Microgrid control methods, software products, and computer equipment based on collaborative optimization

Abstract

This invention provides a microgrid control method, program product, and computer equipment based on collaborative optimization, relating to the field of power supply and distribution systems. The microgrid control method includes: dividing the microgrid into regions based on the topology of electrical equipment to obtain at least one autonomous control region; identifying a region master controller from the intelligent controllers within each autonomous control region; the region master controller receiving dispatch information from the external public power grid and determining operating constraints corresponding to the autonomous control region based on the dispatch information; sending coordination instructions to other intelligent controllers within the autonomous control region based on the operating constraints; and each intelligent controller adjusting the state of its controlled electrical equipment based on its acquired control information and the coordination instructions. The solution of this invention, by guiding each intelligent controller to adjust the state of electrical equipment through coordination instructions, achieves overall collaborative optimization control of the microgrid, enhancing the stability and rationality of microgrid operation.

Description

Technical Field

[0001] This invention relates to the field of power supply and distribution systems, and in particular to a microgrid control method, program product, and computer equipment based on collaborative optimization. Background Technology

[0002] The clean and low-carbon transformation of the power sector has entered a phase of rapid acceleration, particularly with the rapid growth of new energy power generation technologies, such as wind and solar power. The development and utilization model of new energy power generation is gradually shifting from traditional centralized systems to a combination of distributed and centralized approaches. With significant cost reductions in photovoltaic power generation and energy storage, as well as the development of intelligent control technologies for electrical equipment, microgrid systems that combine distributed new energy power generation with energy storage and integrated power utilization are gradually being promoted.

[0003] Microgrids integrate distributed grids, power load regulation facilities, and energy storage technologies to form a compact grid architecture with autonomous monitoring, fine control, intelligent management, and protection functions. A microgrid is not a simple network composed of identical devices, but a complex energy system integrating various devices. The operating characteristics and control methods of different devices vary significantly. Even devices with the same function have different operating parameters and control strategies. For example, the power generation of photovoltaic and wind power depends on natural conditions and is characterized by large fluctuations; energy storage systems have high controllability, but their charging and discharging power and available capacity are significantly limited; and the load characteristics of various electrical load devices differ considerably. These electrical devices each have their own controllers, and with the development of intelligence, the application of intelligent controllers is increasing. These intelligent controllers of electrical devices are used to execute control strategies that meet the control requirements of the devices themselves, but lack consideration for overall optimization goals. This poses a significant challenge to the overall coordination of the microgrid.

[0004] On the other hand, microgrids contain various distributed power sources, such as photovoltaic arrays, wind turbines, energy storage systems, and backup diesel generators. These power sources are distributed across different nodes in the network, and their power supply targets (electrical devices) may differ, resulting in a wide variety of circuit connection methods. Some microgrids are designed as ring or mesh structures and are connected through a large number of switching devices. This complex topology further increases the difficulty of microgrid regulation. Summary of the Invention

[0005] One object of the present invention is to provide a power dispatching method for a microgrid with multiple energy generation systems that at least solves any of the above-mentioned technical problems.

[0006] A further objective of this invention is to prevent the efficiency of microgrid power dispatch schemes from decreasing or even failing due to changes in the operating environment.

[0007] Another further objective of this invention is to improve the operational stability of microgrids.

[0008] Specifically, this invention provides a microgrid control method based on collaborative optimization. The method includes:

[0009] Obtain the topology of the electrical equipment controlled by each smart controller in the microgrid;

[0010] The microgrid is divided into regions based on its topology to obtain at least one autonomous control region. Each autonomous control region has an independent interface for power interaction with the external public power grid.

[0011] One regional master controller is determined from the intelligent controllers within each autonomous control region;

[0012] The regional master controller receives dispatch information from the external public power grid and determines the operating constraints corresponding to the autonomous control area based on the dispatch information;

[0013] The regional master controller sends coordination commands to other intelligent controllers within the autonomous control area based on operational constraints.

[0014] Each intelligent controller adjusts the state of the electrical equipment it controls based on the control information and coordination instructions it acquires.

[0015] Optionally, the above steps for dividing the microgrid into regions based on the topology include:

[0016] Determine all connection interfaces between the microgrid and the external public power grid;

[0017] Identify the electrical equipment connected to each connection interface;

[0018] Determine the connection relationships between electrical devices based on the topology;

[0019] Group the connection interfaces between electrical devices that have a connection relationship into a single independent interface;

[0020] All electrical devices connected to independent interfaces are grouped into a single autonomous control zone.

