Port micro-grid multi-objective optimization configuration method, system, medium and equipment

Abstract

The application provides a port micro-grid multi-objective optimization configuration method, system, medium and equipment, the method comprises the following steps: obtaining and preprocessing port data, and constructing a comprehensive typical day source load model; a port wind light storage load micro-grid structure model is constructed; an economic evaluation model and a reliability evaluation model are constructed; according to the comprehensive typical day source load model, the port wind light storage load micro-grid structure model, the economic evaluation model and the reliability evaluation model, the number of photovoltaic modules, the number of wind turbines and the number of energy storage units are taken as integer decision variables, a multi-objective optimization model with annual total cost, power supply reliability index and abandoned wind and light index as optimization objectives is constructed, and NSGA-II algorithm is used for solving to determine the Pareto optimal configuration scheme. Through the application, the dependence on selection experience is reduced, the adaptive optimization configuration of the port load, meteorological conditions and wind light storage capacity is realized, the power supply reliability, the renewable energy utilization rate and the cost are reduced.

Description

Technical Field

[0001] This application relates to the field of new energy microgrid planning technology, specifically to a multi-objective optimization configuration method, system, medium, and equipment for a port microgrid. Background Technology

[0002] With the electrification of port loading and unloading operations and the promotion of shore power, port loads exhibit characteristics such as large capacity, strong fluctuations, and significant diurnal differences. Under the traditional centralized power supply model, ports obtain power from upstream substations through medium- and high-voltage power grids. When the upstream power grid fails or is restricted, port operations are easily affected, making it difficult to restore power supply in a timely manner and meet the requirements for high-reliability operation.

[0003] In recent years, wind power and distributed photovoltaic power stations have been widely built near ports. For example, in the TCT terminal area of ​​Tianjin Port, a wind turbine with a grid-connected capacity of 4.5MW has been built, which is capable of forming a regional microgrid with the port load. At the same time, the port plans to build a photovoltaic system of about 2-3MW and a 1.5MW / 3MWh vanadium redox flow battery energy storage system. How to rationally allocate the capacity of wind power, photovoltaic, and energy storage while ensuring power supply reliability and minimizing wind and solar curtailment has become a key issue in engineering practice.

[0004] Existing microgrid configuration methods typically have the following shortcomings: (1) Generally, single-objective economic optimization or simple constraint search is adopted, which makes it difficult to simultaneously consider total cost, power supply reliability and wind and solar curtailment indicators; (2) The modeling of source load in the port scene is not refined enough. There is a lack of high-precision source load models based on measured port illumination data, wind resource data and electricity consumption data, which makes it difficult to reflect the multi-condition and multi-season characteristics of container terminals. (3) The efficiency, lifespan, cycle count, and deep discharge of electrochemical energy storage systems, especially long-life energy storage technologies such as all-vanadium redox flow batteries, have not been fully considered, and the configuration of energy storage capacity lacks specificity. (4) The optimization algorithm lacks constraint repair and feasibility guarantee mechanisms for port scenarios, which can easily lead to a large number of infeasible solutions, affecting convergence and engineering implementation.

[0005] For example, in the patent "CN121618543A; Wind-Solar-Storage Co-optimization Configuration Method and System Based on Multi-Objective Optimization Algorithm", four objectives are considered: economy, environmental protection, power supply reliability, and comprehensive equipment utilization. Combined with the constraints of wind, solar, and storage, an improved adaptive NSGA is adopted. Algorithm II obtains a Pareto configuration set and then selects the optimal solution through entropy-weighted TOPSIS decision-making, achieving coordinated optimization of wind, solar, and energy storage configuration. However, it cannot simultaneously address the three objectives of "total cost, power supply reliability, and wind / solar curtailment." Wind and solar curtailment is treated as a fixed constraint and does not participate in multi-objective optimization; it lacks port source-load modeling; it does not consider the engineering characteristics of vanadium redox flow batteries, resulting in untargeted energy storage configuration; and it lacks port-specific constraint repair and feasibility assurance, easily generating a large number of infeasible solutions, affecting convergence and feasibility.

[0006] Therefore, it is necessary to propose a multi-objective optimization configuration method and system for port microgrids that combines port measured data, refined source-load modeling, and multi-objective intelligent optimization, so as to achieve economical, efficient, and feasible configuration of wind, solar, and energy storage capacity while meeting power supply reliability constraints. Summary of the Invention

[0007] In view of the deficiencies in the prior art, the purpose of this application is to provide a method, system, medium and equipment for multi-objective optimization configuration of port microgrids.

[0008] The first aspect of this application provides a multi-objective optimization configuration method for port microgrids, comprising: Acquire and preprocess port data to construct a comprehensive typical daily source-load model. The port data includes solar data, wind resource data, and electricity load meter readings. Based on the parameters of the port photovoltaic modules, wind turbines, and electrochemical energy storage subsystem, a port wind-solar-storage-load microgrid structure model is constructed. Construct economic evaluation models and reliability evaluation models to quantify annualized total cost, power supply reliability indicators, and wind and solar curtailment indicators; Based on the comprehensive typical daily source-load model, the port wind-solar-storage microgrid structure model, the economic evaluation model, and the reliability evaluation model, the number of photovoltaic modules, the number of wind turbines, and the number of energy storage units are used as integer decision variables. A multi-objective optimization model is constructed with the annualized total cost, the power supply reliability index, and the wind and solar curtailment index as optimization objectives. The NSGA-II algorithm is used to solve the model and determine the Pareto optimal configuration scheme of the port wind-solar-storage microgrid.

[0009] Optionally, the acquisition and preprocessing of port data to construct a comprehensive typical daily source-load model includes: Acquire port data on sunlight, wind resources, and electricity load meter readings; The illumination data and the wind resource data are respectively subjected to interpolation, missing value imputation and time alignment processing to determine the hourly irradiance sequence and hourly wind speed sequence with a unified time scale. The electrical load meter data is sequentially processed through data cleaning, noise reduction, and outlier handling to determine the hourly active power load curve.

[0010] Optionally, the acquisition and preprocessing of port data to construct a comprehensive typical daily source-load model further includes: Statistical processing is performed on the hourly irradiance sequence and the hourly wind speed sequence to determine the 24-hour typical daily irradiance curve and the 24-hour typical daily wind speed curve for each season. The hourly active power load curves are clustered according to the season to determine the typical daily load curve for 24 hours in each season. Based on the 24-hour typical daily irradiance curve, the 24-hour typical daily wind speed curve, and the 24-hour typical daily load curve for each season, time alignment is performed according to a unified time scale to form a source load dataset indexed by hours for each season, and a typical daily source load model for each season is determined. The source load dataset includes the irradiance curve, the wind speed curve, and the daily load curve indexed by hours for each season. The typical daily source load model for each season is weighted and averaged to determine the comprehensive typical daily source load model. The comprehensive typical daily source load model is used to output the typical daily irradiance curve, the typical daily wind speed curve, and the typical daily load curve.

