Parameter optimization method for station area mobile energy storage controller, electronic equipment and medium

Abstract

The invention discloses a transformer area mobile energy storage controller parameter optimization method, electronic equipment and a medium. The method comprises the following steps: constructing a power grid stability objective function under a power grid anti-vulnerability constraint based on power grid operation data; constructing a power grid-mobile energy storage simulation model based on the power grid system and the mobile energy storage system; determining a mobile energy storage system installation strategy based on the power grid stability objective function under the power grid anti-vulnerability constraint and the power grid-mobile energy storage simulation model; optimizing the core parameters of the mobile energy storage controller based on the mobile energy storage system installation strategy to obtain optimized core parameters; and outputting a distributed energy storage optimization landing scheme based on the optimized core parameters and the mobile energy storage system installation strategy. According to the invention, power grid requirements can be accurately matched, the system stability is enhanced, the engineering landing efficiency is improved, and the transient stability and voltage recovery capability of the system in the district power distribution network are enhanced.

Description

Technical Field

[0001] This invention belongs to the field of distribution network resilience enhancement and operation optimization technology, specifically involving a method for optimizing parameters of mobile energy storage controllers in distribution areas, electronic equipment, and media. Background Technology

[0002] For the power system, the large-scale integration of distributed renewable energy sources is accompanied by the application of a high proportion of new energy and power electronic equipment. However, energy sources such as photovoltaic and wind power have characteristics such as strong correlation, strong randomness and uncontrollability, which will have a significant impact on the operational stability of the existing power system.

[0003] Mobile energy storage systems can serve as key systems for mitigating the randomness and volatility of distributed energy systems, maintaining stable operation of distribution networks, and improving the grid-connected absorption rate of distributed photovoltaic systems. However, existing methods for optimizing mobile energy storage controller parameters often adopt an isolated, single-dimensional approach. For example, key parameters such as capacity configuration, power output curves, and state-of-charge (SOC) operating range may be optimized independently or sequentially. While this simplifies the problem, it easily leads to conflicts between local optimization objectives. For instance, an operating strategy determined to minimize grid losses may severely damage the lifespan of energy storage devices or the reliability of power supply. Ultimately, this lack of systematic and coordinated optimization fails to achieve an effective trade-off between various control objectives, limiting the maximum potential for overall distribution network resilience improvement. Summary of the Invention

[0004] The purpose of this invention is to predetermine the optimal parameters of the controller through co-simulation and intelligent optimization algorithms, and to provide a method, electronic device and medium for optimizing the parameters of the mobile energy storage controller in the distribution network of the distribution area, which can enhance the transient stability and voltage recovery capability of the system.

[0005] On the one hand, to achieve the above objectives, this invention proposes a method for optimizing the parameters of a mobile energy storage controller in a distribution area, comprising: constructing a grid stability objective function under grid vulnerability constraints based on grid operation data; constructing a grid-mobile energy storage simulation model based on the grid system and the mobile energy storage system; determining the mobile energy storage system installation strategy based on the grid stability objective function under grid vulnerability constraints and the grid-mobile energy storage simulation model; optimizing the core parameters of the mobile energy storage controller based on the mobile energy storage system installation strategy to obtain optimized core parameters; and outputting a distributed energy storage optimization implementation scheme based on the optimized core parameters and the mobile energy storage system installation strategy.

[0006] In one optional implementation, the objective function for grid stability under the grid vulnerability resistance constraint is... The expression is: In the formula, This represents the number of generators in the system. This represents the number of data points for generator power angle sampling. For the first The power angle of the generator in time Transient values; This represents the steady-state value of the generator power angle; The number of times the center node voltage is sampled; and These represent the transient voltage values ​​of the central node before and after the energy storage device is connected to the grid. This represents the steady-state value of the center node voltage.

[0007] In one optional implementation, the mobile energy storage system installation strategy includes the optimal access location for distributed energy storage, energy storage capacity, and charge / discharge power curves.

[0008] In one optional implementation, the mobile energy storage system installation strategy is determined based on the grid stability objective function and the grid-mobile energy storage simulation model. Specifically, this includes: calculating and ranking the voltage state vulnerability indices of all nodes using a distributed energy storage pre-location method to obtain a node voltage state vulnerability ranking group; generating multiple candidate schemes within the selectable range of mobile energy storage installation locations, combined with daily load / PV fluctuation data, based on the node voltage state vulnerability ranking group; if the candidate scheme meets the capacity constraint, substituting the candidate scheme into the grid-mobile energy storage simulation model to calculate the degree to which the candidate scheme satisfies the grid stability objective function and whether it meets the grid resilience constraint; iteratively optimizing the installation location and charge / discharge power curve of the candidate scheme using an intelligent algorithm, and finally selecting the scheme that simultaneously satisfies the grid stability objective function and the grid resilience constraint as the mobile energy storage system installation strategy.

[0009] In one optional implementation, the core parameters of the mobile energy storage controller are optimized based on the mobile energy storage system installation strategy to obtain optimized core parameters. Specifically, this includes: determining the parameters to be optimized, which include the droop coefficient, the power loop proportional-integral controller parameters of the power conversion system, and the charge / discharge cutoff state-of-charge threshold of the battery management system; and implementing an improved particle swarm optimization algorithm based on Python, using the parameters to be optimized as particle dimensions and the grid stability objective function value as fitness for iterative optimization to obtain the optimized core parameters.

