Distributed renewable energy source and stored energy collaborative scheduling system of Internet of Things data

Abstract

The invention relates to the technical field of smart power grids, and discloses a distributed renewable energy source and energy storage cooperative scheduling system of Internet of Things data, comprising a data acquisition and monitoring module used for acquiring the running states of distributed energy sources and energy storage units through the Internet of Things technology; the energy supply and demand prediction module is used for predicting energy supply and load demand according to historical data, real-time data and meteorological information and generating basic data of a scheduling strategy; the collaborative scheduling strategy module formulates a collaborative scheduling scheme of the renewable energy and the energy storage unit based on energy supply and demand prediction, and adjusts the charging and discharging time of the energy storage unit; the double-layer optimization decision module is responsible for long-period energy storage planning and short-period real-time scheduling optimization; and the real-time monitoring and feedback module dynamically adjusts a scheduling strategy based on feedback data. According to the invention, through real-time monitoring, supply and demand prediction and cooperative scheduling of renewable energy sources and energy storage equipment, the system can optimize distribution and use of energy to the greatest extent, and energy waste is reduced.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a distributed renewable energy and energy storage coordinated dispatching system for Internet of Things data. Background Art

[0002] With the transformation of the global energy structure and the development of renewable energy, renewable energy such as wind power and solar energy has gradually become an important part of the power supply. However, renewable energy generation has obvious intermittent and volatile characteristics and is easily affected by the natural environment (such as weather changes), resulting in unstable power generation and posing huge challenges to the operation of the power grid. At the same time, with the development of distributed energy and energy storage technology, distributed power sources (such as photovoltaic power generation, distributed wind power) and energy storage equipment (such as electric vehicles, energy storage power stations) are increasingly connected to the power grid, making the operation and dispatch of the power grid more complicated.

[0003] The existing power grid dispatching system is difficult to reasonably dispatch grid resources according to the real-time fluctuations of renewable energy, resulting in insufficient utilization of power generation at low loads and difficulty in meeting power demand at high loads, resulting in low energy utilization efficiency. For example, when there is excess wind power or photovoltaic power generation, the excess power cannot be absorbed in time, resulting in serious wind and photovoltaic abandonment and widespread waste of resources. Summary of the invention

[0004] In order to make up for the above shortcomings, the present invention provides a distributed renewable energy and energy storage coordinated scheduling system based on Internet of Things data, aiming to improve the problem that the existing power grid scheduling system is difficult to reasonably schedule power grid resources according to the real-time fluctuations of renewable energy.

[0005] In a first aspect, the present invention provides the following technical solution: a distributed renewable energy and energy storage coordinated dispatching system for IoT data, comprising:

[0006] Data collection and monitoring module, used to collect the operating status of distributed energy and energy storage units through Internet of Things technology, and conduct real-time monitoring;

[0007] Energy supply and demand forecasting module, which is used to forecast energy supply and load demand in the future based on historical data, real-time data and meteorological information, and generate basic data for scheduling strategies;

[0008] The coordinated dispatch strategy module formulates a coordinated dispatch plan for renewable energy and energy storage units based on energy supply and demand forecasts, and adjusts the charging and discharging timing of energy storage units;

[0009] Two-layer optimization decision module, responsible for long-term energy storage planning and short-term real-time scheduling optimization;

[0010] The real-time monitoring and feedback module monitors the operation results and dynamically adjusts the scheduling strategy based on the feedback data.

[0011] Preferably, the data acquisition and monitoring module includes:

[0012] The sensing unit collects real-time data from distributed renewable energy equipment, energy storage units and distribution networks through a distributed sensor network to obtain comprehensive operating information;

[0013] The communication unit transmits the data collected by the sensing unit to the central control platform through the Internet of Things communication protocol;

[0014] The equipment status monitoring unit monitors the operating status of energy storage equipment and renewable energy equipment in real time, identifies and reports equipment failures and abnormal conditions.

[0015] Preferably, the energy supply and demand forecasting module includes:

[0016] Renewable energy forecasting unit, which predicts future wind and photovoltaic power generation output based on historical power generation data and meteorological information, and generates time-dependent output curves;

[0017] The load demand forecasting unit analyzes user behavior and historical power consumption data, predicts the power demand of different nodes in the power grid, and generates scheduling suggestions based on the demand;

[0018] The energy storage unit behavior prediction unit predicts the charging and discharging behavior of electric vehicles and other energy storage units by analyzing the operating modes of the electric vehicles and other energy storage units, and generates an energy storage demand curve.

