Methods, apparatus, media, and electronic equipment for determining equipment scheduling strategies in industrial parks based on electricity demand response.

Abstract

This application discloses a method, apparatus, medium, and electronic equipment for determining equipment scheduling strategies for industrial parks based on electricity carbon demand response. The method includes: collecting power information of the target industrial park; determining the target load, output power of energy conversion equipment, and purchased electricity power of the target industrial park based on the power information and the initial carbon emission factor of the target industrial park; determining a corrected carbon emission factor for the target industrial park based on the output power of the energy conversion equipment and the purchased electricity power; when the difference between the corrected carbon emission factor and the initial carbon emission factor is less than or equal to a preset difference threshold, determining the corrected carbon emission factor as the target carbon emission factor for the target industrial park; and determining the equipment scheduling strategy for the target industrial park based on the output power of the energy conversion equipment and the purchased electricity power corresponding to the target carbon emission factor. This application solves the technical problem of inaccurate determination of equipment scheduling strategies for industrial parks in related technologies.

Description

Technical Field

[0001] This application relates to the field of power systems, and more specifically, to a method, apparatus, medium, and electronic equipment for determining equipment scheduling strategies for industrial parks based on electricity carbon demand response. Background Technology

[0002] With the continuous growth of renewable energy installed capacity, how to effectively control carbon emissions while ensuring energy security has become a significant challenge in energy research. As a typical scenario of multi-energy convergence and multi-load aggregation, industrial parks must not only undertake the coordinated and optimized scheduling of various internal equipment such as gas turbines, electric boilers, and energy storage, but also consider demand response management on the user side, guiding users to peak shaving and load adjustment through incentive mechanisms. Therefore, equipment scheduling strategies for industrial parks that balance low-carbon development and demand response have become an important research direction. Related technologies typically optimize equipment scheduling in industrial parks based on minimizing costs, resulting in scheduling strategies that cannot comprehensively address the combined needs of energy security and low-carbon operation. Therefore, related technologies suffer from inaccurate results in determining equipment scheduling strategies for industrial parks.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method, apparatus, medium, and electronic device for determining equipment scheduling strategies for industrial parks based on electricity demand response, in order to at least solve the technical problem of inaccurate determination results of equipment scheduling strategies for industrial parks in related technologies.

[0005] According to one aspect of the embodiments of this application, a method for determining equipment scheduling strategies for a park based on electricity demand response is provided, comprising: collecting power information of a target park; determining a first load adjustment amount and a second load adjustment amount of the loads included in the target park based on the power information, wherein the first load adjustment amount is obtained based on electricity demand response adjustment of the target park, and the second load adjustment amount is obtained based on carbon demand response adjustment of the target park; determining a target load amount of the loads based on the first load adjustment amount, the second load adjustment amount, and an initial carbon emission factor of the target park, wherein the initial carbon emission factor is used to quantify the carbon emission of the target park. Increase capacity; based on the target load, determine the output power of each energy conversion device in the target park, as well as the purchased electricity power of the target park; based on the output power of each energy conversion device and the purchased electricity power, determine the corrected carbon emission factor of the target park; if the difference between the corrected carbon emission factor and the initial carbon emission factor is less than or equal to a preset difference threshold, determine the corrected carbon emission factor as the target carbon emission factor of the target park; based on the output power of each energy conversion device corresponding to the target carbon emission factor, as well as the purchased electricity power corresponding to the target carbon emission factor, determine the equipment scheduling strategy of the target park.

[0006] According to another aspect of the embodiments of this application, a device for determining equipment scheduling strategies for a park based on electricity carbon demand response is provided, comprising: a data acquisition module for acquiring power information of a target park; a first determination module for determining a first load adjustment amount and a second load adjustment amount of loads included in the target park based on the power information, wherein the first load adjustment amount is obtained based on electricity demand response adjustment of the target park, and the second load adjustment amount is obtained based on carbon demand response adjustment of the target park; a second determination module for determining a target load amount of the load based on the first load adjustment amount, the second load adjustment amount, and an initial carbon emission factor of the target park, wherein the initial carbon emission factor is used to quantify the carbon emissions of the target park; the second... The third determination module is used to determine the output power of various energy conversion devices included in the target park and the purchased electricity power of the target park based on the target load. The fourth determination module is used to determine the corrected carbon emission factor of the target park based on the output power of various energy conversion devices and the purchased electricity power. The fifth determination module is used to determine the corrected carbon emission factor as the target carbon emission factor of the target park when the difference between the corrected carbon emission factor and the initial carbon emission factor is less than or equal to a preset difference threshold. The sixth determination module is used to determine the equipment scheduling strategy of the target park based on the output power of various energy conversion devices corresponding to the target carbon emission factor and the purchased electricity power corresponding to the target carbon emission factor.

[0007] According to another aspect of the embodiments of this application, a non-volatile storage medium is provided, which stores multiple instructions, any one of which is adapted to be loaded and executed by a processor for a method of determining a campus equipment scheduling strategy based on carbon demand response.

[0008] According to another aspect of the embodiments of this application, an electronic device is provided, including: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the following methods for determining a campus equipment scheduling strategy based on carbon demand response.

[0009] In this embodiment, power information of the target park is collected; based on the power information, a first load adjustment amount and a second load adjustment amount of the loads included in the target park are determined, wherein the first load adjustment amount is obtained based on the electricity demand response adjustment of the target park, and the second load adjustment amount is obtained based on the carbon demand response adjustment of the target park; based on the first load adjustment amount, the second load adjustment amount, and the initial carbon emission factor of the target park, the target load amount is determined, wherein the initial carbon emission factor is used to quantify the carbon emissions of the target park; based on the target load amount, the output power corresponding to the various energy conversion devices included in the target park and the purchased electricity power of the target park are determined; based on the output power corresponding to the various energy conversion devices and the purchased electricity power, a corrected carbon emission factor of the target park is determined; if the difference between the corrected carbon emission factor and the initial carbon emission factor is less than or equal to a preset difference threshold, the corrected carbon emission factor is determined as the target carbon emission factor of the target park; based on the output power corresponding to the various energy conversion devices corresponding to the target carbon emission factor and the purchased electricity power corresponding to the target carbon emission factor, the equipment scheduling strategy of the target park is determined. The goal is to collect power information from the park, determine the first load adjustment amount based on electricity demand response and the second load adjustment amount based on carbon demand response, and determine the equipment scheduling strategy of the park based on the first load adjustment amount and the second load adjustment amount. This achieves the technical effect of improving the accuracy of the determination result of the equipment scheduling strategy of the park, thereby solving the technical problem of inaccurate determination result of the equipment scheduling strategy of the park in related technologies. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0011] Figure 1This is a flowchart of a method for determining equipment scheduling strategies in a park based on electricity demand response, according to an embodiment of this application.

[0012] Figure 2 This is a flowchart of an optional method for determining equipment scheduling strategies for a park based on electricity carbon demand response, according to an embodiment of this application.

[0013] Figure 3 This is a schematic diagram of an optional integrated energy system park structure provided according to an embodiment of this application;

[0014] Figure 4 This is a flowchart of an optional integrated energy system optimization model solution provided according to an embodiment of this application;

[0015] Figure 5 This is a schematic diagram of an optional incentive price provided according to an embodiment of this application;

[0016] Figure 6 This is an optional peak-valley pricing diagram provided according to an embodiment of this application;

[0017] Figure 7 This is a schematic diagram of load changes before and after an optional demand response in a park, provided according to an embodiment of this application.

[0018] Figure 8 This is a schematic diagram illustrating an optional change in carbon emissions and carbon quotas for a park, provided according to an embodiment of this application.

[0019] Figure 9 This is a schematic diagram of the operation of an optional park gas power related equipment according to an embodiment of this application;

[0020] Figure 10 This is a schematic diagram of an optional park thermal power equipment operation according to an embodiment of this application;

[0021] Figure 11 This is a schematic diagram of an optional park power equipment operation according to an embodiment of this application;

[0022] Figure 12 This is a schematic diagram of an optional park cooling power equipment operation according to an embodiment of this application;

[0023] Figure 13 This is a schematic diagram of an optional device for determining the equipment scheduling strategy of a park based on electricity carbon demand response, according to an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] According to an embodiment of this application, a method embodiment for determining equipment scheduling strategy in a park based on carbon demand response is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] Figure 1 This is a flowchart illustrating a method for determining equipment scheduling strategies in a park based on electricity demand response, according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0028] Step S102: Collect power information of the target park;

[0029] It is understandable that collecting power information from the target industrial park, such as load curves for electrical load, heat load, cooling load, and electric vehicle load, renewable energy output forecasts, comprehensive energy equipment parameters (including carbon emission coefficients for combined heat and power units, gas-fired boilers, and purchased electricity), and the initial carbon emission factor of the target industrial park, can provide a rich data foundation for determining subsequent equipment scheduling strategies for the park.

[0030] Optionally, the initial carbon emission factor can be determined as follows: Obtain historical carbon emission data of the target industrial park, predicted meteorological data of the target industrial park, and predicted renewable energy output of the target industrial park. Based on the aforementioned historical carbon emission data, predicted meteorological data, and predicted renewable energy output, determine the initial carbon emission factor of the target industrial park.

[0031] Step S104: Based on power information, determine the first load adjustment amount and the second load adjustment amount of the loads included in the target park, wherein the first load adjustment amount is obtained based on the electricity demand response adjustment of the target park, and the second load adjustment amount is obtained based on the carbon demand response adjustment of the target park.

[0032] It is understandable that, based on the collected power information of the target industrial park, the first and second load regulation amounts of the loads included in the target industrial park are determined. The first load regulation amount is obtained by adjusting the power load distribution of the target industrial park based on its electricity demand response, and then determining the first load regulation amount based on the adjusted power load distribution. Similarly, the second load regulation amount is obtained by adjusting the carbon emission distribution of the target industrial park based on its carbon demand response, and then determining the second load regulation amount based on the adjusted carbon emission distribution. The combined application of electricity demand response and carbon demand response can reduce the operating costs of the target industrial park, reduce carbon emissions, and improve the rationality and reliability of subsequent equipment scheduling strategies for the target industrial park.

[0033] Optionally, the park, as the initiator of demand response, can use a demand response approach that combines "TV corner" and "carbon perspective," along with load-side incentive mechanisms and guiding prices, to guide users to actively adjust their load distribution, thereby mitigating load peaks at the park level and reducing carbon emissions caused by user behavior. Simultaneously, as the manager of optimized scheduling, the park can use the determined load adjustment results on the user side (i.e., the target load amount) as boundary conditions, comprehensively considering the dynamic operating characteristics of various energy carriers and related equipment, to conduct low-carbon optimized scheduling within the park.

