Load control method and system based on HVAC load cluster flexibility

Abstract

The present application discloses a load control method and system based on the flexibility of HVAC load clusters, the method comprising the following steps: constructing a single HVAC technical constraint model with each HVAC operating parameter in the HVAC load cluster; constructing a single HVAC equivalent energy storage model based on the single HVAC technical constraint model based on equivalent energy storage; constructing a HVAC aggregation model with all single HVAC equivalent energy storage models; acquiring the relative position information of the HVAC and the power station to construct a HVAC load topology map; constructing an optimization objective function based on the HVAC load topology map and the HVAC aggregation model based on minimum energy consumption; outputting a load control strategy with current environmental data and the optimization objective function, and executing load control with the load control strategy. Beneficial effects of the present application: utilizing the HVAC energy storage characteristics to ensure user needs in load control.

Description

Technical Field

[0001] The present application relates to the technical field of HVAC load control, and in particular to a load control method and system based on the flexibility of HVAC load clusters. Background Art

[0002] HVAC load clusters have shown a rapid growth trend in recent years, and the power consumption of HVAC load clusters in large buildings is relatively large. The operation of multiple HVAC load clusters will have a significant impact on the power grid. In actual situations, HVAC load clusters can reflect the characteristics of temperature energy storage, and the energy storage flexibility of HVAC cluster loads can be fully utilized, which will provide greater flexibility for the power grid.

[0003] Load transfer or reduction is achieved through the thermal mass of the building, that is, using the thermal capacity of the building structure and materials to store heat. This is a passive energy storage method that helps to alleviate immediate demand during peak grid load periods. Due to the thermal inertia of buildings, HVAC loads can be adjusted in a short period of time with little impact on user comfort. This characteristic makes HVAC loads a potential flexible load resource that can participate in load regulation of the grid.

[0004] However, in the related art, load control is performed by predicting the overall load and load adjustment is achieved by reducing the air-conditioning power, which cannot take into account both user demand and load balance.

[0005] The Chinese patent "A method for reducing the peak load of the power grid by using air conditioning load control", publication number: CN104990208A, publication date: October 21, 2015, specifically discloses that the method includes the structure of the air conditioning equipment itself, the air conditioning load control method and the classification of the air conditioning system and its load reduction potential analysis; the air conditioning load control method includes the adjustment of the air conditioning system regional parameters, wind system parameters, circulation equipment parameters and central host equipment parameters; the air conditioning load control method is a device recovery method for restoring the equipment to the normal control level after the power peak period. In this solution, the air conditioning parameters are adjusted to adjust the air conditioning load, but it will affect the user experience and cannot take into account the user's needs.

[0006] The Chinese patent "Aggregate air conditioning load control method for power grid peak shaving", publication number: CN110425706A, publication date: November 8, 2019, specifically discloses: obtaining power grid peak shaving instructions, outdoor temperature during peak shaving period, and the number of air conditioners online; grouping aggregate air conditioners according to air conditioner parameter distribution and user electricity type; performing aggregate air conditioner load control based on set temperature adjustment; predicting the load reduction potential of each group of air conditioners during peak shaving period; establishing a peak shaving optimization model, arranging each group of air conditioners to reduce power, and completing peak shaving instructions. This solution avoids load fluctuations after temperature adjustment; grouping and regulating aggregate air conditioners to improve peak shaving flexibility, and establishing a grouping and regulation model for aggregate air conditioners with the goal of minimizing peak shaving deviation. Although grouping and regulation improve peak shaving flexibility in a certain sense, it still causes the power of air conditioners to be reduced, affecting user use. Summary of the invention

[0007] The present application aims at the problem that load control in the prior art cannot take into account both load balance and user needs, and provides a load control method and system based on the flexibility of HVAC load cluster, establishes a HVAC aggregation model based on HVAC operation parameters and equivalent energy storage, and constructs a HVAC load topology map, and constructs an optimization objective function based on the HVAC load topology map and the HVAC aggregation model based on minimum energy consumption. According to the HVAC load topology map, the power flow loss at different relative positions of the power station and the HVAC is obtained. In the case of large regional load, the HVAC load compensation capacity and the power flow loss are considered at the same time, so that the HVAC with greater power flow loss but sufficient energy storage can be added to the load compensation to reduce the remote supply of the power grid, avoid the situation that the HVAC near the power station performs load compensation, but the HVAC operation of the distant power station still causes additional load loss of the power grid, and improve the flexibility of load compensation. Thus, a load control method with minimum energy loss within the HVAC energy storage adjustment range is obtained, and energy storage compensation is used to ensure user needs, and the additional loss caused by energy storage compensation is balanced with the minimum energy consumption, so as to achieve a balance between load balance and user needs.