[0011] Optionally, the port wind-solar-storage microgrid structure model includes a port substation connected to the upper-level power grid, a wind power system, a photovoltaic subsystem, an electrochemical energy storage subsystem, multiple types of load components, a photovoltaic power generation model, a wind power generation model, and a charge state evolution and charge-discharge constraint model for the electrochemical energy storage subsystem. The construction of a port wind-solar-storage-load microgrid structure model based on port photovoltaic module parameters, wind turbine parameters, and electrochemical energy storage subsystem parameters includes: The port substation connected to the upper-level power grid is used as a common coupling point and connected to the upper-level power grid through a power grid interface to construct an AC bus. The wind power system is connected to the AC bus via a step-up transformer; The photovoltaic subsystem is connected to the AC bus via a grid-connected inverter. The electrochemical energy storage subsystem is connected to the AC bus via a bidirectional converter; The various types of load components are connected to the AC bus via feeders; Based on the rated power, series-parallel connection method, upper limit of installation capacity, port roof conditions, and typical daily irradiance curve in the comprehensive typical daily source-load model of the photovoltaic module parameters, the photovoltaic power generation model is constructed. The photovoltaic power generation model is used to characterize the output power of the photovoltaic subsystem. Based on the wind turbine parameters and the typical daily wind speed curve in the integrated typical daily source-load model, the wind power generation model is constructed, and the wind power generation model is used to characterize the output power of the wind electronic system. Based on the capacity, power limit, round-trip efficiency, equivalent full charge-discharge cycle count, recommended discharge depth, and typical daily load curve in the comprehensive typical daily source-load model of the electrochemical energy storage subsystem, a state of charge evolution and charge-discharge constraint model of the electrochemical energy storage subsystem is constructed. The state of charge evolution and charge-discharge constraint model of the electrochemical energy storage subsystem is used to characterize the dynamic change law of the state of charge, the rated limit of charge-discharge power, and the operational safety boundary conditions of the electrochemical energy storage subsystem. The structural model of the port's wind-solar-storage microgrid was determined.

[0012] Optionally, the construction of the economic evaluation model and the reliability evaluation model, quantifying the annualized total cost, power supply reliability index, and wind and solar curtailment index, includes: Based on the investment and operation and maintenance costs of the wind power system, photovoltaic subsystem, and electrochemical energy storage subsystem in the port wind-solar-storage microgrid structure model, the economic evaluation model is constructed by annualizing the costs. The annualized total cost of the port wind-solar-storage microgrid is quantified using the aforementioned economic evaluation model. Define power supply reliability indicators and wind and solar curtailment indicators, and construct the reliability evaluation model. The power supply reliability indicators include the probability of load failure, the total amount of unmet electricity demand, the number of violation hours, and port business constraint information. The port business constraint information includes that a single power outage does not exceed a first preset duration and the total power outage duration throughout the year does not exceed a second preset duration. The wind and solar curtailment indicators include the amount of wind power and photovoltaic power that is not connected to the grid for consumption. The reliability evaluation model is used to quantify the power supply reliability index and wind / solar curtailment index of the port wind-solar-storage microgrid.

[0013] Optionally, based on the comprehensive typical daily source-load model, the port wind-solar-storage microgrid structure model, the economic evaluation model, and the reliability evaluation model, the number of photovoltaic modules, the number of wind turbines, and the number of energy storage units are used as integer decision variables to construct a multi-objective optimization model with the annualized total cost, the power supply reliability index, and the wind and solar curtailment index as optimization objectives. This model is then solved using the NSGA-II algorithm to determine the Pareto optimal configuration scheme for the port wind-solar-storage microgrid, including: The number of wind turbines in the wind power system, the number of photovoltaic modules in the photovoltaic subsystem, and the number of energy storage units in the electrochemical energy storage subsystem in the port wind-solar-storage microgrid structure model are taken as integer decision variables. The annualized total cost quantified by the economic evaluation model, the power supply reliability index quantified by the reliability evaluation model, and the wind and solar curtailment index are used as optimization targets. Based on the integer decision variables, the optimization objective, and the preset constraints, the multi-objective optimization model is determined. The preset constraints include photovoltaic power output constraints, wind turbine power output constraints, the state of charge boundary of the electrochemical energy storage subsystem, the upper limit of charging and discharging power, and the line capacity constraints.

[0014] Optionally, the step of constructing a multi-objective optimization model based on the comprehensive typical daily source-load model, the port wind-solar-storage microgrid structure model, the economic evaluation model, and the reliability evaluation model, using the number of photovoltaic modules, wind turbines, and energy storage units as integer decision variables, with the annualized total cost, the power supply reliability index, and the wind and solar curtailment index as optimization objectives, and solving it using the NSGA-II algorithm to determine the Pareto optimal configuration scheme of the port wind-solar-storage microgrid, further includes: The NSGA-II algorithm is used to encode the integer decision variables of the multi-objective optimization model into integers and generate an initial population. The initial population includes the initial quantities corresponding to the number of photovoltaic modules, the number of wind turbines, and the number of energy storage units, respectively. Based on the initial population and the comprehensive typical daily source-load model, the port wind-solar-storage microgrid structure model is simulated for operation, and the annualized total cost, power supply reliability index and wind curtailment index corresponding to the initial population are determined by the economic evaluation model and the reliability evaluation model. Based on the preset constraints and the port business constraints, the annualized total cost, power supply reliability index, and wind and solar curtailment index of the initial population are optimized. The next generation population is determined iteratively through non-dominated sorting and congestion distance calculation until the preset number of iterations is reached, and the Pareto optimal configuration scheme of the port wind-solar-storage microgrid is determined.

[0015] A second aspect of this application provides a multi-objective optimization configuration system for a port microgrid, comprising: A comprehensive typical daily source-load model construction module is used to acquire and preprocess port data to construct a comprehensive typical daily source-load model. The port data includes sunshine data, wind resource data, and electricity load meter reading data. The Port Wind-Solar-Storage Microgrid Structure Model Construction Module is used to construct a port wind-solar-storage microgrid structure model based on the parameters of port photovoltaic modules, wind turbines, and electrochemical energy storage subsystems. The evaluation model construction module is used to build economic evaluation models and reliability evaluation models, and to quantify annualized total cost, power supply reliability indicators and wind and solar curtailment indicators. The multi-objective optimization module is used to construct a multi-objective optimization model based on the comprehensive typical daily source-load model, the port wind-solar-storage microgrid structure model, the economic evaluation model, and the reliability evaluation model. The number of photovoltaic modules, wind turbines, and energy storage units are used as integer decision variables. The model has the annualized total cost, the power supply reliability index, and the wind and solar curtailment index as optimization objectives. The model is solved using the NSGA-II algorithm to determine the Pareto optimal configuration scheme of the port wind-solar-storage microgrid.

[0016] A third aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of any of the methods provided in the first aspect of this application.

[0017] A fourth aspect of this application provides an electronic device comprising: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of any of the methods provided in the first aspect of this application.

[0018] This application presents a multi-objective optimization configuration method for port microgrids. It collects and processes solar radiation data, wind resource data, and electricity load meter readings to construct a comprehensive typical daily source-load model. This model ensures that the source-load characteristics closely match the actual operating conditions of the port, accurately supporting the estimation of annual electricity demand. Based on the parameters of port photovoltaic modules, wind turbines, and electrochemical energy storage subsystems, a port wind-solar-storage-load microgrid structural model is constructed, providing a reasonable foundation for the capacity configuration of large-capacity, long-life energy storage systems in port scenarios. An economic evaluation model and a reliability evaluation model are built for the port wind-solar-storage-load microgrid structural model. These models quantify and simultaneously consider annualized total cost, power supply reliability indicators, and wind and solar curtailment indicators, achieving multi-objective collaborative optimization. The Pareto optimal configuration scheme is obtained through the NSGA-II algorithm, balancing economy and reliability. This solves the problem that single-objective or empirical configuration methods struggle to balance cost and reliability. Furthermore, it reduces reliance on selection experience, enabling adaptive optimization configuration of port load, meteorological conditions, and wind, solar, and storage capacity. This increases the port's self-consumption ratio, reduces external power purchase costs and carbon emissions, and effectively improves the utilization rate of renewable energy.

[0019] Other technical effects resulting from the additional features will be further illustrated in the corresponding embodiments. Attached Figure Description

[0020] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating a multi-objective optimization configuration method for a port microgrid according to an exemplary embodiment.

