Distributed resource polymerization method, system, equipment and medium based on carbon emission reduction

Abstract

The invention relates to the technical field of resource polymerization, and discloses a distributed resource polymerization method, system, equipment and medium based on carbon emission reduction, and the method comprises the steps: optimizing the reference capacity of each aggregator according to the carbon emission reduction, and obtaining the capacity reference value of the aggregator; calculating a first aggregation index of the aggregator and a second aggregation index of the distributed renewable energy sources according to the aggregator attribute information and the resource operation characteristics; obtaining a resource selection sequence according to the first aggregation index, and obtaining an aggregation selection sequence according to the second aggregation index; calculating a first sensing utility of the aggregator and a second sensing utility of the distributed renewable energy sources according to the resource selection sequence and the aggregation selection sequence; and establishing a bilateral matching model by taking maximization of the first sensing utility and the second sensing utility as an objective function and taking a capacity reference value as a constraint condition, and solving to obtain an optimal aggregation scheme. According to the invention, optimal configuration of power resources can be realized, and the stability and reliability of power grid operation are improved.

Description

Technical Field

[0001] This invention relates to the field of resource aggregation technology, and in particular to a distributed resource aggregation method, system, device and medium based on carbon emission reduction. Background Technology

[0002] Distributed Renewable Energy (DRE) is characterized by its dispersed scale and wide geographical distribution, while also offering advantages such as on-site development, local utilization, flexibility, and efficiency. Currently, aggregator technology is considered an effective means to address the aggregation, control, and scheduling of large-scale distributed DRE resources, enabling the effective aggregation and management of resources through advanced network communication, real-time monitoring, and precise metering.

[0003] The limitations of existing resource aggregation methods are as follows: First, most existing methods are based on a one-sided perspective of maximizing the benefits of aggregators or DREs, lacking sufficient consideration of global information on carbon emission reduction attributes, and failing to guide the aggregation of aggregators and DREs from a globally optimal perspective. Second, existing methods mainly rely on judgment or simple economic indicators when conducting aggregation assessments, lacking systematic assessments based on objective data, resulting in insufficient accuracy of assessment results. In addition, most existing aggregation mechanisms are one-sided matching, lacking bilateral matching mechanisms that consider carbon emission reduction attributes, making it difficult to achieve a balance between stability and optimality in aggregation results. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a distributed resource aggregation method, system, device, and medium based on carbon emission reduction, which can achieve optimal aggregation configuration of the entire power system while fully considering carbon emission reduction attributes.

[0005] In a first aspect, the present invention provides a distributed resource aggregation method based on carbon emission reduction, the method comprising: The baseline capacity of distributed renewable energy for each aggregator is optimized based on carbon emission reduction, resulting in the baseline capacity value for each type of distributed renewable energy for each aggregator. Based on the attribute information of aggregators and the resource operation characteristics of distributed renewable energy, calculate the first aggregation index of aggregators for each distributed renewable energy source and the second aggregation index of distributed renewable energy sources for each aggregator. Based on the first aggregation index, the resource selection sequence of the aggregator is obtained, and based on the second aggregation index, the aggregation selection sequence of distributed renewable energy is obtained; Based on the resource selection sequence and the aggregation selection sequence, the disappointment-joy theory is used to calculate the aggregator's first perceived utility and the distributed renewable energy's second perceived utility. Using the maximization of the first perceived utility and the second perceived utility as the objective function and the capacity benchmark value as the constraint, a two-sided matching model is established, and the two-sided matching model is solved to obtain the optimal aggregation scheme.

[0006] Furthermore, the step of optimizing the baseline capacity of distributed renewable energy for each aggregator based on carbon emission reduction to obtain the baseline capacity value of various types of distributed renewable energy for each aggregator includes: A baseline capacity optimization model is established with the carbon emission reduction, abandoned electricity and total operating cost of the aggregator set as optimization objectives, and the power constraint, quantity constraint and carbon emission flow deviation constraint of distributed renewable energy as constraints. Solving the baseline capacity optimization objective yields the baseline capacity values ​​for various types of distributed renewable energy for each aggregator.

[0007] Furthermore, the step of calculating the first aggregation index of the aggregator for each distributed renewable energy source and the second aggregation index of the distributed renewable energy source for each aggregator based on the attribute information of the aggregator and the resource operation characteristics of the distributed renewable energy source includes: Based on the aggregator's revenue, carbon emission reduction, and external power interaction value before and after resource aggregation, revenue indicators, carbon reduction indicators, and flexibility margin indicators are calculated respectively. Based on the historical operating data of distributed renewable energy, output stability indicators are calculated. The revenue indicator, the carbon reduction indicator, the flexibility margin indicator, and the output stability indicator are used as the first aggregated indicator. Based on the aggregator's actual power generation, aggregator operating costs, and resource output power, production capacity indicators, aggregator cost indicators, and resource stability indicators are calculated respectively, and the production capacity indicators, aggregator cost indicators, and resource stability indicators are used as the second aggregation indicators.

[0008] Further, the steps of obtaining the resource selection sequence of the aggregator based on the first aggregation index, and obtaining the aggregation selection sequence of distributed renewable energy based on the second aggregation index, include: The first aggregation index is normalized and weighted summed to obtain the aggregator's first choice score for various types of distributed renewable energy. The resource selection sequence is obtained by sorting the scores of the first selection. The second aggregation index is normalized and weighted summed to obtain the second selection score of distributed renewable energy for each aggregator. The second selection scores are sorted to obtain the aggregated selection sequence.

[0009] Further, the step of calculating the aggregator's first perceived utility and the distributed renewable energy's second perceived utility using the disappointment-joy theory based on the resource selection sequence and the aggregation selection sequence includes: Based on the resource selection sequence, calculate the aggregator's first preference utility for each distributed renewable energy source; Based on the difference in first preference utility between each type of distributed renewable energy and other distributed renewable energy, the aggregator calculates the first disappointment value and first gratification value for each distributed renewable energy using a preset disappointment function and gratification function; Based on preset disappointment and gratification weights, the first preference utility, the first disappointment value, and the first gratification value are weighted and calculated to obtain the aggregator's first perceived utility for each distributed renewable energy source. Based on the aggregation selection sequence, calculate the second preference utility of distributed renewable energy for each aggregator; Based on the difference in second preference utility between each clusterer and other clusterers, the second disappointment value and second gratification value of distributed renewable energy for each clusterer are calculated using the disappointment function and the gratification function. Based on the disappointment weight and the gratification weight, the second preference utility, the second disappointment value, and the second gratification value are weighted and calculated to obtain the second perceived utility of distributed renewable energy for each aggregator.

[0010] Furthermore, the step of calculating the aggregator's first perceived utility and the distributed renewable energy's second perceived utility using the disappointment-joy theory based on the resource selection sequence and the aggregation selection sequence further includes: Based on historical matching records, calculate the first historical utility of the aggregator for each distributed renewable energy source and the second historical utility of the distributed renewable energy source for each aggregator; The first historical utility and the first perceived utility are weighted and summed to obtain the updated first perceived utility. The second historical utility and the second perceived utility are weighted and summed to obtain the updated second perceived utility.

[0011] Furthermore, the function parameters of the disappointment function and the gratification function, as well as the disappointment weight and the gratification weight, are adaptively adjusted using the following steps: Obtain feedback data after each round of actual matching and aggregation, and based on the feedback data, obtain the utility deviation between the actual utility and the expected utility of each distributed renewable energy source for the aggregator; Based on the utility deviation and the preset adaptive adjustment factor, the aggregator obtains the parameter adjustment values ​​for each distributed renewable energy source, including disappointment parameter adjustment values ​​and euphoria parameter adjustment values; Based on the adjustment value of the disappointment parameter, the original disappointment parameter of the disappointment function is adjusted to obtain the adjusted disappointment parameter; Based on the adjustment value of the euphoria parameter, the original euphoria parameter of the euphoria function is adjusted to obtain the adjusted euphoria parameter; Based on the adjusted disappointment and euphoria parameters, a proportional calculation function is used to obtain the adjusted disappointment weight and euphoria weight.

