Optical storage micro-grid optimization method and system based on Internet of Things

Abstract

The invention relates to the technical field of new energy micro-grid optimization, and discloses an optical storage micro-grid optimization method and system based on the Internet of Things, and the method comprises the steps: carrying out the local processing, and generating an edge data set; calculating node data credibility; generating corrected light storage data; energy scheduling optimization is carried out in combination with the operation cost and the power balance constraint; and generating a control instruction according to the scheme and updating the data set in a closed-loop manner. In the prior art, it is generally assumed that node data is completely reliable or scheduling uncertainty under the condition that node communication is abnormal or aged is not fully considered, especially under the condition that communication conditions of a remote island type micro-grid are limited and node data quality fluctuates. The technical problem that the reliability difference of multi-source data cannot be dynamically identified and safely dispatched in the prior art is solved, and due to the fact that a dynamic credibility evaluation mechanism in the Internet of Things environment is introduced and the credibility is embedded into an optical storage micro-grid energy dispatching model, real-time correction and self-adaptive dispatching optimization of node data are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy microgrid optimization, and in particular to a photovoltaic storage microgrid optimization method and system based on the Internet of Things. Background Art

[0002] Currently, photovoltaic-storage microgrids, as key technologies for absorbing new energy, ensuring energy autonomy, and improving power supply reliability, have been widely adopted in isolated island regions, remote mountainous areas, and areas with weak power grids. These microgrids typically integrate multiple distributed energy resources, including photovoltaic power generation, energy storage systems, electric vehicle charging and discharging, and intelligent load-side control. Energy management systems (EMS) coordinate and dispatch each node to achieve local energy balance and economic optimization. However, existing technologies generally formulate scheduling strategies based on predicted output, state of charge, and load demand data uploaded by sensors at each node. Most solutions assume this data is completely reliable, ignoring the impact of factors such as communication channel fluctuations, node hardware aging, and sensor accuracy degradation in complex environments.

[0003] For example, in remote, isolated microgrids, nodes often face communication link interruptions, high latency, high packet loss rates, and untimely equipment maintenance, leading to abnormal fluctuations or distortion in real-time monitoring data. Failure to promptly identify and correct these abnormal data will directly affect scheduling calculations, potentially leading to overestimation of PV output, underestimation of energy storage capacity, and mismatched electric vehicle output. In severe cases, this can lead to power outages, excessive discharge of energy storage systems, or the frequent activation of expensive backup generators, increasing operating costs and impacting the user experience.

[0004] Furthermore, while some current improvement schemes have introduced data redundancy checks or partial anomaly rejection mechanisms, these are mostly static threshold judgments or simple filtering, lacking quantitative assessment of the dynamic credibility of node data and real-time adaptive updates, and thus failing to meet the comprehensive requirements for security, robustness, and economy under extreme conditions. Therefore, a method is urgently needed that can dynamically identify the reliability of multi-source data, perform real-time corrections, and optimize safe energy scheduling under complex conditions such as unstable node communications, aging equipment, and abnormal data fluctuations, in order to improve the overall stability, economy, and continuous power supply guarantee capabilities of photovoltaic microgrids. Summary of the Invention

[0005] In response to the above-mentioned technical deficiencies, the purpose of the present invention is to propose a photovoltaic energy storage microgrid optimization method based on the Internet of Things, aiming to solve the scheduling uncertainty problem in the existing technology that generally assumes that node data is completely reliable or does not fully consider the situation of node communication abnormalities or aging, especially under the conditions of limited communication conditions and fluctuating node data quality in remote island microgrids, and cannot achieve dynamic identification of reliability differences of multi-source data and safe scheduling.

[0006] To solve the above technical problems, the application adopts the following technical solutions: the application provides an optimization method for a light storage micro-grid based on an Internet of Things, The optimization method for the light storage micro-grid based on the Internet of Things comprises the following steps: Step S10: Real-time collection of node operation state data by an Internet of Things sensor, and local execution of abnormality detection, time stamp alignment and compression processing tasks at each node to generate an edge processing data set D; Step S20: Acquisition of a communication channel quality , a node health state and a data noise index of each node at a current time t from the edge processing data set D, and calculation of a node data credibility ; Step S30: Based on the node data credibility , a credibility weighted correction mechanism is used to correct photovoltaic predicted output, energy storage state of charge and electric vehicle predicted power in the edge processing data set D to obtain corrected light storage data; Step S40: Construction of an operation cost objective function and a power balance constraint, and energy scheduling optimization solving based on the corrected light storage data, the operation cost objective function and the power balance constraint to obtain an optimized scheduling scheme ; Step S50: Generation of a control instruction according to the optimized scheduling scheme and delivery of the control instruction to a preset light storage control unit, and real-time monitoring and feedback of data and updating of the edge processing data set based on a dynamic closed-loop scheduling mechanism during the execution process.