[0034] Optionally, a first load adjustment amount for the loads included in the target park can be determined by constructing an electricity demand response model. The aforementioned electricity demand response model may include an adjustable load model, a load reduction model, a load shifting model, and an electric vehicle load model. Specifically, the adjustable load model is used to determine the adjustable load amount (including the adjustable load amount for cooling and heating), the load reduction model is used to determine the load reduction amount, the load shifting model is used to determine the load shifting amount, and the electric vehicle load model is used to determine the electric vehicle load amount.

[0035] In an optional embodiment, when the first load adjustment amount includes adjustable load amount, load reduction amount, load shifting amount, and electric vehicle load amount, determining the first load adjustment amount of the load included in the target park based on power information includes: determining the adjustable load amount based on the temperature adjustment amount of the load included in the power information; determining the load reduction amount based on the maximum load reduction amount of the load included in the power information; determining the load shifting amount based on the electricity price change before and after the electricity demand response included in the power information; and determining the electric vehicle load amount based on the number of multiple electric vehicles participating in the electricity demand response in the target park included in the power information, and the charging power corresponding to each of the multiple electric vehicles.

[0036] It is understood that when the first load regulation includes adjustable load, load reduction, load shifting, and electric vehicle load, the adjustable load, load reduction, load shifting, and electric vehicle load are determined respectively in the following ways: The adjustable load is determined based on the temperature regulation of the load (e.g., cooling and heating temperature regulation) included in the power information; the load reduction is determined based on the maximum load reduction of the load included in the power information; the load shifting is determined based on the change in electricity price before and after demand response of the load included in the power information; and the electric vehicle load is determined based on the number of electric vehicles participating in demand response in the target area and the charging power of each electric vehicle. By analyzing power information and identifying and quantifying different types of first load regulation, more efficient operation scheduling of park equipment can be achieved, and the accuracy of the park equipment scheduling strategy determination results can be improved.

[0037] Optionally, adjustable load refers to the amount of electrical load, heat load, and cooling load that can be flexibly increased or decreased by adjusting the operating parameters of the load equipment without affecting user comfort; load reduction refers to the amount of electrical load that users actively reduce during critical periods, such as peak and off-peak hours, to alleviate grid pressure; load shifting refers to the transfer of non-immediate demand load from peak to off-peak hours to achieve peak shaving and valley filling; electric vehicle load refers to the electrical load constituted by the charging demand of electric vehicles in the target area, which can be charged at different times through intelligent management to respond to the energy dispatching needs of the target area.

[0038] Alternatively, the adjustable load can be determined using an adjustable load model within the electricity demand response model. The adjustable load model can be constructed as follows:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] in, This represents the adjustable load amount of heat load at time t. This represents the adjustable load of the cooling load at time t. Indicates the volumetric heat / cold coefficient. Indicates the volume of the heating / cooling space. This represents the amount of heating temperature adjustment at time t. This represents the amount of cooling temperature adjustment at time t. This indicates the upper limit of the adjustable temperature range under heating conditions, which is mainly limited by user satisfaction and comfort levels. This indicates the upper limit of the adjustable temperature range under cooling conditions, which is mainly limited by user satisfaction and comfort levels. This indicates the upper limit of the adjustable load for heat load. This indicates the upper limit of the adjustable load for cooling.

[0046] Alternatively, the reducible load can be determined using a reducible load model within the electricity demand response model. The reducible load model can be constructed as follows:

[0047]

[0048] in, This represents the amount of load that can be reduced at time t. This represents the maximum amount of electrical load that can be reduced at time t.

[0049] Alternatively, the movable load quantity can be determined using the movable load model within the electricity demand response model. The movable load model can be constructed as follows:

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] in, This represents the amount of load that can be moved at time t. This represents the upper limit of the movable load at time t, where T represents the total number of times. Represents the elasticity price coefficient matrix, This represents the elasticity of price, indicating the impact of the electricity price at time n on the electricity load at time m. This represents the change in electrical load before and after the demand response at time m. Indicates the original load amount. This represents the original electricity price at time n. This represents the change in electricity price at time n before and after the electricity demand response. This represents the amount of interruptible load that could be interrupted at time t. This represents the change in electricity price at time t before and after the electricity demand response. This represents the original electricity price at time t.

[0056] Alternatively, the electric vehicle load can be determined using the electric vehicle load model within the electricity demand response model. The electric vehicle load model can be constructed as follows:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] in, This represents the SOC (State of Charge) value at the initial moment of charging an electric vehicle. This indicates the set SOC value at the initial moment of charging an electric vehicle. This represents the SOC value at the end of charging for an electric vehicle. The set SOC value indicates the point at which the electric vehicle charging ends. This represents the SOC value of the electric vehicle at time t. This represents the SOC value of the electric vehicle at time t-1. This indicates the charging efficiency of electric vehicles. This represents the charging power of the electric vehicle at time t. Indicates the length of time. Indicates the capacity of electric vehicles. This indicates the start time of charging for the electric vehicle. Indicates the end of charging time for the electric vehicle. and These represent the minimum and maximum charging SOC values ​​of an electric vehicle, respectively. This indicates the rated charging power of the electric vehicle. This represents the total load of electric vehicles at time t after aggregation (i.e., the electric vehicle load in the park), and 'a' represents the number of electric vehicles participating in demand response. Let represent the charging power of the i-th electric vehicle at time t.

[0064] Optionally, a second load adjustment amount for the loads included in the target industrial park can be determined by constructing a carbon demand response model. The carbon demand response model can be constructed as follows:

[0065]

[0066]

[0067]

[0068]

[0069] in, This represents the amount of load increase used for carbon demand response at time t (i.e., the second load adjustment amount at the time of increase). This represents the amount of load reduction used for carbon demand response at time t (i.e., the second load adjustment amount during the reduction). This represents the load adjustment factor used for carbon demand response at time t. This indicates the upper limit of load adjustments caused by carbon demand response. This represents the initial load at time t. This indicates the daily limit for adjusted electricity consumption due to carbon demand response.

[0070] Step S106: Based on the first load adjustment amount, the second load adjustment amount, and the initial carbon emission factor of the target park, determine the target load amount, wherein the initial carbon emission factor is used to quantify the carbon emissions of the target park.

[0071] It is understandable that the target load for the target industrial park is determined based on the first and second load adjustment amounts, combined with the initial carbon emission factor used to quantify the park's carbon emissions. This approach allows for a deep integration of park load regulation and carbon emission control. It not only optimizes the target load and ensures low-carbon operation of the park, but also improves the accuracy and reliability of the determined equipment scheduling strategy through precise carbon emission accounting.

[0072] In one optional embodiment, the target load is determined based on a first load adjustment amount, a second load adjustment amount, and the initial carbon emission factor of the target park. This includes: using a target optimization model to obtain the target load based on the first load adjustment amount, the second load adjustment amount, and the initial carbon emission factor of the target park. The target optimization model includes a first objective function and first constraints. The first objective function is used to minimize the sum of the load volatility of the target park, the carbon emissions of the target park, and the demand response cost of the target park. The first constraints include time-of-use electricity price constraints, incentive price constraints, and satisfaction constraints. The target load includes at least: a target electrical load, a target thermal load, and a target cooling load.

[0073] It is understandable that, based on the first and second load adjustment amounts and the initial carbon emission factor of the target area, a target optimization model for the target area is used to determine the target load amount. This target optimization model consists of a first objective function and first constraints. The first objective function minimizes the sum of the load volatility, carbon emissions, and demand response costs of the target area. The first constraints include time-of-use pricing constraints, incentive price constraints, and satisfaction constraints (i.e., user satisfaction constraints). The target load amount includes at least: target electrical load, target thermal load, and target cooling load. By constructing a target optimization model for the target area, precise load regulation can be achieved, which not only improves energy utilization efficiency but also effectively controls carbon emissions, providing a basis for determining equipment scheduling strategies for the target area.

[0074] Optionally, the aforementioned target electricity load refers to the expected electricity load level of the target industrial park, taking into account both electricity demand response and carbon demand response. It reflects the optimal trade-off between electricity supply and demand balance, cost control, and carbon emission reduction targets. The target heat load refers to the expected heat load level of the target industrial park, taking into account both electricity demand response and carbon demand response. It aims to achieve efficient matching of heat supply and demand and reduce carbon emissions through refined equipment scheduling strategies. The target cooling load refers to the expected cooling load level of the target industrial park, taking into account both electricity demand response and carbon demand response. It can be used to balance the cooling load demand of the target industrial park with energy conservation and emission reduction.

[0075] In an optional embodiment, the first objective function is:

[0076]

[0077] in, Denotes the first objective function. Indicates load volatility. This indicates the carbon emissions of the target industrial park. This represents the demand response cost of the load. This represents the demand response cost of electric vehicles. This represents the weighting coefficient for the demand response cost of the target industrial park. The weighting coefficient representing load volatility. Weighting coefficients representing the carbon emissions of the target industrial park;

[0078] The time-of-use electricity pricing constraint is:

[0079]

[0080]

[0081]

[0082] in, and These represent the lower and upper limits of the load price, respectively. Let h represent the load price at time t, h represent the heat load, c represent the cooling load, and e represent the electrical load. This represents the load price adjustment at time t. and These represent the lower and upper limits of the load price adjustment, respectively. Indicates the initial load price;

[0083] The incentive price constraint is:

[0084]

[0085]

[0086] in, This represents the load incentive price at time t. This indicates the upper limit of the load incentive price. This represents the average load price for electrical load, heating load, and cooling load.

[0087] The satisfaction constraint is:

[0088]

[0089]

[0090] in, This represents a satisfaction index with the target load after demand response, where T indicates the total number of time points. This represents the difference between the target load and the initial load. Indicates the initial load, where P represents electrical load, C represents cooling load, and H represents heating load. This indicates a satisfaction index with load prices.

[0091] It is understandable that the target optimization model for the target industrial park includes a first objective function and first constraints. The first objective function is determined based on the load volatility, carbon emissions, and demand response costs of the target industrial park. The first constraints include time-of-use pricing constraints, incentive price constraints, and satisfaction constraints. Specifically, the time-of-use pricing constraint limits the range of price adjustments to ensure compliance with operating rules and user expectations; the incentive price constraint limits the boundaries of incentive prices to avoid unreasonable pricing; and the satisfaction constraint ensures that user energy satisfaction after adjustments to electrical, thermal, and cooling loads (including load quantity and load price adjustments) is not lower than preset thresholds (including satisfaction indicators for target load quantity and load price after demand response), preventing excessive adjustments that could lead to a decline in user experience. Through the aforementioned first objective function and first constraints, the range of changes in load prices and load incentive prices can be effectively limited, and user satisfaction after load adjustments can be ensured to be no lower than preset thresholds. This, in turn, accurately guides the scheduling scheme of equipment within the industrial park while guaranteeing user experience, improving the rationality and reliability of the park's equipment scheduling strategy.