[0012] Secondly, the present invention provides a distributed resource aggregation system based on carbon emission reduction, the system comprising: The benchmark optimization module is used to optimize the benchmark capacity of distributed renewable energy for each aggregator based on carbon emission reduction, and obtain the benchmark capacity value of each type of distributed renewable energy for each aggregator. The selection and sorting module is used to calculate the first aggregation index of the aggregator for each distributed renewable energy source and the second aggregation index of the distributed renewable energy source for each aggregator based on the attribute information of the aggregator and the resource operation characteristics of the distributed renewable energy source. Based on the first aggregation index, the resource selection sequence of the aggregator is obtained, and based on the second aggregation index, the aggregation selection sequence of distributed renewable energy is obtained; The bilateral matching module is used to calculate the aggregator's first perceived utility and the distributed renewable energy's second perceived utility based on the resource selection sequence and the aggregation selection sequence, using the disappointment-joy theory. Using the maximization of the first perceived utility and the second perceived utility as the objective function and the capacity benchmark value as the constraint, a two-sided matching model is established, and the two-sided matching model is solved to obtain the optimal aggregation scheme.

[0013] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0015] This invention provides a method, system, device, and medium for distributed resource aggregation based on carbon emission reduction. By considering the global optimality of carbon emission reduction attributes, this invention guides the aggregation of aggregators and DRE resources, reducing system operating costs, increasing total system carbon emission reduction, improving capacity utilization, and providing data support for resource allocation and aggregation decisions. Through multi-dimensional objective evaluation indicators, the objectivity and accuracy of the evaluation results are improved. A bilateral matching mechanism enhances the stability and accuracy of resource aggregation results. This invention enables optimized allocation of power resources, thereby improving the stability and reliability of power grid operation. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the distributed resource aggregation method based on carbon emission reduction in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the distributed resource aggregation system based on carbon emission reduction in an embodiment of the present invention; Figure 3 This is an internal structural diagram of the computer device in an embodiment of the present invention.

[0017] Figure label: 10. Benchmark optimization module; 20. Selection sorting module; 30. Bilateral matching module. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 The first embodiment of the present invention proposes a distributed resource aggregation method based on carbon emission reduction, including steps S10 to S50: Step S10: Optimize the baseline capacity of distributed renewable energy for each aggregator based on carbon emission reduction to obtain the baseline capacity value of various types of distributed renewable energy for each aggregator. Step S20: Based on the attribute information of the aggregator and the resource operation characteristics of the distributed renewable energy, calculate the first aggregation index of the aggregator for each distributed renewable energy and the second aggregation index of the distributed renewable energy for each aggregator. Step S30: Based on the first aggregation index, obtain the resource selection sequence of the aggregator, and based on the second aggregation index, obtain the aggregation selection sequence of distributed renewable energy. Step S40: Based on the resource selection sequence and the aggregation selection sequence, the disappointment-joy theory is used to calculate the aggregator's first perceived utility and the distributed renewable energy's second perceived utility. Step S50: Using the maximization of the first perceived utility and the second perceived utility as the objective function and the capacity benchmark value as the constraint, establish a bilateral matching model and solve the bilateral matching model to obtain the optimal aggregation scheme.

[0020] This embodiment optimizes the baseline capacity of various distributed renewable energy (DRE) sources for aggregators while considering global information on carbon emission reduction. In traditional aggregator capacity optimization, the decision-making body typically focuses on the aggregator or DRE, optimizing solely from the perspective of maximizing individual economic benefits. This locally optimal decision-making approach has significant limitations. Firstly, existing methods mostly aim to maximize their own profits, often failing to achieve the highest overall benefit, leading to wasted resource allocation. Secondly, existing methods neglect the carbon emission reduction attributes of distributed renewable energy, failing to incorporate carbon reduction benefits into the optimization objective system.

[0021] Since current calculations of carbon emission flows in power systems and the verification of energy carbon reduction attributes mainly rely on the power flow distribution of the power system, the power grid system can obtain global information such as the carbon emission flow information of the entire network, the operating characteristics data of various resources, and the overall operating status of the system. This allows for a holistic consideration of the comprehensive benefits of aggregated configuration from a system-wide perspective. Therefore, this embodiment proposes a DRE resource capacity benchmark optimization technique for aggregators that considers global carbon reduction information.

[0022] The system's carbon emission reduction directly reflects the contribution of aggregated configuration to carbon reduction. Therefore, carbon emission reduction can be used as the capacity configuration optimization target for aggregators. In addition to carbon emission reduction, other optimization targets can be added, such as a curtailment optimization target, which reflects the level of renewable energy absorption and the flexibility of system operation. A total operating cost optimization target can also be added. By considering the economics of aggregated configuration based on carbon reduction and flexible configuration optimization, the goal is to achieve the optimal aggregated configuration of the power system while taking carbon emission reduction into account. It should be noted that the optimization target settings given in this embodiment are only preferred methods. In addition to carbon emission reduction as the core optimization target, one or more other optimization targets can be added, without specific limitations.

[0023] In a preferred embodiment, the present invention optimizes capacity configuration by taking the carbon emission reduction, power curtailment, and total operating cost of the aggregated aggregator as optimization objectives. Specific steps include: A baseline capacity optimization model is established with the carbon emission reduction, abandoned electricity and total operating cost of the aggregator set as optimization objectives, and the power constraint, quantity constraint and carbon emission flow deviation constraint of distributed renewable energy as constraints. Solving the baseline capacity optimization objective yields the baseline capacity values ​​for various types of distributed renewable energy for each aggregator.

[0024] In this embodiment, optimization calculations are performed based on the global information available to the power grid system to obtain the capacity baseline value for each type of distributed renewable energy (DRE) for each aggregator. The capacity baseline value refers to the upper limit of the total capacity for each aggregator for each type of DRE resource (including wind power, solar power, interruptible loads, and energy storage). This upper limit includes the aggregator's existing resources and any incremental resources to be added. The purpose of this capacity configuration optimization in this embodiment is to ensure that the aggregation configuration matches the overall system operation requirements by comprehensively considering the overall scale and structural configuration of the entire aggregator, and to provide accurate data support for the subsequent resource aggregation and matching process.

[0025] Assume the set of aggregators is V, the set of DREs to be aggregated is K, and the resource types in the DRE set are... ,in This includes wind power (W), photovoltaic (PV), interruptible load (IL), and energy storage (ESS). An aggregator can aggregate multiple DRE resources, but a DRE resource can only be added to one aggregator at a time.

[0026] In this embodiment, carbon emission reduction refers to the system's carbon emission reduction, which is obtained by aggregating the carbon emission reductions of distributed renewable energy sources within all aggregators. Specifically, the carbon emission reduction of each aggregator depends on the capacity baseline output of its internal resources such as wind power, photovoltaics, energy storage, and interruptible loads. These outputs are multiplied by the carbon emission reduction coefficients of the corresponding resource types, and the sum is the carbon emission reduction of that aggregator. The calculation formula can be expressed as: In the formula, F1 represents the carbon emission reduction. This represents the carbon emission reduction of aggregator v, where V represents the set of aggregators. Representing resources The carbon emission reduction coefficient, Indicates the internal structure of aggregator v The capacity baseline value of the DRE resource of the resource type, which is the variable to be solved.

[0027] In the above formula, the carbon emission reduction coefficient reflects the intensity of carbon dioxide emission reduction that can be achieved by different types of renewable energy replacing traditional fossil energy power generation. The capacity benchmark value refers to the power output level calculated based on resource operation characteristics and system scheduling requirements after the capacity configuration scheme is determined. The capacity benchmark value of each type of DRE in the aggregator directly affects the power output level that it can provide, and thus determines the overall carbon emission reduction contribution of the aggregator. Therefore, this embodiment uses the power output level to characterize the capacity benchmark value.

