Control method and device for participation of fixed-frequency air conditioner cluster in demand response

Abstract

The invention relates to a control method and device for a fixed-frequency air conditioner cluster to participate in demand response. The fixed-frequency air conditioner cluster comprises multiple fixed-frequency air conditioners, and the method comprises the steps that for each fixed-frequency air conditioner in the multiple fixed-frequency air conditioners, reference electricity utilization power matched with the first air conditioner state of the fixed-frequency air conditioner in the dispatching time period is predicted; a target function used for representing the air conditioner state adjustment gain is constructed; the air conditioner state adjustment gain is in positive correlation with the difference between the reference electricity consumption power corresponding to each fixed-frequency air conditioner and the target electricity consumption power of the fixed-frequency air conditioner after the air conditioner state is adjusted; solving the target function, and obtaining a second air conditioner state corresponding to each fixed-frequency air conditioner in the scheduling time period under the condition that the air conditioner state adjustment gain in the scheduling time period is maximized; and each fixed-frequency air conditioner is controlled to participate in demand response according to the corresponding second air conditioner state in the scheduling time period. By adopting the method, the control accuracy of the fixed-frequency air conditioner cluster participating in the demand response can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of load operation control of power systems, and in particular to a control method and device for a fixed-frequency air-conditioning cluster participating in demand response. Background Art

[0002] With the rapid increase in renewable energy penetration, power systems face the dual challenges of significant volatility and insufficient flexibility on both the source and load sides. The large-scale integration of intermittent power sources such as wind and photovoltaic power has exacerbated the pressure on the grid to regulate peak power. Traditional thermal power units have limited regulation capabilities and low economic efficiency, necessitating an urgent need to tap into the flexibility potential of demand-side resources. Air conditioning loads, as typical temperature-controlled loads, feature a high proportion, fast response, and significant adjustability. Their participation in peak-shaving services through demand response can alleviate the widening peak-to-valley discrepancy while reducing investment in reserve capacity. This has become a key research direction for optimizing the operation of modern power systems. Fixed-frequency air conditioners, in particular, have become a highly promising flexible resource for peak-shaving services due to their widespread availability and controllable start and stop capabilities.

[0003] Fixed-frequency air conditioners maintain indoor temperatures by periodically starting and stopping. Their clusters can achieve rapid power aggregation and dynamic adjustment through coordinated control. However, traditional peak-shaving strategies often ignore the status of air conditioners within a fixed-frequency air conditioner cluster, resulting in a lack of load diversity and power oscillations. This makes the control accuracy of fixed-frequency air conditioner clusters participating in demand response low. Summary of the Invention

[0004] Based on this, it is necessary to provide a control method and device for a fixed-frequency air-conditioning cluster participating in demand response, which can improve the control accuracy of the fixed-frequency air-conditioning cluster participating in demand response, in order to address the above technical problems.

[0005] In the first aspect, the present application provides a control method for a fixed-frequency air-conditioning cluster to participate in demand response, including: for each fixed-frequency air-conditioner among multiple fixed-frequency air-conditioners, predicting the reference power consumption that matches the first air-conditioning state of the fixed-frequency air-conditioner during the scheduling period; constructing an objective function for characterizing the air-conditioning state adjustment gain; the air-conditioning state adjustment gain is positively correlated with the difference between the reference power consumption corresponding to each fixed-frequency air-conditioner and the target power consumption of the fixed-frequency air-conditioner after adjusting the air-conditioning state; solving the objective function, and obtaining the second air-conditioning state corresponding to each fixed-frequency air-conditioner during the scheduling period when the air-conditioning state adjustment gain during the scheduling period is maximized; controlling each fixed-frequency air-conditioner to participate in demand response according to the corresponding second air-conditioning state during the scheduling period.

[0006] In a second aspect, the present application provides a control device for a fixed-frequency air-conditioning cluster to participate in demand response, wherein the fixed-frequency air-conditioning cluster includes multiple fixed-frequency air-conditioners, and the device includes: a prediction module for predicting, for each of the multiple fixed-frequency air-conditioners, a reference power consumption that matches the first air-conditioning state of the fixed-frequency air-conditioner during the scheduling period; a construction module for constructing an objective function for characterizing the air-conditioning state adjustment gain; the air-conditioning state adjustment gain is positively correlated with the difference between the reference power consumption corresponding to each fixed-frequency air-conditioner and the target power consumption of the fixed-frequency air-conditioner after adjusting the air-conditioning state; a processing module for solving the objective function, and obtaining the second air-conditioning state corresponding to each fixed-frequency air-conditioner during the scheduling period when the air-conditioning state adjustment gain during the scheduling period is maximized; a control module for controlling each fixed-frequency air-conditioner to participate in demand response according to the corresponding second air-conditioning state during the scheduling period.

[0007] The above-mentioned control method and device for a fixed-frequency air conditioner cluster participating in demand response predicts, for each of a plurality of fixed-frequency air conditioners, a reference power consumption matching the fixed-frequency air conditioner's first air conditioning state during a scheduling period, and constructs an objective function representing the air conditioning state adjustment gain. The air conditioning state adjustment gain is positively correlated with the difference between the reference power consumption corresponding to each fixed-frequency air conditioner and the target power consumption of the fixed-frequency air conditioner after the air conditioning state is adjusted. The objective function is solved to maximize the air conditioning state adjustment gain during the scheduling period, thereby obtaining the second air conditioning state corresponding to each fixed-frequency air conditioner during the scheduling period. The method and device then control each fixed-frequency air conditioner to participate in demand response according to the corresponding second air conditioning state during the scheduling period. Thus, by considering the impact of the fixed-frequency air conditioner's first air conditioning state during the scheduling period on its reference power consumption during the scheduling period, it is possible to avoid the lack of load diversity and power oscillation caused by ignoring the air conditioning state within the fixed-frequency air conditioner cluster. This improves the control accuracy of the fixed-frequency air conditioner cluster participating in demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0009] Figure 1 1. A flow chart of a method for controlling a fixed-frequency air conditioning cluster participating in demand response in one embodiment;

[0010] Figure 2 A schematic diagram of a process for constructing an objective function for characterizing an air-conditioning state adjustment gain in one embodiment;

[0011] Figure 3 A flowchart of a control method for a fixed-frequency air conditioning cluster to participate in demand response in another embodiment;

[0012] Figure 4 A flowchart of a control method for a fixed-frequency air conditioning cluster to participate in demand response in another embodiment;

[0013] Figure 5 A flowchart of a control method for a fixed-frequency air conditioning cluster to participate in demand response in another embodiment;

[0014] Figure 6 A flowchart of a control method for a fixed-frequency air conditioning cluster to participate in demand response in another embodiment;

[0015] Figure 7 A schematic diagram of a control process of a method for controlling a fixed-frequency air conditioning cluster participating in demand response in one embodiment;

[0016] Figure 8 This is a schematic diagram showing the total power of a fixed-frequency air conditioning cluster in one embodiment;

[0017] Figure 9 A combined schematic diagram of a user's indoor temperature change under centralized control of a fixed-frequency air conditioner in one embodiment;

[0018] Figure 10 This is a structural block diagram of a control device for a fixed-frequency air conditioning cluster participating in demand response in one embodiment;

[0019] Figure 11 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0021] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0022] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0023] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0024] Traditional peak-shaving strategies often ignore the internal parameter heterogeneity of fixed-frequency AC clusters (such as thermal inertia and user comfort constraints), resulting in a lack of load diversity and power oscillations. Furthermore, the decentralized nature of large-scale AC clusters, high communication costs, and the need for privacy protection further increase control complexity. Therefore, establishing a high-precision aggregation model and designing an optimized control strategy that balances cost-effectiveness and reliability are key issues for fixed-frequency AC systems to participate in demand response.