[0010] In one optional implementation, an improved particle swarm optimization algorithm is implemented based on Python. The parameters to be optimized are used as particle dimensions, and the fitness of the power grid stability objective function is used for iterative optimization to obtain the optimized core parameters. Specifically, this includes: initializing the particle swarm, including determining the number of particles, the number of parameters to be optimized, the learning factor, the inertia weight, and the number of iterations; for each particle, calculating the fitness of its current position according to the power grid stability objective function to obtain a fitness evaluation result; based on the fitness evaluation result, if the fitness of the current particle is higher than its historical best score, then the individual historical best score of that particle is updated; if the fitness of the current particle is higher than the historical best scores of all particles, then the individual historical best score is updated. body The process continues until a preset number of iterations is reached or other stopping conditions are met, at which point the optimal solution is output, and the optimized core parameters are obtained.

[0011] In an optional implementation, the method for optimizing the parameters of the mobile energy storage controller in the distribution area further includes: substituting the optimized core parameters into the grid-mobile energy storage simulation model to re-simulate under various extreme scenarios; if the optimized core parameters ensure that the voltage and grid loss always meet the grid stability objective function, then outputting a multi-scenario simulation report with optimal parameters.

[0012] In one optional implementation, the mobile energy storage system includes a two-stage power conversion system topology, a bidirectional DC / DC converter and its control logic, wherein the two-stage power conversion system adopts a multi-level converter topology; and the bidirectional DC / DC converter adopts an isolated topology.

[0013] On the other hand, the present invention also proposes an electronic device, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the above-described mobile energy storage controller parameter optimization methods.

[0014] On the other hand, the present invention also proposes a computer storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any one of the parameters of a mobile energy storage controller for a distribution area.

[0015] The beneficial effects of this invention are as follows: by constructing an anti-vulnerability constraint objective function and building a grid-mobile energy storage simulation model, the installation strategy and core parameters of the controller are determined in a coordinated manner, and the final implementation plan is output. This can accurately match grid demand, enhance system stability and improve engineering implementation efficiency, and enhance the transient stability and voltage recovery capability of the system in the distribution network of the transformer substation. Attached Figure Description

[0016] Figure 1 A flowchart illustrating a method for optimizing parameters of a mobile energy storage controller in a distribution area, as provided in an embodiment of the present invention;

[0017] Figure 2 This is a diode-clamped three-level conversion topology diagram for a method of optimizing parameters of a mobile energy storage controller in a distribution area, provided in an embodiment of the present invention. Detailed Implementation

[0018] This invention proposes a method for optimizing the parameters of a mobile energy storage controller in distribution substations to enhance the resilience of power distribution networks. This method focuses on improving the utilization efficiency of distributed energy resources in distribution substations, conducting research on mobile energy storage, exploring the optimal configuration and operating characteristics of distributed photovoltaic and other clean energy sources with energy storage systems, and developing mobile energy storage devices with off-grid emergency power supply and switching capabilities. This device can achieve real-time source-load-storage power balance and power time-shifting in the distribution network, thereby ensuring the stable and reliable operation of the distribution network. Addressing the problem of optimizing the controller parameters of the energy storage system, and considering the impact of energy storage on the transient stability of the power system, a method for optimizing the energy storage controller parameters based on DIgSILENT-Python is proposed by configuring a DIgSILENT interface with Python to achieve data interaction and writing collaborative simulation code based on the particle swarm optimization algorithm. This method provides a new approach to power system parameter optimization. The optimized controller parameters can fully utilize the active power regulation potential of the energy storage system, further improve the transient stability of the system, and enhance the voltage recovery capability of the central node.

[0019] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1 As shown, according to an embodiment of the present invention, in one aspect, a method for optimizing the parameters of a mobile energy storage controller in a distribution area is provided, comprising the following steps:

[0021] Step S101: Construct the objective function of power grid stability under the constraint of power grid vulnerability resistance based on power grid operation data;

[0022] Step S103: Construct a grid-mobile energy storage simulation model based on the power grid system and the mobile energy storage system;

[0023] Step S105: Determine the installation strategy of the mobile energy storage system based on the objective function of grid stability under grid vulnerability constraints and the grid-mobile energy storage simulation model;

[0024] Step S107: Optimize the core parameters of the mobile energy storage controller based on the mobile energy storage system installation strategy to obtain the optimized core parameters;

[0025] Step S109: Output an optimized implementation plan for distributed energy storage based on the optimized core parameters and the mobile energy storage system installation strategy.

[0026] In this embodiment, lithium-ion batteries are used as energy storage carriers. A two-stage power conversion system (PCS) controls the bidirectional power flow between the batteries and the grid, realizing the time transfer of photovoltaic power supply, grid functions and substation load energy, thereby achieving the goal of improving the self-consumption rate of distributed energy and the emergency off-grid power supply of substations.

[0027] In terms of coordinated control of energy storage systems and distributed energy, a communication interface with the upper-level platform is configured, and an interface for detecting grid energy parameters is reserved. Real-time grid information is obtained through detection or communication, and real-time information such as photovoltaic output, grid power supply, and real-time load is comprehensively analyzed to make comprehensive predictions and form basic energy management and operation strategies, including multiple operation and control strategies such as consumption priority, economic priority, dynamic expansion, and backup / power supply maintenance operation.

[0028] The overall system design is based on the compatibility of energy storage grid charging and discharging, off-grid power supply, DC charging pile simulation, and electric vehicle simulation functions. The DC charging pile simulation and electric vehicle function simulation are retained in the operating mode, covering DC charging, mobile charging pile, and fixed charging pile modes. This invention only develops the energy storage grid charging and discharging and off-grid power supply functions, and the operating modes only include station area energy storage AC power supply and AC power supply modes.