[0008] To achieve the above technical objectives, a technical solution provided by the present application is a load control method based on the flexibility of HVAC load clusters, including the following steps: S1: constructing a single HVAC technical constraint model based on each HVAC operating parameter in the HVAC load cluster; S2: constructing a single HVAC equivalent energy storage model based on the equivalent energy storage according to the single HVAC technical constraint model; S3: constructing a HVAC aggregation model with all single HVAC equivalent energy storage models; S4: obtaining the relative position information of the HVAC and the power station, and constructing a HVAC load topology map; S5: constructing an optimization objective function based on the minimum energy consumption according to the HVAC load topology map and the HVAC aggregation model; S6: obtaining current environmental data, outputting a load control strategy based on the current environmental data and the optimization objective function, and executing load control with the load control strategy. A single HVAC technical constraint model is constructed corresponding to each HVAC operating parameter to show the power-temperature conversion of each HVAC in different working states, and then the equivalent energy storage corresponding to a single HVAC is obtained according to the power-temperature conversion based on the equivalent energy storage, the relative position information of the HVAC and the power supply station is obtained, and a HVAC load topology map is constructed. A HVAC aggregation model is constructed with all single HVAC equivalent energy storage models, and an optimization objective function is constructed with minimum energy consumption. When the current environmental parameters are obtained, the HVAC control strategy that meets the minimum energy loss, namely the load control strategy, can be output to adjust the power state of the HVAC, and the energy storage flexibility of the HVAC can be used to meet user needs while reducing energy loss. According to the HVAC load topology diagram, the power flow loss at different relative positions of the power station and the HVAC is obtained. When the regional load is large, the HVAC load compensation capacity and the power flow loss are considered at the same time, so that the HVAC with greater power flow loss but sufficient energy storage can be added to the load compensation to reduce the remote supply of the power grid, avoid the situation where the HVAC near the power station performs load compensation, but the operation of the HVAC at the distant power station still causes additional load loss to the power grid, and improve the flexibility of load compensation.

[0009] Furthermore, the constructing of a single HVAC technical constraint model based on each HVAC operating parameter in the HVAC load cluster includes: constructing a single HVAC load power model based on each HVAC operating rated power in the HVAC load cluster; constructing a single HVAC temperature change model based on the heat transfer effect and each HVAC operating room parameter; and constructing a single HVAC technical constraint model based on a single HVAC load power model and a single HVAC temperature change model.

[0010] Furthermore, the method of constructing a single HVAC technical constraint model based on each HVAC operating parameter in the HVAC load cluster also includes: constructing a single HVAC basic model based on each HVAC operating parameter in the HVAC load cluster; obtaining historical HVAC operating data, and training a single HVAC basic model based on the historical HVAC operating data to obtain a single HVAC technical constraint model.

[0011] Furthermore, S1 also includes: calculating theoretical values ​​of thermal insulation performance coefficients of rooms where each HVAC device is located based on historical HVAC operation data; calculating theoretical values ​​of power-temperature conversion parameters of each HVAC device based on historical HVAC operation data; training individual HVAC basic models based on historical HVAC operation data to obtain training values ​​of thermal insulation performance coefficients of rooms where each HVAC device is located and training values ​​of power-temperature conversion parameters of each HVAC device; constructing a thermal insulation performance coefficient fluctuation curve based on theoretical values ​​of thermal insulation performance coefficients of rooms where each HVAC device is located and training values ​​of thermal insulation performance coefficients of rooms where each HVAC device is located; constructing a power-temperature conversion parameter fluctuation curve based on theoretical values ​​of power-temperature conversion parameters of each HVAC device and training values ​​of power-temperature conversion parameters of each HVAC device.

[0012] Furthermore, S1 also includes: training a single HVAC basic model according to historical HVAC operation data, and constructing a single HVAC technical constraint model with the trained single HVAC basic model, the thermal insulation performance coefficient fluctuation curve, and the power-temperature conversion parameter fluctuation curve.

[0013] Furthermore, S4 also includes: acquiring first relative position information between each HVAC and second relative position information between each HVAC and the power station; and constructing a HVAC load topology map according to the first relative position information and the second relative position information.

[0014] Furthermore, S4 also includes: constructing an ambient temperature difference topology map based on the historical operating environment temperatures of each HVAC system and the first relative position relationship; obtaining historical power plant operating data, and constructing an energy loss topology map based on the historical power plant operating data and the second relative position relationship; and constructing a HVAC load topology map using the ambient temperature difference topology map and the energy loss topology map.

[0015] Furthermore, the method of constructing an ambient temperature difference topology map based on the historical operating environment temperatures of each HVAC and the first relative position includes: taking each HVAC as a temperature difference topology node; calculating the difference in the operating environment temperatures of each HVAC in the same time period based on the historical operating environment temperatures of each HVAC, and constructing a historical temperature difference topology edge set between each HVAC based on the difference in the operating environment temperatures in each time period; calculating the temperature difference edge weight based on the historical temperature difference topology edge set; and constructing an ambient temperature difference topology map based on the temperature difference edge weight and the temperature difference topology node.

[0016] Furthermore, the obtaining of historical power plant operation data and constructing an energy loss topology map based on the historical power plant operation data and the second relative position relationship also includes: using the energy storage size of each HVAC and the load bearing capacity of the power plant as loss topology nodes; calculating the energy flow loss between adjacent power plants and HVAC based on the historical power plant operation data, and obtaining the loss edge weight based on the energy flow loss; and constructing an energy loss topology map based on the loss edge weights and loss topology nodes.

[0017] Furthermore, constructing a HVAC load topology map using an ambient temperature difference topology map and an electric energy loss topology map also includes: matching loss topology nodes with temperature difference topology nodes, and reconstructing load topology edges with temperature difference edge weights and loss edge weights.

[0018] The S6 also includes: obtaining historical regional load data, and constructing a load change curve based on the historical regional load data; obtaining current environmental data and current time series, and outputting load fluctuation values ​​for each region based on the current time series and the load change curve; using the load fluctuation values ​​for each region as constraints, and outputting a load control strategy based on the current environmental data, constraints, and optimization objective function.

[0019] Furthermore, the historical HVAC operation data at least includes each HVAC rated power, operating environment temperature and room temperature.

