A virtual power plant dispatching method based on electric carbon flow and related device

Abstract

The application provides a virtual power plant scheduling method based on electric carbon flow and a related device, and belongs to the field of new power system virtual power plant scheduling. The method first acquires an initial scheduling parameter set, counts the installed structure proportion of the station and calculates the dynamic carbon parameter of the time-sharing partition, introduces the dynamic electric carbon factor and the real-time carbon intensity, captures the time-space carbon emission difference caused by the new energy output fluctuation and the transmission loss; then, taking the installed structure as the carbon emission basement, combining the dynamic carbon parameter, performing the multi-dimensional electric carbon flow time-space coupling operation, and synchronously deducing the carbon flow distribution along the electric energy flow path; then, according to the global electric carbon flow distribution result, the macroscopic carbon control index is hierarchically decomposed and matched to the station, the time period and the equipment, and the multi-objective optimization solution is solved in combination with the distribution result, so that the dynamic carbon constraint directly participates in the scheduling decision, thereby realizing the low-carbon emission reduction target while ensuring the economic operation; finally, the scheduling strategy set is obtained, the distributed resources are scheduled and controlled, and the output control instruction is issued.

Description

Technical Field

[0001] This invention belongs to the field of novel power system virtual power plant dispatching technology, specifically relating to a virtual power plant dispatching method and related apparatus based on electric carbon flow. Background Technology

[0002] The current power system is transitioning towards a high proportion of renewable energy and high levels of power electronics. Massive amounts of distributed energy sources, such as distributed photovoltaics, wind power, and energy storage, are being integrated into the distribution network. Traditional centralized dispatching models struggle to efficiently manage and utilize these dispersed resources. Virtual Power Plants (VPPs), as the core carrier for the aggregation and regulation of distributed resources, can aggregate various dispersed resources through communication and control technologies, enabling them to participate in grid dispatching and electricity market transactions. They can effectively smooth out fluctuations in renewable energy output and optimize power resource allocation. This is a key technology for improving power system operating efficiency and reducing carbon emissions, and its application in the power industry is becoming increasingly widespread.

[0003] Existing virtual power plant dispatching has achieved economical and intelligent control of distributed power sources, energy storage, and flexible loads, with core objectives focused on reducing electricity costs and improving the integration rate of renewable energy. With the increasing sophistication of carbon accounting in the power industry and the deep integration of the electricity-carbon market, there is a growing demand for coordinated management of "electricity consumption and carbon emissions" in virtual power plants. Currently, the mainstream low-carbon dispatching solutions in the industry fall into two main categories: one is the electricity-carbon coupling dispatching scheme based on fixed carbon emission factors, which embeds carbon costs and total carbon emissions as static constraints into the dispatching model and uses fixed carbon factors to calculate carbon emissions; the other is the park virtual power plant dispatching scheme based on static carbon flow tracking, which constructs a low-carbon dispatching model through static carbon flow calculation, initially achieving coordinated electricity-carbon dispatching.

[0004] The main problem with existing virtual power plant low-carbon dispatch technologies is the low degree of electricity-carbon coupling. Both mainstream schemes adopt a static accounting model, either using a fixed carbon factor to ignore the dynamic changes in electricity-carbon flow, or only being able to track static carbon flow under steady-state conditions. This fails to accurately capture the carbon emission differences caused by fluctuations in renewable energy output and power transmission losses, resulting in low carbon accounting accuracy and poor traceability. This decoupled dispatch model renders low-carbon constraints merely a formality, making it difficult to balance the economic efficiency and low-carbon aspects of virtual power plant operation. It also fails to achieve refined traceability and differentiated control of carbon responsibility, and cannot meet the dispatch needs of an electricity-carbon coupled market. Summary of the Invention

[0005] Therefore, it is necessary to provide a virtual power plant scheduling method and related devices based on electric carbon flow to address the above-mentioned technical problems. This would solve the problems that existing virtual power plant low-carbon scheduling technologies have low electric carbon coupling, making it difficult to balance the economic efficiency and low carbon emissions of virtual power plant operation, and also failing to achieve refined traceability and differentiated regulation of carbon responsibility, thus failing to meet the scheduling needs of the electric carbon coupling market.

[0006] In a first aspect, the present invention provides a virtual power plant scheduling method based on electric carbon flow, comprising the following steps: Obtain the initial scheduling parameter set; Using the initial scheduling parameter set, the proportion of the installed capacity structure of the station is statistically analyzed and the time-sharing and zoning dynamic carbon parameters are calculated to obtain the station's installed capacity base parameters and the time-sharing and zoning dynamic carbon parameter set. By combining the installed base parameters of the power station with the time-sharing and zone-specific dynamic carbon parameter set, a multidimensional electric carbon flow spatiotemporal coupling calculation is performed to obtain the global time-sharing and zone-specific electric carbon flow distribution results. The multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission base and combines dynamic carbon parameters to deduce the spatiotemporal distribution of carbon flow along the power flow path. Based on the distribution results of time- and zone-based carbon flow across the entire region, the macro carbon control indicators are decomposed and matched hierarchically to obtain a hierarchical set of carbon constraint indicators covering stations, time periods, and equipment. By combining the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results, a multi-objective optimization solution is performed on the virtual power plant to obtain the virtual power plant's scheduling strategy set. Based on the scheduling strategy set, the distributed resources are scheduled and managed to obtain the output control instructions of each device.

[0007] Furthermore, using the initial scheduling parameter set, the proportion of installed capacity at the power station is statistically analyzed and time-sharing and zone-specific dynamic carbon parameters are calculated to obtain the power station's installed capacity base parameters and the time-sharing and zone-specific dynamic carbon parameter set, including: Based on the resource ledger data in the initial scheduling parameter set, the proportion of clean energy installed capacity to total installed capacity of each station is calculated to obtain the installed capacity base parameters of each station. Based on the grid structure, power supply composition, and load peak-valley characteristics of each power supply zone, the dynamic carbon factor is calculated by zone and time. By combining the real-time output conditions and operating loss coefficients of each generator set, the real-time power generation carbon intensity of each unit is calculated. By integrating dynamic electric carbon factor and real-time power generation carbon intensity of the unit, a time-sharing and zone-based dynamic carbon parameter set is obtained.

[0008] Furthermore, by combining the on-site installed base parameters with the time-sharing and zone-specific dynamic carbon parameter sets, multidimensional spatiotemporal coupling calculations of electric carbon flow are performed to obtain the global time-sharing and zone-specific electric carbon flow distribution results, including: Starting from the root node of the power grid, the nodal carbon potential of each node is solved iteratively layer by layer from upstream to downstream along the power flow topology of the distribution network. Based on the carbon potential of each node and the active power of the corresponding branch, the carbon flow of the branch and the carbon emissions of the node load are allocated according to the active power ratio to obtain the source allocation result of the carbon flow of the entire network. The base calibration of the source allocation results is performed using the base parameters of the installed equipment at the site, and the full-domain time-division and zone-division electric carbon flow distribution results of the coverage nodes, branches, and individual equipment are obtained.

[0009] Furthermore, based on the time- and zone-specific distribution of carbon flow across the entire region, macro-level carbon control indicators are hierarchically decomposed and matched to obtain a hierarchical set of carbon constraint indicators covering stations, time periods, and equipment, including: Based on the time-series and zone-specific distribution of carbon flow across the entire region and the installed base parameters of the power station, the time-series carbon emissions of the power station are predicted, and carbon prediction indicators are generated. The total annual carbon budget for the region is broken down by station, day, and time period, generating corresponding time period carbon budget indicators for each level. Using the total annual carbon quota of an enterprise as a hard constraint, the carbon emission limit of a single device can be obtained by breaking it down accordingly. A hierarchical set of carbon constraint indicators is composed of carbon forecast indicators, time-period carbon budget indicators, and single-equipment carbon emission ceiling indicators.

