Multi-mode configuration method for residential air conditioning load to meet various ancillary service needs

Abstract

This invention discloses a multi-mode configuration method for residential air conditioning loads accommodating various ancillary service needs, comprising: constructing a matching mapping relationship between differentiated control modes and ancillary service types; establishing a residential user participation willingness response model based on incentive prices; constructing a physical aggregated power calculation model for residential air conditioning loads under multiple modes; correcting the theoretical aggregated power range to generate a reliable aggregated power range for air conditioning loads; and constructing and solving a collaborative configuration optimization model that balances reliability and economy based on the matching mapping relationship, the residential user participation willingness response model, and the reliable aggregated power range for air conditioning loads, outputting the optimal resource allocation scheme. This invention can differentiate and optimize the configuration combination of three modes—intelligent control switch, intelligent thermostat, and non-intrusive reminder—according to different needs of secondary frequency regulation, peak shaving, and spinning reserve, minimizing system operating costs while ensuring aggregated reliability.

Description

Technical Field

[0001] This invention belongs to the field of power and relates to power system operation control and demand-side response technology, specifically to a multi-mode configuration method for residential air conditioning loads oriented towards various ancillary service needs. Background Technology

[0002] With the accelerated construction of new power systems, the large-scale grid connection of renewable energy sources such as wind power and photovoltaics has led to the system exhibiting low inertia and low damping characteristics, resulting in an increasing demand for ancillary services such as secondary frequency regulation, peak shaving, and spinning reserve. Traditional ancillary services are mainly provided by coal-fired or gas-fired units, but under the "dual carbon" target, relying entirely on traditional units is not only uneconomical, but may also be difficult to cope with the random fluctuations of new energy sources due to insufficient flexibility.

[0003] Residential air conditioning loads, due to their thermal energy storage characteristics and enormous aggregation potential, represent a high-quality demand response resource. However, existing load aggregation configurations often employ mixed control modes, lacking service matching and treating smart control switches, smart thermostats, and non-intrusive alerts as general loads that can be reduced. In reality, smart control methods offer fast response times but are costly, while non-intrusive alerts are inexpensive but slow and reliant on manual user intervention. Indiscriminately using non-intrusive alert resources to respond to second-level secondary frequency regulation demands can easily lead to frequency regulation failures. Furthermore, residential user response behavior is highly uncertain, influenced by factors such as ambient temperature and personal preferences, making it difficult to balance aggregation reliability and economy. Existing methods often employ an "over-call" strategy to ensure regulation completion, resulting in excessively high redundant configuration costs; or they ignore uncertainty, leading to actual response power failing to meet grid safety requirements. Currently, there is a lack of an optimized configuration method that can dynamically combine multiple control modes based on the grid's specific ancillary service needs, comprehensively considering equipment costs, user incentives, and aggregation reliability boundaries. Summary of the Invention

[0004] Purpose of the invention: In order to overcome the shortcomings of the existing technology, a multi-mode collaborative configuration method for residential air conditioning loads that caters to various ancillary service needs is provided. This method can optimize the configuration combination of three modes—intelligent control switch, intelligent thermostat, and non-intrusive reminder—differentiatedly according to the different needs of secondary frequency regulation, peak shaving, and spinning reserve, thereby minimizing the operating cost of the power system while ensuring aggregate reliability.

[0005] Technical Solution: To achieve the above objectives, this invention provides a multi-mode configuration method for residential air conditioning loads accommodating various ancillary service needs, comprising the following steps:

[0006] S1: Establish a matching mapping relationship between differentiated control modes and auxiliary service types;

[0007] S2: Establish a resident user participation willingness response model based on incentive price to establish the functional relationship between incentive price and user participation ratio;

[0008] S3: Construct a physical aggregated power calculation model for residential air conditioning load under multiple modes to obtain the theoretical aggregated power range;

[0009] S4: Based on the combined online learning method, the theoretical aggregated power range of step S3 is corrected to generate the reliable aggregated power range of air conditioning load;

[0010] S5: Based on the matching mapping relationship, the residential user participation willingness response model and the reliable aggregated power range of air conditioning load, construct and solve the collaborative configuration optimization model that takes into account both reliability and economy, and output the optimal resource allocation scheme.

[0011] Furthermore, the construction of the matching mapping relationship in step S1 includes:

[0012] Based on the differences in response time and response duration, the control methods for residential air conditioning loads are divided into three modes: intelligent control switch, intelligent thermostat, and non-intrusive reminder.

[0013] Based on the technical specifications for ancillary services, matching rules are established: Since intelligent control switches and intelligent temperature controllers are controlled by automated equipment and have a fast response speed, they are qualified to participate in secondary frequency regulation, peak shaving, and spinning reserve services; Since non-intrusive reminder modes rely on manual user response, the response time is longer and there is uncertainty, they are only qualified to participate in peak shaving and spinning reserve services, and are prohibited from participating in secondary frequency regulation services.