[0020] Another technical solution provided by the present application is a load control system based on the flexibility of HVAC load clusters, which is used to implement the method as mentioned above, including: a data acquisition module, used to collect HVAC data and environmental data; a data modeling module, used to build a HVAC aggregation model based on the data collected by the data acquisition module; a data topology module, used to build a HVAC load topology map based on the data collected by the data acquisition module; a data analysis module, used to build an optimization objective function based on the HVAC aggregation model and the HVAC load topology map constructed by the data modeling module and the data topology module, and output a load control strategy based on the data collected by the data acquisition module.

[0021] Another technical solution provided by the present application is an electronic device, which includes a processor, a memory and a battery module; the memory is used to store programs; the battery module is used to power the memory; the processor is used to execute the program and implement the load control method based on the flexibility of the HVAC load cluster as mentioned above when executing the program.

[0022] Another technical solution provided by the present application is a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction is executed by a processing device, the load control method based on the flexibility of the HVAC load cluster as described above is implemented.

[0023] The beneficial effects of the present application are as follows: a single HVAC technical constraint model is constructed corresponding to each HVAC operating parameter to show the power-temperature conversion of each HVAC in different working states, and then the equivalent energy storage corresponding to a single HVAC is obtained according to the power-temperature conversion based on the equivalent energy storage, the relative position information of the HVAC and the power supply station is obtained, a HVAC load topology map is constructed, and a HVAC aggregation model is constructed with all single HVAC equivalent energy storage models, and an optimization objective function is constructed with minimum energy consumption, so that when the current environmental parameters are obtained, a HVAC control strategy that satisfies the minimum energy loss, namely a load control strategy, can be output to adjust the power state of the HVAC, and the energy storage flexibility of the HVAC is utilized to meet user needs while reducing energy loss; at the same time, a single HVAC technical constraint model is constructed through the trained single HVAC basic model, the thermal insulation performance coefficient fluctuation curve, and the power-temperature conversion parameter fluctuation curve, and the difference between the theoretical value and the training value is added as a feedback compensation parameter to the single HVAC technical constraint model, and the error of the compensation model training is further improved to improve the accuracy of load control. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of the load control method based on the flexibility of HVAC load cluster in this application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application is further described in detail below in conjunction with the drawings and examples. It should be understood that the specific implementation method described here is only an optimal embodiment of the present application, which is only used to explain the present application and does not limit the scope of protection of the present application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0026] like Figure 1 As shown, as the first embodiment of the present application, the load control method based on the flexibility of the HVAC load cluster includes the following steps:

[0027] S1: Build a single HVAC technical constraint model based on each HVAC operation parameter in the HVAC load cluster;

[0028] S2: Construct a single HVAC equivalent energy storage model based on equivalent energy storage according to the single HVAC technical constraint model;

[0029] S3: Construct HVAC aggregation model with all individual HVAC equivalent energy storage models;

[0030] S4: Obtain the relative location information of HVAC and power station and construct HVAC load topology map;

[0031] S5: Construct optimization objective function based on minimum energy consumption according to HVAC load topology diagram and HVAC aggregation model;

[0032] S6: Acquire current environmental data, output a load control strategy based on the current environmental data and the optimization objective function, and execute load control based on the load control strategy.

[0033] In this embodiment, a single HVAC technical constraint model is constructed corresponding to each HVAC operating parameter to show the power-temperature conversion of each HVAC in different working states, and then the equivalent energy storage corresponding to a single HVAC is obtained according to the power-temperature conversion based on the equivalent energy storage, the relative position information of the HVAC and the power supply station is obtained, a HVAC load topology map is constructed, and a HVAC aggregation model is constructed with all single HVAC equivalent energy storage models, and an optimization objective function is constructed with minimum energy consumption. When the current environmental parameters are obtained, a HVAC control strategy that satisfies the minimum energy loss, namely a load control strategy, can be output to adjust the power state of the HVAC, and the energy storage flexibility of the HVAC can be used to meet user needs while reducing energy loss. According to the HVAC load topology diagram, the power flow loss at different relative positions of the power station and the HVAC is obtained. When the regional load is large, the HVAC load compensation capacity and the power flow loss are considered at the same time, so that the HVAC with greater power flow loss but sufficient energy storage can be added to the load compensation to reduce the remote supply of the power grid, avoid the situation where the HVAC near the power station performs load compensation, but the operation of the HVAC at the distant power station still causes additional load loss to the power grid, and improve the flexibility of load compensation.

[0034] HVAC refers to the system or related equipment responsible for indoor heating, ventilation and air conditioning. The load power of HVAC is:

[0035] ;

[0036] in, Indicates HVAC equipment in The load power at a moment; Indicates HVAC equipment in The opening state at a moment; Indicates The rated power of a HVAC device. That is, when the HVAC is turned on, its load power is the rated power, and when the HVAC is turned off, its load power is 0.

[0037] According to heat transfer, the indoor temperature of the building changes under the action of HVAC as follows:

[0038] ;

[0039] in, Indicates The room where the HVAC equipment is located is in Room temperature at a certain moment; Indicates The room where the HVAC equipment is located is in The ambient temperature at a given moment; Indicates The room where the HVAC equipment is located is in Room temperature at a certain moment; Indicates The thermal insulation performance coefficient of the room where the HVAC equipment is located; Indicates Power-temperature conversion parameters for HVAC equipment; Indicates HVAC equipment in The load power at a moment; Indicates HVAC equipment in The open state at a moment.