[0010] Furthermore, by combining the hierarchical carbon constraint index set with the global time-sharing and zone-based carbon flow distribution results, a multi-objective optimization solution is performed on the virtual power plant to obtain the virtual power plant's scheduling strategy set, including: By combining the hierarchical carbon constraint index set with the results of time- and zone-based distribution of electricity carbon flow across the entire region, a multi-objective optimization function is constructed that takes into account economic operation, low-carbon emission reduction and macro-control. Based on a hierarchical carbon constraint index set as the hard constraint foundation, and combined with the time-series fluctuation characteristics of dynamic carbon parameters in different time zones and regions, dynamic adaptive carbon emission constraints are generated. Based on the dynamic adaptive carbon emission constraint, the optimal solution of the multi-objective optimization function is calculated to obtain a set of scheduling strategies that include the time-series output plans of each device.

[0011] Furthermore, based on the scheduling strategy set, scheduling and control are performed on each distributed resource to obtain output control instructions for each device, including: Based on the predicted operating parameters and scheduling strategy set, the carbon prediction quantification and carbon budget pre-allocation of the station are carried out to obtain the basic output range of each equipment. During the daytime phase, the basic output range is used as the scheduling boundary. The time-sharing and zone-based dynamic carbon parameters are updated according to a fixed rolling cycle to correct carbon emission deviations and adjust the unit output structure, thus obtaining the daytime rolling scheduling scheme. In the real-time phase, the daily rolling scheduling scheme is used as the execution benchmark. Differentiated regulation is carried out on equipment and power supply zones with different carbon intensities according to the ultra-short scheduling cycle to obtain the output regulation instructions of each equipment.

[0012] Secondly, the present invention provides a virtual power plant dispatching device based on electric carbon flow, comprising: The data acquisition module is used to acquire the initial scheduling parameter set; The parameter calculation module is used to use the initial scheduling parameter set to statistically analyze the proportion of the installed structure of the station and calculate the time-sharing and zoning dynamic carbon parameters, so as to obtain the station's installed base parameters and the time-sharing and zoning dynamic carbon parameter set. The electric carbon flow coupling calculation module is used to combine the station's installed base parameters with the time-sharing and zone-based dynamic carbon parameter set to perform multidimensional electric carbon flow spatiotemporal coupling calculations to obtain the global time-sharing and zone-based electric carbon flow distribution results. The multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission base and combines dynamic carbon parameters to deduce the spatiotemporal distribution of carbon flow along the power flow path. The carbon constraint determination module is used to hierarchically decompose and match macro carbon control indicators based on the results of the distribution of time- and zone-wide electrical carbon flow, so as to obtain a hierarchical carbon constraint indicator set covering stations, time periods, and equipment. The scheduling strategy solution module is used to combine the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results to perform multi-objective optimization solutions for the virtual power plant and obtain the scheduling strategy set of the virtual power plant. The scheduling module is used to schedule and manage distributed resources according to the scheduling strategy set and obtain output control instructions for each device.

[0013] Thirdly, the present invention provides a computer device, the device including a processor and a memory: The memory is used to store computer programs and send the instructions of the computer programs to the processor; The processor executes the following steps according to the instructions of the computer program: Obtain the initial scheduling parameter set; Using the initial scheduling parameter set, the proportion of the installed capacity structure of the station is statistically analyzed and the time-sharing and zoning dynamic carbon parameters are calculated to obtain the station's installed capacity base parameters and the time-sharing and zoning dynamic carbon parameter set. By combining the installed base parameters of the power station with the time-sharing and zone-specific dynamic carbon parameter set, a multidimensional electric carbon flow spatiotemporal coupling calculation is performed to obtain the global time-sharing and zone-specific electric carbon flow distribution results. The multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission base and combines dynamic carbon parameters to deduce the spatiotemporal distribution of carbon flow along the power flow path. Based on the distribution results of time- and zone-based carbon flow across the entire region, the macro carbon control indicators are decomposed and matched hierarchically to obtain a hierarchical set of carbon constraint indicators covering stations, time periods, and equipment. By combining the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results, a multi-objective optimization solution is performed on the virtual power plant to obtain the virtual power plant's scheduling strategy set. Based on the scheduling strategy set, the distributed resources are scheduled and managed to obtain the output control instructions of each device.

[0014] Fourthly, the present invention provides a computer-readable storage medium on which a computer program is stored, and when executed by a processor, the computer program performs the following steps: Obtain the initial scheduling parameter set; Using the initial scheduling parameter set, the proportion of the installed capacity structure of the station is statistically analyzed and the time-sharing and zoning dynamic carbon parameters are calculated to obtain the station's installed capacity base parameters and the time-sharing and zoning dynamic carbon parameter set. By combining the installed base parameters of the power station with the time-sharing and zone-specific dynamic carbon parameter set, a multidimensional electric carbon flow spatiotemporal coupling calculation is performed to obtain the global time-sharing and zone-specific electric carbon flow distribution results. The multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission base and combines dynamic carbon parameters to deduce the spatiotemporal distribution of carbon flow along the power flow path. Based on the distribution results of time- and zone-based carbon flow across the entire region, the macro carbon control indicators are decomposed and matched hierarchically to obtain a hierarchical set of carbon constraint indicators covering stations, time periods, and equipment. By combining the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results, a multi-objective optimization solution is performed on the virtual power plant to obtain the virtual power plant's scheduling strategy set. Based on the scheduling strategy set, the distributed resources are scheduled and managed to obtain the output control instructions of each device.

[0015] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, performs the following steps: Obtain the initial scheduling parameter set; Using the initial scheduling parameter set, the proportion of the installed capacity structure of the station is statistically analyzed and the time-sharing and zoning dynamic carbon parameters are calculated to obtain the station's installed capacity base parameters and the time-sharing and zoning dynamic carbon parameter set. By combining the installed base parameters of the power station with the time-sharing and zone-specific dynamic carbon parameter set, a multidimensional electric carbon flow spatiotemporal coupling calculation is performed to obtain the global time-sharing and zone-specific electric carbon flow distribution results. The multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission base and combines dynamic carbon parameters to deduce the spatiotemporal distribution of carbon flow along the power flow path. Based on the distribution results of time- and zone-based carbon flow across the entire region, the macro carbon control indicators are decomposed and matched hierarchically to obtain a hierarchical set of carbon constraint indicators covering stations, time periods, and equipment. By combining the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results, a multi-objective optimization solution is performed on the virtual power plant to obtain the virtual power plant's scheduling strategy set. Based on the scheduling strategy set, the distributed resources are scheduled and managed to obtain the output control instructions of each device.

[0016] In summary, this invention provides a virtual power plant scheduling method and related apparatus based on electric carbon flow. The method first acquires an initial scheduling parameter set, statistically analyzes the proportion of installed capacity at power plants, and calculates time- and zone-specific dynamic carbon parameters. It then introduces dynamic electric carbon factors and real-time carbon intensity to capture the spatiotemporal carbon emission differences caused by fluctuations in renewable energy output and transmission losses, thereby improving calculation accuracy from the source. Subsequently, using the installed capacity structure as the carbon emission basis, it performs multidimensional spatiotemporal coupling calculations of electric carbon flow based on the aforementioned dynamic carbon parameters, synchronously extrapolating carbon flow distribution along the power flow path. This breaks the traditional decoupling model of scheduling first and then calculating, achieving real-time dynamic analysis of power flow and carbon flow. Linkage and refined source tracing: Based on this, the macro-carbon control indicators are hierarchically decomposed and matched to power plants, time periods, and equipment according to the distribution results of the global electricity carbon flow. Multi-objective optimization is then performed in conjunction with the distribution results, so that dynamic carbon constraints can directly participate in scheduling decisions, thereby achieving low-carbon emission reduction targets while ensuring economic operation. Finally, the optimized scheduling strategy set is used to schedule and control each distributed resource and issue power control commands. This solves the shortcomings of traditional technologies that make low-carbon constraints merely a formality and cannot achieve refined source tracing and differentiated control of carbon responsibility. It also meets the collaborative scheduling needs of virtual power plants in the electricity-carbon coupling market. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a virtual power plant scheduling method based on electric carbon flow in one embodiment of the present invention. Figure 2 This is a block diagram of a virtual power plant dispatching device based on electric carbon flow in one embodiment of the present invention; Figure 3 This is a block diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of the invention 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 a 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.

[0021] It should be noted that the user information (including but not limited to user images, user portrait information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations.

[0022] In one embodiment, such as Figure 1 As shown, a virtual power plant scheduling method based on electric carbon flow is provided, including the following steps: S101: Obtain the initial scheduling parameter set.