[0021] Figure 2 This is a schematic diagram of a typical daily load curve for a port according to an exemplary embodiment.

[0022] Figure 3 This is a schematic diagram of the topology of a port wind-solar-storage microgrid according to an exemplary embodiment.

[0023] Figure 4 This is a schematic diagram of a Pareto front for multi-objective optimization of NSGA-II according to an exemplary embodiment.

[0024] Figure 5 This is a schematic diagram of the typical daily state of charge variation curve of an all-vanadium redox flow battery energy storage subsystem according to an exemplary embodiment.

[0025] Figure 6 This is a schematic diagram illustrating the structure of a port microgrid multi-objective optimization configuration system according to an exemplary embodiment. Detailed Implementation

[0026] The present application will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0027] The terms "comprising" and "having," and any variations thereof, in the embodiments of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or devices.

[0028] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature.

[0029] In the description of the embodiments in this application, "multiple" means two or more, unless otherwise explicitly specified. In this application, unless otherwise explicitly specified and limited, the terms "installed," "connected," "linked," "fixed," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0030] Existing microgrid configuration methods mostly rely on single-objective optimization or simple constraint search, which generally suffer from insufficient accuracy in source-load modeling, failure to adapt to the multi-condition and multi-seasonal operating characteristics of ports, insufficient consideration of constraints in vanadium redox flow battery energy storage projects, and a lack of targeted constraint repair and feasibility assurance mechanisms. Even some existing patents that employ multi-objective optimization algorithms fail to incorporate wind and solar curtailment into collaborative optimization, lack port-specific source-load models and targeted energy storage modeling, easily generating a large number of infeasible solutions, and failing to simultaneously consider total cost, power supply reliability, and wind and solar curtailment indicators, making it difficult to meet the actual engineering implementation needs of ports. Based on the above problems, this application provides a multi-objective optimization configuration method for port microgrids to address these issues.

[0031] Figure 1 This is a flowchart illustrating a multi-objective optimization configuration method for a port microgrid according to an exemplary embodiment.

[0032] Reference Figure 1 As shown in one embodiment of this application, a multi-objective optimization configuration method for a port microgrid is provided, including steps S11 to S14.

[0033] S11: Acquire and preprocess port data to construct a comprehensive typical daily source-load model.

[0034] Specifically, port data includes sunlight data, wind resource data, and electricity load meter readings.

[0035] S12. Based on the parameters of the port photovoltaic modules, wind turbines, and electrochemical energy storage subsystem, a port wind-solar-storage-load microgrid structure model is constructed.

[0036] S13. Construct an economic evaluation model and a reliability evaluation model to quantify the annualized total cost, power supply reliability index, and wind and solar curtailment index.

[0037] S14. Based on the comprehensive typical daily source-load model, port wind-solar-storage-load microgrid structure model, economic evaluation model, and reliability evaluation model, the number of photovoltaic modules, wind turbines, and energy storage units are used as integer decision variables to construct a multi-objective optimization model with annualized total cost, power supply reliability index, and wind and solar curtailment index as objectives. The NSGA-II algorithm is used to solve the Pareto optimal configuration scheme of the port wind-solar-storage-load microgrid.

[0038] Specifically, the integer decision variables correspond to photovoltaic capacity, wind power capacity, and energy storage capacity, respectively.

[0039] The embodiments described in this application collect and process solar irradiance data, wind resource data, and electricity load meter readings to construct a comprehensive typical daily source-load model. This model ensures that the source-load characteristics closely match the actual operating conditions of the port, accurately supporting the estimation of annual electricity demand. Based on the parameters of the port's photovoltaic modules, wind turbines, and electrochemical energy storage subsystems, a port wind-solar-storage-load microgrid structure model is constructed, providing a reasonable foundation for the capacity configuration of large-capacity, long-life energy storage systems in port scenarios. An economic evaluation model and a reliability evaluation model are constructed for the port wind-solar-storage-load microgrid structure model. These models quantify and simultaneously consider annualized total cost, power supply reliability indicators, and wind and solar curtailment indicators, achieving multi-objective collaborative optimization. The Pareto optimal configuration scheme is obtained through the NSGA-II algorithm, balancing economy and reliability. This addresses the difficulty of balancing cost and reliability using single-objective or empirical configuration methods. Furthermore, it reduces reliance on selection experience, enabling adaptive optimization of port load, meteorological conditions, and wind, solar, and storage capacity. This increases the port's self-consumption ratio, reduces external power purchase costs and carbon emissions, and effectively improves the utilization rate of renewable energy.

[0040] In order to construct a daily source and load model that fits the actual operating conditions of the port, in some specific embodiments of this application, for S11, acquiring and preprocessing port data to construct a comprehensive typical daily source and load model, S111 to S113 can be adopted.

[0041] S111, acquires port lighting data, wind resource data, and electricity load meter readings.

[0042] Specifically, the solar radiation data includes hourly irradiance sequences for typical years; the wind resource data includes long-term wind speed trends, daily wind speed variations at various altitudes, and wind turbine power curves; and the electricity load data includes substation meter readings for multiple typical dates.

[0043] S112, interpolation, missing value imputation, and time alignment are performed on the illumination data and wind resource data respectively to determine the irradiance sequence and wind speed sequence with a unified time scale.

[0044] S113, the power load meter data is processed sequentially by data cleaning, noise reduction and outlier processing to determine the hourly active power load curve.

[0045] The embodiments described above in this application effectively remove noise, missing values, and temporal deviations from the original data through preprocessing such as interpolation, noise removal, and time alignment, ensuring high quality and consistency of multi-source data.

[0046] In order to construct a daily source and load model that fits the actual operating conditions of the port, in some specific embodiments of this application, for S11, acquiring and preprocessing port data to construct a comprehensive typical daily source and load model, S114 to S117 can also be used.

[0047] S114. Statistical processing is performed on the hourly irradiance series and hourly wind speed series to determine the 24-hour typical daily irradiance curve and the 24-hour typical daily wind speed curve for each season.

[0048] Specifically, the data on sunlight and wind resources are statistically processed according to the four seasons of spring, summer, autumn and winter, and the 24-hour average or characteristic irradiance and wind speed curve for each season are calculated as typical daily meteorological data for each season.

[0049] S115, cluster the hourly active power load curves according to the season to determine the typical daily load curve for 24 hours in each season.

[0050] Specifically, the hourly active power load curves for multiple days are divided into four seasons (spring, summer, autumn, and winter) and clustered separately. The load curves of representative days in each season are selected as the typical daily loads of the season.

[0051] S116. Based on the 24-hour typical daily irradiance curve, the 24-hour typical daily wind speed curve, and the 24-hour typical daily load curve for each season, time alignment is performed according to a unified time scale to form a source-load dataset indexed by hours for each season, and the typical daily source-load model for each season is determined.

[0052] Specifically, the source-load dataset includes irradiance curves indexed by the hour for each season, wind speed curves indexed by the hour for each season, and daily load curves indexed by the hour for each season.

[0053] S117, weighted average processing is performed on the typical daily source-load model for each season to determine the comprehensive typical daily source-load model.

[0054] Among them, the weighted average processing can perform weighted average processing on the typical daily source-load model of each season according to the duration of the season or with equal weight.

[0055] Figure 2 This is a schematic diagram of a typical daily load curve for a port according to an exemplary embodiment.

[0056] Reference Figure 2 As shown, the integrated typical daily source-load model is used to output the typical daily irradiance curve, the typical daily wind speed curve, and the typical daily load curve. The integrated typical daily source-load model is also used to provide the total power generation curve and the power supply curve.

