Method, device and equipment for controlling virtual power plant and storage medium

Abstract

The invention discloses a method and device for controlling a virtual power plant, equipment and a storage medium. The method comprises the following steps: acquiring current actual operation data of equipment in a current virtual power plant; a model-free adaptive controller is utilized to output an operation adjustment target according to the current actual operation data and a preset operation target; and controlling equipment in the current virtual power plant by using the operation adjustment target. According to the technical scheme, accurate mathematical modeling does not need to be carried out on the virtual power plant, model-free self-adaptive control over the virtual power plant is achieved through the model-free self-adaptive controller, and the regulation and control requirements of various devices are better met.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and in particular, to a method, device, equipment, and storage medium for controlling a virtual power plant. Background Art

[0002] With the increasing penetration rate of new energy and the serious shortage of flexible regulation resource capacity on the distribution network side, the power grid faces a great security challenge in ensuring power supply. Against this background, the virtual power plant market that can aggregate power demand-side resources and cooperate with the power grid for regulation has emerged.

[0003] The virtual power plant uses advanced technologies such as digitalization and intelligentization to aggregate, coordinate, and optimize resources such as adjustable loads, distributed power sources, and energy storage in a certain area on the demand side. Combining corresponding power market mechanisms, it constitutes the ability to respond to the operation and regulation of the power grid. The virtual power plant should have the functional performance of conventional generating units (such as peak shaving and frequency modulation, etc.). Its particularity is that the physical form of the power plant is "virtually" formed by aggregating resources such as distributed photovoltaic, energy storage, and load through network calculation in enterprise parks. Therefore, the group control of virtual power plants is one of the core technologies for realizing virtual power plants.

[0004] However, the types of load-side resources aggregated by the virtual power plant include multiple categories such as photovoltaic, energy storage, charging piles, controllable loads, and air source heat pumps. There are also differences in the product technical routes in the system composition of each category (such as the differences between string inverters and centralized inverters in photovoltaic inverters). According to the traditional control method, it is necessary to accurately mathematically model each control loop first and then design the controller algorithm, which is very difficult to achieve in actual business. Because the coupling relationship types of factors such as the types, technical routes, and manufacturer differences of the load-side resources aggregated by the virtual power plant are very numerous, the workload of achieving accurate mathematical modeling is very large. A large amount of human resources are also required to participate in project commissioning and delivery during the technology application stage, and the applicable scenario range of the algorithm is relatively narrow and cannot be applied in batches in actual business. Summary of the Invention

[0005] The present invention provides a method, device, equipment, and storage medium for controlling a virtual power plant to solve the problem of difficult accurate modeling in the control process of the virtual power plant.

[0006] In a first aspect, the present invention provides a method for controlling a virtual power plant, including:

[0007] Obtain the current actual operation data of the devices in the current virtual power plant;

[0008] Use a model-free adaptive controller to output an operation adjustment target according to the current actual operation data and a preset operation target;

[0009] Control the devices in the current virtual power plant using the operation adjustment target.

[0010] Second aspect, the present invention provides a device for controlling a virtual power plant, including:

[0011] An operating data acquisition module, configured to acquire the current actual operating data of the devices in the current virtual power plant;

[0012] An operating adjustment target determination module, configured to use a model-free adaptive controller to output an operating adjustment target according to the current actual operating data and a preset operating target;

[0013] A control module, configured to control the devices in the current virtual power plant by using the operating adjustment target.

[0014] Third aspect, the present invention provides an electronic device, which includes:

[0015] At least one processor;

[0016] And a memory communicatively connected to at least one processor;

[0017] Wherein, the memory stores a computer program executable by at least one processor, and the computer program is executed by at least one processor so that at least one processor can execute the method for controlling a virtual power plant in the first aspect above.

[0018] Fourth aspect, the present invention provides a computer-readable storage medium, which stores computer instructions for causing a processor to implement the method for controlling a virtual power plant in the first aspect above when executed.

