Grid-connected distributed energy system scheduling method and device, equipment and medium

Abstract

The application relates to the fields of power technology and energy storage technology, in particular to a grid-connected distributed energy system scheduling method, device, equipment and medium. The system comprises at least one edge energy node, a distribution bus and an electric energy metering device. When any edge energy node enters a current period, the system obtains the system external net power collected by the electric energy metering device, compares the target power to obtain a power deviation value, calculates the current energy storage output power according to the power deviation value and the energy state information of the edge energy node through droop control, and executes power output and circulation according to the current energy storage output power. The application forms independent control of each edge energy node in the grid-connected distributed energy system through droop control calculation, effectively realizes coordinated control of system-level power without setting a central controller, and improves the reliability, autonomy, adaptability and real-time performance of the grid-connected distributed energy system control.

Description

Technical Field

[0001] This application relates to the fields of power technology and energy storage technology, and in particular to a method, device, equipment and medium for dispatching a grid-connected distributed energy system. Background Technology

[0002] In recent years, distributed photovoltaic (PV) systems, energy storage systems, and electric vehicle charging stations have been widely deployed in homes, industrial parks, and communities. These scenarios are transforming from simple electricity consumption to integrated energy systems that combine power generation, energy storage, and electricity consumption capabilities. Such systems typically operate in a grid-connected manner, maintaining electrical connection with the distribution network, and require the management of various local energy devices to achieve system power balance, energy optimization, and safe operation.

[0003] Such grid-connected distributed energy systems face the following control requirements: photovoltaic power generation fluctuates significantly due to weather conditions; energy storage systems need to maintain a balanced state of charge during charging and discharging and avoid overcharging and over-discharging; load power changes dynamically with user behavior; and the system's net external power needs to be controlled to meet grid connection requirements (such as limiting reverse current and demand response). To achieve these goals, multiple energy devices in the system need to coordinate their operation in real time and jointly adjust their power to track the system's power target.

[0004] There are various solutions for dispatch and control of power grid systems, including centralized, cloud-edge coordination, and droop control. Centralized and cloud-edge coordination have certain problems in grid-connected scenarios, while droop control is not suitable for grid-connected scenarios. It is evident that the existing centralized / cloud-based control and traditional droop control solutions cannot fully meet the comprehensive requirements of grid-connected distributed energy systems for reliability, autonomy, adaptability, and real-time performance.

[0005] Therefore, how to improve dispatch control in grid-connected distributed energy systems to achieve system-level power coordination under conditions of no central controller dependence has become an urgent problem to be solved. Summary of the Invention

[0006] This application provides a grid-connected distributed energy system scheduling method, apparatus, electronic device, and storage medium, aiming to solve the problem of how to improve scheduling control in a grid-connected distributed energy system to achieve system-level power coordination under conditions without a central controller dependency.

[0007] In a first aspect, a grid-connected distributed energy system scheduling method is provided, wherein the grid-connected distributed energy system includes at least one edge energy node, a distribution bus and an energy metering device, all edge energy nodes are connected to the distribution bus and the energy metering device, one end of the distribution bus is used to connect to a load, and the other end of the distribution bus is connected to the power grid through the energy metering device; The grid-connected distributed energy system scheduling method is applied to any end energy node, including: Upon entering the current cycle, the net external power of the system collected by the power metering device is obtained; The power deviation value is obtained by comparing the system's net power output with the target power. Based on the power deviation value and the energy status information of the edge energy node, droop control calculation is performed to obtain the current energy storage output power of the corresponding edge energy node; Based on the current energy storage output power, execute the energy storage power output and enter the next cycle. Take the next cycle as the current cycle and return to execute the process of obtaining the system's net external power collected by the power metering device when entering the current cycle.

[0008] Secondly, a grid-connected distributed energy system coordination and dispatching device is provided. The grid-connected distributed energy system includes at least one edge energy node, a distribution bus, and an energy metering device. All edge energy nodes are connected to the distribution bus and the energy metering device. One end of the distribution bus is used to connect to a load, and the other end of the distribution bus is connected to the power grid through the energy metering device. The grid-connected distributed energy system coordination and dispatch device is applied to any end energy node, including: The external net power acquisition module is used to acquire the system's external net power collected by the power metering device when entering the current cycle; The power deviation calculation module is used to compare the system's net power to the outside with the target power to obtain the power deviation value. The output power determination module is used to perform droop control calculations based on the power deviation value and the energy status information of the edge energy node to obtain the current energy storage output power of the corresponding edge energy node. The loop execution module is used to execute energy storage power output and enter the next cycle according to the current energy storage output power, take the next cycle as the current cycle, and return to execute the process of obtaining the system's external net power collected by the power metering device when entering the current cycle.

[0009] Thirdly, an electronic device is provided, comprising a processor and a memory, wherein, Memory, used to store computer programs; The processor is used to execute the program stored in the memory to implement the grid-connected distributed energy system scheduling method described in the first aspect above.

[0010] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, it implements the grid-connected distributed energy system scheduling method described in the first aspect.