[0040] The switching status of HVAC equipment is converted to:

[0041] ;

[0042] in, Indicates the set temperature of each HVAC unit; Indicates The temperature difference threshold of each HVAC device. The temperature difference threshold can be set according to the temperature difference acceptable to the human body. When the temperature error is within the allowable range of the temperature difference threshold, the HVAC working state remains unchanged; when the temperature exceeds the temperature difference threshold range, the HVAC working state is reversed.

[0043] Therefore, a single HVAC technical constraint model is constructed based on each HVAC operation parameter in the HVAC load cluster, including:

[0044] A single HVAC load power model is constructed based on the rated operating power of each HVAC in the HVAC load cluster;

[0045] According to the heat transfer effect and the parameters of each HVAC operating room, a single HVAC temperature change model is constructed;

[0046] A single HVAC technical constraint model is constructed based on a single HVAC load power model and a single HVAC temperature change model.

[0047] At this time, the HVAC operation parameters at least include the HVAC operation rated power and the HVAC operation room parameters. When the HVAC operation parameters are obtained, the temperature change of the room can be obtained through the HVAC technical constraint model, and the HVAC on / off state at the next moment can be obtained.

[0048] The HVAC model has discrete switching operation states, but in a large-scale HVAC cluster, the number of HVAC equipment is large, so a single HVAC equipment can be regarded as a device model with continuous regulation properties, and the model of each HVAC equipment can be regarded as an energy storage. At this time, based on the equivalent energy storage, a single HVAC equivalent energy storage model is constructed according to the single HVAC technical constraint model:

[0049] ;

[0050] ;

[0051] ;

[0052] in, Indicates HVAC equipment in The power of equivalent energy storage at a moment; Indicates HVAC equipment in The equivalent energy storage at each moment; Indicates HVAC equipment in The equivalent energy storage at each moment; Indicates HVAC equipment in The power of equivalent energy storage at a moment; Indicates Energy loss rate of equivalent energy storage of each HVAC equipment; Indicates Energy conversion rate of equivalent energy storage of each HVAC equipment; Indicates HVAC equipment in The upper bound of energy storage at a moment; Indicates HVAC equipment in The lower bound of the energy storage at a moment.

[0053] The energy loss rate of the equivalent energy storage of HVAC equipment is:

[0054] ;

[0055] Where T represents the total duration of equivalent energy storage; Indicates The room where the HVAC equipment is located is in The ambient temperature at a given moment.

[0056] The energy conversion rate of the equivalent energy storage of HVAC equipment is:

[0057] .

[0058] The upper bound of the energy storage capacity of HVAC equipment is:

[0059] ;

[0060] in, Represents the upper bound of the energy storage capacity of HVAC equipment.

[0061] The lower bound of the energy storage capacity of HVAC equipment is:

[0062] ;

[0063] in, Represents the lower bound of the energy storage capacity of HVAC equipment.

[0064] Construct an HVAC aggregate model with all individual HVAC equivalent energy storage models:

[0065] ;

[0066] ;

[0067] in, Indicates that the HVAC cluster is in The overall merit of a moment; Indicates that the HVAC cluster is in The overall active upper bound at a moment; Indicates that the HVAC cluster is in The upper bound of the overall energy storage at a moment; Indicates that the HVAC cluster is in The lower bound of the overall energy storage energy at a moment.

[0068] The overall active upper bound of the HVAC cluster is:

[0069] ;

[0070] in, represents the overall active upper bound of the HVAC cluster, Indicates the flexibility power aggregation adjustment factor.

[0071] The upper bound of the overall energy storage capacity of the HVAC cluster is:

[0072] ;

[0073] in, represents the upper bound of the overall energy storage capacity of the HVAC cluster, Indicates the flexibility power aggregation adjustment factor.

[0074] The lower bound of the overall energy storage capacity of the HVAC cluster is:

[0075] ;

[0076] in, represents the lower bound of the overall energy storage capacity of the HVAC cluster, Indicates the flexibility power aggregation adjustment factor.

[0077] The flexibility power aggregation adjustment factor is:

[0078] ;

[0079] in, It represents the average value of the rated power of each HVAC equipment; N represents the total number of HVAC equipment. The flexibility power aggregation adjustment factor represents the consistency between the rated power of each HVAC equipment and the average power. If the result is 1, it means that the rated power is completely concentrated without dispersion. If the result is close to 0, it means that the rated power is relatively dispersed.

[0080] The average power ratings of the various HVAC equipment are:

[0081] .

[0082] In this embodiment, a single HVAC technical constraint model obtains the change of the current room temperature through different on / off states of the HVAC in different states of the room and the corresponding power conditions. At this time, a single HVAC technical constraint model is constructed based on each HVAC operating parameter in the HVAC load cluster, and further includes:

[0083] A single HVAC basic model is constructed based on each HVAC operating parameter in the HVAC load cluster;

[0084] The historical HVAC operation data is obtained, and a single HVAC basic model is trained according to the historical HVAC operation data to obtain a single HVAC technical constraint model.