[0021] Optionally, the step of determining a region master controller from the intelligent controllers within each autonomous control region includes:

[0022] Collect the status parameters of all intelligent controllers within the autonomous control area and the operating characteristics of the electrical equipment they control;

[0023] A comprehensive evaluation is conducted based on its own state parameters and operational characteristics using a pre-set scoring model;

[0024] The intelligent controller with the best comprehensive evaluation results will be used as the regional master controller.

[0025] Optionally, the steps described above for comprehensively evaluating based on its own state parameters and operational characteristics using a preset scoring model include:

[0026] The feature analysis module in the scoring model is used to select candidate controllers from the intelligent controllers based on the operating characteristics of the electrical equipment.

[0027] The weighted comprehensive scoring function in the scoring model is called to calculate the weighted score of the candidate controller's own state parameters.

[0028] Optionally, the steps described above for determining the operational constraints corresponding to the autonomous control region based on scheduling information include:

[0029] Establish a regional control model for the autonomous control area and determine the set of control variables for the regional control model;

[0030] The scheduling information is converted into constraints for each parameter in the control variable set, thereby generating operational constraints.

[0031] Optionally, the step of the aforementioned regional master controller sending coordination instructions to other intelligent controllers within the autonomous control area based on operational constraints includes:

[0032] Analyze the control inputs of each intelligent controller within the autonomous control area;

[0033] Extract the constraints corresponding to the control quantities of each intelligent controller from the operational constraints;

[0034] Coordination instructions are generated based on the extracted constraints and sent to the corresponding intelligent controller.

[0035] Optionally, after the step of each intelligent controller adjusting the state of the electrical equipment it controls based on the control information it has acquired and the coordination instructions provided by the regional master controller, the following further step is added:

[0036] Each intelligent controller acquires the adjusted operating status of the electrical equipment it controls;

[0037] Extract state features related to operational constraints from the operational state;

[0038] Feedback of status characteristics to the regional master controller.

[0039] Optionally, after the step of each intelligent controller adjusting the state of the electrical equipment it controls based on the control information it has acquired and the coordination instructions provided by the regional master controller, the following further step is added:

[0040] The regional master controller collects status characteristics fed back by all intelligent controllers within the autonomous control area;

[0041] The collected state features are calculated using a pre-configured penalty function to quantify the evaluation of the control results.

[0042] According to another aspect of the present invention, a computer program product is also provided, comprising a computer program that, when executed by a processor, implements any of the above-described microgrid control methods based on collaborative optimization.

[0043] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements any of the above-described microgrid control methods based on cooperative optimization.

[0044] According to another aspect of the present invention, a computer device is also provided, which includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the above-described microgrid control methods based on collaborative optimization.

[0045] This invention presents a microgrid control method based on collaborative optimization. By acquiring the topology of electrical equipment within the microgrid and dividing it into regions, an autonomous control region with an independent power interaction interface from the external public power grid is formed. This simplifies the complex topology of the microgrid and defines a relatively independent autonomous control region. Within this autonomous control region, the overall coordinated optimization of the control objectives of the intelligent controllers is performed. The regional master controller uniformly processes the dispatch information from the external public power grid, generates operating constraints for the autonomous control region, and further decomposes these constraints into coordination instructions for each intelligent controller. These instructions then guide each intelligent controller to adjust the state of electrical equipment, achieving overall collaborative optimization control of the microgrid. This enhances the stability and rationality of microgrid operation, enabling it to better meet the dispatch requirements of the public power grid and the overall control needs of the microgrid.

[0046] Furthermore, the microgrid control method based on collaborative optimization of the present invention, by collecting the state parameters of the intelligent controller and the operating characteristics of the electrical equipment it controls, and using a preset scoring model for comprehensive evaluation, determines the regional master controller, thus selecting the most suitable intelligent controller to assume the master control role in the autonomous control area. The regional master controller, based on its own and its controlled equipment's state, can better receive external public grid dispatch information and coordinate with other intelligent controllers within the area, thereby optimizing cooperation among intelligent controllers. The feature analysis module in the scoring model selects candidate controllers based on the operating characteristics of the electrical equipment, thereby prioritizing the functional requirements of the electrical equipment in the autonomous control area and reducing the coordination difficulty between controllers. The weighted comprehensive scoring function in the scoring model performs weighted scoring calculations on the state parameters of the candidate controllers, enabling a more accurate comprehensive consideration of the state of each candidate controller, thereby determining the optimal regional master controller.