[0019] The solution for controlling a virtual power plant provided by the present invention acquires the current actual operating data of the devices in the current virtual power plant, uses a model-free adaptive controller to output an operating adjustment target according to the current actual operating data and a preset operating target, and controls the devices in the current virtual power plant by using the operating adjustment target. By adopting the above technical solution, it is not necessary to accurately mathematically model each control loop and control process of the virtual power plant. Based on the control scenario of the virtual power plant, model-free adaptive control of the virtual power plant is realized by using a model-free adaptive controller, which better meets the regulation requirements of various devices and can be well applied to the complex scenarios of the virtual power plant.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0022] Figure 1 is a flowchart of a method for controlling a virtual power plant according to Embodiment 1 of the present invention;

[0023] Figure 2 is a schematic diagram of a model-free adaptive control process according to Embodiment 1 of the present invention;

[0024] Figure 3 is a flowchart of a method for controlling a virtual power plant according to Embodiment 2 of the present invention;

[0025] Figure 4 is a schematic structural diagram of a device for controlling a virtual power plant according to Embodiment 3 of the present invention;

[0026] Figure 5 is a schematic structural diagram of an electronic device according to Embodiment 4 of the present invention. Detailed implementation manners

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the description, claims and the above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In the description of the present invention, unless otherwise specified, "a plurality of" means two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 The flowchart of a method for controlling a virtual power plant is provided for Embodiment 1 of the present invention. This embodiment is applicable to the situation of controlling the equipment in the virtual power plant. This method can be executed by a device for controlling the virtual power plant. The device for controlling the virtual power plant can be implemented in the form of hardware and / or software. The device for controlling the virtual power plant can be configured in an electronic device. The electronic device can be composed of two or more physical entities or one physical entity.

[0031] As Figure 1 shown, a method for controlling a virtual power plant provided in Embodiment 1 of the present invention specifically includes the following steps:

[0032] S101. Obtain the current actual operation data of the equipment in the current virtual power plant.

[0033] Specifically, the goal of virtual power plant control is to maintain the target index (such as active power) level of the adjustable resources after aggregation of the virtual power plant near the expected value. In this embodiment, the current actual operation data of devices such as photovoltaic, energy storage, and charging piles in the current virtual power plant can be collected first, such as the current actual active power, etc.

[0034] S102. Use a model-free adaptive controller to output an operation adjustment target according to the current actual operation data and the preset operation target.

[0035] S103. Control the equipment in the current virtual power plant by using the operation adjustment target.

[0036] In this embodiment, Figure 2 This is a schematic diagram of a model-free adaptive control process. Figure 2 As shown in the figure, if the current actual operating data is active power, and the equipment in the virtual power plant includes distributed photovoltaics, energy storage, and charging piles, the closed-loop control process of model-free adaptive control (MFAC) can include the following four steps:

[0037] 1) According to the current actual operating data volume P p and preset operation targets The difference between the control input and the control input ΔP p , the ΔP p is the input of the MFAC controller. Among them, the preset operation target is the given target value.

[0038] 2) The MFAC controller operates according to the input and output adjustment targets, which can specifically include the active output change ΔP of the distributed photovoltaic in the virtual power plant DG , the active change of energy storage ΔP ES And the active power change of the charging pile ΔP CS .

[0039] 3) Generate corresponding control instructions based on these changes, and send these instructions to photovoltaic, energy storage and charging piles, so that these devices can automatically execute the corresponding instructions to realize the control of the virtual power plant.

[0040] 4) Monitor the active power value of the equipment in the virtual power plant in real time and return to step 1) until the operation adjustment target is small enough or P p and The gap is small enough.

[0041] The method for controlling a virtual power plant provided by an embodiment of the present invention obtains the current actual operation data of the equipment in the current virtual power plant, uses a model-free adaptive controller to output an operation adjustment target according to the current actual operation data and a preset operation target, and uses the operation adjustment target to control the equipment in the current virtual power plant. The technical solution of the embodiment of the present invention does not require accurate mathematical modeling of each control loop and control process of the virtual power plant. Based on the control scenario of the virtual power plant, the model-free adaptive controller is used to realize model-free adaptive control of the virtual power plant, which better meets the regulation requirements of various types of equipment, and can be well applied to the complex scenarios of the virtual power plant.

[0042] Optionally, the data type of the current actual operating data includes active power.