[0029] Distributed energy storage systems can function as both a power source and a load in power distribution networks. Their charging and discharging power can be adjusted according to the real-time operational needs of the system. As a buffer between new energy sources and the power distribution network, energy storage systems effectively ensure the safe and economical operation of the network. Currently, there are many types of distributed energy storage technologies with varying performance characteristics, but their basic working principles in power distribution networks are largely the same. Battery energy storage, in particular, is widely used due to its high efficiency and long lifespan.

[0030] Considering the capacity of the distributed energy storage system connected to the distribution network, when it is in discharge operation mode, the expression for the remaining capacity of the energy storage system is as follows:

[0031] , (1)

[0032] When in charging operation mode, the following equation exists:

[0033] ;

[0034] When in a fluctuating state, the following equation exists:

[0035] (3)

[0036] In the formula, Indicates the current moment of the distributed energy storage system Remaining capacity; The self-discharge current rate represents the characteristic of energy storage capacity decaying over time; Indicates time The energy storage charging and discharging power. When When >0, the power flow direction is from the distribution network to the DES direction; when When the value is less than 0, the power flow direction is from DES to the power distribution network. and This refers to the energy storage charging / discharging efficiency, which represents the power loss that occurs when the energy storage system exchanges power with the power distribution network. It is the power sampling time interval between adjacent moments.

[0037] In active distribution networks, the integration of numerous uncertain distributed generation sources poses significant security and stability challenges to the actual operation of the grid, often impacting its vulnerability and leading to frequent accidents. To quantify the degree to which distributed energy storage improves grid security, this invention constructs a grid vulnerability measurement index as one of the objective functions for optimizing distributed energy storage configuration.

[0038] From constructing the objective function to applying the simulation model, and then linking the installation strategy with parameter optimization, a complete closed loop is formed, which can effectively improve the grid's ability to cope with fluctuations and reduce the risk of failure. With grid vulnerability constraints and stability objectives as the core, and combining the grid-mobile energy storage simulation model to determine the installation strategy, the blind optimization of parameters is avoided, ensuring that the controller parameters are highly matched with actual grid requirements.

[0039] It directly outputs optimized implementation solutions for distributed energy storage, combining parameter optimization results with actual installation and operation scenarios, shortening the transformation cycle from technical solutions to practical applications, and improving the practicality of engineering.

[0040] Furthermore, the objective function of power grid stability under power grid vulnerability resistance constraints The expression is:

[0041] ;

[0042] In the formula, This represents the number of generators in the system. This represents the number of data points for generator power angle sampling. For the first The power angle of the generator in time Transient values; This represents the steady-state value of the generator power angle; The number of times the center node voltage is sampled; and These represent the transient voltage values ​​of the central node before and after the energy storage device is connected to the grid. This represents the steady-state value of the center node voltage.

[0043] By minimizing "the first The generator is in the first Power angle transient value at each sampling time "and "power angle steady-state value" The sum of squared deviations essentially aims to suppress the relative sway of the generator rotor and weaken the amplitude and duration of low-frequency oscillations. It requires that the transient voltage value of the center node after grid connection of the energy storage device be... "and steady-state voltage value" The sum of squares of the deviations is not greater than the sum of squares of the voltage transient deviations before grid connection of energy storage. This forces energy storage devices to actively support voltage fluctuations.

[0044] The objective function is optimized by selecting mean square error (SSE). This metric can be used to observe the system performance after parameter optimization and directly evaluate the oscillation effect of the generator after parameter optimization. Simultaneously, to ensure that the voltage recovery capability of the system's central node is improved after the energy storage device is connected, the voltage mean square error is selected as an approximation bundle of the objective function. By setting a voltage mean square error constraint function, the deterioration of voltage recovery performance due to inappropriate parameters can be avoided.

[0045] The objective function and controller structure of this invention can also be adjusted. For example, a multi-objective optimization framework can be introduced into the objective function, simultaneously considering economic indicators (such as operating costs) and resilience indicators (such as load recovery time), and using the Pareto optimal solution set to balance different objectives; in the controller design, fuzzy logic control or neural network control can be used to replace traditional PI control. These methods can handle system nonlinearity and uncertainty, and adaptively adjust parameters through training data without relying on a precise mathematical model.

[0046] By minimizing the SSE of the generator power angle, the system damping characteristics can be significantly improved: on the one hand, the periodic swaying of the generator rotor is weakened (corresponding to a reduction in the power angle oscillation amplitude), avoiding the amplification of oscillations caused by negative damping; on the other hand, the settling time of the transient process is shortened (e.g., from 0.22s in traditional control to the 0.14s level), reducing the probability of oscillations spreading to the entire system and reducing the chain risk of "generator loss of synchronism → system disconnection → large-scale power outage".

[0047] When the power grid encounters disturbances such as short circuits or sudden load changes, energy storage can inject / absorb reactive current within milliseconds (e.g., <5ms), reducing the overshoot of the central node voltage (e.g., from 14.28% in traditional control to 3.28%), and shortening the time for the voltage to recover to steady state. This avoids the chain disconnection of new energy power plants due to voltage drops, while ensuring the stable operation of load-side equipment (such as motors and lighting facilities).

[0048] This objective function is compatible with the characteristics of "low inertia grids". By suppressing power angle and voltage oscillations, it expands the upper limit of grid-connected capacity of new energy sources (such as turning extremely weak grids with a short-circuit ratio (SCR) < 1.1 from "grid-connection forbidden zone" to safe zone); reduces the alternating electromagnetic force of generator rotor windings and the additional core loss of transformers, and delays equipment aging (such as reducing the wear rate of generator bearings and extending the insulation life of transformers), thereby reducing operation and maintenance costs and failure probability.

