Power distribution area energy optimization system

Abstract

The invention discloses a power distribution area energy optimization system, which comprises an energy optimization controller, a multifunctional protocol converter, a new energy device and a monitoring master station, and is characterized in that the monitoring master station is connected with the energy optimization controller, and the energy optimization controller is connected with the multifunctional protocol converter; the energy optimization system comprises a unified power flow calculation method and an energy optimization method, and the energy optimization system of the power distribution area realizes efficient management and optimization of energy through combination of various technical means, and provides powerful support for development of an intelligent power grid and a distributed energy system.

Description

[Technical field]

[0001] The present invention relates to the field of electric power, and in particular to an energy optimization system for a distribution station area. [Background technology]

[0002] A large number of distributed interactive devices are connected to the distribution network, becoming an important participant in the new distribution network, and at the same time, they also bring more uncertainty and randomness to the distribution system. The emergence of new participants requires that power dispatching must change from the traditional "source follows load" to a high degree of integration of "source, network, load and storage". However, the actual situation of the distribution network is that it has a wide coverage, many professions, and large basic differences. How to build a distribution network that adapts to the large-scale access of distributed interactive devices is an area that power grid companies urgently need to explore. Especially for distribution network substations, with the access of new loads such as distributed photovoltaics and electric vehicle charging piles, many problems have been brought to the substations. Due to the mismatch between the temporal and spatial characteristics of photovoltaic power generation and power load, photovoltaic power generation power is sent out in large quantities during some periods, resulting in reverse flow. At this time, the power grid equipment reversely sends a large amount of power, and even reverse power transmission causes overload of equipment and lines, seriously affecting the safe operation of the power grid. [Summary of the invention]

[0003] In order to solve the above problems, the present invention proposes an energy optimization system for distribution substations to solve the distribution aggregation and coordinated control of distributed photovoltaic, charging piles, energy storage systems, controllable loads and other substations.

[0004] A distribution substation energy optimization system includes an energy optimization controller, a multifunctional protocol converter, new energy equipment and a monitoring main station. The monitoring main station is connected to the energy optimization controller, and the energy optimization controller is connected to the multifunctional protocol converter. The multifunctional protocol converter converts the communication protocols of different manufacturers into a unified format to ensure the compatibility and availability of data. The energy optimization controller is connected to the monitoring main station through a 10 / 100 network port and a 4G / 5G communication method. The multifunctional protocol converter includes an uplink and downlink communication interface and a voltage quality monitoring module. The uplink communicates with the energy optimization controller through an HPLC broadband carrier and a unified communication protocol, and the downlink matches various new energy equipment through RS-485, USB, and CAN interfaces. The voltage quality monitoring module collects the voltage of the new energy equipment and the grid point and uploads it to the energy optimization controller, and provides data support for voltage fluctuations and bidirectional over-limits for the monitoring main station. The software system of the energy optimization controller is divided into an operating system layer and an application layer. The operating system layer includes an operating system, a hardware driver framework, a startup program, a system interface, a hardware abstraction layer, and a system component. The operating system provides a system call interface for APP through a system interface, and provides a hardware device access interface through a hardware abstraction layer. The system component communicates with the application layer through a message bus. The application layer includes application software, which monitors the real-time data and status of the substation load and new energy equipment, exchanges data with system components and other application software through the message bus, and controls the unified power flow calculation method and unified regulation of new energy equipment in the substation based on the monitoring data.

[0005] Furthermore, the energy optimization controller is installed in the intelligent collection and control terminal of the distribution station area, including a data collection module, a power quality monitoring module, a unified power flow calculation module, an equipment operation status monitoring module, an optimization coordination control module, and an operation and maintenance management function module. The data collection module collects data from various equipment in the substation area. The power quality monitoring module monitors the power quality in real time to ensure that parameters such as voltage and frequency are within an appropriate range. The unified power flow calculation module performs unified calculation and analysis of the current and voltage in the substation area and optimizes energy flow. The equipment operation status monitoring module monitors the equipment status in real time, prevents maintenance and locates faults. The optimization coordination control module performs intelligent control based on real-time data to optimize energy use. The operation and maintenance management function module provides remote monitoring and maintenance functions.