[0085] Among them, the historical HVAC operation data at least includes the rated power of each HVAC, the operating environment temperature and the room temperature. The room insulation performance coefficient of the room where each HVAC equipment is located and the power-temperature conversion parameters of the HVAC equipment can be obtained through the historical HVAC operation data. At this time, due to usage needs, HVAC will continue to be added to the HVAC load cluster. When the newly added HVAC and the room where it is located do not match any of the historical HVAC operation data, although the estimated room insulation performance coefficient of the room where the HVAC equipment is located and the power-temperature conversion parameters of the HVAC equipment can be obtained through the constructed single HVAC technical constraint model, there may be inaccuracies in the estimation. Therefore, step S1 also includes:

[0086] Calculate the theoretical value of the thermal insulation performance coefficient of each room where the HVAC equipment is located based on historical HVAC operation data;

[0087] Calculate the theoretical value of the power-temperature conversion parameter of each HVAC equipment based on historical HVAC operation data;

[0088] According to the historical HVAC operation data, a single HVAC basic model is trained to obtain the training value of the room insulation performance coefficient where each HVAC equipment is located and the training value of the power-temperature conversion parameter of each HVAC equipment;

[0089] Constructing a thermal insulation performance coefficient fluctuation curve according to the theoretical value of the thermal insulation performance coefficient of the room where each HVAC equipment is located and the training value of the thermal insulation performance coefficient of the room where each HVAC equipment is located;

[0090] A power-temperature conversion parameter fluctuation curve is constructed according to the power-temperature conversion parameter theoretical value of each HVAC device and the power-temperature conversion parameter training value of each HVAC device.

[0091] At this time, the historical HVAC operation data also includes the theoretical thermal resistance of the room where the HVAC equipment is located, the theoretical heat capacity of the room where the HVAC equipment is located, and the theoretical heat transfer efficiency of the HVAC equipment. Then, according to the theoretical calculation formula of the thermal insulation performance coefficient of the room where the HVAC equipment is located and the theoretical calculation formula of the power-temperature conversion parameter of the HVAC equipment, the theoretical value of the thermal insulation performance coefficient of the room where each HVAC equipment is located and the theoretical value of the power-temperature conversion parameter of each HVAC equipment are calculated respectively.

[0092] The theoretical calculation formula for the thermal insulation performance coefficient of the room where the HVAC equipment is located is:

[0093] ;

[0094] in, Indicates The thermal resistance of the room where the HVAC equipment is located; Indicates The heat capacity of the room where the HVAC equipment is located; The time granularity interval representing the temperature change; It represents assignment, that is, in this embodiment, the response speed of the room where the HVAC equipment is located to the temperature change and the time granularity interval of the temperature change are assigned as the thermal insulation performance coefficient of the room where the HVAC equipment is located.

[0095] The theoretical calculation formula for the power-temperature conversion parameter of HVAC equipment is:

[0096] ;

[0097] in, Indicates The heat transfer efficiency of a HVAC device.

[0098] Therefore, the difference between the theoretical value of the room thermal insulation performance coefficient where the HVAC equipment is located and the theoretical value of the power-temperature conversion parameter of the HVAC equipment and the actual value can be understood that during the training process, since the historical HVAC operation data is used for training, the actual value of the temperature change of the indoor temperature of the building under the action of the HVAC can be obtained, and then the room thermal insulation performance coefficient and the power-temperature conversion parameter under the actual situation can be obtained, and the training value is the actual value. Therefore, step S1 also includes:

[0099] According to the historical HVAC operation data, a single HVAC basic model is trained respectively, and a single HVAC technical constraint model is constructed with the trained single HVAC basic model, the thermal insulation performance coefficient fluctuation curve and the power-temperature conversion parameter fluctuation curve.

[0100] In this embodiment, a single HVAC technical constraint model is constructed by using a trained single HVAC basic model, a thermal insulation performance coefficient fluctuation curve, and a power-temperature conversion parameter fluctuation curve, and the difference between the theoretical value and the training value is added to the single HVAC technical constraint model as a feedback compensation parameter. For example, when a new HVAC is connected, the room thermal insulation performance coefficient training value and the power-temperature conversion parameter training value are output according to the trained single HVAC basic model, and the room thermal insulation performance coefficient theoretical value and the power-temperature conversion parameter theoretical value are calculated according to the new HVAC theoretical parameters. It is calculated whether the difference between the training value and the theoretical value conforms to the thermal insulation performance coefficient fluctuation curve and the power-temperature conversion parameter fluctuation curve. If not, the feedback compensation value is output according to the thermal insulation performance coefficient fluctuation curve and the power-temperature parameter fluctuation curve to compensate the training value.

[0101] Then, a single HVAC equivalent energy storage model is constructed according to the room insulation performance coefficient and power-temperature conversion parameters output by the single HVAC technical constraint model, and the HVAC aggregation model is constructed by integrating all single HVAC equivalent energy storage models.

[0102] Step S4 also includes:

[0103] Acquire first relative position information between each HVAC and second relative position information between each HVAC and the power station;

[0104] A HVAC load topology diagram is constructed according to the first relative position information and the second relative position information.

[0105] The HVAC load topology diagram shows the positional relationship between HVAC and the HVAC and between the HVAC and the power station, and then the loss relationship of electric energy in the flow process and the ambient temperature relationship can be obtained through the relative position relationship. Therefore, step S4 also includes:

[0106] Constructing an ambient temperature difference topology map according to the historical operating ambient temperatures of each HVAC and the first relative position relationship;

[0107] Acquire historical power plant operation data, and construct a power loss topology map according to the historical power plant operation data and the second relative position relationship;

[0108] The HVAC load topology map is constructed based on the ambient temperature difference topology map and the power loss topology map.

[0109] Specifically, constructing an ambient temperature difference topology map according to the historical operating ambient temperatures of each HVAC and the first relative position includes:

[0110] Take each HVAC as a temperature difference topology node;

[0111] According to the historical operating environment temperatures of each HVAC, the differences in the operating environment temperatures of each HVAC in the same time period are calculated, and the historical temperature difference topological edge set between each HVAC is constructed based on the differences in the operating environment temperatures in each time period;

[0112] The temperature difference edge weight is calculated using the historical temperature difference topological edge set;

[0113] The environmental temperature difference topology graph is constructed using the temperature difference edge weights and temperature difference topology nodes.