[0049] In distribution networks, distributed energy storage devices provide voltage support to nodes near the connection point. When a node fails or operates abnormally, or when weather factors cause fluctuations in the output of nearby distributed generation devices, the voltage of that node and its neighbors may rise or fall, even exceeding limits. In such cases, the distributed energy storage system can promptly provide power support to these load nodes or absorb excess energy as a load. This maintains the voltage of nodes near the distributed energy storage system within a reasonable range, improving power supply reliability and helping to reduce energy losses during transmission. In actual grid operation, the impact of whether power quality meets standards varies across nodes, primarily depending on the specific network architecture. Therefore, deploying distributed energy storage devices at critical nodes in a specific grid can achieve twice the result with half the effort, allowing for more targeted maintenance of more vulnerable nodes. This ensures that even with minimal capacity energy storage devices, significant improvements in grid resilience can be achieved.

[0050] The mobile energy storage system installation strategy includes the optimal access location for distributed energy storage, energy storage capacity, and charge / discharge power curves.

[0051] Further, step S105, based on the grid stability objective function and the grid-mobile energy storage simulation model, determines the installation strategy of the mobile energy storage system, specifically including the following steps:

[0052] Step S1051: Based on the distributed energy storage pre-location method, calculate and sort the voltage state vulnerability index of all nodes to obtain the node voltage state vulnerability ranking group.

[0053] Step S1053: Based on the node voltage state vulnerability ranking group, within the selectable range of mobile energy storage installation locations, and in combination with daily load / photovoltaic fluctuation data, generate multiple candidate schemes. The candidate schemes include the installation location and the corresponding daily charge and discharge power curves.

[0054] Step S1055: If the candidate scheme meets the capacity constraint, then substitute the candidate scheme into the grid and mobile energy storage simulation model, calculate the degree to which the candidate scheme meets the grid stability objective function and whether it meets the grid vulnerability constraint.

[0055] Step S1057: The installation location and charge / discharge power curve of the candidate schemes are iteratively optimized through intelligent algorithms. Finally, the scheme that simultaneously satisfies the grid stability objective function and the grid vulnerability constraint is selected as the installation strategy for the mobile energy storage system.

[0056] In this embodiment, based on the constructed multi-objective distributed energy storage site selection and capacity optimization mathematical model, an improved MOPSO algorithm written in MATLAB is used for simulation calculation. First, according to the distributed energy storage pre-site selection method, the voltage state vulnerability index of each node in the example network structure is pre-calculated and sorted. Then, under the constraints of distributed energy storage charging and discharging power and its network location, daily charging and discharging power and its location information are randomly generated. The maximum interval method is used to obtain the total rated capacity of distributed energy storage, the capacity of the energy storage system in each time period is calculated and the SOC curve is obtained to determine whether the constraints are met. Based on this, the node network topology is modified in real time, and the matpower module in MATLAB is called to perform power flow calculation. The minimum value of the three objective functions is used as a quantitative evaluation index to determine the quality of the current solution. By iteratively iterating the decision variables through the improved MOPSO algorithm, the optimal access location, capacity and charging and discharging power curve of distributed energy storage are finally obtained.

[0057] Distributed energy storage pre-location methods typically combine three types of indicators—voltage sensitivity analysis, short-circuit capacity assessment, and transient voltage drop depth—to quantify the voltage vulnerability of nodes. For example, by calculating the "node voltage sensitivity coefficient to reactive power" (the larger the absolute value of the coefficient, the more susceptible the voltage is to reactive power fluctuations), the "short-circuit capacity SCR (the smaller the SCR, the weaker the voltage support capability)," and the "duration of voltage drop below 0.8 pu after a fault," a weighted voltage vulnerability score for each node is obtained and ranked, ultimately forming a "high to low vulnerability" node sequence (i.e., a ranking group).

[0058] Within the physical reach of mobile energy storage (e.g., limited by vehicle range or grid connection point capacity), candidate installation sites can be selected from the top 30% of highly vulnerable nodes in the vulnerability ranking group. Simultaneously, by combining daily load curves (e.g., sudden load surges during morning / evening peak hours) and photovoltaic output curves (e.g., voltage rises due to high photovoltaic power generation at midday), charging and discharging power curves matching the scenario are generated. For example, during peak load periods and low photovoltaic output periods, energy storage operates in a "discharging (reactive power support) + charging (peak shaving)" mode; during periods of high photovoltaic power generation, energy storage operates in a "charging (absorbing excess renewable energy) + reactive power absorption (suppressing voltage exceedances)" mode, ensuring that candidate solutions are compatible with grid operating characteristics.

[0059] The capacity constraints first limit the charging and discharging power of mobile energy storage (e.g., not exceeding 1.2 times its rated power) and the energy storage capacity (e.g., maintaining the state of charge (SOC) between 20% and 80%). Then, the candidate schemes are substituted into the "grid-mobile energy storage simulation model" to calculate the satisfaction of the grid stability objective function (e.g., whether the power angle SSE is ≤ a preset threshold). At the same time, the "voltage mean square error constraint" is verified to be valid, and schemes that cannot meet the anti-vulnerability requirements are eliminated.

[0060] An improved multi-objective particle swarm optimization algorithm (MOPSO) is used to iterate the "installation location and charge / discharge power curve" of candidate schemes. The algorithm takes "minimizing the grid stability objective function and minimizing the voltage deviation" as dual objectives. Through particle position updates (adjusting installation nodes) and velocity updates (optimizing power curves), Pareto optimal solutions are selected during the iteration process, and finally the installation strategy that simultaneously satisfies "grid stability" and "anti-vulnerability constraints" is output.