[0023] The initial scheduling parameter set refers to the set of basic data collected from the parameter perception layer before the virtual power plant performs electricity-carbon flow coupled scheduling.

[0024] In this step, the virtual power plant dispatching system adopts a four-layer interconnected distributed dispatching architecture consisting of a parameter perception layer, a micro-level coupled computation layer, a meso-level optimized dispatching layer, and a macro-level carbon control adaptation layer. The parameter perception layer collects four types of data: resource operation parameters, installed capacity structure parameters, regional carbon parameters, and macro-level control parameters. Among them, resource operation parameters include the real-time output, state of charge, and adjustable capacity of each distributed photovoltaic, wind power, gas turbine, energy storage, and flexible load; installed capacity structure parameters include the total installed capacity of clean energy, fossil energy, and their proportions; regional carbon parameters include the time-of-use dynamic carbon factor and regional grid benchmark carbon intensity for each power supply zone and dispatching period; and macro-level control parameters include annual carbon forecast indicators, regional carbon budget quotas, corporate carbon quota indicators, and carbon emission reduction assessment requirements.

[0025] S102: Using the initial scheduling parameter set, the proportion of the installed capacity structure of the station is statistically analyzed and the time-sharing and zone-based dynamic carbon parameters are calculated to obtain the station's installed capacity base parameters and the time-sharing and zone-based dynamic carbon parameter set.

[0026] Among them, the installed capacity baseline parameters of the power plant refer to the structural parameters that reflect the proportion of clean energy installed capacity in each power plant of the virtual power plant to the total installed capacity. The time-sharing and zone-based dynamic carbon parameter set refers to the set of dynamically updated electrical carbon factors and real-time power generation carbon intensity of the units, divided according to power supply zones and dispatching time periods.

[0027] This step utilizes the initial scheduling parameter set to statistically analyze the proportion of installed capacity at the power station and calculate the time-sharing and zone-specific dynamic carbon parameters.

[0028] S103: Combining the station's installed base parameters with the time-sharing and zone-specific dynamic carbon parameter set, perform multidimensional electric carbon flow spatiotemporal coupling calculation to obtain the global time-sharing and zone-specific electric carbon flow distribution results; the multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission base and combines dynamic carbon parameters to deduce the spatiotemporal distribution of carbon flow along the power flow path.

[0029] Among them, the multidimensional electric carbon flow spatiotemporal coupling calculation is a coupled calculation process that uses the installed structure as the carbon emission basis and combines dynamic carbon parameters to deduce the spatiotemporal distribution of carbon flow along the power flow path. The global time-sharing and zone-sharing electric carbon flow distribution results refer to the time-sharing and zone-sharing carbon flow, carbon density and carbon loss distribution data covering all nodes, branches and individual equipment of the virtual power plant.

[0030] This step couples the power station's installed base parameters with the time-sharing and zone-based dynamic carbon parameter set in multiple dimensions to achieve synchronous and linked calculation of power flow and carbon emission flow.

[0031] S104: Based on the distribution results of time-sharing and zone-sharing carbon flow across the entire region, the macro carbon control indicators are decomposed and matched hierarchically to obtain a set of hierarchical carbon constraint indicators covering stations, time periods, and equipment.

[0032] Among them, the hierarchical carbon constraint index set refers to the set of carbon constraint indicators obtained by breaking down and matching macro carbon control indicators into three levels: station, time period, and equipment.

[0033] Based on the results of the distribution of carbon flow across the entire region and time zone, this step decomposes the macro carbon control indicators into hierarchical levels, enabling the micro-level scheduling to be integrated with the macro carbon control system.

[0034] S105: Combining the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results, the virtual power plant is optimized in multiple objectives to obtain the virtual power plant's scheduling strategy set.

[0035] Multi-objective optimization refers to the process of simultaneously considering objectives such as economic operation, low-carbon emission reduction, and alignment with macroeconomic control, and solving for the optimal scheduling strategy under the constraints of a hierarchical carbon constraint index set. The scheduling strategy set refers to the set of scheduling schemes that include the time-series output plans of each device.

[0036] This step combines the carbon constraint index with the distribution results of electric carbon flow to solve for the optimal scheduling strategy of the virtual power plant through a multi-objective optimization function.

[0037] S106: Based on the scheduling strategy set, schedule and manage each distributed resource to obtain the output control instructions of each device.

[0038] Among them, the output control instruction refers to the specific instruction generated according to the scheduling strategy set and used to control the operating status of each distributed resource.

[0039] This step performs scheduling and control on each distributed resource based on the scheduling policy set, and transforms the optimization policy into device-level execution instructions.

[0040] This embodiment provides a virtual power plant scheduling method based on electric carbon flow. The method first acquires an initial scheduling parameter set, statistically analyzes the proportion of installed capacity at the power plant, and calculates time- and zone-specific dynamic carbon parameters. It introduces dynamic electric carbon factors and real-time carbon intensity to capture the spatiotemporal carbon emission differences caused by fluctuations in renewable energy output and transmission losses, thus improving calculation accuracy from the source. Subsequently, using the installed capacity as the carbon emission basis, it performs multi-dimensional spatiotemporal coupling calculations of electric carbon flow based on the aforementioned dynamic carbon parameters, synchronously extrapolating carbon flow distribution along the power flow path. This breaks the traditional decoupling mode of scheduling first and then calculating, achieving real-time dynamic linkage between power flow and carbon flow. Refined source tracing; based on this, the macro-carbon control indicators are hierarchically decomposed and matched to power plants, time periods and equipment according to the distribution results of the global electricity carbon flow, and multi-objective optimization is performed in combination with the distribution results, so that dynamic carbon constraints directly participate in scheduling decisions, thereby achieving low-carbon emission reduction targets while ensuring economic operation; finally, the scheduling and control of each distributed resource is carried out according to the optimized scheduling strategy set, and power control instructions are issued, which solves the defects of traditional technology in low-carbon constraints being merely a formality and unable to achieve refined source tracing and differentiated control of carbon responsibility, and meets the collaborative scheduling needs of virtual power plants in the electricity-carbon coupling market.

[0041] In an exemplary embodiment, using an initial scheduling parameter set, the proportion of installed capacity at the power station is statistically analyzed and time-sharing and zone-based dynamic carbon parameters are calculated to obtain the power station's installed capacity base parameters and the time-sharing and zone-based dynamic carbon parameter set, including: S201: Based on the resource ledger data in the initial scheduling parameter set, calculate the proportion of clean energy installed capacity of each station to the total installed capacity, and obtain the installed capacity base parameters of the sub-stations.

[0042] Among them, resource ledger data refers to the basic ledger information of each power station's power generation equipment type, installed capacity, commissioning time, and rated parameters, which are centrally recorded in the initial scheduling parameters. Installed capacity baseline parameters refer to the proportion of clean energy installed capacity of each power station to the total installed capacity of the power station.

[0043] For example, based on the collected resource ledger data, the system dynamically generates the base parameters of the virtual power plant's overall installed capacity structure, and statistically analyzes the installed capacity of clean energy, the installed capacity of fossil energy, the installed capacity ratio, and the instantaneous power generation ratio of clean energy.

[0044] It is understandable that there are significant differences in the installed capacity structure of different power plants. Power plants with a high proportion of clean energy have a lower inherent carbon emission base, while power plants with a mix of coal and electricity have a higher carbon emission base. This installed capacity base parameter provides a static installed capacity base constraint for the calculation of electricity carbon flow and provides a classification basis for differentiated scheduling and differentiated carbon quota assessment.

[0045] S202: Based on the grid structure, power supply composition, and load peak-valley characteristics of each power supply zone, the dynamic carbon factor is calculated by zone and time.

[0046] Among them, the dynamic carbon emission factor refers to the parameter that reflects the intensity of carbon emissions from electricity in a given region during a given time period, calculated separately for each region based on the grid structure, power source composition, and peak-valley characteristics of the load in each time period.

