Network construction type energy storage system scheduling method and device, computer equipment and storage medium

Abstract

The invention relates to a network construction type energy storage system scheduling method and device, computer equipment and a storage medium. The method comprises the steps of obtaining photovoltaic output characteristic parameters, electrical strength parameters and performance parameters of an energy storage system, establishing an electromagnetic transient simulation system, simulating a system dynamic response process of photovoltaic output under multi-stage step change based on the electromagnetic transient simulation system, and collecting simulation test result data. Constructing a multi-objective optimization model taking the minimum rated power of the energy storage system, the network voltage fluctuation amplitude and the grid-connected current harmonic distortion rate as optimization objectives, respectively solving the optimal solutions of the multi-objective optimization model in different power grid intensity scenes, and obtaining the optimal power grid intensity probability distribution of the multi-objective optimization model based on the preset power grid intensity probability distribution. And carrying out weighted fusion on the optimal solutions in different power grid strength scenes to obtain a target energy storage capacity and target energy storage control parameter combination, and then scheduling the energy storage system. By adopting the method, the operation stability of the power system can be effectively improved.

Description

Technical Field

[0001] This application relates to the field of new energy grid connection technology, and in particular to a grid-type energy storage system scheduling method, device, computer equipment, computer-readable storage medium and computer program product. Background Technology

[0002] Weak power grids typically exhibit low short-circuit ratios, low system inertia, and high line impedance. These grids are less tolerant of power fluctuations, and the intermittency and volatility of photovoltaic (PV) power sources further exacerbate grid instability. In weak grid scenarios, if PV systems employ traditional grid-following strategies, their output power fluctuates drastically with sunlight and temperature, easily leading to problems such as excessive grid voltage deviations and frequency oscillations. This is especially true when the grid's equivalent impedance is high, where voltage fluctuations may exceed standard limits. Against this backdrop, configuring grid-connected energy storage for existing PV systems becomes the optimal solution to address these issues.

[0003] However, current grid-based energy storage parameter configuration schemes (such as energy storage capacity and control parameters) still have problems such as unreasonable parameter configuration, difficulty in coping with sudden load changes, and impact on the stable operation of the power system. Summary of the Invention

[0004] Therefore, it is necessary to provide a grid-based energy storage system dispatching method, device, computer equipment, computer-readable storage medium, and computer program product that can improve the stable operation of the power system, addressing the aforementioned technical problems.

[0005] Firstly, this application provides a method for scheduling a grid-type energy storage system, including:

[0006] Obtain the photovoltaic output characteristic parameters of the target photovoltaic power plant, the electrical strength parameters of the power grid, and the performance parameters of the energy storage system;

[0007] Based on the photovoltaic output characteristic parameters, the electrical strength parameters, and the performance parameters, an electromagnetic transient simulation system is established, which includes a photovoltaic power generation module, a grid-type energy storage module, an equivalent power grid module, and a measurement module.

[0008] A preset power fluctuation condition is applied to the electromagnetic transient simulation system to simulate the dynamic response process of the photovoltaic power output under multi-level step changes, and simulation test result data is collected.

[0009] Based on simulation test results, a multi-objective optimization model is constructed with the goal of minimizing the rated power of the energy storage system, the voltage fluctuation amplitude at the grid point, and the harmonic distortion rate of the grid-connected current. The optimal solutions of the multi-objective optimization model under different grid intensity scenarios are then solved.

[0010] Based on the preset power grid intensity probability distribution, the optimal solutions under different power grid intensity scenarios are weighted and fused to obtain the target energy storage capacity and the target energy storage control parameter combination;

[0011] The energy storage system is scheduled based on the target energy storage capacity and the target energy storage control parameters.

[0012] In one embodiment, the electromagnetic transient simulation system, based on the photovoltaic output characteristic parameters, the electrical strength parameters, and the performance parameters, comprising a photovoltaic power generation module, a grid-type energy storage module, and an equivalent power grid model, includes:

[0013] The photovoltaic output characteristic parameters are mapped to a photovoltaic power generation module based on the current-voltage characteristic lookup table mechanism;

[0014] The electrical strength parameters are converted into adjustable equivalent impedance parameters and written as series elements into the equivalent power grid module;

[0015] The performance parameters are injected as boundary conditions into the power transfer and state evolution equations of the energy storage unit.

[0016] The grid connection point voltage is collected in real time, and the grid connection point power is determined based on the grid connection point voltage.

[0017] In an exemplary embodiment, applying a preset power fluctuation condition to the electromagnetic transient simulation system to simulate the system dynamic response process of photovoltaic power output under multi-level step changes includes:

[0018] A multi-stage step sequence is retrieved from the preset operating condition library as the photovoltaic power output input signal;

[0019] During the simulation, time-series data of grid connection point voltage, current, frequency, and energy storage state of charge are collected in real time and cached in a temporary data area.

[0020] In an exemplary embodiment, solving for the optimal solution of the multi-objective optimization model under different power grid intensity scenarios includes:

[0021] For each grid strength scenario, a set of candidate solutions is initialized, and each candidate solution corresponds to a set of energy storage capacity and control parameters.

[0022] For each candidate solution, the electromagnetic transient simulation system is invoked to perform a complete disturbance condition simulation, the target value of each candidate solution is determined, and the target values ​​of each candidate solution are weighted to obtain the comprehensive fitness.