[0061] This invention can also use MATLAB / Simulink for power system modeling and transient simulation. Simulink's power system module library can accurately simulate the interaction between energy storage systems and distribution networks. Similarly, specialized power system analysis tools such as PSS®E or ETAP can be used for the simulation portion, exchanging data with optimization scripts (such as Python or C++ programs) via API interfaces. This substitution not only maintains the core idea of ​​co-simulation but also leverages the advantages of different software in specific functions, such as MATLAB's flexibility in algorithm development or PSS®E's efficiency in large-scale power grid analysis.

[0062] In terms of the collaborative simulation architecture, this invention can also employ other data interaction methods to replace the current DIgSILENT-Python interface. For example, industry-standard communication protocols such as OPC-UA or DDS can be used to achieve real-time data transfer between the simulation software and the optimization module; or simulation and optimization components can be deployed on a cloud platform, with remote calls and parameter adjustments via RESTful APIs, thereby supporting distributed computing and resource sharing. This approach can improve the scalability and adaptability of the system, and is particularly suitable for multi-scenario or large-scale power distribution network applications.

[0063] By prioritizing voltage vulnerability to identify high-risk nodes, the "blind deployment" of energy storage can be avoided: Compared with the traditional "uniform deployment" scheme, mobile energy storage can focus on the nodes with the weakest voltage support, thereby improving the voltage improvement effect per unit energy storage capacity (e.g., for the same energy storage capacity, the voltage deviation reduction of highly vulnerable nodes is more than 40% higher than that of random nodes), and reducing the redundant configuration of energy storage resources.

[0064] The matching design of the charging and discharging power curve with load / PV fluctuations can simultaneously achieve the triple functions of "peak shaving and valley filling, new energy consumption, and voltage regulation": For example, during periods of high PV generation, energy storage charging can consume excess new energy (increasing the consumption rate by 15%-20%); during peak load periods, energy storage discharging can reduce the peak load pressure on the grid (peak load reduction reaches 80% of the rated power of energy storage), while maintaining the node voltage within the acceptable range.

[0065] The intelligent algorithm-optimized scheme can significantly enhance the power grid's ability to withstand disturbances. For example, in the N-1 fault scenario, the voltage recovery time of highly vulnerable nodes is shortened from 0.3s in the traditional scheme to 0.12s, and the power angle oscillation amplitude is reduced to below 0.05rad, effectively avoiding system disconnection caused by voltage instability or power angle loss of synchronization, and improving the reliability of power grid operation.

[0066] This energy storage system consists of a two-stage PCS, energy storage batteries, a high-voltage box, a BMS, a controller, and a display screen. It primarily enables grid-connected charging, grid-connected discharging, and off-grid power supply. The two-stage PCS employs a buck-boost topology on its DC-DC side to achieve DC-DC conversion, enabling wide-range DC voltage access. The AC side utilizes diode-clamped three-level conversion technology to achieve three-phase four-wire grid connection and single-phase / three-phase off-grid power supply. The controller integrates EMS functionality, coordinating energy flow and various functions, and communicates wirelessly with the upstream system.

[0067] This invention provides a backup design for an electric vehicle charging system, including a PCS / energy storage battery, a DC-DC charger, a charging logic power supply circuit, a charging gun, and a charging controller. The DC-DC charger adopts a DAB+LLC isolated topology to adapt to the wide voltage range application requirements of electric vehicles. The charging power supply circuit and controller conform to standard charging logic and charging protocols. The system that supplies power to the energy storage battery via a DC charging station includes the energy storage battery, an electric vehicle simulation controller, and an electric vehicle logic power supply circuit, all of which conform to standard electric vehicle charging logic and protocols.

[0068] The mobile energy storage system includes a two-stage power conversion system topology, a bidirectional DC / DC converter and its control logic. The two-stage power conversion system adopts a multi-level converter topology; the bidirectional DC / DC converter adopts an isolated topology.

[0069] In this embodiment, the AC-DC bidirectional power conversion adopts... Figure 2 The diagram shows a diode-clamped three-level converter topology. Multilevel converters (here, three-level) can output voltage waveforms closer to a sine wave: compared to traditional two-level topologies, their output voltage harmonic distortion (THD) is reduced from 5%-8% to below 1.5%, meeting grid harmonic injection standards without requiring additional high-capacity filters, reducing interference to the grid voltage waveform, and simultaneously lowering the operational risk for load-side sensitive equipment (such as precision instruments). Figure 2 As can be seen, the DC side of this circuit topology consists of two capacitors with the same capacitance value connected in series. Therefore, the DC side voltage can be divided into three voltage levels. The midpoint of the upper and lower series capacitors is defined as the neutral point N, and the diode connected to the neutral point in the figure is the clamping diode. Represents the DC bus voltage. and These are the upper and lower capacitors on the DC side. and These are the two switches on the upper arm of phase A. and These are the two switches on the lower arm of phase A. and These are two clamping diodes on phase A bridge arm. The diode-clamped three-level topology distributes the DC bus voltage across the two series capacitors, so that the voltage stress on each switching device is only 1 / 2 of the DC bus voltage.

[0070] Under all operating conditions of energy storage charging and discharging, the DC / DC converter can maintain high voltage conversion efficiency, while the switching frequency of the multi-level topology can be reduced, thereby reducing switching losses.

[0071] Battery energy storage systems (BESS) can balance grid load, improve the utilization rate of renewable energy, provide backup power, and enhance grid stability. A BESS typically consists of three parts: the energy storage battery itself, a monitoring system, and an inverter.

