Two-stage scheduling method for 110kV level air conditioning cluster and IDR

Abstract

The application discloses a two-stage scheduling method for 110kV level air conditioner cluster and IDR, comprising the following steps: S1, according to the smart meter data, the data-driven method is used for non-perception classification of the air conditioner cluster; S2, according to the classification result of the air conditioner cluster, the air conditioner cluster of different types is modeled; S3, the incentive demand response load of 110kV level is classified; S4, a two-stage scheduling framework is constructed, the day-ahead basic scheduling and the day-ahead rolling optimization are completed, and the final power grid scheduling scheme is outputted. The application has the beneficial effects that: the two-stage scheduling method for the air conditioner cluster and IDR on the 110kV voltage level is developed, the accurate scheduling of the 110kV power grid is realized, the day-ahead basic planning-day-ahead rolling correction, thereby effectively breaking through the low-voltage air conditioner load aggregation problem, solving the IDR scheduling mismatch problem, stabilizing the new energy and load fluctuation, improving the 110kV power grid operation efficiency, power supply stability and new energy consumption capacity, and simultaneously, the user electricity comfort is considered through the differentiated regulation.

Description

Technical Field

[0001] This invention belongs to the technical field of air conditioning cluster scheduling, specifically involving a two-stage scheduling method for air conditioning clusters and IDRs at the 110kV level. Background Technology

[0002] With the deepening of the construction of the new power system, the 110kV power grid, as a key hub connecting the transmission network and the low-voltage distribution network, must not only undertake the power allocation tasks of the upper-level power grid, but also cope with the impact of load fluctuations and distributed renewable energy access of the lower-level power grid. The specific problems are reflected in the following two points: On the one hand, air conditioning load, as a core electricity load in summer, accounts for over 40% of the total residential and commercial load, but its regulation has long been limited to the low-voltage distribution network level. Existing technologies mostly focus on small-scale air conditioning clusters at the substation or community level, achieving single-level load regulation through aggregators, but failing to integrate them into the overall dispatching system of the 110kV power grid. From a technical perspective, air conditioning operation relies on the indoor-outdoor temperature difference and user-set temperatures, allowing for flexible power shifting without affecting user comfort. Theoretically, this could provide large-scale, dispatchable load resources for the 110kV power grid. However, due to the traditional mode of localized control of air conditioning load, the 110kV level lacks a unified assessment of the network's air conditioning load regulation potential and has not established a classification standard adapted to the dispatching needs of the high-voltage power grid. It is impossible to accurately distinguish the differences in tolerance of different users to temperature adjustments and forced shutdowns, and it is also difficult to aggregate the dispersed low-voltage air conditioning loads into adjustable resources of the 110kV power grid. Ultimately, the 110kV power grid still needs to rely on conventional units to cope with the air conditioning load impact during peak hours, which not only exacerbates the peak-valley difference contradiction, but may also cause operational risks such as line overload and voltage drop.

[0003] On the other hand, while the types of Incentive-Driven Demand Response (IDR) resources are becoming increasingly diverse, existing scheduling models lack refined and coordinated utilization of the response characteristics of different IDRs. The response speed, regulation capacity, and call costs of different IDR resources vary significantly: some IDRs require a call plan to be prepared one day in advance, while others can respond within a short period of time within a day. Traditional scheduling often adopts a uniform call method, failing to classify IDR types according to their response characteristics and match them with corresponding scheduling time scales. This easily leads to problems such as mismatched call timing and resource waste, making it difficult to adapt to the randomness of renewable energy output and the dynamic deviation of load forecasting. Consequently, the grid's ability to cope with fluctuations is insufficient, and the efficiency of renewable energy absorption is limited.

[0004] Therefore, we designed a two-stage scheduling method for air conditioning clusters and IDRs at the 110kV level. This method can overcome the limitations of local control of air conditioning loads, perform hierarchical scheduling of IDRs according to response characteristics, and construct an adaptive multi-timescale scheduling framework to achieve precise matching between controllable resources and 110kV power grid demand. This can improve the operating efficiency of the 110kV power grid, ensure the stability of power supply, and promote the consumption of new energy sources. Summary of the Invention

[0005] Purpose of the invention: To provide a two-stage scheduling method for 110kV air conditioning clusters and IDRs to solve the above-mentioned problems in the existing technology.

