Multi-load aggregator collaborative response method based on power grid limited adjustment budget

Abstract

The embodiment of the invention relates to the technical field of power system dispatching and control, in particular to a multi-load aggregator collaborative response method based on power grid limited adjustment budget, and the method comprises the steps: combing the interaction relationship among a power grid company, load aggregators and terminal users; constructing an interaction model comprising a power grid company, a load aggregator and a terminal user, wherein the interaction model comprises a power grid company constraint, a load aggregator constraint and a terminal user constraint; constructing a multi-load aggregator collaborative response strategy model considering the power grid limited adjustment budget, and taking the minimum adjustment deviation and the load side adjustment cost as optimization targets; a business solver BARON is called for solving the interaction model and the multi-load aggregator collaborative response strategy model in a GAMS environment, a solving result is output, response results of all scenes are analyzed, the load aggregators are guided to reasonably respond to power grid adjustment requirements, and coordination and balance of interests among multiple subjects are achieved.

Description

Technical Field

[0001] This application relates to the field of power system dispatching and control technology, and in particular to a multi-load aggregator collaborative response method based on a limited power grid regulation budget. Background Technology

[0002] In 2023, my country's installed capacity of renewable energy power generation surpassed that of thermal power for the first time, with newly installed capacity accounting for over 50% of the global total. The intermittent and fluctuating grid connection of large-scale renewable energy sources places higher demands on the flexibility and regulation capabilities of the power system. Load aggregators, as key market players in aggregating and dispatching demand-side resources, can effectively enhance the system's regulation capabilities. However, with the deepening of power market reforms, the coexistence and competition of multiple load aggregators within a single region will become the norm. They compete for the limited budget allocated by the grid company to incentivize regulation, which presents the grid company with the complex challenge of not only incentivizing individual load aggregators but also rationally allocating the budget among multiple competing load aggregators to achieve optimal global regulation.

[0003] CN113098050A discloses a method and system for dynamic aggregation of load resources for power grid regulation. Its core steps are as follows: First, the response characteristics of each adjustable load unit are collected and quantified, and a database containing individual resource models and their adjustable capabilities is constructed and dynamically maintained. Second, the method integrates topology model data from the main power grid and distribution network, combined with the access point information of adjustable loads, to accurately establish the electrical topology relationship between resources and the power grid. Based on this, the system starts from the access feeder of each load and automatically forms a multi-level aggregation entity with hierarchical and zoning characteristics based on the power supply chain through power path tracing. Finally, based on the individual resource models, capability data, and the constructed aggregation entity structure, the system can analyze and evaluate the controllability potential of the aggregation at different levels from multiple dimensions, generating multi-dimensional aggregation information.

[0004] However, existing technologies still have the following problems:

[0005] The study does not consider the coordinated response of multiple competing load aggregators under a limited adjustment budget, and lacks a mechanism for differentiated economic incentives and resource optimization allocation for each aggregator under budget constraints. Summary of the Invention

[0006] To address this, the present invention provides a multi-load aggregator collaborative response method based on a power grid limited regulation budget, which overcomes the problems in the prior art that do not consider the collaborative response of multiple competing load aggregators under a limited regulation budget, as well as the lack of a mechanism for differentiated economic incentives and optimized allocation of regulation resources for each aggregator under budget constraints.

[0007] To achieve the above objectives, the present invention provides a multi-load aggregator coordinated response method based on a finite power grid regulation budget, comprising:

[0008] Step S1: Analyze the interaction between the power grid company, load aggregators, and end users. The power grid company, as the market leader, releases regulation demands and sets incentive mechanisms. Load aggregators aggregate the load resources of end users to form large-scale controllable load resources and formulate response strategies based on the power grid company's incentive mechanisms. End users participate in market interaction through load aggregators.

[0009] Step S2: Construct an interaction model that includes the power grid company, load aggregator, and end users. The interaction model includes constraints on the power grid company, constraints on the load aggregator, and constraints on the end users.

[0010] Step S3: Construct a multi-load aggregator collaborative response strategy model that considers the limited regulation budget of the power grid, with minimizing regulation deviation and load-side regulation cost as the optimization objective;

[0011] Step S4: In the GAMS environment, call the commercial solver BARON to solve the interaction model and the multi-load aggregator collaborative response strategy model, and output the solution results.

[0012] Furthermore, in step S1, the interaction between the power grid company, the load aggregator, and the end user includes:

[0013] In terms of information exchange, the power grid company, as the dominant regulator, is in an information-advantaged position, while the load aggregator is in an information-disadvantaged position.

[0014] In terms of energy interaction, load aggregators form large-scale, controllable load resources by aggregating dispersed end users;

[0015] Regarding the exchange of subsidy funds, the response compensation provided by load aggregators to end users comes from the incentive fund pool established by the power grid company.

[0016] Furthermore, in step S2, the constraints on the power grid company under the interaction model include:

[0017] The total cost of power grid dispatch is constrained by the power grid budget.

[0018] The sum of the actual responses of all load aggregators meets the constraint of grid regulation requirements.

[0019] Furthermore, in step S2, the load aggregator constraint under the interaction model includes:

[0020] Considering the differentiated compensation pricing mechanism for the adjustment value of load aggregators, the adjustment contribution capacity of each load aggregator at different time periods is quantified by the adjustment value weight coefficient and adjustment value.

[0021] Methods for determining the compensation price and response price thresholds for load aggregators;

[0022] Method for calculating the total revenue of load aggregators after participating in load-side regulation;

[0023] The operational constraints of load aggregators include response capacity range constraints, response time period and state variable matching constraints, maximum number of daily responses constraints, power supply and demand balance constraints, and satisfaction constraints.

[0024] Furthermore, in step S2, the end-user constraints under the interaction model include:

[0025] Constraints on the range of adjustable capacity for interruptible loads, load reductions, and load shifting;

[0026] Constraint on the difference in active power before and after adjustable load adjustment;

[0027] Load response time period and response state variable matching constraints;

[0028] Adjustable load satisfaction calculation and satisfaction threshold constraints;

[0029] Consider user-differentiated pricing methods that adjust for value;

[0030] Method for calculating scheduling costs paid by load aggregators to adjustable users.

[0031] Furthermore, in step S2, the end-user constraint further includes:

[0032] Interruptible device model, including interruptible load modeling and electric vehicle power constraints;

[0033] The equipment model can be reduced, including temperature constraints, power constraints, and operating time constraints for air conditioning operation;

[0034] The model for movable equipment includes minimum continuous response runtime constraints and load curve constraints.

[0035] Furthermore, in step S3, the multi-load aggregator collaborative response strategy model takes minimizing the adjustment deviation and load-side adjustment cost as the optimization objective, and incentivizes load aggregators to participate in adjustment with the least scheduling cost under a limited budget.

[0036] Furthermore, the multi-load aggregator collaborative response strategy model considers the overall cost of the power grid company, including:

[0037] Reduced transmission and distribution revenue for power grid companies;

[0038] The compensation fee paid by the power grid to the load aggregator;

[0039] Reduced transmission and distribution capacity costs for power grid companies;

[0040] The power grid company reduced its network loss costs;

[0041] The total dispatch cost of load-side regulation carried out by the power grid company.

[0042] Furthermore, the load aggregator's satisfaction is calculated by comparing the revenue before and after participating in load-side regulation, and a satisfaction threshold is set to ensure the load aggregator's basic revenue requirements.

