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

Abstract

The application relates to a control method and device for a fixed-frequency air conditioner cluster participating in demand response. The fixed-frequency air conditioner cluster comprises multiple fixed-frequency air conditioners. The method comprises the following steps: for each fixed-frequency air conditioner in the multiple fixed-frequency air conditioners, a reference power consumption is predicted which matches a first air conditioner state of the fixed-frequency air conditioner in a scheduling period; a target function is constructed for representing an air conditioner state adjustment gain; the air conditioner 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 conditioner state is adjusted; the target function is solved to obtain a second air conditioner state corresponding to each fixed-frequency air conditioner in the scheduling period under the condition that the air conditioner state adjustment gain is maximized in the scheduling period; and each fixed-frequency air conditioner is controlled to participate in demand response in the scheduling period according to the corresponding second air conditioner state. The method can improve the control accuracy of the fixed-frequency air conditioner cluster participating in demand response.

Description

Technical Field

[0001] This application relates to the field of power system load operation control technology, and in particular to a control method and device for a fixed-frequency air conditioning cluster to participate in demand response. Background Technology

[0002] With the rapid increase in renewable energy penetration, the power system faces the dual challenges of significant volatility and insufficient flexibility on both the source and load sides. The large-scale grid connection of intermittent power sources such as wind and solar power exacerbates the pressure on grid peak shaving, while traditional thermal power units have limited regulation capacity and low economic efficiency, necessitating the exploration of the flexibility potential of demand-side resources. Air conditioning load, as a typical temperature-controlled load, is characterized by its high proportion, fast response speed, and large adjustment potential. Its participation in peak shaving services through demand response can alleviate the widening peak-valley difference and reduce reserve capacity investment, making it an important research direction for the optimized operation of modern power systems. In particular, fixed-frequency air conditioners, due to their widespread availability and controllable start-stop, have become a highly promising flexible resource for peak shaving services.

[0003] Fixed-frequency air conditioners maintain indoor temperature through periodic start-stop cycles, and their clusters can achieve rapid power aggregation and dynamic adjustment through coordinated control. However, traditional peak-shaving strategies often neglect the air conditioning status within the fixed-frequency air conditioning cluster, resulting in a lack of load diversity and causing power oscillations. This makes the control accuracy of fixed-frequency air conditioning clusters participating in demand response low. Summary of the Invention

[0004] Therefore, it is necessary to provide a control method and apparatus for demand response of fixed-frequency air conditioning clusters that can improve the control accuracy of demand response of fixed-frequency air conditioning clusters, in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a control method for a cluster of fixed-frequency air conditioners to participate in demand response, comprising: predicting, for each of the multiple fixed-frequency air conditioners, a reference power consumption matching the first air conditioner state during the scheduling period; constructing an objective function to characterize the air conditioner state adjustment gain; the air conditioner state adjustment gain being 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 conditioner state; solving the objective function to obtain the second air conditioner state corresponding to each fixed-frequency air conditioner during the scheduling period while maximizing the air conditioner state adjustment gain during the scheduling period; and controlling each fixed-frequency air conditioner to participate in demand response according to the corresponding second air conditioner state during the scheduling period.

[0006] Secondly, this application provides a control device for a fixed-frequency air conditioning cluster participating in demand response. The fixed-frequency air conditioning cluster includes multiple fixed-frequency air conditioners. The device includes: a prediction module for predicting a reference power consumption matching a first air conditioning state of each of the multiple fixed-frequency air conditioners during a scheduling period; a construction module for constructing an objective function 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 its state; a processing module for solving 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 a control module for controlling each fixed-frequency air conditioner to participate in demand response according to its corresponding second air conditioning state during the scheduling period.

[0007] The aforementioned control method and apparatus for fixed-frequency air conditioner clusters participating in demand response predicts the reference power consumption matching the first air conditioner state during the scheduling period for each of the multiple fixed-frequency air conditioners, and constructs an objective function to characterize the air conditioner state adjustment gain. The air conditioner 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 its state. The objective function is solved, and by maximizing the air conditioner state adjustment gain during the scheduling period, the second air conditioner state corresponding to each fixed-frequency air conditioner during the scheduling period is obtained. This allows each fixed-frequency air conditioner to participate in demand response according to its corresponding second air conditioner state during the scheduling period. Therefore, by considering the impact of the first air conditioner state on the reference power consumption of the fixed-frequency air conditioners during the scheduling period, the lack of load diversity and power oscillations caused by ignoring the air conditioner states within the fixed-frequency air conditioner cluster can be avoided, thus improving the control accuracy of the fixed-frequency air conditioner cluster participating in demand response. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a control method for a fixed-frequency air conditioning cluster participating in demand response in one embodiment;

[0010] Figure 2 This is a flowchart illustrating the process of constructing an objective function to characterize the gain of air conditioning state adjustment in one embodiment;

[0011] Figure 3 This is a flowchart illustrating a control method for a fixed-frequency air conditioning cluster participating in demand response, as described in another embodiment.

[0012] Figure 4 This is a flowchart illustrating a control method for a fixed-frequency air conditioning cluster participating in demand response, as described in another embodiment.