[0006] Furthermore, the monitoring main station includes a real-time monitoring module, a data analysis module, a load analysis and monitoring module, and an integrated operation and maintenance management module. The monitoring module monitors the power generation of the photovoltaic system and the charging and discharging of energy storage. The data analysis module performs analysis based on cloud data to improve the management level. The load analysis and monitoring module analyzes the load conditions of the distribution transformer area to prevent heavy loading and overload problems. The integrated operation and maintenance management module integrates operation and maintenance resource monitoring, work order management, and operation and maintenance report functions, and supports automatic inspections and preventive maintenance.

[0007] Furthermore, a distribution substation area energy optimization system includes unified power flow calculation and energy optimization method, and the unified power flow calculation includes the following steps:

[0008] 1) Data collection and preprocessing: perform clock synchronization on multi-source data to eliminate errors caused by time differences, classify data according to business attributes and requirements, remove redundant information in the data, and retain valid data; detect and process duplicate data, missing data, and bad data, check the validity and accuracy of measurement data, remove noise and abnormal data, and interpolate or fill in missing data;

[0009] 2) Establish an electrical network topology model and set accurate electrical parameters for each component in the network (such as lines, transformers, loads, etc.);

[0010] 3) Establish a power flow calculation model based on node voltage equations and branch current equations, set initial values,

[0011] 4) Select the power flow calculation algorithm, calculate the iterative convergence, and determine whether the maximum value of the modulus component of the voltage difference between two adjacent iterations is less than the given convergence index. If so, stop the calculation; otherwise, continue the iteration until convergence.

[0012] 6) Result analysis and verification: Check whether the calculated node voltage is within a reasonable range and verify whether the power injected into the network is equal to the power flowing out;

[0013] The data collection and preprocessing can be verified and corrected by using redundant data, and the erroneous data can be corrected before the power flow calculation is performed. The redundant information in the data can be removed by using cluster analysis to identify and process the redundant data.

[0014] The energy optimization method includes the following steps:

[0015] 1) Read the power flow calculation results, obtain the results of the previous power flow calculation as input, and optimize the network structure and equipment configuration according to the power flow calculation results.

[0016] 2) Call the energy optimization algorithm to produce energy optimization solutions, implement load forecasting, formulate reasonable load management strategies based on the load forecasting results, arrange load distribution and scheduling, and avoid overload and voltage fluctuations. Load management strategies include:

[0017] Demand-side management: Through price incentives, policy guidance and other means, users are encouraged to reduce electricity consumption during peak hours and increase electricity consumption during off-peak hours, so as to reduce the peak-to-valley difference and improve the overall efficiency of the power grid.

[0018] Load dispatching: According to the real-time operation status of the power grid and the predicted load situation, reasonably arrange the load distribution and dispatching of each area to avoid overload and voltage fluctuations.

[0019] Energy storage system application: Introduce energy storage systems (such as battery energy storage, pumped storage, etc.) into the power grid to release electrical energy during peak loads and absorb electrical energy during low loads, thereby balancing the supply and demand of the power grid.

[0020] Among them, the energy optimization algorithm first uses a machine learning algorithm to predict the load, and then combines linear programming and nonlinear programming to perform short-term and long-term scheduling optimization; during this period, genetic algorithms and particle swarm optimization can be selected as global optimization tools to solve certain specific complex problems.

[0021] The power distribution area energy optimization system of the present invention realizes efficient management and optimization of energy through the combination of hardware and software technical means, providing strong support for the development of smart grids and distributed energy systems. In distributed energy systems, the optimization system coordinates and manages multiple energy sources to improve overall energy utilization. As part of the smart grid, the system can realize comprehensive management and optimization of new and traditional energy sources and improve the reliability and stability of the power grid.

