Cooperative control method, system and equipment for providing operation reserve for power grid by considering multiple types of adjustable loads on demand side and medium

Abstract

The invention discloses a cooperative control method, system and device for providing operation standby for a power grid by considering multiple types of adjustable loads on a demand side and a medium, and belongs to the technical field of power grid cooperative control, and the method comprises the steps: collecting operation state data and power utilization behavior data of a distributed air conditioner and distributed energy storage on the demand side; based on the collected data, aggregating adjustable and controllable resources of a demand side distributed air conditioner and distributed energy storage, and constructing an energy-storage-like battery energy consumption model of the air conditioner and an energy consumption model of energy storage; a double-layer distributed consensus control algorithm is designed, the upper layer is distributed energy storage resources, and the lower layer is distributed air conditioner resources; and receiving a power regulation and control instruction issued by the power grid dispatching center, and regulating and controlling the aggregated adjustable load based on a double-layer distributed consensus control algorithm so as to respond to the power regulation and control instruction. Based on a two-layer distributed consensus control algorithm, the output of multiple types of adjustable loads can be coordinated, and the response precision and response speed of an adjustable load aggregator are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid cooperative control, and particularly relates to a cooperative control method, system, device and medium for providing operation backup for a power grid by considering multiple types of adjustable loads on the demand side. BACKGROUND

[0002] With the increasing proportion of new energy power generation in the power system, the generation of the power system is continuously affected by the environment, showing the characteristics of large fluctuation and increasing difference between peak and valley within a day, so the power system needs a large amount of operation backup to realize the dynamic balance between source and load. Aggregating the distributed adjustable resources on the demand side and flexibly regulating the demand side resources to realize "load following source" is a very promising solution. Among the demand side resources, energy storage and air conditioners have great adjustment potential: energy storage has fast response speed and high response accuracy; air conditioners have a large proportion of power consumption during peak power consumption periods. With the increasing demand for operation backup of the power system, regulating a single resource cannot meet the regulation and control needs of the power system, and the adjustable resource aggregator needs to cooperatively regulate multiple types of adjustable loads to participate in the service of the power grid, and the control effect of the aggregator will greatly affect the aggregator's own benefits and the stable operation of the power grid.

[0003] To solve this problem, the present application designs a double-layer distributed consensus control framework to realize the cooperative control of multiple types of adjustable resources, effectively improves the response accuracy and response speed of the aggregator, and facilitates the guidance of the participation of multiple types of adjustable loads in the aggregator in the service of the power grid under the new power system. Provide operation backup. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is: how to solve the problem that with the increasing demand for operation backup of the power system, regulating a single resource cannot meet the regulation and control needs of the power system, and the adjustable resource aggregator needs to cooperatively regulate multiple types of adjustable loads to participate in the service of the power grid, and the control effect of the aggregator will greatly affect the aggregator's own benefits and the stable operation of the power grid.

[0006] To solve the above technical problems, the application provides the following technical scheme: a collaborative control method for providing operation backup for a power grid by considering multiple types of adjustable loads on the demand side, comprising: collecting operation state data and power consumption behavior data of distributed air conditioners and distributed energy storage on the demand side; based on the collected data, aggregating adjustable resources of the distributed air conditioners and the distributed energy storage, and constructing an energy consumption model of air conditioner-like energy storage batteries and an energy consumption model of energy storage; designing a double-layer distributed consensus control algorithm, with the upper layer being distributed energy storage resources and the lower layer being distributed air conditioner resources; receiving a power regulation instruction issued by a power grid dispatching center, and based on the double-layer distributed consensus control algorithm, regulating and controlling the aggregated adjustable loads to respond to the power regulation instruction.

[0007] As a preferred scheme of the collaborative control method for providing operation backup for a power grid by considering multiple types of adjustable loads on the demand side, wherein: the collecting operation state data and power consumption behavior data of distributed air conditioners and distributed energy storage on the demand side comprises: obtaining operation state data and power consumption behavior data of the air conditioners and the energy storage devices within a target regulation period; performing time synchronization processing on the obtained data; and pre-processing the time-synchronized data.