[0043] Furthermore, the user-differentiated compensation pricing method calculates the adjustable load adjustment value coefficient and adjustment value, combines the adjustment budget, time-of-use electricity price and adjustment coefficient to determine the compensation price for various adjustable loads, and sets a response price threshold to ensure the basic benefit requirements for users participating in adjustment.

[0044] Compared with existing technologies, the advantages of this invention lie in its introduction of the power grid company as a key system entity and its systematic analysis of the interaction mechanism between the power grid company and load aggregators. Considering key factors such as the components of power grid dispatching costs and the quantification standard of load aggregator regulation value, and with the optimization objective of minimizing the overall dispatching cost and regulation deviation of the power grid company, a multi-load aggregator collaborative response strategy model considering the power grid's limited regulation budget and regulation deviation is constructed. Simulation results show that this method can not only guide load aggregators to respond rationally to power grid regulation demands but also achieve coordination and balance of interests among multiple entities. By introducing a formula for calculating the regulation value of load aggregators to quantify their value, it helps the power grid to more rationally allocate the incentive benefits of each load aggregator under a limited regulation budget, achieving zero dispatch deviation.

[0045] Furthermore, in terms of information interaction, this invention effectively improves market transparency and response efficiency by clarifying the information status and flow direction of each party; in terms of energy interaction, it successfully achieves the scaling and controllability of distributed load resources through the resource integration of load aggregators; in terms of subsidy fund interaction, it achieves optimized allocation and incentive compatibility of limited budgets by constructing a closed-loop fund flow of "incentive fund pool - load aggregator - end user"; through the systematic sorting and mechanism design of the interaction relationship among the three parties, this invention not only solves key technical problems such as information asymmetry, resource dispersion and budget constraints, but also successfully constructs a transparent, efficient and incentive-compatible market environment, providing an innovative business model and technical path for the safe and stable operation of the power system under high proportion of renewable energy access.

[0046] Furthermore, this invention ensures the economic objectives of the power grid company by setting a hard constraint that the total cost of power grid dispatch must not exceed the regulation budget. This constraint forces the power grid company to make optimal decisions within the budget, avoiding cost runaway caused by excessive incentives. Simultaneously, it requires the sum of the actual responses of all load aggregators to meet the system regulation needs, ensuring the effectiveness of regulation. These two core constraints work together to enable the power grid company to accurately guide funds to the links with the highest regulation value with limited financial resources, significantly improving the efficiency of budget fund utilization and the overall economic benefits of regulation. For load aggregators, a fair competition and revenue guarantee mechanism based on regulation value is established. The contribution of each aggregator at different times is quantified through regulation value weighting coefficients and regulation value quantification, and compensation prices are determined accordingly. This allows load aggregators with strong aggregation capabilities and high regulation potential to obtain higher economic returns. Operational constraints ensure… The technical feasibility of load aggregators' participation is ensured, while the power supply and demand balance constraint guarantees the rationality of their internal resource scheduling. The introduction of satisfaction constraints, by comparing the benefits before and after regulation, sets a bottom line for the revenue of load aggregators participating in the market, protecting their basic commercial interests and thus greatly enhancing their willingness and sustainability to participate in load-side regulation. For end users, a refined scheduling model that takes into account both regulation potential and energy satisfaction is constructed. By setting regulation capacity ranges and physical constraints for interruptible, reduceable, and shiftable loads respectively, the regulation potential of distributed loads is maximized while ensuring users' basic energy needs and equipment safety. Differentiated compensation prices are calculated based on users' regulation value coefficients, so that users with high-value regulation capabilities can receive higher compensation during periods and locations when the system urgently needs regulation. This fairly reflects the value differences of regulation behavior among different users at different times.

[0047] Furthermore, the multi-load aggregator collaborative response strategy model constructed in step S3 of this invention not only ensures the effectiveness and economy of system regulation through bi-objective optimization, but also reveals the long-term investment value of load-side regulation through full-cost accounting. Ultimately, this model serves as a powerful decision support tool, enabling power grid companies to make scientific, accurate, and cost-maximizing collaborative response decisions within a limited budget and under complex market conditions, fundamentally improving the utilization efficiency of power grid assets and the overall economic efficiency of system operation.

[0048] Furthermore, by explicitly defining satisfaction as net revenue after adjustment / electricity purchase cost before adjustment, and setting an acceptable satisfaction threshold for load aggregators, this invention effectively solves the problem of load aggregators' lack of willingness to participate due to concerns about uncertain or insufficient revenue to cover their operating costs and risks. When it is foreseen that satisfaction will be lower than the threshold, aggregators can choose not to respond, thereby avoiding losses; conversely, when satisfaction is guaranteed, they will actively invest resources.

[0049] Furthermore, this invention not only solves the problem of solvability of complex models, but also transforms a theoretical collaborative response strategy into a powerful, executable, verifiable, and optimizable decision support system. By ensuring the optimality of the solution, providing comprehensive decision information, and verifying the adaptability of the strategy, this step ultimately transforms the overall solution of this invention from a "blueprint on paper" into a refined and intelligent operation and management tool capable of coping with the complexity and uncertainty of the real world. This provides a solid technical guarantee and core operating platform for power grid companies to achieve collaborative response of multi-load aggregators under limited budgets. Attached Figure Description

[0050] Figure 1 An interaction diagram between power grid companies, load aggregators, and end users provided in this embodiment of the invention;

[0051] Figure 2 A flowchart illustrating a multi-load aggregator coordinated response method based on a finite power grid regulation budget provided in an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of power grid regulation requirements provided in an embodiment of the present invention;

[0053] Figure 4 This is a schematic diagram of the power grid company and various aggregators adjusting the electricity sales volume before the adjustment, provided in an embodiment of the present invention.

[0054] Figure 5(ac) shows typical pre-load adjustment curves for aggregators 1, 2, and 3 provided in the embodiments of the present invention.

[0055] Figures 6(ac) are schematic diagrams showing the market participation and pricing of aggregators 1, 2, and 3 in scenario S1 provided by the embodiments of the present invention.

[0056] Figure 7(ac) are schematic diagrams showing the market participation of aggregators 1, 2, and 3 in scenario S1 provided by the embodiments of the present invention.

[0057] Figure 8(ac) are schematic diagrams of typical load pricing for aggregators 1, 2, and 3 in scenario S1 provided by the embodiments of the present invention.

[0058] Figure 9 This is a schematic diagram illustrating the participation of various aggregators in market and grid demand, provided in an embodiment of the present invention.

[0059] Figure 10 (ac) are schematic diagrams showing the typical load participation of aggregators 1, 2, and 3 in scenario S2 provided by the embodiments of the present invention. Detailed Implementation

[0060] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0061] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0062] Please see Figure 1-Figure 1 As shown in 0, Figure 1 An interaction diagram between power grid companies, load aggregators, and end users provided in this embodiment of the invention; Figure 2 A flowchart of a multi-load aggregator collaborative response strategy based on a limited power grid regulation budget is provided for an embodiment of the present invention. Figure 3 A schematic diagram of power grid regulation requirements provided in an embodiment of the present invention; Figure 4 Figure 5 is a schematic diagram of the power grid company and various aggregators' pre-adjustment electricity sales volume provided in an embodiment of the present invention; Figure 6 is a schematic diagram of aggregators 1, 2, and 3's typical pre-adjustment load curves provided in an embodiment of the present invention; Figure 7 is a schematic diagram of aggregators 1, 2, and 3's market participation and pricing in scenario S1 provided in an embodiment of the present invention; Figure 8 is a schematic diagram of aggregators 1, 2, and 3's typical load participation in scenario S1 provided in an embodiment of the present invention; Figure 9 Figure 10 is a schematic diagram of the market and grid demand situation of each aggregator provided in the embodiment of the present invention; Figure 10 is a schematic diagram of the market participation of each typical load of aggregators 1, 2 and 3 in scenario S2 provided in the embodiment of the present invention.