[0013] Figure 5 This is a flowchart illustrating a control method for a fixed-frequency air conditioning cluster participating in demand response, as described in another embodiment.

[0014] Figure 6 This is a flowchart illustrating a control method for a fixed-frequency air conditioning cluster participating in demand response, as described in another embodiment.

[0015] Figure 7 This is a schematic diagram of the control process of a control method for 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 output of a fixed-frequency air conditioning cluster in one embodiment.

[0017] Figure 9 This is a schematic diagram illustrating the changes in indoor temperature for users 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 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0022] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

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

[0024] Traditional peak-shaving strategies often suffer from a lack of load diversity and power oscillations due to neglecting the heterogeneity of parameters within fixed-frequency air conditioning clusters (such as thermal inertia and user comfort constraints). Furthermore, the dispersed nature of large-scale air conditioning clusters, high communication costs, and privacy requirements further increase control complexity. Therefore, establishing high-precision aggregation models and designing optimized control strategies that balance economy and reliability have become key issues for fixed-frequency air conditioning systems participating in demand response.

[0025] In view of this, such as Figure 1 As shown, this application provides a control method for a fixed-frequency air conditioning cluster participating in demand response. The fixed-frequency air conditioning cluster includes multiple fixed-frequency air conditioners. Taking the application of this method to an air conditioning cluster decision-making system as an example, the method includes the following steps:

[0026] S102, for each of the multiple fixed-frequency air conditioners, predicts the reference power consumption that matches the first air conditioner state during the scheduling period.

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

[0028] In some embodiments, upon reaching a target time, for each of the multiple fixed-frequency air conditioners, a reference power consumption matching the first air conditioner state during the scheduling period is predicted. The target time can refer to any time before the scheduling period.

[0029] In some embodiments, the air conditioning cluster decision-making system can obtain scheduling 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 calls the air conditioning cluster decision-making system, which uses the time periods published by the power grid control center as the scheduling 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 first air conditioning state can be obtained by predicting the air conditioning state of the fixed-frequency air conditioner during the scheduling period based on the historical electricity consumption of the fixed-frequency air conditioner.

[0031] For example, if the air conditioning states of a fixed-frequency air conditioner are a discrete-time Markov chain, then the first air conditioning state... It can be represented as: ,when A value of 0 indicates that the fixed-frequency air conditioner is off. A value of 1 indicates that the fixed-frequency air conditioner is turned on, and when the fixed-frequency air conditioner is turned on, it can operate at its rated power.

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

[0033]

[0034] in, This indicates the probability that a fixed-frequency air conditioner will start cooling from when it is turned off. . This indicates the probability that a fixed-frequency air conditioner will switch from cooling to turning off. .

[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, It is the sigmoid activation function. For example, the coefficients can be obtained by fitting logistic regression in practical engineering applications. For instance, logistic regression can be used to fit historical on / off states, historical user-set temperatures, and historical room temperatures of a fixed-frequency air conditioner.

[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 characterizes 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 within the cluster may be the same or different.

[0041] For example, This represents the reference power consumption of the g-th fixed-frequency air conditioner during the scheduling period t. This indicates the first air conditioner status of the g-th fixed-frequency air conditioner during the scheduling period t. Let represent the rated power of the g-th fixed-frequency air conditioner, then it satisfies: .

[0042] S104, Construct an objective function to characterize the gain of air conditioning state adjustment.

[0043] The air conditioning status adjustment gain characterizes the benefit gained from adjusting the air conditioning status. This gain 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 status 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 status adjustment, the greater the air conditioning status adjustment gain, and thus the greater the benefit.

[0044] There are no restrictions on the implementation of the objective function used to characterize the air conditioning state adjustment gain. The following examples illustrate the possible implementation methods.

[0045] In one implementation, an objective function characterizing the gain of air conditioner state adjustment 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 of the fixed-frequency air conditioners. Here, the compensation attribute value refers to the unit compensation price paid by the air conditioner aggregator to the user to incentivize the user to adjust their power consumption behavior (such as reducing or shifting load) during the scheduling period.

[0046] S106, Solve the objective function to obtain the 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.

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

[0048] The type of solver for the objective function is not limited. Solvers can include linear and mixed integer programming solvers, nonlinear optimization solvers, and constrained programming and heuristic solvers. 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 constrained programming and heuristic solvers include, but are not limited to, OR-Tools and LocalSolver.

[0049] For example, the objective function can be solved using the Gurobi solver to obtain the 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.

[0050] S108 controls each fixed-frequency air conditioner to participate in demand response according to its corresponding second air conditioner status during the scheduling period.

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

[0052] based on Figure 1 The method described above predicts the reference power consumption matching the first air conditioning state of each of multiple fixed-frequency air conditioners during a scheduling period, and constructs an objective function to characterize the air conditioning state adjustment gain. This adjustment gain is positively correlated with the difference between the reference power consumption of each fixed-frequency air conditioner and the target power consumption of the air conditioner after adjusting its state. By solving the objective function and maximizing the adjustment gain during the scheduling period, the second air conditioning state corresponding to each fixed-frequency air conditioner is obtained. This allows each fixed-frequency air conditioner to participate in demand response according to its corresponding second air conditioning state during the scheduling period. Therefore, by considering the impact of the first air conditioning state of the fixed-frequency air conditioners on the reference power consumption during the scheduling period, the method avoids the loss of load diversity and power oscillations caused by ignoring the air conditioning states within the fixed-frequency air conditioner cluster. This improves the accuracy of controlling the participation of the fixed-frequency air conditioner cluster in demand response.