[0011] The advantages of this application compared to the prior art are as follows: The grid-connected distributed energy system of this application includes at least one edge energy node, a distribution bus, and an energy metering device. Each edge energy node is connected to the distribution bus and the energy metering device. One end of the distribution bus is used to connect to the load, and the other end of the distribution bus is connected to the power grid through the energy metering device. This method is applied to any edge energy node. When entering the current cycle, the net power of the system externally collected by the energy metering device is obtained. The net power of the system externally is compared with the target power to obtain the power deviation value. Based on the power deviation value and the energy status information of the edge energy node, droop control calculation is performed to obtain the current energy storage output power of the corresponding edge energy node. Based on the current energy storage output power, the energy storage power is output and the next cycle is entered. The next cycle is taken as the current cycle, and the process returns to the previous cycle. When entering the current cycle, the net power of the system externally collected by the energy metering device is obtained. In grid-connected distributed energy systems, vertical control calculations enable independent control of each edge energy node. Without the need for a central controller, system-level power coordination control can be effectively achieved, improving the reliability, autonomy, adaptability, and real-time performance of grid-connected distributed energy system control. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the structure of a grid-connected distributed energy system provided in Embodiment 1 of this application; Figure 2 This is a flowchart illustrating a grid-connected distributed energy system scheduling method provided in Embodiment 1 of this application; Figure 3 This is a flowchart illustrating a grid-connected distributed energy system scheduling method provided in Embodiment 2 of this application; Figure 4 This is a flowchart illustrating a grid-connected distributed energy system scheduling method provided in Embodiment 3 of this application; Figure 5 This is a schematic diagram of the structure of a grid-connected distributed energy system coordination and dispatching device provided in Embodiment 4 of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in Embodiment 5 of this application. Detailed Implementation

[0013] To illustrate the technical solution of this application, specific embodiments are described below.

[0014] See Figure 1This is a schematic diagram of a grid-connected distributed energy system according to Embodiment 1 of this application. The grid-connected distributed energy system includes N edge energy nodes, a distribution bus, and an energy metering device. All edge energy nodes are connected to the distribution bus and the energy metering device. One end of the distribution bus is used to connect to a load, and the other end of the distribution bus is connected to the power grid through the energy metering device. N is an integer greater than zero. The grid-connected distributed energy system scheduling method is applied to any edge energy node.

[0015] Each node (node ​​1, node 4, ..., node N) in the edge energy nodes of this application has at least the ability to read the system's external net power ( The edge energy node can include interfaces for reading local State of Charge (SOC) / temperature / fault, writing energy storage power commands, etc. The edge energy node can include photovoltaic energy storage node and energy storage node. The photovoltaic energy storage node includes photovoltaic power generation unit, energy storage unit and grid-connected inverter / controller. The energy storage node includes energy storage unit and grid-connected inverter / controller. Of course, it can also be other energy nodes, which are not limited here.

[0016] The aforementioned electricity metering device is installed at the connection point (PCC point) between the system and the power grid for real-time measurement. The signal (i.e., the power exchange value between the system and the power grid, with positive values ​​indicating the purchase of electricity from the power grid and negative values ​​indicating the supply of electricity to the power grid) and the signal of the system's net power to the outside world are transmitted to each node (via wired or wireless).

[0017] The aforementioned loads may include household or park loads. These loads are passive power consumption units that do not participate in power regulation. The distribution bus connects the edge energy nodes, loads, and the power grid. The power grid maintains an electrical connection with the system through an energy metering device, providing power support to the system or receiving excess power from the system.

[0018] In this application, no communication is required between the various edge energy nodes, avoiding communication topology complexity and communication failure risks. When a new node joins, it only needs to connect to the distribution bus and receive power. The signal can automatically participate in power regulation without requiring system reconfiguration. Figure 1 In the diagram, solid lines represent electrical connections (i.e., power flow), and dashed lines represent... Signal transmission.

[0019] like Figure 2 The diagram shown is a flowchart illustrating a grid-connected distributed energy system scheduling method according to Embodiment 1 of this application. The grid-connected distributed energy system scheduling method may include the following steps: Step S201: When entering the current cycle, obtain the net power of the system to the outside world collected by the power metering device.

[0020] The core control objective of this application is to enable... Tracking target power ( ),in, To allow for presets based on requirements, of course, It can also be issued by other systems. If the grid-connected distributed energy system operates independently, the target power is a preset value. If the grid-connected distributed energy system operates in conjunction with other energy systems, the target power is obtained from the other energy systems.

[0021] Specifically, The target value can be configured locally or broadcast over a communication network, with all nodes using the same value. Simultaneously, each node locally stores the "last valid" value. "With its timestamp: In a decentralized scenario, users can set a target through any node, which will broadcast it to other nodes through a communication network. The message can include a timestamp and source identifier so that each node can determine whether to adopt the update; in a cloud-edge collaboration scenario, the target value can be uniformly distributed to each node by the cloud or edge controller."

[0022] In this application, Typical configurations are shown in Table 1 below: Table 1 The net power output of the system in this application satisfies the system balance relationship. For example, a system including photovoltaic energy storage is as follows: ; In the formula, This refers to the system's net external power (positive = purchasing electricity from the grid, negative = sending electricity to the grid). For the target power, For load power, Let i be the photovoltaic output power. The energy storage output power of node i (positive = discharge, negative = charge).

[0023] Each edge energy node in this application is independently controlled, and based on the aforementioned purpose of tracking the target power, it is cyclically controlled. Within one cycle, the power metering device collects the current... And The signal is broadcast to all edge energy nodes, meaning that the edge energy nodes can receive it. Signal.

[0024] Step S202: Compare the system's net power output to the target power to obtain the power deviation value.

[0025] Among these, based on the aforementioned objective of tracking target power, it is necessary to... and A comparison is performed to determine the power deviation value, and then the node control is executed based on this power deviation value.

[0026] The formula for power deviation is as follows: ; in, ( This characterizes energy storage discharge, reducing power draw from the grid. ( This characterizes energy storage charging, increasing power intake from the grid or absorbing local power generation.

[0027] Optionally, after comparing the system's net external power with a preset target power to obtain a power deviation value, the method further includes: Detect whether the power deviation value is greater than the preset dead zone; When the power deviation value is detected to be greater than the dead zone, the droop control calculation is performed based on the power deviation value and the energy state information of the edge energy node to obtain the current energy storage output power of the corresponding edge energy node. When the detected power deviation value is not greater than the dead zone, the previous energy storage output power of the previous cycle of the current cycle is obtained, the previous energy storage output power is used as the current energy storage output power, and the energy storage power output is executed according to the current energy storage output power and the cycle begins.

