Comprehensive energy park operation optimization method, device, equipment and medium

Abstract

The invention belongs to the technical field of energy system optimization, and particularly relates to a comprehensive energy park operation optimization method and device, equipment and a medium. The method comprises the steps of performing economic dispatching based on day-ahead prediction data to obtain a unit output plan; traditional dynamic carbon emission factors are calculated and improved, the abandoned energy of renewable energy sources is considered during improvement, and the equivalent carbon emission benchmark is transmitted to a heat energy and cold energy network through multi-energy coupling equipment; determining a carbon intensity partition threshold based on the improved carbon emission factor and executing partition; generating a carbon reward and punishment price signal; the adjustable load is optimized through a user side objective function, and a responded load curve is obtained; and finally, economic dispatching is executed again to obtain a final output plan. The technical problems that traditional carbon emission factor calculation is inaccurate, the carbon transaction price suddenly changes, and carbon signal transmission is incomplete are solved, and the low-carbon operation effect of the comprehensive energy park is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of energy system optimization, and particularly relates to a comprehensive energy park operation optimization method, device, equipment and medium. BACKGROUND

[0002] As an important technical approach to realize energy efficient utilization and low-carbon transformation, comprehensive energy systems have been widely researched and applied in recent years. As a typical application scenario of comprehensive energy systems, comprehensive energy parks can effectively improve the comprehensive energy utilization efficiency through the collaborative optimization and complementary utilization of electricity, heat, cold, gas and other multiple energies. In this technical field, how to realize the low-carbon economic operation of the park has become a research hotspot. SUMMARY

[0003] The purpose of the present application is to provide a comprehensive energy park operation optimization method, device, equipment and medium to solve the problem of low-carbon economic operation of the park in the prior art.

[0004] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions: In a first aspect, the present application provides a comprehensive energy park operation optimization method, comprising the following steps: Based on the day-ahead renewable energy prediction result, the load prediction result and the system device parameters, economic dispatch is performed with the lowest energy side operation cost as the target to obtain a unit output plan; Based on the unit output plan, a system traditional dynamic carbon emission factor is calculated; Based on the day-ahead renewable energy prediction result, the renewable energy curtailment energy at each time is determined, and the improved dynamic carbon emission factor is calculated in combination with the traditional dynamic carbon emission factor; wherein the improved dynamic carbon emission factor is obtained by taking the average value of the traditional dynamic carbon emission factor of the whole day period as the equivalent carbon emission benchmark for renewable energy consumption, and being transmitted to the heat and cold energy network through the park multi-energy coupling device; the park multi-energy coupling device includes an electric boiler, an electric refrigerator and a cogeneration unit; the cogeneration unit includes a gas turbine and a waste heat boiler; Based on the improved dynamic carbon emission factor, the carbon intensity partition threshold of each energy network is determined, and carbon intensity partition is performed according to the carbon intensity partition threshold; Based on the carbon intensity partition result and the improved dynamic carbon emission factor, a carbon reward and punishment price signal of each period is generated; The carbon reward and punishment price signal is input into a user side target function for optimization and solution, the user side adjustable load is adjusted, and the load curve after user response is obtained; the user side adjustable load includes transferable load and excisable load; the user side adjustable load is adjusted to meet the space-time conservation constraint of the transferable load and the power upper limit constraint of the excisable load; The load curve after the user's response is used as the new load condition to re-execute economic dispatch and obtain an updated unit output plan.

[0005] This solution provides a complete optimization method for the operation of integrated energy parks. By establishing an improved dynamic carbon emission factor calculation model, it solves the problem of inaccurate carbon emission factors caused by the failure to consider renewable energy curtailment in traditional methods. By using the average value of the traditional dynamic carbon emission factor throughout the day as the equivalent carbon emission benchmark for renewable energy consumption, and achieving cross-network transmission of carbon signals through multi-energy coupling devices, the consistency of carbon emission factor calculations across electricity, heat, and cooling energy networks is ensured. Simultaneously, through carbon intensity zoning and the generation of carbon reward / penalty price signals, an effective source-load interaction mechanism is established, enabling users to adjust their energy consumption behavior based on accurate carbon signals. This method achieves synergy between the energy supply side and the user side through closed-loop optimization, improving the low-carbon operation performance of integrated energy parks.