[0053] In one embodiment, such as Figure 2 As shown, a flowchart illustrating the construction of 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 conditioner status and the rated power of the fixed-frequency air conditioner during the scheduling period, determine the target power consumption of the fixed-frequency air conditioner during the scheduling period.

[0055] As can be seen from S106, the second air conditioner state of the fixed-frequency air conditioner during the scheduling period can be obtained by solving the objective function. That is, the second air conditioner state is the parameter that needs to be optimized in the objective function. Based on the second air conditioner 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 conditioner state of the fixed-frequency air conditioner corresponding to the target power consumption is optimized.

[0056] For example, This indicates the status of the g-th fixed-frequency air conditioner during the scheduling period t. This represents the target power consumption of the g-th fixed-frequency air conditioner during the scheduling period t. Let represent the rated power of the g-th fixed-frequency air conditioner, then it satisfies: .

[0057] S204. Based on the difference between the reference power consumption and the target power consumption of each fixed-frequency air conditioner, the penalty coefficient set by the air conditioner aggregator to affect user comfort, and the power attribute values ​​during the scheduling period, an objective function is constructed to characterize the gain of air conditioner state adjustment.

[0058] Among these, the electricity attribute values ​​can be used to characterize electricity prices. For example, the objective function satisfies:

[0059]

[0060] in, This represents the air conditioning status adjustment gain, where G is the total number of air conditioners in the fixed-frequency air conditioning cluster, and T is the number of scheduling periods. The power attribute values ​​within the scheduling period t. This refers to the penalty coefficient set by air conditioning aggregators for any impact on user comfort. Indicates the duration of the scheduling period.

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

[0062] In one embodiment, such as Figure 3 The diagram illustrates a control method for a fixed-frequency air conditioning cluster participating in demand response. Taking the application of this method to an air conditioning cluster decision-making system as an example, it includes:

[0063] S302, obtain the 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 the room parameters, outdoor temperature, target power consumption and energy efficiency ratio of the room where the fixed-frequency air conditioner is located.

[0064] Room parameters can include room temperature, equivalent thermal resistance, and heat capacity of the gas inside the room. The equivalent thermal resistance of the room is the reciprocal of the air heat loss coefficient, and its unit is °C / kW. The heat capacity of the gas inside the room is 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 of the fixed-frequency air conditioner.

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

[0067]

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

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

[0070] There can be multiple scheduling periods, and the next scheduling period refers to the next scheduling period that is adjacent to the current scheduling period.

[0071] For example, by discretizing the scheduling period in the first-order equivalent thermal parameter model, the thermal dynamic process constraints can be obtained, which satisfy the following:

[0072]

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

[0074] Considering the forecast error in the weather information for the location of the fixed-frequency air conditioner, the actual outdoor temperature will fluctuate around the forecast temperature. Therefore, we can define an outdoor temperature uncertainty set that satisfies:

[0075]

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

[0077] In some cases, fixed-frequency air conditioners can be set with different outdoor temperature uncertainty sets for different regions. In this case, the fixed-frequency air conditioner can determine the corresponding outdoor temperature based on the outdoor temperature uncertainty set of the corresponding region.

[0078] S306, under the constraints of the thermal dynamic process, solve the objective function. When the air conditioning state adjustment gain is maximized during the scheduling period, obtain the second air conditioning state corresponding to each fixed frequency air conditioner during the scheduling period.

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

[0080] In one embodiment, such as Figure 4 The diagram illustrates a control method for a fixed-frequency air conditioning cluster participating in demand response. Taking the application of this method to an air conditioning cluster decision-making system as an example, it includes:

[0081] S402, construct room constraints that characterize the room parameters of the room where the fixed-frequency air conditioner is located.

[0082] The content of the room constraints is not limited; the following examples illustrate possible implementation methods.

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

[0084] For example, the first sub-constraint of the room can 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 even when the outdoor temperature is high, and should not fall below the minimum temperature limit even when the outdoor temperature is low. Based on this, a first sub-constraint condition for the room can be defined, which satisfies:

[0086]

[0087] in, This represents the lower limit of the preset temperature range, used to characterize the lower limit of the user's temperature tolerance. This indicates the upper limit of the preset temperature range, used to characterize the user's upper limit of temperature tolerance.

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

[0089] For example, a second sub-constraint of the room can 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 user-set temperature, and the penalty coefficient set by the air conditioning aggregator for impairing user comfort.

[0090] In some situations, a user's optimal temperature is their set temperature for the air conditioner, and this value is unlikely to 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 penalized. Based on this, a second sub-constraint condition for the room can be set, which satisfies the following:

[0091]

[0092] in, This represents the penalty coefficient set by air conditioning aggregators for compromising user comfort, expressed in yuan / (°C·h), which is used to characterize the cost per degree Celsius per hour. This indicates the penalty coefficient set by the air conditioning aggregator for any impact on user comfort. This indicates the user-set temperature for the g-th fixed-frequency air conditioner.