[0047] Furthermore, the microgrid control method based on collaborative optimization of the present invention establishes a regional control model of the autonomous control area and determines the set of control quantities. It then converts scheduling information into constraints for each parameter of the control quantity set to generate operational constraints. This allows for the accurate conversion of external public grid scheduling requirements into specific constraints executable by equipment within the autonomous control area. The regional master controller analyzes the control quantities of each intelligent controller and extracts corresponding constraints from the operational constraints to generate coordination instructions, ensuring that the coordination instructions accurately match the control functions of each intelligent controller.

[0048] Furthermore, the microgrid control method based on collaborative optimization of this invention involves intelligent controllers acquiring the adjusted operating status of equipment and extracting state features related to operating constraints, then feeding this information back to the regional master controller. This allows the regional master controller to understand the status of electrical equipment within its autonomous control area in real time after adjustment. Based on this feedback information, the regional master controller can evaluate the control effect and take timely adjustment measures if deviations are detected, thereby achieving closed-loop management of microgrid control and further improving the accuracy and stability of microgrid control. The regional master controller collects the state features fed back by all intelligent controllers and uses a pre-configured penalty function to calculate and quantitatively evaluate the control results, enabling objective and accurate measurement of whether the control has achieved the expected effect. Through quantitative evaluation, shortcomings in the control process can be clearly identified, providing data support and decision-making basis for subsequent optimization of control strategies.

[0049] The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0050] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0051] Figure 1 This is a schematic diagram of a microgrid control method based on collaborative optimization according to an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the topology of a parallel microgrid;

[0053] Figure 3 This is a schematic diagram of the topology of a series microgrid;

[0054] Figure 4 This is a schematic diagram of the topology of a hybrid distributed interconnected microgrid;

[0055] Figure 5 This is a schematic diagram of the process of dividing autonomous control regions in a microgrid control method based on collaborative optimization according to an embodiment of the present invention;

[0056] Figure 6 This is a flowchart illustrating the process of determining the regional master controller in a microgrid control method based on collaborative optimization according to an embodiment of the present invention.

[0057] Figure 7 This is a schematic diagram of the process by which a regional master controller generates coordination commands in a microgrid control method based on collaborative optimization according to an embodiment of the present invention.

[0058] Figure 8 This is a schematic diagram of the process for evaluating the control effect in a microgrid control method based on collaborative optimization according to an embodiment of the present invention;

[0059] Figure 9 This is a schematic diagram of a computer program product according to an embodiment of the present invention;

[0060] Figure 10 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention;

[0061] Figure 11 This is a schematic block diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0062] Those skilled in the art should understand that the embodiments described below are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. These partial embodiments are intended to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by those skilled in the art without creative effort should still fall within the scope of protection of the present invention.

[0063] Figure 1 This is a schematic diagram of a microgrid control method based on collaborative optimization according to an embodiment of the present invention. The microgrid control method based on collaborative optimization generally includes:

[0064] Step S101: Obtain the topology of the electrical equipment controlled by each smart controller within the microgrid. Electrical equipment may include power generation equipment (e.g., wind power generation equipment, photovoltaic power generation equipment, oil-fired power generation equipment), energy storage equipment (e.g., electrochemical energy storage equipment, mechanical energy storage equipment), and power consumption equipment (e.g., power equipment, electric heating equipment, electronic information equipment, charging equipment, lighting equipment). The smart controllers, through data acquisition and intelligent algorithms, realize the functions of the electrical equipment they control, including, for example, photovoltaic inverter controllers, wind power generation controllers, energy storage system controllers, and load controllers. These smart controllers possess sensing, communication, computing, and execution capabilities, and can collaboratively complete more complex functions through information sharing, such as power dispatching, operating mode switching, power management, and safety protection.

[0065] Step S102: Divide the microgrid into regions according to the topology to obtain at least one autonomous control region. Each autonomous control region has an independent interface for power interaction with the external public power grid. Each autonomous control region may have one or more electrical devices. In the case of multiple electrical devices, each autonomous control region has multiple intelligent controllers.