[0019] Preferably, the collaborative scheduling strategy module includes:

[0020] The dispatch decision unit generates and updates the energy dispatch plan based on the energy supply and demand forecast results and the real-time operation data of the power grid to balance the supply and demand and maximize the use of renewable energy;

[0021] The renewable energy dispatching unit adjusts the power generation in real time according to the grid load demand by adjusting the output of wind and photovoltaic power generation;

[0022] The energy storage unit dispatching unit adjusts the charging and discharging behavior of the energy storage equipment and optimizes the use of the energy storage equipment according to the grid load to alleviate the peak power load and fill the power demand during the valley period;

[0023] The load dispatching unit rationally distributes the output power of renewable energy and energy storage equipment to different load nodes in the power grid.

[0024] Preferably, the two-layer optimization decision module includes:

[0025] The planning unit conducts energy storage unit layout and capacity planning based on the spatiotemporal distribution characteristics of renewable energy and the grid structure, and makes predictions on the long-term demand for energy storage;

[0026] The real-time optimization unit dynamically adjusts the scheduling strategies of energy storage units and renewable energy through optimization algorithms during the real-time operation of the power grid.

[0027] Preferably, the real-time monitoring and feedback module includes:

[0028] Status monitoring unit, used to monitor the real-time operating status of the power grid, energy storage equipment and renewable energy power generation equipment, and transmit the operating data to the central control platform;

[0029] A feedback control unit dynamically adjusts the charging and discharging behavior of the energy storage unit and the generation of renewable energy based on real-time monitoring data;

[0030] The safety assurance unit activates the predetermined emergency dispatch plan when a failure or emergency occurs during operation.

[0031] Preferably, the energy storage unit scheduling unit further comprises:

[0032] Priority setting is used to set different priorities for energy storage units and adjust the charging and discharging order of different energy storage units according to the grid load and renewable energy generation capacity.

[0033] In a second aspect, the present invention provides the following technical solution, a method for coordinated dispatching of distributed renewable energy and energy storage based on IoT data, comprising the following steps:

[0034] S1. Data collection and monitoring:

[0035] The sensing unit collects the operating data of renewable energy equipment and energy storage equipment in real time through a distributed sensor network, including parameters of power generation, energy storage, and charging and discharging status;

[0036] The communication unit uses the Internet of Things technology to transmit the collected real-time data to the central control platform;

[0037] The equipment status monitoring unit monitors the status of the energy storage unit and the power generation equipment, detects whether there is any equipment failure or abnormality, and issues an alarm message;

[0038] S2. Energy supply and demand forecast:

[0039] The renewable energy prediction unit predicts the wind and photovoltaic power generation output in the short term based on meteorological data and historical power generation data, and generates a prediction curve;

[0040] The load demand forecasting unit predicts the power demand of each node and generates a load demand curve by analyzing the historical power consumption data of the power grid and the user behavior patterns;

[0041] The energy storage unit behavior prediction unit analyzes the historical charging and discharging behavior of the energy storage equipment, predicts future energy storage demand, and calculates the scheduling requirements of the energy storage unit in combination with the grid demand;

[0042] S3. Collaborative scheduling strategy formulation:

[0043] The dispatch decision unit formulates a coordinated energy dispatch strategy based on the predicted renewable energy power generation output, load demand and energy storage demand, including the real-time dispatch of wind power and photovoltaic power generation and the charging and discharging plan of energy storage equipment;

[0044] The renewable energy dispatch unit adjusts the power output of wind and photovoltaic power generation equipment according to load demand and weather forecasts;

[0045] The energy storage unit dispatching unit determines the charging and discharging order of the energy storage units by setting priorities;

[0046] The load dispatching unit applies the generated dispatching plan to each load node of the power grid;

[0047] S4. Real-time optimization and adjustment:

[0048] The two-layer optimization decision module performs long-term and short-term energy optimization. The planning unit performs long-term planning for energy storage layout, while the real-time optimization unit dynamically adjusts the output of energy storage units and renewable energy according to the real-time grid operation status.