[0028] The amount of curtailed power is calculated by aggregating the curtailment contributions of wind and solar power resources within all aggregators. Specifically, the curtailment amount for each aggregator depends on the difference between the actual and predicted output of its internal wind and solar power resources under capacity baseline constraints. Curtailment occurs when the predicted output of wind and solar power exceeds the system's absorption capacity or the aggregator's regulation capacity during a specific period. The calculation of curtailment requires comprehensive consideration of the aggregator's wind and solar power capacity baseline, meteorological forecasts, system load demand, and the regulation capacity of flexible resources such as energy storage and interruptible loads. The calculation formula can be expressed as follows: In the formula, F2 represents the amount of electricity wasted. and These represent the predicted output of wind power and photovoltaic resources of aggregator v, respectively, which are predicted based on meteorological data and historical operating characteristics. and These represent the actual output of wind power and photovoltaic resources of aggregator v, respectively, and are directly collected from the real-time monitoring system of the corresponding resources.

[0029] The capacity benchmark values ​​for wind and solar power among aggregators directly determine the installed capacity of these volatile resources, which in turn affects the level of curtailment risk under adverse weather conditions or system constraints.

[0030] Total operating costs are calculated by aggregating the aggregation costs, interruptible load adjustment costs, and deviation power assessment fees of all aggregators. Specifically, the operating cost of each aggregator depends on its actual operation and management costs and market transaction costs under its capacity baseline configuration. Aggregation costs are directly related to the capacity baseline values ​​of various DRE resources accepted by each aggregator, including communication access costs, monitoring costs, data processing costs, and other management fees. The calculation formula can be expressed as: In the formula, F3 represents the total operating cost. , and These represent the aggregation cost, interruptible load adjustment cost, and deviation power assessment fee for aggregator v, respectively. For resources The aggregation cost coefficient generated by aggregator v is calculated based on the resource access cost model and historical aggregation experience data. and The interruptible cost and interruptible power of aggregator v are obtained from the demand response resource database and the real-time load management system, respectively. This represents the deviation assessment coefficient, which is a preset value. and These represent the positive / negative deviation electricity consumption assessment fee coefficients, which are also preset values. and These represent the excess / minimum output of aggregator v, calculated by the difference between the actual output and the planned output.

[0031] A larger capacity baseline value means a larger scale of resources to be managed, and consequently, higher aggregation costs. Interruptible load adjustment costs depend on the aggregator's interruptible load capacity baseline value and actual call frequency; a larger interruptible load capacity baseline value implies higher user compensation and system scheduling costs. Deviation power assessment fees are determined by the degree of deviation between the aggregator's actual output and planned output. This deviation is largely influenced by the capacity baseline configuration of fluctuating resources such as wind power and solar power. Meanwhile, a reasonable setting of capacity baselines for flexible resources such as energy storage can help reduce output deviations, thereby lowering the corresponding assessment fees.

[0032] When optimizing the above three objectives, carbon emission reduction is a positive indicator, while power curtailment and total operating cost are negative indicators. Therefore, this embodiment preferably adopts a normalization and inverse weighted summation method to synthesize the three indicators, and uses the minimization of the comprehensive indicator as the objective function for capacity baseline optimization. The objective function F can be expressed as: In the formula, F1 represents carbon emissions, F2 represents the amount of electricity wasted, and F3 represents the total operating cost. , and These represent the weight coefficients of F1, F2, and F3, respectively. The value range of each weight coefficient is [0,1]. The weight coefficients are set based on historical operation data statistical analysis and scheduling decision-making experience, and the sum of the three weight coefficients is equal to 1.

[0033] Furthermore, based on resource operation characteristics, capacity configuration optimization also needs to satisfy energy storage constraints, interruptible load constraints, and resource quantity constraints. Among these, energy storage constraints refer to the charging and discharging constraints and state of charge constraints of energy storage resources, which are expressed by the following formula: In the formula, This represents the maximum charge and discharge power of the energy storage of aggregator v. and Let v represent the energy storage discharge power and energy storage charging power at time t, respectively. This represents the state of charge of the aggregator v at time t. This represents the maximum charge state of the energy storage of aggregator v. and This indicates the charging and discharging efficiency of energy storage. Let v represent the energy storage capacity of aggregator v, and Δt represent the time difference.

[0034] Interruptible load constraints refer to the power constraints of interruptible loads, and their formula is expressed as: In the formula, This represents the interruptible load power of the aggregate quotient v at time t. This represents the maximum interruptible load power of the aggregator v. This indicates the output period of aggregator v.

[0035] Resource quantity constraint refers to the constraint on the amount of resources allocated to the aggregator, and its formula is expressed as: In the formula, K v Let K be the set of DRE resources allocated to aggregator v; K is the set of DREs to be aggregated, representing all distributed renewable energy resources that can participate in aggregation; the above constraints guarantee the number of distributed resources that can successfully participate in pre-optimization.

[0036] Furthermore, the need for carbon emission reduction can be further considered by adding constraints on carbon emission flow deviations. Aggregators and distributed resources, while gaining benefits, must bear corresponding carbon reduction responsibilities. According to the power system carbon emission flow theory, when the actual power of aggregators and new energy sources deviates from the planned value within a day, changes in unit power and branch power flow will cause implicit carbon emissions, such as increased grid loss carbon emissions and additional carbon emissions from thermal power units. Therefore, it is necessary to constrain the actual carbon emission flow deviation, the formula of which is: In the formula, and These represent the carbon emission adjustment coefficients for positive and negative deviations in the aggregator's (v) electricity, respectively. and These represent the positive / negative deviations of the aggregator quotient v at time t; This represents the upper limit of the carbon emission adjustment margin for aggregator v.

[0037] Among the constraints mentioned above, energy storage constraints ensure that the energy storage system operates within the limits of its physical characteristics, including power constraints, capacity constraints, and energy conservation constraints, guaranteeing the safe and stable operation and lifespan of energy storage devices. Interruptible load constraints ensure that the adjustment amount of interruptible loads does not exceed the user's adjustable capacity limit, protecting the user's basic electricity needs and electricity experience, and maintaining the sustainability of demand response. Resource capacity constraints ensure that the aggregation configuration does not exceed the total limit of available system resources, guaranteeing the physical feasibility of the optimization scheme and the rationality of resource allocation. Carbon emission flow deviation constraints, based on the theory of carbon emission flows in power systems, limit the negative impact of actual operating deviations of aggregators on system carbon emissions, ensuring that the aggregation scheme can still maintain the expected carbon emission reduction effect in actual implementation.

[0038] Based on the above objective function and constraints, a baseline capacity optimization model is established, and by solving the model, the baseline capacity values ​​for various types of distributed renewable energy for each aggregator can be obtained.

[0039] In another preferred embodiment, a multi-objective optimization method based on the ε-constraint method can be used to achieve resource capacity benchmark optimization. Specifically, the ε-constraint method, as a classic multi-objective optimization method, effectively avoids the subjectivity of weight selection by transforming all objectives except the primary objective into constraints, and can find the optimal solution for the entire Pareto front, including the non-convex region. In the DRE resource capacity benchmark optimization problem of aggregators, considering that carbon emission reduction is the core objective of the current energy system, the system carbon emission reduction can be taken as the main optimization objective, and the abandoned electricity and operating costs can be taken as constraints. By adjusting the ε parameter, different Pareto optimal solution sets can be obtained, providing decision-makers with a richer selection of options.

