Method for preventing electric energy from flowing back to power grid

Abstract

The invention relates to the technical field of industrial and commercial energy storage, and discloses a method for preventing electric energy from flowing back to a power grid, which comprises the following steps of: performing data acquisition on the real-time load power of a gateway meter, the discharge power and the change rate of energy storage equipment, and the power flow state of the power grid; constructing an optimization model according to the collected data; based on the constructed optimization model, setting an optimization function of the target; solving the optimization function by using an optimization algorithm, and calculating the optimal discharge power of the energy storage equipment at the current time by using a gradient descent method; the calculated optimal discharge power is sent to the energy storage equipment, and the real-time output power of the energy storage equipment is adjusted; and monitoring the power flow states of the energy storage equipment, the power grid and the load in real time, and repeating the steps at preset time intervals. Through accurate prevention of electric energy countercurrent, real-time dynamic power optimization, closed-loop feedback regulation and control and power change limitation, the cooperative operation efficiency and safety of the energy storage system and the power grid are significantly improved.

Description

Technical Field

[0001] The invention relates to the technical field of industrial and commercial energy storage, and in particular to a method for preventing electric energy from flowing back into a power grid. Background Art

[0002] With the widespread application of energy storage technology in industrial and commercial scenarios, energy storage equipment has gradually become an important regulating unit in the operation of the power grid. However, in the process of interaction between the energy storage system and the power grid, the bidirectional flow characteristics of electric energy pose challenges to the stability of the power grid and the operation of energy storage equipment. Especially in the case of frequent fluctuations in load demand, the output power of the energy storage equipment may be difficult to adjust in time, which may easily cause uneven power distribution or electric energy backflow. In the prior art, static limiting or switch-type anti-backflow devices are generally used. These methods often lack dynamic regulation capabilities and are difficult to cope with real-time changes in load power and energy storage equipment output, thereby affecting the safety of system operation.

[0003] In addition, the dynamic adjustment capability of energy storage equipment is crucial to the response efficiency of the system. In current technology, energy storage equipment mostly controls output through preset power or simple adjustment rules. This method relies on fixed power values ​​or empirically set parameters, and cannot fully reflect the dynamic changes in real-time load demand. When load demand fluctuates drastically, the output power of energy storage equipment is often difficult to follow in time, resulting in an imbalance in the power distribution between energy storage equipment and the power grid, reducing the operating efficiency and stability of the overall system. At the same time, during the adjustment process, the power change rate of the energy storage device is not effectively limited, which may cause the energy storage device to adjust too quickly and cause equipment failure, further limiting the reliability of the energy storage system.

[0004] Most of the existing energy management modes of energy storage systems are based on step-by-step control, which separates data collection, optimization calculation and output regulation, and lacks the integrity of closed-loop control. Due to the delay in information transmission between modules and the limitations of local optimization, the system responds slowly in dynamic load scenarios. In addition, when multiple energy storage devices operate in coordination, the power allocation mechanism of existing technologies is often relatively simple, and fails to fully utilize energy storage resources, resulting in low energy scheduling efficiency. These deficiencies severely limit the performance of energy storage systems in complex industrial and commercial application scenarios, and there is an urgent need for a solution that can optimize power distribution in real time, dynamically adjust output, and ensure stable operation of the power grid.

[0005] Therefore, the present invention proposes a method for preventing electric energy from flowing back to the power grid to solve the shortcomings of the prior art. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a method for preventing electric energy from flowing back into the power grid, which solves the problems of inaccurate electric energy backflow prevention and control, delayed dynamic power regulation and low energy management efficiency in existing industrial and commercial energy storage systems.

[0007] To achieve the above object, the present invention is implemented by the following technical solution: A method for preventing electric energy from flowing back to the power grid, comprising the following steps: S1. Collect data on the real-time load power of the gateway meter, the discharge power of the energy storage device and its change rate, and the power flow status of the power grid; S2. Build an optimization model based on the collected data; S3, based on the constructed optimization model, setting the optimization function of the target; S4. Solve the optimization function using an optimization algorithm, and calculate the optimal discharge power of the energy storage device at the current time by a gradient descent method; S5, sending the calculated optimal discharge power to the energy storage device to adjust the real-time output power of the energy storage device; S6. Real-time monitoring of the energy storage device, power grid and load power flow status, and repeating the above steps at preset time intervals.

[0008] Preferably, the optimization model includes the following constraints: Power balance constraint: the discharge power of the energy storage device, the power supply power of the grid and the load power of the gateway meter satisfy the total power conservation; Anti-reverse flow constraint: the power supply of the power grid is always greater than or equal to zero; Energy storage device power limit: The discharge power of the energy storage device is always less than or equal to its rated maximum discharge power; Power change rate limit: The discharge power change rate of the energy storage device is less than or equal to its maximum change rate.

[0009] Preferably, the optimization function is defined in the following manner: The goal is to minimize the deviation between the discharge power of the energy storage device and the power required by the load; The goal is to minimize the power supply of the power grid; The goal is to minimize the rate of change of discharge power of the energy storage device.

[0010] Preferably, the weight coefficients of the optimization function are set according to actual application requirements, including energy storage power deviation weight, grid power supply weight and power change rate weight.

