Direct current microgrid self-powering system and method

Abstract

The application discloses a direct-current microgrid self-energy supply system and method, and belongs to the technical field of microgrid control. The system comprises a multi-energy flow coupling modeling module, a capacity optimization module and a dynamic regulation and control module. The capacity optimization module takes the full life cycle carbon footprint as a hard constraint, combines the charging load time sequence characteristics, cooperatively plans the optimal capacity of a photovoltaic array, a hydrogen storage tank and an energy storage battery pack, and obtains optimized capacity parameters. The dynamic regulation and control module takes the optimized capacity parameters as a hardware operation boundary, and formulates and dynamically corrects a multi-energy complementary dispatching strategy based on real-time operation data. The multi-energy complementary dispatching strategy comprises photovoltaic priority direct supply, surplus hydrogen production and hydrogen storage, insufficient hydrogen release energy supplement and / or energy storage discharge. The application realizes full-cycle zero-carbon self-energy supply through data-model-control three-flow coupling, and solves the contradiction between low carbonization of charging facilities and energy supply stability.

Description

Technical Field

[0001] This invention relates to the field of microgrid control technology, and particularly to a self-powered DC microgrid system and method. It is especially relevant to self-powered DC microgrid scenarios based on photovoltaic-hydrogen-storage multi-energy flow coupling. Background Technology

[0002] A microgrid (or simply microgrid) is a localized, small-scale power supply network that integrates distributed power sources, local loads, and an energy management system (EMS). It is flexibly interconnected with the mains grid via a point of common coupling (PCC) and operates in two core modes: grid-connected mode, where it supplies power in coordination with the mains grid, and islanded mode, where it relies entirely on its internal power sources for self-balancing. The bus configuration of a microgrid can be AC, DC, or a hybrid AC / DC, depending on the efficiency and cost optimization requirements of the application scenario.

[0003] With the increasing popularity of electric vehicles, the demand for clean, low-carbon charging infrastructure is growing. Traditional charging facilities rely heavily on fossil fuel power generation, resulting in a high carbon footprint over their entire lifecycle. Meanwhile, the volatility of renewable energy sources such as solar power leads to a mismatch between energy supply and demand, affecting system stability.

[0004] Therefore, how to build a charging system that does not rely on an external power grid, is self-sufficient, and operates with zero carbon emissions throughout its entire life cycle has become an urgent problem to be solved. Summary of the Invention

[0005] Purpose of the invention: To address the above problems, the purpose of this invention is to provide a DC microgrid self-powered system and method. This system and method are based on a charging island architecture to ensure the dynamic balance of energy supply and demand throughout the system's entire life cycle while achieving zero-carbon operation.

[0006] Specifically, the approach takes minimizing carbon emissions and minimizing costs throughout the entire life cycle as dual objectives, with a 100% energy self-sufficiency rate as a zero-carbon hard constraint. The rated power of the photovoltaic array, the rated power of the electrolyzer, the effective volume of the hydrogen storage tank, and the rated capacity of the energy storage battery are used as decision variables. Through non-dominated ranking, reference point association selection, and elite retention mechanisms, the Pareto optimal solution set that satisfies the zero-carbon constraint is searched in the objective space, and the fuzzy decision method is used to select the unique optimal configuration scheme.

[0007] Technical solution: In a first aspect, the DC microgrid self-powered system of the present invention includes:

[0008] The multi-energy flow coupling modeling module is configured to construct a multi-physical quantity coupling simulation model, characterize the conversion efficiency and dynamic response of each energy link, and output real-time multi-energy flow state mapping data in real time; the real-time multi-energy flow state mapping data is used to quantify physical constraints.

[0009] The capacity optimization module is configured to be coupled to the multi-energy flow coupling modeling module, receive the multi-energy flow state mapping data, and, with the full life cycle carbon footprint as a hard constraint, combine the charging load timing characteristics to collaboratively plan the optimal capacity of the photovoltaic array, hydrogen storage tank and energy storage battery pack, and obtain the optimized capacity parameters.