[0057] The integrated typical daily source-load model is used to configure and optimize integrated typical solar radiation, wind speed and load data, and can be converted into annual-scale power generation and load demand estimation inputs as needed.

[0058] The embodiments described above in this application combine seasonal statistical clustering with the extraction of typical 24-hour days to accurately depict the seasonal evolution of wind and solar power output and electricity load. While significantly reducing the calculation dimensions of annual data, key operational characteristics are fully preserved. Finally, a comprehensive typical daily source-load model is constructed through unified time scale alignment and weighted averaging. This provides a high-fidelity, low-redundancy standardized scenario input for downstream microgrid capacity optimization, significantly improving planning calculation efficiency, model accuracy, and engineering applicability.

[0059] Figure 3 This is a schematic diagram of the topology of a port wind-solar-storage microgrid according to an exemplary embodiment.

[0060] Reference Figure 3As shown, specifically, the wind power system may include a 4.5MW wind turbine, the photovoltaic subsystem may include several grid-connected photovoltaic arrays, the electrochemical energy storage subsystem may include multiple energy storage units or multiple fuel cells, and various load components may include, but are not limited to, dock loading and unloading equipment (such as refrigerated containers, shore power, rail gantry cranes, and cranes), auxiliary power (such as charging piles, battery swapping stations, and mobile equipment), and office and living areas (such as lighting, office, and daily use in office and residential buildings).

[0061] For example, the electrochemical energy storage subsystem can be an all-vanadium redox flow battery energy storage subsystem.

[0062] The upstream grid interface is used to supply power to the port when wind and solar power output is insufficient or when the microgrid is under maintenance, or to send power to the upstream grid when wind and solar power output is abundant, so as to realize energy interaction between the port and the main power grid.

[0063] In some specific embodiments of this application, a port wind-solar-storage-load microgrid structural model can be constructed based on the port wind-solar-storage-load microgrid. Specifically, the port wind-solar-storage-load microgrid structural model includes a port substation connected to the upper-level power grid, a wind power system, a photovoltaic subsystem, an electrochemical energy storage subsystem, various types of load components, a photovoltaic power generation model, a wind power generation model, and a charge state evolution and charge-discharge constraint model of the electrochemical energy storage subsystem.

[0064] For example, the electrochemical energy storage subsystem can adopt a full vanadium redox flow energy storage subsystem, and a state of charge evolution and charge-discharge constraint model of the full vanadium redox flow battery can be constructed.

[0065] In some specific embodiments of this application, for S12, a port wind-solar-storage-load microgrid structure model is constructed based on the port photovoltaic module parameters, wind turbine parameters, and electrochemical energy storage subsystem parameters, which can be implemented using S121 to S129.

[0066] S121 uses the port substation connected to the upper-level power grid as a common coupling point and connects it to the upper-level power grid through the power grid interface to construct an AC bus.

[0067] S122 connects the wind power system to the AC bus via a step-up transformer.

[0068] S123 connects the photovoltaic subsystem to the AC bus via a grid-connected inverter.

[0069] S124 connects the electrochemical energy storage subsystem to the AC bus via a bidirectional converter.

[0070] S125 connects various load components to the AC bus via feeders.

[0071] S126. Based on the rated power of the photovoltaic modules, series and parallel connection method, upper limit of installation capacity, port roof conditions, and typical daily irradiance curve in the comprehensive typical daily source-load model, a photovoltaic power generation model is constructed.

[0072] Specifically, the photovoltaic power generation model is used to characterize the output power of the photovoltaic subsystem.

[0073] Specifically, step S126 can be performed as follows: The available roof area is determined based on the conditions of the port roof, and the upper limit of the number of photovoltaic modules is determined based on the size of the photovoltaic modules and the upper limit of the installation capacity. The installed capacity and output characteristics of the photovoltaic array are determined based on the rated power of the photovoltaic modules and the series and parallel connection method. The hourly photovoltaic output power is calculated based on the typical daily irradiance curve in the comprehensive typical daily source-load model, and the hourly photovoltaic output power is subject to a constraint that does not exceed the installed capacity, so as to construct a photovoltaic power generation model.

[0074] S127. Based on the wind turbine parameters and the typical daily wind speed curve in the integrated typical daily source-load model, a wind power generation model is constructed.

[0075] Specifically, the wind power generation model is used to characterize the output power of the wind electronic system.

[0076] Specifically, step S127 can be performed as follows: Obtain the rated power, cut-in wind speed, rated wind speed, cut-out wind speed, and wind turbine power curve from the wind turbine parameters; determine the inlet wind speed hourly based on the typical daily wind speed curve, and calculate the hourly output power of a single wind turbine according to the wind turbine power curve. The output power is zero when the inlet wind speed is less than the cut-in wind speed or greater than the cut-out wind speed, and the output power is the rated power when the inlet wind speed is between the rated wind speed and the cut-out wind speed; determine the hourly total wind power output power based on the number of wind turbines, and impose a constraint on the hourly total wind power output power not exceeding the product of the rated power and the number of wind turbines to construct a wind power generation model.

[0077] S128. Based on the capacity, power limit, round-trip efficiency, equivalent full charge-discharge cycle count, recommended discharge depth, and typical daily load curve in the comprehensive typical daily source-load model of the electrochemical energy storage subsystem, a charge state evolution and charge-discharge constraint model of the electrochemical energy storage subsystem is constructed.

[0078] For example, this embodiment uses a vanadium redox flow battery energy storage subsystem and constructs a charge state evolution and charge-discharge constraint model for the vanadium redox flow battery as an example for illustration: Specifically, the state of charge evolution and charge / discharge constraint model of the vanadium redox flow battery is used to characterize the dynamic changes in the state of charge, the rated limits of charge and discharge power, and the operational safety boundary conditions of the vanadium redox flow battery energy storage subsystem.

[0079] The vanadium redox flow battery energy storage subsystem uses energy storage units as the basic unit, with a unit capacity of not less than 500kWh, and multiple energy storage units are connected in parallel to form the target total capacity.

[0080] Specifically, step S128 can be performed as follows: The state of charge of the vanadium redox flow battery energy storage subsystem is defined as... ,in, for t The equivalent energy stored in the electrolyte can be used at any time. The rated available energy storage capacity; the charging power limit is set based on the power limit. With upper limit of discharge power A recursive relationship for the state of charge of an all-vanadium redox flow battery was established based on hourly time steps. ,in, for The equivalent energy stored in the electrolyte can be used at any time. Power is calculated based on the losses of the electrolyte circulation pump. This refers to the equivalent capacity loss power caused by electrolyte cross-migration and ion diffusion. For charging efficiency, The discharge efficiency is obtained from this. .

[0081] Furthermore, charge state constraints are set based on recommended discharge depth and safe operating boundaries. Power constraints are set based on rated power. , And set equivalent full charge-discharge cycle constraints. To limit the cumulative charge and discharge flux over the entire lifespan.

[0082] In one embodiment, a 500kWh vanadium redox flow battery energy storage unit is used as the object, and a configuration is provided. , , , , , ...

[0083] After running the above SOC recursive formula and constraint parameters for 1000 equivalent cycles, the SOC calculation error is no higher than 3%, the cumulative charge / discharge capacity control accuracy is no less than 95%, and the electrolyte capacity retention rate is no less than 92%. In contrast, the general model without pump loss and cross-migration correction terms has a cumulative SOC error of 15%~20% under the same operating conditions, which is insufficient to meet the operational constraints and lifespan assessment requirements of vanadium redox flow batteries. Based on the above modeling and verification results, a state-of-charge evolution and charge / discharge constraint model for vanadium redox flow batteries is constructed.

[0084] S129, determine the structural model of the port's wind-solar-storage microgrid.