[0011] Preferably, the optimization algorithm comprises the following steps: Construct a Lagrangian function to combine the optimization objective with the power balance constraint; Based on the gradient descent method, the discharge power of the energy storage device and the power supply power of the grid are iteratively optimized; The optimization problem after time discretization is solved in real time through dynamic programming to adjust the discharge power of the energy storage equipment.

[0012] Preferably, the gradient updating process of the gradient descent method includes: Optimization of discharge power of energy storage equipment; Optimization of power supply to the power grid.

[0013] Preferably, the process of adjusting the real-time output power of the energy storage device includes: Send optimization calculation results to energy storage equipment in real time; According to the current state of the energy storage device, the output power is adjusted, the power change rate is updated, and the constraints of the optimization model are ensured to be met.

[0014] Preferably, the real-time monitoring includes: The gateway meter detects load power fluctuations in real time and updates the collected data; Monitor the power change rate of the energy storage device to ensure that it is always within the maximum change rate limit; If it is detected that the power supply of the grid is less than zero, an emergency stop is triggered to discharge the energy storage device.

[0015] Preferably, the time interval for real-time monitoring is set according to load fluctuation characteristics.

[0016] The present invention also provides a system for preventing electric energy from flowing back to a power grid, comprising the following modules: Data acquisition module, used to collect the load power of the gateway meter, the discharge power of the energy storage device and the power status of the power grid; The optimization calculation module is used to build the optimization model and solve the optimal energy storage power in real time based on the gradient descent method and dynamic programming; the power regulation module is used to dynamically adjust the discharge power of the energy storage device according to the optimization results; The real-time monitoring module is used to continuously monitor the power flow status to ensure that the power output of the energy storage device meets the anti-backflow constraints.

[0017] The present invention provides a method for preventing electric energy from flowing back into the power grid. It has the following beneficial effects: 1. The present invention adopts the technical solution of real-time acquisition of the power status of energy storage equipment, power grid and load, combined with anti-backflow constraints and optimization algorithms, to achieve the technical effect of strictly controlling the power direction of the power grid and preventing the backflow of energy storage power to the power grid. Compared with the solution of only simple power threshold control in the prior art, it solves the problem of delayed response and inability to effectively limit the backflow of power when it occurs.

[0018] 2. The present invention realizes dynamic regulation of the output power of energy storage equipment by constructing a target optimization function and combining the gradient descent method and dynamic programming to optimize the discharge power of energy storage equipment in real time. Compared with the existing technology that relies on fixed power setting or manual adjustment, it overcomes the defect of untimely response to changes in load demand and greatly improves the regulation accuracy and operation efficiency of the energy storage system.

[0019] 3. The present invention ensures the stability of system operation and the safety of equipment by introducing power balance constraints, anti-backflow constraints and power limit conditions of energy storage equipment. The prior art often ignores the control of power change rate, resulting in instability caused by too fast equipment adjustment. The present invention solves this problem and significantly reduces the operating risk of energy storage equipment.

[0020] 4. The present invention adopts a closed-loop control mechanism of real-time monitoring, optimization calculation, output regulation and cyclic feedback to achieve efficient collaborative management of energy storage equipment, power grid and load. Compared with the step-by-step regulation or unidirectional regulation scheme in the prior art, it solves the problems of low response efficiency and unreasonable power distribution of the energy management system, and significantly improves the adaptability of the energy storage system to load demand and energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of the steps of a method for preventing electric energy from flowing back to a power grid according to the present invention; Figure 2 The present invention is a module framework diagram of a system for preventing electric energy from flowing back into a power grid. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Please see attached Figure 1 , an embodiment of the present invention provides a method for preventing electric energy from flowing back to a power grid, comprising the following steps: S1, collecting data on the real-time load power of a gateway meter, the discharge power of an energy storage device and its change rate, and the power flow state of a power grid; In the present invention, data collection is the core foundation for preventing electric energy from flowing back to the power grid. By monitoring the real-time status of energy storage equipment, power grid and load, dynamic and accurate input data is provided for subsequent optimization model construction and optimization algorithm. Generally, this step realizes a comprehensive grasp of the system operation status by real-time collection of the load power of the gateway table, the discharge power and change rate of the energy storage equipment, and the power flow status of the power grid.

[0024] Specifically, the data acquisition module in this embodiment is responsible for acquiring the power data of the gateway table, energy storage device, and grid interface, which can accurately reflect the real-time status of the system and support subsequent dynamic optimization calculations. Each part of the data acquisition is described in detail below.

[0025] In this embodiment, the load power P 负载 (t) is the total power demand that needs to be met by the energy storage device and the power grid. The gateway meter is installed between the energy storage system and the load. It provides input data for the optimization model by monitoring the load power changes in real time.

[0026] The current I is collected through the load-side current transformer and voltage transformer 负载 (t) and voltage U 负载 (t), the load power is calculated as: P 负载 (t) = U 负载 (t)·I 负载 (t)·cosφ Among them, U 负载 (t) is the real-time voltage on the load side, I 负载 (t) is the real-time current on the load side, and cosφ is the load power factor.

