Virtual power plant low-carbon economy forward-looking scheduling method and system

Abstract

The application discloses a kind of virtual power plant low-carbon economy forward scheduling method and system, belong to virtual power plant low-carbon scheduling technical field.This method collects virtual power plant electric power operation data and carbon market parameter to construct basic database;Based on current scheduling period data, carry out device carbon source tracing, accurately locate new energy power failure, energy storage invalid loss, flexible load high carbon electricity use and other core causes, quantize the maximum carbon emission reduction potential upper limit of various resources, as the rigidity low-carbon boundary constraint of next period scheduling;Build the following period integrated economic benefit maximization, carbon emission minimization as collaborative target carbon-electricity coupling forward optimization scheduling model, fusion power balance, equipment operation, carbon quota performance and other multidimensional constraints, solve optimal power scheduling scheme.The application realizes the cross-period carbon-electricity collaborative scheduling based on current working condition research and judgment, guide next period optimization, effectively improves virtual power plant operation economic benefit and low-carbon emission level.

Description

Technical Field

[0001] This invention belongs to the field of low-carbon optimization scheduling technology for virtual power plants, specifically involving a low-carbon economic forward-looking scheduling method and system for virtual power plants that takes into account carbon-electric coupling constraints. It is applicable to park-type virtual power plants that include distributed new energy, energy storage, and flexible adjustable loads, and is adapted to cross-time period low-carbon economic collaborative scheduling scenarios under the carbon market compliance mechanism. Background Technology

[0002] With the steady progress of the construction of the new power system and the continuous improvement of the national carbon market mechanism, virtual power plants, as the main body for distributed resource aggregation and regulation, need to simultaneously meet multiple requirements such as grid safety operation, power economic operation, and carbon emission compliance management. Traditional virtual power plant dispatch strategies mostly aim at the optimal power economy in a single time period, which has obvious technical defects.

[0003] First, existing dispatching methods lack a deep carbon-electricity coupling mechanism. Most only consider power balance or operational economics, failing to deeply integrate real-time carbon emission levels, carbon quota compliance pressure, and dynamic carbon trading prices into dispatching decisions. This results in insufficient low-carbon features and high carbon compliance costs in dispatching schemes. Second, traditional dispatching is mostly static optimization within a single time period, lacking cross-time period forward-looking dispatching logic based on current operating conditions to guide optimization in the next time period. It cannot proactively optimize subsequent operating strategies based on the current time period's shortcomings in renewable energy consumption, energy storage losses, and high-carbon electricity consumption characteristics of the load. This leads to insufficient resource utilization and significant dispatching lag. Summary of the Invention

[0004] The purpose of this invention is to provide a low-carbon economic forward-looking scheduling method and system for virtual power plants that takes into account carbon-electric coupling constraints.

[0005] To achieve the above objectives, the present invention is implemented according to the following technical solution:

[0006] This invention provides a low-carbon, economically-oriented, forward-looking dispatch method for virtual power plants that takes into account carbon-electric coupling constraints, comprising the following steps:

[0007] S1. Real-time collection of current-period power operation data of distributed new energy sources, energy storage, and flexible loads within the virtual power plant; simultaneous collection of real-time carbon trading prices, phased carbon quota indicators, and multi-dimensional parameters of carbon emission factors from externally purchased thermal power in the regional carbon market; cleaning, denoising, and classifying the collected multi-source heterogeneous data to construct a dynamically updated carbon-electricity coupling basic database, providing standardized data support for subsequent current-period carbon source tracing, carbon emission accounting, carbon emission reduction potential assessment, and forward-looking optimized scheduling for the next period.

[0008] S2. Based on the current single scheduling period's power and carbon emission operation data, construct a carbon source tracing and accounting model and carbon emission reduction potential assessment model adapted to various heterogeneous resources of the virtual power plant. Use the external grid-purchased thermal power as the carbon emission accounting base for the entire domain in the current period; trace the core causes of new energy consumption shortcomings, ineffective energy storage losses, and high carbon electricity consumption of flexible loads in detail to accurately locate the weak links of high carbon emissions in the system in the current period; combine the equipment operation margin and physical constraint upper limit of various adjustable resources to quantify the maximum carbon emission reduction potential upper limit of various resources, which serves as a rigid low-carbon boundary constraint for scheduling optimization in the next period.