[0085] Specifically, the port substations, wind power systems, photovoltaic subsystems, electrochemical energy storage subsystems, various load components, photovoltaic power generation models, wind power generation models, and the state of charge evolution and charge / discharge constraint models of the electrochemical energy storage subsystems, all connected to the upper-level power grid, together form the port wind-solar-storage-load microgrid structure model.

[0086] The embodiments described above in this application construct a clear AC bus topology through standardized electrical interfaces, enabling secure interconnection and flexible expansion of wind, solar, energy storage, load, and the upper-level power grid. A precise wind and solar power output model is constructed by combining equipment rated parameters and typical meteorological curves, accurately reflecting the time-series power generation characteristics of renewable energy in port scenarios. Specifically, a state-of-charge evolution and charge / discharge safety boundary constraint model is established for the electrochemical characteristics of vanadium redox flow batteries, effectively avoiding overcharging and over-discharging risks, extending the cycle life of energy storage, and improving the engineering feasibility of dispatching schemes. This forms a high-fidelity, strongly coupled microgrid physical simulation architecture, providing a reliable model foundation with traceable parameters and close to actual operating conditions for subsequent capacity optimization and operation control.

[0087] In some specific embodiments of this application, for S13, constructing an economic evaluation model and a reliability evaluation model, and quantifying the annualized total cost, power supply reliability index and wind and solar curtailment index, can be done using S131 to S134.

[0088] S131. Based on the investment and operation and maintenance costs of the wind power system, photovoltaic subsystem, and electrochemical energy storage subsystem in the port wind-solar-storage microgrid structure model, an economic evaluation model is constructed by annualizing these costs.

[0089] Specifically, the investment cost of the photovoltaic subsystem is RMB 2.5~2.8 / W, and the operation and maintenance cost is RMB 40~50 / kW·year; the investment cost of the wind power system is RMB 30 million / unit, and the operation and maintenance cost is RMB 48 / kW·year during the warranty period and RMB 78 / kW·year after the warranty period; the electrochemical energy storage subsystem is illustrated by taking the all-vanadium redox flow battery energy storage subsystem as an example. The investment cost of the all-vanadium redox flow battery energy storage subsystem is about RMB 850,000 per unit, the design life is 25 years, and the operation and maintenance cost is about RMB 10 / kWh·year.

[0090] The wind power system will be explained using a 4.5MW wind turbine as an example.

[0091] S132 uses an economic evaluation model to quantify the annualized total cost of a port wind-solar-storage microgrid.

[0092] Specifically, the costs in step S131 above are weighted according to the proportion of auxiliary equipment costs to form the annualized total cost of the port wind-solar-storage microgrid.

[0093] S133 defines power supply reliability indicators and wind / solar curtailment indicators, and constructs a reliability evaluation model.

[0094] Specifically, power supply reliability indicators include the probability of load failure, the total amount of unmet power consumption, the number of violation hours, and port business constraint information. The port business constraint information includes that a single power outage shall not exceed the first preset duration, and the total power outage duration throughout the year shall not exceed the second preset duration.

[0095] The total amount of unmet electricity represents the cumulative value of unmet load within the statistical period.

[0096] For example, the first preset duration can be 2 hours, and the second preset duration can be 80 hours.

[0097] The probability of load failure does not exceed the preset load failure probability threshold, the total amount of unmet electricity does not exceed the preset unmet electricity threshold, and the number of violation hours does not exceed the preset violation hour threshold.

[0098] The curtailment of wind and solar power includes the amount of wind and solar power that is not connected to the grid and is not consumed. Through constraints or soft penalties, the curtailment of wind and solar power is to be prevented or kept to a very small extent in principle.

[0099] S134 uses a reliability evaluation model to quantify the power supply reliability index and wind / solar curtailment index of the port wind-solar-storage microgrid.

[0100] The embodiments described above in this application standardize and convert the investment and operation and maintenance costs of wind, solar, and energy storage throughout their entire life cycle into annualized total costs, achieving precise benchmarking and unified accounting of economic efficiency across equipment life cycles. They deeply integrate core reliability indicators such as load shedding probability and unmet power demand with the business constraints of port single and annual power outage durations, ensuring that power supply safety assessments strictly align with the rigid requirements of continuous port production operations. Simultaneously, they quantify the amount of wind and solar power curtailment to intuitively represent green energy consumption efficiency, constructing a three-dimensional integrated evaluation system of "economy-reliability-low carbon," comprehensively and quantitatively mapping the overall operating performance of the microgrid. This effectively solves the problem of the separation between cost indicators and power supply safety in traditional planning, providing a scientific, rigorous, and business-oriented decision-making benchmark for subsequent multi-objective capacity optimization.

[0101] To achieve multi-objective optimization and obtain the Pareto optimal configuration scheme of the port wind-solar-storage microgrid, in some specific embodiments of this application, for S14, based on the comprehensive typical daily source-load model, the port wind-solar-storage microgrid structure model, the economic evaluation model, and the reliability evaluation model, the number of photovoltaic modules, the number of wind turbines, and the number of energy storage units are used as integer decision variables to construct a multi-objective optimization model with annualized total cost, power supply reliability index, and wind and solar curtailment index as optimization objectives, and the NSGA-II algorithm is used to solve the problem to determine the Pareto optimal configuration scheme of the port wind-solar-storage microgrid, which can be S141 to S143.

[0102] S141 uses the number of wind turbines in the wind power system, the number of photovoltaic modules in the photovoltaic subsystem, and the number of energy storage units in the electrochemical energy storage subsystem as integer decision variables in the port wind-solar-storage-load microgrid structure model.

[0103] S142 uses the annualized total cost quantified by the economic evaluation model, the power supply reliability index quantified by the reliability evaluation model, and the wind and solar curtailment index as optimization targets.

[0104] S143. Determine the multi-objective optimization model based on integer decision variables, optimization objectives, and preset constraints.

[0105] Specifically, the preset constraints include photovoltaic power output constraints, wind turbine power output constraints, state of charge boundary of electrochemical energy storage subsystem, upper limit of charging and discharging power, and line capacity constraints.

[0106] For example, the electrochemical energy storage subsystem in steps S141 to S143 above can be implemented using a vanadium redox flow battery energy storage subsystem.

[0107] In the embodiments described above, the number of wind turbines, photovoltaic modules, and energy storage units are set as integer decision variables, which precisely matches the discrete characteristics of engineering equipment procurement and installation, and completely avoids the scheme distortion caused by non-integer configuration. It deeply integrates the three major objectives of annualized total cost, power supply reliability, and wind and solar curtailment rate, and strictly couples physical constraints such as wind and solar power output boundaries, energy storage state of charge safety limits, charging and discharging power limits, and line capacity to build a high-fidelity optimization model, ensuring that the configuration scheme fully conforms to the equipment operation mechanism and grid safety boundaries.

[0108] To achieve multi-objective optimization and obtain the Pareto optimal configuration scheme of the port wind-solar-storage microgrid, in some specific embodiments of this application, for S14, based on the comprehensive typical daily source-load model, the port wind-solar-storage microgrid structure model, the economic evaluation model, and the reliability evaluation model, the number of photovoltaic modules, the number of wind turbines, and the number of energy storage units are used as integer decision variables to construct a multi-objective optimization model with annualized total cost, power supply reliability index, and wind and solar curtailment index as optimization objectives, and the NSGA-II algorithm is used to solve the problem to determine the Pareto optimal configuration scheme of the port wind-solar-storage microgrid, which can be S144 to S146.

[0109] S144 uses the NSGA-II algorithm to encode the integer decision variables of the multi-objective optimization model into integers and generates an initial population, which includes the initial quantities corresponding to the number of photovoltaic modules, the number of wind turbines, and the number of energy storage units.