[0023] Based on the comprehensive fitness and the preset dominance relationship, each candidate solution is screened, and the individual and global optimal solutions are updated.

[0024] Generate a new generation of candidate solutions and iterate until convergence to obtain the optimal solution.

[0025] In one exemplary embodiment, the method further includes:

[0026] If a candidate solution leads to the energy storage state of charge exceeding the limit or the power quality index exceeding the standard, the candidate solution is marked as an infeasible solution, and its guiding role in the search direction is excluded in subsequent iterations.

[0027] In an exemplary embodiment, solving for the optimal solution of the multi-objective optimization model under different power grid intensity scenarios includes:

[0028] Based on the multi-objective particle swarm optimization algorithm, the Pareto optimal solution of the multi-objective optimization model under different power grid intensity scenarios is obtained.

[0029] Secondly, this application also provides a grid-type energy storage system dispatching device, comprising:

[0030] The data acquisition module is used to acquire photovoltaic output characteristic parameters of the target photovoltaic power station, electrical strength parameters of the power grid, and performance parameters of the energy storage system.

[0031] The simulation system construction module is used to establish an electromagnetic transient simulation system, which includes a photovoltaic power generation module, a grid-type energy storage module, an equivalent power grid module, and a measurement module, based on the photovoltaic output characteristic parameters, the electrical strength parameters, and the performance parameters.

[0032] The simulation module is used to apply a preset power fluctuation condition to the electromagnetic transient simulation system, simulate the system dynamic response process of photovoltaic output under multi-level step changes, and collect simulation test result data.

[0033] The model solving module, based on simulation test results, constructs a multi-objective optimization model with the optimization objectives of minimizing the rated power of the energy storage system, the voltage fluctuation amplitude at the grid point, and the harmonic distortion rate of the grid-connected current. The module then solves for the optimal solution of the multi-objective optimization model under different grid intensity scenarios.

[0034] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the above embodiments of the grid-type energy storage system scheduling method.

[0035] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in any of the above embodiments of the grid-type energy storage system scheduling method.

[0036] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the grid-type energy storage system scheduling method.

[0037] The aforementioned grid-based energy storage system scheduling methods, devices, computer equipment, computer-readable storage media, and computer program products, by establishing a high-fidelity electromagnetic transient simulation system, realistically reproduce the system's dynamic response under severe fluctuations in photovoltaic output, overcoming the problem of insufficient accuracy in traditional models. Applying multi-level step power fluctuation conditions simulates typical harsh operating scenarios, ensuring sufficient robustness of the energy storage configuration. By constructing a multi-objective optimization model centered on minimizing energy storage capacity, voltage fluctuations, and current harmonics, it achieves coordinated optimization of economic efficiency and dynamic performance while satisfying power quality and energy storage safety constraints. Next, the optimal configuration is solved separately for different grid intensities, improving the scheme's adaptability to complex access conditions such as weak grids. Furthermore, based on the probability distribution of grid intensity, the optimal solutions for each scenario are weighted and fused, ensuring that the configuration results take into account both the support requirements of extreme operating conditions and actual operational statistical patterns, avoiding over-design and achieving precise resource allocation. Finally, scheduling based on the optimized target energy storage capacity and control parameters can effectively suppress voltage fluctuations, frequency oscillations and harmonic distortion, significantly improve the grid connection stability and absorption capacity of photovoltaic systems under weak grid conditions, and achieve the comprehensive optimization goal of low cost, high adaptability and strong support. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is an application environment diagram of a grid-type energy storage system scheduling method in one embodiment;

[0040] Figure 2 This is a flowchart illustrating a grid-type energy storage system scheduling method in one embodiment;

[0041] Figure 3 This is a schematic diagram comparing the grid connection point voltage, current, and power before and after optimization using a grid-type energy storage system scheduling method in one embodiment.

[0042] Figure 4 This is a flowchart illustrating the steps involved in constructing an electromagnetic transient simulation system in one embodiment.

[0043] Figure 5This is a schematic diagram of the structure of an electromagnetic transient model in one embodiment;

[0044] Figure 6 This is a flowchart illustrating the scheduling method for a grid-type energy storage system in another embodiment;

[0045] Figure 7 This is a flowchart illustrating the process of solving a multi-objective optimization model in one embodiment;

[0046] Figure 8 This is a structural block diagram of a grid-type energy storage system dispatching device in one embodiment;

[0047] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0050] The grid-type energy storage system scheduling method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0051] Specifically, the operator can send an energy storage system optimization configuration message to the server 104 via terminal 102. The server 104 responds to the message and obtains the photovoltaic output characteristic parameters of the target photovoltaic power station, the electrical strength parameters of the power grid, and the performance parameters of the energy storage system from multiple data sources. Then, based on the photovoltaic output characteristic parameters, electrical strength parameters, and performance parameters, an electromagnetic transient simulation system is established, which includes a photovoltaic power generation module, a grid-type energy storage module, an equivalent power grid module, and a measurement module. A preset power fluctuation condition is applied to the electromagnetic transient simulation system to simulate the dynamic response process of the system under multi-level step changes in photovoltaic output. A multi-objective optimization model is constructed with the optimization objectives of minimizing the rated power of the energy storage system, the voltage fluctuation amplitude at the grid point, and the harmonic distortion rate of the grid-connected current. The optimal solutions of the multi-objective optimization model under different power grid strength scenarios are solved respectively. Based on the preset power grid strength probability distribution, the optimal solutions under different power grid strength scenarios are weighted and fused to obtain the target energy storage capacity and the target energy storage control parameter combination. Finally, the energy storage system is scheduled based on the target energy storage capacity and the target energy storage control parameter combination.

