment, the annual operation cost of the park integrated energy system, and the reduced income for implementing the integrated demand response.

[0033] The above - mentioned total investment mainly includes the initial investment cost of the equipment, operation and maintenance costs, and salvage value.

[0034] The total investment also has the number of equipment units, operation status variables, and capital recovery coefficients.

[0035] In this embodiment, the upper - layer planning model takes the minimum construction investment cost of the park integrated energy system as the objective function, and also considers the impact of the integrated demand response while considering the system investment and operation costs.

[0036] The objective function of the upper - layer planning model is: ; Among them, is the annual value of the total investment of the park integrated energy system equipment; is the annual operation cost of the park integrated energy system; Revenue reduction for implementing integrated demand response; Investment cost for the construction of the park integrated energy system.

[0037] Regarding parameter , the total investment includes the initial investment cost of equipment, operation and maintenance costs, and salvage value. The calculation formula is as follows: ; Among them, is the equipment type in different integrated energy systems. The equipment types include CHP, GB, EC, and AC; N is the total number of equipment types; Among them, CHP represents a combined heat and power equipment; GB represents a gas boiler; EC represents an electric chiller; AC represents an absorption chiller.

[0038] is the set of equipment types; is the equipment initial investment cost; is the equipment salvage value, calculated as 5% of the initial investment; is the operation and maintenance cost, calculated as 3% of the initial investment; is the number of equipment units; is a 0-1 variable of the operation state, 1 for running, 0 for paused; is the capital recovery factor, which can be expressed as: ; Among them, is the discount rate; is the equipment life cycle.

[0039] Regarding parameter , the cooling and heating loads have obvious seasonal characteristics. The heating load is low and the cooling load is high in summer, while the heating load is high and the cooling load is low in winter.

[0040] ; Among them, is the operation and maintenance cost of a typical load day, is the operation and maintenance cost of a typical load day in season ; is the number of days in season days.

[0041] The comprehensive demand response cost is mainly caused by controllable loads. The benefits of controllable loads before and after implementing integrated demand response (IDR) are as follows: ; In the formula: is the controllable benefit before the integrated energy system of the park implements integrated demand response; is the controllable load benefit after the integrated energy system of the park implements integrated demand response.

[0042] In some embodiments, the constraints of the above upper-layer planning model include peak-valley price constraints and capacity constraints; the above capacity constraints include heat maximum load constraints and cold maximum load constraints.

[0043] The upper-layer planning constraints of the integrated energy system mainly consider integrated demand response constraints and capacity constraints. The integrated demand response constraints include peak-valley price constraints.

[0044] When designing peak-valley prices, it is necessary to be within a certain proportion range.

[0045] ; ; ; Among them, is the marginal cost during the valley period; is the energy price during the peak period; is the energy price during the normal period; is the energy price during the valley period; is the lower limit of the peak-valley electricity price ratio; is the upper limit of the peak-valley electricity price ratio; is the lower limit of the type of energy demand response; is the type of energy demand response upper limit.

[0046] In the above heat maximum load constraint, the capacity of a single device is calculated, and the product with the capacity auxiliary parameter is used to obtain the capacity reference parameter value of a single device; For the capacity reference parameter values of each single device, a summation operation is performed to obtain the total first capacity sum value; The above total first capacity sum value is greater than the heat maximum load; In the above cold maximum load constraint, the capacity of a single device is calculated, and the product of the capacity auxiliary parameter is used to obtain the capacity reference parameter value of the single device; Performing a sum operation on the capacity reference parameter value of each single device to obtain a total second capacity sum value; The above-mentioned total second capacity and value is greater than the cold maximum load.

[0047] In this embodiment, the constraint capacity is as follows: ; ; in, For equipment Capacity; is the maximum heat load; is the maximum load for cooling; is the heating efficiency of CHP; is the number of devices; A 0-1 variable indicating the running state, 1 for running and 0 for paused.

[0048] In some embodiments, the objective function of the above-mentioned lower-level optimization model is to minimize the daily operating cost and subsidy cost of the park's integrated energy system.

[0049] The lower-level optimization takes the lowest daily operating cost and subsidy cost of the park’s integrated energy system as its objective function, including fuel consumption cost, electricity purchase cost, and equipment maintenance cost.

[0050] The calculation formula for daily operating cost is as follows: ; in, The daily operating cost of the park's integrated energy system; Cost of natural gas fuel consumption; the cost of purchasing electricity for the public grid; Compensate users for costs.

