Intelligent coordination control method, system and equipment for photovoltaic transformer area and medium

Abstract

The invention relates to the technical field of new energy power system control, and discloses an intelligent cooperative control method, system and device for a photovoltaic transformer area and a medium, and the method comprises the steps: obtaining a target scheduling instruction and carrying out the first decomposition operation, can more accurately distribute a scheduling task to each transformer area, and improves the scheduling accuracy and efficiency. And secondly, the decomposed target scheduling instruction is issued to each transformer area, so that timely transmission and execution of the instruction are ensured, and delay and errors of information transmission are reduced. Moreover, the established first coordination strategy, especially a switching coordination strategy comprising a normal mode and a network disconnection autonomous mode, enables the system to maintain stable performance in different operation states, and improves the reliability and flexibility of the system. And finally, each transformer area adaptively adjusts the voltage and the frequency according to the first coordination strategy and the target scheduling instruction, thereby realizing coordinated operation between the transformer areas and optimizing the overall performance of the power grid.

Description

Technical Field

[0001] This invention relates to the field of new energy power system control technology, and in particular to a method, system, equipment and medium for intelligent collaborative control of photovoltaic power distribution areas. Background Technology

[0002] With the rapid increase in the penetration rate of distributed photovoltaic (PV) power, traditional distribution networks face challenges such as voltage fluctuations, frequency instability, and insufficient communication reliability caused by the high proportion of renewable energy integration. Existing control systems mostly adopt a centralized architecture, which makes it difficult to achieve rapid coordinated response of equipment in complex terrain and weak communication environments, thus restricting the efficiency of PV integration and the level of grid safety operation.

[0003] Existing technologies primarily rely on cloud-based centralized decision-making control systems, which suffer from high communication latency and risks of network outages and loss of control, failing to meet the millisecond-level dynamic response requirements of high-penetration scenarios. The use of a single communication protocol results in insufficient equipment coverage in mountainous areas and cannot adapt to heterogeneous equipment from multiple manufacturers, hindering large-scale deployment. Solutions based on imported chips and operating systems (such as those using ARM+Linux architecture) face supply chain risks and lack national-level security protection mechanisms, making them vulnerable to network attacks and data tampering. Therefore, there is an urgent need for an intelligent collaborative control device and method that can achieve "observable, measurable, adjustable, and controllable" capabilities to address the technical bottlenecks of high-penetration distributed photovoltaic grid connection. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method, system, device, and medium for intelligent collaborative control of photovoltaic power distribution areas, which can solve the problems existing in the prior art, especially in high-penetration distributed photovoltaic grid-connected scenarios, to achieve rapid, reliable, and intelligent collaborative control of photovoltaic power distribution areas, and improve photovoltaic absorption efficiency and grid safety operation level.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for intelligent collaborative control of a photovoltaic power distribution area, comprising:

[0008] Obtain the target scheduling instruction and perform a first decomposition operation on the target scheduling instruction;

[0009] The target scheduling instructions include power limits, peak shaving requirements, and voltage / frequency constraint ranges;

[0010] The target scheduling instruction comes from the superior device;

[0011] The target scheduling instruction after the first decomposition operation is the power control target of several transformer areas;

[0012] The target scheduling instruction after the first decomposition operation is sent to each station area;

[0013] Establish a first coordination strategy, which includes a switching coordination strategy for normal mode and network outage autonomous mode.

[0014] Each transformer substation adaptively adjusts its voltage and frequency based on the first coordination strategy and the target scheduling instructions following the first decomposition operation.

[0015] As a preferred embodiment of the intelligent collaborative control method for photovoltaic power distribution areas described in this invention, the first coordination strategy includes:

[0016] Perform network status monitoring;

[0017] When in normal network status, switch to normal mode, and perform first-level data synchronization and policy update operations.

[0018] When in an abnormal network state, switch to the offline self-governance mode to perform secondary data synchronization and policy update operations.

[0019] This preferred solution ensures stable operation of photovoltaic (PV) distribution areas under various network conditions. Under normal network conditions, first-level data synchronization ensures real-time data consistency across distribution areas, while policy updates allow each area to adaptively adjust based on the latest dispatch instructions and grid status. In abnormal network conditions, such as network failures or communication interruptions, switching to a network outage autonomous mode allows each area to operate autonomously using local data and control strategies, preventing system crashes or loss of control due to network issues. Second-level data synchronization, after network recovery, completes and synchronizes data from the outage period, ensuring data integrity and accuracy. This coordinated strategy not only improves the reliability and stability of PV distribution areas but also enhances their adaptability and robustness to different network environments.

[0020] As a preferred embodiment of the intelligent collaborative control method for photovoltaic power distribution areas described in this invention, the first coordination strategy further includes a communication recovery mechanism;

[0021] The communication recovery mechanism includes automatically synchronizing the operational data during the network outage to the cloud after communication is restored, and re-receiving global optimization parameters;

[0022] A hybrid critical-level task scheduling mechanism is adopted, dividing the task domain into real-time task domains and non-real-time task domains.