[0066] Step S103: Determine a regional master controller from the intelligent controllers in each autonomous control area, so that each autonomous control area corresponds to a regional master controller.

[0067] In step S104, the regional master controller receives dispatch information from the external public power grid and determines the operating constraints corresponding to the autonomous control area based on the dispatch information. The dispatch information from the external public power grid may include: grid power interaction power limit information, electricity price information, grid-connected / islanded mode switching information, and power quality requirement information. Specifically, the grid power interaction power limit information indicates the power limit that the external power grid can provide within a future set time period; the electricity price information indicates the electricity price for each price stage; the grid-connected / off-grid mode switching information indicates whether the microgrid needs to switch between grid-connected and off-grid modes; and the power quality requirement information indicates power quality limits such as voltage deviation, flicker, and harmonic distortion rate limits.

[0068] In step S105, the regional master controller sends coordination instructions to other intelligent controllers within the autonomous control area according to the operating constraints.

[0069] In step S106, each intelligent controller adjusts the state of the electrical equipment it controls based on the control information and coordination commands it has acquired. The intelligent controller itself needs to acquire relevant control information for control. For example, the control information for a photovoltaic inverter controller includes equipment status information such as DC-side voltage, DC-side current, output AC voltage / current, internal temperature, and fault signals, as well as external information such as solar irradiance and photovoltaic half-temperature. The control information for a wind power controller includes equipment status information such as generator speed, output power, pitch angle, vibration signals, and yaw position, as well as external information such as wind speed and wind force. The control information for an energy storage system includes equipment status information such as battery state of charge (SOC), state of health (SOH), charging and discharging current, DC voltage, and internal temperature, as well as external information such as charging and discharging commands and load forecast data.

[0070] The method described above obtains the topology of electrical equipment within the microgrid and divides it into regions to form an autonomous control region with an independent power interaction interface from the external public power grid. This simplifies the complex topology of the microgrid, determines a relatively independent autonomous control region, and performs overall coordination and optimization of the intelligent controller's control objectives within the autonomous control region.

[0071] Different microgrid topologies have different characteristics, and the topologies of electrical equipment can be divided into three main categories: parallel, series, and hybrid distributed connection.

[0072] Figure 2This is a schematic diagram of a parallel microgrid topology. In a parallel microgrid system, each device is connected to the public grid through connection nodes, ensuring that they can operate in parallel and independently. The advantage of this network layout is that the operation of a single device is relatively independent, and when power or energy shortages occur, other devices can respond flexibly to provide the necessary power or energy support. In this topology, although the devices or combinations of devices remain relatively independent, the power distribution network acts as a bridge, enabling flexible allocation and efficient utilization of energy among the microgrids.

[0073] Figure 3 This is a schematic diagram of a series-connected microgrid topology. A series-connected topology links adjacent electrical devices within a region together, forming a unified microgrid and enabling collaborative operation between the microgrids. Series-connected microgrids are susceptible to the state of individual devices within their constituent units.

[0074] Figure 4 This is a schematic diagram of the topology of a hybrid distributed microgrid. A hybrid distributed microgrid refers to a new type of system architecture that utilizes adjacent electrical devices within a region to integrate the distribution network. It can be a ring or mesh structure, and can take the form of a combination of series and parallel connections.

[0075] The method in this embodiment achieves autonomous control area division by analyzing the connection interfaces between the microgrid and the external public power grid one by one, and transforms the topology of the series microgrid and the hybrid distributed connection topology into a topology combination similar to the parallel type. Figure 5 This is a schematic diagram illustrating the process of dividing autonomous control regions in a microgrid control method based on collaborative optimization according to an embodiment of the present invention. The steps of dividing the microgrid into regions based on topology generally include:

[0076] Step S501: Determine all connection interfaces between the microgrid and the external public power grid. These connection interfaces may include connection points of distribution transformers, connection points for setting up electricity meters, and connection points of power conversion equipment.

[0077] Step S502: Determine the electrical equipment connected to each connection interface.

[0078] Step S503: Determine the connection relationship between electrical devices based on the topology.

[0079] Step S504: Merge the connection interfaces between electrical devices that have a connection relationship into an independent interface.

[0080] Step S505: All electrical devices connected to the independent interface are grouped into an autonomous control area.