[0022] The hierarchical architecture design of the energy optimization controller of the present invention can effectively separate hardware resource management from application logic, ensuring the scalability and maintainability of the system. The operating system layer provides a stable foundation and a unified interface, while the application layer focuses on specific business logic and function implementation. The message bus mechanism ensures efficient communication and collaboration between components.

[0023] The multifunctional protocol converter of the present invention can effectively solve the problem of inconsistent communication interfaces and protocols of new energy equipment. It has a built-in voltage quality monitoring module, which can collect the grid connection point voltage in real time and upload it to the energy optimization controller to help monitor the voltage quality of the power grid. At the same time, it provides data support for voltage fluctuations and two-way over-limits for the monitoring main station, which facilitates power quality analysis and problem diagnosis.

Brief Description of the Drawings

[0024] Figure 1 It is the overall connection schematic diagram of the present invention;

[0025] Figure 2 It is a schematic diagram of the energy optimization controller of the present invention;

[0026] Figure 3 A schematic diagram of a multifunctional protocol converter of the present invention;

[0027] Figure 4 It is a schematic diagram of the communication architecture of the present invention;

[0028] Figure 5 It is a schematic diagram of the flow calculation steps of the present invention;

[0029] Figure 6 It is a schematic diagram of the energy optimization steps of the present invention. [Specific implementation method]

[0030] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0031] A distribution substation energy optimization system provides a unified access interface for new energy members in the substation area, monitors the load in the substation area, and monitors the real-time data and status of new energy equipment. It realizes unified power flow calculation and unified regulation of new energy equipment in the substation area, realizes unified operation and maintenance management of new energy equipment in the substation area, and realizes self-management and autonomy of energy in the substation area. It optimizes the operation of the substation area energy system and avoids the phenomenon of transformer overload, unqualified voltage, and other threats to power safety caused by unsupervised and disorderly operation of new energy equipment.

[0032] The details are as follows Figure 1-4 As shown, the system includes an energy optimization controller, a multi-function protocol converter, new energy equipment and a monitoring main station. The monitoring main station is connected to the energy optimization controller, and the energy optimization controller is connected to the multi-function protocol converter. The multi-function protocol converter converts the communication protocols of different manufacturers into a unified format to ensure the compatibility and availability of the data. Due to the existence of the multi-function protocol converter, the system can be compatible with equipment from different manufacturers, greatly improving the scalability and flexibility of the system. As the core control unit of the system, the energy optimization controller is responsible for receiving and processing data from the monitoring main station and various devices, and performing energy scheduling and control according to the optimization algorithm. New energy equipment includes solar panels, wind turbines, energy storage equipment, etc., which provide renewable energy and complement traditional power grids. The monitoring main station is responsible for monitoring and managing the entire system, and obtains real-time data and analyzes it by connecting to the energy optimization controller. The system workflow is as follows:

[0033] Data collection and conversion: New energy equipment and other related equipment convert data formats of different protocols into a unified format through a multi-functional protocol converter and transmit it to the energy optimization controller.

[0034] Data processing and optimization: After receiving the data in a unified format, the energy optimization controller performs real-time processing and analysis and calculates the best energy allocation plan based on the preset optimization algorithm.

[0035] Instruction issuance and execution: The energy optimization controller sends the optimized instructions back to each device, adjusts the distribution and use of energy, and ensures that the system operates in the optimal state.

[0036] Real-time monitoring and feedback: The monitoring master station continuously monitors the system operating status and receives feedback data from the energy optimization controller for further analysis and adjustment.