[0008] As a preferred scheme of the collaborative control method for providing operation backup for a power grid by considering multiple types of adjustable loads on the demand side, wherein: based on the collected data, aggregating adjustable resources of the distributed air conditioners and the distributed energy storage, and constructing an energy consumption model of air conditioner-like energy storage batteries and an energy consumption model of energy storage, comprises: based on the pre-processed operation state data and power consumption behavior data, screening distributed air conditioner resources and distributed energy storage resources with regulation capabilities; aggregating the screened resources according to response capabilities and regulation characteristics; converting thermodynamic behavior of the distributed air conditioner resources into energy storage model parameters to construct the energy consumption model of air conditioner-like energy storage batteries and the energy consumption model of energy storage.

[0009] As a preferred scheme of the collaborative control method for providing operation backup for a power grid by considering multiple types of adjustable loads on the demand side, wherein: the double-layer distributed consensus control algorithm is designed, with the upper layer being distributed energy storage resources and the lower layer being distributed air conditioner resources, comprising: establishing an upper-layer control structure of the distributed energy storage resources; establishing a lower-layer control structure of the distributed air conditioner resources; setting an information interaction mechanism between the upper layer and the lower layer to realize collaborative control between the distributed air conditioner resources and the distributed energy storage resources; and based on a consensus mechanism, determining overall regulation behavior of the aggregated resources and tracking power grid dispatching instructions.

[0010] As a preferred scheme of the collaborative control method for providing operation backup for power grid by considering multiple types of adjustable loads on demand side, in the scheme: the thermodynamic behavior of the distributed air conditioner resource is converted into a parameter of a pseudo energy storage model, an energy model of a pseudo energy storage battery of the air conditioner is constructed, including: obtaining equivalent heat capacity and equivalent thermal resistance of a room to which each distributed air conditioner resource belongs; setting operation parameters of the air conditioner compressor to represent the adjustment rate of indoor temperature per unit time and the compressor refrigeration performance; setting the highest temperature and the lowest temperature range allowed to be adjusted for each air conditioner resource in a target regulation period; determining the adjustable capacity range of the air conditioner based on the operation state data, the operation parameters and the upper and lower limits of the room temperature, and constructing the energy model of the pseudo energy storage battery of the air conditioner.

[0011] The preferred scheme can model the air conditioner load that does not have physical energy storage properties in a pseudo battery form by converting the thermodynamic behavior of the distributed air conditioner resource into a parameter of a pseudo energy storage model, so that the air conditioner has a power response capability consistent with the physical energy storage resource, thereby participating in backup scheduling in a unified control framework.

[0012] As a preferred scheme of the collaborative control method for providing operation backup for power grid by considering multiple types of adjustable loads on demand side, in the scheme: the thermodynamic behavior of the distributed air conditioner resource is converted into a parameter of a pseudo energy storage model, an energy model of a pseudo energy storage battery of the air conditioner is constructed, including: obtaining equivalent heat capacity and equivalent thermal resistance of a room to which each distributed air conditioner resource belongs; setting operation parameters of the air conditioner compressor to represent the adjustment rate of indoor temperature per unit time and the compressor refrigeration performance; setting the highest temperature and the lowest temperature range allowed to be adjusted for each air conditioner resource in a target regulation period; determining the adjustable capacity range of the air conditioner based on the operation state data, the operation parameters and the upper and lower limits of the room temperature, and constructing the energy model of the pseudo energy storage battery of the air conditioner.

[0013] The preferred scheme can model the air conditioner load that does not have physical energy storage properties in a pseudo battery form by converting the thermodynamic behavior of the distributed air conditioner resource into a parameter of a pseudo energy storage model, so that the air conditioner has a power response capability consistent with the physical energy storage resource, thereby participating in backup scheduling in a unified control framework.

[0014] As a preferred scheme of the collaborative control method for providing operation backup for the power grid by considering the demand side multi-type adjustable load, in the scheme, the double-layer distributed consensus control algorithm is used to regulate and control the aggregated adjustable load to respond to the power regulation instruction, and the method comprises the following steps: receiving the regulation instruction issued by the power grid dispatching center, wherein the instruction contains the power demand in a target time period; the double-layer consensus control algorithm is used to schedule the distributed air conditioner resource and the distributed energy storage resource to participate in the regulation; at each time, the response capacity of the aggregated resource is regulated; the response capacity is composed of the power consumption change of the distributed air conditioner resource and the distributed energy storage resource; the distributed air conditioner resource and the distributed energy storage resource are respectively composed of a plurality of regulatable resources, and independently respond to the power change at each regulation time.