[0092] Optionally, an upper-level optimization model (i.e., the target optimization model) can be established with the objective function of minimizing the sum of load volatility, carbon emissions, and demand response costs (i.e., the first objective function). Based on peak-valley electricity trading signals and carbon incentive signals, the target load of the park is determined, including: target electricity load, target heat load, and target cooling load. The upper-level optimization model includes the first objective function and first constraints. The first objective function is determined based on the load volatility, carbon emissions, and demand response costs of the target park, and is used to minimize the sum of these three factors. The first constraints include time-of-use pricing constraints, incentive price constraints, and user satisfaction constraints.

[0093] Optionally, the above-mentioned load volatility Carbon emissions of the target park Demand response cost of load and the demand response cost of electric vehicles It can be determined in the following way:

[0094]

[0095]

[0096]

[0097]

[0098] in, This represents the target electrical load at time t+1. This represents the target electrical load at time t. This represents the target heat load at time t+1. This represents the target heat load at time t. This represents the target cooling load at time t+1. This represents the target cooling load at time t. This represents the carbon emission factor of the electrical load at time t. The carbon emission factor representing the heat load at time t. This represents the carbon emission factor of the cooling load at time t. This indicates the compensation price for users participating in carbon demand response. Let t represent the incentive price of the electric vehicle at time t.

[0099] Optionally, the target electrical load at time t Target heat load at time t and the target cooling load at time t It can be determined in the following way:

[0100]

[0101]

[0102]

[0103] in, This represents the initial electrical load at time t. This represents the initial heat load at time t. This represents the initial cooling load at time t.

[0104] Step S108: Based on the target load, determine the output power of the various energy conversion devices included in the target park, as well as the purchased power of the target park.

[0105] It is understood that, based on the target load of the target industrial park, the output power of each of the various energy conversion devices included in the park, as well as the purchased electricity power, are determined. These various energy conversion devices may include, but are not limited to, combined heat and power units (CHPs), gas boilers (GBs), electric boilers, absorption chillers, and electric chillers. The purchased electricity refers to the electricity purchased from the upper-level power grid when the target industrial park's own power generation cannot meet the load demand. By accurately determining the output power and purchased electricity power corresponding to each of the various energy conversion devices, not only can the park's energy use be optimized and operating costs reduced, but carbon emissions can also be reduced, providing a basis for the precise determination of the park's equipment scheduling strategy.

[0106] In one optional embodiment, based on the target load, the output power corresponding to the various energy conversion devices included in the target park and the purchased electricity power of the target park are determined, including: determining the carbon quota purchase cost, gas purchase cost, electricity purchase cost, equipment operation and maintenance cost, and wind and solar curtailment cost of the target park; based on the carbon quota purchase cost, gas purchase cost, electricity purchase cost, equipment operation and maintenance cost, wind and solar curtailment cost, and demand response cost of the target park, a second objective function is determined to minimize the total operating cost of the target park; based on the target load, the second objective function is used to obtain the output power corresponding to the various energy conversion devices and the purchased electricity power of the target park.

[0107] It is understandable that the costs of carbon allowance purchase, gas purchase, electricity purchase, equipment operation and maintenance, and wind and solar curtailment for the target industrial park are determined. Based on these costs, and considering the demand response cost of the target industrial park, a second objective function is determined to minimize the total operating cost of the target industrial park. Based on the target load, the second objective function is used for optimization to obtain the output power of various energy conversion devices and the externally purchased electricity power of the target industrial park. By constructing the second objective function, the industrial park can achieve precise scheduling of various energy conversion devices, optimize the externally purchased electricity strategy, and improve the accuracy of the industrial park's equipment scheduling strategy determination results.

[0108] Optionally, when supplying energy to the park, it needs to purchase not only electricity but also natural gas when the park's natural gas supply is insufficient. Therefore, when determining the equipment dispatch strategy for the target park, it is necessary to determine not only the amount of electricity to be purchased but also the amount of natural gas to be purchased.

[0109] Optionally, a comprehensive energy system scheduling model can be constructed that incorporates tiered carbon pricing, wind and solar curtailment penalties, equipment operation and maintenance costs, and energy storage operations. This model can then be used to determine the power output of park equipment (e.g., the output power of various energy conversion devices), the charging and discharging power of energy storage, and the amount of purchased energy (e.g., purchased electricity and purchased gas). Using this comprehensive energy system scheduling model, not only can the power output of park equipment be determined, but also the charging and discharging power of energy storage and the amount of purchased gas, thus improving the accuracy of the park's scheduling strategy.

[0110] Optionally, the aforementioned integrated energy system scheduling model may include a second objective function and second constraints. The second objective function can be determined based on carbon allowance purchase costs, gas purchase costs, electricity purchase costs, equipment operation and maintenance costs, wind and solar curtailment costs, and demand response costs, and is used to minimize the total operating cost of the industrial park. The second constraint can be used to limit the output power of energy conversion equipment, etc.

[0111] Alternatively, the two objective functions can be determined in the following way:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] in, This represents the total operating cost. This indicates the cost of purchasing carbon allowances. This indicates the gas purchase cost for the target industrial park. This indicates the electricity purchase cost for the target industrial park. This indicates the equipment operation and maintenance costs of the target park. This indicates the cost of curtailing wind and solar power. This represents the electricity purchase price in the park at time t. This represents the amount of electricity purchased (i.e., the power of externally purchased electricity) at time t in the park. This represents the gas purchase price in the park at time t. This represents the amount of gas purchased from outside the park at time t. Indicates the benchmark carbon price. This indicates the amount of carbon credits purchased by the park. Indicates the length of the carbon emission range. This indicates the growth rate of tiered carbon trading. This represents the collection of all equipment in the park. This represents the unit power operation and maintenance cost of the j-th device. This represents the power of the j-th device at time t. This indicates the cost of curtailing wind and solar power. This represents the amount of wind curtailment at time t. This represents the amount of light discarded at time t.

[0119] Optionally, carbon allowance purchase volume It can be determined in the following way:

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126] in, This represents the carbon emissions of CHP at time t. This represents GB's carbon emissions at time t. This indicates CHP's free carbon allowance. This indicates GB's free carbon credits. This represents the baseline value for generator output, i.e., the free carbon emission allowance per unit of electricity generated. This represents the output electrical power of CHP at time t. This indicates the baseline value for heating based on the unit's category, i.e., the free carbon emission allowance per unit of heat. This represents the output thermal power of CHP at time t. This represents the output thermal power of GB at time t. This represents the carbon emission coefficient of CHP. This represents the carbon emission coefficient of GB. This represents the electrical power consumed by GB at time t. This represents the carbon emissions generated at time t due to purchasing electricity from the upstream power grid. This represents the carbon emission coefficient of the upstream power grid.

[0127] Alternatively, the second constraint can be determined in the following way:

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] in, This represents the amount of electricity purchased from the upstream power grid at time t. This represents the actual power output of wind power generation at time t. This represents the actual power output of solar power generation at time t. This represents the electrical power emitted by CHP at time t. This represents the discharge power of the stored energy at time t. This represents the charging power of the energy storage at time t. This represents the electrical power consumed by AC at time t. This represents the output thermal power of the electric boiler at time t. This represents the discharge power of the thermal storage tank at time t. This represents the charging power of the thermal storage tank at time t. This represents the heat energy consumed by the absorption refrigeration equipment at time t. This represents the gas power purchased at time t. This represents the discharge power of the gas storage tank at time t. This represents the charging power of the gas storage tank at time t. This represents the input gas power of CHP at time t. This represents the input gas power of GB at time t. This represents the output cooling power of the electric refrigeration equipment at time t. This represents the output cooling power of the absorption refrigeration equipment at time t. This represents the charging and discharging state of the stored energy at time t. =1 indicates charging, when =0 indicates discharge. Indicates the rated electrical power of electrical energy storage. and These represent the lower and upper limits of the State of Charge (SOC) value for electrical energy storage, respectively. Indicates the rated capacity of electrical energy storage. This represents the amount of electrical energy stored at time t. This indicates the self-consumption rate of electrical energy storage. This represents the amount of electrical energy stored at time t-1. This indicates the charging efficiency of electrical energy storage. This indicates the discharge efficiency of electrical energy storage. Indicates the energy storage charging power. Indicates the energy storage discharge power. and These represent the lower and upper limits of the electrical power of the CHP unit, respectively. Indicates the gas-to-electricity conversion efficiency. This represents the output electrical power of the CHP unit at time t-1. This indicates the ramp-up power of the CHP unit. and These represent the lower and upper limits of the thermoelectric ratio, respectively. This indicates the gas-to-electric conversion efficiency of GB. and These represent the lower and upper limits of GB output, respectively. This represents the output thermal power of GB at time t-1. Indicates the thermal power of GB. This represents the predicted power output of wind power generation at time t. This represents the predicted output value of solar power generation at time t.

[0146] Step S110: Determine the corrected carbon emission factor of the target park based on the output power of various energy conversion devices and the purchased electricity.

[0147] It is understandable that the initial carbon emission factor of the target park is updated based on the output power of various energy conversion devices and the purchased electricity, thus determining the revised carbon emission factor. By iteratively updating the carbon emission factor of the target park and combining it with real-time data on device output power and purchased electricity, the accuracy of the equipment scheduling strategy determination can be improved, ensuring the safety and reliability of the target park's operation.

[0148] In one optional embodiment, when the multiple energy conversion devices include a combined heat and power (CHP) unit, a gas-fired boiler, an electric boiler, an absorption chiller, and an electric chiller, the corrected carbon emission factor of the target industrial park is determined based on the output power of each of the multiple energy conversion devices and the purchased electricity power. This includes: determining the electrical load carbon emission factor of the target industrial park based on the output electrical power of the CHP unit included in the multiple energy conversion devices and the purchased electricity power; determining the heat load carbon emission factor of the target industrial park based on the output thermal power of the gas-fired boiler included in the multiple energy conversion devices, the output thermal power of the CHP unit included in the multiple energy conversion devices, and the output thermal power of the electric boiler included in the multiple energy conversion devices; determining the cold load carbon emission factor of the target industrial park based on the output cooling power of the absorption chiller included in the multiple energy conversion devices and the output cooling power of the electric chiller included in the multiple energy conversion devices; and summing the electrical load carbon emission factor, the heat load carbon emission factor, and the cold load carbon emission factor to obtain the corrected carbon emission factor.