[0014] Furthermore, the establishment of the resident user participation willingness response model in step S2 includes:

[0015] Based on statistical analysis of user questionnaire survey data, a piecewise linearization method was used to fit the distribution of users' expected subsidy prices. For a given subsidy price, the proportion of users willing to participate in demand response was determined using a resident user participation willingness response model. The model then calculates the total subsidy cost under the incentive level; it reflects the characteristic that the user participation rate increases in a stepwise manner as the subsidy price increases.

[0016] Furthermore, step S2 employs a stepped fitting method to establish a resident user participation intention response model, specifically including:

[0017] Define a series of discrete subsidy schemes, with a total number of schemes. Define the set of subsidized prices as And the corresponding set of user participation ratios are ,in, This represents the subsidy price for the s-th tier. This indicates the percentage of users willing to participate in demand response at subsidized prices;

[0018] Assume that in residential area k, the actual participation rate of users is... The corresponding monthly subsidy price will be given to users in this region. The model determines the optimal step relationship by minimizing the fitting error.

[0019]

[0020]

[0021] In the formula, N is the total number of survey samples. As a feature identifier variable;

[0022]

[0023]

[0024] In the formula, The introduced binary state variable.

[0025] Furthermore, the construction of the physical aggregation power calculation model in step S3 includes:

[0026] Power calculation equations based on first-order thermodynamic equivalent thermal parameters are established for three control modes. The aggregated power of the intelligent control switch mode is calculated based on the total number of users participating in this mode in the area, the air conditioning activation rate, and the energy efficiency coefficient. The aggregated power of the intelligent thermostat mode is based on the upward adjustment of the air conditioning set temperature and the steady-state power difference caused by the temperature difference is calculated using thermodynamic parameters. The aggregated power of the non-intrusive reminder mode introduces the user's actual response probability factor, and its total power is composed of the weighted sum of the user load that turns off the air conditioning in response to the notification and the user load that adjusts the temperature in response to the notification.

[0027] Furthermore, in the physical aggregation power calculation model of step S3:

[0028] For intelligent control switching mode, the formula for calculating aggregated power is:

[0029]

[0030] In the formula, The aggregated power during time period t under intelligent control switching mode. Outdoor temperature Set the average indoor temperature; Total number of users For participation ratio, This represents the current air conditioning usage rate. For the equivalent thermal resistance of the building, The energy efficiency coefficient of an air conditioner;

[0031] For the intelligent thermostat mode, the formula for calculating the aggregated power is:

[0032]

[0033] In the formula, The aggregate power during time period t in intelligent temperature controller mode. Temperatures were raised for the summer.

[0034] For non-intrusive alert modes, the formula for calculating aggregated power is:

[0035]

[0036] In the formula, This represents the aggregated power during time period t in non-intrusive reminder mode. This represents the actual response probability of residential area k within time period t. , The percentages of users who were notified to turn off the air conditioner and adjust the temperature during time period t, respectively.

[0037] Further, step S4 includes:

[0038] Considering the randomness of user response behavior, a combined online learning algorithm is used to process historical demand response interaction data; for each residential area, the actual response deviation under different aggregation targets is statistically analyzed; the maximum compliant aggregation value under the preset aggregation deviation tolerance is selected as the upper bound of reliable aggregation power, and the minimum compliant value is selected as the lower bound of reliable aggregation power, thus forming a reliable aggregation power range, which serves as a safety constraint boundary for subsequent configuration optimization.

[0039] Furthermore, the objective function of the collaborative configuration optimization model in step S5 is:

[0040]

[0041] In the formula, T represents the total number of time periods in the scheduling phase; and These represent the active power output and reserve power of conventional unit i during time period t, respectively. and These represent the available wind power and the actual dispatched wind power of wind farm j during time period t, respectively. The proportion of users participating in DR in residential area k. To provide a monthly subsidy to some users; , and The numbers are respectively the number of conventional generating units, wind farms, and residential areas; and These are the unit marginal generation cost and standby cost of conventional generating units, respectively; and These are the unit capacity power generation cost and wind curtailment cost of a wind farm, respectively. The installation cost of a single intelligent control device. It is the discount rate of investment. It controls the lifespan of the equipment. This represents the number of days in the month; when the residential area k uses a non-intrusive alert method, no control equipment needs to be installed, therefore, a binary variable is introduced. Indicates whether the area is configured with a non-intrusive alert mode.