[0049] The feedback control unit monitors the operating status in real time and makes adjustments based on load changes, the operation of the energy storage equipment, and fluctuations in renewable energy generation;

[0050] S5. Safety and troubleshooting:

[0051] The status monitoring unit continuously monitors the operating status of the power grid and energy storage equipment to detect any abnormal conditions such as overload and low voltage;

[0052] When the safety assurance unit detects equipment failure or grid abnormality, it immediately initiates the emergency dispatch program to adjust the output of power generation or energy storage equipment.

[0053] In the third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the above-mentioned method for coordinated scheduling of distributed renewable energy and energy storage of Internet of Things data when executing the computer program.

[0054] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned distributed renewable energy and energy storage coordinated scheduling method of Internet of Things data is implemented.

[0055] The present invention has the following beneficial effects:

[0056] 1. In the present invention, through real-time monitoring, supply and demand forecasting, and coordinated scheduling of renewable energy and energy storage equipment, the system can optimize the distribution and use of energy to the maximum extent and reduce energy waste. For example, when there is excess renewable energy generation, the system can give priority to charging energy storage equipment; when the grid load is peak, the energy storage equipment is called to discharge first to ensure the rational use of power resources. This effectively improves the utilization rate of renewable energy and reduces dependence on traditional energy.

[0057] 2. In the present invention, the peak load shaving and valley filling function of the power load can be realized through the intelligent scheduling of the energy storage unit. When the power grid load is at its peak, the system will prioritize the energy storage unit discharge; and during the period of low load, the system will arrange the energy storage unit to charge. Such a strategy can effectively balance the supply and demand of electricity, reduce the load fluctuation of the power grid, relieve the pressure of the power grid, and improve the stability of the power system.

[0058] 3. In the present invention, the two-layer optimization decision module combines long-term planning and real-time optimization, so that the system can flexibly respond to short-term fluctuations and long-term changes. In the event of insufficient power generation or sudden increase in load, the system can dynamically adjust the scheduling of energy storage units to provide power support in a timely manner to avoid the occurrence of grid instability. In addition, the system's real-time monitoring and feedback module can detect and handle abnormal situations in a timely manner, further improving the stability and security of the grid.

[0059] 4. In the present invention, the system can effectively reduce the phenomenon of wind and solar power abandonment through the coordinated scheduling of the renewable energy prediction unit and the energy storage unit. According to the weather forecast and power generation forecast curve, the system reasonably arranges the energy storage unit to charge when there is excess wind power or photovoltaic power generation, avoiding the waste of renewable energy power generation due to the inability to absorb it in time, and improving the overall renewable energy power generation absorption capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is the architecture diagram of the distributed renewable energy and energy storage coordinated dispatching system for the Internet of Things data proposed by the present invention;

[0061] Figure 2 This is the architecture diagram of the data acquisition and monitoring module proposed by the present invention;

[0062] Figure 3 This is the architecture diagram of the energy supply and demand prediction module proposed by the present invention;

[0063] Figure 4 This is the architecture diagram of the collaborative scheduling strategy module proposed by the present invention;

[0064] Figure 5This is the architecture diagram of the double-layer optimization decision module proposed by the present invention;

[0065] Figure 6 This is the architecture diagram of the real-time monitoring and feedback module proposed by the present invention;

[0066] Figure 7 This is a flow chart of the method for coordinated scheduling of distributed renewable energy and energy storage based on Internet of Things data proposed by the present invention. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present invention will be described clearly and completely below 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 creative work are within the scope of protection of the present invention.

[0068] Embodiment 1

[0069] Reference Figure 1 - Figure 6 In a first embodiment of the present invention, the present invention provides a distributed renewable energy and energy storage coordinated dispatching system for IoT data, including:

[0070] Data collection and monitoring module, used to collect the operating status of distributed energy and energy storage units through Internet of Things technology, and conduct real-time monitoring;

[0071] Energy supply and demand forecasting module, which is used to forecast energy supply and load demand in the future based on historical data, real-time data and meteorological information, and generate basic data for scheduling strategies;

[0072] The coordinated dispatch strategy module formulates a coordinated dispatch plan for renewable energy and energy storage units based on energy supply and demand forecasts, and adjusts the charging and discharging timing of energy storage units;

[0073] Two-layer optimization decision module, responsible for long-term energy storage planning and short-term real-time scheduling optimization;

[0074] The real-time monitoring and feedback module monitors the operation results and dynamically adjusts the scheduling strategy based on the feedback data.