[0149] It is understood that, when multiple energy conversion devices include combined heat and power (CHP) units, gas-fired boilers, electric boilers, absorption chillers, and electric chillers, the corrected carbon emission factor for the target industrial park is determined as follows: The electrical load carbon emission factor for the target industrial park is calculated based on the output electrical power of the CHP units included in the multiple energy conversion devices, as well as the purchased electrical power. The thermal load carbon emission factor for the target industrial park is calculated based on the output thermal power of the gas-fired boilers, the CHP units, and the electric boilers. The cooling load carbon emission factor for the target industrial park is calculated based on the output cooling power of the absorption chillers and the electric chillers. The calculated electrical load carbon emission factor, thermal load carbon emission factor, and cooling load carbon emission factor are then summed to obtain the corrected carbon emission factor for the target industrial park. By determining the corrected carbon emission factor based on the output power of various energy conversion devices and the purchased power, the accuracy and reliability of the equipment scheduling plan for the target park can be ensured while guaranteeing user satisfaction.

[0150] Optionally, various energy conversion devices may include combined heat and power (CHP) units, gas-fired boilers, electric boilers, absorption chillers, and electric chillers. CHP units and gas-fired boilers achieve synergistic power generation and single-energy heat supply through the combustion of natural gas; electric boilers convert electricity into heat, and electric-to-gas (EPG) devices convert electricity into natural gas; while electric chillers and absorption chillers provide cooling functions. These energy conversion devices can constitute a multi-energy complementary conversion system within the park. Simultaneously, to cope with fluctuations in renewable energy output and load changes, the park can utilize energy storage, thermal storage tanks, and gas storage tanks to achieve energy balance and stable operation across different time scales through the storage and release of energy. Furthermore, in addition to obtaining clean electricity from external renewable energy generation equipment such as wind and solar power, the park can also secure electricity, heat, and cooling loads by purchasing natural gas, electricity, and other new energy sources.

[0151] Optionally, the modified carbon emission factor can be obtained by summing the carbon emission factors of electrical load, thermal load, and cooling load. According to the principle of proportional sharing, the carbon emission factor for the industrial park can be calculated based on the carbon emissions flowing into the park and the load flowing out of the park. Therefore, the carbon emission factor of the park's electrical load at time t is... The heat load and carbon emission factor at time t Carbon emission factor of cooling load at time t It can be determined in the following way:

[0152]

[0153]

[0154]

[0155] in, This indicates the electrothermal conversion efficiency of the electric boiler. This indicates the efficiency of an absorption refrigeration system. This indicates the cooling efficiency of the electric refrigeration equipment.

[0156] Step S112: If the difference between the corrected carbon emission factor and the initial carbon emission factor is less than or equal to a preset difference threshold, the corrected carbon emission factor is determined as the target carbon emission factor for the target park.

[0157] It is understood that the difference between the corrected carbon emission factor and the initial carbon emission factor is determined, and this difference is compared with a preset difference threshold. If the difference is less than or equal to the preset difference threshold, the corrected carbon emission factor is determined as the target carbon emission factor for the target park. By comparing the difference between the corrected carbon emission factor and the initial carbon emission factor and ensuring that it is within the preset threshold, it is possible to ensure that the scheduling results closely match the actual carbon emission situation of the park, thereby effectively improving the accuracy of the equipment scheduling strategy for the target park.

[0158] In an optional embodiment, when the difference is greater than a preset difference threshold, the method further includes: updating and iterating the first load adjustment amount and the second load adjustment amount to obtain a first updated load adjustment amount and a second updated load adjustment amount; updating and iterating the target load amount based on the first updated load adjustment amount, the second updated load adjustment amount, and the corrected carbon emission factor to obtain a target updated load amount; updating and iterating the output power and purchased electricity power corresponding to various energy conversion devices based on the target updated load amount to obtain the output updated power corresponding to various energy conversion devices and the purchased updated electricity power of the target park; updating and iterating the corrected carbon emission factor based on the output updated power and purchased updated electricity power corresponding to various energy conversion devices to obtain the corrected updated carbon emission factor of the target park; stopping the update iteration when the difference between the corrected updated carbon emission factor and the corrected carbon emission factor is less than or equal to a preset difference threshold, and determining the corrected updated carbon emission factor obtained in the last update iteration as the target carbon emission factor.

[0159] It is understandable that if the difference between the corrected carbon emission factor and the initial carbon emission factor exceeds a preset difference threshold, the corrected carbon emission factor needs to be updated iteratively. First, the first and second load adjustments for the target park load are updated iteratively to obtain the first and second updated load adjustments. Second, based on the first and second updated load adjustments and the corrected carbon emission factor, a target optimization model is used to update the target load, obtaining the target updated load. Next, based on the target updated load, a second objective function is used to update the output power of various energy conversion devices and the purchased electricity, obtaining the updated output power of each energy conversion device and the purchased electricity of the target park. Finally, based on the updated output power of each energy conversion device and the purchased electricity, the corrected carbon emission factor is updated iteratively to obtain the corrected updated carbon emission factor for the target park. The difference between the corrected carbon emission factor and the corrected updated carbon emission factor is compared. If the difference is less than or equal to a preset difference threshold, the update iteration stops, and the corrected updated carbon emission factor obtained from the last update iteration is determined as the target carbon emission factor. If the difference is greater than the preset difference threshold, the corrected updated carbon emission factor continues to be updated and iterated in the same way until the update iteration stops (i.e., the difference between the carbon emission factor corresponding to the current iteration number and the carbon emission factor corresponding to the previous iteration number is less than or equal to the preset difference threshold). By iteratively updating the corrected carbon emission factor and finely adjusting the target load and the output power of the energy conversion equipment until the carbon emission factor meets the preset threshold condition, the accuracy and optimization of the park equipment scheduling strategy determination results can be effectively improved, promoting the coordination and unity of operating costs and low-carbon goals, and improving the park's operating efficiency and user satisfaction.

[0160] Step S114: Based on the output power of various energy conversion devices corresponding to the target carbon emission factor and the purchased electricity power corresponding to the target carbon emission factor, determine the equipment scheduling strategy for the target park.

[0161] It is understandable that after determining the target carbon emission factor for the target industrial park, the equipment scheduling strategy for the target park is determined based on the output power of various energy conversion devices corresponding to the target carbon emission factor, as well as the purchased electricity power corresponding to the target carbon emission factor. After determining the target carbon emission factor, by accurately matching the output power of various energy conversion devices and the purchased electricity power, the accuracy of the park's equipment scheduling strategy can be improved, achieving consistency between energy efficiency and emission reduction targets, and optimizing the park's comprehensive energy management.

[0162] Optionally, the equipment in the target park includes not only various energy conversion devices but also various energy storage devices, such as electrical energy storage, thermal storage tanks, and gas storage tanks. Therefore, when determining the equipment scheduling strategy for the target park, it is possible to determine not only the output power of each energy conversion device and the purchased electricity power, but also the charging and discharging power of each energy storage device and the purchased gas power. This allows for coordinated control of the various devices in the target park, reducing costs and carbon emissions. Once the target carbon emission factor for the target park is determined, the optimal demand response curve (i.e., the load curve corresponding to the target load), the equipment scheduling strategy (including the output power of each energy conversion device, the charging and discharging power of the energy storage devices, and the purchased energy), the total operating cost of the park, and the park's carbon emissions are output.

[0163] Optionally, the above-mentioned method for determining the equipment scheduling strategy of a park based on carbon demand response can be applied to a system for determining the equipment scheduling strategy of a park based on carbon demand response. This system may include a data acquisition module, an electricity demand response processing module, a carbon demand response processing module, an upper-level optimization module, and a lower-level scheduling module. The data acquisition module can be used to collect the park's electricity load, heat load, cooling load, and electric vehicle load curves, renewable energy output forecasts, and comprehensive energy equipment parameters. The electricity demand response processing module can be used to analyze the elasticity coefficient of load to time-of-use pricing and incentive signals from the perspective of electricity prices, establish response models according to four types of loads: adjustable load, load that can be reduced, load that can be shifted, and electric vehicle load, calculate the potential power adjustment amount (i.e., the first load adjustment amount) for each time period, and form a candidate set of electricity demand responses to provide data support for subsequent price-incentive joint optimization. The carbon demand response processing module can, based on carbon emission metering methods, associate various user-side loads with their corresponding dynamic carbon emission factors, calculate the load-carbon emission coupling adjustment space that can be increased or decreased under different carbon incentive levels, and output the load adjustment amount of carbon demand response (i.e., the second load adjustment amount) to form the carbon demand response. The system responds to candidate sets, enabling unified modeling from both electricity and carbon perspectives. The upper-level optimization module invokes the upper-level mixed-integer optimization engine to iteratively search for peak-valley electricity price curves and carbon incentive prices, aiming to minimize load volatility, carbon emissions, and demand response costs. Combining the electricity and carbon demand response candidate sets, it generates demand response execution commands and updated target loads on the user side in real time, and distributes these target loads to the lower-level scheduling module. The lower-level scheduling module receives the target load on the system side, integrates equipment operation and maintenance, energy storage charging and discharging strategies, wind and solar curtailment penalties, and tiered carbon pricing constraints, and solves the daily-hourly rolling optimization model of the integrated energy system. This yields output power commands for each device, energy storage status evolution, and external energy purchase plans, and back-calculates actual carbon emissions and dynamic carbon factors (i.e., corrected carbon emission factors), providing an iterative update basis for the upper-level optimization module.

[0164] Through the above steps S102 to S114, the goal of collecting the park's power information, determining the first load adjustment amount based on electricity demand response and the second load adjustment amount based on carbon demand response, and determining the park's equipment scheduling strategy based on the first load adjustment amount and the second load adjustment amount can be achieved. This achieves the technical effect of improving the accuracy of the determination result of the park's equipment scheduling strategy, thereby solving the technical problem of inaccurate determination result of the park's equipment scheduling strategy in related technologies.

[0165] Based on the above embodiments and optional embodiments, this application proposes an optional implementation method for determining the equipment scheduling strategy of a park based on electricity carbon demand response. This implementation method can be understood as a low-carbon optimization scheduling method for a park's integrated energy system that considers electricity-carbon demand response. Figure 2 This is a flowchart illustrating an optional method for determining equipment scheduling strategies for a park based on electricity carbon demand response, according to an embodiment of this application. Figure 2 As shown, the steps of this method include:

[0166] Step S1: Collect the power information of the park (i.e. the target park), including the power load, heat load, cooling load and electric vehicle load curves in the park, the output forecast of renewable energy, the parameters of comprehensive energy equipment (including the carbon emission coefficient of the cogeneration unit, the carbon emission coefficient of the gas boiler and the carbon emission coefficient of the purchased electricity), and the initial carbon emission factor of the target park.