[0081] For example Figure 3 The topology of the series-connected microgrid shown can be divided into a single autonomous control area. Figure 4 The hybrid distributed microgrid shown, within the area highlighted by the dashed line, can be divided into two autonomous control zones when the switch is open; and into one autonomous control zone with four electrical devices when the switch is closed. It should be noted that... Figures 2 to 4 The topology shown is for illustrative purposes only and is not an actual microgrid operating topology. In practical applications, microgrids can be combined based on the above topology according to their functions to form more complex connection structures.

[0082] During the operation of the microgrid, if there are changes in switch status or line modifications, the autonomous control area should be redefined in a timely manner and a new regional master controller should be determined.

[0083] The method in this embodiment can also automatically determine the regional master controller of the autonomous control area based on the characteristics of the electrical equipment and intelligent controllers of the microgrid. Figure 6 This is a flowchart illustrating the process of determining a regional master controller in a microgrid control method based on collaborative optimization according to an embodiment of the present invention. The step of determining a regional master controller from the intelligent controllers within each autonomous control region may include:

[0084] Step S601: Collect the self-state parameters of all intelligent controllers within the autonomous control area and the operating characteristics of the electrical equipment they control. The self-state parameters of the intelligent controllers can include static parameters and state parameters. Static capability parameters can include data processing capabilities (such as CPU clock speed and memory size), communication capabilities (supported maximum bandwidth and communication interface type), and built-in control algorithm libraries. State parameters can include current CPU load, communication link quality (such as signal-to-noise ratio and packet loss rate), power consumption, and the total number of directly controlled devices. The operating characteristics of the controlled electrical equipment can include: equipment functions, power input / output power adjustment range, and start / stop status.

[0085] Step S602: Use a preset scoring model to conduct a comprehensive evaluation based on its own state parameters and operating characteristics.

[0086] Step S603: Select the intelligent controller with the best comprehensive evaluation result as the regional master controller.

[0087] An optional implementation of step S602 is as follows: using the feature analysis module in the scoring model to select a candidate controller from the intelligent controller based on the operating characteristics of the electrical equipment; and calling the weighted comprehensive scoring function in the scoring model to calculate the weighted score of the candidate controller's own state parameters.

[0088] The feature analysis module is used to select suitable intelligent controllers as regional master controllers based on the operating characteristics of electrical equipment. For example, the requirements for an intelligent controller as a regional master controller may include: frequent adjustments to directly controlled electrical equipment and a significant impact on the power distribution of the entire autonomous control area. Therefore, intelligent controllers based on energy storage devices or the largest adjustable load within the area are generally selected as regional master controllers. The feature analysis module can use algorithms such as decision trees, random forests, and support vector machines (SVM) to train a feature analysis model. The operating characteristics of the electrical equipment are input into the trained and optimized feature analysis model, which then selects a suitable intelligent controller as the regional master controller.

[0089] The weighted composite scoring function is used to calculate the score. One possible specific function formula is as follows:

[0090] .

[0091] In the formula, Pi represents the data processing capability score; the higher the CPU performance and the larger the available memory of the controller, the larger the Pi value. Wpro is the weight of the data processing capability score. Ci represents the communication capability score; the larger the communication bandwidth range, the lower the latency, and the higher the link quality of the controller, the larger the Ci value. Wcom is the weight of the communication capability score. Fi represents the control equipment score; the higher the control equipment's adjustment frequency and the larger the adjustable load, the larger the controller's Fi value. Wfre is the weight of the control equipment score. Fci is the reliability score; the lower the failure rate, the larger the Fci value. Wfun is the weight of the reliability score. Si is the calculated weighted score.

[0092] The weights of Wpro, Wcom, Wfre, and Wfun can be determined using hierarchical analysis. The intelligent controller with the highest weighted score becomes the regional master controller, executes the regional coordination optimization algorithm, and sends coordination commands to other intelligent controllers within the region.

[0093] This example illustrates a control zone with three alternative controllers. The first alternative controller is used for energy storage system control, scoring highly in data processing and communication capabilities, and directly controls the energy storage converter (high regulation frequency, wide adjustable power range). The second alternative controller is used for photovoltaic control, scoring moderately in both data processing and communication capabilities, and directly controls the photovoltaic inverter (generally passive control). The third alternative controller is used for load control, scoring highly in both data processing and communication capabilities, and directly controls the electrical load (lower regulation frequency). Under normal circumstances, the first alternative controller is chosen as the zone's master controller due to its strong overall capabilities, especially its direct control of key energy storage devices. If the first alternative controller malfunctions, the third alternative controller can be used as the zone's master controller.