[0009] S3 aims to maximize the comprehensive economic benefits of the virtual power plant in the next time period and minimize the total carbon emissions as a synergistic dual optimization objective. Based on the carbon emission reduction potential upper limit obtained from the current time period data analysis in S2, a carbon-electric coupling low-carbon rigid boundary constraint is constructed. This constraint integrates the constraints of power system safe operation, energy storage operation characteristics, and carbon market compliance management to build a forward-looking optimization scheduling model with deep carbon-electric coupling. The core variables to be solved in the next scheduling period are the power generation of new energy sources, the power charging and discharging of energy storage, the power consumption of flexible loads, and the power purchase and sale of electricity from the external grid. Under all constraints, the optimal power scheduling scheme that adapts to the operating conditions of the next time period and takes into account both economic and low-carbon performance is obtained through intelligent optimization.

[0010] S4. Implement the optimal power dispatch plan for the next time period, and optimize the distributed new energy output plan, the daily charging and discharging sequence of energy storage, and the time period structure of flexible load electricity consumption in a targeted manner. Strictly follow the carbon emission reduction potential boundary obtained from the current time period analysis, maximize the local consumption level of green electricity in the next time period, reduce the loss of ineffective energy storage, reduce the purchase of high-carbon thermal power from the external grid, and achieve the optimal cross-time period low-carbon economic collaborative operation of the virtual power plant.

[0011] This invention provides a virtual power plant low-carbon economic forward-looking scheduling system that takes into account carbon-electric coupling constraints, for executing the above-mentioned scheduling method, including a carbon-electric data acquisition module, a current time period carbon source tracing and potential calculation module, a carbon constraint parameter update module, a cross-time period carbon-electric coupling forward-looking scheduling module, and a scheduling instruction output module.

[0012] The carbon-electricity data acquisition module is used to collect real-time power operation status of various equipment in the virtual power plant, regional carbon trading prices, carbon quota indicators and external grid thermal power carbon emission factors, and dynamically update the raw data to the carbon-electricity coupling basic database after cleaning and normalizing the raw data.

[0013] The current period carbon source tracing and potential calculation module is used to retrieve the current period time series data from the database, complete the quantitative calculation of carbon emissions across the entire domain, trace carbon sources in multiple dimensions, locate high carbon emission problems, quantify the maximum carbon emission reduction potential of various resources, and output low carbon boundary parameters to constrain the scheduling of the next period.

[0014] The carbon constraint parameter update module is used to synchronize dynamic carbon prices, remaining available carbon allowances, and carbon trading reward and punishment rules in real time, providing real-time updated dynamic carbon market constraints and economic accounting parameters for optimizing the scheduling model in the next period.

[0015] The cross-time period carbon-electric coupling forward scheduling module is used to receive the upper limit boundary of carbon emission reduction potential assessed in the current time period, integrate power security constraints and dynamic carbon control constraints, construct and solve a dual-objective scheduling model with the objectives of economic optimization and minimum carbon emissions in the next time period, and output the intraday resource time-series optimal scheduling scheme with cross-time period linkage.

[0016] The dispatch instruction output module is used to parse the forward-looking optimization dispatch scheme, generate and issue instructions for the next time period of new energy output regulation, energy storage charging and discharging control, and flexible load shifting regulation, so as to realize the implementation of forward-looking low-carbon economic dispatch of virtual power plants across time periods.

[0017] The beneficial effects of this invention are:

[0018] 1. This invention constructs a cross-time carbon-electricity linkage scheduling mechanism that traces and analyzes the current operating conditions and optimizes the next time period in advance. It breaks through the lag defect of traditional single-time static scheduling, realizes refined control based on real-time operational shortcomings to optimize subsequent scheduling strategies in advance, and significantly improves the resource utilization rate of virtual power plants.

[0019] 2. This invention uses the carbon emission reduction potential quantified in the current period as a rigid boundary constraint for scheduling in the next period, so that the optimization solution is always carried out within the physical adjustable range of the equipment. This solves the problems of low feasibility and detachment from engineering reality in traditional optimization schemes, and significantly improves the feasibility of scheduling schemes.

[0020] 3. This invention adopts a dual-objective optimization of economic benefits and carbon emissions in the next time period, deeply coupling power security constraints and carbon market compliance constraints, adapting to the carbon-electricity coordinated regulation needs under the dual-carbon background, and can simultaneously improve the economic benefits of virtual power plant operation and low-carbon emission reduction benefits, adapting to the engineering application scenarios of dynamic carbon markets. Attached Figure Description

[0021] Figure 1 This is a flowchart of a virtual power plant low-carbon economic forward-looking scheduling method that takes into account carbon-electric coupling constraints in an embodiment of the present invention.

[0022] Figure 2 This is a module architecture diagram of a virtual power plant low-carbon economic forward-looking dispatch system that takes into account carbon-electric coupling constraints in an embodiment of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but are not intended to limit the present invention.