[0006] Furthermore, the improved dynamic carbon emission factor is calculated using the following formula:

[0007] in, , , These represent the dynamic carbon emission factors of the improved electric, thermal, and cold networks at time t; 、 These are the traditional dynamic carbon emission factors of the electric, thermal, and cold networks at time t, respectively. This represents the energy discarded from renewable energy sources at time t; , These represent the electrothermal conversion efficiency of the electric boiler and the electrocooling conversion efficiency of the electric refrigeration system, respectively. This represents the average value of the traditional dynamic carbon emission factor of the park's power system.

[0008] By introducing a renewable energy curtailment energy correction term, the technical problem that traditional dynamic carbon emission factors cannot reflect the impact of wind and solar curtailment is effectively solved. The formula takes into account the characteristics of different energy networks such as electricity, heat, and cooling, and realizes the accurate transmission of carbon signals in multiple energy networks through the conversion efficiency of electric boilers and electric chillers, ensuring the accuracy and consistency of carbon emission factor calculation and providing a reliable data foundation for subsequent carbon reward and punishment mechanisms.

[0009] Furthermore, in the step of implementing economic dispatch with the goal of minimizing the operating costs on the energy supply side, the objective function of economic dispatch includes a carbon trading cost term. This carbon trading cost term is calculated based on an improved tiered carbon trading model, expressing the carbon trading price as a linear function of the difference between actual carbon emissions and carbon emission allowances. The expression is as follows:

[0010] in, For the improved tiered carbon trading price, For carbon trading costs, This represents the actual total carbon emissions. V represents the carbon emission allowance, and v, a, and d represent the base price, price increase rate, and interval length of the traditional tiered carbon trading model, respectively.

[0011] The improved tiered carbon trading model expresses the carbon trading price as a linear function of the difference between actual carbon emissions and carbon emission allowances, thus resolving the price abruptness problem inherent in traditional tiered carbon trading mechanisms. This enhances the stability of carbon trading cost calculations, avoids convergence difficulties in optimization algorithms caused by price jumps, makes economic scheduling results more reliable, and provides a smooth carbon cost transmission mechanism for the system's low-carbon operation.

[0012] Furthermore, based on the improved dynamic carbon emission factor, the carbon intensity zoning thresholds for each energy network are determined, including: Using linear interpolation, the carbon intensity zoning thresholds for each energy network are calculated based on the maximum and minimum values ​​of the improved dynamic carbon emission factor for the day; among which,

[0013]

[0014] In the formula, x represents the number of energy types within the integrated energy park; , These represent the upper and lower limits of the carbon partitioning threshold, respectively. , These represent the maximum and minimum values ​​of the improved carbon emission factor for the day, respectively.

[0015] A linear interpolation method was used to determine the carbon intensity zoning thresholds, calculated based on the maximum and minimum values ​​of the improved dynamic carbon emission factor for the day, ensuring the rationality and adaptability of the carbon zoning thresholds. This method allows for dynamic adjustment of zoning standards according to the actual daily carbon emission intensity, avoiding the inaccuracies that may result from fixed thresholds. This makes the carbon intensity zoning more scientific and reasonable, providing an accurate basis for the subsequent formulation of carbon reward and penalty prices.

[0016] Furthermore, based on the carbon intensity zoning results and the improved dynamic carbon emission factor, carbon reward and penalty price signals for each time period are generated, specifically calculated using the following formula:

[0017] in, Let be the unit carbon reward / penalty price of energy x at time t; This is the price conversion factor; The improved dynamic carbon emission factor of energy type x in the park at time t; , These represent the different types of energy in the park. The upper and lower thresholds of the carbon partition.

[0018] By establishing a correlation between carbon intensity zoning and incentive / penalty pricing, precise guidance of user energy consumption behavior is achieved. The system can automatically adjust incentives and penalties based on real-time carbon emission intensity, providing positive incentives during low-carbon periods and implementing negative penalties during high-carbon periods. This promotes user participation in system-wide carbon emission reduction and improves the overall economic and environmental efficiency of the integrated energy park operation.