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

[0094]

[0095] in, This indicates the initial room temperature, meaning that the initial room temperature is the same for each fixed-frequency air conditioner in the fixed-frequency air conditioner cluster.

[0096] S404, Based on the relationship between the second air conditioning state of the fixed-frequency air conditioner and the minimum start-up time and minimum shutdown time of the fixed-frequency air conditioner, determine the time constraint conditions characterizing the minimum start-up and shutdown time of the fixed-frequency air conditioner.

[0097] Frequent start-stop cycles of fixed-frequency air conditioners can severely impact their lifespan. To avoid this, the duration of each start-stop cycle should be limited, i.e., a time constraint should be established. The implementation method for determining the time constraint representing the minimum start-up and shutdown time of a fixed-frequency air conditioner, based on the relationship between the second air conditioner state and the minimum start-up and shutdown times, is not limited. Examples of possible implementation methods are provided below.

[0098] In one implementation, the second air conditioner state, start-up action, and stop-down action of the fixed-frequency air conditioner during the scheduling period can be obtained from the air conditioner state set. Then, based on the second air conditioner state, start-up action, and stop-down action, and the relationship between the minimum start-up duration and minimum stop-down duration of the fixed-frequency air conditioner, time constraints characterizing the minimum start-up and stop-down time of the fixed-frequency air conditioner can be determined. These time constraints 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 the difference between the air conditioner's start-up and shut-down actions, and the difference between the second air conditioner state of the fixed-frequency air conditioner during the scheduling period and the second air conditioner state of the fixed-frequency air conditioner during the previous scheduling period. The previous scheduling period refers to the scheduling period preceding the current scheduling period.

[0100] For example, the first time sub-constraint is satisfied:

[0101]

[0102] in, This indicates the start-up action of the g-th fixed-frequency air conditioner during the scheduling period. This indicates the shutdown action of the g-th fixed-frequency air conditioner during the scheduling period. This indicates the status of the g-th fixed-frequency air conditioner as the second air conditioner during the scheduling period. This indicates the status of the g-th fixed-frequency air conditioner as the second air conditioner during the previous scheduling period. , This represents the set of air conditioner statuses, where 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 can be obtained based on the difference between the air conditioner's start-up action and its shutdown action.

[0104] For example, the second time sub-constraint is satisfied:

[0105]

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

[0107] For example, the third time-related sub-constraint is satisfied:

[0108]

[0109] in, This represents the minimum operating time 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 during the scheduling period, and the minimum shutdown duration of the fixed-frequency air conditioner.

[0111] For example, the fourth time sub-constraint is satisfied:

[0112]

[0113] in, This represents the minimum shutdown time for the g-th fixed-frequency air conditioner.

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

[0115] based on Figure 4 The content shown demonstrates that by introducing room constraints and time constraints, and combining them with thermal dynamic process constraints to solve the objective function, the accuracy of the analysis results can be improved, and further, the accuracy of controlling the participation of fixed-frequency air conditioning clusters in demand response can be enhanced.

[0116] In one embodiment, room parameters include the room's outdoor temperature, such as... Figure 5 The diagram illustrates a control method for a fixed-frequency air conditioning cluster participating in demand response. Taking the application of this method to an air conditioning cluster decision-making system as an example, the method 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 conditioner status corresponding to each fixed-frequency air conditioner, performs vector transformation processing on the thermal dynamic process constraints and room constraints to obtain vector constraints.

[0118] In one embodiment, if the room constraints include the first sub-constraint of the room, then 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 constraints and the first sub-constraint of the room are vectorized to obtain vector constraints.

[0119] For example, the vector constraints satisfy:

[0120]

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

[0122] S504 performs an equivalent transformation on the vector constraints to obtain equivalent constraint conditions.

[0123] For vector constraints, assume the solution to the vector constraint is , The above constraints are feasible if and only if the following objective constraints hold, and the objective constraints are satisfied:

[0124]

[0125] Among them, in the objective constraints, yes transpose, It is a unit vector. The j-th element is 1, and the rest are 0. Indicate quantity, G represents the total number of air conditioners in the fixed-frequency air conditioning cluster, and T represents the number of scheduling periods.

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

[0127]

[0128] in, It is a Lagrange multiplier, and is subject to constraints. Correspondingly. Note that the above problem is a convex problem and satisfies the complementary relaxation condition. Therefore, duality holds, and according to the Lagrange duality principle, for 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, it exists. This makes the following equivalent constraints true, and the equivalent constraints are satisfied:

[0131]

[0132]

[0133]

[0134] in, It is the dual norm. This represents a vector or matrix composed of the predicted outdoor temperatures for each room where a fixed-frequency air conditioner is located. Therefore, by simply rewriting the thermal dynamic process constraints and the first sub-constraint of the room as the three equivalent constraints mentioned above (i.e., equivalent constraints), the original problem remains equivalent. That is, after equivalent transformation of the thermal dynamic process constraints and the first sub-constraint of the room, the transformed problem is a mixed-integer linear programming problem, which can be solved directly using a mature commercial solver.