[0047] Specifically, unlike the traditional method of fixing the carbon factor uniformly, this embodiment divides the power grid into multiple power supply zones and scheduling periods based on the grid structure, power source composition, load characteristics, renewable energy output levels, and grid supply pressure during different power supply areas. It then quantitatively calculates the dynamic carbon factor for each region and time period. For example, during peak daytime renewable energy generation, the proportion of clean energy supply in the region is high, and the carbon factor automatically decreases. At night and during peak load periods, the proportion of thermal power supply increases, and the carbon factor dynamically rises, thus restoring the true carbon emission level of the power grid in the spatiotemporal dimensions.

[0048] S203: The real-time power generation carbon intensity of each generator unit is calculated by combining the real-time output conditions and operating loss coefficients of each generator unit.

[0049] Among them, the real-time power generation carbon intensity of the unit refers to the instantaneous power generation carbon emission intensity of the unit calculated on a unit-by-unit basis by combining the real-time output conditions and operating loss coefficients of the generator units.

[0050] In practice, the fixed carbon emission coefficient under rated operating conditions of the unit is abandoned. Instead, the instantaneous carbon intensity of each distributed generation device is calculated by combining the unit's real-time output, fuel consumption, operating load rate, and loss coefficient.

[0051] It should be noted that the carbon intensity of the same gas turbine unit varies significantly under different output conditions. This step can capture the changes in carbon emissions caused by fluctuations in operating conditions and achieve accurate traceability of carbon emissions for a single device.

[0052] S204: Integrate dynamic electric carbon factor and real-time power generation carbon intensity of unit to obtain time-sharing and zone-based dynamic carbon parameter set.

[0053] Among them, the time-sharing and zone-based dynamic carbon parameter set refers to the dynamic carbon parameter set that integrates the dynamic electric carbon factor and the real-time power generation carbon intensity of the unit, covering each zone, each time period, and each unit.

[0054] For example, the formula for calculating the time-sharing and zone-based carbon factor is as follows: (1) in, Indicates partition r During the period t The dynamic electrocarbon factor, expressed in kgCO2 / kWh; Indicates the unit g During the period t Real-time carbon emissions; Indicates partition r During the period t Carbon emissions from external electricity; Indicates the unit g During the period t Those who have made meritorious contributions; Indicates partition r During the period t External network input active power; Indicates partition r During the period t Network loss power. Spatial dimension is divided into independent zones according to the power supply range of the 10kV distribution network feeder. r The time scale is divided into 15-minute time intervals. t The calculation is performed by substituting five types of measured data: unit carbon emissions, external power carbon emissions, zone output, external grid input power, and zone network loss. The unit of the factor result is fixed as kgCO2 / kWh.

[0055] The formula for calculating the real-time carbon intensity of the generating unit is: (2) in, Indicates the unit g During the period t Real-time carbon intensity of electricity generation; , , For the unit g The fitting coefficient of the carbon emission load curve at the plant is obtained by regression fitting of the carbon emission load curve of the unit. For the unit g During the period t Those who have made meritorious contributions; For the unit g During the period t The loss coefficient within the rated efficiency range is 0.82 to 0.95; 44 / 12 is a fixed constant for the stoichiometric conversion of carbon and CO2. For the unit g During the period t The carbon oxidation rate was fixed at the average measured value of 0.995 in the thermal power industry.

[0056] The formula for calculating the baseline carbon emission level of a facility is as follows: (3) in, Indicates the time period of the virtual power plant. t The benchmark carbon emission substrate; Indicates the unit g The rated carbon emission intensity of power generation; Indicates the unit g The rated installed capacity; This indicates the proportion of clean energy installed capacity at the power station, calculated quantitatively based on the rated capacity of photovoltaic, wind power, and energy storage as a percentage of the total installed capacity.

[0057] This embodiment refines the processing of the initial scheduling parameter set. It obtains the installed capacity base parameters by statistically analyzing the proportion of clean energy installed capacity in the power station, calculates the dynamic electric carbon factor by region and time, calculates the real-time carbon intensity of each computer group, and integrates them to form a time-sharing and region-sharing dynamic carbon parameter set. This realizes carbon parameter modeling where the installed capacity structure determines the carbon emission base, the carbon intensity determines the real-time carbon emission, and the time-sharing and region-sharing factor calibrates the regional carbon value, providing underlying parameter support for electric carbon flow coupling calculation.

[0058] In an exemplary embodiment, by combining the on-site installed base parameters and the time-sharing and zone-specific dynamic carbon parameter set, a multidimensional spatiotemporal coupling calculation of the electric carbon flow is performed to obtain the global time-sharing and zone-specific electric carbon flow distribution results, including: S301: Starting from the root node of the power grid, the nodal carbon potential of each node is solved iteratively from upstream to downstream along the power flow topology of the distribution network.

[0059] Among them, nodal carbon potential refers to the carbon emission intensity at a certain node in the power grid topology, reflecting the weighted average carbon emission level of the power sources connected to that node. Distribution network power flow topology refers to the power flow paths and connections between nodes and branches in the distribution network.

[0060] Specifically, starting with the root node of the power grid as the initial iteration point, the nodal carbon potential of each node is solved iteratively layer by layer from upstream to downstream along the power flow topology of the distribution network. It can be understood that this iterative process is based on the principle of power sharing in the power grid, tracing the entire power flow path from generation and transmission to load consumption, and deducing the spatiotemporal migration of carbon flow layer by layer.

[0061] S302: Based on the carbon potential of each node and the active power of the corresponding branch, the carbon flow of the branch and the carbon emissions of the node load are allocated according to the active power ratio to obtain the source allocation result of the carbon flow of the entire network.

[0062] Among them, the source allocation result refers to the source data of carbon flow of the whole network obtained by allocating the carbon flow of the branch and the carbon emission of the node load according to the active power ratio based on the carbon potential of each node and the active power of the corresponding branch.

[0063] In practice, based on the carbon potential of each node and the active power of the corresponding branch, the carbon flow of the branch and the carbon emissions of the node load are allocated according to the proportion of active power. The spatiotemporal migration of carbon flow, carbon loss of the branch, and carbon density distribution of the node are simultaneously simulated to complete the refined carbon source tracing for single equipment, single branch, single node, and single time period. For example, for a certain branch, its carbon flow is equal to the carbon potential of the upstream node multiplied by the active power transmitted by the branch, and the carbon emissions of each load node are equal to the node carbon potential multiplied by the load power, thereby achieving accurate source tracing and allocation of carbon flow across the entire network.

[0064] S303: Use the base parameters of the installed equipment at the site to perform base calibration on the source allocation results to obtain the full-domain time-division and zoned distribution results of the carbon flow of the covered nodes, branches, and individual equipment.

[0065] Baseline calibration refers to the process of correcting the source allocation results using the base parameters of the power plant's installed capacity, so that the carbon flow distribution matches the actual installed capacity structure of the power plant. The time-sharing and zone-based carbon flow distribution results for the entire virtual power plant refer to the time-sharing and zone-based carbon flow, carbon density, and carbon loss distribution data covering all nodes, branches, and individual devices within the entire virtual power plant area.

[0066] Preferably, by introducing a base constraint based on the installed capacity structure, the proportion of clean energy installed capacity, the installed capacity of thermal power units, and the proportion of energy storage in the virtual power plant are used as base constraints for calculating the carbon emission flow: the higher the proportion of clean energy installed capacity, the lower the overall carbon emission base of the power plant; the higher the proportion of thermal power installed capacity, the stricter the carbon intensity control threshold of the power plant. It should be noted that a dynamic carbon emission flow mapping matrix is ​​constructed by using a three-dimensional coupling logic of time-sharing and zone-based factor calibration of regional carbon values, real-time carbon intensity refinement of equipment carbon emissions, and installed capacity structure locking of the power plant carbon base, thereby achieving real-time synchronization and matching between power flow and carbon emission flow.

[0067] For example, the formula for calculating the nodal carbon potential is: (4) in, Represents a node i During the period t The nodal carbon potential; Indicates upstream node j During the period t The nodal carbon potential; Indicates the time period t node j Flow to Node iThe active power. The calculation process is as follows: ① Initialize the carbon potential from the root node of the power grid; ② Iterate layer by layer from upstream to downstream according to the power flow topology; ③ Set the iteration convergence threshold to 10. -6 The calculation stops when the difference between the carbon potentials of two iterations is lower than the threshold. For example, for a chain power grid that reaches the load node through two levels of branches from the root node, the carbon potential of the root node is used as the initial value. First, the carbon potential of the end node of the first-level branch is calculated according to formula (4). Then, the carbon potential of the end node of the second-level branch is calculated as the input. The process is iterated layer by layer until the carbon potential of all nodes in the network converges.