[0028] Among them, power deviation value Before being used for control calculations, dead zone determination is required to avoid frequent adjustments caused by small power fluctuations, reduce the number of energy storage charge and discharge cycles, and extend equipment life.

[0029] ; In the formula, when At this point, the system considers itself to have reached a steady state, and the energy storage maintains its current power level. =0); when At that time, the adjustment action is initiated ( = ).

[0030] Optionally, =30~100W, adjusted according to the control accuracy requirements. If the application scenario has high requirements for power control accuracy (such as strictly limiting reverse power or accurately tracking grid dispatch commands), a smaller dead zone value should be selected to improve the system's response sensitivity to power deviations. If the application scenario does not have high requirements for control accuracy (such as only needing to roughly achieve self-generation and self-consumption), a larger dead zone value can be selected to reduce the frequent charging and discharging switching caused by small power fluctuations in energy storage and extend the life of the energy storage system. The selection of the dead zone value needs to be balanced between control accuracy and energy storage life.

[0031] Step S203: Based on the power deviation value and the energy status information of the edge energy node, perform droop control calculation to obtain the current energy storage output power of the corresponding edge energy node.

[0032] This application employs droop control calculation, which differs from existing droop control calculations. Existing droop control calculations utilize each node to autonomously adjust its output power based on system frequency or voltage deviations, achieving power balance without central coordination. However, in grid-connected operation, the system frequency and voltage are maintained stable by the grid, and local load changes or photovoltaic output fluctuations hardly cause perceptible frequency / voltage deviations, thus rendering existing droop control ineffective. This application uses the power deviation value as a virtual common signal to replace the frequency / voltage signal in existing droop control. This signal directly reflects the system's power balance state, is unaffected by the grid's frequency / voltage constraints, and remains effective in grid-connected scenarios. Each node adjusts its energy storage output power based on this deviation, following existing droop characteristics, thus... Approaching It enables system power target tracking, completes power coordination without inter-node communication, and achieves a response speed in milliseconds.

[0033] The energy state information of the edge energy node can directly include the droop coefficient in the droop control calculation process. Of course, if the droop coefficient is not included, it can include, but is not limited to, the local SOC, temperature, SOC boundary, rated power and other state parameters of the edge energy node. The droop coefficient in the droop control calculation process can be calculated based on the energy state information.

[0034] Based on the droop control calculation, the current energy storage output power in the current cycle can be obtained. This current energy storage output power is the control parameter for the edge energy node, thereby controlling the edge energy node to discharge.

[0035] Optionally, after performing droop control calculations based on the power deviation value and the energy state information of the edge energy node to obtain the current energy storage output power of the corresponding edge energy node, the method further includes: The current energy storage output power is verified, and the verification result is obtained; If the verification result is unsuccessful, the current energy storage output power is corrected to obtain a corrected power. The corrected power is then used as the current energy storage output power. The process of outputting energy storage power based on the current energy storage output power is then executed, and the next cycle begins. If the verification result is passed, then the energy storage power output is executed according to the current energy storage output power, and the cycle begins.

[0036] Before execution, the system must undergo safety checks such as SOC boundary checks (to prevent overcharging and over-discharging), temperature checks (derating at high temperatures), and fault detection (isolation in case of faults) to provide the final safety guarantee for operation in the local controller of the edge energy node, ensuring that the power command does not exceed the safe operating range of the equipment.

[0037] If the verification fails, the current energy storage output power is corrected to form a revised power command. If the verification passes, the current energy storage output power is maintained, a power command is formed, and the final power command is executed. In addition, protection measures such as power slope limiting (to prevent sudden surges) and power limiting are included.

[0038] Step S204: Based on the current energy storage output power, execute energy storage power output and enter the next cycle. Take the next cycle as the current cycle and return to execute the step of obtaining the system's external net power collected by the power metering device when entering the current cycle.

[0039] Specifically, for the edge energy node, the energy storage power output is executed with the current energy storage output power, and then the execution continues to the next cycle. The next cycle is the current cycle that returns to execute the above step S201 until the system's net external power approaches the target power.

[0040] In the k-th period, after each node executes its current energy storage output power, the system power balance relationship is as follows: ; In the formula, This represents the current energy storage output power for the current cycle. The system's net power output for the next cycle is measured by the electricity metering device and fed back to each node in the next cycle, forming a closed loop. If Closer If the deviation decreases, the response in the next cycle will automatically decrease; if the deviation is still large, the adjustment will continue. This negative feedback mechanism allows the system to naturally converge to the target power after multiple iterations without external intervention.

[0041] In a multi-node system, each node independently obtains the same... The signal (measured by the energy metering device and transmitted to each node) is used to independently calculate and execute the energy storage power command. Since all nodes respond to the same deviation signal, their responses are naturally coordinated. When the system needs to discharge, all nodes discharge; when the system needs to charge, all nodes charge, and the response direction is consistent.

[0042] In photovoltaic (PV) scenarios, when all energy storage is fully charged (SOC near its upper limit) and PV output still exceeds load demand, a PV power curtailment strategy can be activated to avoid reverse power transmission to the grid. This strategy balances system power by reducing PV output (curtailment) and is an optional auxiliary strategy.

[0043] The grid-connected distributed energy system of this application embodiment includes at least one edge energy node, a distribution bus, and an energy metering device. Each edge energy node is connected to both the distribution bus and the energy metering device. One end of the distribution bus is used to connect to a load, and the other end is connected to the power grid through the energy metering device. This method is applied to any edge energy node. Upon entering the current cycle, the system's net external power collected by the energy metering device is acquired. The system's net external power is compared with the target power to obtain a power deviation value. Based on the power deviation value and the energy state information of the edge energy node, droop control calculation is performed to obtain the current energy storage output power of the corresponding edge energy node. Based on the current energy storage output power, energy storage power output is executed, and the system enters the next cycle. The next cycle is used as the current cycle, and execution returns to acquire the system's net external power collected by the energy metering device upon entering the current cycle. In this grid-connected distributed energy system, droop control calculation forms independent control for each edge energy node. Without the need for a central controller, system-level power coordination control can be effectively achieved, improving the reliability, autonomy, adaptability, and real-time performance of the grid-connected distributed energy system control.