[0023] As a preferred embodiment of the intelligent collaborative control method for photovoltaic power distribution areas described in this invention, the first-level data synchronization operation and strategy update operation include:

[0024] Each distribution area adaptively adjusts its voltage and frequency according to the target scheduling instructions after the first decomposition operation, and then periodically uploads its operating data to the cloud.

[0025] The cloud includes a pre-trained prediction model used to optimize the droop coefficient;

[0026] The cloud is used to optimize the droop coefficient based on the output of the prediction model and then distribute it to the edge.

[0027] As a preferred embodiment of the intelligent collaborative control method for photovoltaic power distribution areas described in this invention, the second-level data synchronization operation and strategy update operation include:

[0028] Freeze wide-area scheduling commands and enable the local prediction module for short-term power prediction;

[0029] Record all operational data during the network outage and store it on the local edge computing node;

[0030] Only real-time control tasks in the local critical task domain are executed; non-real-time tasks are paused or delayed.

[0031] As a preferred embodiment of the intelligent collaborative control method for photovoltaic power distribution areas described in this invention, the first decomposition operation includes designing an improved ADMM optimization model for decomposition operation;

[0032] The improved ADMM optimization model includes:

[0033] Define the power allocation value for each transformer area and the total power target for the cluster;

[0034] Establish an optimization problem framework that minimizes system cost or maximizes economic benefits while satisfying all technical constraints.

[0035] A dynamically adjusted penalty factor is introduced to accelerate the algorithm's convergence speed, and an asynchronous communication mechanism is adopted based on network conditions to improve the system's robustness and adaptability.

[0036] Each transformer substation independently solves its local optimization problem based on local information and updates its own power allocation value;

[0037] The cluster-level control unit is responsible for aggregating information from all stations, updating global variables, and checking whether the stop conditions are met.

[0038] When the iteration process meets the termination condition, the final power allocation scheme is issued as a power limit to each distribution area execution unit to guide the actual power production and distribution.

[0039] As a preferred embodiment of the intelligent collaborative control method for photovoltaic power distribution areas described in this invention, the network status monitoring includes continuously monitoring the communication connection status with the cloud, and determining whether the network is unobstructed by detecting heartbeat packets. If three consecutive heartbeat packets time out for more than 10 seconds, it is determined that the network is disconnected.

[0040] Secondly, the present invention provides a photovoltaic power distribution area intelligent collaborative control system, comprising:

[0041] The decomposition module is used to acquire the target scheduling instruction and perform a first decomposition operation on the target scheduling instruction;

[0042] The target scheduling instructions include power limits, peak shaving requirements, and voltage / frequency constraint ranges;

[0043] The target scheduling instruction comes from the superior device;

[0044] The target scheduling instruction after the first decomposition operation is the power control target of several transformer areas;

[0045] The instruction issuing module is used to issue the target scheduling instruction after the first decomposition operation to each station area;

[0046] The coordination strategy establishment module is used to establish a first coordination strategy, which includes a switching coordination strategy for normal mode and network outage autonomous mode.

[0047] The coordination module is used for each distribution area to adaptively adjust voltage and frequency according to the first coordination strategy and the target scheduling instruction after the first decomposition operation.

[0048] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0049] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0050] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a smart collaborative control method for photovoltaic power distribution areas, which involves acquiring target scheduling instructions and performing a first decomposition operation on the target scheduling instructions; distributing the target scheduling instructions after the first decomposition operation to each power distribution area; establishing a first coordination strategy, which includes a switching coordination strategy for normal mode and grid-out autonomous mode; and each power distribution area adaptively adjusting voltage and frequency according to the first coordination strategy and the target scheduling instructions after the first decomposition operation. Firstly, by acquiring target scheduling instructions and performing the first decomposition operation, this invention can more accurately allocate scheduling tasks to each power distribution area, improving the accuracy and efficiency of scheduling. Secondly, distributing the decomposed target scheduling instructions to each power distribution area ensures timely transmission and execution of instructions, reducing information transmission delays and errors. Furthermore, the established first coordination strategy, particularly the switching coordination strategy for normal mode and grid-out autonomous mode, enables the system to maintain stable performance under different operating conditions, improving the system's reliability and flexibility. Finally, each power distribution area adaptively adjusts voltage and frequency according to the first coordination strategy and the target scheduling instructions, achieving coordinated operation between power distribution areas and optimizing the overall performance of the power grid. In summary, the intelligent collaborative control system for photovoltaic power distribution areas of the present invention has significant technical advantages and practical application value. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 The present invention provides a method flowchart for a smart collaborative control method for a photovoltaic power station area according to an embodiment of the present invention.

[0053] Figure 2 This is a structural diagram of a device for a photovoltaic power station intelligent collaborative control method according to an embodiment of the present invention.