[0025] In view of this, if Figure 1 As shown, the present application provides a control method for a fixed-frequency air conditioning cluster to participate in demand response. The fixed-frequency air conditioning cluster includes multiple fixed-frequency air conditioners. The method is described by applying it to an air conditioning cluster decision system as an example, including the following steps:

[0026] S102 : For each of the plurality of fixed-frequency air conditioners, predicting a reference power consumption that matches a first air-conditioning state of the fixed-frequency air conditioner within a scheduling period.

[0027] In this embodiment, a scheduling period specifies the period during which a frequency-controlled air conditioner participates in grid demand response. The number of scheduling periods may be one or more. The method for determining a scheduling period is not limited. For example, in some cases, a demand response period published by the grid may be used as a scheduling period. In some cases, the scheduling duration published by the grid, including the demand response start time and the demand response end time, may be divided to obtain at least one scheduling period.

[0028] In some embodiments, when a target time is reached, a reference power consumption matching a first air conditioning state of the fixed-frequency air conditioner during the scheduling period is predicted for each of the plurality of fixed-frequency air conditioners. The target time may be any time before the scheduling period.

[0029] In some embodiments, the air conditioning cluster decision system can obtain scheduling time periods from the power grid control center. For example, the power grid control center determines the peak load situation of the power grid based on the day-ahead load forecast and publishes the demand response capacity and corresponding time periods in the form of price signals. The air conditioning aggregator then calls the air conditioning cluster decision system, which uses the time periods published by the power grid control center as the scheduling time periods.

[0030] In this embodiment, the first air conditioning state refers to the predicted air conditioning state of the fixed-frequency air conditioner during the scheduling period. In some embodiments, the air conditioning state of the fixed-frequency air conditioner during the scheduling period can be predicted based on the historical power consumption of the fixed-frequency air conditioner to obtain the first air conditioning state.

[0031] For example, the state of the fixed-frequency air conditioner is a discrete-time Markov chain, then the first air conditioner state It can be expressed as: ,when When the value is 0, the fixed frequency air conditioner is turned off. When the value is 1, it means that the fixed-frequency air conditioner is turned on. When the fixed-frequency air conditioner is turned on, it can operate at rated power.

[0032] For example, when there are multiple scheduling periods, the state transition probability matrix satisfies:

[0033]

[0034] in, It represents the probability of the fixed-frequency air conditioner starting cooling from off, . represents the probability of the fixed-frequency air conditioner switching from cooling to shutting down, .

[0035] In some embodiments, the state transition probability can be determined based on the room temperature of the room where the fixed-frequency air conditioner is located, the user-set temperature, and the outdoor temperature. For example, the state transition probability satisfies:

[0036]

[0037]

[0038] in, is the sigmoid activation function, In practical engineering applications, the coefficients can be obtained through logistic regression fitting. For example, the historical on / off status, historical user set temperature, and historical room temperature data corresponding to a fixed-frequency air conditioner can be fitted based on logistic regression.

[0039] In this embodiment, the reference power consumption refers to the predicted reference power consumption of the fixed-frequency air conditioner during the scheduling period, and the reference power consumption represents the input power of the fixed-frequency air conditioner.

[0040] In some embodiments, a reference power consumption matching the first air conditioning state of the fixed-frequency air conditioner during the scheduling period can be determined based on the first air conditioning state of the fixed-frequency air conditioner during the scheduling period and the rated power of the fixed-frequency air conditioner. In some cases, the rated power of different fixed-frequency air conditioners in a cluster can be the same or different.

[0041] For example, represents the reference power consumption of the g-th fixed-frequency air conditioner in the scheduling period t, represents the first air-conditioning state of the g-th fixed-frequency air-conditioner in the scheduling period t, represents the rated power of the g-th fixed-frequency air conditioner, then: .

[0042] S104: Construct an objective function for characterizing the air-conditioning state adjustment gain.

[0043] The air conditioning state adjustment gain represents the potential benefit of adjusting the air conditioning state. It is positively correlated with the difference between the reference power consumption of each fixed-frequency air conditioner and the target power consumption of the fixed-frequency air conditioner after the adjustment. In other words, the greater the difference between the reference power consumption of the fixed-frequency air conditioner and the target power consumption of the fixed-frequency air conditioner after the adjustment, the greater the air conditioning state adjustment gain, indicating greater potential benefit.

[0044] There is no limitation on the implementation method of constructing the objective function for characterizing the air-conditioning state adjustment gain. The following is an example of possible implementation methods.

[0045] In one implementation, an objective function representing the air conditioner state adjustment gain can be constructed based on the compensation attribute value during the scheduling period and the difference between the reference power consumption and the target power consumption corresponding to each fixed-frequency air conditioner. The compensation attribute value refers to the unit compensation price paid by the air conditioner aggregator to users to incentivize them to adjust their power consumption (e.g., reduce or shift load) during the scheduling period.

[0046] S106 , solving the objective function, and obtaining the second air-conditioning state corresponding to each fixed-frequency air conditioner in the scheduling period when the air-conditioning state adjustment gain in the scheduling period is maximized.

[0047] The second air-conditioning state is used to represent the actual operating state of the fixed-frequency air-conditioning during the scheduling period. The second air-conditioning state includes air-conditioning off and air-conditioning on.

[0048] The type of solver used to solve the objective function is not limited. Solvers may include linear and mixed integer programming solvers, nonlinear optimization solvers, constraint programming and heuristic solvers, and the like. For example, linear and mixed integer programming solvers include, but are not limited to, Gurobi and CPLEX; nonlinear optimization solvers include, but are not limited to, BARON and CONOPT; and constraint programming and heuristic solvers include, but are not limited to, OR-Tools and LocalSolver.

[0049] For example, the objective function may be solved using a Gurobi solver, and the second air-conditioning state corresponding to each fixed-frequency air conditioner in the scheduling period may be obtained while maximizing the air-conditioning state adjustment gain in the scheduling period.

[0050] S108: Control each fixed-frequency air conditioner to participate in demand response according to the corresponding second air conditioning state during the scheduling period.

[0051] It should be understood that by predicting in advance the reference power consumption that matches the first air-conditioning state of the fixed-frequency air conditioner during the scheduling period, when the scheduling period arrives, each fixed-frequency air conditioner can be controlled to participate in demand response according to the corresponding second air-conditioning state during the scheduling period, which can improve the demand response speed of the fixed-frequency air conditioner.