[0019] Furthermore, the user-side objective function for:

[0020]

[0021]

[0022]

[0023] In the formula, , , These represent user energy supply costs, dissatisfaction costs, and incentive benefits, respectively. This represents the energy price of energy type x at time t; , Representing energy types The response willingness coefficients are inversely proportional to the user's response willingness; that is, the smaller the two coefficients are, the greater the user's response willingness. , , These represent the unit energy cost, carbon incentive price, and load change amount required for load type x at time t, respectively; T and N represent the dispatch cycle and the number of users, respectively; and X represents the load type.

[0024] By comprehensively considering users' energy purchase costs, dissatisfaction costs, and incentive benefits, a scientifically sound user response model was established. This objective function accurately characterizes users' willingness to respond through a combination of quadratic and linear terms, ensuring a balance between user comfort and economic efficiency when participating in the carbon reward and penalty mechanism. This enhances users' enthusiasm for participating in carbon emission reduction and strengthens the effectiveness of source-load interaction.

[0025] Furthermore, following the step of obtaining the updated unit output plan, iterative steps are also included: Determine whether the difference between the updated unit output plan and the previous unit output plan is less than the convergence threshold; if not, re-execute economic dispatch based on the updated unit output plan until the convergence condition is met.

[0026] By introducing iterative optimization steps, a complete closed-loop optimization mechanism was established. The stability and reliability of the optimization results were ensured by determining whether the difference in unit output plans was less than a convergence threshold. This iterative process enabled multiple coordinations between the power supply-side scheduling and the user-side response, gradually approaching the optimal solution, improving the accuracy and practicality of the overall system optimization results, and avoiding the local optima problem that may exist in a single optimization.

[0027] In a second aspect, the present invention provides an integrated energy park operation optimization device, comprising: The first optimization module is used to perform economic dispatch based on the day-ahead renewable energy forecast results, load forecast results and system equipment parameters, with the goal of minimizing the operating cost on the energy supply side, and to obtain the unit output plan. The first calculation module is used to calculate the system's traditional dynamic carbon emission factor based on the unit's output plan; The second calculation module is used to determine the renewable energy curtailment energy at each time point based on the day-ahead renewable energy forecast results, and to calculate the improved dynamic carbon emission factor by combining the traditional dynamic carbon emission factor. The improved dynamic carbon emission factor is obtained by using the average value of the traditional dynamic carbon emission factor over the entire day as the equivalent carbon emission benchmark for renewable energy consumption, and then transmitting it to the thermal and cooling energy networks through the park's multi-energy coupling equipment. The park's multi-energy coupling equipment includes electric boilers, electric chillers, and cogeneration units. The cogeneration units include gas turbines and waste heat boilers. The carbon intensity zoning module is used to determine the carbon intensity zoning threshold for each energy network based on the improved dynamic carbon emission factor, and to perform carbon intensity zoning according to the carbon intensity zoning threshold. The carbon reward and penalty price signal generation module is used to generate carbon reward and penalty price signals for each time period based on the carbon intensity zoning results and the improved dynamic carbon emission factor. The second optimization module is used to input the carbon reward and punishment price signal into the user-side objective function for optimization, adjust the user-side adjustable load, and obtain the load curve after user response. The user-side adjustable load includes transferable load and cut-off load. The user-side adjustable load is adjusted to meet the spatiotemporal conservation constraints of transferable load and the power upper limit constraints of cut-off load. The third optimization module is used to take the load curve after the user's response as the new load condition, re-execute economic dispatch, and obtain an updated unit output plan.

[0028] In a third aspect, the present invention provides an electronic device including a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the integrated energy park operation optimization method described above.