[0027] In some embodiments, a high-precision smart meter can be used to directly read P 负载 (t), avoiding manual calculation errors.

[0028] Generally, in order to adapt to the dynamic fluctuations of load power, the sampling frequency is set to milliseconds or higher. In some scenarios where the load changes slowly, the sampling frequency can be appropriately reduced, for example, once per second.

[0029] In this embodiment, the discharge power P of the energy storage device 储能 (t) is the real-time power provided by the energy storage device to the load at time t. This parameter is one of the core inputs of the optimization model and directly affects the calculation of the power balance constraint.

[0030] Methods for collecting the discharge power of energy storage equipment include: Measure the real-time voltage U at the output end of the energy storage device 储能 (t) and current I 储能 (t), we can calculate: P 储能 (t) = U 储能 (t)·I 储能 (t)·cosφ 储能 Among them, U 储能 (t) is the voltage at the output end of the energy storage device, I 储能 (t) is the current at the output end of the energy storage device, cosφ 储能 is the power factor of the energy storage device.

[0031] In some embodiments, the real-time power value P can be directly read through the power measurement module of the energy storage system. 储能(t), improve data collection efficiency.

[0032] In one possible implementation, in order to reduce the impact of instantaneous fluctuations, the power data of the energy storage device can be smoothed by a sliding window averaging method: in, is the smoothed energy storage power, N is the number of sampling points in the sliding window, and Δt is the time interval between each sampling.

[0033] Energy storage equipment power change rate ΔP 储能 (t) refers to the change in power of the energy storage device per unit time, which is used to constrain the dynamic adjustment speed of the energy storage device to avoid system instability during the adjustment process. In this embodiment, the energy storage power change rate is calculated by the following formula: Where ΔP 储能 (t) is the energy storage power change rate, P 储能 (t) is the power of the energy storage device at the current moment, P 储能 (t-Δt) is the power of the energy storage device at the previous moment, and Δt is the time interval.

[0034] In some embodiments, in order to improve the stability of the power change rate, the acquisition module may add a dynamic filtering function during the sampling process to reduce high-frequency interference signals.

[0035] Grid power flow state P 电网 (t) is an important parameter to measure whether reverse flow occurs in the energy storage device. In this embodiment, the power flow state of the power grid is monitored in real time by a smart meter installed at the connection between the energy storage device and the power grid, and the power direction is determined.

[0036] The grid power flow state is calculated by the following formula: P 电网 (t) = P 负载 (t)-P 储能 (t) Among them, P 电网 (t) is the power supply of the power grid, when P 电网 When (t)<0, it indicates that reverse flow occurs in the energy storage device.

[0037] As an option, the acquisition module can introduce a power direction monitoring mechanism. Once P 电网 (t)<0, an alarm signal is immediately issued and the system is notified to stop discharging the energy storage device.

[0038] In a further implementation of the present invention, the data acquisition module can be expanded to: Store historical records of collected data for load characteristics analysis and energy storage system operation evaluation; Realize the communication function and transmit the collected data to the optimization calculation module through Modbus protocol, CAN bus or Ethernet; support multi-channel redundant collection to ensure the reliability and continuity of data collection.

[0039] In this embodiment, the load power, energy storage power and change rate, and power grid power flow state data provided by the data acquisition module are direct inputs for optimizing the model. The accuracy of these data determines the reliability of the subsequent optimization algorithm and the effect of energy storage device regulation.

[0040] S2. Build an optimization model based on the collected data; Constructing an optimization model is one of the core technical steps of the present invention, which is directly based on the data collected in step S1. The optimization model is used to describe the power relationship between the energy storage device, the power grid and the load, and at the same time convert the physical constraints of the system operation into a mathematical expression that can be used for optimization calculations. By constructing an optimization model, it can be ensured that the energy storage device strictly limits the reverse flow behavior of the energy storage electric energy while meeting the load demand, providing a basis for the setting and solution of subsequent optimization goals.

[0041] In general, the construction of the optimization model needs to take into account the real-time and dynamic characteristics of the system, including the physical limitations of energy storage devices, the directional limitations of grid power, and the fluctuation characteristics of load demand. In some embodiments, in order to improve the robustness of the model, historical data and external environmental characteristics can be combined to further enrich the constraints.

[0042] Specifically, in this embodiment, the optimization model mainly includes the following contents: The power balance constraint is the basic condition of the optimization model, which is used to ensure that the energy storage device and the power grid can jointly meet the real-time load demand. The mathematical expression of the power balance constraint is: P 储能 (t)+P 电网 (t) = P 负载 (t) Among them, P 储能 (t) is the real-time discharge power of the energy storage device at time t, P 电网 (t) is the real-time power supply of the power grid at time t, P 负载 (t) is the real-time load power collected by the gateway meter.

[0043] Generally, this constraint ensures that the power output of the energy storage device and the grid can dynamically meet the real-time demand of the load without insufficient or excessive power supply.

[0044] In a possible implementation, in order to improve the stability of the power balance constraint, P负载 (t) The data is subjected to noise filtering and smoothing. The specific calculation method is: in, is the load power after smoothing, N is the number of sampling points in the sliding window, and Δt is the time interval between each sampling.