[0051] T is the number of operating days; The formula for calculating the fuel cost of natural gas consumption equipment in the park's comprehensive energy system is as follows: ; in, For natural gas Time price; is the natural gas consumption; is the natural gas consumption of the CHP unit; is the natural gas consumption of the GB unit at time t; Regarding the cost of purchasing electricity from the public power grid , the integrated energy system of the park follows the principle of "spontaneous self-use and surplus power grid connection". The calculation formula for the cost of purchasing electricity from the public power grid is as follows: ; Among them, is the selling price of electricity; is the purchase price of electricity; is the electricity sales volume; is the electricity purchase volume.

[0052] User compensation cost The calculation formula is as follows: ; Among them, is the compensation cost of the shiftable electric load; is the compensation cost of the transferable electric load; is the subsidy cost of the curtailable electric load.

[0053] In some embodiments, the constraint conditions of the above-mentioned lower-layer optimization model include power balance constraints, equipment operation constraints, and power purchase and sale constraints; the above-mentioned power purchase and sale constraint is that the park integrated energy system cannot purchase and sell electricity simultaneously.

[0054] The calculation formula for the power balance constraint is as follows: ; ; ; Among them, is the user's electric load demand; is the user's heat load demand; is the user's cooling load demand; is the electric power of the CHP unit at time t; is the power consumed by the EC unit; is the heat power of the CHP unit at time t; is the cooling power of the AC unit at time t; is the thermal power of GB unit at time t; is the cooling power of the EC unit at time t; is the cooling power of the AC unit at time t; Constraints on power purchase and sale: The power purchase and sale of the park's integrated energy system cannot be carried out at the same time, and the following relationship must be met: ; In some embodiments, the above method also includes: dividing the load types in the above integrated energy system into basic loads, shiftable loads, transferable loads and curtailable loads.

[0055] The above-mentioned reducible load is used to change the user's energy consumption characteristics and reduce the corresponding load according to scheduling requirements.

[0056] In some embodiments, the constraint of the above-mentioned load reduction is that the binary variable of the above-mentioned load reduction state is located between the upper limit of the continuous load reduction time and the lower limit of the continuous load reduction time.

[0057] Determine the upper power limit above which the heat load can be reduced.

[0058] The lower limit of the power that can reduce the heat load is determined based on the specific heat capacity of water and the quality of water injected into the user-side heating network within the time interval.

[0059] The subsidy cost of the above-mentioned reducible load is determined based on the electric load power of the above-mentioned reducible load and the subsidy coefficient of the above-mentioned reducible load.

[0060] In this embodiment, the load can be reduced to change the energy consumption characteristics of the user, and the corresponding load can be reduced according to the scheduling requirements. In order to ensure the reasonable reduction of the electric load, the continuous reduction time and the number of reductions are constrained as follows: ; in, To reduce the duration; is a binary variable indicating the state of the curtailed electric load; The upper limit of continuous load reduction time; It is the lower limit of continuous electric load reduction time.

[0061] ; in, It is the maximum number of reductions within the scheduling cycle.

[0062] The subsidy cost of the reducible electric load is: ; where is the power of the reducible electric load; is the subsidy coefficient of the reducible electric load.

[0063] Based on the thermal load cut-off perception delay characteristic, the size of the thermal load can be flexibly adjusted without affecting the perceived temperature of the thermal load. Let the temperature range that does not affect the user's heat perception and comfort be . In this temperature range, the calculation formula for the reducible thermal load is as follows: ; where is the upper limit of the reducible thermal load power; is the lower limit of the reducible thermal load power; is the water injection mass in the user-side heat network within the time interval ; is the specific heat capacity of water; is the reducible thermal load power; is the lower temperature limit that does not affect the user's heat perception and comfort; is the upper temperature limit that does not affect the user's heat perception and comfort; is the initial temperature at a certain moment. The meaning of t is the time interval.

[0064] The calculation formula for the subsidy cost of the reducible thermal load is as follows: ; where is the subsidy coefficient of the reducible thermal load.

[0065] T is the scheduling period; By combining flexible load regulation with the demand response mechanism and adjusting electricity consumption behaviors such as load shifting, transfer, and reduction, the pressure on the system during peak load periods can be effectively alleviated, thus realizing load peak shaving and valley filling. According to different ways of optimizing load demand response, the load types in the integrated energy system are divided into 4 categories: basic load, shiftable load, transferable load, and reducible load.