[0029] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the integrated energy park operation optimization method described above. Attached Figure Description

[0030] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of a method for optimizing the operation of an integrated energy park, according to an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the principle of an integrated energy park operation optimization method according to an embodiment of the present invention; Figure 3 This is a structural block diagram of an integrated energy park operation optimization device according to an embodiment of the present invention; Figure 4 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0031] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0032] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0033] Example 1 like Figure 1 and Figure 2 As shown, a method for optimizing the operation of an integrated energy park includes the following steps: Step 1: Based on the day-ahead renewable energy forecast results, base load forecast results, and system equipment parameters, conduct the first economic dispatch with the goal of minimizing the operating cost on the energy supply side, and generate the initial unit output plan; Step 2: Based on the initial unit output plan from Step 1, calculate the system's traditional dynamic carbon emission factor; Step 3: Based on the current renewable energy forecast results, determine the renewable energy curtailment energy at each time point, and combine it with the traditional dynamic carbon emission factor from Step 2 to calculate the improved dynamic carbon emission factor. The improved dynamic carbon emission factor is obtained by using the average value of the traditional dynamic carbon emission factor throughout the day as the equivalent carbon emission benchmark for absorbing renewable energy, and then transferring it to the thermal and cooling energy network through the multi-energy coupling equipment in the park. Step 4: Based on the improved dynamic carbon emission factor obtained in Step 3, determine the carbon intensity zoning thresholds for the electricity, heat, and cooling energy networks, and perform carbon intensity zoning accordingly. Step 5: Based on the carbon intensity zoning results in Step 4 and the improved dynamic carbon emission factor in Step 3, generate carbon reward and penalty price signals for each energy network at different time periods. Step 6: Input the carbon reward and penalty price signal generated in Step 5 into the user-side optimization model for solution, adjust the adjustable load in the base load forecast result, and output the load curve after user response. Step 7: Using the load curve obtained from the user response in Step 6 as the new load boundary condition, repeat the economic dispatching process in Step 1 to generate the final unit output plan. Step 8: Output the final unit output plan generated in Step 7 and the load curve after user response generated in Step 6, as the day-ahead operation plan for the integrated energy park.

[0034] In the economic scheduling of step 1, the objective function includes a carbon trading cost term, which is calculated based on an improved tiered carbon trading model. Therefore, the following improvement is made to the traditional tiered carbon trading: while ensuring that the total carbon trading cost within a tier remains unchanged, the traditional tiered carbon trading price is changed to the improved tiered carbon trading, which is a linear price, thereby better limiting system carbon emissions.

[0035] The improved tiered carbon trading model is expressed as follows:

[0036]

[0037]

[0038] In the formula, Indicates carbon trading costs, This represents the improved tiered carbon trading price, where v, a, and d represent the base price, price increase rate, and interval length of the traditional tiered carbon trading price, respectively. These represent actual carbon emissions and carbon emission allowances, respectively. , These represent the carbon emissions from the combined heat and power unit and the electricity purchased from the main grid at time t, respectively. , These represent the carbon quota coefficients, , These represent the main grid power purchased at time t and the power of the combined heat and power unit turbine, respectively. In step 1, the system equipment parameters include the operating parameters and constraints of each energy coupling device within the park. The energy coupling devices include electric boilers, combined heat and power units, electric chillers, etc., and are also equipped with wind and solar power, supplemented by energy from the external main power grid and the upstream gas grid.

[0039] Electric boilers and electric chillers can convert electrical energy into heat and cold energy, and their mathematical models can be expressed as follows:

[0040]

[0041] In the formula, , , , Let represent the input electrical power and output thermal power of the electric boiler at time t, and the input electrical power and output cooling power of the electric chiller, respectively. , These represent the upper limit of the input electrical power for the electric boiler and the upper limit of the input electrical power for the electric chiller, respectively. , These represent the electrothermal conversion coefficient of the electric boiler and the electrocooling conversion coefficient of the electric chiller, respectively.

[0042] A combined heat and power (CHP) unit can produce electricity and heat energy by using a gas turbine in conjunction with a waste heat boiler. Its mathematical model is expressed as follows:

[0043]

[0044]

[0045]

[0046]

[0047] In the formula, , , These represent the gas power input, electrical power output, and thermal power output of the gas turbine at time t, respectively. This represents the output thermal power of the waste heat boiler at time t; , , These represent the downhill and uphill limits and the maximum input gas power of the gas-fired boiler, respectively. , , These represent the power generation coefficient and heat generation coefficient of the gas turbine, and the heat recovery coefficient of the waste heat boiler, respectively.