[0015] The preferred scheme establishes a unified collaborative regulation mechanism between the air conditioner resource and the energy storage resource by using the double-layer distributed consensus control algorithm, so that various types of regulatable resources can respond to the power grid regulation instruction as needed, and a stable response capacity is formed at each regulation time, thereby effectively supporting the continuity and dispatchability of the power system operation backup service.

[0016] The application provides a collaborative control system for providing operation backup for the power grid by considering the demand side multi-type adjustable load.

[0017] To solve the above technical problems, the application provides the following technical scheme: a collaborative control system for providing operation backup for the power grid by considering the demand side multi-type adjustable load, comprising: a data acquisition module, a resource aggregation module, a control algorithm module and an execution module; the data acquisition module is used to acquire the operation state data and the power consumption behavior data of the demand side distributed air conditioner and the distributed energy storage; the resource aggregation module is used to aggregate the regulatable resources of the demand side distributed air conditioner and the distributed energy storage based on the acquired data, and construct the air conditioner battery-like energy consumption model and the energy consumption model of the energy storage; the control algorithm module is used to design a double-layer distributed consensus control algorithm, wherein the upper layer is the distributed energy storage resource, and the lower layer is the distributed air conditioner resource; and the execution module is used to receive the power regulation instruction issued by the power grid dispatching center, and regulate and control the aggregated adjustable load to respond to the power regulation instruction based on the double-layer distributed consensus control algorithm.

[0018] The application provides a computer device, comprising a memory and a processor, and the memory stores a computer program, characterized in that the processor implements the steps of the collaborative control method for providing operation backup for the power grid by considering the demand side multi-type adjustable load when executing the computer program.

[0019] The application provides a computer readable storage medium, which stores a computer program, and the computer program is characterized in that the computer program is executed by a processor to realize the steps of the coordinated control method for providing operation backup for a power grid by considering a demand side multi-type adjustable load.

[0020] The application has the beneficial effects that: the application is based on a two-layer distributed consensus control algorithm, can coordinate the output of multi-type adjustable loads, and improves the response accuracy and response speed of an adjustable load aggregator compared with a traditional single-layer distributed consensus control algorithm, thereby facilitating the coordinated control of internal resources of the demand side adjustable load aggregator in a new power system. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 A general flowchart of a coordinated control method for providing operation backup for a power grid by considering a demand side multi-type adjustable load is provided for an embodiment of the application.

[0023] Figure 2 A flowchart of a demand side multi-type adjustable load providing operation backup for a power grid is provided for an embodiment of the application.

[0024] Figure 3 An adjustable load aggregator actual response capacity diagram of a demand side multi-type adjustable load providing operation backup for a power grid is provided for an embodiment of the application. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor should be within the protection scope of the application.

[0026] Embodiment 1, refer to Figure 1 and Figure 2 For an embodiment of the application, the embodiment provides a coordinated control method for providing operation backup for a power grid by considering a demand side multi-type adjustable load, which comprises:

[0027] S1, collecting operation state data and power consumption behavior data of a demand side distributed air conditioner and a distributed energy storage.

[0028] S2, based on the collected data, aggregate the controllable resources of the demand side distributed air conditioner and the distributed energy storage, construct an energy consumption model of the air conditioner and an energy consumption model of the energy storage.

[0029] S3, design a double-layer distributed consensus control algorithm, the upper layer is a distributed energy storage resource, and the lower layer is a distributed air conditioner resource.

[0030] S4, receive the power regulation instruction issued by the power grid dispatching center, and regulate the aggregated adjustable load based on the double-layer distributed consensus control algorithm to respond to the power regulation instruction.

[0031] It should be noted that in the background of wide access of multiple types of distributed power consumption resources to the power system, the traditional single type load regulation mode is difficult to meet the accuracy and flexibility requirements of standby response. The traditional regulation mode is difficult to include the air conditioner load into the real-time scheduling system due to the large thermal inertia and strong regulation delay of the air conditioner load. Therefore, the present application collects the operation state and behavior data of the air conditioner and the energy storage, constructs a class energy storage model and combines a double-layer distributed consensus control algorithm, realizes the aggregation modeling and hierarchical regulation response of multiple types of loads, improves the cooperativity and controllability of the response capacity, and effectively improves the regulation efficiency and resource utilization rate of the power grid operation standby.