[0006] Technical Solution: A two-stage scheduling method for air conditioning clusters and IDR at the 110kV level, including the following steps: S1, classify air conditioning clusters in a data-driven manner without obscuring their operation based on smart meter data; S2, model different types of air conditioning clusters based on the classification results; S3, classify the incentive-driven demand response loads at the 110kV level; S4, construct a two-stage scheduling framework, complete day-ahead basic scheduling and intraday rolling optimization, and output the final power grid scheduling scheme.

[0007] Preferably, S1 specifically includes: S11, extracting four core indicators from electricity meter data—air conditioner electricity consumption ratio, load fluctuation coefficient, peak-valley difference rate, and natural shutdown interval—to quantify the air conditioner's adjustment potential and user tolerance characteristics; S12, constructing a thinking scoring system based on the four core indicators to divide the air conditioners into three clusters.

[0008] Preferably, the three clusters are cluster type one, cluster type two, and cluster type three, respectively; cluster type three allows temperature adjustment of ±2℃ and forced shutdown of ≤ one hour once a day; cluster type two allows temperature adjustment of ±1.5℃ and does not accept shutdown scheduling; the load of cluster type one does not accept any scheduling.

[0009] Preferably, the equivalent energy storage model for cluster type three is: ; in, This represents the equivalent energy storage state of charge for cluster type three. For the cluster j air conditioner t The equivalent state of charge of the energy storage at any given moment; The maximum and minimum states of charge of the equivalent energy storage of this cluster; for t The charging and discharging power of the equivalent energy storage of cluster type 3 at any time; for t The maximum and minimum charge / discharge power of the equivalent energy storage of cluster type 3 at any given time; for t The actual aggregated power of the equivalent energy storage of cluster type 3 at any given time; The equivalent energy storage capacity attenuation coefficient for cluster type three; For cluster type three j Equivalent energy storage attenuation coefficient of a single air conditioner; For cluster type three j Equivalent energy storage charging and discharging efficiency of a Taiwanese air conditioner; For cluster type three j The equivalent heat capacity and equivalent thermal resistance of the air conditioner; It is the third type of cluster. j The energy efficiency ratio of the air conditioner; For cluster type three j The temperature dead zone value of the air conditioner.

[0010] Preferably, the equivalent energy storage model for cluster type two is: ; in, This represents the equivalent energy storage state of charge for cluster type two. The maximum and minimum states of charge for the equivalent energy storage of cluster type two; for t The charging and discharging power of the equivalent energy storage of cluster type 2 at any given time; for t The maximum and minimum charge / discharge power of the equivalent energy storage for cluster type 2 at any given time; for t The actual aggregated power of equivalent energy storage in cluster type 2 at any given moment; The equivalent energy storage capacity attenuation coefficient for cluster type two; For cluster type two j Equivalent energy storage attenuation coefficient of an air conditioner; For cluster type two j Equivalent energy storage charging and discharging efficiency of a Taiwanese air conditioner; For cluster type two j The equivalent energy storage state of charge of an air conditioner; For cluster type two j The equivalent heat capacity and equivalent thermal resistance of the air conditioner; It is the first j The energy efficiency ratio of the air conditioner; For cluster type two j The temperature dead zone value of the air conditioner.

[0011] Preferably, the actual aggregation power of the cluster type at time t It is directly included in the calculation of non-reducible loads.

[0012] Preferably, the incentive-type demand response load in S3 is divided into Class A IDR and Class B IDR; Class A IDR is planned one day in advance and its load call amount is determined in the day-ahead scheduling phase of multi-timescale scheduling; Class B IDR is its load call plan is formulated in the intraday rolling optimization phase and is used to correct the deviation between the day-ahead scheduling plan and the actual working conditions.

[0013] Preferably, S4 specifically includes: S41. Construct the objective function for the day-ahead scheduling phase, where the time scale is 1 hour and the period is 24 hours. S42. Consider the constraints of the day-ahead scheduling phase; S43. Solve the day-ahead scheduling model using a solver, taking the start-stop status and output of thermal power units in each time period, the output of air conditioning clusters in each time period, and the output of Class A IDR as known conditions and substituting them into the intraday rolling optimization stage for solution. S44. Construct the objective function for the intraday rolling optimization stage, with a time scale of 15 minutes and a period of 4 hours. S45. Consider the constraints of the intraday rolling optimization phase; S46. Solve the intraday rolling scheduling model using the solver to solve the charging and discharging capacity of the energy storage power station and the output of Class B IDR in each time period.