[0167] Figure 3 This is a schematic diagram of an optional integrated energy system park structure provided according to an embodiment of this application, such as... Figure 3 As shown, this integrated energy system park includes energy conversion equipment and energy storage facilities. The energy conversion equipment includes combined heat and power (CHP) units and gas-fired boilers, electric boilers, electric refrigeration equipment, and absorption refrigeration equipment. The CHP units and gas-fired boilers achieve synergistic power generation and heat supply, as well as single-source heat energy supply, by burning natural gas; the electric boilers convert electricity into heat, and the electric-to-gas conversion device completes the conversion between electricity and natural gas; while the electric refrigeration equipment and absorption refrigeration equipment provide cooling functions respectively. These energy conversion devices constitute a multi-energy complementary conversion system within the park. To cope with fluctuations in renewable energy output and load changes, the park is also equipped with electric energy storage, thermal pumps (i.e., thermal storage tanks), and gas storage tanks. Through energy storage and release, energy balance and stable operation are achieved at different time scales. Externally, in addition to obtaining clean electricity from wind and solar power generation equipment and electric energy storage equipment, and obtaining natural gas from gas storage tanks, the park can also purchase natural gas from the natural gas grid, electricity from the power grid, and other new energy sources to jointly supply energy to the park's electrical load, heat load, cooling load, and electric vehicles, ensuring the supply of various load forms for the park, including electricity, heat, and cooling. Among them, the natural gas busbar, electrical busbar, hot busbar, and cold busbar are used to transport natural gas, electrical energy, hot energy, and cold energy.

[0168] As the initiator of demand response, the industrial park uses a combination of "TV corner" and "carbon perspective" approaches, along with load-side incentive mechanisms and guiding prices, to guide users to actively adjust their load distribution. This helps mitigate load peaks at the park level and reduce carbon emissions caused by user behavior. Simultaneously, as the manager of optimized scheduling, the park, after determining the load adjustment results on the user side (i.e., the target load), uses these as boundary conditions to comprehensively consider the dynamic operating characteristics of various energy carriers and related equipment, thus conducting low-carbon optimized scheduling for the park.

[0169] Step S2: Construct an electricity demand response model to determine the first load adjustment amount of adjustable load in the park at different time periods, including adjustable load amount, load reduction amount, load shifting amount and electric vehicle load amount.

[0170] The electricity demand response model includes an adjustable load model, a load reduction model, a load shifting model, and an electric vehicle load model. The adjustable load model is used to determine the adjustable load amount (including adjustable load amounts for cooling and heating), the load reduction model is used to determine the load reduction amount, the load shifting model is used to determine the load shifting amount, and the electric vehicle load model is used to determine the electric vehicle load amount.

[0171] The construction methods for the adjustable load model, the load reduction model, the load shifting model, and the electric vehicle load model are the same as those in the above embodiments, and will not be repeated here.

[0172] Step S3: Construct a carbon demand response model and quantify the second load adjustment amount of the park based on carbon demand response.

[0173] The carbon demand response model is constructed in the same way as in the above embodiments, and will not be described again here.

[0174] Step S4: With the minimum sum of load volatility, carbon emissions, and demand response costs as the objective function (i.e., the first objective function), establish an upper-level optimization model (i.e., the target optimization model). Based on peak-valley electricity trading signals and carbon incentive signals, determine the target load of the park, including: target electricity load, target heat load, and target cooling load.

[0175] The upper-level optimization model includes a first objective function and first constraints. The first objective function is determined based on the load volatility, carbon emissions, and demand response cost of the target area, and is used to minimize the sum of these three factors. The first constraints include time-of-use pricing constraints, incentive pricing constraints, and user satisfaction constraints.

[0176] The determination of the first objective function and the first constraint is the same as in the above embodiment, and will not be repeated here.

[0177] Step S5: Using the target load from Step S4 as boundary conditions, construct a comprehensive energy system scheduling model that includes tiered carbon pricing, wind and solar curtailment penalties, equipment operation and maintenance costs, and energy storage operations. Solve for the power of park equipment (i.e., the output power corresponding to various energy conversion devices), energy storage charging and discharging power, and purchased energy (including purchased electricity and purchased gas).

[0178] Figure 4 This is a flowchart of an optional integrated energy system optimization model solution provided according to an embodiment of this application, such as... Figure 4 As shown, the initial carbon emission factor of the park is first determined. The process involves three steps: first, determining the initial cooling, heating, and electrical loads of the industrial park; second, optimizing the park's demand response load by solving the upper-level model; third, determining the park's target load based on the optimization results; fourth, solving the integrated energy system dispatch model to determine the park's equipment dispatch strategy (including equipment operation strategy, purchased electricity, and purchased gas); and finally, determining the park's corrected carbon emission factor based on the aforementioned equipment operation strategy, purchased electricity, and purchased gas. and according to and The difference determines whether the iteration stop condition is met. If it is met, the iteration stops, and the demand response curve, the park's equipment scheduling strategy, etc., are output. If not, the carbon emission factor is adjusted accordingly. The upper-level optimization model is used to further refine the target load, equipment scheduling strategy, and carbon emission factor of the park. The process continues iteratively until the updated carbon emission factor meets the stopping condition.

[0179] The integrated energy system scheduling model includes a second objective function and a second constraint. The second objective function, determined based on carbon allowance purchase costs, gas purchase costs, electricity purchase costs, equipment operation and maintenance costs, wind and solar curtailment costs, and demand response costs, is used to minimize the total operating cost of the industrial park. The second constraint is used to limit the output power of energy conversion equipment, etc.

[0180] The determination of the second objective function and the second constraint is the same as in the above embodiment, and will not be repeated here.

[0181] Step S6: Calculate the corrected carbon emission factor based on the equipment power obtained in step S5, and compare it with the previous carbon emission factor. If the difference between the two is greater than the preset difference threshold, return to step S2 for iteration until convergence.

[0182] The revised carbon emission factor is obtained by summing the carbon emission factors for electricity load, heat load, and cooling load. Based on the principle of proportional sharing, the carbon emission factor for the industrial park can be calculated based on the carbon emissions flowing into and out of the park. Therefore, the carbon emission factors for electricity load, heat load, and cooling load can be calculated based on the inflow and outflow of carbon emissions. Electricity load carbon emission factor. Heat load carbon emission factor and cooling load carbon emission factor The method for determining the value is the same as in the above embodiments, and will not be repeated here.

[0183] Step S7 outputs the optimal demand response curve (i.e., the load curve corresponding to the target load), equipment scheduling strategy (including the output power of various energy conversion devices, the charging and discharging power of energy storage devices and purchased energy), total operating cost of the park and carbon emissions of the park.

[0184] The above-mentioned optional implementation methods are used to determine the equipment scheduling strategy for the industrial park. The penalty for wind and solar curtailment in the park is 0.15 yuan / kWh. The upper limit for adjusting the cooling and heating loads in the park's demand response is 3% of the original cooling and heating loads, and the upper limit for reducing and transferring electricity load is 3% of the original electricity load. The adjustment limits for electricity, cooling, and heating prices in demand response are [-0.5, 0.5], and the user energy satisfaction is 0.7. Three different scenarios are set: Scenario 1 does not consider demand response; Scenario 2 considers electricity demand response; and Scenario 3 considers both electricity demand response and carbon demand response.

[0185] The optimization results for each scenario are shown in Table 1. For the scenario that does not consider demand response (Scenario 1), it is assumed that the electric vehicle starts charging immediately after arrival and stops charging when the user-set SOC value is reached.

[0186] Table 1 Operating Costs in Different Scenarios

[0187]

[0188] As shown in Table 1, in Scenario 1 without any demand response strategy, the total operating cost of the park reached RMB 76.9477 million, and carbon emissions peaked at 4,468 tons. In Scenario 2, after introducing an electricity demand response mechanism, the park's equipment maintenance cost decreased to RMB 57.749 million, and the total operating cost dropped to RMB 60.9449 million, a reduction of approximately 20.79% compared to Scenario 1. Simultaneously, carbon emissions significantly decreased to 3,801.44 tons. This demonstrates that guiding users to adjust their load through electricity pricing can effectively reduce peak loads in the park and significantly lower the park's total operating costs and carbon emissions.

[0189] Scenario 3 further introduces a comprehensive demand response approach from both electricity and carbon perspectives. Although the demand response cost increases significantly to RMB 9.0672 million, resulting in a slightly higher total operating cost (RMB 65.9042 million) compared to Scenario 2, the park's equipment maintenance cost is further reduced to RMB 56.837 million due to more precise guidance for users to use energy during low-carbon periods. Carbon emissions are reduced to a minimum of 3,578.37 tons, a further reduction of approximately 5.87% compared to Scenario 2 and a significant reduction of approximately 19.91% compared to Scenario 1. This demonstrates that comprehensively considering both electricity and carbon demand response in demand response can achieve more effective carbon emission reduction.

[0190] Figure 5 This is a schematic diagram of an optional incentive price provided according to an embodiment of this application. Figure 5 This displays the distribution of incentive prices earned by users participating in demand response across different time periods. The horizontal axis represents time (in hours), and the vertical axis represents the demand response incentive price (in yuan / kWh). Figure 5 As shown, the demand response incentive price varies significantly across different time periods, indicating that the park can guide users to shift their load from peak and high-carbon emission periods to off-peak and low-carbon emission periods through differentiated incentive prices.

[0191] Figure 6 This is an optional peak-valley pricing diagram provided according to an embodiment of this application. Figure 6 This displays the price adjustments for electricity, heat, and cooling loads after the implementation of demand response. The horizontal axis represents time (in hours), and the vertical axis represents price (in yuan / kWh). Figure 6 As shown, the electricity price after demand response (i.e., the optimized electricity price) exhibits a more pronounced peak-valley difference, especially during peak load periods (such as 3-5 am, 10-1 pm, and 6-8 pm), when the price is significantly higher than the initial price. In contrast, the price adjustments for heating and cooling loads are relatively small, fluctuating only slightly during a few periods. This indicates that the park primarily uses electricity price adjustments as its main regulatory tool to guide users to alleviate peak loads and control overall energy costs by reducing or shifting their electricity load. Simultaneously, the price adjustments for heating and cooling loads are moderate and stable, effectively balancing user energy comfort with the park's operating costs.