[0010] as well as

[0011] A dynamic control module is configured to couple the capacity optimization module and the multi-energy flow coupling modeling module. Using the received optimized capacity parameters as the hardware operating boundary, and based on real-time operating data, it formulates and dynamically corrects a multi-energy complementary scheduling strategy. The multi-energy complementary scheduling strategy includes:

[0012] Photovoltaic power supply is prioritized for direct supply, surplus hydrogen is produced and stored, insufficient hydrogen is released for energy replenishment, and / or energy storage is discharged.

[0013] Among them, the life cycle carbon footprint sets an insurmountable boundary for the zero-carbon target as a hard constraint, and incorporates the implicit carbon emissions and operational carbon emissions within the 20-year cycle of the system into a unified accounting framework to ensure that the zero-carbon target is verifiable at the physical level.

[0014] Among them, the optimal capacity planning is used to solve the multi-objective Pareto optimal solution under carbon constraints. It determines the rated capacity of photovoltaic, hydrogen storage and energy storage from the hardware dimension, so that the system has the physical basis to be independent of the external power grid and eliminates the dependence on fossil energy due to insufficient capacity. In other words, the optimal capacity configuration is the minimum and sufficient condition for the system to achieve 100% energy self-sufficiency within the 20-year project cycle. It avoids the gap in purchased electricity (operational carbon emissions) caused by insufficient capacity, and also prevents the waste of resources and the increase of implicit carbon emissions caused by over-configuration.

[0015] Minimizing the deviation between purchased electricity and energy storage status transforms capacity optimization results into dynamic operational targets. Through MPC rolling optimization, zero-carbon constraints are extended from the design phase to the operation phase, ensuring that there is no external carbon source input during real-time scheduling, while maintaining the energy storage system in a healthy charge range.

[0016] The complementary scheduling strategy follows the principle of "source-load-storage" energy cascade utilization and spatiotemporal transfer, achieving on-site mitigation of photovoltaic power output fluctuations and charging load randomness, maximizing on-site photovoltaic power consumption, reducing curtailment losses, and simultaneously achieving energy time shifting through hydrogen energy "electricity-hydrogen-electricity" conversion, reducing reliance on the capacity of high-cost energy storage batteries, and optimizing the economics throughout the entire life cycle. Specifically, photovoltaic power is prioritized for direct supply to charging loads, reducing losses in the energy conversion process and improving the overall energy efficiency of the system; hydrogen energy storage has the characteristics of large capacity and long-term storage, which can convert surplus photovoltaic power into hydrogen energy for cross-day / cross-seasonal storage, making up for the shortcomings of insufficient electrochemical energy storage time and solving the problem of seasonal output and load mismatch of renewable energy; energy storage batteries undertake the mitigation of power fluctuations at the second to minute level, while the hydrogen energy system undertakes the compensation of energy deficits at the hour level and above, and the two work together to achieve wide-frequency power-energy balance.

[0017] Furthermore, the construction of the multi-physical quantity coupled simulation model adopts the time series discretization method, and the specific steps include:

[0018] Divide the data into N discrete time periods of equal length, k=1,2,…,N; and collect the external environment input data, load demand data, and the current initial value data of each energy storage component within each discrete time period.

[0019] A set of multi-physical quantity coupled equations is constructed to map the external environment input to photovoltaic power, the input electrical power to hydrogen production efficiency and hydrogen output, the hydrogen flow rate to changes in hydrogen storage and power generation, and the charging and discharging power to changes in state of charge. The power distribution relationship between each energy link is limited by power balance constraints.

[0020] In each discrete time period, the initial state values ​​of each energy storage link collected at the beginning of the time period are known quantities. The multi-physical quantity coupled equation set is solved simultaneously. The power allocation in the time period is solved according to the preset scheduling logic. The intermediate solution results, including the power commands of each controllable device and the updated energy storage state quantities, are obtained. The energy supply and demand error in each time period is made to be less than the preset threshold through iteration. The solution results are used as the initial state values ​​of the next time period.

[0021] After traversing all discrete time periods, the external environment input data, load demand data, initial state data, and intermediate solution results obtained for each time period are integrated and output to obtain multi-energy flow state mapping data. The multi-energy flow state mapping data is a full-dimensional time-series state trajectory dataset that includes source output time series, hydrogen production efficiency time series, energy storage state of charge change time series, hydrogen storage change time series, and fuel cell output time series.