[0072] The frequency controller selects the droop control function, which ensures that the output power of the converter or generator changes with the frequency, meaning the power output and frequency remain synchronized with a fixed droop rate. When the system load increases, causing the frequency to drop, the converter or generator automatically increases its output power, and vice versa. By properly setting the droop coefficient Kdroop, the system can automatically adjust the power output to maintain frequency stability when the load changes.

[0073] The frequency controller generates active power control commands based on the frequency error between the grid frequency and the rated frequency, and sets a dead zone to prevent frequent operation of the frequency controller. The specific calculation formula is as follows:

[0074] ;

[0075] Among them, dp ref For active power control, Kdroop is the sag coefficient, fref is the frequency command signal, and fe is the frequency measurement signal.

[0076] The power controller selects photovoltaic control mode, i.e., active power-voltage control. The system operation is controlled by adjusting the output power and voltage of the BESS. The active power part adopts active power deviation control to ensure that the system maintains the required active power level under different load conditions. The mathematical model of active power control is shown in formulas (5) and (6).

[0077] (5)

[0078] (6)

[0079] in, This is a reference value for active power. This is the active power measurement signal; Active power command; Active power deviation; current Axis components are ; This is the active power proportionality coefficient; This is the integral coefficient for active power.

[0080] The reactive power component is controlled by constant voltage, which helps manage the flow of reactive power in the system and maintain system stability. The mathematical model of constant voltage control is shown in equation (7).

[0081] (7)

[0082] in, The integral coefficient is ; the Q-axis component of the rotor current is Voltage deviation is . The proportional coefficient is used to set a dead zone with a slope to avoid frequent voltage control adjustments.

[0083] Further, in step S107, the core parameters of the mobile energy storage controller are optimized based on the mobile energy storage system installation strategy to obtain the optimized core parameters, specifically including the following steps:

[0084] Step S1071: Determine the parameters to be optimized. The parameters to be optimized include the droop coefficient, the power loop proportional-integral controller parameters of the power conversion system, and the charge / discharge cutoff state-of-charge threshold of the battery management system.

[0085] Step S1073: Implement an improved particle swarm optimization algorithm based on Python, using the parameters to be optimized as particle dimensions and the objective function value of power grid stability as fitness for iterative optimization to obtain the optimized core parameters.

[0086] In this embodiment, the optimization of the droop coefficient and the power loop PI parameter can shorten the power regulation response time of mobile energy storage. For example, a reasonable configuration of the droop coefficient can enable the energy storage to complete power command tracking within 0.02s when the grid frequency / voltage fluctuates (compared to 0.08s with traditional parameter configuration). Iterative optimization of the PI parameter can reduce the overshoot of power regulation (from 12% to 3%), avoid exacerbating grid oscillations due to power fluctuations, and effectively support the transient stability of the grid.

[0087] The improved particle swarm optimization algorithm uses the "grid stability objective function" as the fitness and can output the optimal parameter combination under multiple operating conditions (such as load mutation and photovoltaic fluctuation). Compared with fixed parameter control, the optimized parameters can reduce the value of the grid stability objective function (such as the power angle SSE) and at the same time be compatible with the voltage support requirements of different vulnerable nodes, avoiding energy storage regulation failure caused by parameter mismatch.

[0088] Optimizing the power loop PI parameters can reduce reactive / active regulation losses in energy storage: for example, proper PI parameter tuning can maintain the power factor of the power conversion system above 0.98, reducing reactive circulating current losses; combined with optimization of the droop coefficient, the energy conversion efficiency of energy storage charging and discharging can be improved, and energy consumption costs can be reduced in the long term.

[0089] Energy storage batteries, power conversion systems (PCS), and DC-DC chargers are interconnected and power distributed via a DC bus, encompassing power sources (grid / energy storage batteries), grid (DC bus), loads (electric vehicle charging / AC loads), and other components.

[0090] Particle Swarm Optimization (PSO) is an intelligent optimization algorithm known for its simplicity, strong search capabilities, and broad applicability. PSO initializes a population of particles in a specified search space, representing potential optimal solutions to the optimization problem. Particles are described by their current position, velocity, and fitness value. After iteration, the position of the next generation of particles is obtained based on their current position and velocity. The fitness value is calculated to determine the quality of the iterative particles. The optimization search is performed iteratively within a randomly initialized particle population.

[0091] Furthermore, in step S1073, an improved particle swarm optimization algorithm is implemented based on Python. The parameters to be optimized are used as particle dimensions, and the objective function value of power grid stability is used as the fitness for iterative optimization to obtain the optimized core parameters. Specifically, the following steps are included:

[0092] Step S10731: Initialize the particle swarm, including determining the number of particles P, the number of parameters to be optimized D, the learning factors C1 and C2, and the inertia weights. And the number of iterations, I. The parameter settings of the algorithm are shown in Table 1.

[0093] Table 1 Algorithm parameter settings

[0094]

[0095] Step S10733: For each particle, calculate the fitness of the current position according to the objective function of power grid stability, and obtain the fitness evaluation result.

[0096] Step S10735: Based on the fitness evaluation results, if the current particle's fitness is higher than its historical best score, then update the particle's individual historical best score.

[0097] Step S10737: If the current particle's fitness is higher than the historical best score of all particles, then update the individual's historical best score. body The process continues until the preset number of iterations is reached or other stopping conditions are met, at which point the optimal solution is output, and the optimized core parameters are obtained.

[0098] In this embodiment, the update and Based on the evaluation results, if the current particle's fitness is higher than its historical best score, then the particle's fitness is updated. If the current particle's fitness is higher than the historical best score of all particles, then update. .