[0032] Embodiment 2 is an embodiment of the present application, which provides a cooperative control method considering the demand side multiple types of adjustable loads providing operation standby for the power grid based on the previous embodiment, comprising:

[0033] In the present application embodiment, the collection of the operation state data and the power consumption behavior data of the demand side distributed air conditioner and the distributed energy storage in step S1 refers to obtaining the operation state data and the power consumption behavior data of the air conditioner and the energy storage device in the target regulation period, wherein the operation state data includes the start-stop state, the power consumption, the set temperature or the power level of the device, and the power consumption behavior data includes the historical load curve, the time period operation characteristics, etc. The obtained data is subjected to time synchronization processing; and the time-synchronized data is preprocessed.

[0034] In an optional embodiment, the collection of the operation state data and the power consumption behavior data of the demand side distributed air conditioner and the distributed energy storage can also be based on the intelligent electric meter and the sensing module installed on the air conditioner and the energy storage device, to collect the power change, the environmental response and the regulation behavior record of the device at each time point in real time, and upload the data through the edge computing gateway.

[0035] In another alternative embodiment, the operation state data and power consumption behavior data of the demand side distributed air conditioner and distributed energy storage can also be the operation data of the air conditioner and energy storage recorded in the building energy management system or home energy management system, and time synchronization and format processing are performed with the centralized control platform to be used as model input.

[0036] The application can accurately construct the device response capability portrait by uniformly collecting the operation state data and power consumption behavior data of the air conditioner and energy storage, improve the integrity and accuracy of the subsequent aggregated modeling and control algorithm input, and provide data support for the development of regulation and control strategy.

[0037] Further, the operation state data and power consumption behavior data of the demand side distributed air conditioner and distributed energy storage collected in step S1 include the following steps A1-A3:

[0038] A1, obtain the operation state data and power consumption behavior data of the air conditioner and energy storage device in the target regulation period.

[0039] A2, perform time synchronization processing on the obtained data.

[0040] A3, pre-process the time-synchronized data.

[0041] Specifically, in step S1, the operation state data includes the start-stop state, power consumption, set temperature or power level, etc. of the device, and the power consumption behavior data includes the historical load curve, time period operation characteristics, etc.

[0042] Specifically, in step S2, the data is aligned to a unified time reference. Time synchronization includes correcting data timestamp deviation, unifying sampling interval, eliminating asynchronous problems caused by communication delay, ensuring the comparability of the operation state of all devices at the same time point, and facilitating subsequent aggregation processing and model input.

[0043] Specifically, in step S3, in the pre-processing process, if continuous missing or mutation points are detected, interpolation is performed according to the historical average load of the device or adjacent time periods for repair.

[0044] In the embodiment of the application, the aggregatable demand side distributed air conditioner and distributed energy storage in step S2 include the operation state data and power consumption behavior data after preprocessing, identifying the distributed air conditioner and energy storage devices with response capability, and constructing the air conditioner resource set and energy storage resource set according to the power regulation range, regulation flexibility and other characteristics, as the basic unit for subsequent regulation modeling and execution.

[0045] In an alternative embodiment, the aggregating controllable resources of demand-side distributed air conditioners and distributed energy storage can also be pre-set threshold filtered by the platform for registered air conditioners and energy storage devices, such as devices with response time less than 5 minutes and adjustable power greater than 10% of rated power can be included in the aggregation range, and on this basis, combined with their geographic location or user category for regional aggregation.

[0046] In another alternative embodiment, the aggregating controllable resources of demand-side distributed air conditioners and distributed energy storage can also be devices with good tracking performance and execution records in participating historical regulation events, which are preferentially included in the aggregation resource pool, and combined with the coordination evaluation results between them and other resources for hierarchical aggregation, to realize the response performance-oriented aggregation control strategy.

[0047] The present application can construct a resource set with unified regulation capability by identifying and aggregating the capacity of air conditioners and energy storage resources, avoid resources without response capability participating in the control process, and improve the response stability of the regulation resources, the scheduling predictability, and the reliability of the grid backup service.

[0048] Further, in step S2, based on the collected data, the aggregating controllable resources of demand-side distributed air conditioners and distributed energy storage, and constructing the energy consumption model of the air conditioner-like energy storage battery and the energy consumption model of the energy storage, including the following steps B1-B3:

[0049] B1, based on the pre-processed operating state data and power consumption behavior data, screening distributed air conditioner resources and distributed energy storage resources with regulation capability.