[0043] Optionally, controlling the devices in the current virtual power plant by using the operation adjustment target includes: if the operation adjustment target is greater than a first preset threshold or the gap between the current actual operation data and the preset operation target is greater than a second preset threshold, issuing an operation data adjustment instruction to the corresponding devices in the current virtual power plant according to the operation adjustment target.

[0044] Specifically, when the operation adjustment target is greater than the first preset threshold, or the gap between the current actual operation data and the preset operation target is greater than the second preset threshold, it indicates that the target indicators of the devices in the virtual power plant still need to be adjusted at present. At this time, an operation data adjustment instruction for each device can be generated according to the operation adjustment target and issued to the corresponding device to achieve the adjustment of the target indicators of the device.

[0045] Optionally, before controlling the devices in the current virtual power plant by using the operation adjustment target, it further includes: respectively judging whether the operation adjustment target meets the corresponding preset adjustment target constraint conditions for each type of device; if not, correcting the operation adjustment target to obtain a corrected operation adjustment target.

[0046] Specifically, in actual system operation, the indicators of the devices in the virtual power plant are all subject to the constraints of physical boundaries, so the operation adjustment target needs to be constrained.

[0047] Exemplarily, if the current actual operation data is active power and the devices in the virtual power plant include distributed photovoltaic, energy storage, and charging piles, then:

[0048] 1) The preset adjustment target constraint condition of the photovoltaic system can be expressed by the following formula:

[0049]

[0050] Where: and are respectively the lower limit and upper limit of the active power of the distributed photovoltaic monitored in real time by the virtual power plant platform, is the upper limit of the active power change rate of the distributed photovoltaic, ΔP DG (k) is the photovoltaic operation adjustment target in the operation adjustment target.

[0051] 2) The preset adjustment target constraint condition of the energy storage system can be expressed by the following formula:

[0052]

[0053] Where: W ES (k) is the value of the energy storage SOC (State of Charge) collected in real time by the virtual power plant platform, and are the lower and upper limits of the energy storage charge capacity respectively, and P ES (k) is the active power of the energy storage collected in real time, and are the lower and upper limits of the active power of the energy storage monitored in real time on the virtual power plant platform respectively, is the upper limit of the change rate of the active power of the energy storage monitored in real time on the virtual power plant platform, and ΔP ES (k) is the operation regulation target of the energy storage in the operation regulation target.

[0054] 3) The preset regulation target constraint conditions of the charging pile system can be expressed by the following formula:

[0055]

[0056] Where: P CS (k) is the active power of the charging pile collected in real time, and are the lower and upper limits of the active power of the charging pile monitored in real time on the virtual power plant platform respectively, is the upper limit of the change rate of the active power of the charging pile monitored in real time on the virtual power plant platform, and ΔP CS (k) is the operation regulation target of the charging pile in the operation regulation target.

[0057] Embodiment 2

[0058] Figure 3 is a flowchart of a method for controlling a virtual power plant provided by Embodiment 2 of the present invention. The technical solution of the embodiment of the present invention is further optimized on the basis of the above optional technical solutions, and a specific method for controlling the equipment in the virtual power plant is given.

[0059] Optionally, the step of using the model-free adaptive controller to output the operation regulation target according to the current actual operation data and the preset operation target includes: using the model-free adaptive controller to determine the pseudo partial derivative estimate value according to the historical control output difference and the control input difference, where the historical control output difference is determined according to the gap between the historical operation regulation targets, and the control input difference is determined according to the deviation between the actual operation data and the preset operation target; using the model-free adaptive controller to output the operation regulation target according to the pseudo partial derivative estimate value and the gap between the preset operation target and the current actual operation data. The advantage of such a setting is that the operation regulation target is output accurately and quickly by estimating the pseudo partial derivative.

[0060] Optionally, the above method further includes: determining whether a first event occurs at a first moment and a second event occurs at a second moment, where the first moment is earlier than the second moment, the first event includes that the operation adjustment target at the first moment is less than or equal to a first preset threshold and the gap between the actual operation data and the preset operation target is less than or equal to a second preset threshold, and the second event includes that the operation adjustment target at the second moment is greater than the first preset threshold or the gap between the actual operation data and the preset operation target is greater than the second preset threshold; if so, the model-free adaptive controller is used to output a new operation adjustment target according to the current actual operation data and the preset operation target. The advantage of such a setting is that, based on the event-driven adaptive update of the operation adjustment target, the optimal operation adjustment target can be adaptively generated when the virtual power plant undergoes dynamic and random changes.