[0048] The objective function for the energy supply side is to minimize the operating cost of the energy supply system in the integrated energy park, specifically expressed as:

[0049]

[0050]

[0051]

[0052]

[0053] In the formula, These represent energy purchase costs, equipment operation and maintenance costs, and penalties for wind and solar curtailment, respectively. This represents the unit maintenance cost for equipment type z; This represents the operating power of type z equipment at time t; it also represents the number of types of equipment in the park. In this case, the equipment includes six types: wind power, photovoltaic, electric boiler, electric refrigeration, and CHP (GT + WHB), which is 6. This represents the unit cost of wind and solar power curtailment. , These represent the main grid purchase prices of electricity and gas at time t; , These represent the electricity and gas purchased from the main grid at time t, respectively. , , , These represent the wind and solar power output and the predicted maximum value at time t, respectively.

[0054] In step 2, when calculating the traditional dynamic carbon emission factor, it is necessary to first calculate the carbon emissions of each device.

[0055] For calculating carbon emissions from combined heat and power (CHP) units, the carbon emissions are allocated based on the energy ratio of power generation to heat supply. The calculation method is as follows:

[0056] In the formula, This represents the total carbon emissions of the CHP unit at time t. These represent natural gas consumption, lower heating value of natural gas, and carbon content per unit calorific value of natural gas, respectively. , Let represent the carbon emissions from power generation and the carbon emissions from heating generated by the cogeneration unit at time t, respectively. , These represent the power supply and heat supply capacity ratios of the combined heat and power (CHP) unit, respectively. The calculation method for carbon emissions from electric boilers, electric refrigeration equipment, and purchased electricity is as follows:

[0057] In the formula, z represents an electric boiler or electric refrigeration equipment, and the carbon emissions are obtained by directly multiplying the equipment output information (electricity purchase power information) with the carbon emission factor. , , These represent the carbon emissions from electric boilers, electric refrigeration systems, and electricity purchased from the main grid at time t, respectively. , , These represent the input electrical power of the electric boiler and electric refrigeration unit, and the main grid-purchased electrical power at time t, respectively. , Let represent the traditional carbon emission factor of the power system and the equivalent carbon emission factor of the main grid's electricity purchase at time t, respectively.

[0058] Therefore, the traditional dynamic carbon emission factor calculation method in the park is as follows:

[0059] In the formula, 、 Let represent the carbon emission factors of conventional electricity, thermal carbon emission, and cold carbon emission at time t, respectively. , , , These represent the carbon emissions from power generation, heating, heating from electric boiler equipment, and cooling from electric refrigeration equipment of the cogeneration unit at time t, respectively. , , , , These represent the power of electrical load, heat load, and cooling load at time t, respectively, as well as the input electrical power of the electric boiler and electric refrigeration system.

[0060] In step 3, the improved dynamic carbon emission factor uses the grid average carbon emission factor throughout the day as the equivalent carbon emission factor for wind and solar power absorption. Energy coupling devices, such as electric boilers and electric chillers, are used to transmit the equivalent carbon emission factor for wind and solar power absorption to the remaining networks.

[0061] The improved method for calculating the dynamic carbon emission factor is as follows:

[0062] In the formula, , , These represent the dynamic carbon emission factors for electricity, heat, and cold at time t, respectively. This represents the average value of the traditional dynamic carbon emission factor of the park's power system. This represents the amount of wind and solar power curtailed at time t; , These represent the electrothermal conversion efficiency of the electric boiler and the electrocooling conversion efficiency of the electric refrigeration system, respectively.

[0063] In step 4, the method for determining the carbon zoning threshold and carbon reward / penalty price using the linear interpolation method is as follows:

[0064]

[0065] In the formula, x represents the number of energy types within the integrated energy park; , These represent the upper limit (maximum value) and lower limit (minimum value) of the carbon partition threshold, respectively. , These represent the maximum and minimum values ​​of the improved carbon emission factor for the day, respectively.