[0052] based on Figure 1 The illustrated method predicts the reference power consumption that matches the first air conditioning state of each of multiple fixed-frequency air conditioners during a scheduling period, and constructs an objective function to represent the air conditioning state adjustment gain. The air conditioning state adjustment gain is positively correlated with the difference between the reference power consumption corresponding to each fixed-frequency air conditioner and the target power consumption of the fixed-frequency air conditioner after the air conditioning state is adjusted. The objective function is then solved to maximize the air conditioning state adjustment gain during the scheduling period, thereby obtaining the second air conditioning state corresponding to each fixed-frequency air conditioner during the scheduling period. This control then controls each fixed-frequency air conditioner to participate in demand response according to the corresponding second air conditioning state during the scheduling period. Thus, by considering the impact of the first air conditioning state of a fixed-frequency air conditioner during the scheduling period on its reference power consumption during the scheduling period, it is possible to avoid the lack of load diversity and power oscillation caused by ignoring the air conditioning state within the fixed-frequency air conditioner cluster. This improves the control accuracy of the fixed-frequency air conditioner cluster's participation in demand response.

[0053] In one embodiment, Figure 2 As shown, a flow chart of constructing an objective function (i.e., S104) for characterizing the air-conditioning state adjustment gain is provided, including the following steps:

[0054] S202 : For each fixed-frequency air conditioner, based on the second air-conditioning state of the fixed-frequency air conditioner in the scheduling period and the rated power of the fixed-frequency air conditioner, determining a target power consumption of the fixed-frequency air conditioner in the scheduling period.

[0055] Combined with S106, it can be known that the second air-conditioning state of the fixed-frequency air conditioner during the scheduling period can be obtained by solving the objective function, that is, the second air-conditioning state is the parameter that needs to be optimized in the objective function. Then, based on the second air-conditioning state of the fixed-frequency air conditioner during the scheduling period and the rated power of the fixed-frequency air conditioner, the target power consumption of the fixed-frequency air conditioner during the scheduling period is determined. The target power consumption is the parameter that needs to be optimized in the objective function, specifically, the second air-conditioning state of the fixed-frequency air conditioner corresponding to the target power consumption is optimized.

[0056] For example, represents the second air-conditioning state of the g-th fixed-frequency air-conditioner in the scheduling period t, represents the target power consumption of the g-th fixed-frequency air conditioner in the scheduling period t, represents the rated power of the g-th fixed-frequency air conditioner, then: .

[0057] S204: Construct an objective function for characterizing the air conditioner state adjustment gain based on the difference between the reference power consumption and the target power consumption corresponding to each fixed-frequency air conditioner, the penalty coefficient set by the air conditioner aggregator for factors affecting user comfort, and the power attribute values ​​within the scheduling period.

[0058] Among them, the power attribute value can be used to represent the electricity price. For example, the objective function satisfies:

[0059]

[0060] in, represents the air conditioner state adjustment gain, G is the total number of air conditioners in the fixed-frequency air conditioner cluster, T is the number of scheduling periods, is the power attribute value in the scheduling period t, The penalty coefficient set by air conditioning aggregators for affecting user comfort, Indicates the duration of the scheduling period.

[0061] based on Figure 2 The content shown in the figure can improve the accuracy of objective function construction by considering the penalty coefficient set by the air-conditioning aggregator for affecting user comfort, the power attribute value during the scheduling period, and the difference between the reference power consumption and the target power consumption corresponding to each fixed-frequency air-conditioning unit. This can further improve the control accuracy of the fixed-frequency air-conditioning cluster participating in demand response.

[0062] In one embodiment, Figure 3 As shown, a flow chart of a control method for a fixed-frequency air conditioning cluster participating in demand response is provided. Taking the method applied to an air conditioning cluster decision system as an example, the method includes:

[0063] S302 : Obtain a first-order equivalent thermal parameter model corresponding to each fixed-frequency air conditioner. The first-order equivalent thermal parameter model is used to characterize the relationship between room parameters of the room where the fixed-frequency air conditioner is located, outdoor temperature, and target power consumption and energy efficiency ratio of the fixed-frequency air conditioner.

[0064] Room parameters include room temperature, room equivalent thermal resistance, and heat capacity of the air in the room. The room equivalent thermal resistance is the inverse of the air heat loss coefficient and is expressed in °C / kW. The heat capacity of the air in the room is expressed in kWh / °C.

[0065] In some embodiments, the room temperature of the room where the fixed-frequency air conditioner is located during the scheduling period can be obtained based on the forecasted weather information of the location where the fixed-frequency air conditioner is located.

[0066] For example, the first-order equivalent thermal parameter model satisfies:

[0067]

[0068] in, represents the room temperature of the room where the g-th fixed-frequency air conditioner is located during the scheduling period t. represents the outdoor temperature of the room where the g-th fixed-frequency air conditioner is located during the scheduling period t, represents the equivalent thermal resistance of the room where the g-th fixed-frequency air conditioner is located, represents the heat capacity of the gas in the room where the g-th fixed-frequency air conditioner is located, Represents the energy efficiency ratio of the g-th fixed-frequency air conditioner.

[0069] S304 , discretizing the first-order equivalent thermal parameter model to obtain thermal dynamic process constraints between the fixed-frequency air conditioner and the room where the fixed-frequency air conditioner is located during the next scheduling period.

[0070] There may be multiple scheduling periods, and the next scheduling period refers to the next scheduling period adjacent to the scheduling period among the multiple scheduling periods.

[0071] For example, by discretizing the scheduling period in the first-order equivalent thermal parameter model, the thermal dynamic process constraint condition can be obtained, and the thermal dynamic process constraint condition satisfies:

[0072]

[0073] in, is the room temperature of the room where the fixed-frequency air conditioner is located in the next scheduling period (t+1), represents the outdoor temperature of the room where the g-th fixed-frequency air conditioner is located at the scheduling period t. .

[0074] Considering that the forecast weather information at the location of the fixed-frequency air conditioner has prediction errors, the actual outdoor temperature will fluctuate around the forecast temperature. Therefore, the outdoor temperature uncertainty set can be defined to satisfy:

[0075]

[0076] in, represents the outdoor temperature uncertainty set, It represents the predicted outdoor temperature value of the room where the g-th fixed-frequency air conditioner is located. is the default value.

[0077] In some cases, different outdoor temperature uncertainty sets may be set for fixed-frequency air conditioners in different areas, and the fixed-frequency air conditioners may determine the corresponding outdoor temperature based on the outdoor temperature uncertainty sets of the corresponding areas.

[0078] S306 , solving the objective function under the constraints of the thermal dynamic process, and when the air conditioning state adjustment gain in the scheduling period is maximized, obtaining the second air conditioning state corresponding to each fixed-frequency air conditioner in the scheduling period.

[0079] based on Figure 3 The content shown is that by solving the objective function under the constraints of the thermal dynamic process, when the air-conditioning state adjustment gain within the scheduling period is maximized, the second air-conditioning state corresponding to each fixed-frequency air-conditioning within the scheduling period is obtained, thereby improving the scheduling accuracy of the fixed-frequency air-conditioning cluster.

[0080] In one embodiment, Figure 4 As shown, a flow chart of a control method for a fixed-frequency air conditioning cluster participating in demand response is provided. Taking the method applied to an air conditioning cluster decision system as an example, the method includes:

[0081] S402: Construct room constraint conditions that characterize room parameters of the room where the fixed-frequency air conditioner is located.

[0082] The content of the room constraint condition is not limited, and the following is an example of possible implementation methods.

[0083] In one implementation, the room parameters include room temperature, and the room constraints include a first room sub-constraint, a second room sub-constraint, and a third room sub-constraint.

[0084] For example, the first sub-constraint condition of the room may be obtained based on the relationship between the room temperature of the room where the fixed-frequency air conditioner is located and the preset temperature range corresponding to the fixed-frequency air conditioner.