[0061] Optionally, after determining whether a first event occurs at a first moment and a second event occurs at a second moment, it further includes: if not, determining whether the operation data threshold range of the devices in the current virtual power plant has changed, where the operation data threshold range is determined according to the actual operation status of the devices in the current virtual power plant; if it has changed, updating the preset adjustment target constraint conditions for each type of device in the current virtual power plant to obtain new constraint conditions; using the model-free adaptive controller to output a new operation adjustment target that satisfies the new constraint conditions according to the current actual operation data and the preset operation target. The advantage of such a setting is that by monitoring whether the operation data threshold range has changed, it can be timely determined whether the availability of the load-side resources in the virtual power plant has changed, and then by adjusting the constraint conditions of the operation adjustment target, the adaptive generation of the optimal operation adjustment target is further realized.

[0062] As Figure 3 shown, a method for controlling a virtual power plant provided in Embodiment 2 of the present invention specifically includes the following steps:

[0063] S201. Obtain the current actual operation data of the devices in the current virtual power plant.

[0064] S202. Use a model-free adaptive controller to determine the pseudo partial derivative estimate value according to the historical control output difference and the control input difference.

[0065] Among them, the historical control output difference is determined according to the gap between the historical operation adjustment targets, and the control input difference is determined according to the deviation between the actual operation data and the preset operation target.

[0066] Specifically, the virtual power plant can be regarded as a multi-input single-output discrete-time nonlinear system, which can be represented by the following formula:

[0067] P p (k + 1) = f(Pp (k), …, P p (k - 1), u(k), …, u(k - N u ))

[0068] where k is the time series, N u is the number of parks aggregated by the virtual power plant, f(·) is the mathematical model of the input and output of the virtual power plant, which is a non - linear system, and the current actual operation data is the active power. The mathematical representation of the above formula using the compact data model is as follows:

[0069]

[0070] where is the PPD (pseudo partial derivative) at time k. P p (k) is the total value of the real - time active power of the virtual power plant at time k, which is the output of the virtual power plant. u(k) is the input quantity array of the virtual power plant at time k and is also the output of the MFAC controller, which can be expressed as:

[0071] u(k)=[ΔP DG (k) ΔP ES (k) ΔP CS (k)]

[0072] where ΔP DG (k) is the active power regulation target of the distributed photovoltaic at time k, ΔP ES (k) is the active power regulation target of the energy storage at time k, ΔP CS (k) is the active power regulation target of the charging pile at time k. The virtual power plant can be partitioned according to the physical park boundary, and the common connection point between the park and the power grid is used as the dominant node. Set the initial value of the control input difference ΔP p (1)=0. The determination method of the pseudo partial derivative estimate value includes:

[0073]

[0074] where is the estimate value of , k is the time series, Δu(k)=u(k)-u(k - 1), ΔP p (k) is the control input difference, and Δu(k - 1) is the operation regulation target at time k - 1, that is, the historical control output difference.

[0075] S203. Use a model-free adaptive controller to output an operation adjustment target according to the pseudo partial derivative estimation value and the gap between the preset operation target and the current actual operation data.

[0076] Specifically, the determination method of the operation adjustment target u(k) includes:

[0077]

[0078] where, is the estimated value of u(k), is the preset operation target at the (k + 1)-th moment, and P p (k) is the actual operation data at the k-th moment, that is, the current actual operation data.

[0079] S204. For the operation adjustment target of each type of device, respectively determine whether the operation adjustment target meets the corresponding preset adjustment target constraint conditions. If not, execute step 205; if so, execute step 206.

[0080] S205. Modify the operation adjustment target to obtain a modified operation adjustment target.

[0081] S206. Determine whether the operation adjustment target is greater than the first preset threshold or the gap between the current actual operation data and the preset operation target is greater than the second preset threshold. If so, execute step 207; if not, execute step 208.