[0066] In step 5, the carbon reward / penalty price is calculated as follows: The calculation method for carbon reward and penalty prices is as follows:

[0067] In the formula, Let be the unit carbon reward / penalty price of energy x at time t; This is the price conversion factor. The improved dynamic carbon emission factor of energy type x in the park at time t; , These represent the different types of energy in the park. The upper and lower thresholds of carbon partitions; By utilizing an improved dynamic carbon emission factor to differentiate carbon reward and penalty pricing, different prices can be formed based on the intensity of carbon emissions. During periods of high carbon emission intensity, the carbon reward and penalty price is negative, representing a penalty and encouraging users to reduce load demand at that time. During periods of low carbon emission intensity, the carbon reward and penalty price is positive, representing a reward and encouraging users to increase load demand.

[0068] For user-side loads, there are mainly three types: rigid loads, transferable loads, and shelvable loads. Rigid loads are used to meet the basic daily needs of users and their energy consumption is relatively fixed. Transferable loads can be transferred over time, while shelvable loads can be directly cut off when needed.

[0069] User load modeling can be expressed as:

[0070]

[0071]

[0072]

[0073] In the formula , , These represent the load change, transferable load change, and shelvable load change of load type x at time t, respectively. , These represent the transferable load and the shelvable load power of load type x at time t, respectively. , These represent the variable proportions of transferable and shelvable loads of load type x at time t.

[0074] Therefore, the user-side objective function (Under the condition of constant energy prices) can be expressed as:

[0075]

[0076]

[0077]

[0078] In the formula, , , These represent user energy supply costs, dissatisfaction costs, and incentive benefits, respectively. Let t represent the energy price of energy type x at time t. , Representing energy types The response willingness coefficients are inversely proportional to the user's response willingness; that is, the smaller the two coefficients are, the greater the user's response willingness. , , These represent the unit energy purchase cost, carbon incentive price, and load change amount required for load type x at time t, respectively; T and N represent the dispatch cycle and the number of users; X represents the load type (energy type), which is 3 here.

[0079] Example 2 like Figure 3As shown, based on the same inventive concept as the above embodiments, the present invention also provides a comprehensive energy park operation optimization device, comprising: The first optimization module is used to perform economic dispatch based on the day-ahead renewable energy forecast results, load forecast results and system equipment parameters, with the goal of minimizing the operating cost on the energy supply side, and to obtain the unit output plan. The first calculation module is used to calculate the system's traditional dynamic carbon emission factor based on the unit's output plan; The second calculation module is used to determine the renewable energy curtailment energy at each time point based on the day-ahead renewable energy forecast results, and to calculate the improved dynamic carbon emission factor by combining the traditional dynamic carbon emission factor. The improved dynamic carbon emission factor is obtained by using the average value of the traditional dynamic carbon emission factor over the entire day as the equivalent carbon emission benchmark for renewable energy consumption, and then transmitting it to the thermal and cooling energy networks through the park's multi-energy coupling equipment. The park's multi-energy coupling equipment includes electric boilers, electric chillers, and cogeneration units. The cogeneration units include gas turbines and waste heat boilers. The carbon intensity zoning module is used to determine the carbon intensity zoning threshold for each energy network based on the improved dynamic carbon emission factor, and to perform carbon intensity zoning according to the carbon intensity zoning threshold. The carbon reward and penalty price signal generation module is used to generate carbon reward and penalty price signals for each time period based on the carbon intensity zoning results and the improved dynamic carbon emission factor. The second optimization module is used to input the carbon reward and punishment price signal into the user-side objective function for optimization, adjust the user-side adjustable load, and obtain the load curve after user response. The user-side adjustable load includes transferable load and cut-off load. The user-side adjustable load is adjusted to meet the spatiotemporal conservation constraints of transferable load and the power upper limit constraints of cut-off load. The third optimization module is used to take the load curve after the user's response as the new load condition, re-execute economic dispatch, and obtain an updated unit output plan.