[0192] Figure 7 This is a schematic diagram of load changes before and after the park's demand response, provided according to an embodiment of this application. Figure 7 This displays the changes in various types of load before and after the implementation of demand response in the park. The horizontal axis represents time in hours, and the vertical axis represents load in kW (kilowatts). Figure 7As shown, through demand response, the park significantly reduced the peak electricity load, decreasing it from approximately 1600kW to below 1400kW, and the overall electricity load curve became smoother. Furthermore, after demand response adjustments, particularly during peak load periods (e.g., 10:00 AM to 12:00 PM), both heating and cooling loads were significantly reduced, alleviating operational pressure on the park during peak hours. Overall, implementing electricity-carbon demand response enabled the park to achieve effective load shifting and reduction, resulting in a smoother load curve and further lowering overall operating costs and carbon emissions, thus achieving the goal of reducing operating costs and carbon emissions.

[0193] Figure 8 This is a schematic diagram illustrating an optional change in park carbon emissions and free carbon allowances, provided according to an embodiment of this application. Figure 8 This displays the park's carbon emissions over a 24-hour period, including CHP carbon emissions, GB carbon emissions, and free carbon allowances. The horizontal axis represents time in hours, and the vertical axis represents carbon emissions and carbon allowances in tons. Figure 8 As shown, the total carbon emission curve of the park exhibits a clear fluctuating trend, with the peak occurring between 6 PM and 8 PM. The CHP unit is the main source of carbon emissions in the park. At the same time, the changes in free carbon allowances are relatively stable, and the park's net carbon emissions (total carbon emissions minus free allowances) are kept at a low level, especially during the nighttime period, when net carbon emissions are even negative, demonstrating the effective control of carbon emissions by demand response and optimized scheduling.

[0194] Figure 9 This is a schematic diagram of the operation of an optional park gas power related equipment according to an embodiment of this application. Figure 9 This displays the natural gas consumption in the park, with the horizontal axis representing time in hours and the vertical axis representing power in kW. For example... Figure 9 As shown, the daily natural gas purchase volume increases significantly during peak hours (4-8 PM), while storage tanks store gas during off-peak hours and release it during peak hours. Furthermore, CHP and GB coordinate their gas allocation, making the overall utilization of gas resources more economical and efficient.

[0195] Figure 10 This is a schematic diagram of an optional park thermal power equipment operation according to an embodiment of this application. Figure 10 The diagram illustrates the park's heat supply situation, with the horizontal axis representing time in hours and the vertical axis representing power in kW. As shown in Figure 10, the CHP boiler handles the majority of the heat load, while the electric boiler and GB boiler provide auxiliary heat load during certain periods. Thermal energy storage charges during off-peak hours and releases heat during peak hours, effectively achieving smooth regulation of the heat load. Absorption refrigeration units provide cooling by consuming thermal energy during the afternoon peak load period.

[0196] Figure 11 This is a schematic diagram of an optional park electrical power equipment operation according to an embodiment of this application. Figure 11 This reflects the park's power load and supply structure, with the horizontal axis representing time in hours and the vertical axis representing power in kW. For example... Figure 11 As shown, during peak electricity load periods, in addition to utilizing the maximum output of wind and solar power, the park also appropriately increases the proportion of purchased electricity and energy storage discharge to reduce the operational pressure on internal equipment during high-cost operating periods. Energy storage charges during off-peak hours and releases power during peak hours, effectively reducing load spikes. CHP (Continuous Power Generation) systems and electric boilers flexibly coordinate with changes in wind and solar power output to improve the overall efficiency of the park's power resources.

[0197] Figure 12 This is a schematic diagram of an optional park cooling power equipment operation according to an embodiment of this application. Figure 12 This illustrates the supply structure of the park's cooling demand, with the horizontal axis representing time in hours and the vertical axis representing power in kW. For example... Figure 12 As shown, the cooling demand of the park is met by electric refrigeration equipment most of the time. During peak load periods, absorption refrigeration equipment effectively shares the cooling load by consuming heat energy.

[0198] The above-mentioned optional implementation can be applied to a low-carbon optimization scheduling system for a park integrated energy system that considers electricity-carbon demand response. The system includes a data acquisition module, an electricity demand response processing module, a carbon demand response processing module, an upper-level optimization module, and a lower-level scheduling module.

[0199] The data acquisition module is used to collect the park's electrical load, heat load, cooling load and electric vehicle load curves, renewable energy output forecasts, and comprehensive energy equipment parameters;

[0200] The electricity demand response processing module is used to analyze the elasticity coefficient of load to time-of-use electricity price and incentive signal from the perspective of electricity price. It establishes response models according to four types of load: adjustable load, load that can be reduced, load that can be shifted, and electric vehicle load. It calculates the potential power adjustment amount (i.e. the first load adjustment amount) in each time period and forms a candidate set of electricity demand response to provide data support for subsequent price-incentive joint optimization.

[0201] The carbon demand response processing module is used to associate various loads on the user side with their corresponding dynamic carbon emission factors based on carbon emission metering methods, calculate the load-carbon emission coupling adjustment space that can be increased or decreased under different carbon incentive levels, output the load adjustment amount of carbon demand response (i.e. the second load adjustment amount), form a carbon demand response candidate set, and realize unified modeling from the dual perspectives of electricity and carbon.

[0202] The upper-level optimization module calls the upper-level mixed integer optimization engine to iteratively search the peak-valley electricity price curve and carbon incentive price with the goal of minimizing the combined load volatility, carbon emissions and demand response costs. It combines the electricity demand response candidate set and the carbon demand response candidate set to generate demand response execution instructions and updated target load on the user side in real time, and then sends the target load to the lower-level scheduling module.

[0203] The lower-level scheduling module receives the target load amount from the system side, integrates equipment operation and maintenance, energy storage charging and discharging strategies, wind and solar curtailment penalties, and tiered carbon price constraints, solves the daily-hour rolling optimization model of the integrated energy system, obtains the output power commands of each device, the evolution of energy storage status, and the external energy purchase plan, and back-calculates the actual carbon emissions and dynamic carbon factor (i.e., the corrected carbon emission factor), providing a basis for iterative updates for the upper-level optimization module.

[0204] The above-mentioned optional implementation methods take into account both energy and carbon perspectives, balancing user flexibility and system cost. While ensuring the efficiency of solving multiple time scales, they provide accurate carbon accounting and can be widely applied to various integrated energy scenarios such as industrial parks and microgrids, which helps to reduce carbon emissions and operating costs of integrated energy systems in parks.

[0205] The above-mentioned optional implementation methods achieve at least the following effects: they integrate user-side demand response and park-side multi-energy scheduling from both electricity and carbon perspectives, integrating load elasticity, carbon incentives, and equipment collaborative operation into the same closed-loop optimization framework to achieve synchronous control of energy flow and carbon flow; they significantly reduce the solution complexity of multi-timescale models through a hierarchical iterative strategy, improving the real-time performance and convergence stability of rolling optimization; they utilize energy storage devices and flexible loads to collaboratively absorb renewable energy output, reducing wind and solar curtailment and smoothing load fluctuations, thereby reducing park operating costs and carbon emissions, and improving the accuracy and rationality of park equipment scheduling strategies.

[0206] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0207] This embodiment also provides a device for determining the equipment scheduling strategy of a park based on electricity carbon demand response. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0208] According to an embodiment of this application, an apparatus embodiment for implementing a method for determining equipment scheduling strategies in a park based on electricity carbon demand response is also provided. Figure 13 This is a schematic diagram of a device for determining equipment scheduling strategies for a park based on electricity demand response, according to an embodiment of this application. Figure 13 As shown, the above-mentioned device for determining the equipment scheduling strategy of the park based on electricity carbon demand response includes a data acquisition module 1302, a first determination module 1304, a second determination module 1306, a third determination module 1308, a fourth determination module 1310, a fifth determination module 1312, and a sixth determination module 1314. The device will be described below.

[0209] Data acquisition module 1302 is used to collect power information of the target park;

[0210] The first determining module 1304 is connected to the data acquisition module 1302 and is used to determine the first load adjustment amount and the second load adjustment amount of the load included in the target park based on power information. The first load adjustment amount is obtained based on the electricity demand response adjustment of the target park, and the second load adjustment amount is obtained based on the carbon demand response adjustment of the target park.

[0211] The second determining module 1306, connected to the first determining module 1304, is used to determine the target load amount based on the first load adjustment amount, the second load adjustment amount, and the initial carbon emission factor of the target park, wherein the initial carbon emission factor is used to quantify the carbon emission amount of the target park.

[0212] The third determining module 1308, connected to the second determining module 1306, is used to determine the output power of the various energy conversion devices included in the target park and the purchased power of the target park based on the target load.

[0213] The fourth determining module 1310, connected to the third determining module 1308, is used to determine the corrected carbon emission factor of the target park based on the output power of various energy conversion devices and the purchased power.

[0214] The fifth determining module 1312, connected to the fourth determining module 1310, is used to determine the corrected carbon emission factor as the target carbon emission factor of the target park when the difference between the corrected carbon emission factor and the initial carbon emission factor is less than or equal to a preset difference threshold.

[0215] The sixth determining module 1314, connected to the fifth determining module 1312, is used to determine the equipment scheduling strategy of the target park based on the output power of various energy conversion devices corresponding to the target carbon emission factor and the purchased power corresponding to the target carbon emission factor.

[0216] This application provides an equipment scheduling strategy determination device for a park based on electricity demand response. By setting up the aforementioned data acquisition module 1302, first determination module 1304, second determination module 1306, third determination module 1308, fourth determination module 1310, fifth determination module 1312, and sixth determination module 1314, it can achieve the purpose of determining the first load adjustment amount based on electricity demand response and the second load adjustment amount based on carbon demand response of the park load by collecting the park's power information, and determining the park's equipment scheduling strategy based on the first load adjustment amount and the second load adjustment amount. This achieves the technical effect of improving the accuracy of the determination result of the park's equipment scheduling strategy, thereby solving the technical problem of inaccurate determination result of the park's equipment scheduling strategy in related technologies.

[0217] As an optional embodiment, the device for determining the equipment scheduling strategy of a park based on electricity demand response provided in this application includes a first determining module comprising: an adjustable load determining unit, used to determine the adjustable load based on the temperature adjustment amount of the load included in the power information; a load reduction determining unit, used to determine the load reduction amount based on the maximum load reduction amount of the load included in the power information; a load shifting determining unit, used to determine the load shifting amount based on the change in electricity price before and after electricity demand response included in the power information; and an electric vehicle load determining unit, used to determine the electric vehicle load based on the number of multiple electric vehicles participating in electricity demand response in the target park included in the power information, and the charging power corresponding to each of the multiple electric vehicles.