[0063] The technical solution provided in this application includes the following steps:

[0064] Step S1: Analyze the interaction between the power grid company, load aggregators, and end users. The power grid company, as the market leader, releases regulation demands and sets incentive mechanisms. Load aggregators aggregate the load resources of end users to form large-scale controllable load resources and formulate response strategies based on the power grid company's incentive mechanisms. End users participate in market interaction through load aggregators.

[0065] Step S2: Construct an interaction model that includes the power grid company, load aggregator, and end users. The interaction model includes constraints on the power grid company, constraints on the load aggregator, and constraints on the end users.

[0066] Step S3: Construct a multi-load aggregator collaborative response strategy model that considers the limited regulation budget of the power grid, with minimizing regulation deviation and load-side regulation cost as the optimization objective;

[0067] Step S4: In the GAMS environment, the interaction model and the multi-load aggregator collaborative response strategy model are solved by calling the commercial solver BARON, the solution results are output, and the response results of each scenario are analyzed.

[0068] This invention introduces the power grid company as a key entity in the system and systematically analyzes the interaction mechanism between it and load aggregators. Considering key factors such as the components of power grid dispatching costs and the quantitative standards for load aggregator regulation value, and with the optimization objective of minimizing the overall dispatching cost and regulation deviation of the power grid company, a multi-load aggregator collaborative response strategy model is constructed, taking into account the power grid's limited regulation budget and regulation deviation. Simulation results show that this method can not only guide load aggregators to respond rationally to power grid regulation demands but also achieve coordination and balance of interests among multiple entities. By introducing a formula for calculating the regulation value of load aggregators to quantify their value, it helps the power grid to more rationally allocate the incentive benefits of each load aggregator under a limited regulation budget, achieving zero dispatch deviation.

[0069] Specifically, in step S1, the interaction between the power grid company, load aggregator, and end-user includes:

[0070] In terms of information exchange, the power grid company, as the dominant regulator, is in an information-advantaged position, while the load aggregator is in an information-disadvantaged position.

[0071] In terms of energy interaction, load aggregators form large-scale, controllable load resources by aggregating dispersed end users;

[0072] Regarding the exchange of subsidy funds, the response compensation provided by load aggregators to end users comes from the incentive fund pool established by the power grid company.

[0073] In this embodiment of the invention, under the traditional power system, end users are generally in a passive response position in the power market due to limitations in their own electricity consumption scale, technical conditions, and information barriers. With the continuous advancement of the new round of power system reform, load aggregators aggregate the load resources of end users, enabling them to form large-scale, controllable load resources and participate in market interactions with the power grid company. In this interaction process, the power grid company, as the market leader, is responsible for issuing regulation demands and setting incentive mechanisms. Its decision-making objectives include system operation economy, power supply and demand balance, and improvement of green energy consumption capacity. The load aggregator, based on the incentive mechanisms set by the power grid company and combined with the response capabilities and regulation characteristics of the user groups it manages, formulates the optimal response strategy to maximize its own profits.

[0074] In terms of information exchange, power grid companies, as the dominant regulators, typically possess more comprehensive and timely market operation data and electricity trading information, placing them at an advantage in this information asymmetry situation. Load aggregators, however, are limited by their access to information, data processing capabilities, and acquisition costs. Their understanding of market dynamics relies primarily on instructions and data disclosed by power grid companies, putting them at a relative disadvantage in information exchange and hindering their ability to independently assess and rapidly respond to market changes.

[0075] In terms of energy interaction, the end-user groups aggregated by load aggregators are characterized by "small size, wide distribution, and large differences in response," making it difficult to achieve direct energy interaction with the power grid company independently. To enhance the response enthusiasm of load aggregators and promote the effective integration of load resources, the power grid company has added a special load-side adjustment incentive price to the existing electricity pricing mechanism to achieve the goal of "peak shaving and valley filling" in the system.

[0076] At the level of subsidy funding interaction, the response compensation provided by load aggregators to end users mainly comes from the incentive fund pool established by the power grid company. Therefore, the amount of incentive subsidies they receive directly determines their maximum allocable compensation space. Since the compensation price directly affects users' willingness to respond, and thus determines the load aggregator's integration capabilities and market response scale, load aggregators need to strive for as much incentive as possible in their interactions with the power grid company to expand their compensation space in order to optimize their revenue. In addition, it is necessary to build a scientific and fair user compensation mechanism to fully stimulate users' willingness to respond while improving resource integration efficiency and enhancing their competitiveness in the market.

[0077] In terms of information interaction, this invention effectively improves market transparency and response efficiency by clarifying the information status and flow direction of each party. Regarding energy interaction, it successfully achieves the scaling and controllability of distributed load resources through resource integration by load aggregators. In terms of subsidy fund interaction, it achieves optimized allocation and incentive compatibility of limited budgets by constructing a closed-loop fund flow of "incentive fund pool - load aggregator - end user." Through a systematic review and mechanism design of the three-party interaction relationship, this invention not only solves key technical problems such as information asymmetry, resource dispersion, and budget constraints, but also successfully constructs a transparent, efficient, and incentive-compatible market environment, providing an innovative business model and technical path for the safe and stable operation of power systems with a high proportion of renewable energy access.

[0078] Specifically, in step S2, the constraints on the power grid company under the interaction model include:

[0079] The total cost of power grid dispatch is constrained by the power grid budget.

[0080] The sum of the actual responses of all load aggregators meets the constraint of grid regulation requirements.

[0081] In this embodiment of the invention, during actual regulation, the power grid company is mainly constrained by the regulation budget, and the total cost of power grid dispatch is limited by the power grid budget.

[0082] ;

[0083] In the formula, For the power grid company's adjustment budget;

[0084] The sum of the actual responses from all load aggregators:

[0085] ;

[0086] In the formula, This refers to the power volume that the load aggregator failed to dispatch to the user response market.

[0087] Specifically, in step S2, the load aggregator constraint under the interaction model includes:

[0088] Considering the differentiated compensation pricing mechanism for the adjustment value of load aggregators, the adjustment contribution capacity of each load aggregator at different time periods is quantified by the adjustment value weight coefficient and adjustment value.

[0089] Methods for determining the compensation price and response price thresholds for load aggregators;

[0090] Method for calculating the total revenue of load aggregators after participating in load-side regulation;

[0091] The operational constraints of load aggregators include response capacity range constraints, response time period and state variable matching constraints, maximum number of daily responses constraints, power supply and demand balance constraints, and satisfaction constraints.