[0080] Example 3 like Figure 4 As shown, the present invention also provides an electronic device 100 for implementing a method for optimizing the operation of integrated energy parks; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0081] The memory 101 can be used to store computer program 103. The processor 102 implements the steps of the integrated energy park operation optimization method of Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0082] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0083] At least one processor 102 may be a Central Processing Unit (CPU), or 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. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0084] The memory 101 in the electronic device 100 stores multiple instructions to implement a comprehensive energy park operation optimization method, and the processor 102 can execute multiple instructions to achieve the following: Based on the recent renewable energy forecast results, load forecast results and system equipment parameters, economic dispatch is executed with the goal of minimizing the operating cost on the energy supply side, and the unit output plan is obtained. Based on the unit output plan, the traditional dynamic carbon emission factor of the calculation system is used. Based on the recent renewable energy forecast results, the energy curtailed from renewable energy at each time point is determined. Combined with the traditional dynamic carbon emission factor, an improved dynamic carbon emission factor is calculated. The improved dynamic carbon emission factor is obtained by using the average value of the traditional dynamic carbon emission factor throughout the day as the equivalent carbon emission benchmark for renewable energy consumption, and then transmitting it to the thermal and cooling energy networks via the park's multi-energy coupling equipment. The park's multi-energy coupling equipment includes electric boilers, electric chillers, and cogeneration units. The cogeneration units include gas turbines and waste heat boilers. The carbon intensity zoning threshold for each energy network is determined based on the improved dynamic carbon emission factor, and carbon intensity zoning is performed according to the carbon intensity zoning threshold. Carbon reward and penalty price signals for each time period are generated based on carbon intensity zoning results and improved dynamic carbon emission factors; The carbon reward and penalty price signal is input into the user-side objective function for optimization. The user-side adjustable load is adjusted to obtain the load curve after user response. The user-side adjustable load includes transferable load and cut-off load. The user-side adjustable load is adjusted to meet the spatiotemporal conservation constraints of transferable load and the power upper limit constraint of cut-off load. The load curve after the user's response is used as the new load condition to re-execute economic dispatch and obtain an updated unit output plan.

[0085] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0086] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0088] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0090] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the operation of an integrated energy park, characterized in that, Includes the following steps: Based on the recent renewable energy forecast results, load forecast results and system equipment parameters, economic dispatch is executed with the goal of minimizing the operating cost on the energy supply side, and the unit output plan is obtained. Based on the unit output plan, the traditional dynamic carbon emission factor of the calculation system is used. Based on the recent renewable energy forecast results, the energy curtailed from renewable energy at each time point is determined. Combined with the traditional dynamic carbon emission factor, an improved dynamic carbon emission factor is calculated. The improved dynamic carbon emission factor is obtained by using the average value of the traditional dynamic carbon emission factor throughout the day as the equivalent carbon emission benchmark for renewable energy consumption, and then transmitting it to the thermal and cooling energy networks via the park's multi-energy coupling equipment. The park's multi-energy coupling equipment includes electric boilers, electric chillers, and cogeneration units. The cogeneration units include gas turbines and waste heat boilers. The carbon intensity zoning threshold for each energy network is determined based on the improved dynamic carbon emission factor, and carbon intensity zoning is performed according to the carbon intensity zoning threshold. Carbon reward and penalty price signals for each time period are generated based on carbon intensity zoning results and improved dynamic carbon emission factors; The carbon reward and penalty price signal is input into the user-side objective function for optimization. The user-side adjustable load is adjusted to obtain the load curve after user response. The user-side adjustable load includes transferable load and cut-off load. The user-side adjustable load is adjusted to meet the spatiotemporal conservation constraints of transferable load and the power upper limit constraint of cut-off load. The load curve after the user's response is used as the new load condition to re-execute economic dispatch and obtain an updated unit output plan.

2. The method for optimizing the operation of integrated energy parks according to claim 1, characterized in that, The improved dynamic carbon emission factor is calculated using the following formula: in, , , These represent the dynamic carbon emission factors of the improved electric, thermal, and cold networks at time t; 、 These are the traditional dynamic carbon emission factors of the electric, thermal, and cold networks at time t, respectively. This represents the energy discarded from renewable energy sources at time t; , These represent the electrothermal conversion efficiency of the electric boiler and the electrocooling conversion efficiency of the electric refrigeration system, respectively. This represents the average value of the traditional dynamic carbon emission factor of the park's power system.