[0037] The monitoring master station is a comprehensive service platform of "Internet +". It can grasp the power generation of the photovoltaic system in the substation and the charging and discharging of the energy storage charging pile in real time. Based on cloud data analysis, it can improve the management level and realize remote monitoring. The monitoring master station system deploys the load analysis and abnormal monitoring module of the distribution transformer substation to realize the analysis and monitoring of various operating states such as heavy load, overload, light load and no load of the distribution transformer. Based on the electrical topology of the substation and combined with the perception data of each node, the time-sharing, segmented and phase-sharing load analysis of the entire substation is realized, and the load situation of the substation is objectively analyzed. The monitoring master station system also deploys a comprehensive operation and maintenance management module that integrates operation and maintenance resource monitoring, work order management, operation and maintenance reports, standard knowledge base, and automatic inspection. It can conduct regular inspections according to plan and provide inspection reports. Preventive maintenance effectively reduces the occurrence rate of equipment failures. Specifically, the monitoring module of the monitoring main station monitors the power generation of the photovoltaic system and the charging and discharging of the energy storage system. The data analysis module performs analysis based on cloud data to improve the management level. The load analysis and monitoring module analyzes the load conditions of the distribution transformer area to prevent heavy load and overload problems. The comprehensive operation and maintenance management module integrates operation and maintenance resource monitoring, work order management, and operation and maintenance reporting functions, and supports automatic inspections and preventive maintenance.

[0038] The software system of the energy optimization controller is divided into the operating system layer and the application layer. The operating system layer includes the operating system, hardware driver framework, startup program, system interface, hardware abstraction layer and system components. The operating system provides a system call interface for the APP through the system interface and provides a hardware device access interface through the hardware abstraction layer. The system components communicate with the application layer through the message bus. The application layer contains application software, which monitors the real-time data and status of the load and new energy equipment in the substation area, exchanges data with system components and other application software through the message bus, and at the same time, according to the monitoring data, the application software controls the unified power flow calculation method of the substation area and the unified regulation of new energy equipment.

[0039] Among them, the energy optimization controller is installed in the intelligent collection and control terminal of the distribution station area, and is connected to the monitoring main station through the 10 / 100 network port and 4G / 5G communication method. It can meet the needs of high-performance concurrency, large-capacity storage, multiple collection objects, local analysis and decision-making, and collaborative computing. It has functions such as data collection, power quality monitoring, unified power flow calculation, equipment operation status monitoring, optimization and coordination control, and operation and maintenance management, supporting the development needs of new energy services in the substation area. The functional modules of the energy optimization controller include data collection module, power quality monitoring module, unified power flow calculation module, equipment operation status monitoring module, optimization and coordination control module, and operation and maintenance management function module. The data collection module collects data from various types of equipment in the substation area. The power quality monitoring module monitors the power quality in real time to ensure that parameters such as voltage and frequency are within the appropriate range. The unified power flow calculation module performs unified calculation and analysis of current and voltage in the substation area and optimizes energy flow. The equipment operation status monitoring module monitors the equipment status in real time, prevents maintenance and fault location. The optimization and coordination control module performs intelligent control based on real-time data to optimize energy use. The operation and maintenance management function module provides remote monitoring and maintenance functions.

[0040] The multifunctional protocol converter mainly solves the problem of inconsistent external communication interfaces and protocols of new energy equipment. It includes uplink and downlink communication interfaces and voltage quality monitoring modules. The uplink communicates with the energy optimization controller through HPLC broadband carrier and unified communication protocol, and the downlink matches with various new energy equipment through RS-485, USB, and CAN interfaces, and converts the protocols of different manufacturers into a unified communication protocol to communicate with the energy optimization device. At the same time, the multifunctional protocol converter is equipped with a voltage quality monitoring module to collect the voltage of the grid connection point of new energy equipment and upload it to the energy optimization controller, while providing data support for voltage fluctuations and two-way over-limit for the monitoring master station.