[0024] like Figure 1 As shown: The present invention provides a low-carbon economic forward-looking dispatch method for virtual power plants that takes into account carbon-electric coupling constraints, comprising the following steps:

[0025] S1. Real-time collection of current-period power operation data of distributed new energy sources, energy storage, and flexible loads within the virtual power plant; simultaneous collection of real-time carbon trading prices, phased carbon quota indicators, and multi-dimensional parameters of carbon emission factors from externally purchased thermal power in the regional carbon market; cleaning, denoising, and classifying the collected multi-source heterogeneous data to construct a dynamically updated carbon-electricity coupling basic database, providing standardized data support for subsequent current-period carbon source tracing, carbon emission accounting, carbon emission reduction potential assessment, and forward-looking optimized scheduling for the next period.

[0026] S2. Based on the current single scheduling period's power and carbon emission operation data, construct a carbon source tracing and accounting model and carbon emission reduction potential assessment model adapted to various heterogeneous resources of the virtual power plant. Use the external grid-purchased thermal power as the carbon emission accounting base for the entire domain in the current period; trace the core causes of new energy consumption shortcomings, ineffective energy storage losses, and high carbon electricity consumption of flexible loads in detail to accurately locate the weak links of high carbon emissions in the system in the current period; combine the equipment operation margin and physical constraint upper limit of various adjustable resources to quantify the maximum carbon emission reduction potential upper limit of various resources, which serves as a rigid low-carbon boundary constraint for scheduling optimization in the next period.

[0027] S3 aims to maximize the comprehensive economic benefits of the virtual power plant in the next time period and minimize the total carbon emissions as a synergistic dual optimization objective. Based on the carbon emission reduction potential upper limit obtained from the current time period data analysis in S2, a carbon-electric coupling low-carbon rigid boundary constraint is constructed. This constraint integrates the constraints of power system safe operation, energy storage operation characteristics, and carbon market compliance management to build a forward-looking optimization scheduling model with deep carbon-electric coupling. The core variables to be solved in the next scheduling period are the power generation of new energy sources, the power charging and discharging of energy storage, the power consumption of flexible loads, and the power purchase and sale of electricity from the external grid. Under all constraints, the optimal power scheduling scheme that adapts to the operating conditions of the next time period and takes into account both economic and low-carbon performance is obtained through intelligent optimization.

[0028] S4. Implement the optimal power dispatch plan for the next time period, and optimize the distributed new energy output plan, the daily charging and discharging sequence of energy storage, and the time period structure of flexible load electricity consumption in a targeted manner. Strictly follow the carbon emission reduction potential boundary obtained from the current time period analysis, maximize the local consumption level of green electricity in the next time period, reduce the loss of ineffective energy storage, reduce the purchase of high-carbon thermal power from the external grid, and achieve the optimal cross-time period low-carbon economic collaborative operation of the virtual power plant.

[0029] Furthermore, in step S2, the sub-equipment carbon source tracing and carbon emission reduction potential assessment model completes the overall carbon emission accounting, multi-resource sub-item carbon source tracing, and carbon emission reduction potential quantification based on the current period's operating data. The three-layer logic is progressively advanced, accurately supporting the scheduling constraint configuration for the next period. The specific calculation formula and accounting method are as follows:

[0030] Let the duration of the current scheduling period be Δt, α be the carbon emission factor of external grid thermal power, and E buy If the electricity generated by thermal power purchased from the external grid in the park during the current period is C, then the formula for calculating the total carbon emissions of the entire area is: total =E buy ·α;

[0031] Multi-resource carbon source traceability specifically includes three parts: new energy carbon source traceability, energy storage carbon source traceability, and flexible load carbon source traceability.

[0032] Carbon source tracing for new energy sources: Distributed photovoltaic and wind power operations emit no carbon from fossil fuels; the actual local consumption of new energy sources, E, is calculated for the current period. green With the theoretical maximum power generation E green-max By comparing the differences between the two, the degree of green electricity waste during the current period can be accurately identified, the surplus unconsumed electricity space during the peak period of new energy generation can be quantified, and the high carbon emission causes of thermal power generation caused by the indirect impact of new energy waste can be identified.

[0033] Energy storage carbon source traceability: Let E ch The charging capacity of the energy storage during the current period, E dis The discharge amount of the energy storage during the current period is ΔE, and the energy loss of the energy storage during the current period is ΔE. bat =E ch -E dis Combined with the theoretical minimum power loss ΔE corresponding to the optimal operating efficiency of energy storage equipment. bat-min By comparing the deviation between the actual loss and the optimal loss in the current period, the system can accurately diagnose the problem of excessive energy storage loss and the high carbon emissions caused by the indirect increase in the purchase of electricity from external thermal power. The greater the ineffective loss of energy storage, the more additional thermal power the system needs to purchase, and the higher the indirect carbon emissions.