[0075] Specifically, in a specific implementation, the distributed renewable energy and energy storage coordinated dispatching system based on IoT data provided by the present invention is composed of multiple modules. The system is based on a data acquisition and monitoring module, and combines an energy supply and demand prediction module with a coordinated dispatching strategy module, and uses a two-layer optimization decision module to achieve dispatch optimization in different time periods. The system relies on a real-time monitoring and feedback module to continuously monitor and feedback the system status to ensure system stability.

[0076] In practical applications, the system can be deployed in a power grid containing a variety of renewable energy equipment (such as wind power, photovoltaic power stations) and energy storage units (such as electric vehicles, energy storage power stations), using 5G, NB-IoT and other Internet of Things technologies to achieve high-speed data transmission and communication between devices. The system uses a central control platform to comprehensively analyze multi-source data, predict future supply and demand, and implement collaborative scheduling strategies to optimize energy distribution in real time and improve energy efficiency.

[0077] The data acquisition and monitoring modules include:

[0078] The sensing unit collects real-time data from distributed renewable energy equipment, energy storage units and distribution networks through a distributed sensor network to obtain comprehensive operating information;

[0079] The communication unit transmits the data collected by the sensing unit to the central control platform through the Internet of Things communication protocol;

[0080] The equipment status monitoring unit monitors the operating status of energy storage equipment and renewable energy equipment in real time, identifies and reports equipment failures and abnormal conditions.

[0081] Specifically, in the specific implementation, the data acquisition and monitoring module is the basic part of the system. This module installs multiple distributed sensors on various renewable energy devices (such as wind turbines, photovoltaic modules) and energy storage devices (such as battery energy storage, charging stations) through the perception unit to collect their working status parameters in real time. Specific parameters include power generation, remaining power, charge and discharge status, device temperature, etc.

[0082] The communication unit uses 5G, LoRa, NB-IoT and other IoT communication technologies to ensure low-latency and high-reliability data transmission, and can achieve remote monitoring and centralized management. The device status monitoring unit in the system is responsible for monitoring the real-time status of each device, detecting faults or abnormal conditions (such as voltage abnormalities, equipment failures, etc.), and sending alarm signals through the central control platform to remind operation and maintenance personnel to handle faults and ensure stable operation of the equipment.

[0083] Energy supply and demand forecasting modules include:

[0084] Renewable energy forecasting unit, which predicts future wind and photovoltaic power generation output based on historical power generation data and meteorological information, and generates time-dependent output curves;

[0085] The load demand forecasting unit analyzes user behavior and historical power consumption data, predicts the power demand of different nodes in the power grid, and generates scheduling suggestions based on the demand;

[0086] The energy storage unit behavior prediction unit predicts the charging and discharging behavior of electric vehicles and other energy storage units by analyzing the operating modes of the electric vehicles and other energy storage units, and generates an energy storage demand curve.

[0087] Specifically, in the specific implementation, the function of the energy supply and demand forecasting module is to realize the forecast of future energy supply and demand. First, the system models and forecasts the output of wind power and photovoltaic power generation based on historical power generation data and meteorological data (such as wind speed, light intensity, temperature, etc.) through the renewable energy forecasting unit. The forecast results are presented in the form of a time curve to help the dispatching system know the future power generation fluctuations in advance.

[0088] The load demand forecasting unit analyzes the user's electricity consumption behavior and combines historical load data to predict the power grid's electricity demand in different periods of the future. This forecast helps formulate dispatch strategies, especially during peak or low periods of power load, to reasonably allocate power resources and avoid power shortages or waste.

[0089] The energy storage unit behavior prediction unit analyzes the charging and discharging behavior of energy storage devices (such as electric vehicles or energy storage power stations) and predicts their future charging and discharging needs and status to ensure that the scheduling strategy can make full use of the energy storage devices and balance the instability of power load and renewable energy generation.