[0040] In the multi-objective optimization method based on ε-constraint, the main objective function is to maximize the system carbon emission reduction F1. This objective function represents the total carbon emission reduction that the power system can achieve through distributed renewable energy aggregation configuration. The carbon emission reduction is calculated based on the carbon emission reduction coefficients of various renewable energy sources and the corresponding capacity benchmarks. The overall carbon reduction benefit of the system is maximized by optimizing the aggregation configuration.

[0041] The constraints include ε-constraints, technical constraints, and logical constraints. Among these, ε-constraints include constraints on the amount of electricity wasted and constraints on the total operating cost. Among them, the curtailment constraint F2 ensures that the curtailment of renewable energy in the system is controlled within an acceptable range, reflecting the power system's requirements for renewable energy absorption capacity and operational flexibility. Excessive curtailment will lead to a waste of clean energy, affecting investment economics and carbon emission reduction effectiveness. The upper limit of the allowable amount of abandoned electricity, in MWh, is the maximum amount of abandoned electricity that the system can accept, determined based on the grid absorption capacity and renewable energy development goals; the total operating cost constraint ensures that the total operating cost of the aggregation scheme is controlled within an economically acceptable range, ensuring the economic feasibility and market competitiveness of the aggregation configuration, and avoiding excessive pursuit of carbon emission reduction targets while ignoring economic benefits; The upper limit of operating costs is the maximum acceptable operating cost determined based on the aggregator's economic capacity and market electricity price levels.

[0042] The technical constraints include energy storage technology constraints, interruptible load constraints, resource capacity constraints, and carbon emission flow deviation constraints. These constraints are similar to those described in the constraint conditions section of the above embodiments and will not be repeated here.

[0043] The logical constraint is a power balance constraint, which defines the power balance relationship within an aggregator, ensuring that the total output of the aggregator equals the algebraic sum of the power of all its internal resources. This is a fundamental physical law of power system operation, namely, that the power injection at any node must equal the power outflow, guaranteeing the power balance and stable operation of the system. in, This represents the output of aggregator v, calculated by collecting and summarizing the actual output data of each resource through a real-time monitoring system. , and These represent the output of aggregator v wind power, photovoltaic power, and interruptible load, respectively, collected directly from the real-time monitoring system of the corresponding resources; and These represent the energy storage discharge power and energy storage charging power of aggregator v, respectively, which are obtained in real time from the energy storage management system (EMS). The load representing aggregator v is obtained through the electricity consumption information collection system and the load forecasting system; For the purchased power of aggregator v, transaction and dispatch data are obtained from the power trading system and the grid dispatch system.

[0044] The multi-objective optimization model based on the ε-constraint method established above belongs to a complex nonlinear mixed integer programming problem. It is characterized by non-convex objective function, complex constraints, and high dimensionality of decision variables. The improved non-dominated sorting genetic algorithm NSGA-III can be used as the core solution algorithm. The specific solution process can refer to the conventional solution steps, which will not be repeated here.

[0045] In the matching process between aggregators and DREs, the cooperation preferences of both parties directly affect the aggregation effect and stability. Traditional assessments of cooperation preferences mainly rely on subjective human judgment or single economic indicators, which have significant drawbacks: First, subjective judgments are easily influenced by the decision-maker's personal preferences and experience limitations, lacking objectivity and consistency; second, single-indicator assessments ignore multi-dimensional considerations in the aggregation process and cannot fully reflect the comprehensive value of cooperation; finally, existing methods lack a systematic evaluation framework and struggle to handle the differentiated characteristics of different types of DRE resources.

[0046] To address the aforementioned shortcomings, this embodiment uses global information such as the attribute information of each aggregator and the operational characteristics data of DRE to calculate the marginal benefits of resources before and after joining aggregators using objective data. It also evaluates the reliability of each resource based on historical data, thereby generating a cooperation preference sequence based on objective parameters. Compared to existing subjective sequence matching methods, this objective data-based approach reduces the influence of human factors and improves the scientific rigor and stability of the matching results.

[0047] In the sequence generation stage, the aggregation effect is first comprehensively evaluated through multiple dimensions. The calculation steps for these multi-dimensional indicators include: Based on the aggregator's revenue, carbon emission reduction, and external power interaction value before and after resource aggregation, revenue indicators, carbon reduction indicators, and flexibility margin indicators are calculated respectively. Based on the historical operating data of distributed renewable energy, output stability indicators are calculated. The revenue indicator, the carbon reduction indicator, the flexibility margin indicator, and the output stability indicator are used as the first aggregated indicator. Based on the aggregator's actual power generation, aggregator operating costs, and resource output power, production capacity indicators, aggregator cost indicators, and resource stability indicators are calculated respectively, and the production capacity indicators, aggregator cost indicators, and resource stability indicators are used as the second aggregation indicators.

[0048] In this embodiment, during the selection of DRE resources by aggregators, revenue indicators, carbon reduction indicators, flexibility margin indicators, and output stability indicators are used as evaluation indicators. Among them, the revenue indicator refers to the incremental revenue brought about by the addition of DRE resource k to aggregator v, reflecting the marginal contribution of the resource to the aggregator's revenue. Its formula is expressed as: in, For DRE resource k, the revenue indicator for aggregator v is represented by the annualized net revenue increment per MW of installed capacity. The total annual revenue after resource k is aggregated to aggregator v includes revenue from electricity, ancillary services, and capacity, and is calculated using electricity market transaction data and a revenue model. The annual revenue from independent operation before resource k aggregation is the expected revenue from independent market participation, calculated based on historical resource operation data and market electricity prices. The annualized additional costs incurred to accommodate resource k include aggregated management fees such as communication access costs, monitoring costs, data processing costs, and risk management costs; The installed capacity of resource k is determined according to the resource type. For wind power and photovoltaic, it is the rated installed capacity; for energy storage, it is the rated power; and for interruptible loads, it is the adjustable capacity.

[0049] The carbon reduction index refers to the proportion of the system's total carbon emission reduction resulting from adding DRE resource k to aggregator v relative to other aggregation options. This index considers not only the resource's inherent carbon reduction capacity but also its marginal contribution to the overall system carbon reduction effect under a specific aggregation configuration. The formula for calculating this index is: In the formula, Let be the carbon reduction index of DRE resource k against aggregator v, representing the relative carbon emission reduction advantage of the resource aggregation choice; The total carbon emission reduction of the system after resource k is aggregated to aggregator v is calculated based on the carbon emission flow of the aggregated system. Let K be the system carbon emission reduction before resource k is aggregated to aggregator v, and let V be the system carbon emission reduction contribution when resource operates independently or chooses other aggregation schemes. The total carbon emission reduction of the system under the current aggregation configuration is equal to the carbon emission reduction generated by the DREs that have been matched with aggregator v plus the independent carbon emission reduction of the DREs that have not yet been matched. It represents the total carbon emission reduction that the entire system can achieve under the current partial aggregation state and is used to measure the relative importance of a single resource joining a certain aggregator.

[0050] The carbon reduction index is calculated based on the system carbon emission flow theory. It takes into account the indirect impact of resource access on the carbon emissions of the entire power system and adopts the concept of marginal contribution rate. It can avoid the scale deviation that may exist in the absolute carbon emission reduction and can identify resource combinations with high carbon emission reduction synergy, guiding low-carbon aggregated configuration.

[0051] The flexibility margin index refers to the proportion of the aggregator's remaining regulation capacity in power interaction with the external grid to its maximum interaction capacity after DRE resource k is added to aggregator v. This index reflects the flexible regulation space that the aggregator can maintain after accepting new resources; the larger the value, the stronger the flexibility. The calculation formula is: in, The flexibility margin index for DRE resource k after adding aggregator v, with a value range of [0,1]. The larger the value, the stronger the aggregator's flexible adjustment capability. This represents the upper limit of power interaction between aggregator v and the external environment. It is determined based on the aggregator's grid access capacity, contractual agreements, and technical capabilities, and indicates the maximum power that the aggregator can purchase from or sell to the grid. The external power demand after resource k is added to aggregator v is calculated through power balance, including the external power interaction demand under normal operation and emergency regulation conditions.