[0052] Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0053] Grid-Forming Energy Storage System (GFM-ESS) is an advanced energy storage technology capable of autonomously establishing grid voltage and frequency and actively supporting the stable operation of the power system. A system composed of a storage converter (PCS) employing grid-forming control and energy storage units (such as lithium batteries) can autonomously establish stable voltage and frequency without external grid support. During grid-connected operation, it provides inertia, damping, and voltage support to weak grids, enhancing the dynamic stability and disturbance rejection capability of the power system.

[0054] In one exemplary embodiment, such as Figure 2 As shown, a scheduling method for a grid-type energy storage system is provided, which is then applied to... Figure 1Taking server 104 as an example, the explanation includes steps 100 to 600. Wherein:

[0055] Step 100: Obtain the photovoltaic output characteristic parameters of the target photovoltaic power station, the electrical strength parameters of the power grid, and the performance parameters of the energy storage system.

[0056] Photovoltaic output characteristic parameters are parameters used to reflect the power generation capacity and output behavior of a photovoltaic power plant, including but not limited to rated power. single-string open-circuit voltage Short circuit current Photovoltaic cell current-voltage characteristic curves, etc. Electrical strength parameters are key indicators characterizing the strength of a power grid, mainly including but not limited to the short-circuit ratio (SCR) and line resistance. Line reactance Line length A lower SCR indicates a weaker power grid. Energy storage system performance parameters describe the physical and operational characteristics of energy storage devices, such as lithium battery energy storage. Charge and discharge efficiency Rated AC voltage Maximum charge / discharge rate, etc.

[0057] In practical applications, the server can receive project configuration files (such as JSON or CSV format) uploaded by users via a web interface or API, or automatically retrieve basic data of registered photovoltaic power plants from relevant energy management systems, as well as grid electrical strength parameters and energy storage system performance parameters. Furthermore, the server can parse, verify, and standardize the retrieved data.

[0058] Step 200: Based on the photovoltaic output characteristic parameters, electrical strength parameters, and performance parameters, establish an electromagnetic transient simulation system that includes a photovoltaic power generation module, a grid-type energy storage module, an equivalent power grid module, and a measurement module.

[0059] In this embodiment, the photovoltaic power generation module is a photovoltaic array model built based on physical mechanisms, capable of simulating the current-voltage response under varying illumination. The grid-type energy storage module is an energy storage converter model employing control strategies (such as VSG) with autonomous voltage build-up and inertia support capabilities. The equivalent grid module can simulate the impedance characteristics of the power grid under different short-circuit ratios using adjustable inductors and resistors. The measurement module is used to collect time-series data of key variables such as voltage, current, frequency, and SOC, including but not limited to current sensors and voltage sensors.

[0060] In practical implementation, the server can build the model based on MATLAB / Simulink. Taking the model built based on Simulink as an example, a dedicated simulation model file (.slx) can be dynamically generated according to the current task parameters, and the model assembly script can be started through the MATLAB automation interface. The server maps the standardized parameters to the parameter fields of each module. Among them, photovoltaic parameters are used to update the IV lookup table module; electrical strength parameters are converted into equivalent impedance values ​​and written into the grid module; energy storage parameters are injected into the state equation and power limit module of the energy storage unit; the measurement module is configured with a sampling frequency (usually ≤10μs) to meet the electromagnetic transient accuracy requirements. Thus, an electromagnetic transient simulation system including a photovoltaic power generation module, a grid-type energy storage module, an equivalent grid module, and a measurement module is constructed.

[0061] Step 300: Apply a preset power fluctuation condition to the electromagnetic transient simulation system to simulate the system dynamic response process of photovoltaic power output under multi-level step changes, and collect simulation test result data.

[0062] Power fluctuation conditions are artificially set sequences of photovoltaic (PV) output changes used to test the system's disturbance rejection capability. Multi-stage step changes refer to the process of PV power undergoing multiple abrupt changes within a short period. The system dynamic response process refers to the trajectory of variables such as voltage, current, and frequency over time, reflecting system stability. Simulation test results include high-sampling-rate time-series data such as voltage fluctuation curves, current waveforms, frequency deviations, THD, and SOC trajectories, used for subsequent performance evaluation.

[0063] In practice, the server can start the simulation engine, drive the model to run for a specified duration (e.g., 35 seconds), load a standard step sequence (configurable as default or measured data in cloudy weather) from the operating condition template library, and bind it to the power input port of the photovoltaic power generation module. Users can choose whether to enable custom disturbance conditions. During the simulation, the server can capture the data stream of each measurement point in real time and record the simulation test results.

[0064] Step 400: Based on the simulation test results, construct a multi-objective optimization model with the optimization objectives of minimizing the rated power of the energy storage system, the voltage fluctuation amplitude at the grid point, and the harmonic distortion rate of the grid-connected current. Solve the optimal solution of the multi-objective optimization model under different grid intensity scenarios.