[0022] Furthermore, the dynamic control module employs dynamic optimization based on the MPC algorithm, and the specific steps include:

[0023] Construct an objective function that minimizes the weighted deviation between purchased electricity and energy storage SOC in the prediction time domain;

[0024] Set constraints, including power balance equation constraints, upper and lower limits of equipment output constraints, energy storage SOC constraints, hydrogen storage constraints, and zero-carbon hard constraints.

[0025] The constrained optimization problem is solved to obtain the optimal control sequence and generate control commands.

[0026] Minimizing purchased electricity is the direct mathematical expression of the zero-carbon hard constraint. Zero purchased electricity is equivalent to the system achieving 100% energy self-sufficiency, cutting off the source of carbon emissions during operation, and is a core indicator for achieving the zero-carbon goal throughout its entire life cycle. Minimizing the energy storage state of charge (SOC) is a necessary means to maintain the system's regulation capability and operational safety. Pulling the energy storage SOC to near the reference value can prevent deep discharge from damaging electrode materials or full charging from causing lithium dendrite formation risks, ensuring that the energy storage system always has a regulation margin to cope with sudden drops in photovoltaic output or sudden increases in load, thus improving system robustness and cycle life. The weighted combination of the two ensures the absolute achievement of the zero-carbon goal, while the latter guarantees the continuous stability of system operation.

[0027] Optionally, the capacity optimization module uses the NSGA-Ⅲ algorithm for multi-objective optimization.

[0028] Specifically, the rated capacity parameter output by NSGA-Ⅲ is used as a hard constraint on the upper and lower limits of equipment output in the MPC optimization problem. That is, the real-time power scheduling of electrolyzers, fuel cells, and energy storage systems must not exceed their rated capacity. This achieves a rigid connection between design parameters and operation control, ensuring that dynamic regulation always takes place within the physically feasible domain. It fundamentally eliminates the risk of being forced to introduce external grid power due to overcapacity operation, and guarantees the absolute satisfaction of zero-carbon constraints at the operational level.

[0029] Optionally, the dynamic optimization step based on the MPC algorithm further includes:

[0030] In the next control cycle, the deviation between the actual state and the predicted state is calculated based on real-time measurement data. If the deviation exceeds the preset threshold, the photovoltaic temperature coefficient and energy storage degradation model parameters are corrected.

[0031] Secondly, a self-powered DC microgrid method according to the present invention includes the following steps:

[0032] A multi-physical quantity coupled simulation model is constructed to characterize the conversion efficiency and dynamic response of each energy link, and to output real-time mapping data of multi-energy flow states in real time; the real-time mapping data of multi-energy flow states is used to quantify physical constraints.

[0033] Based on multi-energy flow state mapping data, with the full life cycle carbon footprint as a hard constraint, and combined with the charging load time sequence characteristics, the optimal capacity of photovoltaic array, hydrogen storage tank and energy storage battery pack is planned in a coordinated manner to obtain the optimized capacity parameters.

[0034] Using the optimized capacity parameters as the hardware operating boundary, and based on real-time operating data, a multi-energy complementary scheduling strategy is formulated and dynamically corrected; the multi-energy complementary scheduling strategy includes: photovoltaic priority direct supply, surplus hydrogen production and storage, insufficient hydrogen release for energy replenishment and / or energy storage discharge.

[0035] Beneficial effects:

[0036] The significant advantages of this invention compared to existing technologies are:

[0037] This invention achieves zero-carbon self-sufficiency throughout the entire lifecycle by coupling data, model, and control, resolving the contradiction between the low-carbonization of charging facilities and the stability of energy supply. Specifically, it uses the full lifecycle carbon footprint as a hard constraint, employs the NSGA-Ⅲ algorithm to complete the dual-objective collaborative optimization of multi-device capacity, and combines it with the MPC algorithm to formulate a photovoltaic-first, multi-energy complementary dynamic scheduling strategy. This provides dual protection at both the hardware and operational levels, ensuring that the system has no external carbon source input and improving energy utilization efficiency. Attached Figure Description

[0038] Figure 1 This is a block diagram showing the relationships between the modules of the DC microgrid self-powered charging island system based on photo-hydrogen-storage multi-energy flow coupling of the present invention;

[0039] Figure 2 This is a data processing block diagram of the photo-hydrogen-storage multi-energy flux coupling modeling module of the present invention;

[0040] Figure 3 This is a block diagram of the NSGA-Ⅲ algorithm optimization for the zero-carbon guided capacity optimization module of the present invention;

[0041] Figure 4 This is a flowchart of the MPC algorithm control process of the DC microgrid dynamic control module of the present invention.