[0050] B2, aggregating the screened resources according to response capability and adjustment characteristics.

[0051] B3, converting the thermodynamic behavior of the distributed air conditioner resources into a class energy storage model parameter, and constructing the energy consumption model of the air conditioner-like energy storage battery and the energy consumption model of the energy storage.

[0052] Specifically, in step B2, the total response capacity of the aggregating demand-side distributed air conditioners and distributed energy storage by the adjustable resource aggregator at time t is:

[0053]

[0054] Where, ΔP Total (t) is the total power change value of the aggregated heterogeneous energy storage resources at time t under the control of the two-layer distributed consensus control algorithm; and are the sets of demand-side air conditioners and energy storage participating in regulation, respectively; ΔP i (t) and ΔP j(t) is the total power change value of the i-th air conditioner and the j-th energy storage at time t, respectively.

[0055] Further, in step B3, the thermodynamic behavior of the distributed air conditioning resource is converted into a class energy storage model parameter, and a class energy storage battery energy model of the air conditioner is constructed, including the following steps B31-B34:

[0056] B31, the equivalent heat capacity and equivalent thermal resistance of the room to which each distributed air conditioning resource belongs are obtained.

[0057] B32, set the operating parameters of the air conditioner compressor, representing the adjustment rate of indoor temperature per unit time and the compressor refrigeration performance.

[0058] B33, set the highest temperature and the lowest temperature range allowed to adjust for each air conditioning resource in the target control period.

[0059] B34, based on the operating state data, operating parameters and room temperature upper and lower limits, determine the adjustable capacity range of the air conditioner, and construct a class energy storage battery energy model of the air conditioner.

[0060] The class thermal energy storage battery energy model of the air conditioner and the energy model of the energy storage are as follows:

[0061]

[0062] Where, x i (t) and x j (t) are the state quantities of the i-th air conditioner and the j-th energy storage at time t, respectively; C i and R i are the equivalent heat capacity and equivalent thermal resistance of the room to which the i-th air conditioner belongs; k i,1 and k i,2 are the operating parameters of the i-th air conditioner compressor; T i max and T i min are the highest and lowest temperatures allowed to adjust in the adjustment period of the house to which the i-th air conditioner belongs; Cap j is the maximum capacity of the j-th energy storage; σ is the self-discharge coefficient of the j-th energy storage; η j is the charge and discharge efficiency of the j-th energy storage.

[0063] Further, in step S3, a double-layer distributed consensus control algorithm is designed, the upper layer is a distributed energy storage resource, and the lower layer is a distributed air conditioning resource, including the following steps C1-C4:

[0064] C1, establish an upper control structure of distributed energy storage resources.

[0065] C2, Establishing the lower-layer control structure of distributed air conditioning resources.

[0066] C3, Setting the information interaction mechanism between the upper and lower layers to realize the collaborative control between the distributed air conditioning resources and the distributed energy storage resources.

[0067] C4, Determining the overall adjustment behavior of the aggregated resources based on the consensus mechanism and tracking the grid dispatching instructions.

[0068] Further, in step C1, the upper-layer control structure of the distributed energy storage resources is established, including the following steps C11-C14:

[0069] C11, Setting the maximum capacity for each distributed energy storage resource to determine the energy adjustment range within the target control period.

[0070] C12, Setting the self-discharge coefficient for each distributed energy storage resource to describe the natural decay characteristics of energy over time.

[0071] C13, Setting the charging efficiency and discharging efficiency of each distributed energy storage resource to represent the energy conversion efficiency in the energy input and output processes, respectively.

[0072] C14, Characterizing the current remaining power of the energy storage resource with the energy storage state meter and using it as a control parameter to participate in the adjustment logic of the upper-layer control structure.

[0073] Further, in step C2, the lower-layer control structure of the distributed air conditioning resources is established, including the following steps C21-C24:

[0074] C21, Setting the equivalent heat capacity and equivalent thermal resistance of the room to which each distributed air conditioning resource belongs to describe the thermal inertia response characteristics of indoor temperature to air conditioning operation.

[0075] C22, Setting the operating parameters of the air conditioner compressor to represent the adjustment rate of the air conditioner to the environment temperature per unit time and the refrigeration capacity.