[0068] The formulas for calculating branch carbon flow and load carbon emissions are as follows: (5) in, Indicates a branch i → j During the period t Carbon flow; Indicates a branch i → j During the period t Transmitted active power; Indicates the first node of the branch. i During the period t The nodal carbon potential; Represents a node i During the period t Carbon emissions from load; Represents a node i During the period t The active power of the load. For example, for a certain branch, its carbon flow is equal to the carbon potential of the upstream node multiplied by the active power transmitted by the branch. The load carbon emissions of each load node are strictly matched with the product of the load active power and the node carbon potential. The network loss carbon emissions are synchronously matched with the loss power traceability. The carbon flow attribution and allocation are not subject to subjective weighting adjustment, which ensures the objectivity and traceability of carbon flow traceability and allocation.

[0069] The formula for calculating the time-of-use carbon flow rate of a single device is: (6) in, Indicates the unit g In partition r Time period t Carbon emissions; Indicates the unit g During the period t Those who have made meritorious contributions; Indicates the unit g During the period t Real-time carbon intensity of electricity generation; Indicates partition r During the period t Dynamic electrocarbon factor; Indicates the baseline carbon emission level of the site; This represents the baseline carbon emission value for a given zone. The formula integrates four quantitative parameters: unit carbon intensity, zoned carbon factor, and station baseline carbon emission, to calculate and output raw time-of-use carbon emission data for a single unit, which is then directly supplied to the scheduling optimization model as a constraint input.

[0070] This embodiment refines the spatiotemporal coupling calculation process of multidimensional electric carbon flow. By iteratively solving the node carbon potential layer by layer starting from the root node of the power grid, the carbon flow of the branch and the carbon emissions of the node load are allocated according to the active power ratio to obtain the source allocation result. The base calibration is performed using the installed base parameters, and a three-dimensional electric carbon flow coupling mechanism of installed base plus spatiotemporal factor plus real-time carbon intensity is constructed. This realizes the real-time linkage, synchronous migration and bidirectional coupling of power flow and carbon emission flow, breaking the traditional electric carbon decoupling scheduling mode.

[0071] In an exemplary embodiment, based on the global time-sharing and zone-based distribution of electrical carbon flow, macro-level carbon control indicators are hierarchically decomposed and matched to obtain a hierarchical set of carbon constraint indicators covering stations, time periods, and equipment, including: S401: Based on the time-series and zone-specific carbon flow distribution results of the entire region and the base parameters of the installed capacity of the power station, the time-series carbon emissions of the power station are predicted, and carbon prediction indicators are generated.

[0072] Among them, the carbon prediction index refers to the carbon emission prediction data generated after predicting the time-series carbon emissions of the power station based on the distribution results of the global time-series and regional electric carbon flow and the base parameters of the power station's installed capacity.

[0073] Specifically, based on historical installed capacity data, time-series power generation data, and dynamic carbon parameters, short-term and medium-term carbon emission forecasts at the power plant level are completed to support carbon forecasting and statistics for the regional power industry.

[0074] S402: The total annual carbon budget for the region is broken down by station, day, and time period, generating the corresponding time period carbon budget indicators for each level.

[0075] Among them, the time-period carbon budget indicator refers to the carbon budget limit corresponding to each level after the total annual carbon budget of the region is broken down by station, day and time period.

[0076] In practice, the overall regional carbon budget targets are broken down into virtual power plants, time periods, and equipment, addressing the industry pain points of difficulty in implementing macro-level carbon budgets and the lack of effective tools. For example, the total annual regional carbon budget is first allocated to power plants, then the annual budget for each power plant is broken down into daily budgets, and then the daily budgets are further broken down into time-period carbon budget limits, thus achieving a seamless transition from macro-level total control to micro-level implementation of carbon budgets.

[0077] S403: Using the total annual carbon quota of an enterprise as a hard constraint, the carbon emission limit of a single device is obtained by breaking it down.

[0078] Among them, the carbon emission ceiling indicator refers to the maximum allowable carbon emission of a single piece of equipment obtained by dismantling the enterprise's total annual carbon quota as a hard constraint.

[0079] It should be noted that strict adherence to the annual carbon quota targets for enterprises is maintained, and the distribution of carbon flow in electricity is continuously optimized to ensure that both the total carbon emissions and carbon intensity of virtual power plants meet the standards. This means that the total annual carbon quota and intensity quota for enterprises serve as hard constraints, locking in the maximum permissible carbon emission limit for dispatch and satisfying carbon quota compliance management.

[0080] S404: A hierarchical set of carbon constraint indicators consisting of carbon forecasting indicators, time-period carbon budget indicators, and single-device carbon emission ceiling indicators.

[0081] The hierarchical carbon constraint index set refers to a set of carbon constraint indicators that covers three levels: station, time period, and equipment, consisting of carbon prediction indicators, time period carbon budget indicators, and single equipment carbon emission ceiling indicators.

[0082] For example, the calculation formula for the three-tiered quantitative decomposition of the carbon budget is as follows: (7) (8) (9) in, This indicates the annual carbon budget allocated to virtual power plant sites within the region. This indicates the region's total annual carbon budget. Indicates the annual baseline carbon emission level of the station. This indicates the annual baseline carbon emission base of the facility; Indicates the time period of the station t Carbon budget; This indicates the daily carbon budget for the facility; This represents the electrocarbon factor for time period t in partition r; This represents the load power during time period t; This indicates the upper limit of carbon emissions for unit g during time period t in zone r; This indicates the rated carbon emission intensity of unit g; This represents the rated installed capacity of unit g. In practice, the annual budget is allocated based on the base carbon emission ratio of the plant; the daily budget of the plant is weighted and broken down into 15-minute time periods based on time-of-use dynamic carbon factor and load power; and the carbon emission limit for a single unit is allocated based on the ratio of the unit's rated carbon intensity and rated output to the total capacity of the plant.

[0083] This embodiment refines the hierarchical decomposition and matching process of macro carbon control indicators. It generates carbon prediction indicators by predicting the time-series carbon emissions of power plants, decomposes the regional annual carbon budget into time-period carbon budget indicators step by step, and decomposes the carbon emission ceiling indicators of single equipment with the enterprise carbon quota as a hard constraint. It constructs a set of carbon constraint indicators covering three levels: power plant, time period, and equipment, and realizes a macro-connection architecture that supports industry carbon prediction with virtual power plant micro data, hierarchical decomposition of regional carbon budget, and dynamic performance verification of main carbon quotas.

[0084] In an exemplary embodiment, by combining a hierarchical carbon constraint index set with the global time-sharing and zone-based carbon flow distribution results, a multi-objective optimization solution is performed on the virtual power plant to obtain a set of scheduling strategies for the virtual power plant, including: S501: Combining the hierarchical carbon constraint index set with the global time-sharing and regional distribution results of electric carbon flow, a multi-objective optimization function is constructed that takes into account economic operation, low-carbon emission reduction and macro-control.

[0085] Among them, the multi-objective optimization function refers to an optimization function that simultaneously considers three dimensions of objectives: economic operation, low-carbon emission reduction, and macro-control alignment. Specifically, the model objective function considers three dimensions of objectives: first, microeconomic objectives, including electricity trading revenue, carbon quota trading revenue, and equipment operating costs; second, on-site low-carbon objectives, with the core objectives of minimizing the average carbon intensity of unit power generation, optimizing the carbon flow density of regional power generation, and maximizing the clean energy consumption rate; and third, macro-control objectives, guided by maximizing the accuracy of on-site carbon prediction, optimizing the carbon budget alignment, and maximizing the carbon quota utilization rate.

[0086] Understandably, the underlying parameters of this multi-objective optimization function incorporate time-sharing and zone-based carbon factors, real-time carbon intensity of unit power generation, proportion of clean energy installed capacity at the power station, and installed capacity structure ratio, while the upper-level constraints are strictly aligned with the carbon control system.