[0085] To ensure user comfort, the room temperature should not exceed the maximum temperature limit when the outdoor temperature is high, and should not fall below the minimum temperature limit when the outdoor temperature is low. Based on this, the first sub-constraint of the room can be defined. The first sub-constraint of the room satisfies:

[0086]

[0087] in, Indicates the lower limit of the preset temperature range, which is used to represent the lower limit of the user's temperature tolerance; Indicates the upper limit of the preset temperature range, which is used to represent the upper limit of the user's temperature tolerance.

[0088] In some embodiments, the preset temperature range corresponding to the fixed-frequency air conditioner can be determined based on a mapping relationship between the air conditioner position and the preset temperature range, and the position of the fixed-frequency air conditioner.

[0089] For example, the second sub-constraint of the room may be obtained based on the relationship between the difference between the room temperature of the room where the fixed-frequency air conditioner is located and the temperature set by the user, and the penalty coefficient set by the air conditioner aggregator for damaging user comfort.

[0090] In some cases, the user's optimal temperature is the air conditioner setting, and this value will not change in the short term. When the room temperature deviates from the optimal temperature, the user's comfort level decreases, and this phenomenon needs to be punished. Based on this, the second sub-constraint of the room can be set. The second sub-constraint of the room satisfies:

[0091]

[0092] in, The penalty coefficient set by the air conditioning aggregator for damaging user comfort is expressed in yuan / (℃·h), which is used to represent the cost per degree Celsius·hour. It represents the penalty coefficient set by the air-conditioning aggregator for affecting the user's comfort. Indicates the user-set temperature of the g-th fixed-frequency air conditioner.

[0093] Exemplarily, based on the relationship between the preset temperature range and the initial room temperature, a third sub-constraint of the room is obtained, which is used to ensure that the initial room temperature is within the comfortable range. The third sub-constraint of the room satisfies:

[0094]

[0095] in, Indicates the initial room temperature. That is, the initial room temperature is the same for each fixed-frequency air conditioner in the fixed-frequency air conditioner cluster.

[0096] S404: Determine a time constraint condition representing the minimum on / off time of the fixed-frequency air conditioner based on a relationship between the second air-conditioning state of the fixed-frequency air conditioner and the minimum on / off time of the fixed-frequency air conditioner.

[0097] Frequent starting and stopping of fixed-frequency air conditioners can seriously affect their service life. To prevent this, the duration of each start and stop should be limited, effectively establishing a time constraint. The time constraint representing the minimum on / off duration of a fixed-frequency air conditioner is determined based on the relationship between its second air-conditioning state and its minimum on / off duration. There are various implementation options for determining this time constraint, but the following examples illustrate possible implementations.

[0098] In one implementation, a second air conditioner state, an air conditioner startup action, and an air conditioner shutdown action of a fixed-frequency air conditioner during a scheduling period can be obtained from an air conditioner state set. Furthermore, based on the relationship between the second air conditioner state, the air conditioner startup action, the air conditioner shutdown action, and the minimum startup duration and minimum shutdown duration of the fixed-frequency air conditioner, a time constraint representing the minimum startup and shutdown time of the fixed-frequency air conditioner is determined. The time constraint can include a first time sub-constraint, a second time sub-constraint, a third time sub-constraint, and a fourth time sub-constraint.

[0099] In some embodiments, the first time sub-constraint is determined based on a difference between an air conditioner power-on action and an air conditioner power-off action, and a difference between a second air conditioning state of the fixed-frequency air conditioner during a scheduled period and a second air conditioning state of the fixed-frequency air conditioner during a previous scheduled period, where the previous scheduled period refers to a previous scheduled period adjacent to the current scheduled period.

[0100] Exemplarily, the time-first sub-constraint satisfies:

[0101]

[0102] in, Indicates the start-up action of the g-th fixed-frequency air conditioner during the scheduling period. Indicates the shutdown action of the g-th fixed-frequency air conditioner during the scheduling period. Indicates the second air-conditioning state of the g-th fixed-frequency air-conditioner during the scheduling period. Indicates the second air-conditioning state of the g-th fixed-frequency air-conditioner in the previous scheduling period. , Indicates the air conditioner status set. 0 indicates that the fixed-frequency air conditioner is off, and 1 indicates that the fixed-frequency air conditioner is on.

[0103] In some embodiments, the second time sub-constraint may be obtained based on the difference between the air conditioner startup action and the air conditioner shutdown action.

[0104] Exemplarily, the second sub-constraint of time satisfies:

[0105]

[0106] In some embodiments, the third time sub-constraint is determined based on the relationship between the air conditioner startup action, the second air conditioner state of the fixed-frequency air conditioner within the scheduling period, and the minimum startup duration of the fixed-frequency air conditioner.

[0107] Exemplarily, the third time sub-constraint satisfies:

[0108]

[0109] in, Indicates the minimum power-on duration of the g-th fixed-frequency air conditioner.

[0110] In some embodiments, the fourth time sub-constraint is determined based on the relationship between the air conditioner shutdown action, the second air conditioner state of the fixed-frequency air conditioner in the scheduling period, and the minimum shutdown duration of the fixed-frequency air conditioner.

[0111] Exemplarily, the fourth sub-constraint of time satisfies:

[0112]

[0113] in, Indicates the minimum shutdown duration of the g-th fixed-frequency air conditioner.

[0114] S406, under the constraints of thermal dynamic process constraints, room constraints and time constraints, solve the objective function, and when the air conditioning state adjustment gain within the scheduling period is maximized, obtain the second air conditioning state corresponding to each fixed-frequency air conditioner within the scheduling period.

[0115] based on Figure 4 As shown in the figure, by introducing room constraints and time constraints, and thus combining the thermal dynamic process constraints to solve the objective function, the accuracy of the analysis results can be improved, and the accuracy of the control of the fixed-frequency air-conditioning cluster participating in demand response can be further improved.

[0116] In one embodiment, the room parameters include the room outdoor temperature, such as Figure 5 As shown, a flow chart of a control method for a fixed-frequency air conditioning cluster participating in demand response is provided. The method is described by taking the application of the method to an air conditioning cluster decision system as an example, and includes the following steps:

[0117] S502 : Based on the outdoor temperature of the room where each fixed-frequency air conditioner is located during the scheduling period and the second air conditioning state corresponding to each fixed-frequency air conditioner, vector conversion processing is performed on the thermal dynamic process constraint condition and the room constraint condition to obtain a vector constraint.

[0118] In one embodiment, the room constraint condition includes the first sub-constraint condition of the room. Based on the outdoor temperature corresponding to the room where each fixed-frequency air conditioner is located and the second air-conditioning state corresponding to each fixed-frequency air conditioner, the thermal dynamic process constraint condition and the first sub-constraint condition of the room are vector-converted to obtain a vector constraint.

[0119] For example, the vector constraint satisfies:

[0120]

[0121] in, are all constant matrices or vectors, is a vector or matrix consisting of the room temperature corresponding to each fixed-frequency air conditioner in the scheduling period and the room temperature corresponding to each fixed-frequency air conditioner in the next scheduling period, that is, Corresponding to the first sub-constraint of the room and the thermal dynamic process constraint; is a vector or matrix composed of the second air-conditioning states corresponding to each fixed-frequency air-conditioner, It is a vector or matrix composed of the outdoor temperatures corresponding to the rooms where each fixed-frequency air conditioner is located.