[0094] Figure 7 This is a schematic flowchart illustrating the generation of coordination commands by a regional master controller in a microgrid control method based on collaborative optimization according to an embodiment of the present invention. Step S104 above, determining the operating constraints corresponding to the autonomous control region based on scheduling information, may include:

[0095] Step S701: Establish a regional control model for the autonomous control area and determine the set of control variables for the regional control model. The specific expression of the set of control variables can be: {P1,P2,…,U1,U2,…,F1,F2,…,C1,C2,…,K1,K2,…}, where P1,P2,… represent each power control point, U1,U2,… represent each voltage control point, F1,F2,… represent each frequency control point, C1,C2,… represent each cost control point, and K1,K2,… represent each switch control point.

[0096] Step S702: The scheduling information is converted into constraints for each parameter in the control quantity set, thereby generating operational constraints. These constraints can be upper control limits, lower control limits, preferred ranges, etc., for example, maximum power, minimum power, preferred power range; maximum voltage, minimum voltage, preferred voltage range; maximum number of switching operations, minimum number of switching operations; maximum frequency, minimum frequency, preferred frequency range.

[0097] In step S105 above, sending coordination instructions to other intelligent controllers within the autonomous control area based on operational constraints may include:

[0098] Step S703: Analyze the control quantity of each intelligent controller within the autonomous control area to determine the control point of each intelligent controller.

[0099] Step S704: Extract the constraints corresponding to the control quantities of each intelligent controller from the running constraints.

[0100] Step S705: Generate coordination instructions according to the extracted constraints and send them to the corresponding intelligent controller.

[0101] In the microgrid control method based on collaborative optimization in this embodiment, the regional master controller analyzes the control quantity of each smart controller and extracts the corresponding constraint conditions from the operating constraints to generate coordination instructions, so that the coordination instructions can accurately match the control functions of each smart controller.

[0102] Figure 8This is a flowchart illustrating the evaluation of control effects in a microgrid control method based on collaborative optimization according to an embodiment of the present invention. After the step of each intelligent controller adjusting the state of its controlled electrical equipment based on the control basis information it has acquired and the coordination instructions provided by the regional master controller, the method may further include:

[0103] In step S801, each intelligent controller obtains the adjusted operating status of its respective controlled electrical equipment.

[0104] Step S802: Extract state features related to operating constraints from the operating state. These state features can be pre-configured based on the operating constraints received by each intelligent controller. For example, the mapping relationship between operating constraints and state features can be configured. If the operating constraints specify the upper limit of the power of the electrical equipment, then the corresponding state feature is the real-time power value of the equipment; if the constraints involve the safe operating temperature range of the equipment, then the state feature is the real-time temperature of the equipment.

[0105] Step S803: Feed back the status characteristics to the regional master controller.

[0106] Step S804: The regional master controller collects the status characteristics fed back by all intelligent controllers within the autonomous control area.

[0107] Step S805: The collected state features are calculated using a pre-configured penalty function to quantitatively evaluate the control results. The penalty function can be a linear function, a quadratic function, or a more complex nonlinear function. For example, for power constraints on electrical equipment, if the actual power of the equipment exceeds the constraint limit, the penalty function can be defined as the square of the excess, to increase the penalty for exceeding the constraint. The area controller calculates the corresponding penalty value according to the defined penalty function. For each state feature, the penalty function is calculated. For example, for the power state features of multiple electrical devices, the penalty value for each device is calculated separately, and then the penalty values ​​of all devices are summed to obtain the total penalty value for the entire autonomous control area. If the total penalty value is zero or within an acceptable range, it indicates that the control results meet the operating constraints and the control effect is good; if the total penalty value is large, it indicates that there are many situations that do not meet the constraints during the control process, and the control strategy needs to be adjusted and optimized.

[0108] This embodiment also provides a computer program product 910, a computer-readable storage medium 920, and a computer device 930. Figure 9 This is a schematic diagram of a computer program product according to an embodiment of the present invention. Figure 10 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention. Figure 11 This is a schematic block diagram of a computer device according to an embodiment of the present invention.