[0041] like Figure 5 As shown, a distribution substation area energy optimization system includes a unified power flow calculation method, and the unified power flow calculation includes the following steps:

[0042] 1) Data collection and preprocessing: perform clock synchronization on multi-source data to eliminate errors caused by time differences, classify data according to business attributes and requirements, remove redundant information in the data, and retain valid data; detect and process duplicate data, missing data, and bad data, check the validity and accuracy of measurement data, remove noise and abnormal data, and interpolate or fill in missing data;

[0043] 2) Establish an electrical network topology model. Generally, the topology of medium and low voltage distribution networks is more complex and may change frequently, with a large number of branches and branch nodes. High voltage distribution networks are generally radial or ring networks, and usually have a relatively fixed topology. Component parameter setting: Set accurate electrical parameters for each component in the network (such as lines, transformers, loads, etc.).

[0044] 3) Establish a power flow calculation model based on node voltage equations and branch current equations. Set the initial value, give the root node voltage of the distribution feeder (usually 1.0pu or nominal voltage), and assign initial values ​​to the voltages of other nodes (usually also 1.0pu or values ​​close to the nominal voltage).

[0045] 4) Select the power flow calculation algorithm, starting from each load node, in the order of child nodes first and then parent nodes, and through forward calculation, calculate the branch power distribution from the node voltage distribution. Back-substitution calculation starts from the root node, first the parent node and then the child node; through back-substitution calculation, calculate the node voltage distribution from the branch power distribution. Iterate and converge, and determine whether the maximum value of the modulus component of the voltage difference between two adjacent iterations is less than the given convergence index (such as 0.001pu). If so, stop the calculation; otherwise, continue to iterate until convergence.

[0046] 6) Result analysis and verification: Check whether the calculated node voltage is within a reasonable range (usually 0.95 to 1.05 pu) to ensure that the voltage quality meets the standard to ensure the safe and stable operation of the power system. At the same time, verify whether the power injected into the network is equal to the power flowing out to ensure power balance and avoid power loss and imbalance problems in the power system.

[0047] Among them, data collection and preprocessing can use redundant data for verification and correction, and the wrong data can be corrected before the power flow calculation. Cluster analysis can be used to identify and process redundant data when removing redundant information in the data. Cluster analysis is mainly used to group data into similar subsets, and it can also identify and process redundant data. Clustering can be used to group similar observations into one category, and select representative values ​​for each category to reduce data redundancy.

[0048] Among them, when performing power flow calculations in medium and low voltage distribution networks, especially when facing redundant measurement data, the calculation requirements may be more complex and sophisticated. Therefore, there are obvious differences in power flow calculations between medium and low voltage distribution networks and high voltage distribution networks, which are mainly reflected in network topology, load characteristics, data acquisition, calculation complexity, voltage level, power quality, reliability, and distributed energy access. Due to its complexity and dynamic changes, medium and low voltage distribution networks require more flexible and efficient calculation methods to adapt to real-time needs.

[0049] Because for the low-voltage distribution network in the substation, due to the change of the original point-line relationship with a certain frequency, it is necessary to adopt a power flow calculation method based on path search to adapt to the dynamic changes of the network topology. At the same time, since all smart meters in the same substation cannot completely achieve synchronous acquisition in the process of acquiring data, the collected data may be wrong. Therefore, redundant data can be used to verify the correctness of each other, and the wrong data can be corrected before the power flow calculation. Specifically, the voltage, current, active power and reactive power data on the low-voltage side of the distribution transformer can be used as the data items of the head end (power supply end), and the voltage, current, active power and reactive power data of the user's smart meter can be used as the data items of the terminal segment. The research paper by the author Zhang Xu et al.: Power flow calculation and application of low-voltage distribution network in the substation for redundant measurement proposes two power flow calculation methods based on current measurement and power measurement, and the results are mutually verified to correct the wrong data. This method can improve the accuracy and reliability of power flow calculation through the verification and correction of redundant data, so as to better adapt to the dynamic changes and complexity of the low-voltage distribution network. Therefore, when selecting a power flow calculation algorithm for medium and low voltage distribution networks, the forward-backward method proposed in this paper can be selected. This method approximates the real power flow state by iteratively calculating the node voltage and branch power distribution. By combining the above steps with the power flow calculation method, the voltage of each node in the distribution network and the power distribution of the branch can be accurately determined, providing important data support for the operation and optimization of the power system.