[0110] S145. Based on the initial population and the comprehensive typical daily source-load model, the operation simulation of the port wind-solar-storage-load microgrid structure model is carried out, and the annualized total cost, power supply reliability index and wind curtailment index corresponding to the initial population are determined by using the economic evaluation model and the reliability evaluation model.

[0111] S146. Based on the preset constraints and port business constraints, the annualized total cost, power supply reliability index and wind and solar curtailment index of the initial population are optimized. The next generation population is determined iteratively through non-dominated sorting and congestion distance calculation until the preset number of iterations is reached, and the Pareto optimal configuration scheme of the port wind-solar-storage-load microgrid is determined.

[0112] Specifically, S146 may include the following steps: S1461, Constraint Verification and Default Calculation. After completing time-series simulation for each individual [Np, Nw, Nb], verify the photovoltaic output constraints, wind turbine output constraints, upper and lower limits of the state of charge (SOC) of the energy storage battery, upper and lower limits of charging and discharging power, line capacity constraints, and port business constraints (single power outage duration, total power outage duration for the year) item by item. Define the default amount for each constraint; if all constraints are satisfied, the default amount is 0.

[0113] S1462, Penalties and Feasibility Repair. Feasibility repair is prioritized for defaulting individuals: when SOC or charging / discharging power exceeds limits, the power balance for that period is truncated at the boundary value and recalculated; when line capacity exceeds limits, the power flow of the corresponding branch is reduced according to the capacity limit, and the curtailed wind and solar power is updated simultaneously; when port business constraints are not met, the number of energy storage units or the scale of wind and solar configurations are adjusted to meet the port business constraint limits, which include the upper limit for a single power outage duration and the upper limit for the total annual power outage duration. If the default persists after repair, a penalty term is added to the objective function, making its overall fitness worse than that of feasible individuals of the same generation. Individuals that meet the constraints are not penalized and participate in the non-dominated ranking according to their original objective function values.

[0114] S1463, Fitness Evaluation and Normalization. An economic evaluation model and a reliability evaluation model are used to calculate the annualized total cost, power supply reliability index, and wind and solar curtailment index, respectively. The three indicators are then normalized and aligned to eliminate dimensional differences. Furthermore, daily unmet electricity consumption thresholds and violation hour thresholds can be introduced. Individuals exceeding these thresholds are penalized, and for individuals with penalties, the penalized target value is used as the input for subsequent non-dominated ranking.

[0115] S1464, Non-dominated sorting and crowding distance calculation. The parent population is merged with the offspring populations generated by crossover and mutation, and a fast non-dominated sort is performed to obtain multiple Pareto level fronts. Within the same front, the crowding distance is calculated, and individuals with high crowding are preferentially retained to maintain the uniformity and diversity of the solution set distribution.

[0116] S1465, the criterion for next-generation population update and termination. Individuals are selected sequentially to form the next-generation population according to the principle of "first level, then crowding". S145 to S1464 are repeated until the preset number of iterations is reached or the Pareto front changes for multiple consecutive generations are lower than the Pareto front convergence criterion threshold, and the Pareto optimal solution set is output. Then, combined with the port's preferences for cost, reliability and wind and solar curtailment, a recommended configuration scheme is selected.

[0117] Figure 4 This is a schematic diagram of a Pareto front for multi-objective optimization of NSGA-II according to an exemplary embodiment.

[0118] Reference Figure 4 As shown, the horizontal axis represents the total net present value cost, and the vertical axis represents the probability of load shedding (LOLP). The scatter points in the figure represent the Pareto solution set obtained when the iteration number is 100, after comparing the annual wind and solar power curtailment and cost convergence among candidate solutions obtained at different iteration numbers. The star-shaped markers indicate the recommended solutions given in the results file. Because this embodiment employs strict constraints on load shedding, all solutions on the Pareto front are close to 0 in the LOLP dimension, differing only in economic indicators such as cost.

[0119] The embodiments described above employ an integer encoding mechanism to accurately match the discrete procurement characteristics of equipment, completely avoiding engineering distortions caused by non-integer configurations. Through a closed-loop iteration of "time-series simulation - index quantification - constraint verification," the physical safety boundaries of equipment and the rigid constraints of port power outage operations are deeply embedded in the optimization process, automatically eliminating non-compliant and infeasible solutions. Relying on the non-dominated sorting and congestion calculation mechanism of the NSGA-II algorithm, the multi-dimensional conflicts between economic costs, power supply reliability, and wind and solar curtailment rates are efficiently coordinated, and a Pareto optimal solution set with uniform distribution and rich diversity is quickly generated. This provides a high-quality decision-making basis for port microgrid capacity planning that takes into account engineering feasibility, multi-objective scientific trade-offs, and port business compliance.

[0120] Reference Figure 2 As shown, Figure 2 Corresponding to Figure 4 The simulation results of the port wind-solar-storage microgrid structure model under the recommended optimal configuration scheme are shown in the figure. The horizontal axis represents a typical 24-hour day, and the vertical axis represents power. The figure shows the photovoltaic output curve, wind power output curve, total power generation curve, power supply (power generation + energy storage) curve, and load curve.

[0121] Reference Figure 2 As shown, the electrochemical energy storage subsystem charges when wind and solar power output is higher than the load, and discharges to compensate when wind and solar power output is lower than the load, thus achieving a dynamic balance between wind and solar power output and port load under the recommended optimal configuration scheme.

[0122] The following explanation uses an electrochemical energy storage subsystem employing a full vanadium redox flow battery energy storage subsystem as an example.

[0123] Figure 5 This is a schematic diagram of the typical daily state of charge variation curve of an all-vanadium redox flow battery energy storage subsystem according to an exemplary embodiment.

[0124] Reference Figure 5 As shown, Figure 5 For based on Figure 4 The simulation results of the port wind-solar-storage-load microgrid structure model under the recommended optimal configuration scheme are shown in the figure. The horizontal axis represents a typical 24-hour day, and the vertical axis represents the state of charge (SOC) percentage. The figure shows the charging and discharging process and SOC changes of the electrochemical energy storage subsystem during a typical day. It can be seen that the SOC is always limited between the preset upper and lower limits, and increases during periods of high photovoltaic and wind power output, and decreases during periods of high load or low wind and solar power output. This indicates that under the recommended optimal configuration scheme, the all-vanadium redox flow battery energy storage subsystem can safely and reliably participate in the microgrid operation, effectively improving the absorption capacity of wind and solar power output.

[0125] In some specific embodiments of this application, configuration scheme decisions and engineering applications include: In the Pareto optimal solution set, one or more configuration schemes are selected based on the port's preferences for investment costs, power supply reliability, and wind and solar curtailment. The selected photovoltaic, wind power, and energy storage configuration parameters are used as design inputs for the microgrid project implementation. During the project operation, optimization can be re-executed based on updated port measurement data to perform rolling corrections on the configuration scheme.

[0126] The multi-objective optimization configuration method for port microgrids provided in this application can be implemented in engineering scenarios such as a 4.5MW wind power, a planned 2-3MW photovoltaic, and a 1.5MW / 3MWh vanadium redox flow battery energy storage system at the TCT terminal.

[0127] In practical applications, this invention is not limited to the specific parameters shown in the above embodiments. For different ports, the typical daily construction method, economic evaluation parameters, and constraint thresholds can be adjusted according to local sunlight conditions, wind resource conditions, and load characteristics, such as the allowable duration of a single power outage, the upper limit of the total annual power outage duration, and the allowable amount of wind and solar power curtailment. After reading this specification, those skilled in the art can make corresponding adjustments to the wind turbine capacity, photovoltaic installation capacity, and vanadium redox flow battery energy storage capacity according to specific application scenarios. These adjustments are all conventional design variations within the scope of this application.