[0014] The beneficial effects of this invention are as follows: This invention develops a two-stage scheduling method for air conditioning clusters and IDR at the 110kV voltage level, which can realize precise scheduling of the 110kV power grid from "day-ahead basic planning to intraday rolling correction". This effectively overcomes the problem of low-voltage air conditioning load aggregation, solves the problem of IDR scheduling mismatch, smooths out the fluctuations of new energy and load, improves the operating efficiency, power supply stability and new energy absorption capacity of the 110kV power grid, and at the same time takes into account the user's power comfort through differentiated regulation. Attached Figure Description Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation

[0016] As attached Figure 1 As shown in this embodiment, the two-stage scheduling method for 110kV level air conditioning clusters and IDRs includes the following steps: S1. Based on smart meter data, perform non-perceptible classification of air conditioning clusters; avoid the reliance on subjective user surveys in traditional classification by using data-driven methods, and at the same time make the classification results deeply compatible with the power grid regulation needs and the actual response capabilities of users.

[0017] Step S11: Extract four core indicators from the electricity meter data to quantify the air conditioning regulation potential and user tolerance characteristics: First, the proportion of air conditioning electricity consumption α, reflecting the weight of air conditioning in the user's load and the scale of adjustable resources; second, the load fluctuation coefficient CV, characterizing the user's tolerance to changes in the air conditioning's operating status; third, the peak-valley difference rate β, measuring the air conditioning's ability to participate in off-peak scheduling; and fourth, the natural shutdown interval T_int, assessing the feasibility of the air conditioning accepting forced shutdown; regulation potential and user tolerance characteristics. Step S12: Construct a four-dimensional scoring system based on the above four core indicators, with weight allocation tailored to power grid control needs: α accounts for 30 points, CV and β each account for 25 points, and T_int accounts for 20 points. A veto mechanism is also implemented: users with CV < 0.3 or α < 20% are directly classified as unadjustable, avoiding ineffective control resource consumption. Specific quantitative indicators are shown in the table below.

[0018] Table 1 Quantitative Indicators .

[0019] S2. Based on the classification results of air conditioning clusters, model different types of air conditioning clusters; The three clusters are cluster type one, cluster type two, and cluster type three, respectively.

[0020] S21. Model the load for cluster type 3. Cluster type 3 allows temperature adjustment of ±2℃ and forced shutdown of ≤1 hour once a day.

[0021] The change in indoor temperature during air conditioning cooling can be approximated using a first-order equivalent thermal parameter model: ; in, Let be a 0-1 variable representing the state of the j-th air conditioner in cluster type 3 at time t; The equivalent heat capacity and equivalent thermal resistance of the j-th air conditioner in cluster type 3; It is the energy efficiency ratio of the j-th air conditioner in cluster type three; These are the outdoor and indoor temperatures of the j-th air conditioner in cluster type three at time t. Let j be the rated power of the j-th air conditioner in cluster type 3.

[0022] Air conditioning systems have thermal inertia, therefore they can be equivalent to virtual energy storage devices at the 110kV power grid level, with the following equivalent parameters: ; in, The set temperature for the j-th air conditioner in cluster type three. Let be the base power of the j-th air conditioner in the cluster at time t. Let be the equivalent energy storage charging and discharging power of the j-th air conditioner in the cluster at time t; This is the temperature dead zone value of the j-th air conditioner in the cluster; This represents the equivalent energy storage state of charge of the j-th air conditioner in the cluster.

[0023] Based on the above derivation, the equivalent energy storage model for cluster type three can be obtained as follows: ; in, This represents the equivalent energy storage state of charge for cluster type three. The maximum and minimum states of charge of the equivalent energy storage of this cluster; Let t be the equivalent energy storage charging and discharging power of cluster type 3 at time t; The maximum and minimum charge / discharge power of the cluster type 3 equivalent energy storage at time t; Let t be the actual aggregated power of the equivalent energy storage of cluster type 3 at time t; The equivalent energy storage capacity attenuation coefficient for cluster type three; Let j be the equivalent energy storage attenuation coefficient of the j-th air conditioner in the cluster; The equivalent energy storage charging and discharging efficiency of the j-th air conditioner in this cluster.

[0024] S22. Model the load for cluster type two. Cluster type two allows temperature adjustment of ±1.5℃ and does not accept shutdown scheduling.