[0065] The voltage fluctuation amplitude at the grid connection point refers to the maximum deviation of the voltage at the grid connection point from its rated value, reflecting voltage stability. Harmonic distortion rate is an indicator used to measure the degree of distortion in the current waveform, affecting power quality indicators.

[0066] In practice, after the simulation is completed, the server performs objective function quantization processing on the simulation test result data in the cache: for example, directly read the maximum energy storage output power set in the current candidate solution, extract the effective value sequence of the grid connection point voltage, calculate its maximum deviation (ΔV_max) relative to the rated value, in percentage, perform fast Fourier transform (FFT) on the steady-state current signal, extract the 2nd to 50th harmonic components, and calculate the grid connection current harmonic distortion rate (THD value).

[0067] Next, based on the aforementioned data such as rated energy storage power, voltage fluctuation amplitude, and grid-connected current harmonic distortion rate, a multi-objective optimization model is constructed. Specifically, the core optimization objectives include:

[0068] 1) Minimum energy storage capacity: based on the rated power of the energy storage system The goal, as a quantifiable indicator, is to achieve the following while ensuring system stability and power quality: Minimize the amount of energy storage while ensuring that the depth of charge and discharge does not exceed a set value (e.g., 80%).

[0069] 2) Minimum grid voltage fluctuation: based on the voltage deviation rate at the grid connection point ( Using ) as an indicator, the goal is to make Minimum;

[0070] 3) Minimum current harmonic content: based on grid-connected current Total Harmonic Distortion (THD) i The indicator is "Harmonic Current Limit at Point of Common Coupling" (THD), which must meet the relevant standards. i ≤5%).

[0071] 4) Optimal control parameters for energy storage: The control parameters for grid-type energy storage include the voltage loop PI regulator parameters ( , ), Current loop PI regulator parameters ( , The parameters include virtual inertia (J) and damping coefficient (D), with the goal of shortening the response time through parameter optimization. And adjust power to reduce overshoot .

[0072] After constructing the multi-objective optimization model containing multiple optimization objectives, the multi-objective optimization model can be solved under different power grid intensity scenarios using a multi-objective optimization algorithm to obtain the optimal solution under different power grid intensity scenarios. In this embodiment, each solution is not a single optimal solution, but a set of optimal solutions.

[0073] Step 500: Based on the preset power grid strength probability distribution, the optimal solutions under different power grid strength scenarios are weighted and fused to obtain the target energy storage capacity and the target energy storage control parameter combination.

[0074] The probability distribution of power grid strength refers to the statistical results of the frequency of occurrence of various short-circuit ratios in a certain region during historical operation, reflecting the distribution of actual operating conditions.

[0075] In practice, the server performs multi-objective optimization under n different power grid intensities, and after obtaining multiple sets of optimal parameters, it can combine the probability distribution of power grid intensity. The optimal solutions under different power grid intensity scenarios are weighted and fused to obtain the optimal target energy storage capacity. And the target energy storage control parameters are combined, where:

[0076]

[0077] Step 600: Based on the target energy storage capacity and the target energy storage control parameters, schedule the energy storage system.

[0078] After obtaining the target energy storage capacity and target energy storage control parameter combination, the server can push the target energy storage capacity and target energy storage control parameter combination to the on-site energy storage monitoring system or energy management system through a secure communication interface, set the operating mode, power command and control parameters of the energy storage system, and then schedule the energy storage system.

[0079] The comparison of voltage, current, and power at the grid connection point (PCC) before and after optimizing the energy storage system parameter configuration using the above-mentioned grid-connected energy storage system scheduling method is as follows: Figure 3 As shown.

[0080] In the aforementioned grid-based energy storage system scheduling method, a high-fidelity electromagnetic transient simulation system is established to realistically reproduce the system's dynamic response under severe fluctuations in photovoltaic output, overcoming the problem of insufficient accuracy in traditional models. Multi-level step power fluctuation conditions are applied to simulate typical harsh operating scenarios, ensuring sufficient robustness of the energy storage configuration. By constructing a multi-objective optimization model centered on minimizing energy storage capacity, voltage fluctuations, and current harmonics, economic efficiency and dynamic performance can be synergistically optimized while meeting power quality and energy storage safety constraints. Next, the optimal configuration is solved separately for different grid intensities to improve the scheme's adaptability to complex access conditions such as weak grids. Furthermore, the optimal solutions for each scenario are weighted and fused based on the grid intensity probability distribution, ensuring that the configuration results take into account both the support requirements of extreme operating conditions and actual operational statistical patterns, avoiding over-design and achieving precise resource allocation. Finally, scheduling is performed based on the optimized target energy storage capacity and control parameters, effectively suppressing voltage fluctuations, frequency oscillations, and harmonic distortion, significantly improving the grid connection stability and absorption capacity of photovoltaic systems under weak grids, and achieving a comprehensive optimization goal of low cost, high adaptability, and strong support.

[0081] In one exemplary embodiment, such as Figure 4 As shown, step 200 includes:

[0082] Step 220: Map the photovoltaic output characteristic parameters to a photovoltaic power generation module based on the current-voltage characteristic lookup table mechanism.

[0083] Step 240: Convert the electrical strength parameters into adjustable equivalent impedance parameters and write them as series elements into the equivalent power grid module.

[0084] Step 260: Inject the performance parameters as boundary conditions into the power transfer and state evolution equations of the energy storage unit.

[0085] Step 280: Collect grid connection point voltage in real time and determine grid connection point power based on grid connection point voltage.