[0054] Figure 3 This is an internal structure diagram of an electronic device for a photovoltaic power station intelligent collaborative control method provided in one embodiment of the present invention. Detailed Implementation

[0055] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0056] Example 1, referring to Figures 1-3 This is the first embodiment of the present invention, which provides a method for intelligent collaborative control of a photovoltaic power distribution area, including:

[0057] Existing technologies have several drawbacks, such as high communication latency and a high risk of network outages and loss of control, which cannot meet the millisecond-level dynamic response requirements in high-penetration scenarios.

[0058] This application provides a method that can effectively solve the problems mentioned above. The following will describe in detail how to implement the intelligent collaborative control method for photovoltaic power distribution areas with multiple embodiments.

[0059] Figure 1 A flowchart of a method for intelligent collaborative control of a photovoltaic power distribution area is shown, including:

[0060] S101, Obtain the target scheduling instruction and perform the first decomposition operation on the target scheduling instruction;

[0061] It should be noted that in order to realize intelligent collaborative control of photovoltaic power distribution areas, it is necessary to design corresponding devices and underlying method logic for device operation. This application considers intelligent collaborative control in both network and offline states to cope with different operating environments and needs.

[0062] In an optional embodiment, under normal network conditions, the system can receive and process target scheduling instructions from the upper-level device in real time, including information such as power limits, peak-shaving requirements, and voltage / frequency constraints. Through a first decomposition operation, these global scheduling instructions are precisely decomposed into power control targets for several distribution areas, ensuring that each distribution area can perform accurate power scheduling according to its own actual situation.

[0063] In an optional embodiment, the process of issuing the target scheduling instruction after the first decomposition operation to each distribution area adopts a distributed architecture. Localized processing and decision-making are performed through edge computing nodes, effectively reducing communication latency caused by centralized decision-making in the cloud and improving system response speed. Simultaneously, each distribution area interacts with information through an efficient and stable communication protocol, ensuring the timeliness and accuracy of data transmission.

[0064] In this embodiment, the target scheduling instruction includes power limits, peak shaving requirements, and voltage / frequency constraints.

[0065] In this embodiment of the application, the target scheduling instruction comes from the superior device.

[0066] In an optional embodiment, the upper-level device may be a distributed photovoltaic cluster intelligent control device.

[0067] In this embodiment of the application, the target scheduling instruction after the first decomposition operation is the power control target of several substations.

[0068] In this embodiment of the application, the first decomposition operation includes designing an improved ADMM optimization model for decomposition.

[0069] The improved ADMM optimization model includes:

[0070] Define the power allocation value for each transformer area and the total power target for the cluster;

[0071] Establish an optimization problem framework that minimizes system costs or maximizes economic benefits while satisfying all technical constraints.

[0072] A dynamically adjusted penalty factor is introduced to accelerate the algorithm's convergence speed, and an asynchronous communication mechanism is adopted based on network conditions to improve the system's robustness and adaptability.

[0073] Each transformer substation independently solves its local optimization problem based on local information and updates its own power allocation value;

[0074] The cluster-level control unit is responsible for aggregating information from all stations, updating global variables, and checking whether the stop conditions are met.

[0075] When the iteration process meets the termination condition, the final power allocation scheme is issued as a power limit to each distribution area execution unit to guide the actual power production and distribution.

[0076] Specifically, the superior device (such as a distributed photovoltaic cluster intelligent control device) issues wide-area dispatch instructions, including power limit P. limit Peak shaving demand ΔP peak Voltage / frequency constraint range [V] min V max ]、[f min ,f max ].

[0077] In an optional embodiment, the objective function of the input wide-area scheduling instruction needs to minimize the total power deviation of the cluster while satisfying the power mutual assistance constraint between stations.

[0078] Furthermore, an improved ADMM (Alternating Direction Method of Multipliers) optimization model is constructed. This model is primarily used in intelligent collaborative control devices for photovoltaic power distribution areas to solve the optimal scheduling problem of distributed power sources. This method improves solution efficiency and system robustness by decomposing the complex global optimization problem into multiple local optimization problems that can be processed in parallel.

[0079] In an optional embodiment, the variables are defined as follows:

[0080] x i : Power allocation value for the i-th transformer area;

[0081] z: Globally consistent variable (cluster total power target).

[0082] In an optional embodiment, the optimization problem is set as follows:

[0083]

[0084] (Power balance constraint)

[0085] (Transportation area capacity constraints)

[0086] Among them, P local,i This represents the local power demand or predicted value for the i-th transformer area. This is the ideal power value for each transformer area calculated based on the current operating state. λ represents the penalty factor, used to balance the difference between local power demand and the globally consistent variable. Its value can be dynamically adjusted according to the real-time operating state to accelerate convergence. N is the total number of transformer areas.

[0087] It should be noted that the improvements in this application are as follows: the dynamic penalty factor is improved by adjusting λ according to the real-time operating status of the transformer area (such as the fluctuation rate of photovoltaic power output) to accelerate convergence; and the asynchronous communication mechanism is improved by allowing the transformer area to asynchronously update the local solution under communication delay, thereby improving robustness.