[0099] The particle's velocity and position are updated based on its velocity and position, using the following formula:

[0100] (9)

[0101] (10)

[0102] in, Represents particles In time The speed at that point, Represents particles In time The location, and It is a random number between [0,1].

[0103] Repeat steps S10733-S10737 until the preset number of iterations is reached or other stopping conditions are met (e.g., the fitness value does not change significantly after more than 20 iterations).

[0104] When the algorithm ends, it outputs the optimal solution (gbest), which is the optimal solution found by the group.

[0105] By employing a dual update mechanism of "individual historical best + group historical best" and combining it with reasonable parameter configuration (such as particle number P=20 and iteration count I=100), the algorithm can quickly converge to the optimal solution. Compared with traditional PSO, it can improve the convergence speed (for example, reducing it from 150 iterations to within 100 iterations). At the same time, the engineering implementation in Python further reduces the computation time, adapting to the "rapid optimization requirements" of mobile energy storage controller parameters.

[0106] Using the "grid stability objective function" as the fitness evaluation logic, combined with the velocity-position update formula (e.g., introducing an inertia weight of 0.7 to balance global / local search), the algorithm can avoid getting trapped in local optima: the optimized controller parameters can reduce the value of the grid stability objective function (such as the power angle SSE), while ensuring the adaptability of the parameter combination under multiple operating conditions, and significantly improving the grid's ability to resist disturbances.

[0107] The collaborative design of learning factors and random numbers retains the exploratory nature of the algorithm while avoiding drastic fluctuations in parameter iteration, thus ensuring the consistency and reliability of the mobile energy storage controller parameters.

[0108] Setting the number of parameters to be optimized to D=7 (covering droop coefficient, PI parameters, etc.) supports collaborative optimization of multi-dimensional parameters: compared to adjusting single parameters one by one, this algorithm can simultaneously optimize 7 core parameters, achieving optimal matching between parameters, thus comprehensively improving the power regulation accuracy and voltage support capability of mobile energy storage. This invention can also use different intelligent optimization algorithms to replace the Particle Swarm Optimization (PSO) algorithm. For example, the Genetic Algorithm (GA) can search the parameter space by simulating the natural selection process, gradually optimizing controller parameters using selection, crossover, and mutation operations; the Simulated Annealing (SA) algorithm, based on the principle of physical annealing, avoids local optima by controlling the "temperature" parameter, and is suitable for high-dimensional nonlinear optimization problems; in addition, the Ant Colony Optimization (ACO) algorithm, by simulating the pheromone feedback mechanism in ant foraging behavior, can be used to solve complex combinatorial optimization problems. These algorithms can also be combined with DIgSILENT or other simulation platforms to achieve automatic parameter optimization, and may exhibit better convergence or robustness in certain scenarios.

[0109] Furthermore, the method for optimizing the parameters of the mobile energy storage controller in the distribution area also includes the following steps: substituting the optimized core parameters into the grid-mobile energy storage simulation model to re-simulate under various extreme scenarios; if the optimized core parameters ensure that the voltage and grid loss always meet the grid stability objective function, then output a multi-scenario simulation report with the optimal parameters.

[0110] A method for optimizing the parameters of mobile energy storage controllers in distribution substations to improve the resilience of distribution networks is proposed. This method uses a co-simulation platform built with DIgSILENT and Python to pre-calculate a fixed set of controller parameters that can significantly improve the transient stability of the distribution network. The complex controller parameter optimization problem is transformed into an offline mathematical optimization process based on a specific objective function. The optimal parameters are determined before the system encounters large disturbances through simulation, thus avoiding the dependence of traditional methods on real-time communication and online computing capabilities.

[0111] This invention utilizes particle swarm optimization (PSO) as the core optimization engine, employs DIgSILENT for power system transient process simulation, and uses Python scripts to automatically process simulation results, execute optimization algorithms, and provide feedback for parameter adjustments. This method systematically optimizes key control parameters such as the droop coefficient in the frequency controller and the proportional-integral parameters in the power controller. Its objective function comprehensively considers the suppression of generator power angle oscillations and the recovery capability of voltages at key system nodes, thereby ensuring that the optimized set of fixed parameters enables the energy storage system to provide optimal stability support during transient processes.

[0112] The complete parameter optimization system constructed in this invention includes the entire process from model establishment, objective function definition, optimization algorithm application to result verification. Relying on pre-optimized parameters, the mobile energy storage device can still autonomously and reliably perform frequency and voltage support functions, greatly enhancing the resilience and robustness of the distribution network.

[0113] Furthermore, this optimization method is closely integrated with the specific hardware architecture of the mobile energy storage system, and the optimized parameters are directly applied to the two-stage PCS topology, bidirectional DC / DC converter, and their control logic. This combination of embedding the calculated optimal parameters with specific hardware control loops ensures that the optimization results can be accurately reproduced in the actual physical system, effectively improving the system's transient stability and voltage recovery capability, thus forming a complete technical solution from software simulation optimization to hardware control execution.

[0114] The core of the method for optimizing the parameters of mobile energy storage controllers in distribution substations to improve the resilience of the distribution network proposed in this invention lies in pre-determining the optimal parameters of the controller through collaborative simulation and intelligent optimization algorithms, so as to enhance the transient stability and voltage recovery capability of the system in the distribution substation.

[0115] On the other hand, the present invention also proposes an electronic device for setting up a data processing module. The electronic device includes: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute any one of the transformer area mobile energy storage controller parameter optimization methods.

[0116] On the other hand, the present invention also proposes a computer storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any one of the parameters of a mobile energy storage controller for a distribution area.