[0050] Furthermore, distributed algorithms can also be used in low-voltage distribution network power flow calculations. Distributed algorithms distribute computing tasks to multiple computing nodes and process data in parallel, thereby significantly improving computing efficiency and scalability. The following are the general steps of distributed algorithms in low-voltage distribution network power flow calculations:

[0051] 1) Data partitioning: The data of the low-voltage distribution network (such as network topology, node parameters, branch parameters, etc.) is divided into multiple subsets according to certain rules, and each subset is assigned to one or more computing nodes.

[0052] 2) Node communication: Establish a communication mechanism between computing nodes to ensure that nodes can transfer data and results to each other.

[0053] 3) Local power flow calculation: Each computing node independently performs local power flow calculation based on the assigned data subset. Here, the forward and backward method or Newton-Raphson method mentioned above can be used to calculate the node voltage and branch power in the local area. It should be noted that during the calculation process, each node needs to process the boundary conditions with the adjacent nodes, such as exchanging power, voltage and other information.

[0054] 4) Iteration and convergence: Iterative calculations are performed between computing nodes, and boundary condition information is exchanged through the communication mechanism. As above, the system convergence is determined based on the iteration results.

[0055] 5) Collecting results: Each computing node sends its local power flow calculation results to the master node or designated collection node.

[0056] 6) Global integration: The master node or collection node globally integrates the received local results to form the overall power flow calculation results of the low-voltage distribution network.

[0057] Among them, in the power flow calculation of high-voltage distribution network, the number of nodes is relatively small and the calculation complexity is low. It is recommended to use the Newton-Raphson method or the fast decoupling method according to the specific grid structure and load characteristics. The fast decoupling method works better when the impedance ratio is moderate. When choosing the Newton-Raphson method, a reasonable initial value can speed up the convergence speed.

[0058] like Figure 6 As shown, a distribution substation area energy optimization system includes an energy optimization method, and the optimization method includes the following steps:

[0059] 1) Read the power flow calculation results, obtain the results of the previous power flow calculation as input, and optimize the network structure and equipment configuration according to the power flow calculation results. The main purpose of this step is to use the results of the power flow calculation to identify bottlenecks, overload areas, low voltage areas and other problems in the distribution network, and optimize the network structure and equipment configuration accordingly. The specific operations are as follows:

[0060] Read the results: First, export or read the calculation results from the power flow calculation software, including the voltage amplitude and phase angle of each node, the power flow of each branch, etc.

[0061] Problem analysis: Based on the power flow calculation results, analyze the operating status of the distribution network and identify possible problem areas, such as overloaded lines, low-voltage nodes, etc.

[0062] Optimize network structure: Consider adjusting the network structure based on the identified problems, such as adding interconnection lines to improve power supply reliability, optimizing the ring network structure to reduce the scope of fault impact, and installing segmentation devices to limit the spread of faults.

[0063] Optimize equipment configuration: Based on the power flow calculation results and equipment operation status, evaluate whether the capacity of existing equipment meets the demand, and consider upgrading or replacing old equipment to improve equipment performance and reliability. At the same time, you can also consider installing intelligent monitoring equipment to achieve real-time monitoring of the distribution network and fault warning.

[0064] 2) Call the energy optimization algorithm to produce energy optimization solutions, implement load forecasting, and formulate reasonable load management strategies based on the load forecasting results. The main purpose of this step is to reasonably arrange load distribution and scheduling through load forecasting and management, avoid overload and voltage fluctuations, and improve the overall efficiency and economy of the power grid. The specific operations are as follows:

[0065] Load forecasting: Use historical load data, meteorological information, holiday factors, etc., and adopt appropriate forecasting models (such as time series analysis, machine learning, etc.) to predict future loads.