[0092] In this embodiment of the invention, considering the differentiated compensation pricing method for the adjustment value of load aggregators, the adjustment value coefficient of load aggregators and the adjustment value are as follows:

[0093] ,

[0094] ;

[0095] In the formula, For aggregators exist The moderating value weighting coefficient for different time periods. for Time-of-use aggregator The sum of the agent user loads before participating in load-side regulation. A typical intraday aggregator Peak and trough values ​​in the daily load curve of agent users. For aggregators exist The regulatory value of time periods;

[0096] The formulas for calculating the load aggregator compensation price and response price threshold are as follows:

[0097] ,

[0098] ,

[0099] ;

[0100] In the formula, for Time-of-use aggregator The sum of the agent user loads after participating in load-side regulation. To adjust the budget coefficient, This is the time-of-use electricity pricing factor. This refers to the price at which load aggregators purchase electricity from the electricity market. For example, if the average price at which load aggregators purchase electricity from the market is 0.45 yuan / kWh, For sufficiently large positive numbers, Does pricing for power grid companies satisfy aggregators? Expected state variables, For aggregators The minimum threshold price required to respond to the market;

[0101] In summary, the total revenue of load aggregators after participating in load-side regulation is:

[0102] ;

[0103] In the formula, The total revenue of load aggregators after participating in load-side regulation. For load aggregators in The difference in active power when participating in load-side regulation at all times. Time-of-use pricing;

[0104] Model constraints:

[0105] At a certain moment, aggregator Response capacity range:

[0106] ;

[0107] This invention uses the following formula to match the aggregator's response time period with the response state variable:

[0108] ;

[0109] Maximum number of market responses per day for load aggregators:

[0110] ,

[0111] ;

[0112] In the formula, For aggregators Whether it responds to market state variables, This is the minimum response capacity threshold constant. This represents the maximum number of times an aggregator can participate in the market in a single day.

[0113] The power supply and demand balance constraint for load aggregators is:

[0114] ;

[0115] In the formula, They represent aggregators The three types of adjustable loads that can be dispatched represent the difference in active power before and after load adjustment in different time periods, i.e., the market response capacity for end-user participation in adjustment. For aggregators The state variables of movable equipment within the region participating in the market-moved electricity volume. For aggregators Number of unresponded;

[0116] As a highly commercialized entity, the satisfaction of load aggregators with participating in regulation is mainly affected by two key factors: first, whether the basic electricity needs of end users in the region are met; and second, whether the revenue they obtain from participating in load-side regulation meets their expectations.

[0117] The aggregator's pre-regulation electricity purchase cost, the aggregator's satisfaction calculation formula, and satisfaction constraints:

[0118] ,

[0119] ,

[0120] ;

[0121] In the formula, The cost of purchasing electricity from the market before aggregators participate in load-side regulation. To increase the satisfaction of aggregators, This represents the aggregator satisfaction threshold.

[0122] Specifically, in step S2, the end-user constraints under the interaction model include:

[0123] Constraints on the range of adjustable capacity for interruptible loads, load reductions, and load shifting;

[0124] Constraint on the difference in active power before and after adjustable load adjustment;

[0125] Load response time period and response state variable matching constraints;

[0126] Adjustable load satisfaction calculation and satisfaction threshold constraints;

[0127] Consider user-differentiated pricing methods that adjust for value;

[0128] Method for calculating scheduling costs paid by load aggregators to adjustable users.

[0129] Specifically, in step S2, the end-user constraints further include:

[0130] Interruptible device model, including interruptible load modeling and electric vehicle power constraints;

[0131] The equipment model can be reduced, including temperature constraints, power constraints, and operating time constraints for air conditioning operation;

[0132] The model for movable equipment includes minimum continuous response runtime constraints and load curve constraints.

[0133] In this embodiment of the invention, the model constraints are as follows: Time Aggregator The adjustment capacity range of dispatchable interruptible loads, loads that can be reduced, and loads that can be shifted, and the difference in active power before and after adjustment of the adjustable load are as follows:

[0134]

[0135] ,

[0136] ,

[0137] ,

[0138] ,

[0139] ;

[0140] In the formula, These represent the differences in active power before and after the adjustment of the three types of adjustable loads in different time periods. These represent the willingness of the three types of adjustable loads to participate in the market. These represent the maximum daily power consumption limits for three types of adjustable loads. These represent the daily power consumption baselines for three types of adjustable loads. These represent the daily power consumption curves after adjusting three types of adjustable loads;

[0141] This invention uses the following formula to match typical load response periods with response state variables:

[0142] ,

[0143] ,

[0144] ,

[0145] ,

[0146] ;

[0147] In the formula, These are three types of adjustable loads participating in the ancillary market state variables, among which... State variables moved in and out by movable load It consists of two parts: the former is the move-out status indicator, and the latter is the move-in status indicator.

[0148] The present invention is set Adjustable loads can only exist in one state: moved in or moved out. The formula for calculating the satisfaction level of adjustable loads and the satisfaction threshold constraints are as follows:

[0149] ,

[0150] ,

[0151] ,

[0152] ,

[0153] ,

[0154] ,

[0155] ,

[0156] ,

[0157] ;

[0158] In the formula, These are the electricity costs before adjustable loads participate in load-side regulation. These are three types of load satisfaction. These are the minimum satisfaction limits for three types of load;

[0159] Consider user-differentiated pricing methods that adjust for value:

[0160] The formula for calculating the adjustable load adjustment value coefficient is as follows:

[0161] ,

[0162] ,

[0163] ;

[0164] In the formula, , , , , , These are the three types of adjustable loads. Peak and trough values ​​in the daily load baseline for this type of load. These represent the adjustment value for each user at each time period of the execution day within each type of adjustable load;

[0165] The formula for calculating the value of adjustable load regulation is as follows:

[0166] ,

[0167] ,

[0168] ;

[0169] In the formula, These represent the percentage of adjustment value for each user in each time period of the execution day for each type of adjustable load;

[0170] The calculation formulas for compensation prices for three typical types of adjustable loads are as follows:

[0171] ,

[0172] ,

[0173] ;

[0174] In the formula, The compensation pricing is set separately for each typical user. To adjust the budget, These are the adjustment coefficients for three types of adjustable load compensation pricing. Time-of-use pricing;

[0175] The three types of adjustable load response thresholds are shown below:

[0176] ,

[0177] ,

[0178] ,

[0179] ,

[0180] ,

[0181] ;

[0182] In the formula, The lowest response price for users to participate in the market;

[0183] In summary, aggregators The scheduling costs payable to the adjustable users are as follows:

[0184] ;

[0185] In the formula, For the scheduling cost of the load aggregator, This refers to the actual adjustment amount for Class III adjustable loads.

[0186] The formula for modeling interruptible loads is as follows:

[0187] ,

[0188] ,

[0189] ,

[0190] ;

[0191] In the formula, This is a status variable for interruptible load interruption; a value of 1 indicates that the user can participate in the market, while a value of 0 indicates that the user does not participate in the market. This represents the maximum permissible number of interruptions for Category I interruptible loads. This represents the typical daily power consumption of a typical individual electric vehicle. This serves as the baseline for daily power consumption for Category I adjustable loads. This is the proportional coefficient for interruptible load power consumption;

[0192] The battery capacity constraints for electric vehicles are as follows:

[0193] ,

[0194] ,

[0195] ;

[0196] In the formula, for Battery charge of each electric vehicle during a given time period. for Battery charge of each electric vehicle during a given time period. To avoid the charging efficiency of 16+ electric vehicles, These represent the minimum and maximum battery capacity for each electric vehicle. The charging power of a certain electric vehicle at any given time. Maximum charging power for each electric vehicle;

[0197] The air conditioning operation constraints in the equipment model can be reduced as follows:

[0198] ,

[0199] ,

[0200] ,

[0201] ,

[0202] ,

[0203] ;

[0204] In the formula, , These represent individual air conditioners in Time period Indoor temperature during the period Indicates individual air conditioners Outdoor temperature during the time period, For unit scheduling time, It is the equivalent thermal resistance. It is the equivalent heat capacity. For electrical energy conversion efficiency, for The minimum indoor temperature during a given time period. for The maximum indoor temperature during the specified time period. To reduce the number of operating state variables of the equipment, To reduce the maximum reduction time of the equipment, Individuals can reduce equipment power consumption. Individuals can reduce the power limit of their devices. To adjust the air conditioning load curves of typical users in the area, This is the maximum number of times equipment can be reduced per day;

[0205] The constraints related to the movable load in the movable equipment model are as follows:

[0206] ,

[0207] ,

[0208] ,

[0209] ;

[0210] In the formula, This is the starting period for the transferable load. For state variables of transferable loads, The minimum continuous response operating time for a load that can be shifted. This is a single-unit, transferable load curve.