[0042] Furthermore, the collaborative configuration optimization model in step S5 includes the following constraints:

[0043] The output power, standby power, ramping power, and minimum start-stop time constraints for conventional synchronous generator sets are as follows:

[0044]

[0045]

[0046]

[0047]

[0048] In the formula, This represents the minimum output power factor for conventional units. This represents the maximum output power of conventional unit i. This represents the maximum reserve factor for conventional unit i. , These represent the start-up and shutdown status of conventional unit i during time periods t and t-1, respectively. , These are the ramp rates of conventional unit i, respectively; , These are the minimum continuous start-up and shutdown times for conventional unit i, respectively;

[0049] The wind farm power constraints, the system's real-time power balance constraints, and the line power flow constraints are as follows:

[0050]

[0051]

[0052]

[0053] In the formula, The susceptance between nodes x and y of the line; , These are the phase angles of nodes x and y respectively during time period t; This represents the maximum active power transmission capacity of line xy;

[0054] Establish corresponding constraints for peak-shaving services:

[0055]

[0056]

[0057]

[0058] In the formula, Peak-shaving capacity provided for Class II users during peak load period t; For residential area k Total adjustment capacity available to user class during time period t; This represents the maximum load for the day. For peak-shaving periods;

[0059] The following relationship is established to represent the secondary frequency regulation demand of residential air conditioning resources:

[0060]

[0061]

[0062]

[0063] In the formula, For disturbance power, Let i be the available secondary frequency regulation power of unit i during time period t; The secondary frequency regulation capacity that can be provided to Class I users in residential area k during time period t;

[0064] A portion of the residential air conditioning load will be reserved for system backup, with the following constraints established:

[0065]

[0066]

[0067] In the formula, The reserve capacity provided to Class II users in residential area k during time period t; This is a safety reserve factor.

[0068] Beneficial Effects: Compared with existing technologies, the collaborative configuration optimization model constructed in this invention can collaboratively optimize the combined application of different control modes. On the one hand, the method of this invention improves the security and matching degree of auxiliary services. By establishing strict matching rules, it physically eliminates the situation of using slow resources to respond to fast services, effectively avoiding the risk of frequency instability caused by response lag. On the other hand, it achieves optimal operational economy. Compared with a single control method, the method of this invention can flexibly combine high-cost but fast-response intelligent control resources with low-cost but slow-response non-intrusive resources according to the actual needs of the system, significantly reducing the system's equipment investment and operating subsidy costs. In addition, it also ensures the reliability of load aggregation. By introducing a "reliable aggregation power range" calculated based on historical data as a hard constraint, it ensures that the optimized scheduling instructions fully consider the uncertainty of user behavior, keeping the actual execution deviation within the allowable range. Attached Figure Description

[0069] Figure 1 This is a flowchart of the method of the present invention;

[0070] Figure 2 This is the revised diagram of the IEEE 14-node system.

[0071] Figure 3 This is a graph showing the aggregated absolute deviation ratio of air conditioning load power in residential areas in Scheme 2;

[0072] Figure 4 This is a graph showing the aggregated absolute deviation ratio of air conditioning load power in residential areas in Scheme 3;

[0073] Figure 5 This is a graph showing the aggregated absolute deviation ratio of air conditioning load power in residential areas in Scheme 4;

[0074] Figure 6 This is a graph showing the aggregated absolute deviation ratio of air conditioning load power in residential areas in Scheme 5.

[0075] Figure 7 A graph showing the unit output under the 7:00 disturbance (with resident participation);

[0076] Figure 8 The system frequency diagram under the 7:00 disturbance;

[0077] Figure 9 A graph showing the unit's output under the disturbance at 13:00 (with resident participation);

[0078] Figure 10 The system frequency diagram under the 13:00 disturbance;

[0079] Figure 11 A graph showing the unit's output under the disturbance at 15:00 (with resident participation);

[0080] Figure 12 This is the system frequency diagram under the 15:00 disturbance. Detailed Implementation

[0081] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0082] Example 1:

[0083] like Figure 1 As shown, this embodiment provides a multi-mode configuration method for residential air conditioning loads to meet various ancillary service needs, including the following steps:

[0084] S1: Establish a matching mapping relationship between differentiated control modes and auxiliary service types;

[0085] Based on the differences in response time and response duration, the control methods for residential air conditioning loads are divided into three modes: intelligent control switch, intelligent thermostat, and non-intrusive reminder.

[0086] Based on the technical specifications for ancillary services, establish differentiated qualification and access matching rules:

[0087] The technical principle of the intelligent control switch mode is to directly control the on / off state of the air conditioner power supply through a smart socket or switch. This method is a fully automated control with an extremely fast response speed. Its measured response time is usually less than 1 minute, which fully meets the power grid's demand for rapid resource adjustment. Therefore, in the mapping relationship, it is defined as having full qualifications to participate in secondary frequency regulation, peak shaving and spinning reserve services.