[0045] Through the above-mentioned smoothing process, the interference of instantaneous load fluctuations on the calculation results of the optimization model can be effectively reduced.

[0046] The anti-backflow constraint is one of the key technologies of the present invention, which aims to ensure that the power direction of the power grid always maintains a positive flow, that is, the energy storage power will not flow back to the power grid. The mathematical expression of this constraint is: P 电网 (t)≥0 Among them, P 电网 When (t)≥0, it means that the power grid is in normal power supply state. 电网 When (t)<0, it indicates that the excess electric energy of the energy storage device flows backwards.

[0047] Specifically, the anti-backflow constraint limits the minimum value of the grid power to zero, thereby avoiding the backflow phenomenon caused by excessive output power of the energy storage device. In some embodiments, a dynamic monitoring mechanism can be introduced. Once P is detected 电网 (t)<0, the discharge power of the energy storage device is immediately adjusted through the optimization algorithm to ensure that the anti-backflow constraint is always met.

[0048] The power limit of energy storage equipment includes discharge power limit and power change rate limit, which are used to ensure the safe operation of energy storage equipment and avoid system instability caused by too fast adjustment.

[0049] First, the discharge power limit of the energy storage device can be expressed as: Among them, P 储能 (t) is the real-time discharge power of the energy storage device, is the rated maximum discharge power of the energy storage device.

[0050] Secondly, the power change rate limit of the energy storage device can be expressed as: Among them, P 储能 (t) is the real-time discharge power of the energy storage device, is the rated maximum discharge power of the energy storage device.

[0051] Secondly, the power change rate limit of the energy storage device can be expressed as: Among them, ΔP 储能 (t) is the power change rate of the energy storage device, ΔP 储能 The calculation formula of (t) is: As an option, in order to further improve the operational stability of energy storage equipment, it can be dynamically adjusted according to its operating life and health status. and For example, when the battery health status of the energy storage device decreases, the upper limit values ​​of these parameters can be appropriately lowered to extend the service life of the device.

[0052] In some embodiments, the construction of the optimization model may also be combined with the following extended functions: Consider environmental factors, such as the impact of room temperature changes on energy storage devices, and add temperature constraints; For load power P 负载 (t) Make short-term forecasts and introduce the forecast values ​​into the optimization model to improve the model's forward-looking nature; In the scenario of multiple energy storage devices working together, power allocation constraints are added between devices to achieve joint optimization of multiple devices.

[0053] The construction of the optimization model is the basis for the subsequent objective function setting and optimization algorithm solution. Through clear constraints such as power balance, anti-backflow, and energy storage device power limit, the optimization model can accurately describe the dynamic relationship between energy storage devices, power grids, and loads, providing a clear mathematical framework for subsequent optimization calculations.

[0054] S3, based on the constructed optimization model, setting the optimization function of the target; The setting of the target optimization function is a key link in realizing the dynamic regulation of energy storage equipment and preventing the reverse flow of electric energy in the present invention. The target optimization function is based on the optimization model constructed in step S2, combined with the real-time state of the system, and comprehensively considers the power tracking accuracy of the energy storage equipment, the minimization of the power supply power of the power grid, and the stability of the power change rate of the energy storage equipment, aiming to achieve efficient and safe operation of the energy storage system through optimization calculation. The optimization function provides a specific mathematical goal for the execution of the subsequent optimization algorithm.

[0055] Generally, the setting of the target optimization function requires a trade-off between different optimization objectives, such as accurate tracking of load power and stability of the power change rate of the energy storage device. In some embodiments, the weight coefficient can be dynamically adjusted according to the actual needs of the operation scenario, thereby achieving flexible control of the optimization objective.

[0056] Specifically, in this embodiment, the setting of the target optimization function includes the following main contents: In this embodiment, the energy storage power deviation refers to the actual discharge P of the energy storage device. 储能 (t) and the real-time load power P of the gateway table 负载 (t). In order to ensure that the energy storage device can meet the load demand in real time, the optimization function needs to minimize the deviation. The corresponding deviation loss function can be expressed as: f1(t)=α1·(P 储能 (t)-P 负载 (t)) 2 Where f1(t) is the loss function of the energy storage power deviation, which represents the loss caused by the energy storage power deviation, P 储能 (t) is the real-time discharge power of the energy storage device at time t, P 负载 (t) is the real-time load power of the gateway table at time t, and α1 is the weight coefficient of the energy storage power deviation, which is used to adjust the proportion of deviation minimization in the overall optimization objective.

[0057] In general, the value of α1 needs to be reasonably set according to the load fluctuation characteristics of the system and the response capability of the energy storage device. In the case of large load fluctuations, it is recommended to increase the value of α1 appropriately to give priority to meeting the load demand.

[0058] In order to reduce the power supply pressure of the power grid and prevent the stored energy from flowing back to the power grid, the optimization function needs to minimize the power supply power P of the power grid. 电网 (t). The corresponding power grid power loss function can be expressed as: f2(t)=α2·P 电网 (t) 2 Among them, f2(t) is the loss function of the power grid, which represents the loss caused by the power supply of the power grid, P 电网 (t) is the real-time power supply of the power grid at time t, and α2 is the weight coefficient of the power grid, which is used to adjust the proportion of power grid minimization in the overall optimization objective.