[0218] As an optional embodiment, the device for determining the equipment scheduling strategy of a park based on electricity carbon demand response provided in this application embodiment includes a second determining module: a target load determining unit, used to obtain the target load based on a first load adjustment amount, a second load adjustment amount, and the initial carbon emission factor of the target park using a target optimization model. The target optimization model includes a first objective function and a first constraint condition. The first objective function is used to minimize the sum of the load volatility of the target park, the carbon emission of the target park, and the demand response cost of the target park. The first constraint condition includes time-of-use electricity price constraint, incentive price constraint, and satisfaction constraint. The target load includes at least: target electricity load, target heat load, and target cooling load.

[0219] As an optional embodiment, the third determining module in the device for determining the equipment scheduling strategy of a park based on carbon demand response provided in this application embodiment includes: a first determining unit, used to determine the carbon quota purchase cost, gas purchase cost, electricity purchase cost, equipment operation and maintenance cost, and wind and solar curtailment cost of the target park; a second determining unit, used to determine a second objective function to minimize the total operating cost of the target park based on the carbon quota purchase cost, gas purchase cost, electricity purchase cost, equipment operation and maintenance cost, wind and solar curtailment cost, and demand response cost of the target park; and an external power purchase determining unit, used to obtain the output power corresponding to various energy conversion devices and the external power purchase of the target park based on the target load and the second objective function.

[0220] As an optional embodiment, the fourth determining module in the equipment scheduling strategy determination device for a park based on electricity carbon demand response provided in this application embodiment includes: a third determining unit, used to determine the electricity load carbon emission factor of the target park based on the output power of the cogeneration unit included in the various energy conversion equipment and the purchased electricity; a fourth determining unit, used to determine the heat load carbon emission factor of the target park based on the output heat power of the gas boiler included in the various energy conversion equipment, the output heat power of the cogeneration unit included in the various energy conversion equipment, and the output heat power of the electric boiler included in the various energy conversion equipment; a fifth determining unit, used to determine the cold load carbon emission factor of the target park based on the output cooling power of the absorption refrigeration equipment included in the various energy conversion equipment and the output cooling power of the electric refrigeration equipment included in the various energy conversion equipment; and a corrected carbon emission factor determining unit, used to sum the electricity load carbon emission factor, the heat load carbon emission factor, and the cold load carbon emission factor to obtain a corrected carbon emission factor.

[0221] As an optional embodiment, the device for determining the equipment scheduling strategy of a park based on electricity carbon demand response provided in this application further includes: a first update iteration unit, used to update and iterate the first load adjustment amount and the second load adjustment amount to obtain a first updated load adjustment amount and a second updated load adjustment amount; a second update iteration unit, used to update and iterate the target load amount based on the first updated load adjustment amount, the second updated load adjustment amount, and a corrected carbon emission factor to obtain a target updated load amount; and a third update iteration unit, used to adjust the output of various energy conversion equipment based on the target updated load amount. The system iterates through the output power and purchased power to obtain the updated output power of various energy conversion devices and the updated purchased power of the target park. The fourth update iteration unit updates the modified carbon emission factor based on the updated output power and the updated purchased power of various energy conversion devices to obtain the modified updated carbon emission factor of the target park. The target carbon emission factor determination unit stops the update iteration when the difference between the modified updated carbon emission factor and the modified carbon emission factor is less than or equal to a preset difference threshold, and determines the modified updated carbon emission factor obtained in the last update iteration as the target carbon emission factor.

[0222] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0223] It should be noted that the data acquisition module 1302, the first determining module 1304, the second determining module 1306, the third determining module 1308, the fourth determining module 1310, the fifth determining module 1312, and the sixth determining module 1314 mentioned above correspond to steps S102 to S114 in the embodiments. The examples and application scenarios implemented by the above modules and their corresponding steps are the same, but they are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0224] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0225] The aforementioned device for determining the equipment scheduling strategy of the park based on electricity carbon demand response may also include a processor and a memory. The data acquisition module 1302, the first determination module 1304, the second determination module 1306, the third determination module 1308, the fourth determination module 1310, the fifth determination module 1312, and the sixth determination module 1314 are all stored in the memory as program units. The processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0226] The processor contains a core that retrieves the corresponding program unit from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0227] This application provides a non-volatile storage medium storing a program that, when executed by a processor, implements a method for determining a park's equipment scheduling strategy based on electricity demand response.

[0228] This application provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: collecting power information of a target industrial park; determining a first load adjustment amount and a second load adjustment amount for the loads included in the target industrial park based on the power information, wherein the first load adjustment amount is obtained based on the electricity demand response adjustment of the target industrial park, and the second load adjustment amount is obtained based on the carbon demand response adjustment of the target industrial park; and determining a target load amount based on the first load adjustment amount, the second load adjustment amount, and the initial carbon emission factor of the target industrial park, wherein the initial carbon emission factor... This method is used to quantify the carbon emissions of a target industrial park; based on the target load, it determines the output power of various energy conversion devices within the target park, as well as the purchased electricity power of the target park; based on the output power of the various energy conversion devices and the purchased electricity power, it determines the corrected carbon emission factor of the target park; if the difference between the corrected carbon emission factor and the initial carbon emission factor is less than or equal to a preset difference threshold, the corrected carbon emission factor is determined as the target carbon emission factor of the target park; based on the output power of the various energy conversion devices corresponding to the target carbon emission factor, and the purchased electricity power corresponding to the target carbon emission factor, it determines the equipment scheduling strategy for the target park. The equipment in this article can be servers, PCs, etc.

[0229] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: collecting power information of a target industrial park; based on the power information, determining a first load adjustment amount and a second load adjustment amount for the loads included in the target industrial park, wherein the first load adjustment amount is obtained based on the electricity demand response adjustment of the target industrial park, and the second load adjustment amount is obtained based on the carbon demand response adjustment of the target industrial park; based on the first load adjustment amount, the second load adjustment amount, and the initial carbon emission factor of the target industrial park, determining the target load amount, wherein the initial carbon emission factor is used to quantify the target industrial park's... Carbon emissions; based on the target load, determine the output power of each energy conversion device in the target park and the purchased electricity power of the target park; based on the output power of each energy conversion device and the purchased electricity power, determine the corrected carbon emission factor of the target park; if the difference between the corrected carbon emission factor and the initial carbon emission factor is less than or equal to a preset difference threshold, the corrected carbon emission factor is determined as the target carbon emission factor of the target park; based on the output power of each energy conversion device corresponding to the target carbon emission factor and the purchased electricity power corresponding to the target carbon emission factor, determine the equipment scheduling strategy of the target park.

[0230] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0231] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0232] 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.

[0233] 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.

[0234] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0235] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0236] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0237] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0238] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0239] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining a device scheduling strategy of a park based on an electricity carbon demand response, characterized in that, The method comprises: collecting power information of a target park; based on the power information, determining a first load adjustment amount of a load included in the target park, and a second load adjustment amount of the load, wherein the first load adjustment amount is obtained based on demand response adjustment of the target park, and the second load adjustment amount is obtained based on carbon demand response adjustment of the target park; based on the first load adjustment amount, the second load adjustment amount, and an initial carbon emission factor of the target park, determining a target load amount of the load, wherein the initial carbon emission factor is used to quantify the carbon emission amount of the target park; based on the target load amount, determining the output power corresponding to each of a plurality of energy conversion devices included in the target park, and the purchased power of the target park; based on the output power corresponding to each of the plurality of energy conversion devices and the purchased power, determining a corrected carbon emission factor of the target park; in the case where the difference between the corrected carbon emission factor and the initial carbon emission factor is less than or equal to a preset difference threshold, determining the corrected carbon emission factor as a target carbon emission factor of the target park; based on the output power corresponding to each of the plurality of energy conversion devices corresponding to the target carbon emission factor, and the purchased power corresponding to the target carbon emission factor, determining a device scheduling strategy of the target park; wherein, based on the first load adjustment amount, the second load adjustment amount, and the initial carbon emission factor of the target park, determining the target load amount of the load comprises: based on the first load adjustment amount, the second load adjustment amount, and the initial carbon emission factor of the target park, using a target optimization model to obtain the target load amount of the load, wherein the target optimization model comprises a first objective function and a first constraint condition, the first objective function is used to minimize the sum of the load fluctuation rate of the target park, the carbon emission amount of the target park and the demand response cost of the target park, and the first constraint condition includes time-of-use electricity price constraint, incentive price constraint and satisfaction constraint, the target load amount at least includes: target electric load amount, target heat load amount and target cold load amount; wherein, the target optimization model comprises: the first objective function is: ； wherein, represents a first objective function, represents a load fluctuation rate, represents a carbon emission of the target park, represents a demand response cost of the load, represents a demand response cost of the electric vehicle, represents a weight coefficient of the demand response cost of the target park, represents a weight coefficient of the load fluctuation rate, represents a weight coefficient of the carbon emission of the target park; The load fluctuation rate is determined in the following manner: The carbon emission of the target park is determined in the following manner: The demand response cost of the load is determined in the following manner: The demand response cost of the electric vehicle is determined in the following manner: The demand response cost of the electric vehicle is determined in the following manner: ; ; ; wherein, T represents the total T represent the time, represents t the target electric load amount at time t+1, the target electric load amount at time t, the target thermal load amount at time t+1, the target thermal load amount at time t, the target cold load amount at time t+1, the target cold load amount at time t, the electric load carbon emission factor at time t, the thermal load carbon emission factor at time t, the cold load carbon emission factor at time t, represents the compensation price of user participation in carbon demand response, represents t the incentive price of the electric vehicle at time t, represents the adjustable load amount of the thermal load at time t, represents the adjustable load amount of the cold load at time t, represents the reducible load amount at time t, represents the second load adjustment amount at time t when the load is increased, represents the second load adjustment amount at time t when the load is reduced, represents the electric vehicle load amount at time t, represents t the thermal load price at time t, represents t the cold load price at time t, represents t the electric load price at time t.

2. The method of claim 1, wherein, in the case where the first load adjustment amount includes adjustable load amount, reducible load amount, translatable load amount and electric vehicle load amount, the method further comprises: based on the temperature adjustment amount of the load included in the power information, determining the adjustable load amount; based on the maximum reducible electric load amount of the load included in the power information, determining the reducible load amount; based on the price change amount before and after the demand response included in the power information, determining the translatable load amount; based on the number of electric vehicles participating in the demand response of the target park included in the power information, and the charging power corresponding to each of the plurality of electric vehicles, determining the electric vehicle load amount.