[0087] S502: Based on a hierarchical carbon constraint index set as the hard constraint foundation, and combined with the time-series fluctuation characteristics of dynamic carbon parameters in different time zones and regions, dynamic adaptive carbon emission constraint conditions are generated.

[0088] Among them, the dynamic adaptive carbon emission constraint condition refers to the adaptive constraint condition generated by dynamically adjusting the carbon emission constraint threshold based on the time-series fluctuation characteristics of dynamic carbon parameters in different time zones and using a hierarchical carbon constraint index set as the hard constraint basis.

[0089] It should be noted that the model abandons the traditional static fixed carbon constraint and adopts a dynamic parameter constraint mechanism: based on the fluctuation of the electric carbon factor in different time periods and regions, and combined with the real-time changes in the carbon intensity of the units, the carbon emission constraint threshold is dynamically adjusted. In specific implementation, the carbon quota assessment weights for new energy power plants and those including thermal power plants are set differently according to the differences in the installed capacity structure of virtual power plants. This ensures both the accuracy of the dispatching plan and the complete adaptation of micro-dispatch to the requirements of macro-carbon control policies, avoiding the dispatching rigidity and economic and low-carbon imbalances caused by traditional fixed constraints.

[0090] S503: Based on dynamic adaptive carbon emission constraints, the optimal solution of the multi-objective optimization function is calculated to obtain a set of scheduling strategies that includes the time-series output plans of each device.

[0091] The scheduling strategy set refers to the set of scheduling schemes that include the time-series output plans of each device, obtained after calculating the optimal solution of the multi-objective optimization function based on the dynamic adaptive carbon emission constraints.

[0092] Understandably, by solving the optimal solution of the multi-objective optimization function through algorithms such as linear programming and robust optimization, the time-series output plans of each distributed power source, energy storage, and flexible load are output, forming a set of scheduling strategies that take into account economy, low carbon emissions, and macro-control compliance.

[0093] For example, the overall objective function of multi-objective scheduling optimization F The calculation formula is: (10) in, F This represents the overall optimization target value for scheduling; This represents the scalarized expression of sub-objectives of economic operation (such as the sum of equipment power generation cost, fuel cost, and operation and maintenance cost). This represents the scalarized expression of a low-carbon emission reduction sub-target (such as the minimum total carbon emissions of the entire network). This represents a scalarized expression for macro-level carbon control alignment with sub-targets (such as the minimum deviation from the annual carbon budget). , , These are the weighting coefficients for the three sub-objectives: economic, low-carbon, and regulatory. The weighting constraints are... The default baseline weights are 0.4 / 0.35 / 0.25, which can be adjusted according to the control requirements. The model is solved using the interior-point method of linear programming, with a fixed iteration step size and convergence termination condition.

[0094] In practical implementation, the following dynamic constraint formula can be used to generate dynamic adaptive carbon emission constraints: (11) in, Indicates time periodt Total carbon emissions from all units across all zones; Indicates time period t The basic carbon budget limit; This represents the dynamic correction coefficient for each zone, quantified according to quarterly carbon control targets. It should be noted that dynamic carbon constraints abandon the static threshold limitations of traditional fixed carbon constraints, incorporating the temporal fluctuation characteristics of time- and zone-specific dynamic carbon parameters into the constraint conditions. Based on the fluctuations in the electric carbon factor in different time periods and regions, and combined with real-time changes in unit carbon intensity, the carbon emission constraint threshold is dynamically adjusted, ensuring that the constraint conditions and the electric carbon flow distribution results remain spatiotemporally synchronized, thus guaranteeing the feasibility and optimality of the optimization results under a dynamic carbon environment.

[0095] It should be noted that Equation (10) defines the quantitative solution framework for the three-dimensional objectives of economy, low carbon, and control, while Equation (11) defines the adaptive tightening mechanism of dynamic carbon constraints. The two are linked by weighting coefficients. ω and dynamic correction coefficient k r The collaborative process ensures that the multi-objective optimization solution process takes into account commercial value, low-carbon value and policy alignment value, and that all solution rules and constraint rules are solidified and reproducible.

[0096] This embodiment refines the multi-objective optimization solution process. By constructing a multi-objective optimization function that takes into account economic operation, low-carbon emission reduction, and macro-control, dynamic adaptive carbon emission constraints are generated based on a hierarchical carbon constraint index set as a hard constraint, and the optimal scheduling strategy set is solved. This achieves a two-level optimization that couples micro-operation and macro-control, breaking through the limitations of traditional single-objective optimization that focuses on optimal technology cost or optimal carbon emission, and realizing the unity of commercial value, low-carbon value, and policy compliance value.

[0097] In an exemplary embodiment, according to a set of scheduling strategies, the distributed resources are scheduled and managed to obtain output control instructions for each device, including: S601: In the day-ahead phase, based on the predicted operating parameters and scheduling strategy set, the carbon prediction quantification and carbon budget pre-allocation of the station are carried out to obtain the basic output range of each equipment.

[0098] The basic output range refers to the output range of each device in each time period of the following day, obtained after the carbon prediction quantification and carbon budget pre-allocation of the site are carried out based on the predicted operating parameters and scheduling strategy set in the day-ahead phase.

[0099] Specifically, based on the proportion of clean energy installed capacity in virtual power plants and the installed capacity structure of thermal power units, combined with the time-of-use and zone-based carbon factor forecasts for each time period of the following day, as well as new energy output and load forecasts, the total time-series carbon emissions of the power plants for the following day are predicted, generating an accurate power plant-level carbon forecast report. Simultaneously, in conjunction with the regional annual or quarterly carbon budget allocated by higher authorities, the time-of-use carbon budget for virtual power plants is dynamically broken down on a daily and time-period basis, achieving the micro-level implementation of the macro-carbon budget and locking in the output range and carbon emission ceiling for each resource unit for the following day.

[0100] S602: During the intraday phase, the basic output range is used as the scheduling boundary. The time-sharing and zone-based dynamic carbon parameters are updated according to a fixed rolling cycle to correct carbon emission deviations and adjust the unit output structure, thus obtaining the intraday rolling scheduling scheme.

[0101] Among them, the intraday rolling scheduling scheme refers to the intraday scheduling scheme obtained by taking the basic output range as the scheduling boundary, updating the time-sharing and zone dynamic carbon parameters according to a fixed rolling cycle, correcting carbon emission deviations and adjusting the unit output structure.

[0102] In practice, the carbon factor of each power supply zone and the real-time carbon intensity of each unit are updated in real time with a 15-minute rolling cycle. Combined with the actual clean energy power generation output of the day, the day-ahead carbon forecast results and carbon budget allocation plan are revised.

[0103] It should be noted that, in response to carbon emission deviations caused by fluctuations in new energy sources and load shifts, the unit output structure is fine-tuned, with priority given to controlling high-carbon-intensity units to ensure that the total daily carbon emissions of the virtual power plant strictly adhere to the budget targets. At the same time, the utilization efficiency of the installed capacity resources within the station is optimized to balance low-carbon operation and power supply reliability.

[0104] S603: In the real-time phase, the intraday rolling scheduling scheme is used as the execution benchmark. Differentiated control is carried out on equipment and power supply zones with different carbon intensities according to the ultra-short scheduling cycle to obtain the output control instructions of each equipment.

[0105] Among them, the output control instruction refers to the specific operation control instruction generated for each device after the daily rolling scheduling scheme is used as the execution benchmark and the devices and power supply zones with different carbon intensities are differentiated and controlled according to the ultra-short scheduling cycle in the real-time stage.

[0106] Preferably, a 1-minute ultra-short cycle is used to implement differentiated regulation based on real-time electrical-carbon flow coupling calculation results and combined with the characteristics of the power station's installed capacity structure: for zones with a high proportion of clean energy installed capacity, new energy sources are fully utilized during periods of low electrical-carbon factor to increase the proportion of low-carbon power generation; for zones with concentrated thermal power and high carbon intensity, the output of high-carbon units is strictly limited, and node carbon density is reduced through energy storage discharge and flexible load staggering.