[0122] S504: Perform equivalent conversion processing on the vector constraint to obtain equivalent constraint conditions.

[0123] For vector constraints, assume that the solution of the vector constraint is , The above constraints are feasible if and only if the following target constraints hold:

[0124]

[0125] Among them, in the target constraint, yes The transpose of is a unit vector, The jth element of is 1 and the rest are 0. Indicates quantity, , G is the total number of air conditioners in the fixed-frequency air conditioner cluster, and T is the number of scheduling periods.

[0126] The target constraint holds if and only if , then the optimal value of the following optimization problem is negative, and the optimization problem satisfies:

[0127]

[0128] in, is the Lagrange multiplier, and the constraint Corresponding. Note that the above problem is a convex problem and satisfies the complementary relaxation condition. Therefore, the duality holds. According to the Lagrange duality principle, , and its dual problem is as follows:

[0129]

[0130] right , the above maximization problem has a non-negative optimal value if and only if the optimal value of the problem is not less than 0, that is, there exists , so that the following equivalence constraints are established, the equivalence constraints are satisfied:

[0131]

[0132]

[0133]

[0134] in, is the dual norm, A vector or matrix representing the predicted outdoor temperature values ​​for each room where the fixed-frequency air conditioner is located. Therefore, the original problem remains equivalent by simply rewriting the thermal process constraints and the first sub-constraints of the room as the three equivalent constraints (i.e., equivalent constraints). This conversion of the thermal process constraints and the first sub-constraints of the room into equivalents results in a mixed-integer linear programming problem that can be solved directly using mature commercial solvers.

[0135] S506 , solving the objective function under the constraints of the equivalence constraint and the time constraint, and when the air conditioning state adjustment gain in the scheduling period is maximized, obtaining the second air conditioning state corresponding to each fixed-frequency air conditioner in the scheduling period.

[0136] In some embodiments, the room constraint further includes a second room sub-constraint and a third room sub-constraint, and the time constraint includes a first time sub-constraint, a second time sub-constraint, a third time sub-constraint, and a fourth time sub-constraint.

[0137] Specifically, under the constraints of the equivalence constraint, the second sub-constraint of the room, the third sub-constraint of the room, and the first sub-constraint of time, the second sub-constraint of time, the third sub-constraint of time and the fourth sub-constraint of time, the objective function is solved. When the air-conditioning state adjustment gain within the scheduling period is maximized, the second air-conditioning state corresponding to each fixed-frequency air conditioner within the scheduling period is obtained.

[0138] based on Figure 5The content shown can improve data processing efficiency by converting the thermal dynamic process constraints and the room constraints to solve the objective function.

[0139] In combination with the above, in one embodiment, Figure 6 As shown, a flow chart of a control method for a fixed-frequency air conditioning cluster participating in demand response is provided. The method is described by taking the application of the method to an air conditioning cluster decision system as an example, and includes the following steps:

[0140] S602: For each of the plurality of fixed-frequency air conditioners, predict a reference power consumption that matches a first air-conditioning state of the fixed-frequency air conditioner within a scheduling period.

[0141] For example, The reference power consumption of the g-th fixed-frequency air conditioner in the scheduling period t is: represents the first air-conditioning state of the fixed-frequency air-conditioner in the scheduling period t, represents the rated power of the g-th fixed-frequency air conditioner, then: .

[0142] S604 : For each fixed-frequency air conditioner, based on the second air-conditioning state of the fixed-frequency air conditioner in the scheduling period and the rated power of the fixed-frequency air conditioner, determine the target power consumption of the fixed-frequency air conditioner in the scheduling period.

[0143] For example, represents the second air-conditioning state of the g-th fixed-frequency air-conditioner in the scheduling period t, represents the target power consumption of the g-th fixed-frequency air conditioner in the scheduling period t, then: .

[0144] S606: Construct an objective function for characterizing the air conditioner state adjustment gain based on the difference between the reference power consumption and the target power consumption corresponding to each fixed-frequency air conditioner, the penalty coefficient set by the air conditioner aggregator for factors affecting user comfort, and the power attribute values ​​within the scheduling period.

[0145] For example, the objective function satisfies:

[0146]

[0147] in, represents the air conditioner state adjustment gain, G represents the total number of air conditioners in the fixed-frequency air conditioner cluster, T represents the number of scheduling periods, represents the power attribute value within the scheduling period t, It represents the penalty coefficient set by the air-conditioning aggregator for affecting the user's comfort. Indicates the duration of the scheduling period.

[0148] S608: Obtain a first-order equivalent thermal parameter model corresponding to each fixed-frequency air conditioner.

[0149] Among them, the first-order equivalent thermal parameter model is used to characterize the relationship between the room parameters of the room where the fixed-frequency air conditioner is located, the outdoor temperature, and the target power consumption and energy efficiency ratio of the fixed-frequency air conditioner.

[0150] For example, the first-order equivalent thermal parameter model satisfies:

[0151]

[0152] in, represents the room temperature of the room where the g-th fixed-frequency air conditioner is located during the scheduling period t. represents the outdoor temperature of the room where the g-th fixed-frequency air conditioner is located during the scheduling period t, represents the equivalent thermal resistance of the room where the g-th fixed-frequency air conditioner is located, represents the heat capacity of the gas in the room where the g-th fixed-frequency air conditioner is located, Represents the energy efficiency ratio of the g-th fixed-frequency air conditioner.

[0153] S610 , discretizing the scheduling period in the first-order equivalent thermal parameter model to obtain thermal dynamic process constraints between the fixed-frequency air conditioner and the room where the fixed-frequency air conditioner is located, representing the next scheduling period.

[0154] For example, the thermal dynamic process constraints satisfy:

[0155]

[0156] in, is the room temperature of the room where the fixed-frequency air conditioner is located in the next scheduling period (t+1), represents the outdoor temperature of the room where the g-th fixed-frequency air conditioner is located during the scheduling period t, .

[0157] S612: Obtain a first sub-constraint condition of the room based on the relationship between the room temperature of the room where the fixed-frequency air conditioner is located and the preset temperature range corresponding to the fixed-frequency air conditioner.

[0158] For example, the first sub-constraint of the room satisfies:

[0159]

[0160] in, Indicates the lower limit of the preset temperature range, which is used to represent the lower limit of the user's temperature tolerance; Indicates the upper limit of the preset temperature range, which is used to represent the upper limit of the user's temperature tolerance.

[0161] S614 , based on the relationship between the difference between the room temperature of the room where the fixed-frequency air conditioner is located and the user-set temperature, and the penalty coefficient set by the air conditioner aggregator for damaging user comfort, obtain a second sub-constraint condition for the room.

[0162] For example, the second sub-constraint of the room satisfies:

[0163]

[0164] in, represents the penalty coefficient set by the air conditioning aggregator for damaging user comfort, It represents the penalty coefficient set by the air-conditioning aggregator for affecting the user's comfort. Indicates the user-set temperature of the g-th fixed-frequency air conditioner.

[0165] S616: Obtain a third sub-constraint condition of the room based on the relationship between the preset temperature range and the initial room temperature.

[0166] For example, the third sub-constraint of the room satisfies:

[0167]

[0168] in, Indicates the initial room temperature. That is, the initial room temperature is the same for each fixed-frequency air conditioner in the fixed-frequency air conditioner cluster.