[0135] S506, under the constraints of equivalent constraints and time constraints, solve the objective function. When the air conditioning state adjustment gain is maximized during the scheduling period, obtain the second air conditioning state corresponding to each fixed frequency air conditioner during the scheduling period.

[0136] In some embodiments, the room constraints further include a second room sub-constraint and a third room sub-constraint, and the time constraints include 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 equivalent constraints, room second sub-constraints, room third sub-constraints, and time first sub-constraints, time second sub-constraints, time third sub-constraints, and time fourth sub-constraints, the objective function is solved. When the air conditioning state adjustment gain is maximized during the scheduling period, the second air conditioning state corresponding to each fixed-frequency air conditioner during the scheduling period is obtained.

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

[0139] In conjunction with the above, in one embodiment, such as Figure 6 The diagram illustrates a control method for a fixed-frequency air conditioning cluster participating in demand response. Taking the application of this method to an air conditioning cluster decision-making system as an example, the method includes the following steps:

[0140] S602, for each of multiple fixed-frequency air conditioners, predicts the reference power consumption that matches the first air conditioner state during the scheduling period.

[0141] For example, Reference power consumption of the g-th fixed-frequency air conditioner during the dispatch period t. This indicates the first air conditioner state of a fixed-frequency air conditioner within the scheduling period t. Let represent the rated power of the g-th fixed-frequency air conditioner, then it satisfies: .

[0142] S604, for each fixed-frequency air conditioner, based on the second air conditioner status and the rated power of the fixed-frequency air conditioner during the scheduling period, determine the target power consumption of the fixed-frequency air conditioner during the scheduling period.

[0143] For example, This indicates the status of the g-th fixed-frequency air conditioner during the scheduling period t. Let represent the target power consumption of the g-th fixed-frequency air conditioner during the scheduling period t, then the following condition is met: .

[0144] S606, based on the difference between the reference power consumption and the target power consumption of each fixed-frequency air conditioner, the penalty coefficient set by the air conditioner aggregator to affect user comfort, and the power attribute values ​​during the scheduling period, constructs an objective function to characterize the gain of air conditioner state adjustment.

[0145] For example, the objective function satisfies:

[0146]

[0147] in, This represents the air conditioning status adjustment gain, where G represents the total number of air conditioners in the fixed-frequency air conditioning cluster, and T represents the number of scheduling periods. This represents the power attribute value within the scheduling period t. This indicates the penalty coefficient set by the air conditioning aggregator for any impact on user comfort. Indicates the duration of the scheduling period.

[0148] S608, obtain the 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, This represents the room temperature in the room where the g-th fixed-frequency air conditioner is located during the scheduling period t. This represents the outdoor temperature of the room where the g-th fixed-frequency air conditioner is located during the scheduling period t. This represents the equivalent thermal resistance of the room where the g-th fixed-frequency air conditioner is located. This represents the heat capacity of the gas in the room where the g-th fixed-frequency air conditioner is located. This represents the energy efficiency ratio of the g-th fixed-frequency air conditioner.

[0153] S610 discretizes the scheduling period in the first-order equivalent thermal parameter model to obtain the 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 are satisfied as follows:

[0155]

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

[0157] S612, 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 first sub-constraint condition of the room.

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

[0159]

[0160] in, This represents the lower limit of the preset temperature range, used to characterize the lower limit of the user's temperature tolerance. This indicates the upper limit of the preset temperature range, used to characterize the user's upper limit of 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's set temperature, and the penalty coefficient set by the air conditioner aggregator for impairing user comfort, the second sub-constraint condition of the room is obtained.

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

[0163]

[0164] in, This indicates the penalty coefficient set by air conditioning aggregators for any disruption to user comfort. This indicates the penalty coefficient set by the air conditioning aggregator for any impact on user comfort. This indicates the user-set temperature for the g-th fixed-frequency air conditioner.

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

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

[0167]

[0168] in, This indicates the initial room temperature, meaning that the initial room temperature is the same for each fixed-frequency air conditioner in the fixed-frequency air conditioner cluster.

[0169] S618: Obtain the second air conditioner status, air conditioner start-up action, and air conditioner shutdown action of the fixed-frequency air conditioner during the scheduling period from the air conditioner status set.

[0170] S620, based on the difference between the air conditioner's start-up action and the air conditioner's shut-down action, and the difference between the second air conditioner state of the fixed-frequency air conditioner during the scheduling period and the second air conditioner state of the fixed-frequency air conditioner during the previous scheduling period, the first time sub-constraint condition is determined.

[0171] For example, the first time sub-constraint is satisfied:

[0172]

[0173] in, This indicates the start-up action of the g-th fixed-frequency air conditioner during the scheduling period. This indicates the shutdown action of the g-th fixed-frequency air conditioner during the scheduling period. This indicates the status of the g-th fixed-frequency air conditioner as the second air conditioner during the scheduling period. This indicates the status of the second air conditioner of the g-th fixed-frequency air conditioner during the previous scheduling period.

[0174] S622, based on the difference between the air conditioner's start-up action and the air conditioner's shut-off action, obtain the second time sub-constraint condition.