[0076] C23, Setting the highest temperature and the lowest temperature range allowed within the target control period for each distributed air conditioning resource as the boundary conditions for air conditioning participation in regulation.

[0077] C24, Characterizing the current room temperature level of the air conditioner with the operating state meter and determining the adjustable ability interval of the air conditioner based on the temperature upper and lower limits and the operating parameters to participate in the adjustment logic of the lower-layer control structure as a control parameter.

[0078] The double-layer distributed consensus control algorithm, whose upper layer is the distributed energy storage resource and whose lower layer is the distributed air conditioning resource, has the following specific structure:

[0079]

[0080] wherein, X I (t) are state variables of the lower air conditioning cluster and the upper energy storage cluster, respectively; A J (t) are state variables of the lower air conditioning cluster and the upper energy storage cluster, respectively; A I and A J are Laplacian matrices corresponding to the communication topological graph of the lower air conditioning cluster and the upper energy storage cluster, respectively; K g is a gain of the distributed control system; γ is a gain of the transmission signal of the upper energy storage group to the lower air conditioning group; R E is an interaction matrix between the upper energy storage group and the lower air conditioning group; ΔP reg is a power dispatch instruction sent by the superior power dispatch center to the aggregator; ΔP Total (t) is the total response capacity of the aggregator at time t.

[0081] In the embodiment of the application, the information interaction mechanism in step C3 includes establishing a data communication structure for transmitting state variables and control variables between the upper and lower layers in the double-layer distributed consensus control algorithm, so that the distributed energy storage resources of the upper layer can transmit state information, power regulation targets and other data to the distributed air conditioning resources of the lower layer, and cross-level collaborative control is realized.

[0082] In an optional embodiment, the information interaction mechanism can also use a multi-level data exchange module in the aggregator platform to collect the regulation data generated by the controllers of each layer at regular intervals, and broadcast downward after centralized collection through a communication bus or an edge server, so as to realize bidirectional data channel maintenance and regulation instruction feedback.

[0083] In another optional embodiment, the information interaction mechanism can also build a unified format control protocol to specify the field structure, frequency and fault tolerance mechanism of information transmission between the upper and lower layers, so that the running state, regulation boundary, power response instruction and other data between the air conditioner and the energy storage can be reliably interacted under the unified protocol.

[0084] The application realizes state synchronization and control collaboration between the energy storage resources and the air conditioning resources by setting the information interaction mechanism between the upper and lower control structures, and improves the coordination, response consistency and standby regulation capability of the double-layer control system.

[0085] In the embodiment of the application, the determination of the overall regulation behavior of the aggregated resources based on the consensus mechanism in step C4 includes establishing a state coordination mechanism between the distributed energy storage resources and the distributed air conditioning resources through the double-layer distributed consensus control algorithm, so that various resources can realize distributed negotiation and consistent decision-making of the response power after receiving the power grid dispatching instruction.

[0086] In an alternative embodiment, the determination of the overall adjustment behavior of the aggregated resource based on the consensus mechanism can also be in the upper layer control structure, and each energy storage node reaches an agreement on the response amount based on the local state quantity and the scheduling target by exchanging state information with adjacent energy storage nodes, and the result is taken as the adjustment reference amount for the lower layer air conditioning resource.

[0087] In another alternative embodiment, the determination of the overall adjustment behavior of the aggregated resource based on the consensus mechanism can also be that the lower layer air conditioning resource shares information among multiple nodes through a local communication topology network according to the adjustment instruction transmitted by the upper layer and its own adjustable capacity, realizes the joint response of the air conditioning resource under the distributed structure, and forms a total load adjustment amount that meets the system requirements.

[0088] The present application can realize the orderly coordination of response capacity in the case of distributed deployment of resources by determining the overall adjustment behavior of the aggregated resource based on the consensus mechanism, and improve the synergy and convergence efficiency of the joint participation of multiple types of load resources in the backup service of power grid operation.

[0089] Further, in step S4, the power regulation instruction issued by the power grid dispatching center is received, and the aggregated adjustable load is regulated to respond to the power regulation instruction based on a double-layer distributed consensus control algorithm, including the following steps D1-D5:

[0090] D1, receiving the regulation instruction issued by the power grid dispatching center, and the instruction contains the power demand in the target time period.