[0211] This invention ensures the economic objectives of the power grid company by setting a hard constraint that the total cost of power grid dispatch must not exceed the regulation budget. This constraint forces the power grid company to make optimal decisions within the budget, avoiding cost runaway caused by excessive incentives. Simultaneously, it requires the sum of the actual responses of all load aggregators to meet the system regulation needs, ensuring the effectiveness of regulation. These two core constraints work together to enable the power grid company to accurately guide funds to the links with the highest regulation value with limited financial resources, significantly improving the efficiency of budget fund utilization and the overall economic benefits of regulation. For load aggregators, a fair competition and revenue guarantee mechanism based on regulation value is established. The contribution of each aggregator at different times is quantified through regulation value weighting coefficients and regulation value, and compensation prices are determined accordingly. This allows load aggregators with strong aggregation capabilities and high regulation potential to obtain higher economic returns. Operational constraints ensure... The technical feasibility of load aggregators' participation is assessed, while the power supply and demand balance constraint ensures the rationality of their internal resource scheduling. The introduction of satisfaction constraints, by comparing the benefits before and after regulation, sets a bottom line for the revenue of load aggregators participating in the market, protecting their basic commercial interests and thus greatly enhancing their willingness and sustainability to participate in load-side regulation. For end users, a refined scheduling model that takes into account both regulation potential and energy satisfaction is constructed. By setting regulation capacity ranges and physical constraints for interruptible, reduceable, and shiftable loads respectively, the regulation potential of distributed loads is maximized while ensuring users' basic energy needs and equipment safety. Differentiated compensation prices are calculated based on users' regulation value coefficients, so that users with high-value regulation capabilities can receive higher compensation during periods and locations when the system urgently needs regulation. This fairly reflects the value differences of regulation behaviors of different users at different times.

[0212] Specifically, in step S3, the multi-load aggregator collaborative response strategy model takes minimizing the adjustment deviation and load-side adjustment cost as the optimization objective, and uses the minimum scheduling cost to incentivize load aggregators to participate in adjustment under a limited budget.

[0213] Specifically, the multi-load aggregator collaborative response strategy model considers the overall cost of the power grid company, including:

[0214] Reduced transmission and distribution revenue for power grid companies;

[0215] The compensation fee paid by the power grid to the load aggregator;

[0216] Reduced transmission and distribution capacity costs for power grid companies;

[0217] The power grid company reduced its network loss costs;

[0218] The total dispatch cost of load-side regulation carried out by the power grid company.

[0219] In this embodiment of the invention, the optimization objective is to minimize the adjustment deviation and load-side adjustment cost, aiming to incentivize load aggregators to participate in adjustment with the least scheduling cost under a limited budget.

[0220] ;

[0221] In the formula, Let the objective function of the power grid company be... The power grid company adjusts the total cost. For the power grid company's own regulation needs, This represents the sum of the actual response volumes of all load aggregators in the market. The unit cost of the power grid failing to dispatch resources to the market;

[0222] Reduced transmission and distribution revenue for power grid companies:

[0223] ;

[0224] In the formula, The reduction in transmission and distribution revenue for the power grid company This refers to the power grid company's transmission and distribution price, for example, a transmission price of 0.08 yuan / kWh. Aggregator for a certain period of time In response to the power grid company's adjustment of electricity volume, The number of load aggregators participating in the response. This represents the total number of scheduling periods;

[0225] The power grid provides compensation to load aggregators:

[0226] ;

[0227] In the formula, The compensation cost provided by the power grid company to the load aggregator. For the power grid company during the time period For load aggregator The compensation price;

[0228] This avoids transmission and distribution capacity costs, resulting in reduced transmission and distribution capacity costs for power grid companies.

[0229] ;

[0230] In the formula, This reduces the transmission and distribution capacity costs for power grid companies. This reduces the unit cost of transmission and distribution capacity for power grid companies;

[0231] This avoids transmission and distribution network losses, resulting in reduced network loss costs for power grid companies.

[0232] ;

[0233] In the formula, The reduction in transmission and distribution network losses for power grid companies to carry out load-side regulation. The average transaction price of electricity in the market. For power grid transmission and distribution network loss coefficient;

[0234] In summary, the dispatching costs for power grid companies to carry out load-side regulation are as follows:

[0235] ;

[0236] In the formula, Total cost of load-side regulation and dispatch by power grid companies.

[0237] The multi-load aggregator collaborative response strategy model constructed in step S3 of this invention not only ensures the effectiveness and economy of system regulation through bi-objective optimization, but also reveals the long-term investment value of load-side regulation through full-cost accounting. Ultimately, this model serves as a powerful decision support tool, enabling power grid companies to make scientific, accurate, and cost-maximizing collaborative response decisions within limited budgets in complex market environments, fundamentally improving the utilization efficiency of power grid assets and the overall economic efficiency of system operation.

[0238] Specifically, the load aggregator's satisfaction is calculated by comparing the revenue before and after participating in load-side regulation, and a satisfaction threshold is set to ensure the basic revenue requirements of the load aggregator.

[0239] In this embodiment of the invention, the satisfaction assessment of load aggregators is an important basis for their decision-making regarding participation in the regulation market. A specific implementation case illustrates this: A load aggregator's pre-regulation electricity purchase cost on a typical day is 500,000 yuan. After participating in load-side regulation, the incentive revenue obtained by responding to grid regulation demands is 80,000 yuan. After deducting compensation costs of 30,000 yuan paid to end users and other operating costs of 10,000 yuan, the net profit is 40,000 yuan. The load aggregator's satisfaction is then calculated as the ratio of the net profit after regulation to the pre-regulation electricity purchase cost, i.e., 4 / 50 = 8%. If the load aggregator's set satisfaction threshold is 5%, then the actual satisfaction rate of 8% is greater than the threshold of 5%, and the aggregator will continue to participate in the regulation market. This mechanism ensures that load aggregators can obtain basic revenue guarantees after participating in regulation, maintaining their long-term enthusiasm for market participation.

[0240] Specifically, the user-differentiated compensation pricing method calculates the adjustable load adjustment value coefficient and adjustment value, combines the adjustment budget, time-of-use electricity price and adjustment coefficient to determine the compensation price for various adjustable loads, and sets a response price threshold to ensure the basic benefit requirements for users participating in adjustment.