[0175] For example, the second time sub-constraint is satisfied:

[0176]

[0177] S624, based on the relationship between the air conditioner's start-up action, the second air conditioner status of the fixed-frequency air conditioner during the scheduling period, and the minimum start-up duration of the fixed-frequency air conditioner, determine the third time sub-constraint condition.

[0178] For example, the third time-related sub-constraint is satisfied:

[0179]

[0180] in, This represents the minimum operating time of the g-th fixed-frequency air conditioner.

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

[0182] For example, the fourth time sub-constraint is satisfied:

[0183]

[0184] in, This represents the minimum shutdown time for 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 conditioner status corresponding to each fixed-frequency air conditioner, the thermal dynamic process constraints and the first sub-constraint of the room are equivalently transformed to obtain equivalent constraints.

[0186] Specifically, the thermal dynamic process constraints and the first sub-constraint of the room are subjected to equivalent transformation to obtain equivalent constraints. This includes: performing vector transformation on the thermal dynamic process constraints and the first sub-constraint of the room to obtain vector constraints; and performing equivalent transformation on the vector constraints to obtain equivalent constraints.

[0187] For example, the equivalent constraint condition is satisfied:

[0188]

[0189]

[0190]

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

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

[0193] S632 controls each fixed-frequency air conditioner to participate in demand response according to its corresponding second air conditioner status during the scheduling period.

[0194] The specific content of S602-S632 can be found in the aforementioned description.

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

[0196] Table 1

[0197]

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

[0199] Based on the above, by discretizing the time (i.e., the scheduling period) in the first-order equivalent thermal parameter model, and taking a time step of , When the outdoor temperature is If the time remains constant, Indoor temperature at any time satisfy:

[0200]

[0201] In conjunction with the above, in one embodiment, such as Figure 7 The diagram shown illustrates the control process of a control method for a fixed-frequency air conditioning cluster participating in demand response, wherein:

[0202] The power grid control center 702 can determine the peak load situation of the power grid 704 based on the day-ahead load forecast results, and then publish the demand response capacity and corresponding time periods in the form of price signals. High demand often corresponds to high prices. The air conditioning aggregator 706, based on peak-shaving demand, invokes the 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, the air conditioning aggregator 706 can invoke the air conditioning cluster decision system 708 to use the time periods published by the power grid as scheduling time periods; the number of scheduling time periods can be multiple. The air conditioning aggregator 706 can, based on its own interests, invoke the air conditioning cluster decision system 708 to control each air conditioning unit to participate in the power grid's demand response.

[0203] Specifically, the air conditioning cluster decision system 708 establishes and solves the objective function based on the air conditioning information (such as on / off status and temperature parameters) reported by the intelligent terminals of each air conditioning unit for the corresponding fixed-frequency air conditioners. This yields the air conditioning status of each fixed-frequency air conditioner during the scheduling period. Therefore, for each air conditioning unit, the intelligent terminal controls the fixed-frequency air conditioner to perform on / off actions during the scheduling period based on the corresponding air conditioning status. Temperature parameters may include room parameters such as those of 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 centralized control by the aggregator. Furthermore, this application can construct the state transition probability matrix of the fixed-frequency air conditioners based on a discrete-time Markov chain. This state transition probability matrix can then be represented as a probabilistic response model of the fixed-frequency air conditioner cluster based on the Markov chain, indicating that it is not subject to centralized control by the aggregator.

[0205] As can be seen from the above, this application considers temperature uncertainty and can robustly guarantee user comfort. Specifically, under appropriate constraints, this application achieves optimized control of each fixed-frequency air conditioner within a fixed-frequency air conditioning cluster by solving the objective function. The goal is to maximize the profits of the air conditioning aggregator while ensuring robustness in user comfort. For example, the objective function can be solved using the Gurobi software package in a Matlab environment.

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

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

[0208] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0209] Based on the same inventive concept, this application also provides a control device for implementing the control method for fixed-frequency air conditioning clusters participating in demand response as described above. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more embodiments of the control device for fixed-frequency air conditioning clusters participating in demand response provided below can be found in the limitations of the control method for fixed-frequency air conditioning clusters participating in demand response described above, and will not be repeated here.

[0210] In one exemplary embodiment, such as Figure 10 As shown, a control device for a fixed-frequency air conditioning cluster participating in demand response is provided. The fixed-frequency air conditioning 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. Specifically: the prediction module 1002 is used to predict the reference power consumption matching the first air conditioning state of each of the multiple fixed-frequency air conditioners during the scheduling period; the construction module 1004 is used to construct an objective function 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 its state; the processing module 1006 is used to solve the objective function to obtain the second air conditioning state corresponding to each fixed-frequency air conditioner during the scheduling period while maximizing the air conditioning state adjustment gain; the control module 1008 is used to control each fixed-frequency air conditioner to participate in demand response according to its corresponding second air conditioning state during the scheduling period.

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

[0212] In one embodiment, the processing module 1006 is further configured to: obtain the first-order equivalent thermal parameter model corresponding to each fixed-frequency air conditioner, wherein 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; discretize the first-order equivalent thermal parameter model to obtain the thermal dynamic process constraints characterizing the fixed-frequency air conditioner and the room where the fixed-frequency air conditioner is located in the next scheduling period; solve the objective function under the constraints of the thermal dynamic process constraints, and when the air conditioner state adjustment gain is maximized in the scheduling period, obtain the second air conditioner state corresponding to each fixed-frequency air conditioner in the scheduling period.