[0117] Those skilled in the art will understand that implementing all or part of the processes in the above embodiments can be accomplished by a computer program instructing related hardware, and can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Dual Data SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM). The various embodiments described in this specification are presented in a progressive manner, and similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, for embodiments of apparatus, devices, and non-volatile computer storage media, since they are substantially similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments.

[0118] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for optimizing the parameters of a mobile energy storage controller in a distribution area, characterized in that, include: Construct a power grid stability objective function under power grid vulnerability resistance constraints based on power grid operation data; A grid-mobile energy storage simulation model is constructed based on the grid system and the mobile energy storage system. The installation strategy for the mobile energy storage system is determined based on the objective function of grid stability under the grid anti-vulnerability constraint and the grid-mobile energy storage simulation model. Based on the mobile energy storage system installation strategy, the core parameters of the mobile energy storage controller are optimized to obtain the optimized core parameters; Based on the optimized core parameters and the mobile energy storage system installation strategy, an optimized implementation plan for distributed energy storage is output.

2. The method for optimizing the parameters of a mobile energy storage controller in a distribution area according to claim 1, characterized in that, The objective function for power grid stability under the constraint of power grid resilience The expression is: ； In the formula, This represents the number of generators in the system. This represents the number of data points for generator power angle sampling. For the first The power angle of the generator in time Transient values; This represents the steady-state value of the generator power angle; The number of times the center node voltage is sampled; and These represent the transient voltage values ​​of the central node before and after the energy storage device is connected to the grid. This represents the steady-state value of the center node voltage.

3. The method for optimizing the parameters of a mobile energy storage controller in a distribution area according to claim 1, characterized in that, The mobile energy storage system installation strategy includes the optimal access location for distributed energy storage, energy storage capacity, and charge / discharge power curves.

4. The method for optimizing the parameters of a mobile energy storage controller in a distribution area according to claim 1, characterized in that, Based on the aforementioned grid stability objective function and the grid-mobile energy storage simulation model, the installation strategy for the mobile energy storage system is determined, specifically including: Based on the distributed energy storage pre-situation method, the voltage state vulnerability index of all nodes is calculated and sorted to obtain the node voltage state vulnerability ranking group. Based on the node voltage state vulnerability ranking group, within the selectable range of mobile energy storage installation locations, and combined with daily load / photovoltaic fluctuation data, multiple candidate schemes are generated. The candidate schemes include the installation location and the daily charge and discharge power curve of the corresponding location. If the candidate scheme satisfies the capacity constraint, then the candidate scheme is substituted into the power grid and mobile energy storage simulation model to calculate the degree to which the candidate scheme satisfies the power grid stability objective function and whether it meets the power grid resilience constraint. The installation location and charge / discharge power curves of the candidate schemes are iteratively optimized using intelligent algorithms. Finally, the scheme that simultaneously satisfies the grid stability objective function and the grid vulnerability constraint is selected as the installation strategy for the mobile energy storage system.

5. The method for optimizing the parameters of a mobile energy storage controller in a distribution area according to any one of claims 1 to 4, characterized in that, Based on the aforementioned mobile energy storage system installation strategy, the core parameters of the mobile energy storage controller are optimized to obtain the optimized core parameters, which specifically include: Determine the parameters to be optimized, including the droop coefficient, the power loop proportional-integral controller parameters of the power conversion system, and the charge / discharge cutoff state of charge threshold of the battery management system; An improved particle swarm optimization algorithm is implemented based on Python. The parameters to be optimized are used as particle dimensions, and the objective function value of power grid stability is used as the fitness for iterative optimization to obtain the optimized core parameters.

6. The method for optimizing the parameters of a mobile energy storage controller in a distribution area according to claim 5, characterized in that, An improved particle swarm optimization algorithm is implemented using Python. The parameters to be optimized are used as particle dimensions, and the objective function value for power grid stability is used as the fitness for iterative optimization to obtain the optimized core parameters, specifically including: Initialize the particle swarm, including determining the number of particles, the number of parameters to be optimized, the learning factor, the inertia weight, and the number of iterations; For each particle, the fitness of its current position is calculated based on the power grid stability objective function, and the fitness evaluation result is obtained. Based on the fitness evaluation results, if the current particle's fitness is higher than its historical best score, then the particle's individual historical best score is updated. If the current particle's fitness is higher than the historical best score of all particles, then update the individual's historical best score. body The process continues until a preset number of iterations is reached or other stopping conditions are met, at which point the optimal solution is output, and the optimized core parameters are obtained.

7. The method for optimizing the parameters of a mobile energy storage controller in a distribution area according to claim 5, characterized in that... It lies in, It also includes: substituting the optimized core parameters into the grid-mobile energy storage simulation model to re-simulate under various extreme scenarios; if the optimized core parameters ensure that the voltage and network loss always meet the grid stability objective function, then outputting a multi-scenario simulation report with optimal parameters.

8. The method for optimizing the parameters of a mobile energy storage controller in a distribution area according to any one of claims 1 to 4, characterized in that, The mobile energy storage system includes a two-stage power conversion system topology, a bidirectional DC / DC converter and its control logic. The two-stage power conversion system adopts a multi-level converter topology; the bidirectional DC / DC converter adopts an isolated topology.

9. An electronic device, characterized in that, include: At least one processor; A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the method for optimizing the parameters of the mobile energy storage controller for the distribution area as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that, The device stores a computer program, which, when executed by a processor, implements the method for optimizing parameters of a mobile energy storage controller for distribution areas as described in any one of claims 1 to 8.

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