3. The method of claim 1, wherein, The target optimization model comprises: The time-of-use electricity price constraint is: ； ； ； wherein, and respectively represent a lower limit and an upper limit of the load price, represents t the load price at the time, h represents a heating load, c represents a cooling load, e represents an electric load, represents t the load price adjustment amount at the time, and respectively represent a lower limit and an upper limit of the load price adjustment amount, represents an initial load price; The incentive price constraint is: ； ； wherein, denotes t the load incentive price at the time, denotes an upper limit of the load incentive price, denotes an average load price of the electric load, the heat load, and the cold load; The satisfaction constraint is: ； ； wherein, represents a satisfaction index for the target load amount after the demand response, T represents a total T times, represents a difference between the target load amount and the initial load amount, represents the initial load amount, P represents an electric load, C represents a cooling load, H represents a heating load, represents a satisfaction index for the load price.

4. The method of claim 1, wherein, The target load amount, the output power corresponding to each of the plurality of energy conversion devices included in the target park, and the external purchase power of the target park are determined based on the target load amount, and the target park comprises: The carbon quota purchase cost of the target park, the gas purchase cost of the target park, the electricity purchase cost of the target park, the equipment operation and maintenance cost of the target park, and the wind and light curtailment cost of the target park are determined. A second target function for minimizing the total operation cost of the target park is determined based on the carbon quota purchase cost, the gas purchase cost, the electricity purchase cost, the equipment operation and maintenance cost, the wind and light curtailment cost, and the demand response cost of the target park. Based on the target load amount, the second target function is used to obtain the output power corresponding to each of the plurality of energy conversion devices and the external purchase power of the target park.

5. The method of claim 1, wherein, In the case where the plurality of energy conversion devices comprise a combined heat and power unit, a gas boiler, an electric boiler, an absorption refrigeration device, and an electric refrigeration device, the modified carbon emission factor of the target park is determined based on the output power of the combined heat and power unit and the external purchase power. The electric load carbon emission factor of the target park is determined based on the output electric power of the combined heat and power unit and the external purchase power. The thermal load carbon emission factor of the target park is determined based on the output thermal power of the gas boiler, the output thermal power of the combined heat and power unit, and the output thermal power of the electric boiler. The cold load carbon emission factor of the target park is determined based on the output cold power of the absorption refrigeration device and the output cold power of the electric refrigeration device. The electric load carbon emission factor, the thermal load carbon emission factor, and the cold load carbon emission factor are added to obtain the modified carbon emission factor.

6. The method according to any one of claims 1 to 5, characterized in that, In the case where the difference is greater than the preset difference threshold, the method further comprises: The first load adjustment amount and the second load adjustment amount are updated and iterated to obtain a first updated load adjustment amount of the load and a second updated load adjustment amount of the load. The target load amount is updated and iterated based on the first updated load adjustment amount, the second updated load adjustment amount, and the modified carbon emission factor to obtain a target updated load amount of the load. The output power corresponding to each of the plurality of energy conversion devices and the external purchase power of the target park are updated and iterated based on the target updated load amount to obtain output updated power corresponding to each of the plurality of energy conversion devices and external purchase updated power of the target park. updating iteration is performed on the corrected carbon emission factor based on the output power corresponding to each of the plurality of energy conversion devices and the purchased power, to obtain a corrected updated carbon emission factor of the target park; in a case where a difference between the corrected updated carbon emission factor and the corrected carbon emission factor is less than or equal to the preset difference threshold, the updating iteration is stopped, and the corrected updated carbon emission factor obtained in the last updating iteration is determined as the target carbon emission factor.

7. An equipment scheduling strategy determination device for a park based on electric carbon demand response, characterized by, comprising: a data collection module configured to collect power information of a target park; a first determination module configured to determine, based on the power information, a first load adjustment amount of a load included in the target park and a second load adjustment amount of the load, wherein the first load adjustment amount is obtained based on an electricity demand response adjustment of the target park, and the second load adjustment amount is obtained based on a carbon demand response adjustment of the target park; a second determination module configured to determine, based on the first load adjustment amount, the second load adjustment amount, and an initial carbon emission factor of the target park, a target load amount of the load, wherein the initial carbon emission factor is used to quantify a carbon emission amount of the target park; a third determination module configured to determine, based on the target load amount, output power corresponding to each of a plurality of energy conversion devices included in the target park and purchased power of the target park; a fourth determination module configured to determine, based on the output power corresponding to each of the plurality of energy conversion devices and the purchased power, a corrected carbon emission factor of the target park; a fifth determination module configured to determine, in a case where a difference between the corrected carbon emission factor and the initial carbon emission factor is less than or equal to a preset difference threshold, the corrected carbon emission factor as a target carbon emission factor of the target park; a sixth determination module configured to determine, based on the output power corresponding to each of the plurality of energy conversion devices corresponding to the target carbon emission factor and the purchased power corresponding to the target carbon emission factor, a device scheduling strategy of the target park; wherein the second determination module is further configured to obtain the target load amount of the load based on the first load adjustment amount, the second load adjustment amount, and the initial carbon emission factor of the target park by using a target optimization model, wherein the target optimization model includes a first objective function and a first constraint condition, the first objective function is used to minimize a sum of a load fluctuation rate of the target park, a carbon emission amount of the target park, and a demand response cost of the target park, and the first constraint condition includes a time-of-use electricity price constraint, an incentive price constraint, and a satisfaction constraint, and the target load amount at least includes a target electric load amount, a target heat load amount, and a target cold load amount; wherein the target optimization model comprises: the first objective function is: ； wherein, represents a first objective function, represents a load fluctuation rate, represents a carbon emission of the target park, represents a demand response cost of the load, represents a demand response cost of the electric vehicle, represents a weight coefficient of the demand response cost of the target park, represents a weight coefficient of the load fluctuation rate, represents a weight coefficient of the carbon emission of the target park; The load fluctuation rate is determined in the following manner: The carbon emission of the target park is determined in the following manner: The demand response cost of the load is determined in the following manner: The demand response cost of the electric vehicle is determined in the following manner: The demand response cost of the electric vehicle is determined in the following manner: ; ; ; ; wherein, T represents the total T represents the total represents t represents the target electric load amount at t+1 time, represents the target electric load amount at t time, represents the target thermal load amount at t+1 time, represents the target thermal load amount at t time, represents the target cold load amount at t+1 time, represents the target cold load amount at t time, represents the electric load carbon emission factor at t time, represents the thermal load carbon emission factor at t time, represents the cold load carbon emission factor at t time, represents the compensation price of user participation in carbon demand response, represents t represents the incentive price of electric vehicle at t time, represents the adjustable load amount of thermal load at t time, represents the adjustable load amount of cold load at t time, represents the reducible load amount at t time, represents the second load adjustment amount at t time when load is increased, represents the second load adjustment amount at t time when load is reduced, represents the electric vehicle load amount at t time, represents t represents the thermal load price at t time, represents t represents the cold load price at t time, represents t represents the electric load price at t time.

8. The apparatus of claim 7, wherein, the first determination module comprises: an adjustable load amount determination unit configured to determine the adjustable load amount based on a temperature adjustment amount of the load included in the power information; The cuttable load amount determination unit is configured to determine the cuttable load amount based on a maximum cuttable electric load amount of the load included in the power information; The shiftable load amount determination unit is configured to determine the shiftable load amount based on a price change amount before and after the demand response included in the power information; The electric vehicle load amount determination unit is configured to determine the electric vehicle load amount based on a number of electric vehicles participating in the demand response of the target park included in the power information, and charging powers corresponding to the electric vehicles, respectively.

9. The apparatus of claim 7, wherein, The third determination module comprises: The first determination unit is configured to determine a carbon quota purchase cost of the target park, a gas purchase cost of the target park, an electricity purchase cost of the target park, an equipment operation and maintenance cost of the target park, and a wind and light curtailment cost of the target park; The second determination unit is configured to determine a second target function for minimizing a total operation cost of the target park based on the carbon quota purchase cost, the gas purchase cost, the electricity purchase cost, the equipment operation and maintenance cost, the wind and light curtailment cost, and a demand response cost of the target park; The externally purchased electric power determination unit is configured to obtain an output power corresponding to each of the plurality of energy conversion devices and an externally purchased electric power of the target park based on the target load amount and the second target function.

10. The apparatus of claim 7, wherein, The fourth determination module comprises: The third determination unit is configured to determine an electric load carbon emission factor of the target park based on a combined heat and power unit output electric power of a combined heat and power unit included in the plurality of energy conversion devices and the externally purchased electric power; The fourth determination unit is configured to determine a thermal load carbon emission factor of the target park based on a gas boiler output thermal power of a gas boiler included in the plurality of energy conversion devices, a combined heat and power unit output thermal power of the combined heat and power unit, and an electric boiler output thermal power of an electric boiler included in the plurality of energy conversion devices; The fifth determination unit is configured to determine a cold load carbon emission factor of the target park based on an absorption refrigeration device output cold power of an absorption refrigeration device included in the plurality of energy conversion devices and an electric refrigeration device output cold power of an electric refrigeration device included in the plurality of energy conversion devices; The corrected carbon emission factor determination unit is configured to add the electric load carbon emission factor, the thermal load carbon emission factor, and the cold load carbon emission factor to obtain the corrected carbon emission factor.

11. The apparatus of any one of claims 7 to 10, wherein, The device further comprises: The first update iteration unit is configured to update iterate the first load adjustment amount and the second load adjustment amount to obtain a first updated load adjustment amount of the load and a second updated load adjustment amount of the load; The second update iteration unit is configured to update iterate the target load amount based on the first updated load adjustment amount, the second updated load adjustment amount, and the corrected carbon emission factor to obtain a target updated load amount of the load. a third updating iteration unit, configured to perform updating iteration on the output power of the plurality of energy conversion devices and the purchased power based on the target updating load, to obtain updated output power of the plurality of energy conversion devices and updated purchased power of the target park; a fourth updating iteration unit, configured to perform updating iteration on the corrected carbon emission factor based on the updated output power of the plurality of energy conversion devices and the updated purchased power, to obtain a corrected updated carbon emission factor of the target park; a target carbon emission factor determination unit, configured to stop the updating iteration when a difference between the corrected updated carbon emission factor and the corrected carbon emission factor is less than or equal to the preset difference threshold, and determine the corrected updated carbon emission factor obtained in the last updating iteration as the target carbon emission factor.

12. A non-volatile storage medium, comprising: The non-volatile storage medium stores a plurality of instructions, and the instructions are adapted to be loaded and executed by the processor to implement the device scheduling strategy determination method of the park based on the electric carbon demand response according to any one of claims 1 to 6.

13. An electronic device, comprising: comprise: one or more processors and a memory, the memory being configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the device scheduling strategy determination method of the park based on the electric carbon demand response according to any one of claims 1 to 6.

Images