[0088] In an optional embodiment, distributed iterative solution

[0089] Local update of transformer areas: Each transformer area solves the local optimization problem in parallel and updates the local area.

[0090]

[0091] In one optional embodiment, a globally consistent update is performed, aggregating all station area solutions at the cluster level and updating...

[0092] global variable z (k+1) :

[0093]

[0094] Termination condition: When |z (k+1) -z (k) Iteration stops when | < ∈ (preset threshold).

[0095] In an optional embodiment, a control target at the station / area level is issued, which will ultimately resolve... The power limit is issued to each distribution unit, along with dynamic adjustment parameters (such as the reactive power compensation coefficient K). Q ).

[0096] It should be noted that acquiring the target dispatch instruction and performing a first decomposition operation on it ensures that the power control targets received by each distribution area in subsequent steps are accurately calculated and optimized, thereby improving the accuracy and efficiency of power dispatch. Furthermore, the first decomposition operation transforms global dispatch instructions into local control targets, enabling each distribution area to flexibly perform power dispatch according to its own actual situation, enhancing the system's flexibility and adaptability. In subsequent steps, these decomposed power control targets will be distributed to each distribution area to guide actual power production and distribution, thereby achieving intelligent collaborative control between photovoltaic distribution areas.

[0097] S102, the target scheduling instruction after the first decomposition operation is sent to each station area;

[0098] In an optional embodiment, the target scheduling instruction after the first decomposition operation can be sent to each distribution area through a secure and reliable communication channel to ensure the timeliness and accuracy of instruction transmission. After receiving the target scheduling instruction, each distribution area will perform corresponding power dispatching operations according to its own actual situation and preset control strategies.

[0099] In an optional embodiment, the number of each transformer substation can be flexibly configured according to actual needs to meet the intelligent collaborative control requirements of photovoltaic transformer substations of different sizes and complexities. To ensure the reliability and stability of communication, redundant communication links are designed between each transformer substation and between the transformer substation and the cluster-level control unit. When a communication link fails, the system can automatically switch to a backup link to ensure continuous information transmission.

[0100] It should be noted that sending the target scheduling instructions after the first decomposition operation to each distribution area allows for independent power dispatch based on the received precise power control targets, without waiting for a unified global instruction, thus significantly shortening the dispatch response time. Simultaneously, since each distribution area dispatches based on its own actual conditions, photovoltaic energy can be utilized more effectively, improving energy efficiency. Furthermore, this distributed dispatch method enhances system robustness; even if some distribution areas experience faults or communication interruptions, other distribution areas can still operate normally, ensuring the stable operation of the entire system. In the following steps, each distribution area will adaptively adjust voltage and frequency according to its own conditions and the received power control targets to achieve more precise and efficient power dispatch.

[0101] S103, Establish the first coordination strategy, which includes the switching coordination strategy between normal mode and network outage autonomous mode;

[0102] In an optional embodiment, the first coordination strategy is to ensure that the photovoltaic power station area can maintain stable performance and efficient power dispatch under different operating conditions.

[0103] In an optional embodiment, the first coordination strategy can be implemented through preset rules and algorithms that take into account the actual conditions and scheduling needs of the photovoltaic distribution areas. In normal mode, each distribution area will perform power dispatching according to the received power control target and the preset control strategy to ensure the stable operation of the power grid and the efficient utilization of photovoltaic energy.

[0104] In an optional embodiment, when the system detects a communication failure or network outage, it will automatically switch to the network outage autonomous mode. At this time, each distribution area will carry out independent power dispatching according to the preset autonomous strategy to ensure that a certain power supply and grid stability can still be maintained in the network outage state.

[0105] It should be noted that this switching coordination strategy design enables the photovoltaic power distribution area intelligent control system to maintain stable performance and efficient power dispatch under different operating conditions, improving the system's reliability and flexibility. Furthermore, the first coordination strategy can be flexibly configured and adjusted according to actual needs to meet the intelligent control requirements of photovoltaic power distribution areas of varying scales and complexities.

[0106] In an optional embodiment, under normal mode, each distribution area adaptively adjusts voltage and frequency based on the received target dispatch instructions and its own actual situation to achieve precise power generation and distribution. However, under the grid-out autonomous mode, due to the loss of communication with the upstream equipment, each distribution area needs to rely on its own prediction and control capabilities to maintain the stable operation of the power system.

[0107] In this embodiment of the application, the first coordination strategy includes:

[0108] Perform network status monitoring;

[0109] When in normal network status, switch to normal mode, and perform first-level data synchronization and policy update operations.

[0110] When in an abnormal network state, switch to the offline self-governance mode to perform secondary data synchronization and policy update operations.

[0111] In this application embodiment, the first-level data synchronization operation and policy update operation include:

[0112] Each distribution area adaptively adjusts its voltage and frequency according to the target scheduling instructions after the first decomposition operation, and then periodically uploads its operating data to the cloud.