[0007] Preferably, in step S10, the Internet of Things sensor is deployed at a photovoltaic array, an energy storage unit, an electric vehicle charging and discharging device and a load side, and the node operation state data comprises photovoltaic predicted output, energy storage state of charge, electric vehicle predicted power, real-time power demand of a load, communication channel quality, node health state and data noise index.

[0008] Preferably, in step S20, wherein the communication channel quality , the node health state and the data noise index are all normalized values mapped to a value range of [0, 1], , and are weight coefficients, and satisfy .

[0009] Preferably, in step S40, the operation cost objective function is as follows:

[0010] in, is the operating cost objective function; is the cost coefficient of the energy storage system, which is used to express the unit cost of energy storage discharge; The cost coefficient for charging and discharging electric vehicles is used to represent the unit cost of electric vehicles as dispatch resources; is the cost coefficient of the photovoltaic system, which is used to express the unit cost of photovoltaic power generation; is the cost coefficient of the unpowered part; is the corrected energy storage discharge power; is the corrected electric vehicle power; is the corrected photovoltaic output; Unsupplied power is used to indicate power whose demand cannot be met.

[0011] Preferably, in step S40, the power balance constraint constructed is:

[0012] in, is the load demand, which is used to represent the power demand that needs to be met at time t.

[0013] Preferably, in step S30, based on the node data credibility , a credibility weighted correction mechanism is used to correct the photovoltaic predicted output, energy storage charge state, and electric vehicle predicted power in the edge processing data set D to obtain the steps of correcting the photovoltaic storage data, specifically including: correcting the photovoltaic predicted output according to the credibility of the photovoltaic node to obtain the corrected photovoltaic available output; correcting the energy storage maximum discharge power according to the credibility of the energy storage node to obtain the corrected energy storage available power; correcting the electric vehicle predicted power according to the credibility of the electric vehicle node to obtain the corrected electric vehicle available power; combining the corrected photovoltaic output, energy storage discharge power, and electric vehicle power into a corrected photovoltaic storage data set D, and using it as input data for the subsequent step S40 energy scheduling optimization solution.

[0014] Preferably, in step S50, according to the optimized scheduling scheme Generate control instructions and send them to the preset solar storage control unit. During execution, the steps of real-time monitoring of feedback data and updating edge processing data sets based on the dynamic closed-loop scheduling mechanism include: According to the optimized scheduling plan Generate control instructions and send them to the preset photovoltaic storage control unit, where the optimized scheduling plan is executed During the process, the latest operating status data uploaded by each node is received in real time through the IoT communication network, including the current actual photovoltaic output, the current real-time energy storage discharge power, the current real-time electric vehicle power and the current actual load demand, as well as the corresponding current node channel quality, current node health status, and current data noise index; According to the latest operating status data and optimized scheduling plan The error ratio of each node is calculated based on the expected state data in the edge processing dataset to obtain the node deviation value. When the node deviation value is greater than the preset deviation threshold, the node data credibility is adjusted through feedback. The feedback adjustment of the node data credibility is achieved by adjusting and updating the edge processing dataset.

[0015] The present invention also provides a photovoltaic storage microgrid optimization system based on the Internet of Things, comprising: The data collection and preprocessing module is used to collect node operation status data in real time through IoT sensors, and perform anomaly detection, timestamp alignment and compression processing tasks locally on each node to generate edge processing data set D; The credibility calculation module is used to obtain the communication channel quality of each node at the current time t from the edge processing data set D Node health status and data noise indicators , and calculate the node data credibility ; Credibility weighted correction module, used to adjust the credibility of node data , a credibility weighted correction mechanism is used to correct the photovoltaic predicted output, energy storage charge state, and electric vehicle predicted power in the edge processing dataset D to obtain the corrected photovoltaic storage data; The scheduling optimization solution module is used to construct the operating cost objective function and power balance constraints, and perform energy scheduling optimization based on the modified solar storage data, operating cost objective function and power balance constraints to obtain the optimized scheduling plan. ; Closed-loop control execution module is used to optimize the scheduling plan Generate control instructions and send them to the preset optical storage control unit. During execution, feedback data is monitored in real time and the edge processing data set is updated based on the dynamic closed-loop scheduling mechanism.