[0090] The collaborative scheduling strategy module includes:

[0091] The dispatch decision unit generates and updates the energy dispatch plan based on the energy supply and demand forecast results and the real-time operation data of the power grid to balance the supply and demand and maximize the use of renewable energy;

[0092] The renewable energy dispatching unit adjusts the power generation in real time according to the grid load demand by adjusting the output of wind and photovoltaic power generation;

[0093] The energy storage unit dispatching unit adjusts the charging and discharging behavior of the energy storage equipment and optimizes the use of the energy storage equipment according to the grid load to alleviate the peak power load and fill the power demand during the valley period;

[0094] The load dispatching unit rationally distributes the output power of renewable energy and energy storage equipment to different load nodes in the power grid.

[0095] Specifically, in a specific implementation, the coordinated scheduling strategy module realizes the coordinated scheduling of renewable energy and energy storage equipment through the following units. First, the scheduling decision unit in the system formulates a coordinated energy scheduling strategy based on the prediction results of the energy supply and demand prediction module and the real-time operation data of the power grid. The strategy includes the power output adjustment of wind power and photovoltaic power generation equipment and the charging and discharging operation timing of the energy storage unit, with the aim of achieving a dynamic balance between power generation and power consumption.

[0096] The renewable energy dispatching unit adjusts the output power of wind power and photovoltaic power generation equipment in real time to match the load demand of the power grid. When there is excess power generation, it can choose to reduce power output or give priority to charging the energy storage unit; when the power grid load is high, the system can give priority to dispatching more renewable energy generation.

[0097] The energy storage unit dispatching unit is responsible for dispatching and controlling the charging and discharging of energy storage equipment, and determines when to charge or discharge the energy storage equipment according to the load of the power grid to achieve the effect of peak shaving and valley filling. Through reasonable charging and discharging strategies, the fluctuation of the power grid can be reduced and the stability of the power grid can be improved.

[0098] The load dispatching unit distributes the output power of renewable energy and energy storage equipment to different grid load nodes, especially giving priority to allocating power to key load nodes to ensure the efficient use of power and reduce power costs.

[0099] The two-layer optimization decision module includes:

[0100] The planning unit conducts energy storage unit layout and capacity planning based on the spatiotemporal distribution characteristics of renewable energy and the grid structure, and makes predictions on the long-term demand for energy storage;

[0101] The real-time optimization unit dynamically adjusts the scheduling strategies of energy storage units and renewable energy through optimization algorithms during the real-time operation of the power grid.

[0102] Specifically, in a specific implementation, the two-layer optimization decision module realizes long-term and short-term scheduling optimization through a planning unit and a real-time optimization unit. The planning unit first determines the layout and capacity planning of the energy storage unit by analyzing the long-term historical data of the system. The unit can make reasonable plans for future energy storage needs based on the historical operation of the power grid and determine the optimal deployment location of the energy storage equipment.

[0103] The real-time optimization unit dynamically adjusts the real-time operation status of the power grid. The unit obtains the operation data of the power grid in real time, combines the load fluctuation, the current status of the energy storage unit and the real-time output of renewable energy generation, optimizes the dispatching strategy, and ensures the stability and economy of power supply.

[0104] The two-layer optimization decision-making module not only improves the system's long-term planning capabilities through coordinated operation of long and short cycles, but also enhances its ability to respond to emergencies.

[0105] Real-time monitoring and feedback modules include:

[0106] Status monitoring unit, used to monitor the real-time operating status of the power grid, energy storage equipment and renewable energy power generation equipment, and transmit the operating data to the central control platform;

[0107] A feedback control unit dynamically adjusts the charging and discharging behavior of the energy storage unit and the generation of renewable energy based on real-time monitoring data;

[0108] The safety assurance unit activates the predetermined emergency dispatch plan when a failure or emergency occurs during operation.

[0109] Specifically, in the specific implementation, the real-time monitoring and feedback module is responsible for monitoring the system operation status and making dynamic adjustments. First, the status monitoring unit obtains the key parameters of energy storage equipment, renewable energy equipment and power grid operation in real time through the sensor network, such as power generation, battery power, charging and discharging status, etc. All data will be uploaded to the central control platform, which can analyze the data and detect abnormal situations.

[0110] When an abnormal operation is detected, the feedback control unit will adjust the dispatch strategy in a timely manner based on real-time monitoring data. For example, when the energy storage device is close to full charge, the system will stop charging it; when the grid load is high, the system will prioritize the energy storage device for discharge.