[0169] S618: Acquire the second air-conditioning state, air-conditioning startup action, and air-conditioning shutdown action of the fixed-frequency air-conditioning within the scheduling period from the air-conditioning state set.

[0170] S620: Determine a first time sub-constraint based on a difference between the air conditioner startup action and the air conditioner shutdown action, and a difference between the second air conditioning state of the fixed-frequency air conditioner in the scheduling period and the second air conditioning state of the fixed-frequency air conditioner in the previous scheduling period.

[0171] Exemplarily, the time-first sub-constraint satisfies:

[0172]

[0173] in, Indicates the start-up action of the g-th fixed-frequency air conditioner during the scheduling period. Indicates the shutdown action of the g-th fixed-frequency air conditioner during the scheduling period. Indicates the second air-conditioning state of the g-th fixed-frequency air-conditioner during the scheduling period. Indicates the second air-conditioning state of the g-th fixed-frequency air conditioner in the previous scheduling period.

[0174] S622: Obtain a second time sub-constraint based on the difference between the air conditioner startup action and the air conditioner shutdown action.

[0175] Exemplarily, the second sub-constraint of time satisfies:

[0176]

[0177] S624 : Determine a third time sub-constraint based on the relationship between the air conditioner startup action, the second air conditioner state of the fixed-frequency air conditioner within the scheduling period, and the minimum startup duration of the fixed-frequency air conditioner.

[0178] Exemplarily, the third time sub-constraint satisfies:

[0179]

[0180] in, Indicates the minimum power-on duration of the g-th fixed-frequency air conditioner.

[0181] S626 , determining a fourth time sub-constraint based on the relationship between the air conditioner shutdown action, the second air conditioner state of the fixed-frequency air conditioner in the scheduling period, and the minimum shutdown duration of the fixed-frequency air conditioner.

[0182] Exemplarily, the fourth sub-constraint of time satisfies:

[0183]

[0184] in, Indicates the minimum shutdown duration of the g-th fixed-frequency air conditioner.

[0185] S628: Based on the outdoor temperature of the room where each fixed-frequency air conditioner is located during the scheduling period and the second air conditioning state corresponding to each fixed-frequency air conditioner, perform equivalent conversion processing on the thermal dynamic process constraint condition and the first sub-constraint condition of the room to obtain an equivalent constraint condition.

[0186] Among them, performing equivalent conversion processing on the thermal dynamic process constraint condition and the first sub-constraint condition of the room to obtain equivalent constraint conditions includes: performing vector conversion processing on the thermal dynamic process constraint condition and the first sub-constraint condition of the room to obtain vector constraints; performing equivalent conversion processing on the vector constraints to obtain equivalent constraint conditions.

[0187] For example, the equivalence constraint satisfies:

[0188]

[0189]

[0190]

[0191] S630, solve the objective function under the constraints of the equivalence constraint, the second sub-constraint of the room, the third sub-constraint of the room, the first sub-constraint of time, the second sub-constraint of time, the third sub-constraint of time and the fourth sub-constraint of time. When the air-conditioning state adjustment gain within the scheduling period is maximized, the second air-conditioning state corresponding to each fixed-frequency air conditioner within the scheduling period is obtained.

[0192] For example, the objective function may be solved based on a Gurobi solver, and when the air-conditioning state adjustment gain in the scheduling period is maximized, the second air-conditioning state corresponding to each fixed-frequency air-conditioner in the scheduling period is obtained.

[0193] S632: Control each fixed-frequency air conditioner to participate in demand response according to the corresponding second air conditioning state during the scheduling period.

[0194] Among them, the specific content of S602-S632 can refer to the above content for adaptation description.

[0195] In combination with the above content, as shown in Table 1, a Monte Carlo simulation algorithm is provided to simulate the reference target power of the fixed-frequency air conditioner and the total cluster power of the fixed-frequency air conditioner cluster. The total simulation time is consistent with the number of scheduling periods, and the time step is consistent with the duration of the scheduling period, where:

[0196] Table 1

[0197]

[0198] In Table 1, the outdoor temperature series Including the outdoor temperature corresponding to each fixed-frequency air conditioner in the fixed-frequency air conditioner cluster during the scheduling period t.

[0199] In combination with the above content, by discretizing the time (i.e. scheduling period) in the first-order equivalent thermal parameter model, the time step is taken as , when the outdoor temperature is If the time remains unchanged, Indoor temperature at the moment satisfy:

[0200]

[0201] In combination with the above, in one embodiment, Figure 7 As shown, a control process diagram of a control method for a fixed-frequency air conditioning cluster to participate in demand response is provided, wherein:

[0202] Grid control center 702 can determine load peaks for grid 704 based on day-ahead load forecasts and then publish demand response capacity and corresponding time periods in the form of price signals. Prices are typically higher when load demand is high. Based on peak-shaving requirements, air conditioning aggregator 706 invokes air conditioning cluster decision system 708 to control each fixed-frequency air conditioner in the fixed-frequency air conditioning cluster to participate in demand response. Specifically, air conditioning aggregator 706 can invoke air conditioning cluster decision system 708 to use the time periods published by the grid as scheduling periods, and there can be multiple scheduling periods. Based on its own interests, air conditioning aggregator 706 can invoke air conditioning cluster decision system 708 to control each air conditioning unit's participation in grid demand response.

[0203] Specifically, the air conditioning cluster decision system 708 establishes and solves an objective function based on the air conditioning information (e.g., on / off status, temperature parameters) of the corresponding fixed-frequency air conditioner reported by the smart terminal of each air conditioning unit. This function determines the air conditioning status of each fixed-frequency air conditioner during the scheduling period. Consequently, for each air conditioning unit, the smart terminal of the air conditioning unit controls the fixed-frequency air conditioner to turn on or off during the scheduling period based on the corresponding air conditioning status. The temperature parameters may include room parameters for the room where the fixed-frequency air conditioner is located.

[0204] It should be understood that air conditioner aggregators can respond based on the status of each fixed-frequency air conditioner. Therefore, the fixed-frequency air conditioner cluster is not subject to the centralized control of the aggregator. At the same time, this application can construct a state transition probability matrix for fixed-frequency air conditioners based on a discrete-time Markov chain. The state transition probability matrix of fixed-frequency air conditioners can then be represented as a probabilistic response model based on the Markov chain for the fixed-frequency air conditioner cluster that is not subject to the centralized control of the aggregator.

[0205] Based on the above, it can be seen that this application takes temperature uncertainty into account and can ensure user comfort in a robust manner. Specifically, this application solves the objective function under appropriate constraints to achieve optimal control of each fixed-frequency air conditioner in a fixed-frequency air conditioner cluster. The goal is to maximize the benefits of the air conditioner aggregator while ensuring robust user comfort. For example, the Gurobi software package can be called in the Matlab environment to solve the objective function.

[0206] like Figure 8 As shown, a schematic diagram of the total cluster power of a fixed-frequency air-conditioning cluster is provided. It can be seen that by optimizing the on / off status of the fixed-frequency air-conditioning, the total cluster power of the fixed-frequency air-conditioning cluster during the peak electricity price period (11:00-13:00) is significantly reduced, and the maximum value is reduced from 0.66MW (at 12:59) when uncontrolled to 0MW (at 12:59) when controlled, and the peak reduction amount reaches 100%, which shows that the method provided in this application can effectively smooth the peak load of the power grid.