[0044] like Figure 3 The diagram shown is a flowchart illustrating a grid-connected distributed energy system scheduling method according to Embodiment 2 of this application. In step S203, the step of performing droop control calculations based on the power deviation value and the energy state information of the edge energy node to obtain the current energy storage output power of the corresponding edge energy node may include the following steps: Step S301: Based on the power deviation value and the energy status information of the edge energy node, perform a droop control calculation to obtain the current adjustment amount corresponding to the edge energy node.

[0045] If the energy state information directly includes the droop coefficient in the droop control calculation process, the current adjustment amount is directly calculated using the droop coefficient and the power deviation value. If the energy state information does not include the droop coefficient, the droop coefficient is calculated based on the local SOC, temperature, SOC boundary, rated power and other state parameters of the edge energy node, and then the current adjustment amount is calculated using the droop coefficient and the power deviation value.

[0046] Step S302: Obtain the energy storage output power of the previous cycle of the current cycle.

[0047] Specifically, for historical data, the energy storage output power calculated during the execution of the previous cycle before the current cycle is obtained and recorded as the previous energy storage output power.

[0048] Step S303: Add the previous energy storage output power to the current adjustment amount to obtain the current energy storage output power of the corresponding edge energy node.

[0049] The current energy storage output power is obtained by adding the previous energy storage output power to the current adjustment amount. That is, based on the energy storage output power calculated in the previous cycle, the current adjustment amount calculated in step S301 is used to make adjustments to form the output power of the current cycle.

[0050] The actual output power of energy storage is the sum of the power of the previous cycle and the adjustment amount of the current cycle, as follows: ; In the formula, This represents the power adjustment amount for node i in this period (positive = increase discharge, negative = increase charge). Let be the energy storage output power of node i in the kth period (positive = discharge, negative = charge). Let be the energy storage output power of node i in the (k-1)th cycle.

[0051] In this embodiment, based on the power deviation value and the energy status information of the edge energy node, a droop control-like calculation is performed to obtain the current adjustment amount of the corresponding edge energy node, obtain the previous energy storage output power of the previous cycle of the current cycle, add the previous energy storage output power to the current adjustment amount, and obtain the current energy storage output power of the corresponding edge energy node, thereby achieving accurate calculation of the current energy storage output power.

[0052] like Figure 4 The diagram shown is a flowchart illustrating a grid-connected distributed energy system scheduling method according to Embodiment 3 of this application. Step S301 above, which involves performing droop control calculations based on the power deviation value and the energy state information of the edge energy nodes to obtain the current adjustment amount corresponding to the edge energy node, may include the following steps: Step S401: Obtain the energy status information of the edge energy node, which includes the current state of charge and the rated power of energy storage.

[0053] Step S402: Determine the state of charge adjustment function based on the power deviation value.

[0054] Step S403: Calculate the response weight based on the current state of charge and the state of charge adjustment function.

[0055] Step S404: Multiply the response weight by the rated energy storage power to obtain the droop coefficient.

[0056] Step S405: Multiply the droop coefficient, the power deviation value, and the preset global droop gain coefficient to obtain the current adjustment amount corresponding to the edge energy node.

[0057] Specifically, the state of charge (SCC) adjustment function is determined based on the power deviation value. This power deviation value represents whether the device is discharging or charging. That is, the SCC adjustment function corresponding to discharging is used when discharging, and the SCC adjustment parameter corresponding to charging is used when charging.

[0058] The response weight is calculated based on the current state of charge (SOC) and the SOC regulation function. This response weight characterizes the relationship between the SOC state and the SOC protection threshold. Multiplying the response weight by the rated power of the energy storage yields the droop coefficient, as shown in the following formula: ; In the formula, The rated energy storage power of node i reflects the node's regulation capability; The response weights are calculated for the SOC adjustment function and dynamically adjusted according to the current SOC state and adjustment direction.

[0059] Subsequently, the current adjustment amount of the energy storage power for each control cycle is calculated. The formula is as follows: In the formula, This is the power deviation value after dead zone determination. The droop coefficient is... This is the global droop gain coefficient, which controls the trade-off between response speed and stability.

[0060] In this application, the current adjustment amount of each node is determined by the droop coefficient. Decide, Depends on the node's rated power Based on the current State of Charge (SOC), nodes with higher rated power exhibit larger responses, while nodes with SOC states more suited to the current adjustment direction also show larger responses. This differentiated response based on local state enables the system to achieve reasonable power allocation and SOC equalization without central coordination. The convergence speed is determined by the global droop gain. adjust, The larger the value, the larger the single response and the faster the convergence, but too large a value may cause oscillations; The smaller the value, the smoother the convergence, but the slower the response speed. By choosing an appropriate value... It can achieve a balance between response speed and stability.

[0061] Optionally, determining the state of charge adjustment function based on the power deviation value includes: Detect whether the power deviation value is greater than zero; If the detected power deviation value is greater than zero, then determine Let be the state-of-charge adjustment function, where, In response to weights, SOC L State of Charge (SOC) is the minimum protection threshold. i The current state of charge, n is 1 or 2; or If the detected power deviation value is less than zero, then determine Let be the state-of-charge adjustment function, where, For the response weights, SOC H The upper limit protection threshold for state of charge (SOC) i The current state of charge is n, which is 1 or 2.