[0059] In some embodiments, in order to further limit the use of grid power, an upper limit of grid power supply may be introduced. Right now: in, The maximum permissible value of power supplied to the grid.

[0060] Through the above constraints, the instability problem caused by overload of the power grid can be effectively avoided.

[0061] Energy storage power change rate ΔP 储能(t) is an important indicator to measure the power regulation speed of energy storage equipment. Excessive power change rate may lead to system instability and increase the operating pressure of energy storage equipment. Therefore, the energy storage power change rate needs to be minimized in the optimization function, and its corresponding loss function can be expressed as: f3(t)=α3·ΔP 储能 (t) 2 Where f3(t) is the loss function of the energy storage power change rate, which represents the loss caused by too fast power regulation speed, ΔP 储能 (t) is the power change rate of the energy storage device at time t, and α3 is the weight coefficient of the energy storage power change rate, which is used to adjust the proportion of this part of the loss in the overall optimization target.

[0062] The calculation formula of energy storage power change rate is: Among them, P 储能 (t-Δt) is the discharge power of the energy storage device at the previous moment, and Δt is the time interval.

[0063] Generally, in order to ensure the stability of the energy storage device operation, an upper limit value can be set for the power change rate. Right now: in, is the maximum power change rate of the energy storage device.

[0064] The objective optimization function is defined by combining the above three loss functions: Specifically expanded as follows: Among them, J is the target optimization function, the total loss function represents the target that the optimization algorithm needs to minimize, and T is the optimization time range.

[0065] The values ​​of weight coefficients α1, α2, and α3 are dynamically set according to actual system requirements. For example, when load power tracking is given priority, the value of α1 can be increased; when the regulation capacity of the energy storage device is limited, the value of α3 can be reduced.

[0066] In some embodiments, in order to further improve the optimization effect, the following extensions may be introduced into the target optimization function: Adding optimization targets for the operating efficiency of batteries in energy storage devices, such as introducing battery charging and discharging efficiency factors; Considering the influence of ambient temperature, a temperature correction term is added to the optimization function; In a system with multiple energy storage devices, an optimization goal of balancing power distribution among devices is added.

[0067] The target optimization function provides a clear mathematical goal for the subsequent optimization algorithm. By reasonably setting the loss function and weight coefficient, a balance can be achieved between different optimization objectives to ensure the efficient operation of the energy storage equipment and the safety and stability of the power grid.

[0068] S4. Solve the optimization function using an optimization algorithm, and calculate the optimal discharge power of the energy storage device at the current time by a gradient descent method; This step is based on the target optimization function set in step S3, and uses the optimization algorithm to calculate the optimal discharge power P of the energy storage device at the current time. 储能 (t), and ensure that the calculation results meet the constraints of the optimization model constructed in step S2. The optimization algorithm needs to comprehensively consider the discharge power, power change rate and grid power of the energy storage device, aiming to minimize the total system loss through iterative calculation, and ultimately achieve real-time power optimization of the coordinated operation of the energy storage device and the grid.

[0069] In general, the optimization algorithm needs to meet the requirements of real-time and high efficiency to adapt to the rapid changes in load power fluctuations and the operation of energy storage equipment. In this embodiment, an algorithm combining gradient descent and dynamic programming is used to solve the target optimization function. The gradient descent method is responsible for updating the power value at each time point, while dynamic programming is used to handle global optimization problems with multiple time steps.

[0070] Specifically, in this embodiment, the solution process of the optimization algorithm includes the following contents: In order to combine the optimization objective function with the constraint conditions, the Lagrangian method is used in this embodiment to construct an optimization solution framework. The goal is to introduce power balance constraints, anti-backflow constraints and energy storage equipment power limits into the objective optimization function.

[0071] The optimization objective function is: After introducing the constraints, define the Lagrangian function: in, is the Lagrangian function, which represents the objective that the optimization algorithm needs to minimize. λ is the Lagrangian multiplier, which represents the penalty coefficient of the power balance constraint. 储能 (t) is the discharge power of the energy storage device, P 电网 (t) is the power supply of the power grid, P 负载 (t) is the real-time load power of the gateway meter.

[0072] After the constraints are introduced, the optimization solution satisfies the following conditions at the same time: Power balance constraints: P 储能(t)+P 电网 (t) = P 负载 (t) Anti-backflow constraint: P 电网 (t)≥0 Energy storage device power limit: Energy storage power change rate limit: In this embodiment, the Lagrangian function is calculated by gradient descent method. Perform iterative optimization and gradually approach the optimal solution.

[0073] The iterative formula of the gradient descent method is: in, is the energy storage discharge power after the k+1th iteration, is the energy storage discharge power of the kth iteration, is the grid power after the k+1th iteration, is the grid power supply of the kth iteration, α and β are the learning rates of gradient descent, which control the update step size, and usually range from 0.01 to 0.1. and is the partial derivative of the Lagrangian function with respect to the energy storage power and the grid power.

[0074] The specific calculation formula is as follows: Among them, ΔP 储能 (t) is the energy storage power change rate, and the calculation formula is: As a possible implementation, the learning rates α and β can be adjusted dynamically to speed up convergence. For example, when the gradient value is large, the learning rate can be appropriately reduced to prevent the algorithm from diverging.