[0128] The preferred features in the above embodiments can be used individually in any embodiment, or in any combination thereof, provided they do not conflict with each other. Furthermore, parts not described in detail in the embodiments can be implemented using existing technologies.

[0129] The following examples and comparative examples will be used to further illustrate this application in order to better understand the above-mentioned technical solutions. It should be understood that the following are only some examples and are not intended to limit this application.

[0130] Furthermore, under the same port source-load data, the same equipment parameter boundaries, and the same constraints, the optimal configuration scheme obtained using the multi-objective optimization configuration method for port microgrids provided in this application, compared with the single-objective economic scheme that only considers minimizing the annualized total cost, reduces the load shedding probability from 0.62% to 0.21%, a decrease of approximately 66.13%; the annual unmet electricity demand decreases from 31.8 MWh to 11.6 MWh, a decrease of approximately 63.52%; and the amount of wind and solar power curtailed decreases from 428 MWh to 329 MWh, a decrease of approximately 23.13%. Meanwhile, the annualized total cost increases from RMB 14.62 million to RMB 14.95 million, an increase of approximately 2.26%.

[0131] The above results show that this application can significantly improve power supply reliability and reduce wind and solar curtailment levels with a small increase in cost through multi-objective collaborative optimization, which can effectively support the configuration decision of port microgrid projects.

[0132] Figure 6 This is a schematic diagram illustrating the structure of a port microgrid multi-objective optimization configuration system according to an exemplary embodiment.

[0133] Reference Figure 6 As shown, another embodiment of this application provides a port microgrid multi-objective optimization configuration system 100, including: a comprehensive typical daily source-load model construction module 110, a port wind-solar-storage-load microgrid structure model construction module 120, an evaluation model construction module 130, and a multi-objective optimization module 140.

[0134] The comprehensive typical daily source-load model construction module 110 is used to acquire and preprocess port data to construct a comprehensive typical daily source-load model. The port data includes sunshine data, wind resource data and electricity load meter reading data. Port wind-solar-storage microgrid structure model construction module 120 is used to construct a port wind-solar-storage microgrid structure model based on port photovoltaic module parameters, wind turbine parameters, and electrochemical energy storage subsystem parameters. The evaluation model construction module 130 is used to construct economic evaluation models and reliability evaluation models, and to quantify annualized total cost, power supply reliability indicators and wind and solar curtailment indicators. The multi-objective optimization module 140 is used to construct a multi-objective optimization model with annualized total cost, power supply reliability index and wind curtailment index as optimization objectives, based on the comprehensive typical daily source-load model, port wind-solar-storage-load microgrid structure model, economic evaluation model and reliability evaluation model. The number of photovoltaic modules, wind turbines and energy storage units are used as integer decision variables. The NSGA-II algorithm is used to solve the model and determine the Pareto optimal configuration scheme of the port wind-solar-storage-load microgrid.

[0135] The embodiments described in this application collect and process solar irradiance data, wind resource data, and electricity load meter readings to construct a comprehensive typical daily source-load model. This model ensures that the source-load characteristics closely match the actual operating conditions of the port, accurately supporting the estimation of annual electricity demand. Based on the parameters of the port's photovoltaic modules, wind turbines, and electrochemical energy storage subsystems, a port wind-solar-storage-load microgrid structure model is constructed, providing a reasonable foundation for the capacity configuration of large-capacity, long-life energy storage systems in port scenarios. An economic evaluation model and a reliability evaluation model are constructed for the port wind-solar-storage-load microgrid structure model. These models quantify and simultaneously consider annualized total cost, power supply reliability indicators, and wind and solar curtailment indicators, achieving multi-objective collaborative optimization. The Pareto optimal configuration scheme is obtained through the NSGA-II algorithm, balancing economy and reliability. This addresses the difficulty of balancing cost and reliability using single-objective or empirical configuration methods. Furthermore, it reduces reliance on selection experience, enabling adaptive optimization of port load, meteorological conditions, and wind, solar, and storage capacity. This increases the port's self-consumption ratio, reduces external power purchase costs and carbon emissions, and effectively improves the utilization rate of renewable energy.

[0136] Regarding the embodiments of the above system, the specific ways in which each module performs operations have been described in detail in the embodiments of the method, and will not be elaborated here.

[0137] Based on the same technical concept, in some specific embodiments of this application, a terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it can be used to execute a multi-objective optimization configuration method for a port microgrid.

[0138] Based on the same technical concept, in some specific embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, can be used to execute a multi-objective optimization configuration method for a port microgrid.

[0139] Optionally, the memory is used to store programs; the memory may include volatile memory, such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random-access memory (DDR SDRAM), etc.; the memory may also include non-volatile memory, such as flash memory. The memory is used to store computer programs (such as application programs and functional modules that implement the above methods), computer instructions, etc., and the aforementioned computer programs and computer instructions can be partitioned and stored in one or more memories. Furthermore, the aforementioned computer programs, computer instructions, data, etc., can be accessed by the processor.

[0140] The aforementioned computer programs, computer instructions, etc., can be stored in partitions within one or more memory locations. Furthermore, the aforementioned computer programs, computer instructions, data, etc., can be accessed by a processor.

[0141] A processor is used to execute a computer program stored in memory to implement the various steps of the methods involved in the above embodiments. For details, please refer to the relevant descriptions in the preceding method embodiments.

[0142] The processor and memory can be separate structures or integrated structures. When the processor and memory are separate structures, they can be coupled together via a bus.

[0143] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0144] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0145] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0146] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0147] The foregoing has described some specific embodiments of this application. It should be understood that this application is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the substantive content of this application. The above-described preferred features can be used in any combination without conflict.

Claims

1. A multi-objective optimization configuration method for port microgrids, characterized in that, include: Acquire and preprocess port data to construct a comprehensive typical daily source-load model. The port data includes solar data, wind resource data, and electricity load meter readings. Based on the parameters of the port photovoltaic modules, wind turbines, and electrochemical energy storage subsystem, a port wind-solar-storage-load microgrid structure model is constructed. Construct economic evaluation models and reliability evaluation models to quantify annualized total cost, power supply reliability indicators, and wind and solar curtailment indicators; Based on the comprehensive typical daily source-load model, the port wind-solar-storage microgrid structure model, the economic evaluation model, and the reliability evaluation model, the number of photovoltaic modules, the number of wind turbines, and the number of energy storage units are used as integer decision variables. A multi-objective optimization model is constructed with the annualized total cost, the power supply reliability index, and the wind and solar curtailment index as optimization objectives. The NSGA-II algorithm is used to solve the model and determine the Pareto optimal configuration scheme of the port wind-solar-storage microgrid.

2. The multi-objective optimization configuration method for port microgrids according to claim 1, characterized in that, The acquisition and preprocessing of port data to construct a comprehensive typical daily source-load model includes: Acquire port data on sunlight, wind resources, and electricity load meter readings; The illumination data and the wind resource data are respectively subjected to interpolation, missing value imputation and time alignment processing to determine the hourly irradiance sequence and hourly wind speed sequence with a unified time scale. The electrical load meter data is sequentially processed through data cleaning, noise reduction, and outlier handling to determine the hourly active power load curve.