[0113] The cloud includes pre-trained prediction models, which are used to optimize the droop coefficient;

[0114] The cloud is used to optimize the droop coefficient based on the output of the prediction model and then distribute it to the edge.

[0115] In this embodiment of the application, the second-level data synchronization operation and the policy update operation include:

[0116] Freeze wide-area scheduling commands and enable the local prediction module for short-term power prediction;

[0117] Record all operational data during the network outage and store it on the local edge computing node;

[0118] Only real-time control tasks in the local critical task domain are executed; non-real-time tasks are paused or delayed.

[0119] Specifically, in the normal mode (cloud collaboration) for first-level data synchronization and policy update operations, data synchronization involves the periodic (every 5 minutes) upload of operational data (such as P) by the regional unit. pv V bus The strategy is updated to use a cloud-based LSTM prediction model trained on historical data, dynamically optimizing the droop coefficient K. Q K P And distribute it to the edge.

[0120] Second-order data synchronization and policy update operations in the network-disconnected autonomous mode include freezing wide-area scheduling commands and locking the current power limit x. i * ;

[0121] Enable the local AI prediction module to generate short-term (future 15 minutes) power predictions based on LSTM;

[0122] Switch to the hybrid mode of "local droop control + predictive feedforward", i.e., the autonomous mode when the network is disconnected:

[0123] [Q ref =K Q ·(V ref -V meas )+Q predict ]

[0124] Among them, Q predict To predict reactive power, this represents the amount of reactive power that needs to be injected or absorbed to maintain system voltage stability. It is a calculated target value used to guide the inverter in adjusting its output. K Q The dynamic adjustment coefficient is a key parameter used to regulate the relationship between reactive power and voltage. It adaptively updates based on the impedance characteristics of the transformer substation to ensure optimal voltage regulation under various operating conditions.ref This is the reference voltage, the ideal voltage value that is expected to be achieved. It is typically set by the system designer to ensure that the grid voltage level remains within a safe and stable range. V meas This is the measured voltage, i.e., the voltage value actually measured at present. This value reflects the current actual operating state of the power grid. Q predict Predicted reactive power is the reactive power demand predicted based on factors such as load changes and weather conditions over a future period. Introducing a predictive feedforward mechanism can adjust reactive power output in advance, improving system response speed and stability.

[0125] In this embodiment of the application, the first coordination strategy further includes a communication recovery mechanism;

[0126] The communication recovery mechanism includes automatically synchronizing the operational data during the network outage to the cloud and re-receiving global optimization parameters once communication is restored.

[0127] A hybrid critical-level task scheduling mechanism is adopted, dividing the task domain into real-time task domains and non-real-time task domains.

[0128] In this embodiment of the application, network status monitoring includes continuously monitoring the communication connection status with the cloud, and determining whether the network is unobstructed by detecting heartbeat packets. If three consecutive heartbeat packets time out for more than 10 seconds, it is determined that the network is disconnected.

[0129] It should be noted that establishing a primary coordination strategy ensures the efficient and stable operation of the photovoltaic power distribution area intelligent control system under different network conditions. Under normal network conditions, through primary data synchronization and strategy update operations, the system can acquire real-time operating data of the distribution area, optimize the droop coefficient using cloud-based predictive models, and distribute the updated parameters to the edge, achieving precise power dispatch. When the system detects a communication failure or network outage, it automatically switches to an autonomous outage mode. Through secondary data synchronization and strategy update operations, it relies on the local prediction module for short-term power prediction and executes real-time control tasks in the local critical task domain, ensuring a certain level of power supply and grid stability even during network outages. Furthermore, the communication recovery mechanism allows the system to automatically synchronize operating data from the outage period to the cloud and re-receive global optimization parameters after network recovery, ensuring system continuity and stability. Simultaneously, the introduction of a hybrid critical task scheduling mechanism enables the system to flexibly schedule tasks based on their importance and urgency, improving overall system performance and response speed.

[0130] S104, each distribution area adaptively adjusts its voltage and frequency according to the first coordination strategy and the target scheduling instructions after the first decomposition operation.

[0131] In an optional embodiment, the steps of adaptively adjusting the voltage and frequency can be as follows:

[0132] Step 1: Data Acquisition and Preprocessing

[0133] Real-time data acquisition: Obtaining inverter output P pv Energy storage SOCS ess Bus voltage V bus Data such as frequency f (sampling period ≤ 1 second).

[0134] Anomaly filtering: A sliding window mean filter is used to remove noise interference.

[0135] Step 2: Local control policy generation

[0136] Voltage regulation (QV droop control):

[0137] [Q ref =K Q ·(V ref -V meas )]

[0138] Among them, K Q The coefficients are dynamically adjusted and updated adaptively based on the impedance characteristics of the transformer area.