[0082] Specifically, if not, it means |u(k - N)| ≤ ε and At this time, it is considered that the control target has been achieved. If N = 0, 1, 2, 3, 4, and 5, it means that the virtual power plant achieves dynamic stability within 5 control cycles. ε represents the control accuracy target, that is, the first preset threshold and the second preset threshold, and its value can be 5% based on the current evaluation and assessment rules of the virtual power plant.

[0083] S207. Send an operation data adjustment instruction to the corresponding device in the current virtual power plant according to the operation adjustment target.

[0084] S208. Determine whether a first event occurs at the first moment and a second event occurs at the second moment. If so, execute step 209; if not, execute step 210.

[0085] Among them, the first moment is earlier than the second moment. The first event includes that the operation adjustment target at the first moment is less than or equal to a first preset threshold and the gap between the actual operation data and the preset operation target is less than or equal to a second preset threshold. The second event includes that the operation adjustment target at the second moment is greater than the first preset threshold or the gap between the actual operation data and the preset operation target is greater than the second preset threshold.

[0086] Specifically, if the current virtual power plant has a first event at the first moment and a second event at the second moment, it means that after the current virtual power plant uses the MFAC controller to make the virtual power plant reach the ideal steady state, due to reasons such as equipment failures, the virtual power plant system may undergo a structural change, resulting in the occurrence of the second event, and the target index that the virtual power plant wants to control is once again far from the ideal level. At this time, if the operation adjustment target is not updated in time, it will lead to a large deviation in the control effect.

[0087] S209. Use the model-free adaptive controller to output a new operation adjustment target according to the current actual operation data and the preset operation target, and execute step 204.

[0088] Specifically, after it is determined that a first event occurs at the first moment and a second event occurs at the second moment, the model-free adaptive controller can be used to re-output a new operation adjustment target according to the current actual operation data and the preset operation target.

[0089] S210. Judge whether the threshold range of the operation data of the equipment in the current virtual power plant has changed. If so, execute step 211. If not, execute step 208.

[0090] Among them, the threshold range of the operation data is determined according to the actual operation state of the equipment in the current virtual power plant.

[0091] Specifically, the online state and access capacity of the equipment in the virtual power plant can be monitored in real time. If a device fails and exits, it will cause the upper or lower limit of the threshold range of the operation data of the faulty device to change, such as the upper or lower limit of the active power of the photovoltaic or energy storage to change.

[0092] S211. Update the preset adjustment target constraint conditions for each type of equipment in the current virtual power plant to obtain new constraint conditions; use the model-free adaptive controller to output a new operation adjustment target that meets the new constraint conditions, and execute step 206.

[0093] Specifically, the preset adjustment target constraint conditions can be adjusted according to the operating data threshold range of the device. For example, if the upper limit of the operating data threshold range becomes smaller, the preset adjustment target constraint conditions can be tightened correspondingly to obtain new constraint conditions. Then, the model-free adaptive controller is used to re-output a new operation adjustment target based on the current actual operating data and the preset operation target, and the new operation adjustment target is within the new constraint conditions.

[0094] The method for controlling a virtual power plant provided by the embodiment of the present invention accurately and quickly outputs an operation adjustment target by estimating the pseudo partial derivative, and based on the event-driven adaptive update of the operation adjustment target, it can adaptively generate an optimal operation adjustment target when the virtual power plant undergoes dynamic and random changes. By monitoring whether the operating data threshold range changes, it is timely determined whether the availability of load-side resources in the virtual power plant changes, and then by adjusting the constraint conditions of the operation adjustment target, the adaptive generation of the optimal operation adjustment target is further realized. This method is applicable to dealing with various random events such as equipment failures and load shedding during the operation of the virtual power plant, has good engineering application significance, helps the virtual power plant operator improve the regulation accuracy, and effectively reduces the losses caused by regulation deviations.

[0095] Embodiment III

[0096] Figure 4 It is a schematic structural diagram of a device for controlling a virtual power plant provided by Embodiment III of the present invention. As Figure 4 shown, the device includes: an operating data acquisition module 301, an operation adjustment target determination module 302, and a control module 303, where:

[0097] The operating data acquisition module is used to acquire the current actual operating data of the devices in the current virtual power plant;

[0098] The operation adjustment target determination module is used to output an operation adjustment target by using a model-free adaptive controller according to the current actual operating data and the preset operation target;

[0099] The control module is used to control the devices in the current virtual power plant by using the operation adjustment target.