[0025] The change in indoor temperature during air conditioning cooling can be approximated using a first-order equivalent thermal parameter model: ; in, The equivalent heat capacity and equivalent thermal resistance of the j-th air conditioner in cluster type two; It is the energy efficiency ratio of the j-th air conditioner; These are the outdoor and indoor temperatures of the j-th air conditioner in cluster type two at time t. Let j be the rated power of the j-th air conditioner in cluster type two.

[0026] Air conditioning systems have thermal inertia, therefore they can be equivalent to virtual energy storage devices at the 110kV power grid level, with the following equivalent parameters: ; in, The set temperature for the j-th air conditioner in cluster type two. Let t be the base power of the j-th air conditioner in cluster type two. Let t be the equivalent energy storage charging and discharging power of the j-th air conditioner in cluster type two at time t; The temperature dead zone value of the j-th air conditioner in cluster type two; This represents the equivalent energy storage state of charge of the j-th air conditioner in cluster type two.

[0027] Based on the above derivation, the equivalent energy storage model for cluster type two can be obtained as follows: ; in, This represents the equivalent energy storage state of charge for cluster type two. The maximum and minimum states of charge for the equivalent energy storage of cluster type two; Let t be the charging and discharging power of the equivalent energy storage of cluster type 2 at time t; Let t be the maximum and minimum charge / discharge power of the equivalent energy storage of cluster type 2 at time t; The actual aggregated power of the equivalent energy storage of cluster type 2 at time t; The equivalent energy storage capacity attenuation coefficient for cluster type two; Let j be the equivalent energy storage attenuation coefficient of the j-th air conditioner in cluster type 2; Let be the equivalent energy storage charging and discharging efficiency of the j-th air conditioner in cluster type 2.

[0028] S23. Model the load for cluster type one, which does not accept any scheduling. The actual aggregated power of cluster type one at time t. It is directly included in the calculation of non-reducible loads.

[0029] S3. Classify the incentive-driven demand response loads at the 110kV level; Type A IDR: The plan is formulated one day in advance, and its load call volume is determined during the day-ahead scheduling phase, serving as one of the basic parameters for subsequent intraday rolling optimization.

[0030] Type B IDR: During the intraday rolling optimization phase, a load dispatch plan is developed to correct the deviation between the day-ahead scheduling plan and the actual operating conditions.

[0031] S4. Construct a two-stage scheduling framework to complete day-ahead basic scheduling and intraday rolling optimization, and output the final power grid scheduling scheme.

[0032] Basic dispatching (timescale 1 hour, cycle 24 hours). This involves determining the start-up and shutdown of conventional generating units and their output, the discharge volume of the air conditioning cluster, and the Class A IDR (Independent Power Demand) call volume. It also addresses the uncertainties in renewable energy and load forecasting, providing a basic dispatching plan. The objective function includes the operating costs of thermal power units, the penalty costs for renewable energy curtailment, the operating costs of energy storage power stations, the penalty costs for load shedding, and the adjustment costs of the air conditioning cluster.

[0033] Intraday rolling optimization (15-minute timescale, 4-hour cycle). Based on measured data and ultra-short-term forecasts for the next 4 hours, the electrochemical energy storage charge / discharge volume and Class B IDR call volume are determined based on the day-ahead plan, correcting the deviation between the day-ahead plan and the actual situation. The objective function includes the operating cost of conventional units, the cost of electrochemical energy storage power stations, the cost of renewable energy abandonment penalties, and load-related costs.

[0034] S4 specifically includes: S41. Construct the objective function for the day-ahead scheduling phase, where the time scale is 1 hour and the period is 24 hours; its expression is: ; ; in, This represents the total cost of day-ahead dispatching; The cost of generating electricity from thermal power units; Costs associated with the absorption of new energy sources; Cost of energy storage power stations; Cost of load for incentive-based demand response; Cost of air conditioning cluster scheduling. This represents the number of thermal power units. Let be the power of the i-th unit at time t; The start-up and shutdown cost of the i-th generating unit; Let be the power generation cost coefficient of the i-th generating unit; It is a 0-1 variable, representing the start-up and shutdown status of unit i at time t; The number of new energy power plants; Let be the actual power of the i-th new energy power station at time t; The cost function representing the cost of a new energy power plant at time t; The variable is 0-1, representing the start-up and shutdown status of the i-th new energy power station at time t; The penalty coefficient for abandoning new energy sources; Let t be the predicted power of the new energy source; Let t be the actual power of the new energy source. This refers to the number of energy storage power stations; Let be the power of the i-th energy storage power station at time t; Let be the cost function of the i-th energy storage power station at time t; Let be the maintenance cost function of the i-th energy storage power station at time t; It is a 0-1 variable, representing the start-up and shutdown status of the i-th energy storage power station at time t; This refers to the depreciation cost of energy storage power stations. Cost coefficient for calling IDR of type A; Let A be the number of times IDR is called at time t; For scheduling time periods; The number of air conditioners to be dispatched; To compensate for electricity prices; Let be a 0-1 variable, representing the start / stop status of the i-th air conditioner at time t; Let represent the power of the i-th air conditioner at time t; This is the scheduling time interval.