[0111] The safety assurance unit ensures that in the event of a system failure or emergency (such as equipment failure, grid overload, etc.), the emergency dispatch strategy is activated to ensure the safety and stability of grid operation and prevent system collapse or interruption.

[0112] The energy storage unit scheduling unit further includes:

[0113] Priority setting is used to set different priorities for energy storage units and adjust the charging and discharging order of different energy storage units according to the grid load and renewable energy generation capacity.

[0114] Specifically, in a specific implementation, the energy storage unit scheduling unit sets priorities for different energy storage devices through a priority setting unit. According to the grid load and renewable energy generation capacity, the system can dynamically adjust the charging and discharging order of the energy storage devices. For example, during periods of high grid load, energy storage devices with high-efficiency charging capabilities are prioritized for discharge, or when there is excess renewable energy generation, energy storage units with larger capacity are prioritized for charging.

[0115] The priority setting unit can dynamically adjust the priority of each energy storage unit according to the remaining power, charge and discharge rate and health status of the equipment to ensure that the system can maintain efficient and stable operation under complex load and power generation environments.

[0116] Embodiment 2:

[0117] Reference Figure 7 In a second embodiment of the present invention, the present invention provides an image recognition system, comprising the following steps:

[0118] S1: Data Collection and Monitoring

[0119] First, the system's data collection and monitoring module starts working. Through the sensing unit, the system obtains operating data from renewable energy equipment such as wind power and photovoltaics, and energy storage equipment such as electric vehicles and energy storage power stations. The collected data includes key parameters such as power generation power, equipment power, charging and discharging status, and equipment operating temperature.

[0120] The communication unit uses IoT technology (such as 5G, NB-IoT) to transmit the collected real-time data to the central control platform. The system ensures efficient transmission and security of data to facilitate the formulation of subsequent scheduling strategies.

[0121] At the same time, the equipment status monitoring unit continuously monitors the real-time status of each device. When an abnormal situation is detected (such as equipment failure, voltage abnormality, etc.), an alarm signal is immediately issued and the operation and maintenance personnel are notified to handle it.

[0122] S2: Energy supply and demand forecast

[0123] After receiving the data, the system's energy supply and demand forecasting module starts working. First, the renewable energy forecasting unit calculates the output of wind and photovoltaic power generation in the short term in the future based on historical power generation data and real-time meteorological information (such as wind speed, light intensity, temperature, etc.) through a forecasting model, generates a forecast curve, and provides data support for subsequent scheduling.

[0124] The load demand forecasting unit analyzes the historical load data of the power grid and the electricity consumption behavior patterns of users to predict the electricity demand of the power grid in different time periods, especially the load changes during peak periods, in order to formulate reasonable scheduling strategies.

[0125] The energy storage unit behavior prediction unit combines the historical charging and discharging records of the energy storage equipment to predict future charging and discharging needs and status, and generates an energy storage demand curve to ensure that the energy storage equipment can be effectively mobilized to relieve grid pressure during peak loads.

[0126] S3: Collaborative Scheduling Strategy Development

[0127] The system's coordinated dispatch strategy module formulates a dispatch strategy based on the forecast results and real-time monitoring data. First, the dispatch decision unit comprehensively analyzes the data based on the predicted renewable energy generation, energy storage demand and grid load to generate a coordinated dispatch plan.

[0128] The renewable energy dispatching unit ensures that the power generation matches the real-time grid load by adjusting the power generation of renewable energy equipment such as wind power and photovoltaic power. When there is excess power generation, the system will give priority to charging the energy storage equipment; during peak load periods, the system will maximize the mobilization of wind power and photovoltaic power generation capacity.

[0129] The energy storage unit dispatching unit arranges the charging and discharging sequence reasonably according to the priority setting of the energy storage equipment, ensuring that the energy storage unit can discharge in time and balance the grid load when the grid load is peak.

[0130] The load dispatching unit is responsible for allocating electricity from renewable energy and energy storage equipment to different load nodes, giving priority to supplying power to key load equipment to avoid power waste.

[0131] S4: Real-time optimization and dynamic adjustment

[0132] During system operation, the two-layer optimization decision module starts the optimization strategy. First, the planning unit makes long-term adjustments to the layout and capacity planning of the energy storage unit to ensure the reasonable configuration of future energy storage equipment.