[0114] Take each HVAC as a temperature difference topological node, calculate the temperature difference edge weights between each HVAC according to the historical temperature difference topological edges, and construct an ambient temperature difference topological map with the temperature difference edge weights and temperature difference topological nodes. Then, the temperature rise and fall between each topological temperature difference topological node can be obtained, and the ambient temperature difference topological map can be used to show the ambient temperature differences of HVAC in different geographical locations.

[0115] Acquiring historical power plant operation data, and constructing a power loss topology map according to the historical power plant operation data and the second relative position relationship also includes:

[0116] The energy storage size of each HVAC and the load bearing capacity of the power station are used as loss topology nodes;

[0117] The power flow loss between the adjacent power station and the HVAC is calculated based on the historical power station operation data, and the loss edge weight is obtained based on the power flow loss;

[0118] Construct an energy loss topology graph using loss edge weights and loss topology nodes.

[0119] Taking each HVAC and power station as a loss topological node, an energy loss topological graph with edges as loss edge weights can be constructed. Based on the difference in the load bearing capacity of the power station and the energy storage size of the HVAC, the difference is reflected in the energy loss topological graph through the size of the topological node, which can show the energy flow between HVAC with different energy storage sizes and power stations with different load bearing capacities.

[0120] Furthermore, the HVAC load topology map constructed by using the ambient temperature difference topology map and the power loss topology map also includes:

[0121] The temperature difference topology nodes are matched with the loss topology nodes, and the load topology edges are reconstructed with the temperature difference edge weights and the loss edge weights.

[0122] Since the temperature difference topology node does not show the energy storage size of the HVAC, matching the loss topology node with the temperature difference topology node can retain nodes with more information and avoid information loss during the topology fusion process. At the same time, the temperature difference edge weight and the loss edge weight reconstruct the load topology edge. At this time, the load topology edge shows the temperature difference and loss, which is convenient for subsequent HVAC cluster adjustment according to the topology diagram.

[0123] In this embodiment, the difference in HVAC ambient temperature in different geographical locations can be obtained by constructing an ambient temperature difference topology map. Under normal circumstances, the ambient temperature of the HVAC is directly obtained according to the weather conditions of the entire area or according to its own temperature sensor. However, the weather in the entire area will also present different conditions due to changes in geographical location. Directly obtaining the ambient temperature according to the weather conditions of the entire area for adjustment will result in adjustment deviation and low accuracy. However, according to the acquisition of its own temperature sensor, in the entire HVAC cluster deployment process, the sensor data of all HVAC clusters is obtained, and there is a large amount of data processing and analysis. Once the sensor of a certain HVAC is damaged, it will directly lead to the inability to perform regulation. Therefore, according to the ambient temperature difference topology map, the ambient temperature of all HVACs can be directly obtained according to the ambient temperature of a certain HVAC, or the ambient temperature of all HVAC clusters can be obtained according to the regional temperature. The accuracy is high, and it is not affected by sensor damage, and the stability is stronger. At the same time, when constructing the power loss topology diagram, the size of the loss topology node is used to show the energy storage size of the HVAC and the load bearing capacity of the power station. Therefore, in the process of constructing the optimization objective function, the relationship between the energy storage size, power flow loss, and load bearing capacity can be directly obtained, which is convenient for selecting HVAC with stronger or farther energy storage size to share the load when the load is high.

[0124] Then, based on the minimum energy consumption, the optimization objective function is constructed according to the HVAC load topology diagram and the HVAC aggregation model:

[0125] ;

[0126] in, Indicates The energy demand of each HVAC equipment Indicates The power loss coefficient between each HVAC equipment and the nearest power station.

[0127] The energy demand is obtained according to the ambient temperature difference topology map and the HVAC aggregation model. That is, the actual ambient temperature of each HVAC can be obtained according to the ambient temperature difference topology map and the current environmental data. Then, the HVAC aggregation model is used to output the equivalent energy storage. The power variation range of the HVAC and the different start and close times of the HVAC under the corresponding power can be obtained with the equivalent energy storage, that is:

[0128] ;

[0129] HVAC can use technologies such as ground source heat pumps, water source heat pumps or air source heat pumps to achieve equivalent energy storage. The stored energy can be released when needed. Therefore, HVAC can change its working state according to the flexibility of energy storage. It can also use energy storage release and electric energy conversion to jointly achieve energy supply.

[0130] In another embodiment, step S5 also includes: constructing a first optimization objective function based on the minimum energy consumption according to the HVAC load topology map and the HVAC aggregation model, constructing a second optimization objective function based on the HVAC load topology map and the HVAC aggregation model based on the maximum energy storage, and taking the maximum balanced load of the power grid as the constraint condition of the second optimization objective function. At this time, step S6 also includes: obtaining the current load data, and judging whether to perform the first optimization objective function optimization or the second optimization objective function optimization according to the current load data. If the current load data exceeds the preset load threshold, the first optimization objective function optimization is performed, and the HVAC that realizes load compensation is selected with the minimum energy consumption; if the current load data is less than the preset load threshold, the second optimization objective function optimization is performed, and the maximum energy storage is used to make each HVAC perform energy storage under load balance. In this embodiment, the different situations of load compensation or energy storage of the HVAC are divided by the preset load threshold, that is, the maximum balanced load of the power grid, so that the HVAC stores energy when the power grid load is small, and releases energy to compensate the power grid load when the power grid load is large. Thus, the calculated HVAC working state is used as the load control strategy, and the load control strategy is used to perform load control.