[0034] Carbon source traceability of flexible loads: Total electricity consumption of flexible loads E load The electricity supply is broken down into local green electricity and external grid thermal power, and the proportion of thermal power supply on the load side (η) is calculated for the current period. fire It accurately identifies the load characteristics of high thermal power ratio and high carbon emissions during the current period.

[0035] The carbon emission reduction potential assessment model is based on the high-carbon causes and resource waste shortcomings identified through source tracing and diagnosis. It quantifies the upper limit of low-carbon optimization for various resources based on the difference in current operating electricity consumption across different dimensions. The total carbon emission reduction potential is calculated using the formula: ΔC = (ΔE) green +ΔEload +ΔE bat-loss )·α; where the differences in the three types of potential electricity are all calculated from the differences in the current quantity traced back to their source, and the specific calculation method is as follows:

[0036] New energy sources can salvage the potential for green electricity consumption: ΔE green =E green-max -E green This characterizes the characteristics of power curtailment in the current period. In the next period, power curtailment can be recovered and the maximum amount of electricity consumed locally can be increased through scheduling optimization.

[0037] Flexible loads can reduce the potential of thermal power: ΔE load =η·E load-fire‌ E in the formula load-fire =η fire ·E load η is the flexible load shifting adjustment coefficient, which is taken from 0 to 1 according to the equipment's adjustable margin. It is determined comprehensively based on load process constraints, adjustable margin, and user power comfort, and quantifies the upper limit of thermal power consumption that can be shifted to the next period of abundant green electricity and can be replaced and reduced.

[0038] Energy storage can optimize loss potential: ΔE bat-loss =ΔE bat -ΔE bat-min Based on the energy storage loss deviation in the current period, the upper limit of energy loss that can be reduced through charging and discharging timing optimization in the next period can be quantified.

[0039] Furthermore, in step S3, the carbon-electricity coupled forward-looking optimization scheduling model uses the emission reduction potential assessed in the current time period as a constraint and the operating efficiency and carbon emissions in the next time period as optimization objectives. It constructs a multi-dimensional coupled constraint system including dual optimization objective functions, power security constraints, energy storage operation constraints, carbon market management constraints, and low-carbon potential boundary constraints. The model then considers the next scheduling period Δt... +1 The core variables to be solved are the various resources and electricity quantities of the virtual power plant. The specific formulas and constraints are as follows:

[0040] Dual optimization objective function: max F = R + (CC) total1 )·P carbon1 -F run1 ,min F carbon1 =C total1 ;

[0041] Where: F is the comprehensive operating revenue for the next period, R is the net revenue from electricity purchase and sale for the next period, C is the total carbon allowance for the next period, and P is the total carbon allowance for the next period. carbon1 For the next period's real-time carbon trading price, F run1 To adjust the operating costs of the equipment in the next period, C carbon1 This represents the total carbon emissions of the system in the next time period;

[0042] R=E sell1 ·c sell1 -E buy1 ·c buy1

[0043] C total1 =E buy1 ·α

[0044] F run1 =E green1 ·c green1 +E ‌ch1 ·c ‌ch1 +E dis1 ·c dis1 +E load1 ·c load1

[0045] In the formula: c buy1 The electricity purchase price for the next time period; c sell1 The electricity price for the next time period; c green1 c ‌ch1 c dis1 c load1 These represent the unit adjustment costs for new energy sources, energy storage charging, energy storage discharging, and flexible loads, respectively; E buy1 Purchase electricity for the next time period; E sell1 E represents the electricity sold in the next time period. green1 E represents the amount of electricity generated by new energy sources in the next time period. ‌ch1 E is the amount of energy stored for charging in the next time period. dis1 E represents the energy storage discharge capacity for the next time period. load1 This will generate electricity for the flexible load in the next time period.

[0046] Electrical safety constraints include:

[0047] Next period system power balance constraint: E green1 +E dis1 +E buy1 =E load1 +E ‌ch1 +E sell1 ;

[0048] New Energy Forward-Looking Optimization Boundary Constraints: E green ≤E green1 ≤E green +ΔE green-predict In the formula, ΔE green-predict To incorporate the renewable energy output forecast data for the next period, the upper limit of the curtailment recovery potential ΔE calculated for the current period is determined. green The predicted boundary value is dynamically adjusted. The increase in renewable energy consumption in the next period is limited to the upper limit of the current period's assessed potential for recovering wasted electricity.