[0062] Among them, when SOC i ≤SOC L When the discharge adjustment factor is 0 (discharge is prohibited), when the SOC i ≥SOC H The charging adjustment factor is 0 (charging is prohibited) when the system needs to discharge. >0): Nodes with higher SOC undertake more discharge tasks when the system needs to be charged ( <0): Nodes with lower SOC take on more charging tasks. The exponent n controls the aggressiveness of the allocation. n=1 is a linear allocation, and n=2 makes the differences more obvious. Recommended value.

[0063] When power deviation exists ( Each node independently calculates and executes energy storage power adjustment based on the deviation. The charging and discharging of energy storage directly changes the system power balance, driving... and Directional movement, power deviation value This decreases accordingly. Due to the current adjustment amount of power. and Proportional to the deviation, the response in the next control cycle automatically decreases as the deviation decreases, and the system exhibits exponential convergence characteristics. When the power deviation enters the dead zone ( When the system reaches a steady state, each node stops adjusting, and the system automatically converges through a negative feedback mechanism.

[0064] This embodiment uses a residential photovoltaic-storage system in a detached house as an example to illustrate the application process of the edge-end autonomous coordination and scheduling method proposed in this application. The system adopts a grid-connected operation mode, with the goal of self-generation and self-consumption (…). This means that local photovoltaic power generation should prioritize supplying local loads, with excess power absorbed by energy storage, minimizing the purchase of electricity from the grid, and prohibiting reverse power transmission to the grid.

[0065] For example, the system includes 3 edge autonomous energy nodes and 1 electricity metering device, as shown in Table 2 below: Table 2 The electricity metering device is installed at the entrance of the indoor distribution box to measure electricity in real time. The sampling period is 100ms, and each node acquires the signal via wired or wireless means.

[0066] The parameter configuration table 3 is as follows: Table 3 The droop coefficient is dynamically calculated based on the power deviation direction: during discharge. During charging .

[0067] To visually demonstrate how this technical solution works, the following data were precisely calculated by the simulation program, assuming that each node calculates synchronously. In actual operation, each node executes independently and asynchronously, with identical convergence characteristics.

[0068] Scenario 1: Photovoltaic output exceeds load (charging scenario): Initial state: Load Photovoltaics Energy storage System net power (Sending power to the grid, deviating from the target).

[0069] Control cycle 1 calculation process (taking node 1 as an example): collection: Calculate the deviation: Dead zone determination: A response is required. Determine direction: Charging direction Calculate the droop coefficient: Calculate the power adjustment amount: Update energy storage capacity: Node 2 and Node 3 execute the same process independently: Node 2: , Node 3: , The multi-cycle convergence process is shown in Table 4 below: Table 4 Convergence result: The system converged from [previous state] after 10 control cycles (approximately 1 second). After converging to a steady state, the final charging power of each node is: 734W for node 1, 673W for node 2, and 551W for node 3, totaling 1958W.

[0070] Scenario 2: Load greater than photovoltaic (discharge scenario): Initial state: Load Photovoltaics Energy storage System net power (Purchasing electricity from the power grid).

[0071] Discharge droop coefficient: , , The multi-cycle convergence process is shown in Table 5 below: Table 5 Convergence result: The system converged from [previous state] after 6 control cycles (approximately 0.6 seconds). After converging to a steady state, the final discharge power of each node is: 735W for node 1, 368W for node 2, and 858W for node 3. Node 3, with its higher SOC, undertook more discharge tasks.

[0072] Scenario 3: Sudden Changes in Photovoltaic Output (Dynamic Response): Scenario Description: During normal system operation, the photovoltaic output suddenly drops from 4kW to 2kW in the 5th cycle (simulating cloud cover), verifying the algorithm's ability to respond to sudden power changes.

[0073] Initial state: , (initial), The multi-cycle convergence process is shown in Table 6 below: Table 6 Convergence results: After a sudden drop in photovoltaic power, the system completes the mode switch from charging to discharging within 6 control cycles (approximately 0.6 seconds) and reaches steady state again in the 11th cycle (the 6th cycle after the sudden change). The algorithm can respond quickly and smoothly to power surges without overshoot oscillations.

[0074] Scenario 4: Dynamically adding nodes (plug and play): Scenario description: Initially, only nodes 1 and 2 are running in the system. In the 5th cycle, node 3 is added to the system to verify the algorithm's ability to adapt to dynamic changes in nodes.

[0075] Initial state: , Initial 2-node system The multi-cycle convergence process (precise calculation) is shown in Table 7 below: Table 7 Convergence results: After the new node was added, the system reached steady state again within 7 control cycles. The new node (with an initial power of 0) automatically participated in power sharing, and the power of the existing nodes was automatically adjusted without manual intervention or parameter reconfiguration, achieving true plug-and-play functionality.

[0076] Autonomous coordinated scheduling at the edge of the grid-connected low-voltage distributed energy system is achieved through droop-like control. The effects achieved by this technical solution are as follows: I. System reliability is significantly improved. This application adopts a pure edge-end autonomous architecture, which does not rely on a central controller or cloud nodes. Each node is based on local measurements. The signal makes independent decisions, and the failure or exit of any single node does not affect the overall operation of the system, fundamentally eliminating the risk of single point of failure. Compared with existing centralized solutions (where the failure of the central node means system paralysis) or cloud solutions (where network interruption means loss of scheduling capability), this solution achieves true "decentralization and fault resistance".

[0077] Second, the power regulation response speed is significantly improved. The droop control response time of this application is in the millisecond range, which can track load changes and photovoltaic output fluctuations in real time. Compared with cloud control solutions (the response delay is usually in the second range or even longer), this solution improves the power regulation from "second-level response" to "millisecond-level response", enabling the system to smooth power fluctuations in a timely manner, accurately track power targets, and avoid power overruns or reverse current caused by response lag.