[0066] The shiftable electrical load can be shifted across multiple time periods as a whole by setting the shift interval. Assume that within the scheduling period, the shiftable interval of the electrical load is , the shiftable duration is , and the set of starting time periods for shifting is: ; The compensation cost of the shiftable electrical load is: ; Wherein, is the shifted electrical power within the shiftable interval; is the subsidy coefficient for the shiftable electrical load. The shiftable thermal load refers to the overall thermal load that can be reasonably adjusted within a certain time period on the premise of ensuring the user's heating experience. Assume that within the scheduling period, the shiftable interval of the thermal load is , the shiftable duration is , then the set of starting time periods for shifting is: ; The compensation cost of the shiftable thermal load is: ; In the formula: The shifted thermal load power within the shiftable interval.

[0067] is the subsidy coefficient for the shiftable thermal load.

[0068] The shiftable load is flexibly adjusted in each time period within the cycle on the premise of meeting the load demand within the scheduling period. Due to the particularity of the user's heating demand, the shiftable thermal load is not considered. Define the shiftable electrical load interval within the cycle as , and constrain the minimum continuous operation time of the equipment and the transfer power to solve the problem of frequent start and stop of the equipment. The specific constraint conditions are shown in the following formula: ; Wherein, is a binary variable used to represent the state of the shiftable load; is the minimum time for continuous operation.

[0069] ; Wherein, is the upper limit of the shiftable electrical load power; is the shiftable electrical load; is the lower limit of the shiftable electrical load power. The compensation cost of the translatable electrical load is: ; Wherein, is the subsidy coefficient of the transferable electrical load; is the power of the transferable electrical load.

[0070] This application establishes a two - layer collaborative planning model for equipment capacity configuration and operation strategy. The upper - layer capacity configuration model aims to minimize the total planning cost, and the lower - layer optimal operation model takes the minimum operation cost as the objective function to realize the accurate analysis of the response capacity of the park integrated energy system. The technical solution of this application integrates the discrete characteristics of equipment, uncertainty, and multi - time - scale dynamic response mechanisms, optimizes the system configuration, improves the equipment utilization rate, and reduces the abandonment of clean energy. Combining multi - stage planning and robustness design, it enhances the applicability of the configuration scheme, establishes an efficient and accurate model, and solves the problems of low accuracy and utilization rate of traditional methods. This application comprehensively improves the overall performance of the integrated energy system.

[0071] In a second aspect, this application proposes a data processing device for a park integrated energy system. Referring to the appendix Figure 2 , the data processing device 2 for the park integrated energy system includes: An acquisition module 21, configured to acquire the initial data of the park integrated energy system.

[0072] A processing module 22, configured to input the initial data into a pre - constructed upper - layer planning model and a lower - layer optimization model to obtain the optimized result data of the park integrated energy system.

[0073] The optimized result data includes: the equipment models, capacities, and quantities of each device in the park integrated energy system; and the output power plan of each device.

[0074] The upper - layer planning model is used to determine the equipment models, capacities, and quantities of each device in the park integrated energy system with the lowest planning cost of the integrated energy system as the optimization goal.

[0075] The lower - layer optimization model is used to minimize the operation cost and subsidy cost of the park integrated energy system and formulate the output power plan of each device in the park integrated energy system.

[0076] In a third aspect, this application proposes an electronic device 3. Referring to the appendix Figure 3 , the electronic device 3 includes: a processor 31; a memory 32 for storing executable instructions of the processor 31. Wherein, the processor 31 is configured to execute the steps of implementing the method described in any one of the above.

[0077] The above-mentioned electronic device 3 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. Those skilled in the art can understand that Figure 3 These are merely examples of the electronic device 3 and do not constitute a limitation on the electronic device 3. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, the above-mentioned electronic device 3 may further include input / output devices, network access devices, buses, etc.

[0078] The so-called processor 31 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0079] The above-mentioned memory 32 may be an internal storage unit of the above-mentioned electronic device 3, such as the hard disk or memory of the electronic device 3. The above-mentioned memory 32 may also be an external storage device of the above-mentioned electronic device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the above-mentioned electronic device 3. Further, the above-mentioned memory 32 may also include both the internal storage unit and the external storage device of the electronic device 3. The above-mentioned memory 32 is used to store the above-mentioned computer program and other programs and data required by the above-mentioned electronic device. The above-mentioned memory 32 may also be used to temporarily store the data that has been output or will be output.

[0080] In a fourth aspect, the present application proposes a computer-readable storage medium, on which computer program instructions are stored, and when the program instructions are executed by a processor, the steps of any one of the above-mentioned methods are implemented.

[0081] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above-mentioned functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the above-mentioned device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above-mentioned system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0082] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0083] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0084] In the embodiments provided in this application, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are merely illustrative. For example, the above-mentioned division of modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0085] The units described as separate components above may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0086] In addition, in each embodiment of the present application, each functional unit may be integrated into a processing unit, may exist separately as individual physical units, or two or more units may be integrated into one unit. The above integrated units may be implemented in the form of hardware or in the form of software functional units.