[0052] Power system flexibility is a key capability for coping with the intermittency of renewable energy and market price fluctuations. The flexibility of aggregators directly affects their competitive advantage and risk resilience in the electricity market. The flexibility margin index in this embodiment comprehensively considers the aggregator's internal adjustment capabilities and external interaction capabilities, and can pre-assess the impact of new resource access on the aggregator's flexibility, thereby providing a quantitative basis for the aggregator's risk management and operational strategy optimization.

[0053] The output stability index refers to the consistency and predictability of output performance of DRE resource k based on historical operating data. This index quantifies stability using the reciprocal of the output variance; a smaller variance indicates more stable output, and a higher stability index value. The calculation formula is: In the formula, This is the output stability index for DRE resource k, with a value range of (0,1]. The larger the value, the more stable the resource output. The output variance of resource k is calculated based on historical operating data. It is derived by analyzing the output fluctuations of the resource within a typical operating cycle, reflecting the dispersion of the resource's output. It should be noted that the historical operating data should include hourly output data for at least one year, covering operating conditions under different seasons and weather conditions, to ensure the representativeness and reliability of the variance calculation.

[0054] The safe and stable operation of the power system requires all participating entities to have predictable and controllable output characteristics. The output stability index is obtained based on the statistical analysis of historical data, which is objective and verifiable. Moreover, the use of the inverse of variance can intuitively reflect the degree of power stability, which is convenient for aggregators to conduct risk assessment and resource combination optimization.

[0055] Since the four indicators mentioned above have different dimensions and orders of magnitude, in order to ensure fair weighting of each indicator during the selection sequence generation process, and to improve the comparability and interpretability of the indicators, they need to be normalized. After normalization, the indicators are weighted and summed according to the pre-set indicator weights to obtain the final score of aggregator v for DRE resource k. By sorting the scores in descending order, the resource selection sequence of each aggregator for each distributed renewable energy source is obtained.

[0056] In a bilateral matching aggregation mechanism, not only do aggregators need to select and evaluate DRE resources, but DRE resources, as independent market entities, also need to compare and select different aggregators. Traditional research often overlooks the subjective initiative of DRE resources, assuming they passively accept the aggregator's selection. This one-way decision-making model carries the risk of system instability due to unilateral forced aggregation. Therefore, this embodiment also employs multi-dimensional indicators for selection and evaluation in the process of DRE resources selecting aggregators.

[0057] In this embodiment, when selecting aggregators for DRE resources, three indicators were chosen: production capacity, aggregation cost, and resource stability. Resource production capacity reflects the power generation and dispatch capabilities of the DRE resources within the aggregator, and is typically related to its installed capacity, power generation efficiency, and the climate conditions of the region (such as wind speed and sunshine). For wind power and photovoltaic resources, production capacity refers to the amount of electricity the resource can output within a certain timeframe. For energy storage systems, production capacity reflects their energy storage and discharge capabilities. Specifically, the production capacity indicator is the ratio of the aggregator's actual annual power generation to its maximum possible annual power generation. The actual annual power generation can be obtained through historical power generation data or simulation models, while the maximum possible annual power generation can be predicted based on meteorological data and resource type.

[0058] The aggregation cost metric refers to the access and operating costs incurred by aggregators when aggregating resources. This metric reflects the economic investment required by aggregators to connect DRE resources to the power grid and participate in cooperation. Access costs include the costs of infrastructure construction and equipment installation for connecting the resources to the grid; operating costs involve the maintenance and management costs during the daily operation of the resources. Specifically, access cost and operating cost scores can be obtained by dividing the access cost by the reciprocal of the annual operating cost, respectively. The access cost is the cost of equipment and infrastructure construction required for the aggregator to connect the DRE resources to the grid, which can be obtained through market quotations or industry standards; the annual operating cost is the maintenance, management, and operating costs incurred by the aggregator during the operation of the DRE resources, which can be referenced from historical data of similar resources.

[0059] Resource stability index refers to the stability of an aggregator's DRE resources in actual power output. For resources such as wind and solar power, stability is usually related to the volatility of output power and the consistency of power generation; for energy storage systems, stability reflects the reliability of their charging and discharging capabilities. Specifically, the resource stability index is quantified by calculating the standard deviation of the output power of DRE resources within an aggregator, which is the reciprocal of the standard deviation of the output power of all resources within the aggregator. Historical output power data of resources within the aggregator can be obtained through power dispatching systems or real-time monitoring platforms.

[0060] After calculating the above three indicators, the indicators are normalized and weighted by using preset indicator weights to obtain the final score of each aggregator for each type of DRE resource. Through the score sequence, the aggregation selection sequence of distributed renewable energy for aggregators is obtained.

[0061] After obtaining the resource selection sequence and the aggregation selection sequence, this embodiment uses the disappointment-joy theory for bilateral matching of the sequences to obtain the optimal resource aggregation scheme. First, the perceived utility is calculated based on the disappointment function and the joy function of the disappointment-joy theory. The specific steps include: Based on the resource selection sequence, calculate the aggregator's first preference utility for each distributed renewable energy source; Based on the difference in first preference utility between each type of distributed renewable energy and other distributed renewable energy, the aggregator calculates the first disappointment value and first gratification value for each distributed renewable energy using a preset disappointment function and gratification function; Based on preset disappointment and gratification weights, the first preference utility, the first disappointment value, and the first gratification value are weighted and calculated to obtain the aggregator's first perceived utility for each distributed renewable energy source. Based on the aggregation selection sequence, calculate the second preference utility of distributed renewable energy for each aggregator; Based on the difference in second preference utility between each clusterer and other clusterers, the second disappointment value and second gratification value of distributed renewable energy for each clusterer are calculated using the disappointment function and the gratification function. Based on the disappointment weight and the gratification weight, the second preference utility, the second disappointment value, and the second gratification value are weighted and calculated to obtain the second perceived utility of distributed renewable energy for each aggregator.

[0062] In this embodiment, perceived utility includes the aggregator's first perceived utility for each distributed renewable energy source and the distributed renewable energy source's second perceived utility for each aggregator. The calculation process for the two types of perceived utility is explained below.

[0063] For the first perceived utility, the aggregator's preference utility for each DRE resource is first calculated based on the resource selection sequence. The calculation formula is as follows: In the formula, Let v be the aggregator's (v) preferred utility for DRE resource k, and k represent the total number of DER resources to be aggregated. The ranking value of aggregator v for DRE resource k in the resource selection sequence is the sequence position. The smaller the position, the higher the aggregator's tendency to aggregate that resource. Preference utility is the sequence position of each resource converted to (0,1]. The larger the preference utility, the higher the aggregator's tendency to aggregate that resource.

[0064] In the disappointment-joy theory, the disappointment function and the joy function It can be represented as: In the formula, α and β represent the disappointment parameter and the gratification parameter, respectively.

[0065] When the above function is applied to this embodiment, it is necessary to set disappointment and delight parameters for the aggregator for each type of DRE resource. In this case, the function can be expressed as: In the formula, and Let be the parameters of disappointment and delight for aggregator v regarding DRE resource k, respectively. , . The smaller the value, the greater the perceived disappointment of aggregators and DRE resources with the matching results, and the less inclined they are to cooperate. The smaller the value, the greater the sense of satisfaction that aggregators and DRE resources have with the matching results, and the greater their tendency to cooperate.