[0078] Third, enhanced deployment flexibility: This application supports plug-and-play nodes. New nodes automatically participate in regulation after going online without manual parameter tuning. When nodes are added or removed, the system automatically adapts without modifying the configuration of other nodes. Moreover, only an energy metering device needs to be configured at the system inlet, and no special hardware is required for each energy storage node. Compared with the existing centralized solution (where node changes require reconfiguration of scheduling strategies), this solution significantly reduces system deployment and maintenance costs.

[0079] Fourth, data security is enhanced. All decisions in this application are completed locally at the edge. Sensitive data such as electricity consumption behavior and SOC status do not need to be uploaded to the cloud, which eliminates the risk of data privacy leakage from the architecture. This is especially important for home users and enterprise users who value privacy protection.

[0080] Fifth, the lifespan of the energy storage system is extended. Through the design of the SOC adjustment function, the system automatically achieves SOC balancing among multiple energy storage nodes: nodes with low SOC charge more during charging and discharge less during discharging, while nodes with high SOC do the opposite. After long-term operation, the SOC of each node tends to be consistent, avoiding overuse of some batteries, balancing the charge-discharge cycle count of each battery, and extending the overall lifespan of the system.

[0081] VI. System stability assurance: This application uses methods such as direct response to current power deviation, power slope limitation, dead zone design and closed-loop feedback to make the power deviation gradually decrease with the response and the system naturally converge to a steady state. It can operate stably under various operating scenarios without overshoot or oscillation.

[0082] The technical solutions provided in this application are flexible. In addition to the independent operation mode described in detail in the preceding chapters, alternative deployment methods can be adopted, such as cloud or edge controllers cooperating with the edge autonomous layer. The core control algorithm (vertical control-like) of these alternative solutions is completely consistent with the independent operation mode; the difference lies in the generation and distribution mechanism of the system power target.

[0083] In the cloud + edge collaboration solution, the system architecture is divided into two layers: Cloud Layer: Deploys a cloud-based energy management system (Cloud EMS) responsible for global optimization and scheduling decisions on long-term scales (minutes to hours). Based on information such as electricity price signals, weather forecasts, load forecasts, and demand response commands, the cloud calculates the system-level target power value (i.e., the system's net external power target) using optimization algorithms (such as linear programming, dynamic programming, and model predictive control), and broadcasts the same target power value to all edge nodes via the communication network at a low frequency (such as every 5 minutes or every 15 minutes).

[0084] Edge Autonomous Layer: Each edge autonomous energy node runs the droop-like control algorithm described in the preceding chapters, responsible for real-time power tracking and rapid response on short timescales (milliseconds to seconds). Each node collects the system's net power output, calculates the deviation from the target power value sent from the cloud, and independently adjusts the energy storage power according to the droop-like characteristics to achieve real-time power balance.

[0085] Collaboration Mechanism: The cloud and edge are decoupled in terms of time scale. The cloud does not participate in real-time control, but only updates the target power value periodically; the edge does not rely on real-time communication with the cloud, and only needs to receive target power updates. When cloud communication is interrupted, the edge autonomous layer continues to operate, tracking the last target power value sent, and the system will not go out of control.

[0086] Technical benefits: This solution combines the economic advantages of cloud optimization (optimizing operation using information such as electricity prices and forecasts) with the real-time performance and reliability of edge autonomy (millisecond-level response, no loss of control even when communication is interrupted), making it suitable for scenarios such as accessing virtual power plants and park systems that require economic optimization.

[0087] In the cloud + edge gateway + edge-device collaboration solution, the system architecture is divided into three layers: Cloud layer: Similar to solution 6.1, it is responsible for global optimization and long-term scheduling, but the communication frequency can be further reduced (e.g., once per hour or per day to send optimization targets or strategy parameters).

[0088] Edge Layer: Locally deployed edge controllers (such as small gateways, edge servers, industrial PCs, etc.) are responsible for local optimization and strategy calculation at medium time scales (seconds to minutes). Based on the optimization targets or strategy parameters sent from the cloud, and combined with local real-time measured information such as photovoltaic output, load power, and SOC status, the edge controller generates a real-time system-level target power curve through local optimization algorithms (such as rolling optimization, rule engines, etc.), and broadcasts the same target power value to each edge node at a high frequency (such as every second or every 10 seconds).

[0089] Edge Autonomous Layer: Each edge autonomous energy node runs a vertical control algorithm to track the target power value issued by the edge controller and achieve real-time power balance at the millisecond to second level.

[0090] Collaboration mechanism: The cloud is responsible for global policy generation, the edge is responsible for local real-time optimization, and the edge device is responsible for rapid execution. The three layers are decoupled in terms of time scale and do not depend on each other for real-time communication. When cloud communication is interrupted, the edge layer continues to operate; when edge communication is interrupted, the edge autonomous layer continues to operate, and the system can be progressively degraded.

[0091] Technical benefits: This solution adds local optimization capabilities at the edge layer on top of cloud optimization, reduces reliance on real-time cloud communication, and improves the system's robustness to network fluctuations. It is suitable for scenarios with limited network conditions but requiring local optimization (such as remote parks, island microgrids, etc.).

[0092] Corresponding to the grid-connected distributed energy system scheduling method in the above embodiment, Figure 5 This diagram illustrates the structural block diagram of the grid-connected distributed energy system coordination and dispatch device provided in Embodiment 4 of this application. For ease of explanation, only the parts relevant to the embodiments of this application are shown. The grid-connected distributed energy system described in this application includes at least one edge energy node, a distribution bus, and an energy metering device. All edge energy nodes are connected to the distribution bus and the energy metering device. One end of the distribution bus is used to connect to a load, and the other end of the distribution bus is connected to the power grid through the energy metering device. The grid-connected distributed energy system coordination and dispatching device is applied to any edge energy node, such as... Figure 5 As shown, the grid-connected distributed energy system coordination and dispatch device includes: The external net power acquisition module 51 is used to acquire the system's external net power collected by the power metering device when entering the current cycle. The power deviation calculation module 52 is used to compare the system’s net power to the outside with the target power to obtain the power deviation value. The output power determination module 53 is used to perform droop control calculation based on the power deviation value and the energy status information of the edge energy node to obtain the current energy storage output power of the corresponding edge energy node. The loop execution module 54 is used to execute energy storage power output and enter the next cycle according to the current energy storage output power, take the next cycle as the current cycle, and return to execute the process of obtaining the system's external net power collected by the power metering device when entering the current cycle.