[0049] Energy storage operation constraints: E ‌ch-min ≤E ‌ch1 ≤E ‌ch-max E dis-min ≤E dis1 ≤E dis-max In the formula, E ‌ch-max E ‌ch-min E represents the upper and lower limits of the charging capacity for energy storage. dis-max E dis-min These are the upper and lower limits of the energy storage discharge capacity;

[0050] Energy storage loss look-ahead optimization constraint: ΔE bat-loss ≤E ‌ch1 -E dis1 ≤ΔE bat To ensure that the reduction in energy storage losses in the next period does not exceed the potential for optimized losses assessed in the current period;

[0051] Boundary constraints on the low-carbon potential of flexible loads: E load-fire -E load-fire1 ≤ΔE load In the formula, E load-fire E represents the amount of electricity supplied by external grid thermal power to the flexible load during the current period. load-fire1 This refers to the amount of electricity supplied by external grid thermal power to the flexible load in the next time period.

[0052] Carbon market dynamics and carbon control constraints: C total1 ≤C, to achieve carbon quota compliance management in the next period, and to dynamically calculate carbon trading profits and losses and the cost of penalties for excessive emissions in conjunction with real-time carbon prices;

[0053] Rigid boundary constraints for low carbon emissions across time periods: In the process of optimizing the model for the next time period, the increase in new energy consumption, the reduction in flexible load thermal power, and the reduction in energy storage losses are all strictly limited to the corresponding carbon emission reduction potential upper limit calculated by S2 based on the current time period data, ensuring that the scheduling scheme for the next time period is physically feasible and optimized to a certain extent.

[0054] Furthermore, the forward-looking scheduling execution strategy in step S4 specifically includes: using the carbon emission reduction potential of the three types of resources obtained from the source analysis of the current period as a rigid optimization upper limit, optimizing the operating conditions of the next period in a targeted manner, and orderly shifting the high-energy-consuming flexible loads of the next period from the high-carbon power supply period of thermal power to the low-carbon period of abundant new energy generation and green electricity surplus, so as to fully release the potential of new energy curtailment and the potential of flexible load replacement by thermal power; optimizing the intraday charging and discharging sequence of energy storage in the next period, avoiding high-loss operating conditions, minimizing the loss of ineffective energy storage, and reducing the additional demand for external thermal power purchase caused by energy storage loss; within the boundary constraint of cross-period low-carbon potential, reducing the demand for external thermal power purchase in the next period across the entire region, so as to achieve the optimal synergy between the cross-period economic benefits, carbon trading benefits and low-carbon emission reduction benefits of virtual power plants.

[0055] In summary, this invention executes S1 to complete multi-source data acquisition and database construction; executes S2 to conduct carbon source tracing based on measured data of the current single scheduling period, quantifying the upper limit of carbon emission reduction potential as a rigid boundary for optimization in the next period; executes S3 to construct a carbon-electricity coupled look-ahead optimization scheduling model with the coordinated goal of maximizing the comprehensive economic benefits and minimizing the total carbon emissions in the next period, and solves the optimal power scheduling scheme by combining multiple constraints; executes S4 to complete the implementation of the scheduling strategy for the next period, realizing cross-period low-carbon economic collaborative scheduling.

[0056] like Figure 2 As shown, the present invention is a virtual power plant low-carbon economic forward-looking scheduling system that takes into account carbon-electric coupling constraints. It is used to execute the scheduling method described above, including a carbon-electric data acquisition module, a current time period carbon source tracing and potential calculation module, a carbon constraint parameter update module, a cross-time period carbon-electric coupling forward-looking scheduling module, and a scheduling instruction output module.

[0057] The carbon-electricity data acquisition module is used to collect real-time power operation status of various equipment in the virtual power plant, regional carbon trading prices, carbon quota indicators and external grid thermal power carbon emission factors, and dynamically update the raw data to the carbon-electricity coupling basic database after cleaning and normalizing the raw data.

[0058] The current period carbon source tracing and potential calculation module is used to retrieve the current period time series data from the database, complete the quantitative calculation of carbon emissions across the entire domain, trace carbon sources in multiple dimensions, locate high carbon emission problems, quantify the maximum carbon emission reduction potential of various resources, and output low carbon boundary parameters to constrain the scheduling of the next period.

[0059] The carbon constraint parameter update module is used to synchronize dynamic carbon prices, remaining available carbon allowances, and carbon trading reward and punishment rules in real time, providing real-time updated dynamic carbon market constraints and economic accounting parameters for optimizing the scheduling model in the next period.

[0060] The cross-time period carbon-electric coupling forward scheduling module is used to receive the upper limit boundary of carbon emission reduction potential assessed in the current time period, integrate power security constraints and dynamic carbon control constraints, construct and solve a dual-objective scheduling model with the objectives of economic optimization and minimum carbon emissions in the next time period, and output the intraday resource time-series optimal scheduling scheme with cross-time period linkage.

[0061] The dispatch instruction output module is used to parse the forward-looking optimization dispatch scheme, generate and issue instructions for the next time period of new energy output regulation, energy storage charging and discharging control, and flexible load shifting regulation, so as to realize the implementation of forward-looking low-carbon economic dispatch of virtual power plants across time periods.