[0016] The present invention also provides a photovoltaic and storage microgrid optimization device based on the Internet of Things, comprising: a memory, a processor, and a photovoltaic and storage microgrid optimization program based on the Internet of Things stored in the memory and runnable on the processor. When the photovoltaic and storage microgrid optimization program based on the Internet of Things is executed by the processor, a photovoltaic and storage microgrid optimization method based on the Internet of Things is implemented.

[0017] The present invention also provides a computer program product, including a photovoltaic and storage microgrid optimization program based on the Internet of Things. When the photovoltaic and storage microgrid optimization program based on the Internet of Things is executed by a processor, the photovoltaic and storage microgrid optimization method based on the Internet of Things is implemented.

[0018] The beneficial effects of the present invention are as follows: by introducing a dynamic credibility assessment mechanism based on the Internet of Things, the present invention can identify the reliability of data at each node in real time, actively correct the available output of photovoltaics, energy storage and electric vehicles, avoid scheduling deviations and power supply interruptions caused by abnormal or distorted data, and significantly improve the safety and stability of microgrids under complex conditions such as unstable communications or node aging.

[0019] The present invention directly embeds the credibility factor into the energy scheduling optimization model to achieve dynamic adjustment and optimal allocation of node resources, fully utilize high-credibility node resources, reduce spare capacity and redundant scheduling requirements, thereby reducing operating costs and improving new energy utilization and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 This is a flow chart of the first embodiment of a photovoltaic microgrid optimization method based on the Internet of Things of the present invention.

[0022] Figure 2 This is a schematic diagram of the equipment of a photovoltaic storage microgrid optimization method based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] Example 1: Figure 1 As shown, it is a flow chart of the first embodiment of the photovoltaic storage microgrid optimization method based on the Internet of Things of the present invention, and the first embodiment of the photovoltaic storage microgrid optimization method based on the Internet of Things of the present invention is proposed.

[0025] In a first embodiment, the photovoltaic energy storage microgrid optimization method based on the Internet of Things includes: Step S10: Collect node operating status data in real time through IoT sensors, and perform anomaly detection, timestamp alignment, and compression processing tasks locally on each node to generate an edge processing dataset D; It should be noted that the anomaly detection algorithm integrated locally in the node adopts a multi-layer discrimination method based on a sliding window statistical model combined with first-order difference threshold detection.

[0026] It's understandable that by locally executing the dual-threshold anomaly detection algorithm, nodes can not only promptly remove sudden spikes, outliers, or short-duration communication interference data, but also effectively preserve the characteristics of physical dynamic changes, avoiding misjudgment of actual power fluctuations. For example, in the case of photovoltaic power fluctuations caused by rapid cloud cover, simple filtering alone may misinterpret it as an anomaly. However, combining standard deviation and first-order difference dual judgment can accurately distinguish between true physical dynamics and interference pulses.

[0027] It should be understood that compared with traditional centralized big data anomaly elimination solutions, the edge local detection adopted by the present invention not only reduces the amount of redundant data upload (reducing the amount of original data by more than 50%), but also realizes real-time early warning and self-correction of data. It is particularly suitable for application scenarios with limited communication bandwidth and dispersed nodes in remote isolated microgrids, thereby ensuring the immediacy and high accuracy of the input data of the scheduling algorithm.

[0028] Step S20: Obtain the communication channel quality of each node at the current time t from the edge processing data set D Node health status and data noise indicators , and calculate the node data credibility ; It should be noted that the calculation of node data credibility is based on a weighted fusion of communication channel quality, node health status, and data noise indicators. The value range of node data credibility is [0, 1], which quantitatively represents the reliability of the current node data. Specifically, communication channel quality is measured through multi-dimensional channel characteristics such as average received signal strength indicator (RSSI) and packet loss rate. Node health status is assessed based on device self-test results, aging index, and maintenance cycle information. Data noise indicators are dynamically calculated using noise characteristic indicators such as data variance and mean square error within the window.