[0066] Load management: Develop a reasonable load management strategy based on load forecast results. This includes but is not limited to:

[0067] Demand-side management: Through price incentives, policy guidance and other means, users are encouraged to reduce electricity consumption during peak hours and increase electricity consumption during off-peak hours, so as to reduce the peak-to-valley difference and improve the overall efficiency of the power grid.

[0068] Load dispatching: According to the real-time operation status of the power grid and the predicted load situation, reasonably arrange the load distribution and dispatching of each area to avoid overload and voltage fluctuations.

[0069] Energy storage system application: Introduce energy storage systems (such as battery energy storage, pumped storage, etc.) into the power grid to release electrical energy during peak loads and absorb electrical energy during low loads, thereby balancing the supply and demand of the power grid.

[0070] Among them, the energy optimization algorithm first uses a machine learning algorithm to predict the load, and then combines linear programming and nonlinear programming to perform short-term and long-term scheduling optimization; during this period, genetic algorithms and particle swarm optimization can be selected as global optimization tools to solve certain specific complex problems.

[0071] In summary, the energy optimization system for distribution substations can realize the coordinated aggregation of distributed energy storage and distributed photovoltaics in the substation area; break through the coordinated control of distributed energy storage and distributed power sources and regional energy allocation management technology, and improve the distribution network's ability to absorb distributed photovoltaics. The following functions can be achieved through aggregation and optimized regulation:

[0072] (1) New energy access: Photovoltaic energy is first stored in batteries, and the surplus power is supplied to the load. When the photovoltaic energy is insufficient, the load is powered by the energy storage system, and the power generation and power consumption are smoothed through the energy storage system. The instability and random fluctuations of photovoltaic power generation are eliminated, the power quality is improved, and a continuous power supply is provided.

[0073] (2) Severe overload control in substations: When the transformer capacity in the substation is insufficient or the transformer is severely overloaded, the substation energy storage system improves the power supply capacity of the substation through discharge, supports the normal operation of the substation, saves the investment cost of capacity increase and site deployment, shortens the transformation cycle, and improves power supply reliability.

[0074] (3) Terminal low voltage control: When the voltage at the user end of the distribution station area is lower than the normal voltage and is unqualified, the energy storage system in the station area will discharge according to the set coefficient through automatic detection and output reactive power support to increase the power factor of the line, ensure reliable electricity supply to residents, and improve power supply reliability.

[0075] (4) System backup power: In the event of planned or temporary power outages, the energy storage system can serve as a backup power source to ensure the power supply of important loads in the substation area and improve the reliability of power use.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A distribution area energy optimization system, characterized in that : It includes an energy optimization controller, a multifunctional protocol converter, new energy equipment and a monitoring main station. The monitoring main station is connected to the energy optimization controller, and the energy optimization controller is connected to the multifunctional protocol converter. The energy optimization controller is connected to the monitoring main station through a 10 / 100 network port and a 4G / 5G communication method. The multifunctional protocol converter includes an uplink and downlink communication interface and a voltage quality monitoring module. The uplink communicates with the energy optimization controller through an HPLC broadband carrier and a unified communication protocol, and the downlink matches various new energy equipment through RS-485, USB, and CAN interfaces. The voltage quality monitoring module collects the grid voltage of the new energy equipment and uploads it to the energy optimization controller, and at the same time provides data support for voltage fluctuations and two-way over-limits for the monitoring main station. The software system of the energy optimization controller is divided into an operating system layer and an application layer. The operating system layer includes an operating system, a hardware driver framework, a startup program, a system interface, a hardware abstraction layer, and a system component. The operating system provides a system call interface for APP through the system interface, and provides a hardware device access interface through the hardware abstraction layer. The system component communicates with the application layer through a message bus.