[0133] The real-time optimization unit analyzes the real-time operation data of the power grid and dynamically adjusts the dispatching strategy based on the charging and discharging status of the energy storage unit. For example, when the load on the power grid rises rapidly, the system will promptly mobilize the energy storage equipment to discharge; when the power generation of renewable energy fluctuates greatly, the system will optimize the power output to ensure the stability of the system.

[0134] S5: Real-time monitoring and feedback mechanism

[0135] The real-time monitoring and feedback module continuously monitors the operating status of the system. The status monitoring unit obtains the operating parameters of the energy storage equipment, renewable energy equipment and power grid in real time through the sensor network and transmits them to the central control platform.

[0136] The feedback control unit adjusts the charging and discharging behavior of the energy storage device and the power generation output of renewable energy based on real-time data. For example, when the load is peak, the energy storage device automatically starts discharging; when there is excess power generation, the energy storage device stops charging to optimize the allocation of power resources.

[0137] When an abnormality or emergency occurs during system operation, the safety assurance unit immediately initiates the emergency dispatch program to reallocate power resources to prevent system collapse and ensure the safety of the power grid.

[0138] Throughout the system operation, the system will continuously update the model and strategy based on the feedback data. By optimizing the prediction model and dispatch strategy, the system can continuously improve the overall operation efficiency over time and adapt to the actual changes in the power grid and renewable energy.

[0139] Embodiment 3

[0140] The third embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the distributed renewable energy and energy storage coordinated scheduling method of the Internet of Things data of the above embodiment.

[0141] Embodiment 4

[0142] The fourth embodiment of the present invention is based on the same inventive concept. The present invention proposes a terminal, which includes: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory, and execute the distributed renewable energy and energy storage coordinated scheduling method of the Internet of Things data of the above embodiment.

[0143] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0144] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Distributed renewable energy and energy storage coordinated dispatching system based on IoT data, characterized by: include: Data collection and monitoring module, used to collect the operating status of distributed energy and energy storage units through Internet of Things technology, and conduct real-time monitoring; Energy supply and demand forecasting module, which is used to forecast energy supply and load demand in the future based on historical data, real-time data and meteorological information, and generate basic data for scheduling strategies; The coordinated dispatch strategy module formulates a coordinated dispatch plan for renewable energy and energy storage units based on energy supply and demand forecasts, and adjusts the charging and discharging timing of energy storage units; Two-layer optimization decision module, responsible for long-term energy storage planning and short-term real-time scheduling optimization; The real-time monitoring and feedback module monitors the operation results and dynamically adjusts the scheduling strategy based on the feedback data.

2. The distributed renewable energy and energy storage coordinated dispatching system of Internet of Things data according to claim 1 is characterized in that: The data acquisition and monitoring module includes: The sensing unit collects real-time data from distributed renewable energy equipment, energy storage units and distribution networks through a distributed sensor network to obtain comprehensive operating information; The communication unit transmits the data collected by the sensing unit to the central control platform through the Internet of Things communication protocol; The equipment status monitoring unit monitors the operating status of energy storage equipment and renewable energy equipment in real time, identifies and reports equipment failures and abnormal conditions.

3. The distributed renewable energy and energy storage coordinated dispatching system of Internet of Things data according to claim 1 is characterized in that: The energy supply and demand forecasting module includes: Renewable energy forecasting unit, which predicts future wind and photovoltaic power generation output based on historical power generation data and meteorological information, and generates time-dependent output curves; The load demand forecasting unit analyzes user behavior and historical power consumption data, predicts the power demand of different nodes in the power grid, and generates scheduling suggestions based on the demand; The energy storage unit behavior prediction unit predicts the charging and discharging behavior of electric vehicles and other energy storage units by analyzing the operating modes of the electric vehicles and other energy storage units, and generates an energy storage demand curve.

4. The distributed renewable energy and energy storage coordinated dispatching system of Internet of Things data according to claim 1 is characterized in that: The collaborative scheduling strategy module includes: The dispatch decision unit generates and updates the energy dispatch plan based on the energy supply and demand forecast results and the real-time operation data of the power grid to balance the supply and demand and maximize the use of renewable energy; The renewable energy dispatching unit adjusts the power generation in real time according to the grid load demand by adjusting the output of wind and photovoltaic power generation; The energy storage unit dispatching unit adjusts the charging and discharging behavior of the energy storage equipment and optimizes the use of the energy storage equipment according to the grid load to alleviate the peak power load and fill the power demand during the valley period; The load dispatching unit rationally distributes the output power of renewable energy and energy storage equipment to different load nodes in the power grid.