[0042] Figure 5 This is a diagram of the Pareto front obtained by optimizing the NSGA-Ⅲ algorithm of this invention.

[0043] Figure 6 This is a diagram showing the results of the MPC dynamic control strategy of the present invention. Detailed Implementation

[0044] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and not intended to limit the scope of the invention. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the embodiments of the present invention, and not all structures.

[0045] In the following description, specific details such as target system architecture and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0046] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0047] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0048] Furthermore, in the description of this application and the appended claims, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0049] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include the target features, structures, or characteristics described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0050] The following is in conjunction with the appendix Figures 1 to 6 The embodiments of the present invention will be described in further detail below.

[0051] In one embodiment, a DC microgrid self-powered charging island system based on photovoltaic-hydrogen-storage multi-energy flow coupling is provided. Taking the DC microgrid self-powered charging island as an example, it is a professional implementation of microgrid technology in the electric vehicle charging scenario: simplifying the bus to a DC architecture, limiting the load to charging piles, and focusing on local green electricity consumption and zero-carbon operation. This subtractive design makes the charging island superior to general microgrids in terms of efficiency, economy, and reliability. At the same time, the charging island, as a sub-unit, can be nested into a larger campus-level microgrid to assume the role of controllable load or distributed energy storage, forming a hierarchical and reusable energy collaboration system.

[0052] See Figure 1 The self-powered charging island system of the DC microgrid in the figure includes a photovoltaic-hydrogen-storage multi-energy flow coupling modeling module, a zero-carbon guided capacity optimization module, and a DC microgrid dynamic control module.

[0053] The photovoltaic-hydrogen-storage multi-energy flow coupled modeling module integrates photovoltaic power generation characteristics, electrolytic hydrogen production kinetics, hydrogen storage buffering characteristics, fuel cell response characteristics, energy storage system behavior, and charging load characteristics to construct a multi-physical quantity coupled simulation model. This model can accurately characterize the conversion efficiency and dynamic response of each energy link, and realize real-time mapping of multi-energy flow states such as photovoltaic output, hydrogen energy state, energy storage SOC, and load demand.

[0054] Those skilled in the art should know that the mathematical principles and implementation methods of the above-mentioned individual mapping relationships are publicly recorded in their respective fields and belong to common knowledge in the field. For example, the output characteristics of photovoltaic cells can be mapped to current-voltage (IV) output characteristics by using their equivalent circuit model (such as a single diode or dual diode model) and engineering fitting methods with light intensity and temperature as inputs, thereby obtaining photovoltaic power.

[0055] However, this embodiment is not a simple listing of these known equations or algorithms, but rather integrates them into a unified multi-physical quantity coupled simulation framework. This enables time-synchronous joint state updates of photovoltaic, hydrogen production, hydrogen storage, fuel cells, energy storage, and load components, allowing the originally independently modeled components to operate collaboratively on the same time axis. It outputs complete multi-energy flow state mapping data in real time, including photovoltaic power, hydrogen production efficiency, hydrogen production, fuel cell power, energy storage SOC, and hydrogen storage capacity. This data provides quantifiable physical constraints for capacity optimization, such as output boundaries, efficiency curves, SOC limits, and hydrogen storage capacity, giving the optimization model an engineering-feasible constraint space. Furthermore, it provides precise initial state values ​​and prediction benchmarks for dynamic control, enabling real-time allocation commands under power balance constraints to be accurately issued based on the actual adjustable capacity of each component.