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

[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 configured to: obtain the first sub-constraint 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 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 impairing user comfort; and obtain the third sub-constraint based on the relationship between the preset temperature range and the initial room temperature.

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

[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 configured to: obtain the second air conditioner state, air conditioner start-up action, and air conditioner shutdown action of the fixed-frequency air conditioner during the scheduling period from the air conditioner state set; determine the first time sub-constraint based on the difference between the air conditioner start-up action and the air conditioner shutdown action, and the difference between the second air conditioner state of the fixed-frequency air conditioner during the scheduling period and the second air conditioner state of the fixed-frequency air conditioner during the previous scheduling period; obtain the second time sub-constraint based on the difference between the air conditioner start-up action and the air conditioner shutdown action; determine the third time sub-constraint based on the relationship between the air conditioner start-up action, the second air conditioner state of the fixed-frequency air conditioner during the scheduling period, and the minimum start-up duration of the fixed-frequency air conditioner; and determine the 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 during the scheduling period, and the minimum shutdown duration of the fixed-frequency air conditioner.

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

[0218] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data such as reference power consumption. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a control method for a fixed-frequency air conditioning cluster participating in demand response.

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

[0220] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0221] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

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

[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, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0224] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, 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 many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0225] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this application. The above embodiments only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of this application. It should be noted that for those skilled in the art, several modifications and improvements can be made without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A control method for a constant frequency air conditioner cluster participating in demand response, characterized in that, The fixed-frequency air conditioning cluster includes multiple fixed-frequency air conditioners, and the method includes: For each of the multiple fixed-frequency air conditioners, predict the reference power consumption that matches the first air conditioner state during the scheduling period. An objective function is constructed to characterize the air conditioner state adjustment gain; the air conditioner state adjustment gain 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 air conditioner state is adjusted. Solving the objective function, while maximizing the air conditioner state adjustment gain during the scheduling period, yields the second air conditioner state corresponding to each of the fixed-frequency air conditioners during the scheduling period. Each of the fixed-frequency air conditioners is controlled to participate in demand response according to its corresponding second air conditioner status during the scheduling period; The construction of the objective function for characterizing the air conditioner state adjustment gain includes: for each fixed-frequency air conditioner, determining the target power consumption of the fixed-frequency air conditioner during the scheduling period based on the second air conditioner state and the rated power of the fixed-frequency air conditioner during the scheduling period; and constructing the 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 affecting user comfort, and the power attribute values ​​during the scheduling period. The method further includes: 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 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; discretizing the first-order equivalent thermal parameter model to obtain thermal dynamic process constraints characterizing the fixed-frequency air conditioner and the room where the fixed-frequency air conditioner is located in the next scheduling period; constructing room constraints characterizing the room parameters of the room where the fixed-frequency air conditioner is located; determining time constraints characterizing the minimum on / off time of the fixed-frequency air conditioner based on the relationship between the second air conditioner state of the fixed-frequency air conditioner and the minimum on / off time of the fixed-frequency air conditioner; and solving the objective function to obtain the second air conditioner state corresponding to each of the fixed-frequency air conditioners in the scheduling period under the condition of maximizing the air conditioner state adjustment gain in the scheduling period, including: solving the objective function under the constraints of the thermal dynamic process constraints, the room constraints, and the time constraints, and obtaining the second air conditioner state corresponding to each of the fixed-frequency air conditioners in the scheduling period when the air conditioner state adjustment gain in the scheduling period is maximized.

2. The method of claim 1, wherein, 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 room constraints that characterize the room parameters of the room where the fixed-frequency air conditioner is located include: 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, the first sub-constraint condition of the room is obtained. Based on the relationship between the difference between the room temperature and the user-set temperature in the room where the fixed-frequency air conditioner is located, and the penalty coefficient set by the air conditioner aggregator for impairing user comfort, the second sub-constraint condition of the room is obtained. Based on the relationship between the preset temperature range and the initial room temperature, the third sub-constraint condition of the room is obtained.

3. The method of claim 1, wherein, The room parameters include the room's outdoor temperature; under the constraints of the thermal dynamic process, the room constraints, and the time constraints, the objective function is solved, and when the air conditioning state adjustment gain is maximized during the scheduling period, the second air conditioning state corresponding to each of the fixed-frequency air conditioners during the scheduling period is obtained, 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 constraints and the room constraints are vectorized to obtain vector constraints. Perform equivalent transformations on vector constraints to obtain equivalent constraint conditions; Under the constraints of the equivalent constraints and the time constraints, the objective function is solved. When the air conditioning state adjustment gain is maximized during the scheduling period, the second air conditioning state corresponding to each of the fixed-frequency air conditioners during the scheduling period is obtained.