5. The distributed renewable energy and energy storage coordinated dispatching system of Internet of Things data according to claim 1 is characterized in that: The two-layer optimization decision module includes: The planning unit conducts energy storage unit layout and capacity planning based on the spatiotemporal distribution characteristics of renewable energy and the grid structure, and makes predictions on the long-term demand for energy storage; The real-time optimization unit dynamically adjusts the scheduling strategies of energy storage units and renewable energy through optimization algorithms during the real-time operation of the power grid.

6. The distributed renewable energy and energy storage coordinated dispatching system of Internet of Things data according to claim 1 is characterized in that: The real-time monitoring and feedback module includes: Status monitoring unit, used to monitor the real-time operating status of the power grid, energy storage equipment and renewable energy power generation equipment, and transmit the operating data to the central control platform; A feedback control unit dynamically adjusts the charging and discharging behavior of the energy storage unit and the generation of renewable energy based on real-time monitoring data; The safety assurance unit activates the predetermined emergency dispatch plan when a failure or emergency occurs during operation.

7. The distributed renewable energy and energy storage coordinated dispatching system of Internet of Things data according to claim 4 is characterized in that: The energy storage unit scheduling unit further includes: Priority setting is used to set different priorities for energy storage units and adjust the charging and discharging order of different energy storage units according to the grid load and renewable energy generation capacity.

8. A method for coordinated dispatching of distributed renewable energy and energy storage based on IoT data, characterized in that: A distributed renewable energy and energy storage coordinated dispatching system for Internet of Things data according to any one of claims 1 to 7, comprising the following steps: S1. Data collection and monitoring: The sensing unit collects the operating data of renewable energy equipment and energy storage equipment in real time through a distributed sensor network, including parameters of power generation, energy storage, and charging and discharging status; The communication unit uses the Internet of Things technology to transmit the collected real-time data to the central control platform; The equipment status monitoring unit monitors the status of the energy storage unit and the power generation equipment, detects whether there is any equipment failure or abnormality, and issues an alarm message; S2. Energy supply and demand forecast: The renewable energy prediction unit predicts the wind and photovoltaic power generation output in the short term based on meteorological data and historical power generation data, and generates a prediction curve; The load demand forecasting unit predicts the power demand of each node and generates a load demand curve by analyzing the historical power consumption data of the power grid and the user behavior patterns; The energy storage unit behavior prediction unit analyzes the historical charging and discharging behavior of the energy storage equipment, predicts future energy storage demand, and calculates the scheduling requirements of the energy storage unit in combination with the grid demand; S3. Collaborative scheduling strategy formulation: The dispatch decision unit formulates a coordinated energy dispatch strategy based on the predicted renewable energy power generation output, load demand and energy storage demand, including the real-time dispatch of wind power and photovoltaic power generation and the charging and discharging plan of energy storage equipment; The renewable energy dispatch unit adjusts the power output of wind and photovoltaic power generation equipment according to load demand and weather forecasts; The energy storage unit dispatching unit determines the charging and discharging order of the energy storage units by setting priorities; The load dispatching unit applies the generated dispatching plan to each load node of the power grid; S4. Real-time optimization and adjustment: The two-layer optimization decision module performs long-term and short-term energy optimization. The planning unit performs long-term planning for energy storage layout, while the real-time optimization unit dynamically adjusts the output of energy storage units and renewable energy according to the real-time grid operation status. The feedback control unit monitors the operating status in real time and makes adjustments based on load changes, the operation of the energy storage equipment, and fluctuations in renewable energy generation; S5. Safety and troubleshooting: The status monitoring unit continuously monitors the operating status of the power grid and energy storage equipment to detect any abnormal conditions such as overload and low voltage; When the safety assurance unit detects equipment failure or grid abnormality, it immediately initiates the emergency dispatch program to adjust the output of power generation or energy storage equipment.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the distributed renewable energy and energy storage coordinated scheduling method of Internet of Things data as described in claim 8 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by the processor, the distributed renewable energy and energy storage coordinated scheduling method of Internet of Things data as claimed in claim 8 is implemented.

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