[0093] Optionally, the output power determination module 53 includes: The adjustment amount determination unit is used to perform droop control calculations based on the power deviation value and the energy status information of the edge energy node to obtain the current adjustment amount corresponding to the edge energy node; The historical data acquisition unit is used to acquire the energy storage output power of the previous cycle in the current cycle. The output power calculation unit is used to add the previous energy storage output power to the current adjustment amount to obtain the current energy storage output power corresponding to the edge energy node.

[0094] Optionally, the adjustment amount determining unit includes: The status information acquisition subunit is used to acquire the energy status information of the edge energy node, which includes the current state of charge and the rated power of energy storage. The function-determining sub-unit is used to determine the state-of-charge adjustment function based on the power deviation value; The weight calculation subunit is used to calculate the response weight based on the current state of charge and the state of charge adjustment function; The droop coefficient calculation subunit is used to multiply the response weight by the rated energy storage power to obtain the droop coefficient; The adjustment amount determination unit is used to multiply the droop coefficient, the power deviation value and the preset global droop gain coefficient to obtain the current adjustment amount corresponding to the edge energy node.

[0095] Optionally, the function determining the subunit is specifically used for: Detect whether the power deviation value is greater than zero; If the detected power deviation value is greater than zero, then determine Let be the state-of-charge adjustment function, where, In response to weights, SOC L State of Charge (SOC) is the minimum protection threshold. i The current state of charge, n is 1 or 2; or If the detected power deviation value is less than zero, then determine Let be the state-of-charge adjustment function, where, For the response weights, SOC H The upper limit protection threshold for state of charge (SOC) i The current state of charge is n, which is 1 or 2.

[0096] Optionally, the grid-connected distributed energy system coordination and dispatch device also includes: The dead zone detection module is used to detect whether the power deviation value is greater than the preset dead zone after the system's net external power is compared with the preset target power to obtain the power deviation value. The first determination module is used to perform droop control calculation based on the power deviation value and the energy state information of the edge energy node when the power deviation value is detected to be greater than the dead zone, so as to obtain the current energy storage output power of the corresponding edge energy node. The second determination module is used to obtain the previous energy storage output power of the previous cycle of the current cycle when the power deviation value is detected to be no greater than the dead zone, take the previous energy storage output power as the current energy storage output power, and execute the step of outputting energy storage power according to the current energy storage output power and entering the next cycle.

[0097] Optionally, the grid-connected distributed energy system coordination and dispatch device also includes: The verification module is used to verify the current energy storage output power after performing droop control calculation based on the power deviation value and the energy state information of the edge energy node to obtain the current energy storage output power of the corresponding edge energy node, and obtain the verification result. The correction module is used to correct the current energy storage output power if the verification result is unsuccessful, obtain the corrected power, use the corrected power as the current energy storage output power, and execute the step of outputting energy storage power according to the current energy storage output power and entering the next cycle. The uncorrected module is used to execute the step of outputting energy storage power according to the current energy storage output power and entering the next cycle if the verification result is passed.

[0098] Optionally, if the grid-connected distributed energy system operates independently, the target power is a preset value; or If the grid-connected distributed energy system operates in conjunction with other energy systems, the target power is obtained from the other energy systems.

[0099] It should be noted that the information interaction and execution process between the above modules, units, and sub-units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0100] Embodiment 5 of this application also provides an electronic device 60, please refer to... Figure 6 It includes a memory 61 and a processor 62, wherein the memory 61 is used to store computer programs; the processor 62 is used to execute the programs stored in the memory 61 to implement the grid-connected distributed energy system scheduling method described in any embodiment of this application.

[0101] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the grid-connected distributed energy system scheduling method described in any embodiment of this application.

[0102] In this application, "multiple" refers to two or more.

[0103] In this application, unless otherwise expressly defined, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0104] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0105] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0106] Unless otherwise specified, all steps in this application may be performed sequentially or randomly. For example, if the method includes steps A and B, it means that the method may include steps A and B performed sequentially, or it may include steps B and A performed sequentially. For example, if the method may also include step C, it means that step C may be added to the method in any order. For example, the method may include steps A, B, and C, or it may include steps A, C, and B, or it may include steps C, A, and B, etc.