[0029] It can be understood that compared with traditional static health labels or simple data integrity scoring methods, the credibility calculation mechanism of the present invention combines multi-dimensional dynamic parameters, and forms a flexible credibility numerical expression by quantifying the communication, hardware and signal noise conditions of the node in real time. It can reflect the reliability of node data in a more fine-grained and continuous manner, avoid the overall abandonment of nodes due to the abnormality of a single indicator, and improve the availability and flexibility of the overall scheduling.

[0030] Step S30: Based on the node data credibility , a credibility weighted correction mechanism is used to correct the photovoltaic predicted output, energy storage charge state, and electric vehicle predicted power in the edge processing dataset D to obtain the corrected photovoltaic storage data; It can be understood that by introducing a credibility factor to dynamically reduce the power prediction value, it is equivalent to adaptively configuring a "trust upper limit" for each node based on its current state. Even if the prediction value is high, when the credibility of the node is low, the available output will be automatically reduced to avoid over-reliance on node resources that may be abnormal or distorted, thereby improving the overall safety redundancy of scheduling.

[0031] It should be understood that compared with traditional scheduling methods that are directly based on node rated or predicted power allocation, the credibility weighted correction mechanism of the present invention can reflect the comprehensive impact of the node's health status, communication quality and data noise in real time, and dynamically correct resource participation. It is particularly suitable for scenarios such as isolated microgrids that lack external backup power supplies and have extremely high requirements for self-sufficiency, and significantly reduces the risk of scheduling failures caused by node data deviations.

[0032] Step S40: Construct the operation cost objective function and power balance constraint, and perform energy scheduling optimization based on the modified solar storage data, the operation cost objective function and the power balance constraint to obtain the optimized scheduling solution. ; It should be noted that this step first dynamically constructs an energy scheduling optimization model that matches the actual node availability based on the corrected photovoltaic storage data obtained in step S30. This model introduces the operating costs of photovoltaics, energy storage, electric vehicles and unpowered power, comprehensively considers the economy and sustainability of each resource, and performs optimal output distribution from an overall perspective. The innovation here is that not only the rated or predicted output of the node is considered, but also the credibility of each node at the current moment is incorporated into the scheduling variables to form a dynamic optimization based on credibility. Compared with traditional solutions that only calculate scheduling based on a single economic cost or static output value, this step emphasizes multi-source information fusion and flexible safety factor design, so that the scheduling solution can still effectively adapt to and ensure power supply reliability when facing extreme environmental changes, abnormal node data or unstable communications.

[0033] It's understandable that the so-called operating cost objective function isn't simply the sum of electricity prices or fuel costs. Instead, it incorporates the marginal cost of photovoltaic power generation (typically near zero), the cycle decay cost of energy storage batteries, the discounted backup capacity of electric vehicles, and the loss costs of unpowered power within a global minimization framework. Furthermore, a power balance constraint ensures that at any given moment, the total power supplied by all nodes equals the sum of the load demand and the unpowered power. This constraint also verifies in real time that the corrected node output does not exceed an upper limit based on credibility constraints. This design concept embodies a shift from "hard prediction results" to "credibility-weighted flexible resources," better aligning with the essential requirement of dynamically balancing output and demand in actual microgrid operation.

[0034] It should be understood that traditional microgrid scheduling usually assumes that node sensor data is stable and reliable in the long term, and only makes a single static correction to the load or output at the prediction level; the present invention, on the other hand, uses real-time output information after credibility correction to continuously and dynamically reconstruct the optimization problem of each scheduling cycle, so that it has adaptive adjustment capabilities. For example, when it is detected that a node's communication channel has severe packet loss or the health of the equipment has decreased, its credibility will decrease, and the scheduling will automatically reduce its reliance on the node, thereby promptly increasing the output reservation of other nodes or backup energy sources to prevent overall power supply imbalances caused by emergencies. In addition, the mechanism can also quickly adjust the scheduling plan after the node returns to normal, resume the use of high-credibility nodes, and improve overall energy utilization efficiency. Through this dynamic, closed-loop optimization mechanism, the microgrid can maintain a safe, stable, and efficient energy supply state in unknown disturbances or extreme environments.