[0100] The device for controlling a virtual power plant provided by the embodiment of the present invention does not require accurate mathematical modeling of each control loop and control process of the virtual power plant. Based on the control scenario of the virtual power plant, it realizes model-free adaptive control of the virtual power plant by using a model-free adaptive controller, better meets the control requirements of various devices, and can be well applied to the complex scenarios of the virtual power plant.

[0101] Optionally, the operation adjustment target determination module includes:

[0102] A pseudo partial derivative determination unit, which is used to utilize a model-free adaptive controller to determine an estimated value of the pseudo partial derivative according to the historical control output difference and the control input difference, where the historical control output difference is determined according to the gap between historical operation adjustment targets, and the control input difference is determined according to the deviation between the actual operation data and the preset operation target;

[0103] An adjustment target determination unit, which is used to utilize a model-free adaptive controller to output an operation adjustment target according to the estimated value of the pseudo partial derivative and the gap between the preset operation target and the current actual operation data.

[0104] Optionally, the device further includes:

[0105] An event judgment module, which is used to judge whether a first event occurs at a first moment and a second event occurs at a second moment, where the first moment is earlier than the second moment, the first event includes that the operation adjustment target at the first moment is less than or equal to a first preset threshold and the gap between the actual operation data and the preset operation target is less than or equal to a second preset threshold, and the second event includes that the operation adjustment target at the second moment is greater than the first preset threshold or the gap between the actual operation data and the preset operation target is greater than the second preset threshold;

[0106] A first output module, which is used to, if the information output by the event judgment module is yes, utilize the model-free adaptive controller to output a new operation adjustment target according to the current actual operation data and the preset operation target.

[0107] Optionally, the device further includes:

[0108] A threshold range judgment module, which is used to, after judging whether the first event occurs at the first moment and the second event occurs at the second moment, if the information output by the event judgment module is no, judge whether the operation data threshold range of the devices in the current virtual power plant changes, where the operation data threshold range is determined according to the actual operation state of the devices in the current virtual power plant;

[0109] An update module, which is used to, if the information output by the threshold range judgment module is that it changes, update the preset adjustment target constraint conditions of each type of device in the current virtual power plant to obtain new constraint conditions;

[0110] A second output module, which is used to utilize the model-free adaptive controller to output a new operation adjustment target that meets the new constraint conditions according to the current actual operation data and the preset operation target.

[0111] Optionally, the control module is specifically configured to, if the operation adjustment target is greater than the first preset threshold or the gap between the current actual operation data and the preset operation target is greater than the second preset threshold, issue an operation data adjustment instruction to the corresponding device in the current virtual power plant according to the operation adjustment target.

[0112] Optionally, the device further includes:

[0113] A judgment module, configured to, before controlling the devices in the current virtual power plant by using the operation adjustment target, respectively judge whether the operation adjustment target meets the corresponding preset adjustment target constraint conditions for the operation adjustment target of each type of device;

[0114] A correction module, configured to, if the information output by the judgment module is negative, correct the operation adjustment target to obtain a corrected operation adjustment target.

[0115] Optionally, the data type of the current actual operation data includes active power.

[0116] The device for controlling a virtual power plant provided by an embodiment of the present invention can execute the method for controlling a virtual power plant provided by any embodiment of the present invention, and has corresponding function modules and beneficial effects for executing the method.

[0117] Embodiment 4

[0118] Figure 5 FIG. shows a schematic structural diagram of an electronic device 40 that can be used to implement an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processing, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0119] As Figure 5As shown, the electronic device 40 includes at least one processor 41 and a memory communicatively connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc. The memory stores a computer program executable by the at least one processor. The processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 into the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0120] Multiple components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disc, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0121] The processor 41 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as the method for controlling a virtual power plant.

[0122] In some embodiments, the method for controlling a virtual power plant can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the method for controlling a virtual power plant described above can be executed. Alternatively, in other embodiments, the processor 41 can be configured to execute the method for controlling a virtual power plant by any other appropriate means (e.g., by means of firmware).