[0139] Frequency adjustment (Pf droop control):

[0140] [P ref =K P ·(f ref -f meas )]

[0141] Energy storage PCS according to P ref Adjust the charging and discharging power.

[0142] Step 3: Command Issuance and Equipment Control

[0143] Inverter control: Send Q signals to the photovoltaic inverter via Modbus / TCP protocol. ref ;

[0144] Energy storage PCS control: P is set via IEC 61850 protocol. ref and charging / discharging modes;

[0145] Protection action: If an over-limit voltage is detected (e.g., V), meas >1.1V rated This immediately triggers the intelligent circuit breaker to trip.

[0146] It should be noted that after receiving the target dispatch instruction following the first decomposition operation, each distribution area will, based on its own actual situation and preset control strategy, adaptively adjust voltage and frequency to achieve precise power production and distribution. This adaptive adjustment mechanism ensures that the distribution area maintains stable performance and efficient power dispatch under different operating conditions. Simultaneously, by collecting real-time data such as inverter output, energy storage SOC, bus voltage, and frequency, and performing anomaly filtering and preprocessing, the accuracy and reliability of the data can be further improved, providing strong support for the subsequent generation of local control strategies. For voltage regulation, a QV droop control strategy is adopted, adaptively updating the dynamic adjustment coefficient according to the impedance characteristics of the distribution area to achieve stable voltage regulation. For frequency regulation, the charging and discharging power is adjusted through the energy storage PCS to achieve stable frequency control. Finally, control commands are sent to the photovoltaic inverter and energy storage PCS via the Modbus / TCP protocol and the IEC 61850 protocol to achieve precise equipment control. If abnormal conditions such as voltage exceeding limits are detected, the intelligent circuit breaker will be immediately triggered to ensure the safe operation of the system. By adaptively adjusting voltage and frequency, each distribution area can achieve more precise and efficient power dispatching based on its own actual situation and the received power control target, thereby improving the performance and efficiency of the entire photovoltaic distribution area intelligent collaborative control system.

[0147] In summary, this invention proposes an intelligent collaborative control method for photovoltaic power distribution areas. The method involves acquiring target scheduling instructions and performing a first decomposition operation on these instructions; distributing the decomposed instructions to each distribution area; establishing a first coordination strategy, which includes a switching coordination strategy for normal mode and grid-out autonomous mode; and each distribution area adaptively adjusting voltage and frequency according to the first coordination strategy and the target scheduling instructions after the first decomposition operation. Firstly, by acquiring the target scheduling instructions and performing the first decomposition operation, this invention can more accurately allocate scheduling tasks to each distribution area, improving the accuracy and efficiency of scheduling. Secondly, distributing the decomposed target scheduling instructions to each distribution area ensures timely transmission and execution of instructions, reducing information transmission delays and errors. Furthermore, the established first coordination strategy, particularly the switching coordination strategy for normal mode and grid-out autonomous mode, enables the system to maintain stable performance under different operating conditions, improving system reliability and flexibility. Finally, each distribution area adaptively adjusts voltage and frequency according to the first coordination strategy and the target scheduling instructions, achieving coordinated operation between distribution areas and optimizing the overall performance of the power grid. In conclusion, the intelligent collaborative control system for photovoltaic power distribution areas of this invention has significant technical advantages and practical application value.

[0148] Example 2, in a preferred embodiment, such as Figure 2As shown, the hardware layer adopts a domestically produced highly integrated SoC chip, equipped with the HarmonyOS power operating system, and uses a dual-core heterogeneous architecture to support containerized deployment; it collects the photovoltaic inverter's IV curve, temperature, and power data in real time; and it integrates the national cryptographic SM4 algorithm and hardware TCM trusted module to realize device identity authentication and encrypted data transmission.

[0149] Communication layer: integrates HPLC, LoRa / NB-IoT, and low-power wireless multimode communication modules, supporting various wireless hybrid networking methods;

[0150] Control Layer: Construct a two-level collaborative architecture of "cluster-area". The cluster level receives wide-area scheduling instructions and decomposes the wide-area scheduling instructions based on the improved ADMM algorithm to generate control targets at the area level. It adopts a "cloud-edge collaboration" mode and switches to a local prediction + vertical control hybrid mode when the network is down to ensure controllability in extreme scenarios.

[0151] The hardware configuration of the intelligent collaborative control device for photovoltaic power distribution areas is as follows:

[0152] 1. Main control processor module

[0153] Core chip: Domestically developed multi-core SoC chip (HiSilicon Hi3861), integrating dual-core Cortex-A55 (1.8GHz) + Cortex-M7 real-time core, supporting hardware virtualization and floating-point operation acceleration.

[0154] Operating System: Deeply adapted to the HarmonyOS operating system for power systems, featuring a lightweight microkernel architecture and supporting multi-container isolated operation.

[0155] Storage configuration: Onboard 4GB LPDDR4X memory, 64GB eMMC flash storage;

[0156] The expansion interface supports MicroSD cards (up to 1TB).