3. The method for optimizing the operation of integrated energy parks according to claim 1, characterized in that, In the steps of implementing economic dispatch with the goal of minimizing energy supply-side operating costs, the objective function of economic dispatch includes a carbon trading cost term. This carbon trading cost term is calculated based on an improved tiered carbon trading model, expressing the carbon trading price as a linear function of the difference between actual carbon emissions and carbon emission allowances. The expression is as follows: in, For the improved tiered carbon trading price, For carbon trading costs, This represents the actual total carbon emissions. V represents the carbon emission allowance, and V, A, and D represent the base price, price increase rate, and interval length of the traditional tiered carbon trading model, respectively.

4. The method for optimizing the operation of integrated energy parks according to claim 1, characterized in that, The carbon intensity zoning thresholds for each energy network are determined based on the improved dynamic carbon emission factor, including: Using linear interpolation, the carbon intensity zoning thresholds for each energy network are calculated based on the maximum and minimum values ​​of the improved dynamic carbon emission factor for the day; among which, In the formula, x represents the number of energy types within the integrated energy park; , These represent the upper and lower limits of the carbon partitioning threshold, respectively. , These represent the maximum and minimum values ​​of the improved carbon emission factor for the day, respectively.

5. The method for optimizing the operation of integrated energy parks according to claim 1, characterized in that, Based on the carbon intensity zoning results and the improved dynamic carbon emission factor, carbon reward and penalty price signals for each time period are generated, specifically calculated using the following formula: in, Let be the unit carbon reward / penalty price of energy x at time t; This is the price conversion factor; The improved dynamic carbon emission factor of energy type x in the park at time t; , These represent the different types of energy in the park. The upper and lower thresholds of the carbon partition.

6. The method for optimizing the operation of integrated energy parks according to claim 1, characterized in that, User-side objective function for: In the formula, , , These represent user energy supply costs, dissatisfaction costs, and incentive benefits, respectively. This represents the energy price of energy type x at time t; , Representing energy types The response willingness coefficient; , , These represent the unit energy cost, carbon incentive price, and load change amount required for load type x at time t, respectively; T and N represent the dispatch cycle and the number of users, respectively; and X represents the load type.

7. The method for optimizing the operation of integrated energy parks according to claim 1, characterized in that, Following the step of obtaining the updated unit output plan, there are also iterative steps: Determine whether the difference between the updated unit output plan and the previous unit output plan is less than the convergence threshold; if not, re-execute economic dispatch based on the updated unit output plan until the convergence condition is met.

8. A comprehensive energy park operation optimization device, characterized in that, include: The first optimization module is used to perform economic dispatch based on the day-ahead renewable energy forecast results, load forecast results and system equipment parameters, with the goal of minimizing the operating cost on the energy supply side, and to obtain the unit output plan. The first calculation module is used to calculate the system's traditional dynamic carbon emission factor based on the unit's output plan; The second calculation module is used to determine the renewable energy curtailment energy at each time point based on the day-ahead renewable energy forecast results, and to calculate the improved dynamic carbon emission factor by combining the traditional dynamic carbon emission factor. The improved dynamic carbon emission factor is obtained by using the average value of the traditional dynamic carbon emission factor over the entire day as the equivalent carbon emission benchmark for renewable energy consumption, and then transmitting it to the thermal and cooling energy networks through the park's multi-energy coupling equipment. The park's multi-energy coupling equipment includes electric boilers, electric chillers, and cogeneration units. The cogeneration units include gas turbines and waste heat boilers. The carbon intensity zoning module is used to determine the carbon intensity zoning threshold for each energy network based on the improved dynamic carbon emission factor, and to perform carbon intensity zoning according to the carbon intensity zoning threshold. The carbon reward and penalty price signal generation module is used to generate carbon reward and penalty price signals for each time period based on the carbon intensity zoning results and the improved dynamic carbon emission factor. The second optimization module is used to input the carbon reward and punishment price signal into the user-side objective function for optimization, adjust the user-side adjustable load, and obtain the load curve after user response. The user-side adjustable load includes transferable load and cut-off load. The user-side adjustable load is adjusted to meet the spatiotemporal conservation constraints of transferable load and the power upper limit constraints of cut-off load. The third optimization module is used to take the load curve after the user's response as the new load condition, re-execute economic dispatch, and obtain an updated unit output plan.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the integrated energy park operation optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the integrated energy park operation optimization method as described in any one of claims 1 to 7.

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