[0107] For example, the quantitative determination of differentiated power output control can adopt the following three-tier threshold classification rule: based on the proportion of clean energy installed capacity in the power station. S VPP Differentiated control thresholds are defined, including: high-energy power plants S VPP When the power generation capacity is ≥60%, priority should be given to increasing the output of clean energy; for conventional thermal power plants, the increase should be 20% or less. S VPP When output is less than 60%, the power output is dispatched according to the balanced output strategy; high-carbon thermal power units S VPP When the power output is ≤20%, power output is limited to reduce carbon emissions. Power output priority follows a fixed order: new energy → energy storage first → conventional units. Control commands are generated for each unit according to priority. For example, for a mixed power station containing 30% photovoltaic, 20% wind power, 10% energy storage, and 40% thermal power (… S VPP =60%), the system judges it as a high-tech new energy power station, and prioritizes the dispatch of photovoltaic and wind power at the maximum output. Energy storage is given priority to discharge during the low output of photovoltaic and wind power to fill the gap. Thermal power units only undertake the base load and operate at the minimum safe output.

[0108] In practice, the following dynamic correction formula can be used for the multi-timescale bias correction closed loop of carbon prediction error: (12) in, This indicates the relative bias in carbon predictions; This indicates the equipment's real-time actual carbon emissions; This indicates the predicted carbon emissions from the equipment. Three correction thresholds are set: ① When δ≤5%, no parameter adjustment is made, and the current scheduling strategy continues to execute; ② When 5%<δ≤15%, the dynamic electrical carbon factor of the corresponding partition is fine-tuned. EF r,t After correcting the carbon flow distribution results, the constraints are re-solved; ③ When δ>15%, the real-time carbon intensity of the power generation of the computer group is recalculated based on the deviation signal. e.g. g,t and station benchmark carbon emission base E base,VPP,t After updating the end-to-end parameters, the three-level scheduling (day-to-day-to-real-time) is re-executed. After the correction is completed, a rolling review is performed on a three-level time scale (daily, weekly, and monthly), and the corrected parameters are sent back to the link starting point to form a closed loop.

[0109] It should be noted that the three threshold rules for differentiated output control and the deviation correction closed loop of formula (12) together constitute the standardized control system of the hierarchical scheduling execution unit. The former solidifies the output priority and control threshold according to the differences in the installed structure, while the latter triggers parameter recalculation and strategy re-optimization according to the size of carbon emission deviation. The two form a complete closed loop through rolling review at multiple time scales, ensuring the accuracy, timeliness and traceability of the scheduling control instructions.

[0110] This embodiment refines the scheduling and control process. It employs a multi-timescale hierarchical scheduling system, including day-ahead carbon prediction quantification and carbon budget pre-allocation, intraday carbon parameter updates to correct carbon emission deviations, and real-time differentiated control. This system achieves day-ahead prediction, intraday rolling updates, and real-time precision. Furthermore, after the scheduling cycle ends, carbon emission responsibility can be traced and broken down based on full-cycle time-segmented and zoned power plant carbon data, unit carbon intensity data, and installed capacity operation data. This allows for a review of the deviations between actual carbon emissions and carbon predictions, carbon budgets, and carbon quotas. The reviewed data is then used to iterate back to the prediction model, continuously improving carbon prediction accuracy. This forms a closed-loop scheduling system encompassing prediction, allocation, scheduling, verification, and iteration, possessing self-learning and adaptive capabilities. Its long-term stability, accuracy, and market adaptability far surpass existing static and open-loop scheduling schemes.

[0111] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0112] Based on the same inventive concept, this application also provides an electric carbon flow-based virtual power plant scheduling device for implementing the above-mentioned electric carbon flow-based virtual power plant scheduling method. The solution provided by this device is similar to the solution described in the above-described method. Therefore, the specific limitations in the embodiments of the electric carbon flow-based virtual power plant scheduling device provided below can be found in the limitations of the electric carbon flow-based virtual power plant scheduling method described above, and will not be repeated here.

[0113] Please see Figure 2 This invention also provides a virtual power plant dispatching device based on carbon flow, comprising: The data acquisition module is used to acquire the initial scheduling parameter set; The parameter calculation module is used to use the initial scheduling parameter set to statistically analyze the proportion of the installed structure of the station and calculate the time-sharing and zoning dynamic carbon parameters, so as to obtain the station's installed base parameters and the time-sharing and zoning dynamic carbon parameter set. The electric carbon flow coupling calculation module is used to combine the station's installed base parameters with the time-sharing and zone-based dynamic carbon parameter set to perform multidimensional electric carbon flow spatiotemporal coupling calculations to obtain the global time-sharing and zone-based electric carbon flow distribution results. The multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission base and combines dynamic carbon parameters to deduce the spatiotemporal distribution of carbon flow along the power flow path. The carbon constraint determination module is used to hierarchically decompose and match macro carbon control indicators based on the results of the distribution of time- and zone-wide electrical carbon flow, so as to obtain a hierarchical carbon constraint indicator set covering stations, time periods, and equipment. The scheduling strategy solution module is used to combine the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results to perform multi-objective optimization solutions for the virtual power plant and obtain the scheduling strategy set of the virtual power plant. The scheduling module is used to schedule and manage distributed resources according to the scheduling strategy set and obtain output control instructions for each device.

[0114] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0115] Reference Figure 3 This invention also provides a computer device, including: a memory and a processor, and a computer program stored in the memory. When the computer program is executed on the processor, it performs the following steps: Obtain the initial scheduling parameter set; Using the initial scheduling parameter set, the proportion of the installed capacity structure of the station is statistically analyzed and the time-sharing and zoning dynamic carbon parameters are calculated to obtain the station's installed capacity base parameters and the time-sharing and zoning dynamic carbon parameter set. By combining the installed base parameters of the power station with the time-sharing and zone-specific dynamic carbon parameter set, a multidimensional electric carbon flow spatiotemporal coupling calculation is performed to obtain the global time-sharing and zone-specific electric carbon flow distribution results. The multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission base and combines dynamic carbon parameters to deduce the spatiotemporal distribution of carbon flow along the power flow path. Based on the distribution results of time- and zone-based carbon flow across the entire region, the macro carbon control indicators are decomposed and matched hierarchically to obtain a hierarchical set of carbon constraint indicators covering stations, time periods, and equipment. By combining the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results, a multi-objective optimization solution is performed on the virtual power plant to obtain the virtual power plant's scheduling strategy set. Based on the scheduling strategy set, the distributed resources are scheduled and managed to obtain the output control instructions of each device.

[0116] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 3 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. They may include more or fewer components than shown in the illustration, or combinations of certain components, or different components. For example, they may also include input / output devices, network access devices, etc.

[0117] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0118] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, boot loader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0119] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the following steps: Obtain the initial scheduling parameter set; Using the initial scheduling parameter set, the proportion of the installed capacity structure of the station is statistically analyzed and the time-sharing and zoning dynamic carbon parameters are calculated to obtain the station's installed capacity base parameters and the time-sharing and zoning dynamic carbon parameter set. By combining the installed base parameters of the power station with the time-sharing and zone-specific dynamic carbon parameter set, a multidimensional electric carbon flow spatiotemporal coupling calculation is performed to obtain the global time-sharing and zone-specific electric carbon flow distribution results. The multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission base and combines dynamic carbon parameters to deduce the spatiotemporal distribution of carbon flow along the power flow path. Based on the distribution results of time- and zone-based carbon flow across the entire region, the macro carbon control indicators are decomposed and matched hierarchically to obtain a hierarchical set of carbon constraint indicators covering stations, time periods, and equipment. By combining the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results, a multi-objective optimization solution is performed on the virtual power plant to obtain the virtual power plant's scheduling strategy set. Based on the scheduling strategy set, the distributed resources are scheduled and managed to obtain the output control instructions of each device.