[0157] Real-time control unit: integrates an FPGA coprocessor for millisecond-level control command generation and issuance.

[0158] 2. Communication module

[0159] Multimode communication chipset:

[0160] HPLC module: Based on domestic chip (Dongsoft Carrier HR7P), supports OFDM modulation, communication rate ≥2Mbps;

[0161] LoRa / NB-IoT module: Semtech SX1276 chip, supports 470-510MHz frequency band, coverage radius ≥5km;

[0162] Low-power wireless module: Si4463 chip, supports frequency hopping and adaptive power adjustment.

[0163] Protocol compatibility: Built-in Modbus, IEC 61850, and DL / T645 protocol conversion engine, compatible with mainstream inverters and energy storage devices.

[0164] Interface expansion: Dual Gigabit Ethernet interfaces (supports fiber optic / electrical port auto-negotiation);

[0165] 2-channel RS485 / RS232 industrial bus;

[0166] One CAN 2.0B interface.

[0167] 3. Power Management Module

[0168] Input range: Wide voltage DC 12-48V input, supports direct power supply from photovoltaic DC bus;

[0169] Redundant design: Dual power input (automatic switching between primary and backup), built-in supercapacitor (5F / 16V) to achieve seamless switching in 10ms;

[0170] Dynamic power management: Light load mode: Standby power consumption ≤2W (basic communication and status monitoring only);

[0171] Heavy load mode: Peak power consumption ≤15W (full-function operation).

[0172] 4. Data Acquisition and Processing Module

[0173] Analog signal acquisition: 16-bit high-precision ADC (1MHz sampling rate), supporting 8-channel synchronous acquisition;

[0174] Voltage measurement range: 0-1000V (accuracy ±0.2%);

[0175] Current measurement range: 0-100A (accuracy ±0.5%).

[0176] Digital inputs / outputs: 8-channel DI (optically isolated, supports dry / wet contacts);

[0177] 4-channel DO (relay output, contact capacity 5A / 250V AC).

[0178] AI coprocessor: integrates Cambricon MLU acceleration chip, supports local inference of LSTM models (2 TOPS computing power).

[0179] 5. Safety Protection Module

[0180] Hardware encryption: Domestic TCM security chip, supporting hardware acceleration of SM2 / SM3 / SM4 national cryptographic algorithms;

[0181] Trusted Execution Environment (TEE): Isolates and stores keys and sensitive data, and supports remote secure OTA upgrades;

[0182] Physical protection: Anti-tamper detection sensor, which automatically erases the key and locks the system upon triggering.

[0183] 6. Structural and environmental adaptability design

[0184] Protection rating: IP65 protective enclosure, wide operating temperature range of -40℃ to +85℃;

[0185] Heat dissipation design: Fanless heat dissipation structure, passive heat dissipation is achieved through aluminum fins and thermally conductive silicone.

[0186] Installation method: DIN rail mounting (standard 35mm), supports wall mounting / cabinet deployment.

[0187] Hardware co-operation logic: Real-time guarantee: The Cortex-M7 core is dedicated to control instruction generation (cycle ≤ 1ms), and the Cortex-A55 core handles data aggregation and protocol conversion;

[0188] Fault tolerance: Dual communication modules with hot backup, automatically switching to the backup link when the main module fails;

[0189] Dynamic load balancing: Dynamically allocate container resources based on task priority (e.g., AI-predicted tasks are downgraded to low priority).

[0190] Example 3: This example also provides a photovoltaic power distribution area intelligent collaborative control system, including:

[0191] The decomposition module is used to acquire the target scheduling instruction and perform the first decomposition operation on the target scheduling instruction;

[0192] The target scheduling instructions include power limits, peak shaving requirements, and voltage / frequency constraints.

[0193] The target scheduling instructions come from the superior unit;

[0194] The target scheduling instruction after the first decomposition operation is the power control target of several transformer areas;

[0195] The instruction issuance module is used to issue the target scheduling instruction after the first decomposition operation to each station area;

[0196] The coordination strategy establishment module is used to establish the first coordination strategy, which includes the switching coordination strategy between normal mode and network outage autonomous mode.

[0197] The coordination module is used to adaptively adjust the voltage and frequency of each distribution area according to the first coordination strategy and the target scheduling instructions after the first decomposition operation.

[0198] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0199] This embodiment also provides an electronic device, which can be a terminal, and its internal structure diagram can be as follows: Figure 3 As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for intelligent collaborative control of a photovoltaic power station area. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the device's casing, or an external keyboard, touchpad, or mouse.

[0200] This embodiment also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it performs the following steps:

[0201] Obtain the target scheduling instruction and perform the first decomposition operation on the target scheduling instruction;

[0202] The target scheduling instructions include power limits, peak shaving requirements, and voltage / frequency constraints.