[0241] In this embodiment of the invention, the implementation process of differentiated compensation pricing for users is illustrated through a specific example. Taking the air conditioning load (reducible load) of an industrial park as an example, the typical daily baseline load peak of this user is 2000kW, and the valley load is 800kW. The adjustment value coefficient is calculated as 0.15 based on its proportion in the total load of the area. During the midday peak period (13:00-15:00), the time-of-use electricity price coefficient is 1.8, the adjustment budget coefficient is 0.2, the market purchase price of electricity is 0.45 yuan / kWh, and the adjustment coefficient for air conditioning load is set to 1.2. Then, the compensation price for this user during this period is calculated as: 1.2 × 0.2 × 1.8 × 0.45 = 0.194 yuan / kWh. At the same time, a response price threshold of 0.45 yuan / kWh is set to ensure that the compensation price does not exceed the price at which the user purchases electricity from the market, avoiding unreasonable economic incentives. Through this differentiated pricing method, both the actual value contribution of users in system adjustment and the economic rationality of the compensation mechanism are considered, effectively stimulating the enthusiasm of various users to participate in adjustment.

[0242] This invention effectively addresses the problem of load aggregators' lack of willingness to participate due to concerns about uncertain or insufficient revenue to cover their operating costs and risks. By clearly defining satisfaction as net revenue after adjustment / electricity purchase cost before adjustment, and by setting an acceptable satisfaction threshold for load aggregators, this invention provides a solution. When it is anticipated that satisfaction will fall below the threshold, aggregators can choose not to respond, thereby avoiding losses. Conversely, when satisfaction is guaranteed, they will actively invest resources.

[0243] Specifically, in step S4, the interaction model and the multi-load aggregator collaborative response strategy model are solved by calling the commercial solver BARON in the GAMS environment, the solution results are output, and the response results of each scenario are analyzed.

[0244] In this embodiment of the invention, this step is the key process of transforming the aforementioned constructed mathematical model into an executable computational task and obtaining the optimal decision solution through a high-performance commercial solver. The specific implementation process is as follows:

[0245] Model Conversion and Encoding: First, the mixed-integer nonlinear programming model established in steps S2 and S3 is encoded according to the syntax rules of GAMS, including:

[0246] Set definition: Define the set of load aggregators, the set of end-user types, the set of time segments, etc.

[0247] Parameter and scalar input: Input all known parameters such as grid regulation budget, time-of-use electricity price, upper and lower limits of regulation capacity for various types of loads, and satisfaction threshold;

[0248] Variable declaration: Declare decision variables, such as the response volume of each aggregator, compensation price, adjustment power of various types of loads on the user side, and various state variables;

[0249] Equation Construction: All objective functions and constraints, including grid company budget constraints, load aggregator revenue and operation constraints, end-user physical model and satisfaction constraints, etc., are fully translated into GAMS equations;

[0250] Solver Invocation and Solving: In the GAMS model, the BARON solver is specified. BARON is a solver specializing in global optima, particularly suitable for the non-convex nonlinear problems present in this application. It effectively handles nonlinear aspects of the model, such as compensation pricing and satisfaction calculation, ensuring that the found solution is globally optimal or close to globally optimal. It is invoked in the GAMS environment using the following statement:

[0251] OptionMINLP = BARON;

[0252] SolveMyModelUsingMINLPMinimizingObjective_F;

[0253] Where MyModel is the model name, and Objective_F is the objective function of the power grid company;

[0254] Results Output and Analysis: After the solution is completed, GAMS will output a complete solution report and results;

[0255] Core output results: Output the optimal values ​​of key decision variables, mainly including: a) the optimal incentive budget allocated by the power grid company to each load aggregator; b) the optimal response capacity of each load aggregator at different time periods; c) the differentiated compensation price sequence calculated based on the adjustment value; d) the specific scheduling plan for various types of end-user loads (such as which electric vehicles will interrupt charging, and when which air conditioners will adjust their temperature).

[0256] Multi-scenario comparative analysis: Simulations are conducted across multiple scenarios by changing key parameters (such as adjusting the total budget, fluctuations in renewable energy output, and base electricity prices). For example:

[0257] Scenario 1 (High Budget Scenario): A high adjustment budget is set, and the analysis results show that the total adjustment demand of the system can be fully met, and the revenue of each load aggregator and user satisfaction are at a high level.

[0258] Scenario 2 (Low Budget Scenario): With a tight adjustment budget, the analysis results will show how this method, through a differentiated compensation mechanism, prioritizes incentivizing aggregators and users with high adjustment value to participate in the response, thereby maximizing the adjustment effect under budget constraints, and revealing the adjustment bias caused by insufficient budget;

[0259] Scenario 3 (Peak Load Scenario): Simulate the peak load period of the system and analyze how this method can effectively guide the shiftable load to fill the valley and significantly reduce the peak pressure of the system.

[0260] This invention not only solves the problem of solvability of complex models, but also transforms a theoretical collaborative response strategy into a powerful, executable, verifiable, and optimizable decision support system. By ensuring the optimality of the solution, providing comprehensive decision information, and verifying the adaptability of the strategy, this step ultimately transforms the overall solution of this invention from a "blueprint on paper" into a refined and intelligent operation and management tool capable of coping with the complexity and uncertainty of the real world. This provides a solid technical guarantee and core operating platform for power grid companies to achieve collaborative response of multi-load aggregators under limited budgets.

[0261] One embodiment of the present invention provides a multi-load aggregator collaborative response strategy based on a limited power grid regulation budget. To verify the beneficial effects of the present invention, comparative experiments are conducted for scientific demonstration.

[0262] (1) Basic data

[0263] To verify the effectiveness of the above model, this invention introduces three load aggregators. Each load aggregator includes two types of typical interruptible loads, two types of loads that can be reduced, and two types of loads that can be shifted. The specific equipment power consumption curves are shown in Figure 5. The scheduling cycle is set to T=24h, and the unit scheduling time is... Adjusting demand, such as Figure 3 As shown, Figure 3 The demand curves are shown in Table 1, which decomposes the total grid regulation demand based on the user volume represented by the three load aggregators. Time-of-use pricing is also shown in Tables 2-4. Relevant parameters for the grid, load aggregators, and users are shown in Tables 2-4.

[0264]

[0265] To verify the superiority of the method proposed in this invention, the following two scenarios are set up to compare its load scheduling optimization results and fund utilization. The scenarios are as follows:

[0266] Scenario S1: Set compensation prices for typical users based on the adjustment value of each time period, with an adjustment budget of 5 million yuan;

[0267] Scenario S2: Fixed compensation pricing, the compensation price for aggregators 1, 2 and 3 is 3800 yuan / MWh, and the compensation prices for interruptible, reduceable and transferable resources are 3500 yuan / MWh, 3500 yuan / MWh and 3500 yuan / MWh respectively;

[0268] (2) Analysis of the optimization results of the scenario Based on the above optimization model and data, the optimization results of the scenario are shown in Figure 6-8. Table 5 shows the revenue and cost of each aggregator.

[0269]

[0270] 1) In response to market analysis, the total dispatch cost of the power grid company in scenario S1 is RMB 1003.37, of which the total cost of load-side regulation and dispatch by the power grid company is RMB 1849.58, the power grid dispatch deviation cost is RMB 0, the capacity cost avoided by the power grid in the total power grid dispatch cost is RMB 344.01, the reduced network loss cost is RMB 627.75, the dispatch cost to the load aggregator is RMB 1849.58, and the reduced transmission and distribution revenue is RMB 125.55.