[0109] Computer program product 910 includes computer program 911, which, when executed by processor 931, implements any of the aforementioned microgrid control methods based on cooperative optimization. Computer-readable storage medium 920 stores the aforementioned computer program 911, which, when executed by processor 931, implements any of the aforementioned embodiments of the microgrid control method based on cooperative optimization. Computer device 930 may include memory 932, processor 931, and computer program 911 stored in memory 932 and running on processor 931.

[0110] The computer program 911 used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages.

[0111] Computer program 911 can execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of the invention, electronic circuits including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) can execute computer-readable program instructions to personalize the electronic circuits by utilizing state information of computer-readable program instructions.

[0112] For the purposes of this embodiment, computer program product 910 is a related product that includes computer program 911.

[0113] For the purposes of this embodiment, a computer-readable storage medium 920 is a tangible device capable of holding and storing a computer program 911. It can be any device capable of containing, storing, communicating, propagating, or transmitting the computer program 911 for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable storage medium 920 include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical encoding device, and any suitable combination thereof.

[0114] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.

Claims

1. A microgrid control method based on collaborative optimization, characterized in that... include: Obtain the topology of the electrical equipment controlled by each intelligent controller in the microgrid; The microgrid is divided into regions according to the topology to obtain at least one autonomous control region. Each autonomous control region has an independent interface for power interaction with the external public power grid. One regional master controller is determined from the intelligent controllers within each of the autonomous control regions; The regional master controller receives dispatch information from the external public power grid, establishes a regional control model for the autonomous control area, and determines the set of control variables for the regional control model. The scheduling information is converted into constraints for each parameter in the control quantity set, thereby generating operating constraints. The control quantity set is expressed as a set of multiple power control points, multiple voltage control points, multiple frequency control points, multiple cost control points, and multiple switching control points. The regional master controller analyzes the control quantity of each of the intelligent controllers within the autonomous control region; Extract the constraints corresponding to the control quantity of each intelligent controller from the operating constraints; generate coordination instructions according to the extracted constraints and send them to the corresponding intelligent controllers; the constraints include the upper control limit, lower control limit and preferred range of each control point; Each of the intelligent controllers adjusts the state of the electrical equipment it controls based on the control information it acquires and the coordination instructions.

2. The microgrid control method based on collaborative optimization according to claim 1, characterized in that... The step of dividing the microgrid into regions according to the topology includes: Identify all connection interfaces between the microgrid and the external public power grid; Identify the electrical equipment connected to each of the aforementioned connection interfaces; The connection relationships between the electrical devices are determined based on the topology; The connection interfaces that are connected between the electrical devices are merged into one independent interface; All electrical devices connected to the independent interface are grouped into one autonomous control zone.

3. The microgrid control method based on collaborative optimization according to claim 1, characterized in that, The step of determining a region master controller from the intelligent controllers in each of the autonomous control regions includes: Collect the self-state parameters of all intelligent controllers within the autonomous control area and the operating characteristics of the electrical equipment they control; A comprehensive evaluation is performed based on the self-state parameters and the operational characteristics using a preset scoring model; The intelligent controller with the best comprehensive evaluation result is selected as the regional master controller.

4. The microgrid control method based on collaborative optimization according to claim 3, characterized in that, The step of using a preset scoring model to perform a comprehensive evaluation based on its own state parameters and operational characteristics includes: The feature analysis module in the scoring model is used to select a candidate controller from the intelligent controller based on the operating characteristics of the electrical equipment. The weighted comprehensive scoring function in the scoring model is invoked to calculate the weighted score of the candidate controller's own state parameters.

5. The microgrid control method based on collaborative optimization according to claim 1, characterized in that, After the step of each intelligent controller adjusting the state of the electrical equipment it controls based on the control information it has acquired and the coordination instructions provided by the regional master controller, the following is also included: Each of the intelligent controllers acquires the adjusted operating status of the electrical equipment it controls; Extract state features related to the operating constraints from the operating state; The status characteristics are fed back to the regional master controller.

6. The microgrid control method based on collaborative optimization according to claim 5, characterized in that, After the step of each intelligent controller adjusting the state of the electrical equipment it controls based on the control information it has acquired and the coordination instructions provided by the regional master controller, the following is also included: The regional master controller collects the state characteristics fed back by all the intelligent controllers within the autonomous control area; The collected state features are calculated using a pre-configured penalty function to quantify the evaluation of the control results.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the microgrid control method based on collaborative optimization as described in any one of claims 1 to 6.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the microgrid control method based on collaborative optimization as described in any one of claims 1 to 6.

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