[0066] Then, based on the difference in preference utility between this resource and other resources, the disappointment value and the gratification value are calculated using the disappointment function and the gratification function, respectively. Specifically, for DRE resource k, by comparing preference utility, K resources i with preference utility greater than resource k are obtained. D There are K resources, and K resources j whose preference utility is less than resource k. E Then, the difference in preference utility between resource i and resource k is used as an input parameter to input the disappointment function, obtaining the disappointment value between resource k and resource i. By averaging the disappointment values ​​of each resource, the disappointment value of the aggregator v for resource k can be obtained. Its formula is expressed as: In the formula, This represents the total number of resources for which the preference utility is greater than resource k, i.e., the number of choices that result in disappointment. Let aggregator v be the preference utility for DRE resource i. Let $k$ be the ranking value of the aggregator $v$ for DRE resource $i$ in the resource selection sequence. Resource $i$ is the resource whose preference utility is greater than that of resource $k$, that is, the resource whose ranking value in the resource selection sequence is less than that of resource $k$. .

[0067] Similarly, by using the difference in preference utility between resource k and resource j as an input parameter to the euphoria function, we obtain the euphoria value between resource k and resource j. By averaging the euphoria values ​​of each resource, we can obtain the euphoria value of the aggregator v for resource k. Its formula is expressed as: In the formula, This represents the total number of resources for which the preference utility is less than resource k, i.e., the number of choices that produce a pleasing outcome. Let v be the aggregator's (v) preference utility for DRE resource j. Let $v$ be the ranking value of the aggregator $v$ for DRE resource $j$ in the resource selection sequence. Resource $j$ is the resource whose preference utility is less than that of resource $k$, that is, the resource whose ranking value in the resource selection sequence is greater than that of resource $k$. .

[0068] Finally, by using preset disappointment and euphoria weights, the disappointment and euphoria values ​​are weighted and calculated. The perceived utility of aggregator v for DRE resource k is then obtained by subtracting the weighted disappointment value from the perceived utility and adding the weighted euphoria value. Its formula is expressed as: In the formula, and These are the weights for the disappointment function and the euphoria function, respectively. These weights are preset values ​​that satisfy... , and .

[0069] Following the steps described above, the perceived utility of each aggregator for each distributed renewable resource, i.e., the first perceived utility, can be calculated.

[0070] For the second perceived utility, the calculation steps are similar to those for the first perceived utility. First, the preference utility of DRE resources for each aggregator is calculated based on the aggregation selection sequence. The calculation formula is as follows: In the formula, Let DRE resource k be the preference utility for aggregator v. Let V be the sorting value for the selection of aggregator v for DRE resource k, where V is the total number of aggregators.

[0071] Then, using the aforementioned disappointment and euphoria functions, the disappointment value of DRE resource k with respect to aggregator v is calculated. And joy value Its formula is expressed as: In the formula, This represents the total number of aggregators whose preference utility is greater than the aggregator's value (v), i.e., the number of choices that result in disappointment. Let DRE resource k be the preference utility for aggregator m. Let m be the ranking value of DRE resource k in the aggregation selection sequence for aggregator m, where aggregator m is the aggregator whose preference utility is greater than aggregator v, i.e. . This represents the total number of aggregators whose preference utility is less than aggregator v, i.e., the number of choices that produce a pleasing outcome. Let DRE resource k be the preference utility of aggregator n. Let be the ranking value of DRE resource k in the aggregation selection sequence for aggregator quotient n, where aggregator quotient n is the aggregator whose preference utility is less than aggregator quotient v, i.e. .

[0072] Finally, the perceived utility of DRE resource k to aggregator v is obtained through weight calculation. for: The above steps allow us to calculate the perceived utility of each DRE resource for each aggregator, i.e., the second perceived utility.

[0073] Then, with the goal of maximizing the perceived utility of the aggregator and the perceived utility of DRE resources, a two-sided matching model is established, whose objective function can be expressed as: In the formula, Z1 and Z2 represent the first objective function and the second objective function, respectively. This is a binary variable representing the aggregation matching degree between aggregator v and DRE resource k. The value is 1 when aggregator v matches DRE resource k, and 0 otherwise.

[0074] In addition to the objective function mentioned above, the model also needs to satisfy the baseline capacity constraint and the aggregation matching quantity constraint. The baseline capacity constraint means that the number of DRE resources aggregated by each aggregator for each type cannot exceed its capacity limit, which is the baseline capacity value obtained through capacity configuration optimization. The aggregation matching quantity constraint means that each DRE resource can only be matched with one aggregator, and each aggregator is matched with at least one DRE resource. These constraints can be expressed by the following formula: In the formula, This indicates that the aggregator v already has Type of resource stock, Indicates to be aggregated The capacity of the k-th resource in the type. Indicates to be aggregated The total number of resources of each type Indicates the internal structure of aggregator v Capacity baseline value for DRE resources of resource type. This represents the aggregation matching degree between aggregator v and DRE resource i, where resource i is the resource with a preference utility greater than resource k, i.e. , This represents the aggregation matching degree between aggregator m and DRE resource k, where aggregator m is the aggregator whose preference utility is greater than that of aggregator v. .

[0075] The objective function and constraints mentioned above together form a bilateral matching model. Then, a multi-objective solution algorithm or a weighted summation method of membership functions is used to transform the multi-objective optimization model into a single-objective optimization model. The single-objective optimization solution algorithm is then used to solve the model, thereby obtaining the optimal aggregation scheme between the aggregator and the distributed renewable energy.

[0076] In the above embodiments, the perceived utility of aggregators and distributed renewable energy is calculated using fixed function parameters and weights. This static algorithm cannot adapt to the influence of factors such as demand fluctuations and DRE resource power generation fluctuations, resulting in a large deviation between expected utility and actual utility. For example, when the power generation of a certain DRE resource is lower than expected, the aggregator's disappointment will increase. However, since the disappointment parameter is fixed, the increase in disappointment cannot be reflected in the matching process in a timely manner, thereby reducing the matching efficiency.

[0077] To improve the stability and accuracy of aggregation matching, in a preferred embodiment, an adaptive adjustment algorithm is used to dynamically adjust the function parameters, disappointment weights, and joy weights of the disappointment and joy functions. Specific steps include: Obtain feedback data after each round of actual matching and aggregation, and based on the feedback data, obtain the utility deviation between the actual utility and the expected utility of each distributed renewable energy source for the aggregator; Based on the utility deviation and the preset adaptive adjustment factor, the aggregator obtains the parameter adjustment values ​​for each distributed renewable energy source, including disappointment parameter adjustment values ​​and euphoria parameter adjustment values; Based on the adjustment value of the disappointment parameter, the original disappointment parameter of the disappointment function is adjusted to obtain the adjusted disappointment parameter; Based on the adjustment value of the euphoria parameter, the original euphoria parameter of the euphoria function is adjusted to obtain the adjusted euphoria parameter; Based on the adjusted disappointment and euphoria parameters, a proportional calculation function is used to obtain the adjusted disappointment weight and euphoria weight.

[0078] In this embodiment, after each matching aggregation, the weights of disappointment and delight are dynamically adjusted based on the actual feedback data from the participants. Specifically, if the actual matching results between DRE resources and aggregators deviate significantly from expectations (significant disappointment), the weight of disappointment is increased; conversely, if the actual matching results exceed expectations (significant delight), the weight of delight is increased.

[0079] Taking aggregator v as an example, suppose its initial disappointment and initial delight parameters for DRE resource k are as follows: and Adjustments are made based on the aggregator's utility difference (i.e., the deviation between actual and expected utility), using the following formula: in, and The adaptive adjustment factor of aggregator v to DRE resource k determines the sensitivity of the disappointment and gratification parameter adjustments, and is used to control the adjustment magnitude of disappointment and gratification. The utility deviation of aggregator v for DRE resource k is obtained by subtracting expected utility from actual utility. Actual utility is the difference between the actual benefit and cost after matching DRE resource with aggregator, and expected utility is the first perceived utility calculated in the previous bilateral matching process. Normalization is required when calculating utility deviation to ensure consistency of dimensions.