[0088] In the intelligent thermostat mode, the power is flexibly controlled by adjusting the temperature set point of the air conditioner through remote commands. Although the adjustment process involves thermal inertia, its response time can still be controlled within 2 minutes, which also meets the fast adjustment standard. Therefore, it is also considered to be qualified to participate in secondary frequency regulation, peak shaving and spinning standby services.

[0089] In contrast, the non-intrusive alert mode operates by sending demand response notifications to users via SMS platforms or mobile apps. This relies entirely on the user's subjective will and manual operation after receiving the information. This method not only typically has a response time exceeding 10 minutes, but also exhibits significant randomness and uncertainty in the final response power. Given the extremely high requirements for response timeliness and determinism in secondary frequency regulation services, this mode cannot meet its technical threshold. Therefore, the constructed matching mapping relationship explicitly limits the non-intrusive alert mode to only participating in long-term peak shaving and spinning reserve services, and forcibly prohibits its participation in secondary frequency regulation services. This avoids the risk of grid frequency regulation instability caused by response lag at the physical mechanism level.

[0090] S2: Establish a resident user participation willingness response model based on incentive price to establish the functional relationship between incentive price and user participation ratio;

[0091] Based on statistical analysis of user questionnaire survey data, a piecewise linearization method was used to fit the distribution of users' expected subsidy prices. For a given subsidy price, the proportion of users willing to participate in demand response was determined using a resident user participation willingness response model. The model then calculates the total subsidy cost under the incentive level; it reflects the characteristic that the user participation rate increases in a stepwise manner as the subsidy price increases.

[0092] To accurately describe the nonlinear relationship between incentive prices and user participation intentions, a step-fitting method is used to establish a response model for residents' participation intentions, specifically including:

[0093] Define a series of discrete subsidy schemes, with a total number of schemes. Define the set of subsidized prices as And the corresponding set of user participation ratios are ,in, This represents the subsidy price for the s-th tier. This indicates the percentage of users willing to participate in demand response at subsidized prices;

[0094] Assume that in residential area k, the actual participation rate of users is... The corresponding monthly subsidy price will be given to users in this region. The model determines the optimal step relationship by minimizing the fitting error.

[0095]

[0096]

[0097] In the formula, N is the total number of survey samples. As a feature identifier variable;

[0098]

[0099]

[0100] In the formula, The introduced binary state variable.

[0101] S3: Construct a physical aggregated power calculation model for residential air conditioning load under multiple modes to obtain the theoretical aggregated power range;

[0102] This step aims to accurately assess the theoretical adjustable potential of residential air conditioning load under different control methods based on physical principles. First, a first-order thermodynamic equivalent thermal parameter model is used to describe the heat exchange process in the air-conditioned room. This model comprehensively considers the building's heat capacity, indoor-outdoor temperature difference, building equivalent thermal resistance, and the heat transfer relationship generated by air conditioning cooling / heating. Considering that residential indoor temperatures are typically set to fluctuate around a certain comfort value, the model uses the average indoor set temperature. To approximate real-time indoor temperature, combined with the air conditioning energy efficiency coefficient It is deduced that the steady-state aggregate power of the regional air conditioning load is directly proportional to the indoor-outdoor temperature difference and inversely proportional to the building's thermal resistance and energy efficiency coefficient.

[0103] Power calculation equations based on first-order thermodynamic equivalent thermal parameters are established for three control modes. The aggregated power of the intelligent control switch mode is calculated based on the total number of users participating in this mode in the area, the air conditioning activation rate, and the energy efficiency coefficient. The aggregated power of the intelligent thermostat mode is based on the upward adjustment of the air conditioning set temperature and the steady-state power difference caused by the temperature difference is calculated using thermodynamic parameters. The aggregated power of the non-intrusive reminder mode introduces the user's actual response probability factor, and its total power is composed of the weighted sum of the user load that turns off the air conditioning in response to the notification and the user load that adjusts the temperature in response to the notification.

[0104] In the physical aggregation power calculation model:

[0105] For intelligent control switching mode, the regulation mechanism is to directly cut off the power supply; therefore, the aggregated regulating power it can provide is the sum of the operating power of the disconnected equipment. Thus, the formula for calculating the aggregated power is:

[0106]

[0107] In the formula, The aggregated power during time period t under intelligent control switching mode. Outdoor temperature Set the average indoor temperature during the summer. hour, =0; Total number of users For participation ratio, This represents the current air conditioning usage rate. For the equivalent thermal resistance of the building, The energy efficiency coefficient of an air conditioner;

[0108] In intelligent thermostat mode, the control mechanism is to increase the set temperature. In this embodiment, data from a questionnaire survey is used to adjust the temperature by 2°C in summer to reduce load demand without completely cutting off the power supply. Therefore, the aggregated power is represented by the difference in steady-state power before and after the adjustment. The formula for calculating aggregated power is:

[0109]

[0110] In the formula, The aggregate power during time period t in intelligent temperature controller mode. The temperature was raised for summer, during the summer. hour, =0;

[0111] For non-intrusive notification modes, since they rely on manual responses from users after receiving notifications, there is not only uncertainty in the response, but the response behavior also falls into two categories: "direct shutdown" and "temperature adjustment." Therefore, the aggregation model of this mode is a weighted combination of the two physical models mentioned above, and its aggregation power is calculated using the following formula:

[0112]

[0113] In the formula, This represents the aggregated power during time period t in non-intrusive reminder mode. This represents the actual response probability of residential area k within time period t. , The percentages of users notified to turn off the air conditioner and adjust the temperature during time period t, respectively; similarly, in summer and winter hour, It is 0.

[0114] S4: Based on the combined online learning method, the theoretical aggregated power range of step S3 is corrected to generate the reliable aggregated power range of air conditioning load;

[0115] Considering the randomness of user response behavior, a combined online learning algorithm is used to process historical demand response interaction data; for each residential area, the actual response deviation under different aggregation targets is statistically analyzed; the maximum compliant aggregation value under the preset aggregation deviation tolerance is selected as the upper bound of reliable aggregation power, and the minimum compliant value is selected as the lower bound of reliable aggregation power, thus forming a reliable aggregation power range, which serves as a safety constraint boundary for subsequent configuration optimization.

[0116] An online learning simulation and deviation statistics strategy based on historical interaction data is adopted. First, a set of candidate aggregation target values ​​covering values ​​from zero to the theoretical maximum capacity is defined, along with an allowable aggregation deviation tolerance threshold. For each candidate aggregation target in the set, a combined online learning algorithm is used to conduct simulations on a historical demand response event set. During the simulations, the algorithm dynamically adjusts its selection strategy based on users' historical feedback data, attempting to select the optimal combination of resident users to approximate the current aggregation target, and records the actual response deviation for each simulation.

[0117] Based on the simulation results above, the absolute proportion of aggregation deviation under each candidate aggregation target is calculated, that is, the proportion of the absolute value of the difference between the actual response power and the target value to the target value. Through comparative analysis, aggregation targets whose aggregation deviation proportion is consistently lower than the preset tolerance threshold in a statistical sense are selected and identified as "reliable targets". Finally, among all the reliable targets that pass the screening, the target with the largest value is selected as the upper bound of reliable aggregation power. The target with the smallest value is selected as the lower bound of reliable aggregation power. This allows for the construction of a reliable aggregated power range for the residential area during the current time period. This range effectively eliminates high-risk areas caused by overly ambitious targets or insufficient user base, providing a probabilistically guaranteed safe decision boundary for subsequent economic allocation models.

[0118] S5: Based on the matching mapping relationship, the residential user participation willingness response model and the reliable aggregated power range of air conditioning load, construct and solve the collaborative configuration optimization model that takes into account both reliability and economy, and output the optimal resource allocation scheme.

[0119] The core of this step is to establish and solve a mixed-integer linear programming model to determine the optimal resource allocation scheme for each residential area. First, an objective function is constructed to minimize the total system operating cost F, which consists of three parts: conventional unit operating cost, wind farm operating cost, and air conditioning load resource regulation cost. This optimization model is used to determine the optimal allocation of residential air conditioning load resources among different ancillary services within the planning period. The objective function of the collaborative allocation optimization model is:

[0120]

[0121] In the formula, T is the total number of time periods in the scheduling phase, which is taken as 24 hours in this embodiment; and These represent the active power output and reserve power of conventional unit i during time period t, respectively. and These represent the available wind power and the actual dispatched wind power of wind farm j during time period t, respectively. The proportion of users participating in DR in residential area k. This is to provide a monthly subsidy to these users; , and The numbers are respectively the number of conventional generating units, wind farms, and residential areas; and These represent the unit marginal generation cost and standby cost of conventional generating units, respectively, in yuan / MWh; and These are the unit capacity power generation cost and wind curtailment cost of a wind farm, respectively. The installation cost of a single intelligent control device. It is the discount rate of investment. It controls the lifespan of the equipment. This represents the number of days in the month; when the residential area k uses a non-intrusive alert method, no control equipment needs to be installed, therefore, a binary variable is introduced. Indicates whether the area is configured with a non-intrusive alert mode.

[0122] In terms of constraints, the collaborative configuration optimization model considers constraints such as the power and ramp-up of conventional units, safety constraints such as the real-time power balance and reserve of the system, as well as related configuration constraints.