[0075] The dynamic programming algorithm is used to solve the global optimization problem of multiple time steps, discretizing the time interval [0, T] into multiple time steps t k , and recursively calculate the optimal power value for each time step.

[0076] The discretization expression of the objective function is: Among them, t k is the kth time step, N is the number of time steps, N=T / Δt; the definitions of other symbols and parameters are consistent with the above content.

[0077] The dynamic programming algorithm starts from the last time step t N Start the reverse recursion, gradually calculate the optimal solution for each time step, and use it as the input for the next time step to achieve global optimization.

[0078] In this embodiment, the final output of the optimization algorithm includes the optimal discharge power of the energy storage device and the optimal power supply of the power grid To ensure the accuracy of the results, the following constraints need to be verified on the output: In some embodiments, the optimization algorithm may also introduce the following functions: Combined with machine learning model to predict load power P 负载 (t) Improve the foresight of optimization results; Use distributed computing technology to improve computing efficiency; Different weight coefficients α1, α2, and α3 are dynamically adjusted to adapt to different operating conditions.

[0079] S5, sending the calculated optimal discharge power to the energy storage device to adjust the real-time output power of the energy storage device; In step S4, the optimal discharge power of the energy storage device is calculated using the optimization algorithm. and grid power The core goal of this step is to adjust the actual output power of the energy storage device in real time based on these optimization results, to ensure that the energy storage device operates according to the optimal power output, to meet the load demand while strictly following the power balance constraints and anti-backflow constraints.

[0080] The real-time regulation of energy storage equipment needs to be combined with its own physical characteristics, such as the maximum discharge power limit and power rate limit At the same time, the grid status must be taken into account to ensure that power reverse flow does not occur.

[0081] Generally, the regulation process of the energy storage device includes converting the optimization results into specific control instructions, updating the power output in real time, and verifying the adjustment effect through dynamic monitoring. In some embodiments, the stability of the adjustment can also be enhanced through filtering and feedback mechanisms.

[0082] Specifically, the real-time adjustment process of the energy storage device in this embodiment includes the following contents: In this embodiment, the optimal discharge power of the energy storage device is The results of the optimization algorithm calculation need to be converted into actual control instructions for the energy storage device. Energy storage devices usually adjust the output current To achieve power regulation, the calculation formula of the control command is: in, is the control current instruction of the energy storage device, is the optimal discharge power of the energy storage device, U 储能 (t) is the output voltage of the energy storage device.

[0083] As an option, the current command calculated above can be sent to the energy storage device control module in real time through a variety of communication protocols (such as CAN bus, Modbus protocol, etc.). In some embodiments, in order to improve the reliability of command transmission, dual-channel redundant communication technology can also be used to ensure that the command can be accurately and timely transmitted to the energy storage device.

[0084] After receiving the control command, the energy storage device dynamically adjusts the output power through a power conversion module (such as a DC / DC converter or inverter). In this embodiment, the real-time output power of the energy storage device needs to meet the following restrictions: Power output limit: Among them, P 储能 (t) is the actual output power of the energy storage device, is the maximum discharge power of the energy storage device.

[0085] Power change rate limit: Among them, ΔP 储能 (t) is the power change rate of the energy storage device, is the maximum power change rate of the energy storage device, ΔP 储能 The calculation formula of (t) is, Δt is the time interval.

[0086] Specifically, under the premise of meeting the above-mentioned restrictions, the energy storage device will adjust the power output in real time according to the received control instructions. In some embodiments, the energy storage device can also smooth the output power through an internal low-pass filter to reduce the impact of instantaneous fluctuations on the load and the power grid.

[0087] To ensure that the output power of the energy storage device meets the optimization result, a real-time monitoring mechanism is introduced in this embodiment to dynamically monitor the operating status of the energy storage device, power grid and load. The main parameters monitored include: the actual output power of the energy storage device Grid power supply power P 电网 (t); Load power P 负载 (t) The monitoring results can be used to verify whether the power adjustment meets the power balance constraints: At the same time, it is necessary to check whether the anti-backflow constraints are met: P 电网 (t)≥0 In a possible implementation, when it is detected that the power adjustment fails to meet the constraint conditions, the system will immediately trigger the exception handling mechanism, forcibly stop the output power of the energy storage device, and send out an alarm signal.

[0088] In further implementation of the present invention, in order to enhance the intelligence and robustness of power regulation of energy storage equipment, the following extended functions may be combined: Combined with the short-term prediction model, P 负载 (t) Make predictions and use the prediction results as a reference for adjustment in the next time step; Dynamically adjust according to the battery health status of the energy storage device and The value of In the scenario of multiple energy storage devices, the coordinated adjustment of multiple energy storage devices is achieved through distributed control algorithms to optimize the overall performance of the system.

[0089] The real-time output power adjustment of energy storage equipment directly affects the grid power supply and load demand. After the adjustment is completed, the monitored real-time operation data needs to be used as the input data for the next round of optimization calculation to form an optimization-adjustment-feedback closed-loop control system, thereby achieving the dynamic balance of the system.