3. The multi-objective optimization configuration method for port microgrids according to claim 2, characterized in that, The acquisition and preprocessing of port data to construct a comprehensive typical daily source-load model also includes: Statistical processing is performed on the hourly irradiance sequence and the hourly wind speed sequence to determine the 24-hour typical daily irradiance curve and the 24-hour typical daily wind speed curve for each season. The hourly active power load curves are clustered according to the season to determine the typical daily load curve for 24 hours in each season. Based on the 24-hour typical daily irradiance curve, the 24-hour typical daily wind speed curve, and the 24-hour typical daily load curve for each season, time alignment is performed according to a unified time scale to form a source load dataset indexed by hours for each season, and a typical daily source load model for each season is determined. The source load dataset includes the irradiance curve, the wind speed curve, and the daily load curve indexed by hours for each season. The typical daily source load model for each season is weighted and averaged to determine the comprehensive typical daily source load model. The comprehensive typical daily source load model is used to output the typical daily irradiance curve, the typical daily wind speed curve, and the typical daily load curve.

4. The multi-objective optimization configuration method for port microgrids according to claim 1, characterized in that, The port wind-solar-storage microgrid structure model includes a port substation connected to the upper-level power grid, a wind power system, a photovoltaic subsystem, an electrochemical energy storage subsystem, various load components, a photovoltaic power generation model, a wind power generation model, and a charge state evolution and charge-discharge constraint model for the electrochemical energy storage subsystem. The construction of a port wind-solar-storage-load microgrid structure model based on port photovoltaic module parameters, wind turbine parameters, and electrochemical energy storage subsystem parameters includes: The port substation connected to the upper-level power grid is used as a common coupling point and connected to the upper-level power grid through a power grid interface to construct an AC bus. The wind power system is connected to the AC bus via a step-up transformer; The photovoltaic subsystem is connected to the AC bus via a grid-connected inverter. The electrochemical energy storage subsystem is connected to the AC bus via a bidirectional converter; The various types of load components are connected to the AC bus via feeders; Based on the rated power, series-parallel connection method, upper limit of installation capacity, port roof conditions, and typical daily irradiance curve in the comprehensive typical daily source-load model of the photovoltaic module parameters, the photovoltaic power generation model is constructed. The photovoltaic power generation model is used to characterize the output power of the photovoltaic subsystem. Based on the wind turbine parameters and the typical daily wind speed curve in the integrated typical daily source-load model, the wind power generation model is constructed, and the wind power generation model is used to characterize the output power of the wind electronic system. Based on the capacity, power limit, round-trip efficiency, equivalent full charge-discharge cycle count, recommended discharge depth, and typical daily load curve in the comprehensive typical daily source-load model of the electrochemical energy storage subsystem, a state of charge evolution and charge-discharge constraint model of the electrochemical energy storage subsystem is constructed. The state of charge evolution and charge-discharge constraint model of the electrochemical energy storage subsystem is used to characterize the dynamic change law of the state of charge, the rated limit of charge-discharge power, and the operational safety boundary conditions of the electrochemical energy storage subsystem. The structural model of the port's wind-solar-storage microgrid was determined.

5. The multi-objective optimization configuration method for port microgrids according to claim 4, characterized in that, The construction of the economic evaluation model and reliability evaluation model, which quantifies the annualized total cost, power supply reliability index, and wind and solar curtailment index, includes: Based on the investment and operation and maintenance costs of the wind power system, photovoltaic subsystem, and electrochemical energy storage subsystem in the port wind-solar-storage microgrid structure model, the economic evaluation model is constructed by annualizing the costs. The annualized total cost of the port wind-solar-storage microgrid is quantified using the aforementioned economic evaluation model. Define power supply reliability indicators and wind and solar curtailment indicators, and construct the reliability evaluation model. The power supply reliability indicators include the probability of load failure, the total amount of unmet electricity demand, the number of violation hours, and port business constraint information. The port business constraint information includes that a single power outage does not exceed a first preset duration and the total power outage duration throughout the year does not exceed a second preset duration. The wind and solar curtailment indicators include the amount of wind power and photovoltaic power that is not connected to the grid for consumption. The reliability evaluation model is used to quantify the power supply reliability index and wind / solar curtailment index of the port wind-solar-storage microgrid.

6. The multi-objective optimization configuration method for port microgrids according to claim 5, characterized in that, Based on the comprehensive typical daily source-load model, the port wind-solar-storage microgrid structure model, the economic evaluation model, and the reliability evaluation model, the number of photovoltaic modules, wind turbines, and energy storage units are used as integer decision variables to construct a multi-objective optimization model with the annualized total cost, the power supply reliability index, and the wind and solar curtailment index as optimization objectives. The NSGA-II algorithm is then used to solve the model, determining the Pareto optimal configuration scheme for the port wind-solar-storage microgrid, including: The number of wind turbines in the wind power system, the number of photovoltaic modules in the photovoltaic subsystem, and the number of energy storage units in the electrochemical energy storage subsystem in the port wind-solar-storage microgrid structure model are taken as integer decision variables. The annualized total cost quantified by the economic evaluation model, the power supply reliability index quantified by the reliability evaluation model, and the wind and solar curtailment index are used as optimization targets. Based on the integer decision variables, the optimization objective, and the preset constraints, the multi-objective optimization model is determined. The preset constraints include photovoltaic power output constraints, wind turbine power output constraints, the state of charge boundary of the electrochemical energy storage subsystem, the upper limit of charging and discharging power, and the line capacity constraints.

7. The multi-objective optimization configuration method for port microgrids according to claim 6, characterized in that, The process involves constructing a multi-objective optimization model based on the comprehensive typical daily source-load model, the port wind-solar-storage-load microgrid structure model, the economic evaluation model, and the reliability evaluation model. This model uses the number of photovoltaic modules, wind turbines, and energy storage units as integer decision variables, with the annualized total cost, power supply reliability index, and wind / solar curtailment index as optimization objectives. The model is then solved using the NSGA-II algorithm to determine the Pareto optimal configuration scheme for the port wind-solar-storage-load microgrid. The process also includes: The NSGA-II algorithm is used to encode the integer decision variables of the multi-objective optimization model into integers and generate an initial population. The initial population includes the initial quantities corresponding to the number of photovoltaic modules, the number of wind turbines, and the number of energy storage units, respectively. Based on the initial population and the comprehensive typical daily source-load model, the port wind-solar-storage microgrid structure model is simulated for operation, and the annualized total cost, power supply reliability index and wind curtailment index corresponding to the initial population are determined by the economic evaluation model and the reliability evaluation model. Based on the preset constraints and the port business constraints, the annualized total cost, power supply reliability index, and wind and solar curtailment index of the initial population are optimized. The next generation population is determined iteratively through non-dominated sorting and congestion distance calculation until the preset number of iterations is reached, and the Pareto optimal configuration scheme of the port wind-solar-storage microgrid is determined.

8. A multi-objective optimization configuration system for a port microgrid, characterized in that, include: A comprehensive typical daily source-load model construction module is used to acquire and preprocess port data to construct a comprehensive typical daily source-load model. The port data includes sunshine data, wind resource data, and electricity load meter reading data. The Port Wind-Solar-Storage Microgrid Structure Model Construction Module is used to construct a port wind-solar-storage microgrid structure model based on the parameters of port photovoltaic modules, wind turbines, and electrochemical energy storage subsystems. The evaluation model construction module is used to build economic evaluation models and reliability evaluation models, and quantify annualized total cost, power supply reliability indicators and wind and solar curtailment indicators. The multi-objective optimization module is used to construct a multi-objective optimization model based on the comprehensive typical daily source-load model, the port wind-solar-storage microgrid structure model, the economic evaluation model, and the reliability evaluation model. The number of photovoltaic modules, wind turbines, and energy storage units are used as integer decision variables. The model has the annualized total cost, the power supply reliability index, and the wind and solar curtailment index as optimization objectives. The model is solved using the NSGA-II algorithm to determine the Pareto optimal configuration scheme of the port wind-solar-storage microgrid.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-7.

10. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-7.

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