[0271] Based on the power grid dispatch data, the proposed load-side regulation strategy demonstrates excellent performance in both dispatch accuracy and cost-effectiveness. On the one hand, regarding dispatch accuracy, the load aggregators and their represented adjustable loads can accurately respond to the regulation demands issued by the power grid company at various time periods, achieving complete matching of dispatch objectives. As shown in Figure 6, the regulation tasks at different time periods are mainly undertaken by aggregators 2 and 3, demonstrating their strong resource integration and load regulation capabilities. On the other hand, regarding dispatch cost control, although the initial dispatch budget set by the power grid company in scenario S1 was 10,000 yuan, after optimization, system supply and demand balance was achieved at only 1,003.37 yuan, resulting in a budget saving rate of up to 90%, significantly improving the economic efficiency of regulation.

[0272] By comparison Figure 4 As shown in Table 5, in scenario S1, the actual response scale of each aggregator is inversely proportional to its pre-adjustment electricity sales. According to the formula, aggregator 1's compensation pricing is positively correlated with the proportion of its agent users' electricity consumption in the total market during each time period. Therefore, aggregator 1's compensation price is significantly higher than aggregator 2 and aggregator 3. Under the premise that the optimization objective is to minimize grid dispatch costs and dispatch deviations, aggregator 1, with its higher compensation price, has a relatively lower priority in the dispatch ranking. Meanwhile, due to the differences in the types of users represented by aggregators 2 and 3, their response behaviors also exhibit different time-period characteristics: aggregator 2's response is concentrated between 6:00 and 24:00, mainly meeting market demand through its agent's typical interruptible and reducible loads, with these two types of loads mostly operating alternately, only operating simultaneously in a few periods; while aggregator 3's response is concentrated from the evening of the first day to the morning of the next day, with its market response mainly undertaken by typical interruptible equipment 1. The total response amounts of the two during the dispatch cycle are 461.91kW and 793.17kW, respectively.

[0273] It should be noted that among the typical load types represented by Aggregators 2 and 3, only the typical movable load did not participate in market response throughout the entire dispatch cycle. The reasons can be attributed to the following: First, as commercial entities focused on economic efficiency, load aggregators' dispatch strategies must balance dispatch revenue and cost control. Compared to interruptible and reducible loads, movable equipment involves both "moving out" and "moving in," resulting in them bearing dispatch costs for two separate periods. As shown in Figure 8, the unit dispatch cost of movable loads does not offer a significant economic advantage in most periods, and is even significantly higher than other typical loads in some periods. Second, dispatching movable loads is significantly more complex than other load types. It requires achieving equivalence between "moving out" and "moving in" electricity within a single dispatch cycle to ensure a constant total electricity consumption, thus significantly increasing the difficulty of dispatch strategy design. While interruptible and reduceable loads are also subject to certain constraints, their total power volume allows for a certain degree of fluctuation and adjustment, resulting in greater dispatch flexibility. Furthermore, considering that the adjustment demand proposed by the power grid company in this model is smaller in scale than the load scale that the three aggregators can integrate, the aggregators can meet the grid-side demand by relying on interruptible and reduceable loads when completing market response tasks, further reducing the necessity of dispatching shiftable loads, thus causing them not to be included in the priority dispatch category.

[0274] 2) Pricing analysis: By comparing the compensation pricing formula for load aggregators with that for users, it is evident that there are significant differences in the underlying pricing logic between the two.

[0275] In the interaction between power grid companies and load aggregators, power grid companies prioritize high-energy-consuming industrial users or large enterprise users during dispatching. Compared to the large number of small and medium-sized adjustable loads that are distributed widely, industrial users are usually given higher dispatching priority and compensation prices due to their advantages such as large electricity consumption, convenient communication, simple control, high load forecasting accuracy, and excellent regulation precision. In the load aggregator compensation pricing model proposed in this study, the regulation coefficient is located in the numerator, while the aggregator's electricity sales after regulation are located in the denominator (the former is used to measure the proportion of electricity consumption of typical equipment in each period to its own and other equipment's electricity consumption in the market). This structure means that within the same dispatching period, if an aggregator has a larger market share and a higher response depth, its compensation unit price will increase accordingly. This design logic is in line with the actual strategy of power grid companies to prioritize dispatching large users to a certain extent. However, it should be noted that the goals of power grid companies in carrying out load-side regulation are not limited to supply and demand balance and dispatching convenience, but also need to comprehensively consider multiple objectives such as dispatching cost control and minimizing regulation deviation. Therefore, in scenario S1, although aggregator 1 has significantly better adjustment value than aggregator 2 and aggregator 3 in each time period, and its agent users' responsiveness is also in a leading position, its unit scheduling cost is much higher than other aggregators due to its high compensation unit price, thus failing to achieve deep market participation.

[0276] As shown in Figure 6, at t=19, although aggregator 1 represented the largest user electricity consumption among the three, it was not prioritized for dispatch by the power grid company due to its high compensation pricing. In contrast, aggregator 2 and aggregator 3 both achieved a certain scale of market response during this period. Among them, aggregator 2 was superior to aggregator 3 in both market share and response depth; therefore, its compensation unit price at this moment was also significantly higher than that of aggregator 3.

[0277] (3) Comparative analysis of fixed compensation pricing mechanisms

[0278] To verify the effectiveness of the method proposed in this invention, various indicators such as scheduling cost, adjustment deviation, and average satisfaction of each subject are introduced. The optimization results are as follows: Figure 9-1 0. The relevant indicators for scenarios S1 and S2 are shown in Table 6-8.

[0279]

[0280] 1) Analysis of the results of scenario S2: As shown in Figure 10 and Table 7, the grid regulation demand in scenario S2 is mainly met by aggregator 1, accounting for about 85% of the regulation market transaction scale, with grid compensation revenue of RMB 4028.13 and net dispatch revenue of RMB 261.91. Aggregator 2 only participates in the market on a small scale during the evening period, with a response scale of about 14.85%, grid compensation revenue of RMB 710.76 and net dispatch revenue of RMB 56.11.

[0281] Regarding scheduling accuracy, by Figure 9 As shown in Table 8, the interruptible device 1 distributed by aggregator 1 has an adjustment deviation of 11.22kW during the t=1 period. This is attributed to the fact that the interruptible device of the present invention is set as an electric vehicle, which is subject to the constraints of typical device power supply, maximum number of responses, etc., resulting in a response deviation during the t=1 period.

[0282] 2) Validity analysis

[0283] From an economic perspective, as shown in Table 6, the total dispatch cost of the power grid company in scenario S1 is significantly lower than that in scenario S2, with a reduction of approximately 74.57%. Specifically, capacity cost, network loss cost, aggregator dispatch cost, and transmission and distribution revenue decreased by 0.9%, 0.9%, 61.23%, and 0.9%, respectively. This result fully demonstrates that the load aggregator compensation mechanism based on regulation value proposed in this invention can significantly reduce multiple dispatch-related costs incurred by the power grid company in implementing load-side regulation while ensuring the regulation effect of the power grid, achieving a synergistic response between economic efficiency and system operating efficiency. Further analysis of the revenue indicators of each aggregator in Table 7 shows that in scenario S1, the net revenue of aggregator 2 and aggregator 3 decreased by 4200% and 36.69%, respectively, compared to scenario S2. Although aggregator 1 achieved an increase of 4026.6 yuan in revenue on the grid side, its net revenue only increased by 261.53 yuan, with a revenue growth rate significantly lower than its revenue obtained at the grid end. This indicates that, under the background of the power grid company's differentiated compensation pricing strategy, the aggregator's final net profit depends not only on its dispatch participation level but also on the combined influence of unit dispatch cost and incentive subsidy efficiency. Overall, the total net profit of the three aggregators in scenario S1 reaches 681.72 yuan, while in scenario S2, this figure is only 320.55 yuan, with the former being approximately 2.13 times that of the latter. This result further verifies the effectiveness of the load aggregator regulation value quantification and differentiated compensation mechanism proposed in this invention under limited budget constraints. This mechanism not only achieves the optimized allocation of aggregator dispatch revenue and enhances their market participation enthusiasm but also promotes the efficient allocation of power grid resources, realizing a win-win situation for all parties involved, including the power grid company and the aggregators.