[0123] The collaborative configuration optimization model includes the following constraints:

[0124] The output power, standby power, ramping power, and minimum start-stop time constraints for conventional synchronous generator sets are as follows:

[0125]

[0126]

[0127]

[0128]

[0129] In the formula, This represents the minimum output power factor for conventional units. This represents the maximum output power of conventional unit i. This represents the maximum reserve factor for conventional unit i. , These represent the start-up and shutdown status of conventional unit i during time periods t and t-1, respectively. , These are the ramp rates of conventional unit i, respectively; , These are the minimum continuous start-up and shutdown times for conventional unit i, respectively;

[0130] The wind farm power constraints, the system's real-time power balance constraints, and the line power flow constraints are as follows:

[0131]

[0132]

[0133]

[0134] In the formula, The susceptance between nodes x and y of the line; , These are the phase angles of nodes x and y respectively during time period t; This represents the maximum active power transmission capacity of line xy;

[0135] For different residential areas, the available capacity of different ancillary services needs to be constrained. For peak-shaving services, this embodiment defines the peak load period t, sets the peak-shaving capacity to 5% of the daily peak load, and establishes corresponding constraints:

[0136]

[0137]

[0138]

[0139] In the formula, Peak-shaving capacity provided for Class II users during peak load period t; For residential area k Total adjustment capacity available to user class during time period t; This represents the maximum load for the day. For peak-shaving periods;

[0140] To quantify the system's demand for air conditioning load resources to participate in secondary frequency regulation, this embodiment only considers traditional units and residential air conditioning loads sharing the secondary frequency regulation task. As determined in step S1, when the residential area k is configured with a non-intrusive reminder method, secondary frequency regulation service cannot be provided. Therefore, the following relationship is established to represent the secondary frequency regulation demand of residential air conditioning resources:

[0141]

[0142]

[0143]

[0144] In the formula, For disturbance power, Let i be the available secondary frequency regulation power of unit i during time period t; The secondary frequency regulation capacity that can be provided to Class I users in residential area k during time period t;

[0145] To ensure the safety of system operation, in addition to considering the secondary frequency regulation requirements, it is also necessary to reserve a certain amount of backup to cope with sudden equipment failures, changes in load demand, etc. In this embodiment, a portion of the residential air conditioning load resources are used as system backup, and the following constraints are established:

[0146]

[0147]

[0148] In the formula, The reserve capacity provided to Class II users in residential area k during time period t; For safety margin, this embodiment uses a margin of 0.1.

[0149] For each residential area k, in order to achieve reliable aggregation, the aggregation target needs to be constrained to the reliable aggregation power range generated in step S4. If the system does not require auxiliary services, the target is 0.

[0150]

[0151] The collaborative configuration optimization model proposed in this invention can differentiate the collaborative configuration combination of three modes—intelligent control switch, intelligent temperature controller, and non-intrusive reminder—based on different needs of secondary frequency regulation, peak shaving, and spinning reserve, thereby enabling the system to achieve higher operating economy and aggregate reliability. For secondary frequency regulation needs, the method of this invention can automatically exclude the participation of the non-intrusive reminder mode to ensure response speed; for peak shaving and spinning reserve needs, it can prioritize the use of lower-cost non-intrusive reminder mode resources; simultaneously, by constraining the reliable aggregated power range, it effectively avoids the risk of regulation deviation caused by user response uncertainty.

[0152] Example 2:

[0153] To verify the effectiveness and superiority of the method of this invention, this embodiment constructs an improved IEEE 14-node distribution network system for example analysis. Figure 2As shown, the system includes two different types of synchronous generator sets and two wind farms with installed capacities of 80MW and 100MW respectively. Residential areas at nodes 3, 4, 9, and 13 are selected as the air conditioning load resource allocation objects, denoted as residential areas A, B, C, and D, respectively, containing 12,500, 10,000, 9,000, and 8,000 households, with an air conditioning installation rate set at 98.5%. The simulation is based on the MATLAB platform and uses the GUROBI solver for solving. For comparative analysis, five typical schemes are set: Scheme 1 is where all residential air conditioning resources do not participate in ancillary services; Schemes 2 to 4 are single control modes where all areas uniformly adopt intelligent switches, intelligent temperature control, and non-intrusive reminders; Scheme 5 is a collaborative optimization configuration scheme generated using the method of this invention.

[0154] Table 1 - Comparison of Costs of Different Options

[0155]

[0156] Table 2 - Results of User Participation in Different Residential Areas

[0157]

[0158] As shown in Tables 1 and 2, the cost and user participation results of the five options were compared.