[0056] Specifically, the zero-carbon-oriented capacity optimization module is coupled to the photovoltaic-hydrogen-storage multi-energy flow coupled modeling module. This module receives multi-energy flow state mapping data and, with the full life-cycle carbon footprint as a hard constraint, combines the charging load timing characteristics and uses an improved non-dominated sorting genetic algorithm (NSGA-III) to collaboratively plan the optimal capacity of the photovoltaic array, hydrogen storage tank, and energy storage battery pack. This optimization process aims to prevent the introduction of external fossil fuel power due to insufficient capacity at the hardware level, while also preventing energy waste and hidden carbon emissions caused by over-configuration. The optimized capacity parameters are transmitted to the DC microgrid dynamic control module; here, the optimized capacity parameters, also known as the optimized capacity parameters, refer to the unique optimal capacity configuration scheme obtained by fuzzy decision screening of the Pareto optimal solution set output by the zero-carbon-oriented capacity optimization module after iterative optimization using the NSGA-III algorithm, including the rated power of the photovoltaic array, the rated power of the electrolyzer, the effective volume of the hydrogen storage tank, and the rated capacity of the energy storage battery.

[0057] It should be noted that using the full life-cycle carbon footprint as a hard constraint means that the total carbon emissions of the system over its 20-year project cycle (including the implicit carbon emissions from each stage of equipment manufacturing, transportation, installation, operation and maintenance, and decommissioning) must be zero or negative. In other words, the carbon emission reductions avoided by the system through its own photovoltaic power generation replacing external fossil fuel power must be greater than or equal to the total carbon emissions generated during the entire construction and operation process. This constraint is achieved through a 100% energy self-sufficiency rate, ensuring that the system does not draw power from the external grid at any point in its operation, thereby fundamentally cutting off the source of carbon emissions during the operational phase.

[0058] Furthermore, the DC microgrid dynamic control module is coupled to the zero-carbon guided capacity optimization module and the photovoltaic-hydrogen-storage multi-energy flow coupled modeling module. This module uses the received optimized capacity parameters as the hardware operating boundary and, based on the Model Predictive Control (MPC) algorithm, collects real-time operating data such as photovoltaic output, hydrogen energy system pressure, energy storage battery state of charge (SOC), and charging load. By solving an objective function that minimizes the deviation between purchased electricity and energy storage state of charge, a multi-energy complementary scheduling strategy is formulated and continuously revised to ensure that there is no external carbon source input throughout the entire operating cycle. The multi-energy complementary scheduling strategy includes priority direct photovoltaic power supply, surplus hydrogen production and storage, insufficient hydrogen release for energy replenishment, and / or energy storage discharge.

[0059] In some embodiments, the DC microgrid dynamic control module also feeds back actual operating data to the photovoltaic-hydrogen-storage multi-energy flow coupled modeling module. This feedback mechanism is used to correct parameters in the coupled simulation model, such as the temperature coefficient of photovoltaic modules and the degradation model of energy storage batteries, thereby forming a closed-loop optimization architecture from modeling, optimization to control, and then back to modeling, continuously improving the zero-carbon stability and energy utilization efficiency of the system.

[0060] In one embodiment, see Figure 1 and Figure 2 The specific working process of the photo-hydrogen-storage multi-energy flux coupling modeling module is as follows:

[0061] First, operational data such as real-time photovoltaic power output, hydrogen energy system status, energy storage system data, and charging load data are collected. Then, this data is deeply integrated and simulated with built-in photovoltaic power output characteristic equations, electrolysis hydrogen production kinetic equations, fuel cell response models, and energy storage charge / discharge characteristic curves. By coupling calculations on core parameters such as conversion efficiency and dynamic response rate of each energy link, accurate real-time mapping data of multi-energy flow states is finally output.

[0062] To achieve efficient simulation, this module employs a time-series discretization method. Specifically, the system's operating cycle is divided into N consecutive discrete time periods of equal length. Within each discrete time period k, it is assumed that parameters such as light intensity, ambient temperature, and charging load remain stable (taking the average value within that time period). This transforms the continuous physical equations into discrete equations for rapid solution, outputting photovoltaic output curves, hydrogen production efficiency time-series data, and energy storage SOC variation curves for N discrete time periods k, providing high-precision foundational data for subsequent modules.