[0062] In summary, the carbon-electricity data acquisition module completes data acquisition and storage; the current period carbon source tracing and potential calculation module completes carbon emission calculation, carbon source tracing, and outputs emission reduction potential boundaries; the carbon constraint parameter update module synchronizes dynamic parameters of the carbon market; the cross-period carbon-electricity coupling forward scheduling module completes the dual-objective optimization solution for the next period; and the scheduling instruction output module outputs various equipment control instructions to achieve closed-loop scheduling.

[0063] This embodiment selects a virtual power plant in a park containing distributed photovoltaic, wind power, energy storage systems, and flexible loads that can be moved as the research object. The scheduling time is hourly scheduling within the day. The carbon market parameters adopt the regional real-time carbon price and the monthly remaining carbon quota. The carbon emission factor of the external grid thermal power adopts the grid benchmark emission factor.

[0064] First, execute step S1 to collect in real time the current period's new energy power generation, energy storage charging and discharging power, flexible load power consumption, and external grid power purchase and sale power. Simultaneously collect real-time carbon trading prices, remaining carbon quotas, and thermal power carbon emission factors, complete data cleaning and regularization, and construct a dynamic carbon-electricity coupling database.

[0065] Then, step S2 is executed to conduct carbon source tracing and potential quantification based on the current single scheduling period data: the total carbon emissions for the current period are calculated based on the amount of electricity purchased from external grid thermal power; the current period's abandoned renewable energy, ineffective energy storage losses, and the proportion of flexible load thermal power supply are traced to accurately pinpoint the causes of high carbon emissions in the current period; among them, flexible load electricity consumption is only distinguished between local green electricity supply and external grid thermal power supply, and energy storage is only accounted for indirect carbon emissions caused by its own losses and does not participate in load power supply splitting; based on the current operating condition difference quantification, the potential for renewable energy consumption improvement, the potential for flexible load thermal power reduction, and the potential for energy storage loss optimization are obtained, and the upper limits of the three types of potential are used as rigid constraints for the scheduling of the next period.

[0066] Then, step S3 is executed to build a cross-time period forward-looking optimization scheduling model. The dual objectives are to maximize the comprehensive benefits and minimize carbon emissions in the next time period. The model incorporates power balance constraints, equipment output constraints, carbon quota constraints, and emission reduction potential boundary constraints generated in the current time period. The power of various resources in the next time period is used as optimization variables. The model is solved through intelligent optimization algorithms to obtain the optimal power generation and consumption of new energy, the optimal charging and discharging power of energy storage, the optimal power consumption sequence of flexible loads, and the optimal power purchase and sale power of the external grid in the next time period.

[0067] Finally, step S4 is executed, which involves resource regulation for the next time period based on the optimal scheduling scheme. This shifts flexible loads from high-carbon periods to green electricity surplus periods, optimizes the charging and discharging sequence of energy storage to reduce ineffective losses, and enhances the local consumption capacity of new energy. Under the premise of not exceeding the upper limit of emission reduction potential, the purchase of external thermal power is reduced to the maximum extent, achieving the optimal synergy between economy and low carbon.

[0068] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. A virtual power plant low-carbon economic forward-looking dispatch method, characterized in that, Includes the following steps: S1. Real-time collection of power operation data of distributed new energy, energy storage and flexible loads within the virtual power plant; simultaneous collection of real-time carbon trading prices, phased carbon quota indicators and multi-dimensional operating parameters of carbon emission factors of purchased thermal power from external grids in the regional carbon market; cleaning, denoising and classifying the collected multi-source heterogeneous data to build a carbon-electricity coupling basic database that can be dynamically iteratively updated. S2. Based on the measured data of power operation and carbon emissions during the current scheduling period, construct a carbon source traceability and carbon emission reduction potential assessment model for each device that is adapted to various heterogeneous resources of the virtual power plant. Use the purchased thermal power from the external grid as the base for carbon emission accounting of the entire operation. Conduct sub-item carbon source traceability for the distributed new energy consumption status, energy storage power loss, and flexible load power consumption structure during the current scheduling period to locate the core causes of carbon emissions in the system. By combining the actual operational margins of various adjustable resources with the upper limits of equipment constraints, the maximum carbon emission reduction potential of various resources is quantified and used as a rigid low-carbon boundary constraint for the next scheduling period. S3. Taking the maximization of comprehensive economic benefits and the minimization of total carbon emissions of the virtual power plant in the next scheduling period as the synergistic dual optimization objectives, and constructing a low-carbon rigid boundary constraint based on the upper limit of the maximum carbon emission reduction potential, integrating the power system safety operation constraint and the carbon market dynamic compliance management constraint, a joint optimization scheduling model with deep coupling of carbon emissions and power scheduling is constructed; taking the power generation of new energy sources, the power charging and discharging of energy storage, the power consumption of flexible loads, and the power purchase and sale of external grids in the next scheduling period as the core variables to be solved, the optimal power scheduling scheme of various resources of the virtual power plant in the next scheduling period is obtained within the constraint conditions. S4. Based on the optimal power dispatch scheme, collaboratively optimize the distributed new energy output plan, energy storage charging and discharging sequence, and flexible load power consumption time structure in the next dispatch period; under the constraint of the maximum carbon emission reduction potential upper limit, execute the forward dispatch strategy to improve green power consumption, reduce energy storage losses, and reduce the amount of thermal power purchased.