[0035] S42. Consider the constraints of the day-ahead scheduling phase, where the time scale is 1 hour and the period is 24 hours.

[0036] 1. Power balance constraints are satisfied: ; in, This is a non-reducible load; Let be the change in type A IDR load at time t.

[0037] 2. The constraints of the thermal power unit are satisfied: ; in, These are the upper and lower limits of the output of the i-th thermal power unit, respectively; Let be the gradient rate of the i-th thermal power unit.

[0038] 3. Constraints on new energy output are met: ; 4. Constraints of the energy storage power station are satisfied: ; in, Let t be the rated charging power and rated discharging power of the energy stored at time t; Let t be the state of charge of the energy storage power station. These represent the lower and upper limits of the state of charge (SOC) of an energy storage power station.

[0039] 5. Class A IDR constraints are satisfied: ; in, These are the upper and lower limits for calling IDR of class A.

[0040] 6. Equivalent energy storage constraints for air conditioning clusters, where the equivalent energy storage constraints for cluster type three satisfy: ; The equivalent energy storage constraints for cluster type two satisfy: .

[0041] 7. The power flow constraints of the 110kV power grid are satisfied: ; in, These represent the active power and reactive power injected at time t and node i, respectively. Let be the active load and reactive load at node i at time t, respectively; The admittance between nodes i and j; The voltage phase angle difference between nodes i and j; and U j,t Let be the voltages of node i and node j at time t, respectively.

[0042] S43. Solve the day-ahead scheduling model using a solver, taking the start-stop status and output of thermal power units at each time period, the output of air conditioning clusters at each time period, and the output of Class A IDR as known conditions and substituting them into the intraday rolling optimization stage for solution.

[0043] S44. Construct the objective function for the intraday rolling optimization phase, where the time scale is 15 minutes and the period is 4 hours; the expression of the objective function is: ; ; in, Cost coefficient for Class B IDR calls; This represents the number of times Class B IDR is invoked at time t.

[0044] The start / stop status and output of thermal power units at various times, the output of air conditioning clusters at various times, and the Class A IDR output have been determined during the day-ahead dispatch phase. Therefore, the intraday rolling optimization only considers the generation cost of thermal power units, the cost of renewable energy consumption, the cost of energy storage power stations, and the cost of incentive-based demand response loads. The expressions for the generation cost of power units, the cost of renewable energy consumption, and the cost of energy storage power stations are the same as in S41 and will not be repeated here.

[0045] S45. Consider the constraints of the intraday rolling optimization phase; the time scale is 15 minutes and the period is 4 hours.

[0046] The constraints on power balance, thermal power unit, new energy output, energy storage power station, equivalent energy storage of air conditioning clusters, and power flow of 110kV power grid are the same as those in S42 and will not be repeated here.

[0047] Class B IDR constraints are satisfied: ; in, This sets the upper and lower limits for the number of calls to Class B IDR.

[0048] S46. Solve the intraday rolling scheduling model using the solver to solve the charging and discharging capacity of the energy storage power station and the output of Class B IDR in each time period.

[0049] The preferred embodiments have been shown and described, but should not be construed as limiting the invention itself. Various changes in form and detail may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A two-stage scheduling method for 110kV air conditioning clusters and IDRs, characterized by: Includes the following steps: S1. Based on smart meter data, air conditioning clusters are classified in a data-driven manner without being noticed. S2. Based on the classification results of air conditioning clusters, model different types of air conditioning clusters; S3. Classify the incentive-driven demand response loads at the 110kV level; S4. Construct a two-stage scheduling framework to complete day-ahead basic scheduling and intraday rolling optimization, and output the final power grid scheduling scheme.