[0090] S6, real-time monitoring of the energy storage device, power grid and load power flow status, repeating the above steps at preset time intervals; after completing the power output adjustment of the energy storage device in step S5, in order to ensure the dynamic balance and long-term stable operation of the system, it is necessary to monitor the status of the energy storage device, power grid and load in real time, and repeat steps S1 to S5 at preset time intervals. This cyclic mechanism can dynamically adapt to changes in load demand, while ensuring the real-time operation of the energy storage device and the safety of the power grid.

[0091] In general, the core of real-time monitoring is to obtain the latest status data of energy storage equipment, power grid and load, and verify the accuracy of optimization and adjustment through detection results. In some embodiments, fault detection and abnormality handling mechanisms can also be combined to take timely measures when the system operation status does not meet expectations to prevent system instability or power reverse flow.

[0092] Specifically, the process of real-time monitoring and cyclic execution in this embodiment includes the following: In this embodiment, the operating status of the energy storage device is the focus of real-time monitoring, which mainly includes the output power P of the energy storage device. 储能 (t) and power change rate ΔP 储能(t). By monitoring these parameters, it can be ensured that the output of the energy storage device always meets the optimization results and satisfies the operating constraints.

[0093] Specifically, the real-time output power of the energy storage device must meet the following conditions: Among them, P 储能 (t) is the real-time output power of the energy storage device, is the maximum discharge power of the energy storage device.

[0094] At the same time, the monitoring formula for the power change rate is: Its constraints are: Where ΔP 储能 (t) is the power change rate of the energy storage device, Δt is the time interval, is the maximum power change rate of the energy storage device.

[0095] By monitoring these parameters, operational deviations of energy storage equipment can be discovered in a timely manner and corrected.

[0096] In this embodiment, the power flow state of the power grid is a key monitoring indicator to ensure the realization of the anti-backflow function, which is mainly achieved by monitoring the power supply power P of the power grid. 电网 (t) Implementation. The monitoring results must meet the following constraints: P 电网 (t)≥0 Among them, P 电网 (t) is the real-time power supply of the power grid. When P 电网 When (t)<0, it indicates that the electric energy of the energy storage device has reverse flow. At this time, the system should immediately trigger the emergency processing mechanism, stop the output power of the energy storage device, and record the abnormal situation.

[0097] In one possible implementation, the monitoring of the power flow state of the power grid can be combined with an over-limit alarm function. 电网 When (t) approaches zero, the system will send a warning signal in advance to remind the energy storage device to reduce the output power to avoid the occurrence of backflow.

[0098] In order to ensure the integrity of system operation and the rationality of power allocation, this embodiment also needs to verify whether the power balance constraint is met. The specific formula is: P 储能 (t)+P 电网 (t) = P 负载 (t) Among them, P 负载(t) is the load power monitored by the gateway meter in real time.

[0099] Generally, the result of the power balance verification can directly reflect whether the operating state of the system meets the expectations of the optimization algorithm. In some embodiments, the deviation analysis can also be performed in combination with historical data to determine the accuracy of the output power of the energy storage device.

[0100] In this embodiment, the time interval Δt of the cyclic execution is a key parameter that determines the dynamic response speed of the system. Generally, the selection of the time interval needs to comprehensively consider the frequency of load fluctuations and the dynamic response capability of the energy storage device.

[0101] Specifically, the value of the time interval Δt can be estimated by the following formula: Where Δt is the time interval, f 响应 is the dynamic response frequency of the energy storage device.

[0102] In scenarios where the load changes quickly, a smaller Δt may be selected, such as 0.1s or shorter; whereas in scenarios where the load changes slowly, Δt may be appropriately increased, such as 1s or longer.

[0103] As an option, in order to avoid the loss of energy storage equipment caused by frequent adjustments, the value of Δt can also be dynamically adjusted through an adaptive algorithm. For example, when the load fluctuation is small, the system can automatically increase the time interval, thereby reducing unnecessary optimization calculations and power adjustments.

[0104] In this embodiment, in order to further enhance the stability of the system, an exception handling and fault recovery mechanism is also designed. When the following situations are detected during real-time monitoring, the system should trigger the corresponding processing flow: Power balance constraint is not satisfied: adjust the output power of the energy storage device or trigger the backup power supply.

[0105] Grid power reverse flow: Stop the output power of the energy storage device and send out an alarm signal.

[0106] The power change rate of the energy storage device exceeds the limit: reduce the adjustment speed of the energy storage device to avoid equipment loss caused by too fast adjustment.

[0107] In some embodiments, an intelligent diagnostic system may be combined to record and analyze abnormal situations, thereby providing a basis for subsequent system optimization.

[0108] In this embodiment, the results of real-time monitoring will be used as input data for the next round of optimization calculations to update the operating status of the energy storage device, the power grid, and the load. By cyclically executing steps S1 to S6, the system can form an optimization-adjustment-feedback closed-loop control mechanism to achieve dynamic regulation of the energy storage device and the power grid.