[0086] The current-voltage characteristic lookup table mechanism is a mechanism that pre-discretizes the IV curve into a two-dimensional lookup table (light intensity × temperature → current / voltage), and then quickly obtains the output current under the current operating condition through interpolation in the simulation.

[0087] The server can extract the IV curve under standard test conditions from the aforementioned photovoltaic output characteristic parameters and, combined with a temperature and irradiance correction model, generate a multi-dimensional IV lookup table covering a typical operating range (e.g., G=200~1000 W / m², T=10~60℃). This table is stored in memory in matrix form. During simulation, the output current of the photovoltaic array is dynamically calculated using a bilinear interpolation algorithm based on real-time input irradiance and temperature signals. The adjustable equivalent impedance parameter is the grid-side equivalent impedance (R + jX) obtained by back-derived from the SCR, used to simulate line impedance. The equivalent grid module is a simplified model representing the external AC system in electromagnetic transient simulation, typically composed of voltage source series impedances.

[0088] In practice, the construction process of the electromagnetic transient simulation system can be as follows:

[0089] For photovoltaic power generation modules: a multi-series parallel photovoltaic array model is adopted. Based on the preset photovoltaic cell current-voltage characteristic curve, the output voltage under certain illumination conditions is obtained from the port voltage. The output current is obtained by looking up the table. And calculate its output power. :

[0090]

[0091] DC side capacitor voltage of converter The dynamic equation is:

[0092]

[0093] Where C is the DC bus capacitance value. This refers to the output power of the photovoltaic converter.

[0094] The dq-axis component equations of the AC side voltage and current of the converter are as follows:

[0095]

[0096] in, , This refers to the dq-axis current on the grid side of the photovoltaic converter. , This refers to the dq-axis voltage on the valve side of the photovoltaic converter. , This refers to the dq-axis voltage on the grid side of the photovoltaic converter. Angular frequency, The AC equivalent resistance of the photovoltaic converter. This is the AC equivalent inductance of the photovoltaic converter.

[0097] Converter output active power reactive power is :

[0098]

[0099] The MPPT algorithm is a perturbation-observation method that determines the optimal terminal voltage reference value based on illumination conditions and photovoltaic output power. .

[0100] The photovoltaic converter employs dual closed-loop vector control, using the grid-side dq-axis voltage to control the converter's DC voltage and output reactive power. Specifically, the outer power loop obtains the inner current reference value through the DC voltage and the output reactive power deviation.

[0101]

[0102] in, This is the outer ring proportional adjustment coefficient. This is the outer loop integral adjustment coefficient.

[0103] The inner current loop regulates the converter output voltage via a proportional-integral (PI) controller, and obtains the dq-axis control duty cycle. , :

[0104]

[0105] in, This is the current proportional adjustment coefficient. This is the current integral adjustment coefficient.

[0106] Then, the valve-side control voltage is obtained through a voltage limiting circuit. , :

[0107]

[0108] Where min(·) is the function for finding the minimum value.

[0109] For grid-type energy storage modules: the performance parameters of the energy storage system are injected as boundary conditions into the power transfer and state evolution equations of the energy storage unit. The energy storage unit (energy storage battery) is equivalent to a power source, and its output power... Due to frequency deviation and rate of change Decide:

[0110]

[0111] in, This is the frequency deviation coefficient. This represents the rate of change coefficient of frequency deviation. Battery output voltage. It is usually quite stable, taking into account the internal resistance loss coefficient. (When the value range is greater than 0 and less than 1), the converter output power for:

[0112]

[0113] The dq-axis component equations of the AC side voltage and current of the converter are as follows:

[0114]

[0115] in, , This refers to the dq-axis current on the grid side of the energy storage converter. , This refers to the dq-axis voltage on the valve side of the energy storage converter. , This refers to the dq-axis voltage on the grid side of the energy storage converter. Angular frequency, The AC equivalent resistance of the energy storage converter. This is the AC equivalent inductance of the energy storage converter.

[0116] Converter output active power reactive power is :

[0117]

[0118] The energy storage converter uses virtual synchronous machine control.

[0119] The active power control loop generates an internal potential angle θ based on the system frequency deviation and active power deviation.

[0120]

[0121] in, This is the frequency adjustment coefficient. is the active power regulation coefficient, s is the differential operator, J is the equivalent inertia, and D is the equivalent damping coefficient.

[0122] The reactive power control loop generates an internal potential reference value E based on the AC voltage amplitude deviation and reactive power variation.

[0123]

[0124] in, This is the voltage regulation coefficient. is the reactive power regulation coefficient, and s is the differential operator.

[0125] The converter output voltage is adjusted by a proportional-integral (PI) controller, and the dq-axis current reference value is obtained. , :

[0126]

[0127] in, This is the voltage proportional adjustment coefficient. This is the voltage integral adjustment coefficient.

[0128] The converter output voltage is adjusted by a proportional-integral (PI) controller, and the dq-axis control duty cycle is obtained. , :

[0129]

[0130] in, This is the current proportional adjustment coefficient. This is the current integral adjustment coefficient.

[0131] The valve-side control voltage is obtained by controlling the voltage limiting circuit. , :

[0132]

[0133] Where min(·) is the function for finding the minimum value.