[0087] If the above integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above embodiment methods of the present application, it may also be completed by a computer program instructing relevant hardware. The above computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments may be implemented. Among them, the above computer program includes computer program code, and the above computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The above computer-readable medium may include: any entity or device capable of carrying the above computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the above computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0088] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

[0089] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the protection scope of the present invention. Any simple modification or equivalent replacement made by those of ordinary skill in the art to the technical solution of the present invention does not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A data processing method for a park integrated energy system, characterized in that: include: Obtain initial data on the park's integrated energy system; Inputting the initial data into a pre-built upper-level planning model and a lower-level optimization model to obtain optimization result data of the park integrated energy system; The optimization result data includes: the equipment model, capacity and quantity of each equipment in the park comprehensive energy system; and the output plan of each equipment; The upper-level planning model is used to determine the equipment model, capacity and quantity of each equipment in the park integrated energy system with the lowest planning cost of the integrated energy system as the optimization goal; The lower-level optimization model is used to minimize the operating cost and subsidy cost of the park's integrated energy system and to formulate an output plan for each device in the park's integrated energy system.

2. The method for processing data of a park integrated energy system according to claim 1, characterized in that: The objective function of the upper-level planning model includes: the annual value of the total investment in equipment for the park's integrated energy system, the annual operating cost of the park's integrated energy system, and the reduced revenue for implementing integrated demand response; The total investment amount mainly includes the initial investment cost of the equipment, operation and maintenance costs and residual value; The total investment amount also includes the number of equipment, operating state variables and capital recovery coefficient.

3. The method for processing data of a park integrated energy system according to claim 1, characterized in that: The constraints of the upper-level planning model include: peak-valley price constraints and capacity constraints; The capacity constraints include a hot maximum load constraint and a cold maximum load constraint; In the thermal maximum load constraint, the capacity of a single device is calculated, and the product of the capacity auxiliary parameter and the capacity reference parameter value of the single device is obtained; Performing a sum operation on the capacity reference parameter value of each single device to obtain a total first capacity sum value; The total first capacity and value is greater than the thermal maximum load; In the cold maximum load constraint, the capacity of a single device is calculated, and the product of the capacity auxiliary parameter is obtained to obtain the capacity reference parameter value of the single device; A sum operation is performed on the capacity reference parameter value of each single device to obtain a total second capacity sum value; the total second capacity sum value is greater than the maximum cold load.

4. The method for processing data of a park integrated energy system according to claim 1, characterized in that: The objective function of the lower optimization model is to minimize the daily operating cost and subsidy cost of the park's integrated energy system.

5. The method for processing data of a park integrated energy system according to claim 4, characterized in that: The constraints of the lower-level optimization model include power balance constraints, equipment operation constraints, and power purchase and sales constraints; the power purchase and sales constraints are that the park's comprehensive energy system cannot purchase and sell power at the same time.

6. The method for processing data of a park integrated energy system according to claim 1, characterized in that: The method further includes: dividing the load types in the integrated energy system into basic loads, shiftable loads, transferable loads and curtailable loads; The reducible load is used to change the energy consumption characteristics of the user and reduce the corresponding load according to the scheduling requirements.

7. The method for processing data of a park integrated energy system according to claim 6, characterized in that: The constraint of the load that can be reduced is that the binary variable of the reduction state of the load that can be reduced is between the upper limit of the continuous load reduction time and the lower limit of the continuous load reduction time; Determining the upper power limit of the heat load that can be reduced; Determining the lower limit of the power that can reduce the heat load according to the specific heat capacity of water and the quality of water injected into the user-side heat network within the time interval; The subsidy cost of the curtailable load is determined according to the electric load power of the curtailable load and the subsidy coefficient of the curtailable load.

8. A data processing device for a park integrated energy system, characterized in that: include: The acquisition module is used to obtain the initial data of the park's integrated energy system; A processing module, used for inputting the initial data into a pre-built upper-level planning model and a lower-level optimization model to obtain optimization result data of the park integrated energy system; The optimization result data includes: the equipment model, capacity and quantity of each equipment in the park comprehensive energy system; and the output plan of each equipment; The upper-level planning model is used to determine the equipment model, capacity and quantity of each equipment in the park integrated energy system with the lowest planning cost of the integrated energy system as the optimization goal; The lower-level optimization model is used to minimize the operating cost and subsidy cost of the park's integrated energy system and to formulate an output plan for each device in the park's integrated energy system.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to execute the steps of implementing the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

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