[0080] Based on this, the adjusted disappointment parameter can be obtained. And delightful parameters ,Right now: In addition, the weights of the disappointment function and the euphoria function ( and The parameters of disappointment and joy determine the degree to which disappointment and joy influence the final result throughout the matching process. When the disappointment or joy parameters change, their weights need to be adjusted accordingly to ensure that the impact of disappointment and joy better aligns with the perception of the current aggregator and DRE resources. Specifically, the weights are dynamically adjusted based on changes in the disappointment and joy parameters. and The weight adjustment formula can be expressed as: In the formula, This indicates the adjusted disappointment weight. This indicates the adjusted "happy weight".

[0081] When the function parameters change, the corresponding weights are dynamically adjusted, causing perceived utility to adjust accordingly, thus maintaining a balance between the disappointment and gratification functions. After each round of matching, the weights of disappointment and gratification are dynamically updated based on the deviation between actual and expected utility, and the matching results are recalculated based on the new weights. This ensures that the satisfaction of aggregators and DRE resources can be promptly fed back and adjusted.

[0082] Furthermore, to ensure the convergence and stability of the adaptive parameter adjustment process, this embodiment also designs the following guarantee mechanism: 1) Parameter boundary constraint mechanism To prevent unlimited variations in the parameters of disappointment and delight, parameter boundary constraints are set: , in, These represent the lower limits of the disappointment and gratification parameters, respectively. These represent the upper limits of the disappointment and gratification parameters, respectively, and are preferred. , When the adjusted parameters exceed the boundaries, the boundary values ​​are used as replacements.

[0083] 2) Phased adjustment mechanism To balance convergence speed and stability, a phased adjustment strategy is adopted: Initial exploration phase (first 2 rounds of matchmaking): Make significant parameter adjustments to enable the parameters to respond quickly to changes in utility deviation; Fine-tuning phase (round 3 and beyond): Make minor parameter adjustments to ensure that the parameters converge smoothly to near the optimal value.

[0084] This mechanism ensures that the algorithm can converge quickly to a reasonable range and remain stable in the later stages, avoiding oscillations of parameters near the optimal value.

[0085] 3) Utility Bias Threshold Mechanism When the absolute value of the utility deviation is less than 0.02 for N consecutive rounds (usually N=3), the disappointment and elation parameters are considered to have stabilized and adjustment is stopped.

[0086] After adaptively adjusting the parameters and weights, the adjusted perceptual utility is calculated using the adjusted parameters and weights to build the bilateral matching model for the next round of matching, thereby improving the accuracy of the matching results.

[0087] In another preferred embodiment, in the electricity market, participants' behavior typically exhibits long-term trends, and historical behavioral data can provide more accurate preference predictions and decision support. Therefore, this embodiment introduces historical data to make the matching process more stable and avoid frequent adjustments caused by short-term fluctuations. Specific steps include: Based on historical matching records, calculate the first historical utility of the aggregator for each distributed renewable energy source and the second historical utility of the distributed renewable energy source for each aggregator; The first historical utility and the first perceived utility are weighted and summed to obtain the updated first perceived utility. The second historical utility and the second perceived utility are weighted and summed to obtain the updated second perceived utility.

[0088] In this embodiment, historical data refers to the historical matching records between aggregators and DRE resources, including their performance in past aggregations, such as the number of aggregations and the stability of resources after aggregation. Based on the historical matching records, the historical utility between aggregators and DRE resources is preset, for example, by normalizing and weighting various types of historical performance data to obtain the historical utility.

[0089] Then, through a weighted mechanism, historical utility is incorporated into perceived utility, thereby integrating historical data into the matching decision-making process. Specifically, a historical preference weight is set to adjust the ratio between current and historical utility; that is, the adjusted utility is the weighted sum of historical and current utility. This embodiment, through a matching priority mechanism based on historical data, can reduce the interference of short-term utility fluctuations on the matching results, making the matching decision more stable.

[0090] In another preferred embodiment, combining the parameter adaptive adjustment mechanism and the historical data matching priority mechanism from the two embodiments above, the constructed bilateral matching model can be expressed as: In the formula, This represents the historical preference utility between aggregator v and DRE resource k. This represents the aggregator v's first perceived utility after adjusting for DRE resource k. This represents the adjusted second perceived utility of DRE resource k to aggregator v.

[0091] This embodiment introduces an adaptive disappointment-joy parameter and a matching priority mechanism based on historical data. This not only dynamically adjusts the preference parameters of aggregators and DRE resources, but also takes into account the long-term behavior changes of aggregators and DRE resources, thereby optimizing the matching results and effectively improving the stability, adaptability and accuracy of the bilateral matching model.

[0092] This embodiment provides a distributed resource aggregation method based on carbon emission reduction. This embodiment guides the aggregation of aggregators and DRE resources from the perspective of considering the global optimality of carbon emission reduction attributes. This can reduce system operating costs, increase the total carbon emission reduction of the system, improve capacity utilization, and provide data support for resource allocation and aggregation decisions. By evaluating aggregation selection through multi-dimensional objective evaluation indicators, the objectivity and accuracy of the evaluation results can be improved. Through a bilateral matching mechanism, the stability and accuracy of resource aggregation results can be improved, thereby achieving optimal allocation of power resources and improving the stability and reliability of power grid operation.

[0093] Please see Figure 2 Based on the same inventive concept, the second embodiment of this invention proposes a distributed resource aggregation system based on carbon emission reduction, comprising: The baseline optimization module 10 is used to optimize the baseline capacity of distributed renewable energy for each aggregator based on carbon emission reduction, so as to obtain the baseline capacity value of each type of distributed renewable energy for each aggregator. The sorting module 20 is used to calculate the first aggregation index of the aggregator for each distributed renewable energy source and the second aggregation index of the distributed renewable energy source for each aggregator based on the attribute information of the aggregator and the resource operation characteristics of the distributed renewable energy source. Based on the first aggregation index, the resource selection sequence of the aggregator is obtained, and based on the second aggregation index, the aggregation selection sequence of distributed renewable energy is obtained; The bilateral matching module 30 is used to calculate the aggregator's first perceived utility and the distributed renewable energy's second perceived utility based on the resource selection sequence and the aggregation selection sequence, using the disappointment-joy theory. Using the maximization of the first perceived utility and the second perceived utility as the objective function and the capacity benchmark value as the constraint, a two-sided matching model is established, and the two-sided matching model is solved to obtain the optimal aggregation scheme.

[0094] The technical features and effects of the carbon reduction-based distributed resource aggregation system proposed in this invention are the same as those of the method proposed in this invention, and will not be repeated here. Each module in the above-mentioned carbon reduction-based distributed resource aggregation system can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0095] Furthermore, embodiments of the present invention also propose a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0096] Please see Figure 3 The diagram illustrates the internal structure of a computer device in one embodiment. This computer device can specifically be a terminal or a server. The computer device includes a processor, memory, network interface, display, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface of the computer device is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a distributed resource aggregation method based on carbon reduction. The display screen of the computer device can be a liquid crystal display (LCD) or an e-ink display. The input devices of the computer device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse, etc.

[0097] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computing devices may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0098] Furthermore, embodiments of the present invention also propose a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0099] In summary, the embodiments of this invention propose a distributed resource aggregation method, system, device, and medium based on carbon emission reduction. The method optimizes the baseline capacity of distributed renewable energy for each aggregator based on carbon emission reduction, obtaining baseline capacity values ​​for various types of distributed renewable energy for each aggregator. Based on the attribute information of the aggregator and the resource operation characteristics of the distributed renewable energy, a first aggregation index for each distributed renewable energy and a second aggregation index for each aggregator are calculated. Based on the first aggregation index, a resource selection sequence for the aggregator is obtained, and based on the second aggregation index, an aggregation selection sequence for the distributed renewable energy is obtained. Based on the resource selection sequence and the aggregation selection sequence, the disappointment-joy theory is used to calculate the first perceived utility of the aggregator and the second perceived utility of the distributed renewable energy. With the maximization of the first and second perceived utilities as the objective function and the baseline capacity as the constraint, a bilateral matching model is established and solved to obtain the optimal aggregation scheme. This invention guides the aggregation of aggregators and DRE resources from the perspective of considering the global optimality of carbon emission reduction attributes. It can reduce system operating costs, increase the total carbon emission reduction of the system, improve capacity utilization, and provide data support for resource allocation and aggregation decisions. By evaluating aggregation selection through multi-dimensional objective evaluation indicators, it can improve the objectivity and accuracy of evaluation results. Through a bilateral matching mechanism, it can improve the stability and accuracy of resource aggregation results. This invention can achieve the optimal allocation of power resources, thereby improving the stability and reliability of power grid operation.