[0134] For the power grid equivalent module: electrical strength parameters are converted into adjustable equivalent impedance parameters and written as series elements into the equivalent power grid module. Specifically, an equivalent adjustable reactance is used to simulate different power grid short-circuit ratios. The formula for calculating the equivalent parameters is as follows:

[0135]

[0136] in, It represents the reactance / resistance ratio.

[0137] For the measurement module: Voltage at the grid connection point is collected in real time using voltage and current sensors. Current data And based on the grid connection point voltage Current data The power at the grid connection point (PCC point) is calculated.

[0138] Based on the above process, an electromagnetic transient simulation system is constructed, comprising photovoltaic power generation modules, grid-type energy storage modules, and an equivalent power grid model. The electromagnetic transient model structure is referenced. Figure 5 .

[0139] In this embodiment, the real dynamic characteristics of the three main components—photovoltaics, power grid, and energy storage—are fully restored through methods such as table lookup mechanism, equivalent impedance modeling, and state equation injection. All model parameters are derived from actual engineering data, avoiding empirical assumptions and enhancing simulation credibility. The high-fidelity simulation output provides a high-quality data foundation for multi-objective optimization, ensuring that the final recommended energy storage capacity and control parameters have strong adaptability and engineering feasibility.

[0140] like Figure 6 As shown, in an exemplary embodiment, step 300 includes:

[0141] Step 320: Retrieve a multi-stage step sequence from the preset operating condition library as a photovoltaic output command signal and inject it into the photovoltaic power generation module. During the simulation operation, collect time series data of grid connection point voltage, current, frequency and energy storage charge status in real time, and cache the time series data in the temporary data area.

[0142] A multi-stage step sequence refers to a sequence of photovoltaic power abrupt changes set in chronological order (e.g., 0% → 100% → 50% → 80% of rated power), used to simulate the most severe power disturbances caused by drastic fluctuations in sunlight under cloudy weather conditions. Grid connection point voltage, current, and frequency refer to the effective values ​​of the three-phase voltage and current, and the system frequency at the point of connection (PCC) between the photovoltaic power generation system and the grid, reflecting power quality and stability. Energy storage state of charge (SOC) represents the proportion of remaining energy in the energy storage battery relative to its rated capacity, and is a core state variable for assessing its operational safety.

[0143] In practice, the server reads the disturbance condition type required for the current optimization scenario through the task configuration interface, and then retrieves a standard multi-stage step sequence (configurable as a default template or user-defined) from the preset condition library, injecting it as a photovoltaic output command signal into the photovoltaic power generation module. This signal drives the model into a dynamic operating state, simulating the most severe operating conditions under real weather changes, such as a step jump in photovoltaic power from 0 to rated power. ( (This represents the rated power of the photovoltaic system), and after maintaining this level for a certain period, it steps down to 0.5. After maintaining this level for a certain period, it stepps to 0.8. This operating condition covers typical sudden changes in sunlight under cloudy weather conditions, placing the highest demands on energy storage regulation capabilities. During the simulation operation, time-series data of grid-connected point voltage, current, frequency, and energy storage state of charge are collected in real time and cached in a temporary data area. Then, data from each channel is extracted from the temporary data area to calculate the required indicators for the objective function, such as voltage fluctuation amplitude (ΔV_max), frequency deviation (Δf), and harmonic distortion rate (THD), which serve as the input basis for constructing a multi-objective optimization model.

[0144] In this embodiment, a multi-stage step sequence based on historical meteorological statistics is used to closely approximate the most severe power fluctuation scenario in actual operation, thereby improving the robustness of the optimization scheme. By collecting key state quantities at all times with high precision, the accurate capture of transient processes (such as voltage drop and SOC over-limit) is ensured.

[0145] like Figure 7 As shown, in an exemplary embodiment, solving for the optimal solution of the multi-objective optimization model under different power grid intensity scenarios includes:

[0146] Step 520: For each grid strength scenario, initialize a set of candidate solutions, where each candidate solution corresponds to a set of energy storage capacity and control parameters.

[0147] Step 540: For each candidate solution, call the electromagnetic transient simulation system to perform a complete disturbance condition simulation, determine the target value of each candidate solution, and perform weighted processing on the target values ​​of each candidate solution to obtain the comprehensive fitness.

[0148] Step 560: Based on the comprehensive fitness and the preset dominance relationship, each candidate solution is screened, and the individual and global optimal solutions are updated.

[0149] Step 580: Generate a new generation of candidate solutions and iterate until convergence to obtain the optimal solution.

[0150] In practice, the multi-objective optimization model can be solved using a multi-objective particle swarm optimization algorithm. The specific process is as follows:

[0151] 1) Initialization: Randomly generate 50 particles (candidate solutions), each particle corresponding to a set of parameters ( , , , , (J, D);

[0152] 2) Fitness calculation: The target value (C, ...) of each particle is obtained through electromagnetic transient simulation. THD i , , (), where C represents the cost of the energy storage system. To balance multiple objectives, the values ​​of each objective need to be normalized and a weighted average taken to obtain the overall fitness:

[0153]

[0154] in The weighting coefficients;

[0155] 3) Particle Swarm Optimization: Simulates the foraging behavior of bird flocks, and the parameters of each particle are iteratively updated based on the particle's historical best solution and the current population best solution.