[0035] For example, in a remote island microgrid demonstration project, the peak load demand on the island was approximately 120 kW. Under normal circumstances, photovoltaic power generation could provide 80 kW, energy storage 30 kW, and electric vehicles 10 kW. However, during a severe convective weather event, cloud cover and communication interference affected the PV node, reducing its reliability to 0.6. After correction, the available power dropped to 48 kW. Traditional methods would continue to assume that the PV output was 80 kW, causing the unpowered power to instantly increase to approximately 30 kW, leading to frequent startup of backup diesel engines, significantly increasing operating costs and risking deep discharge of the energy storage. In our method, the dispatcher immediately adjusts the available PV power based on dynamic reliability. During optimization, the available energy storage power and electric vehicle power are automatically adjusted to higher levels. Furthermore, diesel generator backup power is reserved in advance. Ultimately, power outages and over-discharge of energy storage are avoided. Over the entire operating cycle, the use of excess backup power is reduced by over 40%, the load guarantee ratio remains above 99%, and the total operating cost is reduced by approximately 18% compared to traditional methods.

[0036] Step S50: According to the optimized scheduling plan The control instruction is generated and sent to a preset optical storage control unit. During execution, real-time feedback data is monitored and the edge processing data set is updated based on a dynamic closed-loop scheduling mechanism.

[0037] It should be noted that this step is not only a simple dispatching instruction, but also combines the dynamic closed-loop control concept to track and adaptively update the execution effect of the control instruction. After the instruction is executed, the feedback information of each node is collected immediately through the Internet of Things channel, including actual power output, node instantaneous voltage, battery state of charge, communication link quality and other key indicators, and compared with the expected scheduling instruction to form a feedback error matrix for guiding subsequent scheduling correction.

[0038] It can be understood that this closed-loop mechanism is equivalent to adding a "self-correcting" capability to the entire optical storage micro-grid. When a significant deviation is detected between the execution result and the preset scheduling plan (for example, due to unstable node output or sudden offline), the subsequent scheduling dependence on the node will be reduced, or a backup energy source will be dynamically called to make up for the power supply gap. At the same time, the node real-time state information is immediately written into the edge processing data set D for use in the next optimization scheduling, realizing deep coupling between the scheduling scheme and the real state of the node.

[0039] It should be understood that the traditional centralized control method usually adopts an open-loop mode, i.e., it is executed at a fixed period after pre-planning, and does not have the ability to adjust immediately according to node abnormalities during execution; while the dynamic closed-loop scheduling mechanism of the present application iteratively updates based on real-time feedback results, greatly improving the adaptability to sudden events such as communication interruption, node hardware failure, load mutation, etc. In particular, in the remote island micro-grid scenario, a single point failure can easily lead to a large area of load power shortage, and the present application can automatically switch the scheduling logic within seconds to minutes to ensure the continuity of key load power supply.

[0040] For example, in an actual island micro-grid measurement, the battery of the energy storage node appeared a battery thermal runaway warning after executing the instruction, and the real-time monitoring showed that the output power of the node deviated from the planned value by as high as 25%, and the channel quality decreased significantly to 0.4. The traditional scheme needs manual intervention to close the energy storage node and enable the backup generator, with an average response time of more than 15 minutes, resulting in a load power shortage of about 30 kW for a short time, and voltage abnormal alarm on the user side multiple times. In the present application, the available power of the energy storage is automatically reduced in real time according to the feedback, the output of the standby diesel engine is instantly increased to the compensation level, and the reliability and output correction data are automatically updated in the next optimization period, ensuring that there is no load interruption in the whole process, and the overall reliability is improved by about 25%, significantly reducing the risk of safety accidents and the number of times of starting the backup energy source.