[0100] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0101] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A distributed resource aggregation method based on carbon emission reduction, characterized in that, include: The baseline capacity of distributed renewable energy for each aggregator is optimized based on carbon emission reduction, resulting in the baseline capacity value for each type of distributed renewable energy for each aggregator. Based on the attribute information of aggregators and the resource operation characteristics of distributed renewable energy, calculate the first aggregation index of aggregators for each distributed renewable energy source and the second aggregation index of distributed renewable energy sources for each aggregator. Based on the first aggregation index, the resource selection sequence of the aggregator is obtained, and based on the second aggregation index, the aggregation selection sequence of distributed renewable energy is obtained; Based on the resource selection sequence and the aggregation selection sequence, the disappointment-joy theory is used to calculate the aggregator's first perceived utility and the distributed renewable energy's second perceived utility. Using the maximization of the first perceived utility and the second perceived utility as the objective function and the capacity benchmark value as the constraint, a two-sided matching model is established, and the two-sided matching model is solved to obtain the optimal aggregation scheme.

2. The distributed resource aggregation method based on carbon emission reduction according to claim 1, characterized in that, The step of optimizing the baseline capacity of distributed renewable energy for each aggregator based on carbon emission reduction to obtain the baseline capacity value of various types of distributed renewable energy for each aggregator includes: A baseline capacity optimization model is established with the carbon emission reduction, abandoned electricity and total operating cost of the aggregator set as optimization objectives, and the power constraint, quantity constraint and carbon emission flow deviation constraint of distributed renewable energy as constraints. Solving the baseline capacity optimization objective yields the baseline capacity values ​​for various types of distributed renewable energy for each aggregator.

3. The distributed resource aggregation method based on carbon emission reduction according to claim 1, characterized in that, The steps of calculating the first aggregation index of the aggregator for each distributed renewable energy source and the second aggregation index of the distributed renewable energy source for each aggregator based on the attribute information of the aggregator and the resource operation characteristics of the distributed renewable energy source include: Based on the aggregator's revenue, carbon emission reduction, and external power interaction value before and after resource aggregation, revenue indicators, carbon reduction indicators, and flexibility margin indicators are calculated respectively. Based on the historical operating data of distributed renewable energy, output stability indicators are calculated. The revenue indicator, the carbon reduction indicator, the flexibility margin indicator, and the output stability indicator are used as the first aggregated indicator. Based on the aggregator's actual power generation, aggregator operating costs, and resource output power, production capacity indicators, aggregator cost indicators, and resource stability indicators are calculated respectively, and the production capacity indicators, aggregator cost indicators, and resource stability indicators are used as the second aggregation indicators.

4. The distributed resource aggregation method based on carbon emission reduction according to claim 3, characterized in that, The steps of obtaining the resource selection sequence of the aggregator based on the first aggregation index and obtaining the aggregation selection sequence of distributed renewable energy based on the second aggregation index include: The first aggregation index is normalized and weighted summed to obtain the aggregator's first choice score for various types of distributed renewable energy. The resource selection sequence is obtained by sorting the scores of the first selection. The second aggregation index is normalized and weighted summed to obtain the second selection score of distributed renewable energy for each aggregator. The second selection scores are sorted to obtain the aggregated selection sequence.

5. The distributed resource aggregation method based on carbon emission reduction according to claim 1, characterized in that, The step of calculating the aggregator's first perceived utility and the distributed renewable energy's second perceived utility using the disappointment-joy theory, based on the resource selection sequence and the aggregation selection sequence, includes: Based on the resource selection sequence, calculate the aggregator's first preference utility for each distributed renewable energy source; Based on the difference in first preference utility between each type of distributed renewable energy and other distributed renewable energy, the aggregator calculates the first disappointment value and first gratification value for each distributed renewable energy using a preset disappointment function and gratification function; Based on preset disappointment and gratification weights, the first preference utility, the first disappointment value, and the first gratification value are weighted and calculated to obtain the aggregator's first perceived utility for each distributed renewable energy source. Based on the aggregation selection sequence, calculate the second preference utility of distributed renewable energy for each aggregator; Based on the difference in second preference utility between each clusterer and other clusterers, the second disappointment value and second gratification value of distributed renewable energy for each clusterer are calculated using the disappointment function and the gratification function. Based on the disappointment weight and the gratification weight, the second preference utility, the second disappointment value, and the second gratification value are weighted and calculated to obtain the second perceived utility of distributed renewable energy for each aggregator.

6. The distributed resource aggregation method based on carbon emission reduction according to claim 5, characterized in that, The step of calculating the aggregator's first perceived utility and the distributed renewable energy's second perceived utility using the disappointment-joy theory based on the resource selection sequence and the aggregation selection sequence further includes: Based on historical matching records, calculate the first historical utility of the aggregator for each distributed renewable energy source and the second historical utility of the distributed renewable energy source for each aggregator; The first historical utility and the first perceived utility are weighted and summed to obtain the updated first perceived utility. The second historical utility and the second perceived utility are weighted and summed to obtain the updated second perceived utility.

7. The distributed resource aggregation method based on carbon emission reduction according to claim 5, characterized in that, The function parameters of the disappointment function and the gratification function, as well as the disappointment weight and the gratification weight, are adaptively adjusted using the following steps: Obtain feedback data after each round of actual matching and aggregation, and based on the feedback data, obtain the utility deviation between the actual utility and the expected utility of each distributed renewable energy source for the aggregator; Based on the utility deviation and the preset adaptive adjustment factor, the aggregator obtains the parameter adjustment values ​​for each distributed renewable energy source, including disappointment parameter adjustment values ​​and euphoria parameter adjustment values; Based on the adjustment value of the disappointment parameter, the original disappointment parameter of the disappointment function is adjusted to obtain the adjusted disappointment parameter; Based on the adjustment value of the euphoria parameter, the original euphoria parameter of the euphoria function is adjusted to obtain the adjusted euphoria parameter; Based on the adjusted disappointment and euphoria parameters, a proportional calculation function is used to obtain the adjusted disappointment weight and euphoria weight.

8. A distributed resource aggregation system based on carbon emission reduction, characterized in that, include: The benchmark optimization module is used to optimize the benchmark capacity of distributed renewable energy for each aggregator based on carbon emission reduction, and obtain the benchmark capacity value of each type of distributed renewable energy for each aggregator. The selection and sorting module is used to calculate the first aggregation index of the aggregator for each distributed renewable energy source and the second aggregation index of the distributed renewable energy source for each aggregator based on the attribute information of the aggregator and the resource operation characteristics of the distributed renewable energy source. Based on the first aggregation index, the resource selection sequence of the aggregator is obtained, and based on the second aggregation index, the aggregation selection sequence of distributed renewable energy is obtained; The bilateral matching module is used to calculate the aggregator's first perceived utility and the distributed renewable energy's second perceived utility based on the resource selection sequence and the aggregation selection sequence, using the disappointment-joy theory. Using the maximization of the first perceived utility and the second perceived utility as the objective function and the capacity benchmark value as the constraint, a two-sided matching model is established, and the two-sided matching model is solved to obtain the optimal aggregation scheme.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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