[0156] 4) Pareto Optimization: To accommodate multi-objective optimization, a Pareto optimal solution set is constructed. A "dominance relationship" is defined—if all objectives of solution A are superior to solution B, then A dominates B and constitutes a Pareto optimal set, serving as a reference benchmark for population optimization.

[0157] 5) External archiving mechanism: Set up an external archive to store Pareto optimal solutions, and eliminate redundant solutions by calculating the crowding degree (measuring the distance between solutions) to maintain the diversity of the solution set and avoid the algorithm from converging to local optima.

[0158] 6) Convergence check: After iterating to the maximum number of times (e.g., 100 times), output the capacity solution in the current Pareto optimal set. , , , , J, D).

[0159] 7) Repeat the above multi-objective optimization process under n different power grid intensities to obtain multiple sets of optimal solutions.

[0160] If a candidate solution leads to the energy storage state of charge exceeding the limit or the power quality index exceeding the standard, the candidate solution will be marked as an infeasible solution, and its guiding role in the search direction will be excluded in subsequent iterations.

[0161] It is understood that in other embodiments, the multi-objective optimization model can also be solved using a non-dominated sorting genetic algorithm, a multi-objective differential evolution algorithm, or other multi-objective optimization algorithms.

[0162] In this embodiment, by introducing an external archive mechanism to store non-dominated solutions and combining congestion distance for diversity maintenance, a uniformly distributed Pareto front can be stably output, enabling the energy storage configuration scheme to achieve the optimal balance between economy, dynamic performance and engineering feasibility.

[0163] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0164] Based on the same inventive concept, this application also provides a grid-type energy storage system scheduling device for implementing the grid-type energy storage system scheduling method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the grid-type energy storage system scheduling device provided below can be found in the limitations of the grid-type energy storage system scheduling method described above, and will not be repeated here.

[0165] In one exemplary embodiment, such as Figure 8 As shown, a grid-type energy storage system scheduling device 600 is provided, including: a data acquisition module 610, a simulation system construction module 620, a simulation module 630, a model solving module 640, a weighting module 650, and a scheduling module 660, wherein:

[0166] The data acquisition module 610 is used to acquire photovoltaic output characteristic parameters of the target photovoltaic power station, electrical strength parameters of the power grid, and performance parameters of the energy storage system.

[0167] The simulation system construction module 620 is used to establish an electromagnetic transient simulation system, which includes a photovoltaic power generation module, a grid-type energy storage module, an equivalent power grid module, and a measurement module, based on the photovoltaic output characteristic parameters, the electrical strength parameters, and the performance parameters.

[0168] The simulation module 630 is used to apply a preset power fluctuation condition to the electromagnetic transient simulation system, simulate the system dynamic response process of photovoltaic power output under multi-level step changes, and collect simulation test result data.

[0169] The model solving module 640, based on simulation test results data, constructs a multi-objective optimization model with the optimization objectives of minimizing the rated power of the energy storage system, the voltage fluctuation amplitude at the grid point, and the harmonic distortion rate of the grid-connected current. It then solves the optimal solution of the multi-objective optimization model under different grid intensity scenarios.

[0170] The weighting module 650 is used to perform weighted fusion of the optimal solutions under different grid intensity scenarios based on a preset grid intensity probability distribution, so as to obtain the target energy storage capacity and the target energy storage control parameter combination.

[0171] The scheduling module 660 is used to schedule the energy storage system based on the target energy storage capacity and the target energy storage control parameters.

[0172] In one embodiment, the simulation system construction module 620 is further configured to map the photovoltaic output characteristic parameters to a photovoltaic power generation module based on the volt-ampere characteristic lookup table mechanism, convert the electrical strength parameters into adjustable equivalent impedance parameters and write them as series elements into the equivalent grid module, inject the performance parameters as boundary conditions into the power transmission and state evolution equations of the energy storage unit, collect the grid connection point voltage in real time, and determine the grid connection point power based on the grid connection point voltage.

[0173] In one embodiment, the simulation module 630 is also used to retrieve a multi-stage step sequence from a preset operating condition library as a photovoltaic output command signal and inject it into the photovoltaic power generation module. During the simulation operation, it collects time series data of grid connection point voltage, current, frequency and energy storage charge status in real time and caches the time series data in a temporary data area.

[0174] In one embodiment, the model solving module 640 is further configured to initialize a set of candidate solutions for each power grid intensity scenario, each candidate solution corresponding to a set of energy storage capacity and control parameters, and for each candidate solution, call the electromagnetic transient simulation system to perform a complete disturbance condition simulation to determine the target value of each candidate solution, perform weighted processing on the target value of each candidate solution to obtain the comprehensive fitness, and based on the comprehensive fitness and the preset dominance relationship, screen each candidate solution, update the individual and global optimal solutions, generate a new generation of candidate solutions and iterate until convergence to obtain the optimal solution.

[0175] In one embodiment, the model solving module 640 is further configured to mark the candidate solution as an infeasible solution if the candidate solution causes the energy storage state of charge to exceed the limit or the power quality index to exceed the standard, and to exclude the guiding role of the candidate solution in the search direction in subsequent iterations.

[0176] In one embodiment, the model solving module 640 is further configured to solve the Pareto optimal solution of the multi-objective optimization model under different power grid intensity scenarios based on the multi-objective particle swarm optimization algorithm.