[0207] like Figure 9 As shown in Figure 1, a combined schematic diagram of the user's indoor temperature changes under the centralized control of fixed-frequency air conditioners is provided. It can be seen that although the temperature fluctuates under the optimized control, it always remains within the comfortable range (user-set temperature ±3°C).

[0208] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0209] Based on the same inventive concept, an embodiment of the present application further provides a control device for a fixed-frequency air conditioning cluster participating in demand response, which is used to implement the aforementioned control method for a fixed-frequency air conditioning cluster participating in demand response. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the embodiments of the control device for one or more fixed-frequency air conditioning clusters participating in demand response provided below can be found in the above-mentioned limitations of the control method for a fixed-frequency air conditioning cluster participating in demand response, and will not be repeated here.

[0210] In an exemplary embodiment, Figure 10 As shown, a control device for a fixed-frequency air conditioner cluster participating in demand response is provided. The fixed-frequency air conditioner cluster includes multiple fixed-frequency air conditioners. The device includes: a prediction module 1002, a construction module 1004, a processing module 1006, and a control module 1008. The prediction module 1002 is configured to predict, for each of the multiple fixed-frequency air conditioners, a reference power consumption that matches a first air conditioning state of the fixed-frequency air conditioner during a scheduling period; the construction module 1004 is configured to construct an objective function for representing an air conditioning state adjustment gain; the air conditioning state adjustment gain is positively correlated with the difference between the reference power consumption corresponding to each fixed-frequency air conditioner and the target power consumption of the fixed-frequency air conditioner after the air conditioning state is adjusted; the processing module 1006 is configured to solve the objective function to obtain a second air conditioning state corresponding to each fixed-frequency air conditioner during the scheduling period while maximizing the air conditioning state adjustment gain during the scheduling period; and the control module 1008 is configured to control each fixed-frequency air conditioner to participate in demand response according to the corresponding second air conditioning state during the scheduling period.

[0211] In one embodiment, the construction module 1004 is further used to: determine the target power consumption of the fixed-frequency air conditioner during the scheduling period based on the second air-conditioning state of the fixed-frequency air conditioner during the scheduling period and the rated power of the fixed-frequency air conditioner for each fixed-frequency air conditioner; and construct an objective function for characterizing the air-conditioning state adjustment gain based on the difference between the reference power consumption and the target power consumption corresponding to each fixed-frequency air conditioner, the penalty coefficient set by the air-conditioning aggregator for affecting user comfort, and the power attribute value during the scheduling period.

[0212] In one embodiment, the processing module 1006 is further used to: obtain a first-order equivalent thermal parameter model corresponding to each fixed-frequency air conditioner, the first-order equivalent thermal parameter model being used to characterize the relationship between the room parameters, outdoor temperature, target power consumption, and energy efficiency ratio of the room where the fixed-frequency air conditioner is located; discretize the first-order equivalent thermal parameter model to obtain a thermal dynamic process constraint condition between the fixed-frequency air conditioner and the room where the fixed-frequency air conditioner is located that characterizes the next scheduling period; solve the objective function under the constraints of the thermal dynamic process constraint condition, and when the air conditioning state adjustment gain within the scheduling period is maximized, obtain the second air conditioning state corresponding to each fixed-frequency air conditioner within the scheduling period.

[0213] In one embodiment, the processing module 1006 is further used to: construct room constraints that characterize room parameters of the room where the fixed-frequency air conditioner is located; determine a time constraint that characterizes the minimum on / off time of the fixed-frequency air conditioner based on the relationship between the second air-conditioning state of the fixed-frequency air conditioner and the minimum on-time and minimum off-time of the fixed-frequency air conditioner; solve the objective function under the constraints of the thermal dynamic process constraints, the room constraints, and the time constraints, and when the air-conditioning state adjustment gain within the scheduling period is maximized, obtain the second air-conditioning state corresponding to each fixed-frequency air conditioner within the scheduling period.

[0214] In one embodiment, the room parameters include room temperature, and the room constraints include a first sub-constraint, a second sub-constraint, and a third sub-constraint; the processing module 1006 is further used to: obtain the first sub-constraint of the room based on the relationship between the room temperature of the room where the fixed-frequency air conditioner is located and the preset temperature range corresponding to the fixed-frequency air conditioner; obtain the second sub-constraint of the room based on the relationship between the difference between the room temperature of the room where the fixed-frequency air conditioner is located and the user-set temperature, and the penalty coefficient set by the air conditioning aggregator for damaging user comfort; obtain the third sub-constraint of the room based on the relationship between the preset temperature range and the initial room temperature.

[0215] In one embodiment, the room parameters include the outdoor temperature of the room; the processing module 1006 is further used to: based on the outdoor temperature of the room where each fixed-frequency air conditioner is located during the scheduling period and the second air-conditioning state corresponding to each fixed-frequency air conditioner, perform vector conversion processing on the thermal dynamic process constraint condition and the room constraint condition to obtain a vector constraint; perform equivalent conversion processing on the vector constraint to obtain an equivalent constraint condition; solve the objective function under the constraints of the equivalent constraint condition and the time constraint condition, and when the air-conditioning state adjustment gain during the scheduling period is maximized, obtain the second air-conditioning state corresponding to each fixed-frequency air conditioner during the scheduling period.

[0216] In one embodiment, the time constraint includes a first time sub-constraint, a second time sub-constraint, a third time sub-constraint, and a fourth time sub-constraint; the processing module 1006 is further used to: obtain the second air-conditioning state, the air-conditioning startup action, and the air-conditioning shutdown action of the fixed-frequency air conditioner within the scheduling period from the air-conditioning state set; determine the first time sub-constraint based on the difference between the air-conditioning startup action and the air-conditioning shutdown action, and the difference between the second air-conditioning state of the fixed-frequency air conditioner within the scheduling period and the second air-conditioning state of the fixed-frequency air conditioner within the previous scheduling period; obtain the second time sub-constraint based on the difference between the air-conditioning startup action and the air-conditioning shutdown action; determine the third time sub-constraint based on the relationship between the air-conditioning startup action, the second air-conditioning state of the fixed-frequency air conditioner within the scheduling period, and the minimum startup time of the fixed-frequency air conditioner; determine the fourth time sub-constraint based on the relationship between the air-conditioning shutdown action, the second air-conditioning state of the fixed-frequency air conditioner within the scheduling period, and the minimum shutdown time of the fixed-frequency air conditioner.

[0217] Each module in the aforementioned control device for a fixed-frequency air conditioning cluster participating in demand response can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0218] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 11As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data such as reference power consumption. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a control method for a fixed-frequency air-conditioning cluster to participate in demand response is implemented.

[0219] Those skilled in the art will understand that Figure 11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0220] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0221] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0222] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0223] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0224] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0225] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present application. The above-mentioned embodiments only express several implementation methods of the present application. The description is relatively specific and detailed, but it cannot be understood as a limitation on the scope of the patent of this application. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several variations and improvements can be made, which all fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be based on the attached claims.