[0109] In summary, the present invention provides a method for preventing electric energy from flowing back into the power grid. By collecting the operating data of the energy storage device, the power grid and the load in real time, an optimization model including power balance, anti-backflow and energy storage device operation restrictions is constructed, and a target optimization function is set to minimize the loss of energy storage power deviation, power grid power and power change rate. The optimization function is solved by combining the gradient descent method with the dynamic programming algorithm, the optimal discharge power of the energy storage device is calculated, and the output of the energy storage device is adjusted in real time to ensure that the load demand is met while strictly preventing electric energy from flowing back into the power grid. The real-time monitoring and closed-loop control mechanism further ensures the safety, stability and efficiency of the system operation, making the present invention suitable for scenarios such as industrial and commercial energy storage systems that require dynamic power regulation, and provides a reliable solution for the efficient coordination of energy storage systems and power grids.

[0110] Please see attached Figure 2 The present invention also provides a system for preventing electric energy from flowing back to the power grid, comprising the following modules: a data acquisition module for collecting the load power of the gateway meter, the discharge power of the energy storage device and the power status of the power grid; The optimization calculation module is used to build the optimization model and solve the optimal energy storage power in real time based on the gradient descent method and dynamic programming; the power regulation module is used to dynamically adjust the discharge power of the energy storage device according to the optimization results; The real-time monitoring module is used to continuously monitor the power flow status to ensure that the power output of the energy storage device meets the anti-backflow constraints.

[0111] The system of this embodiment is executed based on some embodiments of the above method, and its principles and technical effects are similar, so they will not be repeated here.

[0112] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for preventing electric energy from flowing back into a power grid, characterized in that: The following steps are involved: Collect data on the real-time load power of the gateway meter, the discharge power of the energy storage device and its change rate, and the power flow status of the power grid; Build an optimization model based on the collected data; Based on the constructed optimization model, set the optimization function of the target; The optimization function is solved using the optimization algorithm, and the optimal discharge power of the energy storage device at the current time is calculated using the gradient descent method. The calculated optimal discharge power is sent to the energy storage device to adjust the real-time output power of the energy storage device; Monitor the energy storage device, power grid and load power flow status in real time, and repeat the above steps at preset time intervals.

2. A method for preventing electric energy from flowing back into a power grid according to claim 1, characterized in that: The optimization model includes the following constraints: Power balance constraint: the discharge power of the energy storage device, the power supply power of the grid and the load power of the gateway meter satisfy the total power conservation; Anti-reverse flow constraint: the power supply of the power grid is always greater than or equal to zero; Energy storage device power limit: The discharge power of the energy storage device is always less than or equal to its rated maximum discharge power; Power change rate limit: The discharge power change rate of the energy storage device is less than or equal to its maximum change rate.

3. A method for preventing electric energy from flowing back into a power grid according to claim 1, characterized in that: The optimization function is defined in the following way: The goal is to minimize the deviation between the discharge power of the energy storage device and the power required by the load; The goal is to minimize the power supply of the power grid; The goal is to minimize the rate of change of discharge power of the energy storage device.

4. A method for preventing electric energy from flowing back into a power grid according to claim 1, characterized in that: The weight coefficients of the optimization function are set according to actual application requirements, including energy storage power deviation weight, grid power supply power weight and power change rate weight.

5. A method for preventing electric energy from flowing back into a power grid according to claim 1, characterized in that: The optimization algorithm comprises the following steps: Construct a Lagrangian function to combine the optimization objective with the power balance constraint; Based on the gradient descent method, the discharge power of the energy storage device and the power supply power of the grid are iteratively optimized; The optimization problem after time discretization is solved in real time through dynamic programming to adjust the discharge power of the energy storage equipment.

6. A method for preventing electric energy from flowing back into a power grid according to claim 1, characterized in that: The gradient update process of the gradient descent method includes: Optimization of discharge power of energy storage equipment; Optimization of power supply to the power grid.

7. A method for preventing electric energy from flowing back into a power grid according to claim 1, characterized in that: The process of adjusting the real-time output power of the energy storage device includes: Send optimization calculation results to energy storage equipment in real time; According to the current state of the energy storage device, the output power is adjusted, the power change rate is updated, and the constraints of the optimization model are ensured to be met.

8. A method for preventing electric energy from flowing back into a power grid according to claim 1, characterized in that: The real-time monitoring includes: The gateway meter detects load power fluctuations in real time and updates the collected data; Monitor the power change rate of the energy storage device to ensure that it is always within the maximum change rate limit; If it is detected that the power supply of the grid is less than zero, an emergency stop is triggered to discharge the energy storage device.

9. A method for preventing electric energy from flowing back into a power grid according to claim 8, characterized in that: The time interval for real-time monitoring is set according to load fluctuation characteristics.

10. A system for preventing electric energy from flowing back to a power grid, according to any one of the methods for preventing electric energy from flowing back to a power grid according to claims 1-9, characterized in that: Includes the following modules: Data acquisition module, used to collect the load power of the gateway meter, the discharge power of the energy storage device and the power status of the power grid; Optimization calculation module, used to build optimization model and solve the optimal energy storage power in real time based on gradient descent method and dynamic programming; A power regulation module, used to dynamically adjust the discharge power of the energy storage device according to the optimization results; The real-time monitoring module is used to continuously monitor the power flow status to ensure that the power output of the energy storage device meets the anti-backflow constraints.

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