[0120] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0121] This invention provides a computer program product, including a computer program that, when executed by a processor, performs the following steps: Obtain the initial scheduling parameter set; Using the initial scheduling parameter set, the proportion of the installed capacity structure of the station is statistically analyzed and the time-sharing and zoning dynamic carbon parameters are calculated to obtain the station's installed capacity base parameters and the time-sharing and zoning dynamic carbon parameter set. By combining the installed base parameters of the power station with the time-sharing and zone-specific dynamic carbon parameter set, a multidimensional electric carbon flow spatiotemporal coupling calculation is performed to obtain the global time-sharing and zone-specific electric carbon flow distribution results. The multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission base and combines dynamic carbon parameters to deduce the spatiotemporal distribution of carbon flow along the power flow path. Based on the distribution results of time- and zone-based carbon flow across the entire region, the macro carbon control indicators are decomposed and matched hierarchically to obtain a hierarchical set of carbon constraint indicators covering stations, time periods, and equipment. By combining the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results, a multi-objective optimization solution is performed on the virtual power plant to obtain the virtual power plant's scheduling strategy set. Based on the scheduling strategy set, the distributed resources are scheduled and managed to obtain the output control instructions of each device.

[0122] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0123] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0124] In the embodiments disclosed in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0125] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A virtual power plant scheduling method based on carbon flow, characterized in that, Includes the following steps: Obtain the initial scheduling parameter set; Using the initial scheduling parameter set, the proportion of the installed capacity structure of the power station is statistically analyzed and the time-sharing and zone-based dynamic carbon parameters are calculated to obtain the power station's installed capacity base parameters and the time-sharing and zone-based dynamic carbon parameter set; By combining the on-site installed base parameters and the time-sharing and zone-based dynamic carbon parameter set, a multidimensional electric carbon flow spatiotemporal coupling calculation is performed to obtain the global time-sharing and zone-based electric carbon flow distribution results. The multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission basis, combines dynamic carbon parameters, and deduces the spatiotemporal distribution of carbon flow along the power flow path. Based on the results of the global time-zone distribution of carbon flow, the macro carbon control indicators are decomposed and matched hierarchically to obtain a hierarchical carbon constraint indicator set covering stations, time periods, and equipment. Combining the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results, a multi-objective optimization solution is performed on the virtual power plant to obtain the virtual power plant's scheduling strategy set; Based on the set of scheduling strategies, the distributed resources are scheduled and managed to obtain the output control instructions for each device.

2. The virtual power plant scheduling method based on carbon flow according to claim 1, characterized in that, Using the initial scheduling parameter set, the proportion of installed capacity at the power station is statistically analyzed and time-sharing and zone-based dynamic carbon parameters are calculated to obtain the power station's installed capacity base parameters and the time-sharing and zone-based dynamic carbon parameter set, including: Based on the resource ledger data in the initial scheduling parameter set, the proportion of clean energy installed capacity of each station to the total installed capacity is calculated to obtain the installed capacity base parameters of the sub-stations. Based on the grid structure, power supply composition, and load peak-valley characteristics of each power supply zone, the dynamic carbon factor is calculated by zone and time. By combining the real-time output conditions and operating loss coefficients of each generator set, the real-time power generation carbon intensity of each unit is calculated. By integrating the dynamic electric carbon factor and the real-time power generation carbon intensity of the unit, the time-sharing and zone-based dynamic carbon parameter set is obtained.

3. The virtual power plant scheduling method based on electric carbon flow according to claim 1, characterized in that, By combining the aforementioned on-site installed base parameters with the time-sharing and zone-specific dynamic carbon parameter set, a multidimensional spatiotemporal coupling calculation of electric carbon flow is performed to obtain the global time-sharing and zone-specific electric carbon flow distribution results, including: Starting from the root node of the power grid, the nodal carbon potential of each node is solved iteratively layer by layer from upstream to downstream along the power flow topology of the distribution network. Based on the carbon potential of each node and the active power of the corresponding branch, the carbon flow of the branch and the carbon emissions of the node load are allocated according to the active power ratio to obtain the source allocation result of the carbon flow of the entire network. The base parameters of the installed equipment at the site are used to perform base calibration on the source allocation results to obtain the full-domain time-division and zoned distribution results of the carbon flow of the covered nodes, branches, and individual devices.

4. The virtual power plant scheduling method based on carbon flow according to claim 1, characterized in that, Based on the aforementioned global time- and zone-based distribution of carbon flow, macro-level carbon control indicators are hierarchically decomposed and matched to obtain a hierarchical set of carbon constraint indicators covering stations, time periods, and equipment, including: Based on the results of the global time-zone-based carbon flow distribution and the on-site installed base parameters, the time-series carbon emissions of the site are predicted, and carbon prediction indicators are generated. The total annual carbon budget for the region is broken down by station, day, and time period, generating corresponding time period carbon budget indicators for each level. Using the total annual carbon quota of an enterprise as a hard constraint, the carbon emission limit of a single device can be obtained by breaking it down accordingly. The hierarchical carbon constraint index set consists of the carbon prediction index, the time period carbon budget index, and the single device carbon emission limit index.

5. The virtual power plant scheduling method based on carbon flow according to claim 4, characterized in that, Combining the hierarchical carbon constraint index set with the global time-sharing and zone-based carbon flow distribution results, a multi-objective optimization solution is performed on the virtual power plant to obtain a set of scheduling strategies for the virtual power plant, including: Combining the hierarchical carbon constraint index set with the global time- and regional distribution results of carbon flow, a multi-objective optimization function is constructed that takes into account economic operation, low-carbon emission reduction, and macro-control. Based on the hierarchical carbon constraint index set as the hard constraint basis, and combined with the time-series fluctuation characteristics of time-division and regional dynamic carbon parameters, dynamic adaptive carbon emission constraint conditions are generated. Based on the dynamic adaptive carbon emission constraints, the optimal solution of the multi-objective optimization function is calculated to obtain a set of scheduling strategies that includes the time-series output plans of each device.

6. The virtual power plant scheduling method based on carbon flow according to claim 1, characterized in that, Based on the aforementioned scheduling strategy set, scheduling and control are performed on each distributed resource to obtain output control instructions for each device, including: Based on the predicted operating parameters and the aforementioned scheduling strategy set, the carbon prediction quantification and carbon budget pre-allocation of the station are carried out to obtain the basic output range of each device. During the daytime phase, the basic output range is used as the scheduling boundary. The time-sharing and zone-based dynamic carbon parameters are updated according to a fixed rolling cycle to correct carbon emission deviations and adjust the unit output structure, thus obtaining the daytime rolling scheduling scheme. In the real-time phase, the intraday rolling scheduling scheme is used as the execution benchmark. Differentiated regulation is carried out on equipment and power supply zones with different carbon intensities according to the ultra-short scheduling cycle to obtain the output regulation instructions of each equipment.

7. A virtual power plant dispatching device based on carbon flow, characterized in that, include: The data acquisition module is used to acquire the initial scheduling parameter set; The parameter calculation module is used to use the initial scheduling parameter set to statistically analyze the proportion of the installed structure of the station and calculate the time-sharing and zoning dynamic carbon parameters to obtain the station installed base parameters and the time-sharing and zoning dynamic carbon parameter set. The electrocarbon flow coupling calculation module is used to combine the on-site installed base parameters and the time-sharing and zone-based dynamic carbon parameter set to perform multi-dimensional electrocarbon flow spatiotemporal coupling calculations to obtain the global time-sharing and zone-based electrocarbon flow distribution results. The multidimensional electric carbon flow spatiotemporal coupling calculation is a coupling calculation process that uses the installed structure as the carbon emission basis, combines dynamic carbon parameters, and deduces the spatiotemporal distribution of carbon flow along the power flow path. The carbon constraint determination module is used to perform hierarchical decomposition and matching of macro carbon control indicators based on the global time-segmented and zoned carbon flow distribution results, so as to obtain a hierarchical carbon constraint indicator set covering stations, time periods and equipment. The scheduling strategy solution module is used to combine the hierarchical carbon constraint index set with the global time-sharing and zone-sharing carbon flow distribution results to perform multi-objective optimization solution for the virtual power plant and obtain the scheduling strategy set of the virtual power plant. The scheduling module is used to schedule and manage each distributed resource according to the scheduling strategy set, and obtain the output control instructions of each device.

8. A computer device, characterized in that, The device includes a processor and a memory: The memory is used to store computer programs and send the instructions of the computer programs to the processor; The processor executes a virtual power plant scheduling method based on electric carbon flow as described in any one of claims 1-6 according to the instructions of the computer program.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements a virtual power plant scheduling method based on electric carbon flow as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements a virtual power plant scheduling method based on electric carbon flow as described in any one of claims 1-6.

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