[0177] Each module in the aforementioned grid-type energy storage system dispatching device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0178] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data such as photovoltaic output characteristic parameters of the target photovoltaic power plant, electrical strength parameters of the power grid, performance parameters of the energy storage system, and simulation test results. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a grid-based energy storage system scheduling method.

[0179] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0180] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the above embodiments of the grid-type energy storage system scheduling method.

[0181] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps in any of the above embodiments of the grid-type energy storage system scheduling method.

[0182] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the grid-type energy storage system scheduling method.

[0183] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0184] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0185] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0186] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A scheduling method for a grid-type energy storage system, characterized in that, The method includes: Obtain the photovoltaic output characteristic parameters of the target photovoltaic power plant, the electrical strength parameters of the power grid, and the performance parameters of the energy storage system; Based on the photovoltaic output characteristic parameters, the electrical strength parameters, and the performance parameters, an electromagnetic transient simulation system is established, which includes a photovoltaic power generation module, a grid-type energy storage module, an equivalent power grid module, and a measurement module. A preset power fluctuation condition is applied to the electromagnetic transient simulation system to simulate the dynamic response process of the photovoltaic power output under multi-level step changes, and simulation test result data is collected. Based on simulation test results, a multi-objective optimization model is constructed with the goal of minimizing the rated power of the energy storage system, the voltage fluctuation amplitude at the grid point, and the harmonic distortion rate of the grid-connected current. The optimal solutions of the multi-objective optimization model under different grid intensity scenarios are then solved. Based on the preset power grid intensity probability distribution, the optimal solutions under different power grid intensity scenarios are weighted and fused to obtain the target energy storage capacity and the target energy storage control parameter combination; The energy storage system is scheduled based on the target energy storage capacity and the target energy storage control parameters.

2. The method according to claim 1, characterized in that, The electromagnetic transient simulation system, established based on the photovoltaic output characteristic parameters, the electrical strength parameters, and the performance parameters, includes a photovoltaic power generation module, a grid-type energy storage module, and an equivalent power grid model. The photovoltaic output characteristic parameters are mapped to a photovoltaic power generation module based on the current-voltage characteristic lookup table mechanism; The electrical strength parameters are converted into adjustable equivalent impedance parameters and written as series elements into the equivalent power grid module; The performance parameters are injected as boundary conditions into the power transfer and state evolution equations of the energy storage unit. The grid connection point voltage is collected in real time, and the grid connection point power is determined based on the grid connection point voltage.

3. The method according to claim 1, characterized in that, The process of applying a preset power fluctuation condition to the electromagnetic transient simulation system to simulate the dynamic response of the photovoltaic power output under multi-level step changes includes: A multi-stage step sequence is retrieved from the preset operating condition library as the photovoltaic power output input signal; During the simulation, time-series data of grid connection point voltage, current, frequency, and energy storage state of charge are collected in real time and cached in a temporary data area.

4. The method according to claim 1, characterized in that, The process of finding the optimal solution of the multi-objective optimization model under different power grid intensity scenarios includes: For each grid strength scenario, a set of candidate solutions is initialized, and each candidate solution corresponds to a set of energy storage capacity and control parameters. For each candidate solution, the electromagnetic transient simulation system is invoked to perform a complete disturbance condition simulation, the target value of each candidate solution is determined, and the target values ​​of each candidate solution are weighted to obtain the comprehensive fitness. Based on the comprehensive fitness and the preset dominance relationship, each candidate solution is screened, and the individual and global optimal solutions are updated. Generate a new generation of candidate solutions and iterate until convergence to obtain the optimal solution.

5. The method according to claim 4, characterized in that, The method further includes: If a candidate solution leads to the energy storage state of charge exceeding the limit or the power quality index exceeding the standard, the candidate solution is marked as an infeasible solution, and its guiding role in the search direction is excluded in subsequent iterations.

6. The method according to claim 1, characterized in that, The process of finding the optimal solution of the multi-objective optimization model under different power grid intensity scenarios includes: Based on the multi-objective particle swarm optimization algorithm, the Pareto optimal solution of the multi-objective optimization model under different power grid intensity scenarios is obtained.

7. A grid-type energy storage system dispatching device, characterized in that, The device includes: The data acquisition module is used to acquire photovoltaic output characteristic parameters of the target photovoltaic power plant, electrical strength parameters of the power grid, and performance parameters of the energy storage system. The simulation system construction module is used to establish an electromagnetic transient simulation system, which includes a photovoltaic power generation module, a grid-type energy storage module, an equivalent power grid module, and a measurement module, based on the photovoltaic output characteristic parameters, the electrical strength parameters, and the performance parameters. The simulation module is used to apply a preset power fluctuation condition to the electromagnetic transient simulation system, simulate the system dynamic response process of photovoltaic output under multi-level step changes, and collect simulation test result data. The model solving module, based on simulation test results, constructs a multi-objective optimization model with the optimization objectives of minimizing the rated power of the energy storage system, the voltage fluctuation amplitude at the grid point, and the harmonic distortion rate of the grid-connected current. The module then solves for the optimal solution of the multi-objective optimization model under different grid intensity scenarios. The weighting module is used to perform weighted fusion of the optimal solutions under different grid intensity scenarios based on a preset grid intensity probability distribution, so as to obtain the target energy storage capacity and the target energy storage control parameter combination. The scheduling module is used to schedule the energy storage system based on the target energy storage capacity and the target energy storage control parameters.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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