2. The virtual power plant low-carbon economic forward-looking dispatch method according to claim 1, characterized in that, In step S2, the sub-equipment carbon source traceability and carbon emission reduction potential assessment model includes three layers of calculation logic: full-domain carbon emission accounting, multi-resource sub-item carbon source traceability, and carbon emission reduction potential quantification. The specific calculation formula and accounting method are as follows: Let the duration of the current scheduling period be Δt, α be the carbon emission factor of external grid thermal power, and E buy If the electricity generated by thermal power purchased from the external grid in the park during the current period is C, then the formula for calculating the total carbon emissions of the entire area is: total =E buy ·α; Multi-resource carbon source tracing specifically includes: Carbon source tracing for new energy sources: Distributed photovoltaic and wind power operations emit no carbon from fossil fuels; the actual local consumption of new energy sources, E, is calculated for the current period. green With the theoretical maximum power generation E green-max By comparing the differences between the two, we can identify the degree of green electricity waste in the current period, quantify the space of surplus unconsumed electricity during the period of large-scale new energy generation, and locate the high carbon emission causes of thermal power generation caused by the indirect increase of new energy curtailment. Energy storage carbon source traceability: Let E ch The charging capacity of the energy storage during the current period, E dis The energy storage discharge amount during the current period is represented by ΔE, and the energy storage loss amount during the current period is also represented by ΔE. bat =E ch -E dis Combined with the theoretical minimum power loss ΔE corresponding to the optimal operating efficiency of energy storage equipment. bat-min Compare the deviation between the actual loss and the optimal loss in the current period; Carbon source traceability of flexible loads: Total electricity consumption of flexible loads E load The electricity supply is broken down into local green electricity and external grid thermal power, and the proportion of thermal power supply on the load side (η) is calculated for the current period. fire ; In the carbon emission reduction potential assessment model, the formula for calculating the total carbon emission reduction potential is as follows: ΔC=(ΔE green +ΔE load +ΔE bat-loss )·α; The three types of potential energy differences are all calculated from the current energy difference based on source tracing. The specific calculation method is as follows: New energy sources can salvage the potential for green electricity consumption: ΔE green =E green-max -E green ; Flexible loads can reduce the potential of thermal power: ΔE load =η·E load-fire‌ E in the formula load-fire =η fire ·E load‌ η is the flexible load transferable adjustment coefficient, which is taken from 0 to 1 according to the equipment's adjustable margin, and is determined comprehensively based on the load process constraints, adjustable margin, and user power comfort. Energy storage can optimize loss potential: ΔE bat-loss =ΔE bat -ΔE bat-min .

3. The virtual power plant low-carbon economic forward-looking dispatch method according to claim 1, characterized in that, In step S3, the joint optimization scheduling model includes a dual optimization objective function, power security energy constraints, energy storage operation constraints, carbon market management constraints, and low-carbon potential boundary constraints. The model is based on the next scheduling period Δt +1 The core variables to be solved are the various resources and electricity quantities of the virtual power plant. The specific formulas and constraints are as follows: Dual optimization objective function: max F = R + (CC) total1 )·P carbon1 -F run1 ,min F carbon1 =C total1 Where: F is the comprehensive operating revenue for the next period, R is the net revenue from electricity purchase and sale for the next period, C is the total carbon allowance for the next period, and P is the total carbon allowance for the next period. carbon1 For the next period's real-time carbon trading price, F run1 To adjust the operating costs of the equipment in the next period, C carbon1 This represents the total carbon emissions of the system in the next time period; R=E sell1 ·c sell1 -E buy1 ·c buy1 C total1 =E buy1 ·a F run1 =E green1 ·c green1 +E ‌ch1 ·c ‌ch1 +E dis1 ·c dis1 +E load1 ·c load1 In the formula: c buy1 The electricity purchase price for the next time period; c sell1 The electricity price for the next time period; c green1 c ‌ch1 c dis1 c load1 These are the unit adjustment costs for new energy sources, energy storage charging, energy storage discharging, and flexible loads, respectively. E buy1 Purchase electricity for the next time period; E sell1 E represents the electricity sold in the next time period. green1 E represents the amount of electricity generated by new energy sources in the next time period. ‌ch1 E is the amount of energy stored for charging in the next time period. dis1 E represents the energy storage discharge capacity for the next time period. load1 This will generate electricity for the flexible load in the next time period.