[0203] The target scheduling instructions come from the superior unit;

[0204] The target scheduling instruction after the first decomposition operation is the power control target of several transformer areas;

[0205] The target scheduling command after the first decomposition operation is sent to each station area;

[0206] Establish a primary coordination strategy, which includes coordination strategies for switching between normal mode and offline autonomous mode;

[0207] Each distribution area adaptively adjusts its voltage and frequency according to the first coordination strategy and the target scheduling instructions after the first decomposition operation.

[0208] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0209] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented using various computer languages.

[0210] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0211] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0212] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0213] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0214] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for intelligent collaborative control of a photovoltaic power distribution area, characterized in that, include: Obtain the target scheduling instruction and perform a first decomposition operation on the target scheduling instruction; The target scheduling instructions include power limits, peak shaving requirements, and voltage / frequency constraint ranges; The target scheduling instruction comes from the superior device; The target scheduling instruction after the first decomposition operation is the power control target of several transformer areas; The target scheduling instruction after the first decomposition operation is sent to each station area; Establish a first coordination strategy, which includes a switching coordination strategy for normal mode and network outage autonomous mode. Each transformer substation adaptively adjusts its voltage and frequency based on the first coordination strategy and the target scheduling instructions following the first decomposition operation.

2. The intelligent collaborative control method for photovoltaic power distribution areas as described in claim 1, characterized in that, The first coordination strategy includes: Perform network status monitoring; When in normal network status, switch to normal mode, and perform first-level data synchronization and policy update operations. When in an abnormal network state, switch to the offline self-governance mode to perform secondary data synchronization and policy update operations.

3. The intelligent collaborative control method for photovoltaic power distribution areas as described in claim 2, characterized in that, The first coordination strategy also includes a communication recovery mechanism; The communication recovery mechanism includes automatically synchronizing the operational data during the network outage to the cloud after communication is restored, and re-receiving global optimization parameters; A hybrid critical-level task scheduling mechanism is adopted, dividing the task domain into real-time task domains and non-real-time task domains.

4. The intelligent collaborative control method for photovoltaic power distribution areas as described in claim 3, characterized in that, The first-level data synchronization operation and policy update operation include: Each distribution area adaptively adjusts its voltage and frequency according to the target scheduling instructions after the first decomposition operation, and then periodically uploads its operating data to the cloud. The cloud includes a pre-trained prediction model used to optimize the droop coefficient; The cloud is used to optimize the droop coefficient based on the output of the prediction model and then distribute it to the edge.

5. The intelligent collaborative control method for photovoltaic power distribution areas as described in claim 4, characterized in that, The second-level data synchronization operation and policy update operation include: Freeze wide-area scheduling commands and enable the local prediction module for short-term power prediction; Record all operational data during the network outage and store it on the local edge computing node; Only real-time control tasks in the local critical task domain are executed; non-real-time tasks are paused or delayed.

6. The intelligent collaborative control method for photovoltaic power distribution areas as described in claim 5, characterized in that, The first decomposition operation includes designing an improved ADMM optimization model for decomposition. The improved ADMM optimization model includes: Define the power allocation value for each transformer area and the total power target for the cluster; Establish an optimization problem framework that minimizes system cost or maximizes economic benefits while satisfying all technical constraints. A dynamically adjusted penalty factor is introduced to accelerate the algorithm's convergence speed, and an asynchronous communication mechanism is adopted based on network conditions to improve the system's robustness and adaptability. Each transformer substation independently solves its local optimization problem based on local information and updates its own power allocation value; The cluster-level control unit is responsible for aggregating information from all stations, updating global variables, and checking whether the stop conditions are met. When the iteration process meets the termination condition, the final power allocation scheme is issued as a power limit to each distribution area execution unit to guide the actual power production and distribution.

7. The intelligent collaborative control method for a photovoltaic distribution area as described in claim 6, characterized in that, The network status monitoring includes continuously monitoring the communication connection status with the cloud, and determining whether the network is unobstructed by detecting heartbeat packets. If three consecutive heartbeat packets time out for more than 10 seconds, it is determined that the network is disconnected.

8. A photovoltaic distribution area intelligent collaborative control system, using the method described in any one of claims 1 to 7, characterized in that, include: The decomposition module is used to acquire the target scheduling instruction and perform a first decomposition operation on the target scheduling instruction; The target scheduling instructions include power limits, peak shaving requirements, and voltage / frequency constraint ranges; The target scheduling instruction comes from the superior device; The target scheduling instruction after the first decomposition operation is the power control target of several transformer areas; The instruction issuing module is used to issue the target scheduling instruction after the first decomposition operation to each station area; The coordination strategy establishment module is used to establish a first coordination strategy, which includes a switching coordination strategy for normal mode and network outage autonomous mode. The coordination module is used for each distribution area to adaptively adjust voltage and frequency according to the first coordination strategy and the target scheduling instruction after the first decomposition operation.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent collaborative control method for photovoltaic power distribution areas as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent collaborative control method for photovoltaic power distribution areas as described in any one of claims 1 to 7.

Images