[0159] In the economic analysis and comparison of the five schemes, simulation results show that the system using the method of this invention has the lowest total operating cost of RMB 2,075,715, which is significantly lower than the total operating cost of RMB 2,121,273 for Scheme 1, which does not participate in regulation at all, demonstrating a significant economic advantage. Compared with Schemes 2 and 4, which use a single control mode, the cost of Scheme 5 is reduced by approximately 0.12% and 1.02%, respectively. Scheme 5, through collaborative optimization, configures intelligent control switches in residential areas B, C, and D to meet the high-value secondary frequency regulation needs, while configuring low-cost non-intrusive alerts in residential area A to meet peak shaving and spinning reserve needs. This avoids the high investment caused by excessive configuration of intelligent equipment in Scheme 2, thus finding the optimal balance between equipment investment and operational efficiency.

[0160] This embodiment verifies the effectiveness of the method of the present invention in ensuring the safe and stable operation of the power grid by analyzing the aggregate reliability under different configuration schemes and the participation of secondary frequency regulation. Figures 3-6The absolute deviation ratio of air conditioning load power aggregation in residential areas was analyzed for Schemes 2 to 5. Regarding the uncertainty of user response, simulation results show that after introducing a reliable aggregation interval constraint, the actual aggregated power deviation of each residential area at different time periods is consistently controlled within the preset 5% tolerance range, proving the reliability of the aggregation strategy.

[0161] Table 3 - Comparison of Results Regarding Whether Residential Areas Participate in the Secondary Frequency Modulation System

[0162]

[0163] Figures 7-12 The system's secondary frequency regulation (PMD) under different disturbances at different times is shown. Table 3 compares the results of PMD results regardless of whether residential areas participate in the system's PMD. Figures 7-12 Table 3 further analyzes the system's frequency recovery under high-power disturbances. Taking the 64.19MW large power deficit disturbance occurring at 13:00 as an example, without residential participation, the system's steady-state frequency deviation would be as high as -0.28665Hz. However, after adopting the configuration scheme of this invention, the residential air conditioning load and conventional units work together to significantly reduce the steady-state frequency deviation to -0.00607Hz, effectively maintaining the frequency within a safe range. This indicates that the method of this invention not only achieves optimal economic efficiency but also effectively improves the system's frequency regulation capability and operational safety from a technical perspective.

Claims

1. A method for multi-mode configuration of residential air conditioning load to meet various ancillary service needs, characterized in that, Includes the following steps: S1: Establish a matching mapping relationship between differentiated control modes and auxiliary service types; S2: Establish a resident user participation willingness response model based on incentive price to establish the functional relationship between incentive price and user participation ratio; S3: Construct a physical aggregated power calculation model for residential air conditioning load under multiple modes to obtain the theoretical aggregated power range; S4: Based on the combined online learning method, the theoretical aggregated power range of step S3 is corrected to generate the reliable aggregated power range of air conditioning load; S5: Based on the matching mapping relationship, the residential user participation willingness response model and the reliable aggregated power range of air conditioning load, construct and solve the collaborative configuration optimization model that takes into account both reliability and economy, and output the optimal resource allocation scheme; The construction of the matching mapping relationship in step S1 includes: Based on the differences in response time and response duration, the control methods for residential air conditioning loads are divided into three modes: intelligent control switch, intelligent thermostat, and non-intrusive reminder. Based on the technical specifications for ancillary services, matching rules are established: Since the intelligent control switch and intelligent temperature controller modes are controlled by automated equipment and have a fast response speed, they are qualified to participate in secondary frequency regulation, peak shaving and spinning reserve services; Since the non-intrusive reminder mode relies on manual user response, the response time is longer and there is uncertainty, it is only qualified to participate in peak shaving and spinning reserve services, and is prohibited from participating in secondary frequency regulation services. In the physical aggregation power calculation model of step S3: For intelligent control switching mode, the formula for calculating aggregated power is: ； In the formula, The aggregated power during time period t under intelligent control switching mode. Outdoor temperature Set the average indoor temperature; Total number of users For participation ratio, This represents the current air conditioning usage rate. For the equivalent thermal resistance of the building, The energy efficiency coefficient of an air conditioner; For the intelligent thermostat mode, the formula for calculating the aggregated power is: ； In the formula, The aggregate power during time period t in intelligent temperature controller mode. Temperatures were raised for the summer. For non-intrusive alert modes, the formula for calculating aggregated power is: ； In the formula, This represents the aggregated power during time period t in non-intrusive reminder mode. This represents the actual response probability of residential area k within time period t. , The percentages of users who were notified to turn off the air conditioner and adjust the temperature during time period t, respectively. Step S4 includes: Considering the randomness of user response behavior, a combined online learning algorithm is used to process historical demand response interaction data; for each residential area, the actual response deviation under different aggregation targets is statistically analyzed; the maximum compliant aggregation value under the preset aggregation deviation tolerance is selected as the upper bound of reliable aggregation power, and the minimum compliant value is selected as the lower bound of reliable aggregation power, thus forming a reliable aggregation power range, which serves as a safety constraint boundary for subsequent configuration optimization.