[0041] Embodiment 2: In addition, the present invention provides a photovoltaic microgrid optimization system based on the Internet of Things, which adopts a photovoltaic microgrid optimization method based on the Internet of Things in the above embodiment, and can solve the technical problem of photovoltaic microgrid optimization based on the Internet of Things. Compared with the prior art, the beneficial effects of the photovoltaic microgrid optimization system based on the Internet of Things provided by the present invention are the same as the beneficial effects of the photovoltaic microgrid optimization method based on the Internet of Things provided by the above embodiment, and the other technical features of the photovoltaic microgrid optimization system based on the Internet of Things are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0042] Example 3: The present invention provides a photovoltaic microgrid optimization device based on the Internet of Things, please refer to Figure 2A photovoltaic microgrid optimization device based on the Internet of Things (IoT) includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the IoT-based photovoltaic microgrid optimization method of the first embodiment described above. The IoT-based photovoltaic microgrid optimization device in this embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The IoT-based photovoltaic microgrid optimization device is merely an example and should not limit the functionality or scope of use of the embodiments of the present invention. The IoT-based photovoltaic microgrid optimization device may include a processing device 1001 that can perform various appropriate actions and processes based on a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. Random access memory 1004 also stores various programs and data required for the operation of an IoT-based photovoltaic microgrid optimization device. The processing device 1001, read-only memory 1002, and random access memory 1004 are interconnected via a bus 1005. An I / O interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: an input device 1007, such as a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008, such as a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003; and a communication device 1009. The communication device 1009 allows the IoT-based photovoltaic microgrid optimization device to communicate with other devices wirelessly or wired to exchange data. While the figure illustrates an IoT-based photovoltaic microgrid optimization device with various systems, it should be understood that implementation or inclusion of all illustrated systems is not required. More or fewer systems may alternatively be implemented or included.

[0043] Embodiment Four: The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of an Internet of Things-based optimization method for a photovoltaic energy storage micro-grid as described above. The computer program product provided by the present application can solve the technical problem of an Internet of Things-based optimization method for a photovoltaic energy storage micro-grid. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the Internet of Things-based optimization method for a photovoltaic energy storage micro-grid provided by the above-described embodiments, and are not described here in detail.

[0044] In particular, according to the embodiments disclosed by the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the embodiments disclosed by the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for executing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, the above-mentioned functions defined in the method of the embodiments disclosed by the present application are executed.

[0045] It should be understood that various parts of the present application can be realized in hardware, software, firmware, or a combination thereof. In the description of the above-described embodiments, specific features, structures, materials or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0046] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A photovoltaic storage microgrid optimization method based on the Internet of Things, characterized in that: Methods include: Step S10: Collect node operating status data in real time through IoT sensors, and perform anomaly detection, timestamp alignment, and compression processing tasks locally on each node to generate an edge processing dataset D; Step S20: Obtain the communication channel quality of each node at the current time t from the edge processing data set D Node health status and data noise indicators , and calculate the node data credibility ; Step S30: Based on the node data credibility , a credibility weighted correction mechanism is used to correct the photovoltaic predicted output, energy storage charge state, and electric vehicle predicted power in the edge processing dataset D to obtain the corrected photovoltaic storage data; Step S40: Construct the operation cost objective function and power balance constraint, and perform energy scheduling optimization based on the modified solar storage data, the operation cost objective function and the power balance constraint to obtain the optimized scheduling solution. ; Step S50: According to the optimized scheduling plan Generate control instructions and send them to the preset optical storage control unit. During execution, feedback data is monitored in real time and the edge processing data set is updated based on the dynamic closed-loop scheduling mechanism.

2. The method for optimizing a photovoltaic microgrid based on the Internet of Things according to claim 1, wherein: In step S10, IoT sensors are deployed on the photovoltaic array, energy storage unit, electric vehicle charging and discharging equipment, and load side. The node operating status data includes photovoltaic predicted output, energy storage charge state, electric vehicle predicted power, load real-time power demand, communication channel quality, node health status, and data noise index.

3. The method for optimizing a photovoltaic microgrid based on the Internet of Things according to claim 1, wherein: In step S20, , where the communication channel quality Node health status and data noise indicators are all normalized values ​​mapped to the range [0,1]. 、 and is the weight coefficient, satisfying .

4. The method for optimizing a photovoltaic microgrid based on the Internet of Things according to claim 1, wherein: In step S40, the operating cost objective function is: in, is the operating cost objective function; is the cost coefficient of the energy storage system, which is used to express the unit cost of energy storage discharge; The cost coefficient for charging and discharging electric vehicles is used to represent the unit cost of electric vehicles as dispatch resources; is the cost coefficient of the photovoltaic system, which is used to express the unit cost of photovoltaic power generation; is the cost coefficient of the unpowered part; is the corrected energy storage discharge power; is the corrected electric vehicle power; is the corrected photovoltaic output; Unsupplied power is used to indicate power whose demand cannot be met.