2. A distribution station area energy optimization system, characterized by: The energy optimization system includes unified power flow calculation and energy optimization method. The unified power flow calculation comprises the following steps: 1) Data collection and preprocessing: perform clock synchronization on multi-source data to eliminate errors caused by time differences, classify data according to business attributes and requirements, remove redundant information in the data, and retain valid data; detect and process duplicate data, missing data, and bad data, check the validity and accuracy of measurement data, remove noise and abnormal data, and interpolate or fill in missing data; 2) Establish an electrical network topology model and set accurate electrical parameters for each component in the network (such as lines, transformers, loads, etc.); 3) Establish a power flow calculation model based on node voltage equations and branch current equations, set initial values, 4) Select the power flow calculation algorithm, calculate the iterative convergence, and determine whether the maximum value of the modulus component of the voltage difference between two adjacent iterations is less than the given convergence index. If so, stop the calculation; otherwise, continue the iteration until convergence. 6) Result analysis and verification: Check whether the calculated node voltage is within a reasonable range and verify whether the power injected into the network is equal to the power flowing out; The energy optimization method comprises the following steps: 1) Read the power flow calculation results, obtain the results of the previous power flow calculation as input, and optimize the network structure and equipment configuration according to the power flow calculation results. 2) Call the energy optimization algorithm to produce energy optimization solutions, implement load forecasting, formulate reasonable load management strategies based on the load forecasting results, and arrange load distribution and adjustment.

3. The power distribution area energy optimization system according to claim 1, characterized in that: The energy optimization controller is installed in the intelligent collection and control terminal of the distribution station area, including a data acquisition module, a power quality monitoring module, a unified power flow calculation module, an equipment operation status monitoring module, an optimization coordination control module, and an operation and maintenance management function module. The data acquisition module collects data from various equipment in the substation area. The power quality monitoring module monitors the power quality in real time to ensure that parameters such as voltage and frequency are within an appropriate range. The unified power flow calculation module performs unified calculation and analysis of the current and voltage in the substation area and optimizes energy flow. The equipment operation status monitoring module monitors the equipment status in real time, prevents maintenance and locates faults. The optimization coordination control module performs intelligent control based on real-time data to optimize energy use. The operation and maintenance management function module provides remote monitoring and maintenance functions.

4. The power distribution area energy optimization system according to claim 1, characterized in that: The monitoring main station includes a real-time monitoring module, a data analysis module, a load analysis and monitoring module, and a comprehensive operation and maintenance management module. The monitoring module monitors the power generation of the photovoltaic system and the charging and discharging of energy storage. The data analysis module performs analysis based on cloud data to improve the management level. The load analysis and monitoring module analyzes the load conditions of the distribution transformer area to prevent heavy load and overload problems. The comprehensive operation and maintenance management module integrates operation and maintenance resource monitoring, work order management, and operation and maintenance report functions, and supports automatic inspections and preventive maintenance.

5. The power distribution area energy optimization system according to claim 1, characterized in that: The multifunctional protocol converter converts the communication protocols of different manufacturers into a unified format to ensure the compatibility and availability of data.

6. The power distribution area energy optimization system according to claim 1, characterized in that: The application layer includes application software, which monitors the real-time data and status of the area load and new energy equipment, exchanges data with system components and other application software through the message bus, and controls the unified power flow calculation method and unified regulation of new energy equipment in the area based on the monitoring data.

7. The power distribution area energy optimization system according to claim 2, characterized in that: The data collection and preprocessing can be verified and corrected using redundant data, and the erroneous data can be corrected before the power flow calculation is performed. The removal of redundant information in the data can use cluster analysis to identify and process redundant data.

8. The power distribution area energy optimization system according to claim 2, characterized in that: The energy optimization algorithm first uses a machine learning algorithm to perform load forecasting, and then combines linear programming and nonlinear programming to perform short-term and long-term scheduling optimization; genetic algorithms and particle swarm optimization are selected as global optimization tools to solve certain specific complex problems.

9. The power distribution area energy optimization system according to claim 2, characterized in that: The load management strategy includes demand side management, load scheduling and energy storage system application.

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