[0107] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for scheduling a grid-connected distributed energy system, characterized in that, The grid-connected distributed energy system includes at least one edge energy node, a distribution bus, and an energy metering device. All edge energy nodes are connected to the distribution bus and the energy metering device. One end of the distribution bus is used to connect to the load, and the other end of the distribution bus is connected to the power grid through the energy metering device. The grid-connected distributed energy system scheduling method is applied to any end energy node, including: Upon entering the current cycle, the edge energy node independently acquires the net external power of the system collected by the power metering device; The power deviation value is obtained by comparing the system's net power output with the target power. The edge energy node independently performs droop control calculations based on the power deviation value and the energy state information of the edge energy node to obtain the current energy storage output power of the corresponding edge energy node; Based on the current energy storage output power, execute energy storage power output and enter the next cycle. Take the next cycle as the current cycle and return to execute the process of obtaining the system's net external power collected by the power metering device when entering the current cycle. The step of performing droop control calculations based on the power deviation value and the energy state information of the edge energy node to obtain the current energy storage output power of the corresponding edge energy node includes: Based on the power deviation value and the energy status information of the edge energy node, a droop control calculation is performed to obtain the current adjustment amount corresponding to the edge energy node; Obtain the energy storage output power of the previous energy storage cycle in the current cycle; Add the previous energy storage output power to the current adjustment amount to obtain the current energy storage output power corresponding to the edge energy node; The step of performing droop control calculations based on the power deviation value and the energy state information of the edge energy node to obtain the current adjustment amount corresponding to the edge energy node includes: Obtain the energy status information of the edge energy node, including the current state of charge and the rated power of energy storage; Based on the power deviation value, determine the state of charge adjustment function; The response weights are calculated based on the current state of charge and the state of charge adjustment function. Multiply the response weight by the rated energy storage power to obtain the droop coefficient; Multiply the droop coefficient, the power deviation value, and the preset global droop gain coefficient to obtain the current adjustment amount corresponding to the edge energy node; Determining the state of charge adjustment function based on the power deviation value includes: Detect whether the power deviation value is greater than zero; If the detected power deviation value is greater than zero, then determine Let be the state-of-charge adjustment function, where, In response to weights, SOC L State of Charge (SOC) is the minimum protection threshold. i The current state of charge, n is 1 or 2; or If the detected power deviation value is less than zero, then determine Let be the state-of-charge adjustment function, where, For the response weights, SOC H The upper limit protection threshold for state of charge (SOC) i The current state of charge is n, which is 1 or 2.

2. The grid-connected distributed energy system scheduling method according to claim 1, characterized in that, After comparing the system's net external power with a preset target power to obtain a power deviation value, the method further includes: Detect whether the power deviation value is greater than the preset dead zone; When the power deviation value is detected to be greater than the dead zone, the droop control calculation is performed based on the power deviation value and the energy state information of the edge energy node to obtain the current energy storage output power of the corresponding edge energy node. When the detected power deviation value is not greater than the dead zone, the previous energy storage output power of the previous cycle of the current cycle is obtained, the previous energy storage output power is used as the current energy storage output power, and the energy storage power output is executed according to the current energy storage output power and the cycle begins.

3. The grid-connected distributed energy system dispatching method according to claim 1, characterized in that, After performing droop control calculations based on the power deviation value and the energy state information of the edge energy node to obtain the current energy storage output power of the corresponding edge energy node, the method further includes: The current energy storage output power is verified, and the verification result is obtained; If the verification result is unsuccessful, the current energy storage output power is corrected to obtain a corrected power. The corrected power is then used as the current energy storage output power. The process of outputting energy storage power based on the current energy storage output power is then executed, and the next cycle begins. If the verification result is passed, then the energy storage power output is executed according to the current energy storage output power, and the cycle begins.

4. The grid-connected distributed energy system dispatching method according to any one of claims 1 to 3, characterized in that, If the grid-connected distributed energy system operates independently, then the target power is a preset value; or If the grid-connected distributed energy system operates in conjunction with other energy systems, the target power is obtained from the other energy systems.

5. A coordinated dispatching device for a grid-connected distributed energy system, characterized in that, The grid-connected distributed energy system includes at least one edge energy node, a distribution bus, and an energy metering device. All edge energy nodes are connected to the distribution bus and the energy metering device. One end of the distribution bus is used to connect to the load, and the other end of the distribution bus is connected to the power grid through the energy metering device. The grid-connected distributed energy system coordination and dispatch device is applied to any end energy node, including: The external net power acquisition module is used to acquire the system's external net power collected by the power metering device when entering the current cycle; The power deviation calculation module is used to compare the system's net power to the outside with the target power to obtain the power deviation value. The output power determination module is used to perform droop control calculations based on the power deviation value and the energy status information of the edge energy node to obtain the current energy storage output power of the corresponding edge energy node. The loop execution module is used to execute energy storage power output and enter the next cycle according to the current energy storage output power, take the next cycle as the current cycle, and return to execute the process of obtaining the system's external net power collected by the power metering device when entering the current cycle. The output power determination module includes: The adjustment amount determination unit is used to perform droop control calculations based on the power deviation value and the energy status information of the edge energy node to obtain the current adjustment amount corresponding to the edge energy node; The historical data acquisition unit is used to acquire the energy storage output power of the previous cycle in the current cycle. The output power calculation unit is used to add the previous energy storage output power to the current adjustment amount to obtain the current energy storage output power corresponding to the edge energy node; The adjustment amount determination unit includes: The status information acquisition subunit is used to acquire the energy status information of the edge energy node, which includes the current state of charge and the rated power of energy storage. The function-determining sub-unit is used to determine the state-of-charge adjustment function based on the power deviation value; The weight calculation subunit is used to calculate the response weight based on the current state of charge and the state of charge adjustment function; The droop coefficient calculation subunit is used to multiply the response weight by the rated energy storage power to obtain the droop coefficient; The adjustment amount determination unit is used to multiply the droop coefficient, the power deviation value and the preset global droop gain coefficient to obtain the current adjustment amount corresponding to the edge energy node; The function determines the specific use of the sub-unit: Detect whether the power deviation value is greater than zero; If the detected power deviation value is greater than zero, then determine Let be the state-of-charge adjustment function, where, In response to weights, SOC L State of Charge (SOC) is the minimum protection threshold. i The current state of charge, n is 1 or 2; or If the detected power deviation value is less than zero, then determine Let be the state-of-charge adjustment function, where, For the response weights, SOC H The upper limit protection threshold for state of charge (SOC) i The current state of charge is n, which is 1 or 2.

6. An electronic device, characterized in that, Including processor and memory, among which, Memory, used to store computer programs; A processor is used to execute a program stored in memory to implement the grid-connected distributed energy system scheduling method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the grid-connected distributed energy system scheduling method according to any one of claims 1-4.

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