5. The method for optimizing a photovoltaic microgrid based on the Internet of Things according to claim 4, wherein: In step S40, the power balance constraint constructed is: in, is the load demand, which is used to represent the power demand that needs to be met at time t.

6. The method for optimizing a photovoltaic microgrid based on the Internet of Things according to claim 1, wherein: In step S30, based on the node data credibility , a credibility weighted correction mechanism is used to correct the photovoltaic predicted output, energy storage charge state, and electric vehicle predicted power in the edge processing data set D to obtain the steps of correcting the photovoltaic storage data, specifically including: correcting the photovoltaic predicted output according to the credibility of the photovoltaic node to obtain the corrected photovoltaic available output; correcting the energy storage maximum discharge power according to the credibility of the energy storage node to obtain the corrected energy storage available power; correcting the electric vehicle predicted power according to the credibility of the electric vehicle node to obtain the corrected electric vehicle available power; combining the corrected photovoltaic output, energy storage discharge power, and electric vehicle power into a corrected photovoltaic storage data set D, and using it as input data for the subsequent step S40 energy scheduling optimization solution.

7. The method for optimizing a photovoltaic microgrid based on the Internet of Things according to claim 1, wherein: In step S50, according to the optimized scheduling scheme Generate control instructions and send them to the preset solar storage control unit. During execution, the steps of real-time monitoring of feedback data and updating edge processing data sets based on the dynamic closed-loop scheduling mechanism include: According to the optimized scheduling plan Generate control instructions and send them to the preset photovoltaic storage control unit, where the optimized scheduling plan is executed During the process, the latest operating status data uploaded by each node is received in real time through the IoT communication network, including the current actual photovoltaic output, the current real-time energy storage discharge power, the current real-time electric vehicle power and the current actual load demand, as well as the corresponding current node channel quality, current node health status, and current data noise index; According to the latest operating status data and optimized scheduling plan The error ratio of each node is calculated based on the expected state data in the edge processing dataset to obtain the node deviation value. When the node deviation value is greater than the preset deviation threshold, the node data credibility is adjusted through feedback. The feedback adjustment of the node data credibility is achieved by adjusting and updating the edge processing dataset.

8. A photovoltaic microgrid optimization system based on the Internet of Things, applied to a photovoltaic microgrid optimization method based on the Internet of Things according to any one of claims 1 to 7, characterized in that: The photovoltaic storage microgrid optimization system based on the Internet of Things includes: The data collection and preprocessing module is used to collect node operation status data in real time through IoT sensors, and perform anomaly detection, timestamp alignment and compression processing tasks locally on each node to generate edge processing data set D; The credibility calculation module is used to obtain the communication channel quality of each node at the current time t from the edge processing data set D Node health status and data noise indicators , and calculate the node data credibility ; Credibility weighted correction module, used to adjust the credibility of node data , a credibility weighted correction mechanism is used to correct the photovoltaic predicted output, energy storage charge state, and electric vehicle predicted power in the edge processing dataset D to obtain the corrected photovoltaic storage data; The scheduling optimization solution module is used to construct the operating cost objective function and power balance constraints, and perform energy scheduling optimization based on the modified solar storage data, operating cost objective function and power balance constraints to obtain the optimized scheduling plan. ; Closed-loop control execution module is used to optimize the scheduling plan Generate control instructions and send them to the preset optical storage control unit. During execution, feedback data is monitored in real time and the edge processing data set is updated based on the dynamic closed-loop scheduling mechanism.

9. A photovoltaic storage microgrid optimization device based on the Internet of Things, characterized in that: The IoT-based photovoltaic and storage microgrid optimization device includes: a memory, a processor, and an IoT-based photovoltaic and storage microgrid optimization program stored in the memory and executable on the processor. When the IoT-based photovoltaic and storage microgrid optimization program is executed by the processor, an IoT-based photovoltaic and storage microgrid optimization method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product includes a photovoltaic and energy-storage microgrid optimization program based on the Internet of Things. When the photovoltaic and energy-storage microgrid optimization program based on the Internet of Things is executed by a processor, it implements a photovoltaic and energy-storage microgrid optimization method based on the Internet of Things according to any one of claims 1 to 7.

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