[0123] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0124] The computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer program can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0125] The computer device provided above can be used to execute the method for controlling a virtual power plant provided in any of the above embodiments, and has the corresponding functions and beneficial effects.

[0126] Embodiment 5

[0127] In the context of the present invention, a computer-readable storage medium can be a tangible medium, and the computer-executable instructions are used to execute a method for controlling a virtual power plant when executed by a computer processor, and the method includes:

[0128] Obtaining current actual operation data of devices in the current virtual power plant;

[0129] Using a model-free adaptive controller to output an operation adjustment target according to the current actual operation data and a preset operation target;

[0130] Controlling the devices in the current virtual power plant by using the operation adjustment target.

[0131] In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include electrical connections based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0132] The computer device provided above can be used to execute the method for controlling a virtual power plant provided in any of the foregoing embodiments, and has the corresponding functions and beneficial effects.

[0133] It should be noted that in the embodiments of the apparatus for controlling a virtual power plant described above, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and do not limit the protection scope of the present invention.

[0134] Note that the above is only a preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for controlling a virtual power plant, characterized in that: include: Obtain the current actual operation data of the equipment in the current virtual power plant; The model-free adaptive controller is used to output the operation adjustment target according to the current actual operation data and the preset operation target; The operation adjustment target is used to control the equipment in the current virtual power plant.

2. The method according to claim 1, characterized in that The method of using the model-free adaptive controller to output the operation adjustment target according to the current actual operation data and the preset operation target includes: Using a model-free adaptive controller, a pseudo partial derivative estimate is determined based on a historical control output difference and a control input difference, wherein the historical control output difference is determined based on the gap between historical operation adjustment targets, and the control input difference is determined based on the deviation between actual operation data and a preset operation target; A model-free adaptive controller is used to output an operation adjustment target according to the pseudo partial derivative estimate and the gap between the preset operation target and the current actual operation data.

3. The method according to claim 1, characterized in that Also includes: Determine whether a first event occurs at a first moment and a second event occurs at a second moment, wherein the first moment is earlier than the second moment, the first event includes that the operation adjustment target at the first moment is less than or equal to a first preset threshold and the difference between the actual operation data and the preset operation target is less than or equal to a second preset threshold, and the second event includes that the operation adjustment target at the second moment is greater than the first preset threshold or the difference between the actual operation data and the preset operation target is greater than the second preset threshold; If so, the model-free adaptive controller is used to output a new operation adjustment target according to the current actual operation data and the preset operation target.

4. The method according to claim 3, characterized in that After determining whether the first event occurs at the first moment and the second event occurs at the second moment, the method further includes: If not, determining whether the operating data threshold range of the equipment in the current virtual power plant has changed, wherein the operating data threshold range is determined according to the actual operating status of the equipment in the current virtual power plant; If there is a change, the preset regulation target constraint conditions of each type of equipment in the current virtual power plant are updated to obtain new constraint conditions; The model-free adaptive controller is used to output a new operation adjustment target that meets the new constraint condition according to the current actual operation data and the preset operation target.

5. The method according to claim 1, characterized in that The controlling of the equipment in the current virtual power plant by using the operation adjustment target includes: If the operation adjustment target is greater than the first preset threshold or the gap between the current actual operation data and the preset operation target is greater than the second preset threshold, an operation data adjustment instruction is sent to the corresponding equipment in the current virtual power plant according to the operation adjustment target.

6. The method according to claim 1, characterized in that Before using the operation adjustment target to control the equipment in the current virtual power plant, the method further includes: For each type of equipment, determining whether the operation adjustment target satisfies the corresponding preset adjustment target constraint condition; If not, the operation adjustment target is corrected to obtain a corrected operation adjustment target.

7. The method according to any one of claims 1 to 6, characterized in that The data type of the current actual operation data includes active power.

8. A device for controlling a virtual power plant, characterized in that: include: An operation data acquisition module is used to obtain the current actual operation data of the equipment in the current virtual power plant; An operation regulation target determination module is used to output an operation regulation target according to current actual operation data and preset operation targets by using a model-free adaptive controller; A control module is used to control the equipment in the current virtual power plant using the operation adjustment target.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the method for controlling a virtual power plant according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for controlling a virtual power plant according to any one of claims 1 to 7 when executed.

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