2. The two-stage scheduling method for 110kV level air conditioning clusters and IDRs according to claim 1, characterized in that: S1 specifically includes: S11. Based on electricity meter data, extract four core indicators to quantify the air conditioner's adjustment potential and user tolerance characteristics: air conditioner electricity consumption ratio, load fluctuation coefficient, peak-valley difference rate, and natural shutdown interval. S12. Based on four core indicators, a thinking scoring system is constructed, and air conditioners are divided into three clusters.

3. The two-stage scheduling method for 110kV level air conditioning clusters and IDRs according to claim 2, characterized in that: The three clusters are cluster type one, cluster type two, and cluster type three, respectively. Cluster type three allows temperature adjustment of ±2℃ and a forced shutdown of ≤1 hour once a day. Cluster type two allows temperature adjustment of ±1.5℃ and does not accept shutdown scheduling. The load of cluster type one does not accept any scheduling.

4. The two-stage scheduling method for 110kV level air conditioning clusters and IDRs according to claim 3, characterized in that: The equivalent energy storage model for cluster type three is as follows: ； in, This represents the equivalent energy storage state of charge for cluster type three. For the cluster j air conditioner t The equivalent state of charge of the energy storage at any given moment; The maximum and minimum states of charge of the equivalent energy storage of this cluster; for t The charging and discharging power of the equivalent energy storage of cluster type 3 at any time; for t The maximum and minimum charge / discharge power of the equivalent energy storage of cluster type 3 at any given time; for t The actual aggregated power of the equivalent energy storage of cluster type 3 at any given time; The equivalent energy storage capacity attenuation coefficient for cluster type three; For cluster type three j Equivalent energy storage attenuation coefficient of an air conditioner; For cluster type three j Equivalent energy storage charging and discharging efficiency of a Taiwanese air conditioner; For cluster type three j The equivalent heat capacity and equivalent thermal resistance of the air conditioner; It is the third type of cluster. j The energy efficiency ratio of the air conditioner; For cluster type three j The temperature dead zone value of the air conditioner.

5. The two-stage scheduling method for 110kV level air conditioning clusters and IDRs according to claim 4, characterized in that: The equivalent energy storage model for cluster type two is as follows: ； in, This represents the equivalent energy storage state of charge for cluster type two. The maximum and minimum states of charge for the equivalent energy storage of cluster type two; for t The charging and discharging power of the equivalent energy storage of cluster type 2 at any given time; for t The maximum and minimum charge / discharge power of the equivalent energy storage for cluster type 2 at any given time; for t The actual aggregated power of equivalent energy storage in cluster type 2 at any given moment; The equivalent energy storage capacity attenuation coefficient for cluster type two; For cluster type two j Equivalent energy storage attenuation coefficient of an air conditioner; For cluster type two j Equivalent energy storage charging and discharging efficiency of a Taiwanese air conditioner; For cluster type two j The equivalent energy storage state of charge of an air conditioner; For cluster type two j The equivalent heat capacity and equivalent thermal resistance of the air conditioner; It is the first j The energy efficiency ratio of the air conditioner; For cluster type two j The temperature dead zone value of the air conditioner.

6. The two-stage scheduling method for 110kV level air conditioning clusters and IDRs according to claim 5, characterized in that: Cluster type one t Actual aggregate power at time It is directly included in the calculation of non-reducible loads.

7. The two-stage scheduling method for 110kV level air conditioning clusters and IDRs according to claim 6, characterized in that: In the S3, incentive-based demand response loads are divided into Class A IDR and Class B IDR. Class A IDR is planned one day in advance and its load call volume is determined in the day-ahead scheduling phase of multi-timescale scheduling. Class B IDR is its load call plan is formulated in the intraday rolling optimization phase and is used to correct the deviation between the day-ahead scheduling plan and the actual operating conditions.

8. The two-stage scheduling method for 110kV level air conditioning clusters and IDRs according to claim 7, characterized in that: S4 specifically includes: S41. Construct the objective function for the day-ahead scheduling phase, where the time scale is 1 hour and the period is 24 hours. S42. Consider the constraints of the day-ahead scheduling phase; S43. Solve the day-ahead scheduling model using a solver, and substitute the start-stop status and output of thermal power units in each time period, the output of air conditioning clusters in each time period, and the output of Class A IDR as known conditions into the intraday rolling optimization stage for solution. S44. Construct the objective function for the intraday rolling optimization stage, with a time scale of 15 minutes and a period of 4 hours. S45. Consider the constraints of the intraday rolling optimization phase; S46. Solve the intraday rolling scheduling model using the solver to solve the charging and discharging capacity of the energy storage power station and the output of Class B IDR in each time period.

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