[0091] D2, the double-layer consensus control algorithm schedules the distributed air conditioning resource and the distributed energy storage resource to participate in the regulation.

[0092] D3, at each time, the response capacity of the aggregated resource is regulated.

[0093] D4, the response capacity is composed of the change of the power consumption of the distributed air conditioning resource and the distributed energy storage resource.

[0094] D5, the distributed air conditioning resource and the distributed energy storage resource are respectively composed of a plurality of controllable resources, and independently respond to the power change at each regulation time.

[0095] Embodiment 3, refer to Figure 3 An embodiment of the present application provides a collaborative control method considering the demand side multiple types of adjustable load for providing backup operation for the power grid. In order to verify the beneficial effects of the present application, scientific demonstration is carried out through experiments.

[0096] For a typical adjustable load aggregator on the demand side, the total rated power of the distributed energy storage inside is 30kW, and it contains 800 distributed air conditioners with different parameters, and the parameters are shown in Table 1.

[0097] Table 1 parameter setting table

[0098]

[0099] wherein represents a uniform distribution; represents a normal distribution. The control gain of the distributed control algorithm is K g = [-10, 0.15], and the control gain of the interaction matrix between the upper layer system and the lower layer system is γ = 0.2.

[0100] After receiving the instruction ΔP reg = 200 kW from the superior power dispatching center. The actual response capacity of the adjustable load aggregator is as shown in Table 2. Figure 3 It can be seen that the double-layer distributed control method designed in the application has a faster response speed and higher response accuracy than the traditional distributed consensus control algorithm, which is convenient for guiding the adjustable load aggregator containing multiple resources to realize resource control, providing operation backup, providing a basis for the adjustable load to participate in the power grid service, and realizing the technical effect.

[0101] Embodiment 4 is an embodiment of the application, which provides a collaborative control system considering that multiple types of adjustable loads on the demand side provide operation backup for the power grid, comprising a data acquisition module, a resource aggregation module, a control algorithm module, and an execution module.

[0102] The data acquisition module is used to acquire the operation state data and power consumption behavior data of the distributed air conditioner and the distributed energy storage on the demand side.

[0103] The resource aggregation module is used to aggregate the adjustable resources of the distributed air conditioner and the distributed energy storage on the demand side based on the acquired data, and construct the energy consumption model of the air conditioner and the energy consumption model of the energy storage.

[0104] The control algorithm module is used to design a double-layer distributed consensus control algorithm, with the upper layer being the distributed energy storage resource and the lower layer being the distributed air conditioner resource.

[0105] The execution module is used to receive the power regulation instruction issued by the power dispatching center, and regulate the aggregated adjustable load based on the double-layer distributed consensus control algorithm to respond to the power regulation instruction.

[0106] The embodiment also provides an electronic device suitable for the case of the collaborative control method considering that multiple types of adjustable loads on the demand side provide operation backup for the power grid, comprising a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the collaborative control method considering that multiple types of adjustable loads on the demand side provide operation backup for the power grid as proposed in the above embodiment.

[0107] The embodiment further provides a storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method for providing operation backup for a power grid by considering demand side multi-type adjustable loads proposed in the above embodiment.

[0108] The storage medium proposed in the embodiment belongs to the same inventive concept as the method for providing operation backup for a power grid by considering demand side multi-type adjustable loads proposed in the above embodiment, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0109] From the above description about the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary universal hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a ROM, a RAM, a FLASH, a hard disk, or an optical disc, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for coordinated control of providing operational reserve for power grid by considering demand side multi-type adjustable loads, characterized in that: The method comprises the following steps: Collecting operation state data and electricity consumption behavior data of distributed air conditioners and distributed energy storage devices on the demand side; Based on the collected data, aggregating the controllable resources of distributed air conditioners and distributed energy storage devices, and constructing a class energy storage battery energy consumption model of air conditioners and an energy consumption model of energy storage; Designing a double-layer distributed consensus control algorithm, with the upper layer being distributed energy storage resources and the lower layer being distributed air conditioner resources; Receiving power regulation instructions issued by a power grid dispatching center, and regulating the aggregated adjustable load based on the double-layer distributed consensus control algorithm to respond to the power regulation instructions.