4. The virtual power plant low-carbon economic forward-looking dispatch method according to claim 3, characterized in that, In step S3, the power safety constraints include: Next period system power balance constraint: E green1 +E dis1 +E buy1 =E load1 +E ‌ch1 +E sell1 ; New Energy Forward-Looking Optimization Boundary Constraints: E green ≤E green1 ≤E green +ΔE green-predict In the formula, ΔE green-predict To incorporate the renewable energy output forecast data for the next period, the upper limit of the current period's calculated curtailment recovery potential ΔE is determined. green The predicted boundary value after dynamic correction limits the increase in renewable energy consumption in the next period to no more than the upper limit of the curtailment recovery potential assessed in the current period. Energy storage operation constraints: E ‌ch-min ≤E ‌ch1 ≤E ‌ch-max E dis-min ≤E dis1 ≤E dis-max In the formula, E ‌ch-max E ‌ch-min E represents the upper and lower limits of the charging capacity for energy storage. dis-max E dis-min These are the upper and lower limits of the energy storage discharge capacity; Energy storage loss look-ahead optimization constraint: ΔE bat-loss ≤E ‌ch1 -E dis1 ≤ΔE bat To ensure that the reduction in energy storage losses in the next period does not exceed the potential for optimized losses assessed in the current period; Boundary constraints on the low-carbon potential of flexible loads: E load-fire -E load-fire1 ≤ΔE load In the formula, E load-fire E represents the amount of electricity supplied by external grid thermal power to the flexible load during the current period. load-fire1 The amount of electricity supplied by external grid thermal power to the flexible load in the next time period; Carbon market dynamics and carbon control constraints: C total1 ≤C, to achieve carbon quota compliance management in the next period; Rigid boundary constraints for low-carbon energy across time periods: In the process of optimizing the model for the next time period, the increase in new energy consumption, the reduction in flexible load thermal power, and the reduction in energy storage losses shall all strictly not exceed the corresponding carbon emission reduction potential upper limit calculated by S2 based on the data of the current time period.

5. The virtual power plant low-carbon economic forward-looking dispatch method according to claim 1, characterized in that, In step S4, the forward scheduling strategy includes: shifting high-energy-consuming flexible loads in the next time period from the thermal power supply period to the new energy generation period; optimizing the intraday charging and discharging sequence of energy storage in the next time period to reduce energy storage losses; and reducing the demand for external grid thermal power purchases in the next time period within the upper limit constraint of the maximum carbon emission reduction potential.

6. A virtual power plant low-carbon economic forward-looking dispatch system, characterized in that, The method for executing the scheduling method according to any one of claims 1-5 includes a carbon-electricity data acquisition module, a current time period carbon source tracing and potential calculation module, a carbon constraint parameter update module, a cross-time period carbon-electricity coupling forward scheduling module, and a scheduling instruction output module. The carbon-electricity data acquisition module is used to collect real-time power operation status of various equipment in the virtual power plant, regional carbon trading prices, carbon quota indicators and external grid thermal power carbon emission factors, and dynamically update the raw data to the carbon-electricity coupling basic database after cleaning and normalizing the raw data. The current period carbon source tracing and potential calculation module is used to retrieve the current period time series data from the database, complete the quantitative calculation of carbon emissions across the entire domain, trace carbon sources in multiple dimensions, locate high carbon emission periods in different time periods, quantify the maximum carbon emission reduction potential of various resources, and output low-carbon boundary parameters to constrain the scheduling of the next period. The carbon constraint parameter update module is used to synchronize dynamic carbon prices, remaining available carbon allowances, and carbon trading reward and punishment rules in real time, providing real-time updated dynamic carbon market constraints and economic accounting parameters for optimizing the scheduling model in the next period. The cross-time period carbon-electric coupling forward scheduling module is used to receive the upper limit boundary of carbon emission reduction potential assessed in the current time period, integrate power security constraints and dynamic carbon control constraints, construct and solve a dual-objective scheduling model with the objectives of economic optimization and minimum carbon emissions in the next time period, and output the intraday resource time-series optimal scheduling scheme with cross-time period linkage. The scheduling instruction output module is used to parse the forward-looking optimization scheduling scheme, generate and issue instructions for the next period of new energy output regulation, energy storage charging and discharging control, and flexible load shifting regulation.

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