4. The method of claim 1, wherein, The time constraints include a first time sub-constraint, a second time sub-constraint, a third time sub-constraint, and a fourth time sub-constraint. The method further includes: Obtain the second air conditioner status, air conditioner start-up action, and air conditioner shutdown action of the fixed-frequency air conditioner during the scheduling period from the air conditioner status set; The relationship between the second air conditioning state of the fixed-frequency air conditioner and the minimum start-up and minimum shutdown times of the fixed-frequency air conditioner determines the time constraints characterizing the minimum start-up and shutdown times of the fixed-frequency air conditioner, including: Based on the difference between the air conditioner's start-up action and the air conditioner's shutdown action, and the difference between the second air conditioner state of the fixed-frequency air conditioner during the scheduling period and the second air conditioner state of the fixed-frequency air conditioner during the previous scheduling period, the first time sub-constraint condition is determined. Based on the difference between the air conditioner's start-up action and the air conditioner's shut-off action, the second time sub-constraint condition is obtained; Based on the relationship between the air conditioner's start-up action, the second air conditioner status of the fixed-frequency air conditioner during the scheduling period, and the minimum start-up duration of the fixed-frequency air conditioner, the third time sub-constraint condition is determined. Based on the relationship between the air conditioner shutdown action, the second air conditioner state of the fixed-frequency air conditioner during the scheduling period, and the minimum shutdown duration of the fixed-frequency air conditioner, the fourth time sub-constraint condition is determined.

5. A control device for a fixed-frequency air conditioning cluster participating in demand response, characterized in that, The fixed-frequency air conditioning cluster includes multiple fixed-frequency air conditioners, and the device includes: The prediction module is used to predict the reference power consumption that matches the first air conditioner state of each of the multiple fixed-frequency air conditioners during the scheduling period. A construction module is used to construct an objective function to characterize the air conditioner state adjustment gain; the air conditioner state adjustment gain 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 air conditioner state is adjusted. The processing module is used to solve the objective function and, under the condition of maximizing the air conditioner state adjustment gain during the scheduling period, obtain the second air conditioner state corresponding to each of the fixed-frequency air conditioners during the scheduling period. 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 status during the scheduling period. The construction module is further configured to: for each fixed-frequency air conditioner, determine the target power consumption of the fixed-frequency air conditioner during the scheduling period based on the second air conditioner status of the fixed-frequency air conditioner during the scheduling period and the rated power of the fixed-frequency air conditioner; and construct an objective function to characterize the air conditioner status 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 affecting user comfort, and the power attribute value during the scheduling period. The processing module is further configured to: obtain 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 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; discretize the first-order equivalent thermal parameter model to obtain the thermal dynamic process constraints characterizing the fixed-frequency air conditioner and the room where the fixed-frequency air conditioner is located in the next scheduling period; construct room constraints characterizing the room parameters of the room where the fixed-frequency air conditioner is located; determine the time constraints characterizing the minimum on / off time of the fixed-frequency air conditioner based on the relationship between the second air conditioner state of the fixed-frequency air conditioner and the minimum on / off time of the fixed-frequency air conditioner; and solve the objective function under the constraints of the thermal dynamic process constraints, the room constraints, and the time constraints, and obtain the second air conditioner state corresponding to each of the fixed-frequency air conditioners in the scheduling period when the air conditioner state adjustment gain is maximized during the scheduling period.

6. The apparatus according to claim 5, characterized in that, 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 is further configured to: 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, the first sub-constraint condition of the room is obtained. Based on the relationship between the difference between the room temperature and the user-set temperature in the room where the fixed-frequency air conditioner is located, and the penalty coefficient set by the air conditioner aggregator for impairing user comfort, the second sub-constraint condition of the room is obtained. Based on the relationship between the preset temperature range and the initial room temperature, the third sub-constraint condition of the room is obtained.

7. The apparatus according to claim 5, characterized in that, The room parameters include the room's outdoor temperature; the processing module is also used for: 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 constraints and the room constraints are vectorized to obtain vector constraints. Perform equivalent transformations on vector constraints to obtain equivalent constraint conditions; Under the constraints of the equivalent constraints and the time constraints, the objective function is solved. When the air conditioning state adjustment gain is maximized during the scheduling period, the second air conditioning state corresponding to each of the fixed-frequency air conditioners during the scheduling period is obtained.

8. The apparatus according to claim 5, characterized in that, The time constraints include a first time sub-constraint, a second time sub-constraint, a third time sub-constraint, and a fourth time sub-constraint; the processing module is further configured to: Obtain the second air conditioner status, air conditioner start-up action, and air conditioner shutdown action of the fixed-frequency air conditioner during the scheduling period from the air conditioner status set; Based on the difference between the air conditioner's start-up action and the air conditioner's shutdown action, and the difference between the second air conditioner state of the fixed-frequency air conditioner during the scheduling period and the second air conditioner state of the fixed-frequency air conditioner during the previous scheduling period, the first time sub-constraint condition is determined. Based on the difference between the air conditioner's start-up action and the air conditioner's shut-off action, the second time sub-constraint condition is obtained; Based on the relationship between the air conditioner's start-up action, the second air conditioner status of the fixed-frequency air conditioner during the scheduling period, and the minimum start-up duration of the fixed-frequency air conditioner, the third time sub-constraint condition is determined. Based on the relationship between the air conditioner shutdown action, the second air conditioner state of the fixed-frequency air conditioner during the scheduling period, and the minimum shutdown duration of the fixed-frequency air conditioner, the fourth time sub-constraint condition is determined.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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