2. The method for multi-mode configuration of residential air conditioning load oriented towards multiple ancillary service needs according to claim 1, characterized in that, The establishment of the resident user participation willingness response model in step S2 includes: Based on statistical analysis of user questionnaire survey data, a piecewise linearization method was used to fit the distribution of users' expected subsidy prices. For a given subsidy price, the proportion of users willing to participate in demand response was determined using a resident user participation willingness response model. Then, the total subsidy cost under the incentive level is calculated.

3. The method for multi-mode configuration of residential air conditioning load oriented towards multiple ancillary service needs according to claim 2, characterized in that, Step S2 employs a stepped fitting method to establish a resident user participation intention response model, specifically including: Define a series of discrete subsidy schemes, with a total number of schemes. Define the set of subsidized prices as And the corresponding set of user participation ratios are ,in, This represents the subsidy price for the s-th tier. This indicates the percentage of users willing to participate in demand response at subsidized prices; Assume that in residential area k, the actual participation rate of users is... The corresponding monthly subsidy price will be given to users in this region. The model determines the optimal step relationship by minimizing the fitting error. ； ； In the formula, N is the total number of survey samples. As a feature identifier variable; ； ； In the formula, The introduced binary state variable.

4. A method for multi-mode configuration of residential air conditioning load oriented towards multiple ancillary service needs as described in claim 3, characterized in that, The construction of the physical aggregation power calculation model in step S3 includes: Power calculation equations based on first-order thermodynamic equivalent thermal parameters are established for three control modes. The aggregated power of the intelligent control switch mode is calculated based on the total number of users participating in this mode in the area, the air conditioning activation rate, and the energy efficiency coefficient. The aggregated power of the intelligent thermostat mode is based on the upward adjustment of the air conditioning set temperature and the steady-state power difference caused by the temperature difference is calculated using thermodynamic parameters. The aggregated power of the non-intrusive reminder mode introduces the user's actual response probability factor, and its total power is composed of the weighted sum of the user load that turns off the air conditioning in response to the notification and the user load that adjusts the temperature in response to the notification.

5. A method for multi-mode configuration of residential air conditioning load oriented towards multiple ancillary service needs as described in claim 4, characterized in that, The objective function of the collaborative configuration optimization model in step S5 is: ； In the formula, T represents the total number of time periods in the scheduling phase; and These represent the active power output and reserve power of conventional unit i during time period t, respectively. and These represent the available wind power and the actual dispatched wind power of wind farm j during time period t, respectively. The proportion of users participating in DR within residential area k. To provide a monthly subsidy to some users; , and The numbers are respectively the number of conventional generating units, wind farms, and residential areas; and These are the unit marginal generation cost and standby cost of conventional generating units, respectively; and These are the unit capacity power generation cost and wind curtailment cost of a wind farm, respectively. The installation cost of a single intelligent control device. It is the discount rate of investment. It controls the lifespan of the equipment. This represents the number of days in the month; when a non-intrusive reminder method is used for residential area k, a binary variable is introduced. Whether the marked area is configured with a non-intrusive alert mode.

6. A method for multi-mode configuration of residential air conditioning load oriented towards multiple ancillary service needs as described in claim 5, characterized in that, The collaborative configuration optimization model in step S5 includes the following constraints: The output power, standby power, ramping power, and minimum start-stop time constraints for conventional synchronous generator sets are as follows: ； ； ； ； In the formula, This represents the minimum output power factor for conventional units. This represents the maximum output power of conventional unit i. This represents the maximum reserve factor for conventional unit i. , These represent the start-up and shutdown status of conventional unit i during time periods t and t-1, respectively. , These are the ramp rates of conventional unit i, respectively; , These are the minimum continuous start-up and shutdown times for conventional unit i, respectively; The wind farm power constraints, the system's real-time power balance constraints, and the line power flow constraints are as follows: ； ； ； In the formula, The susceptance between nodes x and y of the line; , These are the phase angles of nodes x and y respectively during time period t; This represents the maximum active power transmission capacity of line xy; Establish corresponding constraints for peak-shaving services: ； ； ； In the formula, Peak-shaving capacity provided for Class II users during peak load period t; For residential area k Total adjustment capacity available to user class during time period t; This represents the maximum load for the day. For peak-shaving periods; The following relationship is established to represent the secondary frequency regulation demand of residential air conditioning resources: ； ； ； In the formula, For disturbance power, Let i be the available secondary frequency regulation power of unit i during time period t; The secondary frequency regulation capacity that can be provided to Class I users in residential area k during time period t; A portion of the residential air conditioning load will be reserved for system backup, with the following constraints established: ； ； In the formula, The reserve capacity provided to Class II users in residential area k during time period t; This is a safety reserve factor.

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