[0131] As a second embodiment of the present application, considering the load changes in different regions, for example, the high load peak in the industrial area is likely to be concentrated during the day, while the high load peak in the living area is likely to be concentrated at night, it is necessary to reasonably adjust the start and stop of the HVAC according to the load changes in different regions. At this time, step S6 also includes:

[0132] Obtain historical regional load data and construct a load change curve based on the historical regional load data;

[0133] Obtain current environmental data and current time series, and output load fluctuation values ​​for each region based on the current time series and load change curve;

[0134] Taking the load fluctuation value of each region as the constraint condition, the load control strategy is output according to the current environmental data, constraint conditions and optimization objective function.

[0135] In this embodiment, by constructing a load change curve, the load change situation of each region at different time points is obtained, and then the load fluctuation value of each current region compared with the next time sequence is obtained according to the current time sequence. It can be understood that the default current time sequence load does not generate an overload alarm, that is, the load of the current time sequence must be lower than the load alarm value. If the load of the next time sequence is greater than the load of the current time sequence, the load fluctuation value is negative. If the load of the next time sequence is less than the load of the current time sequence, the load fluctuation value is positive. That is, the load fluctuation is estimated based on the historical load change situation, so as to determine the load fluctuation direction of the HVAC load clusters located in different regions. That is, if the load fluctuation value is negative, the constraint condition is that the total power of the HVAC load cluster in the current region is less than the total power of the HVAC load cluster in the previous time sequence. If the load fluctuation value is positive, the constraint condition is that the total power of the HVAC load cluster in the current region can be greater than the total power of the HVAC load cluster in the previous time sequence. In other cases, the load fluctuation value of each region is the difference between the current time series load of each region and the next time series load of each region. The constraint condition also includes that the difference between the total power fluctuation value of the HVAC load cluster in all regions and the load fluctuation value is less than or equal to 0, so as to avoid the power adjustment of the HVAC load cluster exceeding the load bearing value.

[0136] As a third embodiment of the present application, a load control system based on the flexibility of a HVAC load cluster includes:

[0137] Data acquisition module, used to collect HVAC data and environmental data;

[0138] A data modeling module, used to build a HVAC aggregation model based on the data collected by the data acquisition module;

[0139] A data topology module is used to construct a HVAC load topology diagram based on the data collected by the data acquisition module;

[0140] The data analysis module is used to construct an optimization objective function based on the HVAC aggregation model and HVAC load topology diagram constructed by the data modeling module and the data topology module, and output a load control strategy based on the data collected by the data acquisition module.

[0141] In this embodiment, the data acquisition module is connected to the data modeling module, the data topology module and the data analysis module respectively, and the data modeling module is connected to the data topology module and the data analysis module.

[0142] The HVAC data at least includes HVAC operation parameters, historical HVAC operation data, historical power plant operation data, and historical regional load data.

[0143] The data analysis module includes at least a function construction unit and a processing and analysis unit. The function construction unit is used to construct an optimization objective function based on the HVAC aggregation model and the HVAC load topology diagram constructed by the data modeling module and the data topology module. The processing and analysis module receives the current environmental data and current time series output by the data acquisition module, and outputs the load control strategy using the optimization objective function.

[0144] As a fourth embodiment of the present application, the electronic device includes a processor, a memory and a battery module; the memory is used to store programs; the battery module is used to power the memory; the processor is used to execute the program and implement the load control method based on the flexibility of the HVAC load cluster as mentioned above when executing the program.

[0145] As a fifth embodiment of the present application, a computer-readable storage medium is used to store a computer program or instruction. When the computer program or instruction is executed by a processing device, the above-mentioned load control method based on the flexibility of the HVAC load cluster is implemented. The computer-readable storage medium can be any available medium that can be stored by a computing device or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state hard disk), etc.

[0146] The specific implementation method described above is a preferred implementation method of the load control method and system based on the flexibility of the HVAC load cluster of this application, and is not intended to limit the specific implementation scope of this application. The scope of this application includes but is not limited to this specific implementation method. All equivalent changes made in accordance with the shape and structure of this application are within the protection scope of this application.