[0284] Analysis of the aggregator's response accuracy and responsiveness reveals that the differentiated pricing mechanism used in scenario S1 performed best in terms of regulation effectiveness, with no regulation deviations occurring throughout the entire dispatch cycle. In contrast, in scenario S2, the interruptible device 1 represented by aggregator 1 experienced a regulation deviation of 11.22kW at time t=1, which not only led to a decrease in the grid company's dispatch plan execution effectiveness but also further compressed its net profit margin. Furthermore, aggregator 1 responded to grid regulation demands almost throughout the day in scenario S2, indicating that the overall electricity consumption plans of its represented users were significantly disrupted. This phenomenon, to some extent, reflects that the traditional fixed pricing mechanism failed to fully consider the differences in load characteristics and response capabilities among different aggregators, lacking precise guidance for user-side behavior, thus causing resource misallocation and regulation deviations.

[0285] Analyzing from the perspective of average satisfaction of aggregators and users, and combining the relevant data in Tables 7 and 8, it appears that the average satisfaction of aggregators and their agents in scenario S2 is higher than that in scenario S1. However, further analysis using the formula reveals that the satisfaction of aggregators and users is significantly affected by changes in electricity consumption before and after regulation. Specifically, in this invention, the total daily electricity consumption of aggregator 1 is 41689.89 kW, while the total daily grid regulation demand is only 1255.5 kW. The regulation scale is relatively small compared to the total electricity consumption. Therefore, in scenario S2, despite adopting a fixed pricing strategy, the average satisfaction of aggregator 1 and its agents after market response remains high. However, this result does not indicate that the fixed pricing mechanism has an advantage in improving user satisfaction. In fact, in the operation of real power systems, grid regulation demand is usually much higher than the scale set in this study. When a fixed pricing method is used to prioritize the dispatch of a high-load aggregator and its users, it will inevitably lead to excessive regulation intensity, thereby significantly reducing their response satisfaction. In contrast, the differentiated compensation pricing mechanism proposed in this invention can formulate a more reasonable compensation price based on a full consideration of the aggregator / user's adjustment capabilities, electricity consumption behavior characteristics, and willingness to respond, thereby substantially improving user participation and satisfaction.

[0286] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.

Claims

1. A multi-load aggregator collaborative response method based on a finite power grid regulation budget, characterized in that, include: Step S1: Analyze the interaction between the power grid company, load aggregators, and end users. The power grid company, as the market leader, releases regulation demands and sets incentive mechanisms. Load aggregators aggregate the load resources of end users to form large-scale controllable load resources and formulate response strategies based on the power grid company's incentive mechanisms. End users participate in market interaction through load aggregators. Step S2: Construct an interaction model that includes the power grid company, load aggregator, and end users. The interaction model includes constraints on the power grid company, constraints on the load aggregator, and constraints on the end users. Step S3: Construct a multi-load aggregator collaborative response strategy model that considers the limited regulation budget of the power grid, with minimizing regulation deviation and load-side regulation cost as the optimization objective; Step S4: In the GAMS environment, call the commercial solver BARON to solve the interaction model and the multi-load aggregator collaborative response strategy model, and output the solution results.

2. The multi-load aggregator coordinated response method based on a finite power grid regulation budget according to claim 1, characterized in that, In step S1, the interaction between the power grid company, load aggregator, and end-user includes: In terms of information exchange, the power grid company, as the dominant regulator, is in an information-advantaged position, while the load aggregator is in an information-disadvantaged position. In terms of energy interaction, load aggregators form large-scale, controllable load resources by aggregating dispersed end users; Regarding the exchange of subsidy funds, the response compensation provided by load aggregators to end users comes from the incentive fund pool established by the power grid company.

3. The multi-load aggregator coordinated response method based on a finite power grid regulation budget according to claim 1, characterized in that, In step S2, the constraints on the power grid company under the interaction model include: The total cost of power grid dispatch is constrained by the power grid budget. The sum of the actual responses of all load aggregators meets the constraint of grid regulation requirements.

4. The multi-load aggregator coordinated response method based on a finite power grid regulation budget according to claim 1, characterized in that, In step S2, the load aggregator constraint under the interaction model includes: Considering the differentiated compensation pricing mechanism for the adjustment value of load aggregators, the adjustment contribution capacity of each load aggregator at different time periods is quantified by the adjustment value weight coefficient and adjustment value. Methods for determining the compensation price and response price thresholds for load aggregators; Method for calculating the total revenue of load aggregators after participating in load-side regulation; The operational constraints of load aggregators include response capacity range constraints, response time period and state variable matching constraints, maximum number of daily responses constraints, power supply and demand balance constraints, and satisfaction constraints.

5. The multi-load aggregator coordinated response method based on a finite power grid regulation budget according to claim 1, characterized in that, In step S2, the end-user constraints under the interaction model include: Constraints on the range of adjustable capacity for interruptible loads, load reductions, and load shifting; Constraint on the difference in active power before and after adjustable load adjustment; Load response time period and response state variable matching constraints; Adjustable load satisfaction calculation and satisfaction threshold constraints; Consider user-differentiated pricing methods that adjust for value; Method for calculating scheduling costs paid by load aggregators to adjustable users.

6. The multi-load aggregator coordinated response method based on a finite power grid regulation budget according to claim 5, characterized in that, In step S2, the end-user constraint further includes: Interruptible device model, including interruptible load modeling and electric vehicle power constraints; The equipment model can be reduced, including temperature constraints, power constraints, and operating time constraints for air conditioning operation; The model for movable equipment includes minimum continuous response runtime constraints and load curve constraints.

7. The multi-load aggregator coordinated response method based on a finite power grid regulation budget according to claim 1, characterized in that, In step S3, the multi-load aggregator collaborative response strategy model takes minimizing the adjustment deviation and load-side adjustment cost as the optimization objective, and uses the minimum scheduling cost to incentivize load aggregators to participate in adjustment under a limited budget.

8. The multi-load aggregator coordinated response method based on a finite power grid regulation budget according to claim 7, characterized in that, The multi-load aggregator collaborative response strategy model considers the overall cost of the power grid company, including: Reduced transmission and distribution revenue for power grid companies; The compensation fee paid by the power grid to the load aggregator; The power grid company's reduced transmission and distribution capacity costs; The power grid company reduced its network loss costs; The total dispatch cost of load-side regulation carried out by the power grid company.

9. The multi-load aggregator coordinated response method based on a finite power grid regulation budget according to claim 4, characterized in that, The load aggregator satisfaction is calculated by comparing the revenue before and after participating in load-side regulation, and a satisfaction threshold is set to ensure the basic revenue requirements of the load aggregator.

10. The multi-load aggregator coordinated response method based on a finite power grid regulation budget according to claim 5, characterized in that, The user-differentiated compensation pricing method calculates the adjustable load adjustment value coefficient and adjustment value, combines the adjustment budget, time-of-use electricity price and adjustment coefficient to determine the compensation price for various adjustable loads, and sets a response price threshold to ensure the basic benefit requirements for users participating in adjustment.

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