Claims

1. A control method for a fixed-frequency air conditioning cluster to participate in demand response, characterized in that: The fixed-frequency air conditioner cluster includes multiple fixed-frequency air conditioners, and the method includes: For each of the plurality of fixed-frequency air conditioners, predicting a reference power consumption that matches a first air-conditioning state of the fixed-frequency air conditioner within a scheduling period; Constructing an objective function for characterizing an air conditioning state adjustment gain; wherein the air conditioning state adjustment gain is positively correlated with a difference between a reference power consumption corresponding to each of the fixed-frequency air conditioners and a target power consumption of the fixed-frequency air conditioner after adjusting the air conditioning state; Solving the objective function, and obtaining the second air-conditioning state corresponding to each of the fixed-frequency air conditioners in the scheduling period while maximizing the air-conditioning state adjustment gain in the scheduling period; Control each of the fixed-frequency air conditioners to participate in demand response according to the corresponding second air conditioner state during the scheduling period.

2. The method according to claim 1, characterized in that The objective function for characterizing the air conditioning state adjustment gain is constructed, including: For each of the fixed-frequency air conditioners, determining a target power consumption of the fixed-frequency air conditioner in the scheduling period based on the second air-conditioning state of the fixed-frequency air conditioner in the scheduling period and the rated power of the fixed-frequency air conditioner; Based on the difference between the reference power consumption and the target power consumption corresponding to each of the fixed-frequency air conditioners, the penalty coefficient set by the air conditioner aggregator for factors affecting user comfort, and the power attribute value within the scheduling period, an objective function for characterizing the air conditioner state adjustment gain is constructed.

3. The method according to claim 1, characterized in that The method further comprises: Obtaining a first-order equivalent thermal parameter model corresponding to each of the fixed-frequency air conditioners, wherein the first-order equivalent thermal parameter model is used to characterize the relationship between room parameters of the room where the fixed-frequency air conditioner is located, the outdoor temperature, and the target power consumption and energy efficiency ratio of the fixed-frequency air conditioner; Discretizing the first-order equivalent thermal parameter model to obtain thermal dynamic process constraints between the fixed-frequency air conditioner and the room where the fixed-frequency air conditioner is located, representing a next scheduling period; The solving of the objective function to obtain the second air-conditioning state corresponding to each of the fixed-frequency air conditioners in the scheduling period when the air-conditioning state adjustment gain in the scheduling period is maximized includes: The objective function is solved under the constraints of the thermal dynamic process constraints, and when the air-conditioning state adjustment gain in the scheduling period is maximized, the second air-conditioning state corresponding to each of the fixed-frequency air conditioners in the scheduling period is obtained.

4. The method according to claim 3, characterized in that The method further comprises: Constructing room constraints representing room parameters of the room where the fixed-frequency air conditioner is located; Determining a time constraint condition representing the minimum on / off time of the fixed-frequency air conditioner based on a relationship between the second air-conditioning state of the fixed-frequency air conditioner and the minimum on / off time of the fixed-frequency air conditioner; Solving the objective function under the constraints of the thermal dynamic process, and obtaining the second air-conditioning state corresponding to each of the fixed-frequency air conditioners in the scheduling period when the air-conditioning state adjustment gain in the scheduling period is maximized, includes: Under the constraints of the thermal dynamic process constraints, the room constraints and the time constraints, the objective function is solved, and when the air-conditioning state adjustment gain within the scheduling period is maximized, the second air-conditioning state corresponding to each of the fixed-frequency air conditioners within the scheduling period is obtained.

5. The method according to claim 4, characterized in that The room parameters include room temperature, and the room constraints include a first room sub-constraint, a second room sub-constraint, and a third room sub-constraint; The step of constructing room constraint conditions representing room parameters of the room where the fixed-frequency air conditioner is located includes: Obtaining a first sub-constraint condition for the room based on a relationship between a room temperature of the room where the fixed-frequency air conditioner is located and a preset temperature range corresponding to the fixed-frequency air conditioner; Obtaining a second sub-constraint condition for the room based on a relationship between a difference between a room temperature of the room where the fixed-frequency air conditioner is located and a user-set temperature, and a penalty coefficient set by an air conditioning aggregator for damaging user comfort; The third sub-constraint condition of the room is obtained based on the relationship between the preset temperature range and the initial room temperature.

6. The method according to claim 4, characterized in that The room parameters include the room outdoor temperature; and solving the objective function under the constraints of the thermal dynamic process constraint, the room constraint, and the time constraint, and obtaining the second air-conditioning state corresponding to each of the fixed-frequency air conditioners in the scheduling period when the air-conditioning state adjustment gain in the scheduling period is maximized, including: Based on the outdoor temperature of the room where each fixed-frequency air conditioner is located during the scheduling period and the second air-conditioning state corresponding to each fixed-frequency air conditioner, the thermal dynamic process constraint condition and the room constraint condition are vector-converted to obtain a vector constraint; Perform equivalent transformation on the vector constraints to obtain equivalent constraint conditions; The objective function is solved under the constraints of the equivalent constraint condition and the time constraint condition, and when the air-conditioning state adjustment gain in the scheduling period is maximized, the second air-conditioning state corresponding to each of the fixed-frequency air conditioners in the scheduling period is obtained.

7. The method according to claim 4, characterized in that The time constraint condition includes a first time sub-constraint condition, a second time sub-constraint condition, a third time sub-constraint condition and a fourth time sub-constraint condition; The method further comprises: Acquire a second air-conditioning state, an air-conditioning startup action, and an air-conditioning shutdown action of the fixed-frequency air-conditioning within the scheduling period from the air-conditioning state set; The determining of the time constraint condition representing the minimum on / off time of the fixed-frequency air conditioner based on the relationship between the second air-conditioning state of the fixed-frequency air conditioner and the minimum on / off time of the fixed-frequency air conditioner includes: determining the first time sub-constraint based on a difference between the air conditioner startup action and the air conditioner shutdown action, and a difference between a second air conditioning state of the fixed-frequency air conditioner in the scheduling period and a second air conditioning state of the fixed-frequency air conditioner in a previous scheduling period; Obtaining the second time sub-constraint based on a difference between the air conditioner startup action and the air conditioner shutdown action; determining the third time sub-constraint based on a relationship between the air conditioner startup action, the second air conditioning state of the fixed-frequency air conditioner within the scheduling period, and the minimum startup duration of the fixed-frequency air conditioner; The fourth time sub-constraint is determined based on a relationship between the air conditioner shutdown action, the second air conditioner state of the fixed-frequency air conditioner in the scheduling period, and a minimum shutdown duration of the fixed-frequency air conditioner.

8. A control device for a fixed-frequency air conditioning cluster participating in demand response, characterized in that: The fixed-frequency air conditioner cluster includes multiple fixed-frequency air conditioners, and the device includes: a prediction module, configured to predict, for each of the plurality of fixed-frequency air conditioners, a reference power consumption that matches a first air-conditioning state of the fixed-frequency air conditioner within a scheduling period; a construction module for constructing an objective function for characterizing an air conditioning state adjustment gain; wherein the air conditioning state adjustment gain is positively correlated with a difference between a reference power consumption corresponding to each of the fixed-frequency air conditioners and a target power consumption of the fixed-frequency air conditioner after adjusting the air conditioning state; A processing module is configured to solve the objective function, and obtain a second air-conditioning state corresponding to each of the fixed-frequency air conditioners in the scheduling period when the air-conditioning state adjustment gain in the scheduling period is maximized; The control module is used to control each of the fixed-frequency air conditioners to participate in demand response according to the corresponding second air conditioner state during the scheduling period.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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