Claims

1. A load control method based on the flexibility of HVAC load cluster, characterized by: The steps include: S1: Build a single HVAC technical constraint model based on each HVAC operation parameter in the HVAC load cluster; S2: Construct a single HVAC equivalent energy storage model based on equivalent energy storage according to the single HVAC technical constraint model; S3: Construct HVAC aggregation model with all individual HVAC equivalent energy storage models; S4: Obtain the relative location information of HVAC and power station and construct HVAC load topology map; S5: Construct optimization objective function based on minimum energy consumption according to HVAC load topology diagram and HVAC aggregation model; S6: obtaining current environmental data, outputting a load control strategy based on the current environmental data and the optimization objective function, and executing load control based on the load control strategy; S5 also includes: constructing a first optimization objective function based on minimum energy consumption according to the HVAC load topology map and the HVAC aggregation model, constructing a second optimization objective function based on maximum energy storage according to the HVAC load topology map and the HVAC aggregation model, and taking the maximum balanced load of the power grid as a constraint condition of the second optimization objective function; S6 also includes: determining to perform the first optimization objective function optimization or the second optimization objective function optimization according to the current load data; Among them, a single HVAC basic model is constructed based on each HVAC operating parameter in the HVAC load cluster; Obtain historical HVAC operation data, train individual HVAC basic models based on the historical HVAC operation data, and obtain individual HVAC technical constraint models; Calculate the theoretical value of the room insulation performance coefficient where each HVAC equipment is located and the theoretical value of the power-temperature conversion parameter of each HVAC equipment based on the historical HVAC operation data; According to the historical HVAC operation data, a single HVAC basic model is trained to obtain the training value of the room insulation performance coefficient where each HVAC equipment is located and the training value of the power-temperature conversion parameter of each HVAC equipment; Constructing a thermal insulation performance coefficient fluctuation curve according to the theoretical value of the thermal insulation performance coefficient of the room where each HVAC equipment is located and the training value of the thermal insulation performance coefficient of the room where each HVAC equipment is located; Constructing a power-temperature conversion parameter fluctuation curve according to the power-temperature conversion parameter theoretical value of each HVAC device and the power-temperature conversion parameter training value of each HVAC device; Acquire first relative position information between each HVAC and second relative position information between each HVAC and the power station; Constructing an ambient temperature difference topology map according to the historical operating ambient temperatures of each HVAC and the first relative position relationship; Acquire historical power plant operation data, and construct a power loss topology map according to the historical power plant operation data and the second relative position relationship; The HVAC load topology map is constructed based on the ambient temperature difference topology map and the power loss topology map.

2. The load control method based on HVAC load cluster flexibility according to claim 1, characterized in that: The method of constructing a single HVAC technical constraint model based on each HVAC operation parameter in the HVAC load cluster includes: A single HVAC load power model is constructed based on the rated operating power of each HVAC in the HVAC load cluster; According to the heat transfer effect and the parameters of each HVAC operating room, a single HVAC temperature change model is constructed; A single HVAC technical constraint model is constructed based on a single HVAC load power model and a single HVAC temperature change model.

3. The load control method based on HVAC load cluster flexibility according to claim 1, characterized in that: The S1 further comprises: According to the historical HVAC operation data, a single HVAC basic model is trained respectively, and a single HVAC technical constraint model is constructed with the trained single HVAC basic model, the thermal insulation performance coefficient fluctuation curve and the power-temperature conversion parameter fluctuation curve.

4. The load control method based on HVAC load cluster flexibility according to claim 1, characterized in that: The step of constructing an ambient temperature difference topology map according to the historical operating ambient temperatures of each HVAC system and the first relative position includes: Take each HVAC as a temperature difference topology node; According to the historical operating environment temperatures of each HVAC, the differences in the operating environment temperatures of each HVAC in the same time period are calculated, and the historical temperature difference topological edge set between each HVAC is constructed based on the differences in the operating environment temperatures in each time period; The temperature difference edge weight is calculated using the historical temperature difference topological edge set; The environmental temperature difference topology graph is constructed using the temperature difference edge weights and temperature difference topology nodes.

5. The load control method based on HVAC load cluster flexibility according to claim 4, characterized in that: The acquiring of historical power plant operation data and constructing a power loss topology map according to the historical power plant operation data and the second relative position relationship further includes: The energy storage size of each HVAC and the load bearing capacity of the power station are used as loss topology nodes; The power flow loss between the adjacent power station and the HVAC is calculated based on the historical power station operation data, and the loss edge weight is obtained based on the power flow loss; Construct an energy loss topology graph using loss edge weights and loss topology nodes.

6. The load control method based on HVAC load cluster flexibility according to claim 5, characterized in that: The construction of the HVAC load topology map using the ambient temperature difference topology map and the power loss topology map also includes: The temperature difference topology nodes are matched with the loss topology nodes, and the load topology edges are reconstructed with the temperature difference edge weights and the loss edge weights.

7. The load control method based on HVAC load cluster flexibility according to claim 1, characterized in that: The S6 further includes: Obtain historical regional load data and construct a load change curve based on the historical regional load data; Obtain current environmental data and current time series, and output load fluctuation values ​​for each region based on the current time series and load change curve; Taking the load fluctuation value of each region as the constraint condition, the load control strategy is output according to the current environmental data, constraint conditions and optimization objective function.

8. The load control method based on HVAC load cluster flexibility according to claim 1, characterized in that: The historical HVAC operation data at least includes the rated power, operating environment temperature and room temperature of each HVAC.

9. A load control system based on the flexibility of HVAC load cluster, used to implement the method according to any one of claims 1 to 8, characterized in that: include: Data acquisition module, used to collect HVAC data and environmental data; A data modeling module, used to build a HVAC aggregation model based on the data collected by the data acquisition module; A data topology module is used to construct a HVAC load topology diagram based on the data collected by the data acquisition module; The data analysis module is used to construct an optimization objective function based on the HVAC aggregation model and HVAC load topology diagram constructed by the data modeling module and the data topology module, and output a load control strategy based on the data collected by the data acquisition module.

10. An electronic device, characterized in that: The electronic device includes a processor, a memory and a battery module; The memory is used to store programs; The battery module is used to supply power to the memory; The processor is used to execute the program and implement the load control method based on the flexibility of the HVAC load cluster as described in any one of claims 1 to 8 when executing the program.

11. A computer-readable storage medium, characterized in that: The storage medium stores a computer program or instruction. When the computer program or instruction is executed by a processing device, the load control method based on the flexibility of the HVAC load cluster as described in any one of claims 1 to 8 is implemented.

Images