2. The method of claim 1, wherein the method is characterized by: The method comprises the following steps: Collecting operation state data and electricity consumption behavior data of distributed air conditioners and distributed energy storage devices on the demand side; The method comprises the following steps: Collecting operation state data and electricity consumption behavior data of distributed air conditioners and distributed energy storage devices on the demand side; The method comprises the following steps:

3. The method of claim 2, wherein the method further comprises: determining the operating reserve of the power grid based on the demand side multi-type adjustable load. Based on the pre-processed operation state data and electricity consumption behavior data, screening distributed air conditioner resources and distributed energy storage resources with regulation capability; Aggregating the screened resources according to response capability and regulation characteristics; Converting the thermodynamic behavior of distributed air conditioner resources into class energy storage model parameters to construct a class energy storage battery energy consumption model of air conditioners and an energy consumption model of energy storage. The method comprises the following steps:

4. The method of claim 3, wherein the method further comprises: Establishing an upper-layer control structure of distributed energy storage resources; Establishing a lower-layer control structure of distributed air conditioner resources; Setting an information interaction mechanism between the upper and lower layers to realize collaborative control between distributed air conditioner resources and distributed energy storage resources; Based on the consensus mechanism, determining the overall regulation behavior of the aggregated resources and tracking the power grid dispatching instructions. The method comprises the following steps:

5. The method of claim 4, wherein the method further comprises: Obtaining the equivalent heat capacity and equivalent thermal resistance of the room to which each distributed air conditioner resource belongs; Setting the operation parameters of the air conditioner compressor to represent the adjustment rate of indoor temperature per unit time and the compressor refrigeration performance; In the target regulation period, setting the highest temperature and the lowest temperature range allowed to be adjusted for each air conditioner resource; Based on the operation state data, operation parameters, and upper and lower limits of room temperature, determining the adjustable capability range of the air conditioner to construct a class energy storage battery energy consumption model of the air conditioner. The method comprises the following steps:

6. The method of claim 5, wherein the method further comprises: Setting the maximum capacity of each distributed energy storage resource to determine the energy regulation range in the target regulation period; Setting the self-discharge coefficient of each distributed energy storage resource to describe the natural attenuation characteristics of energy over time; Setting the charging efficiency and discharging efficiency of each distributed energy storage resource to represent the energy conversion efficiency in the energy input and output processes, respectively; Using the energy storage state to represent the current remaining power of the energy storage resource, and using it as a control parameter to participate in the regulation logic of the upper-layer control structure. ​ 7. The method of claim 6, wherein the method further comprises: determining the operating reserve of the power grid based on the demand side multi-type adjustable load. The double-layer distributed consensus control algorithm is used for regulating the aggregated adjustable load to respond to the power regulation instruction, including, receiving a regulation instruction issued by a power grid dispatching center, the instruction containing a power demand in a target time period; the double-layer consensus control algorithm schedules the distributed air conditioner resources and the distributed energy storage resources to participate in regulation; at each time, the response capacity of the aggregated resources is regulated; the response capacity is composed of the power consumption changes of the distributed air conditioner resources and the distributed energy storage resources; the distributed air conditioner resources and the distributed energy storage resources are respectively composed of multiple regulatable resources, and independently respond to the power changes at each regulation time.

8. A coordinated control system for providing operational reserve for power grid considering multiple types of demand side adjustable loads, applying the coordinated control method for providing operational reserve for power grid considering multiple types of demand side adjustable loads according to any one of claims 1-7, characterized in that, comprising: a data acquisition module, a resource aggregation module, a control algorithm module, and an execution module; the data acquisition module is used for acquiring the operating state data and the power consumption behavior data of the demand side distributed air conditioner and the distributed energy storage; the resource aggregation module is used for aggregating the regulatable resources of the demand side distributed air conditioner and the distributed energy storage based on the acquired data, and constructing an air conditioner energy storage battery-like energy consumption model and an energy consumption model of the energy storage; the control algorithm module is used for designing a double-layer distributed consensus control algorithm, the upper layer being the distributed energy storage resource and the lower layer being the distributed air conditioner resource; the execution module is used for receiving a power regulation instruction issued by a power grid dispatching center, and regulating the aggregated adjustable load based on the double-layer distributed consensus control algorithm to respond to the power regulation instruction. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the collaborative control method for providing operating backup for the power grid by considering the demand side multi-type adjustable load according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the collaborative control method for providing operating backup for the power grid by considering the demand side multi-type adjustable load according to any one of claims 1 to 7.

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