[0063] This embodiment employs the following steps for deep data and model fusion and discretization solution:

[0064] Data preprocessing and time period segmentation: Divide the system operation cycle into N equal-length time intervals. For each discrete time period k (k=1,2,…,N), the real-time photovoltaic power output within that discrete time period k is collected. Energy storage battery state of charge Hydrogen storage tank pressure and charging load The sampling frequency is higher than the time period resolution, so the arithmetic mean of the time period is taken as the representative value of that time period.

[0065] Modeling simultaneous equations and coupling relationships includes:

[0066] Based on the photovoltaic power output characteristic equation:

[0067] ;

[0068] In the formula, Let k be the photovoltaic output power within the discrete time period k; Let k be the photovoltaic power output characteristic function within the discrete time period k; Let be the average illumination intensity within the discrete time period k; Let be the average ambient temperature within the discrete time period k.

[0069] Electrolytic hydrogen production kinetic equation:

[0070] ;

[0071] In the formula, The efficiency of hydrogen production by electrolysis within the discrete time period k; The mass of hydrogen produced by the electrolyzer during discrete time period k; It has the lowest calorific value of hydrogen. Let be the input electrical power of the electrolytic cell within the discrete time period k.

[0072] Energy storage charge state dynamic equation:

[0073] ;

[0074] In the formula, The state of charge of the energy storage battery is given for the discrete time period k+1. The state of charge of the energy storage battery during discrete time period k; Let k be the charging and discharging power of the energy storage battery during discrete time period k. This refers to the rated capacity of the energy storage battery.

[0075] And fuel cell response models:

[0076] ;

[0077] In the formula, Let k be the fuel cell output power during discrete time intervals. This refers to the energy conversion efficiency of a fuel cell.

[0078] Based on the above equations and model, a system of multi-energy flow coupled algebraic-differential equations is constructed. The various components are coupled through power balance constraints, resulting in:

[0079] ;

[0080] In the formula, Let k be the charging load power demand during discrete time periods; Let k be the input electrical power of the electrolytic cell during discrete time period k.

[0081] Coupled Iterative Solution: In each time period, using the initial state variables as known inputs, the above equations are solved simultaneously, and based on preset scheduling logic, such as photovoltaic priority and surplus hydrogen production, the power allocation within the time period is determined. Specifically, an explicit Euler method is used for time progression, transforming continuous differential equations into discrete difference equations. Iterative calculations ensure that the energy supply and demand error in each time period is less than a preset threshold. This iterative process simultaneously updates the input power of the electrolyzer, the output power of the fuel cell, the charging and discharging power of the energy storage, and the hydrogen mass in the hydrogen storage tank, thereby obtaining the new state variables at the end of the time period.

[0082] Multi-energy flow status output: After traversing all time periods, the photovoltaic output curve, hydrogen production efficiency time series, energy storage state of charge change curve, hydrogen storage amount change curve, and fuel cell output curve for each time period are obtained, forming a complete real-time mapping data of multi-energy flow status, which is used by the capacity optimization module and the dynamic control module.

[0083] In one embodiment, see Figure 1 and Figure 3 The zero-carbon guided capacity optimization module employs the NSGA-III algorithm for multi-objective optimization. Its core optimization variables include the rated power of the photovoltaic array. Rated power of electrolytic cell Effective volume of hydrogen storage tank and rated capacity of energy storage batteries The optimization objective is to minimize carbon emissions over the entire life cycle. The algorithm aims to minimize the lifecycle cost (LCC). A key constraint is the zero-carbon constraint, meaning the system must maintain 100% energy self-sufficiency throughout its entire lifecycle, allowing no externally purchased electricity. The algorithm iteratively optimizes the system through non-dominated sorting, reference point association selection, and crossover mutation, ultimately generating a set of Pareto optimal solutions. Based on actual engineering requirements, a unique optimal capacity configuration scheme is selected and passed to the dynamic control module.

[0084] Specifically, the execution process of the NSGA-III algorithm is as follows:

[0085] First, the population size is set to 100, the maximum number of iterations to 200, the crossover probability to 0.9, and the mutation probability to 0.1, generating an initial population of 100 individuals, each corresponding to a set of capacity parameter combinations. Second, for each individual, a power balance simulation of 8760 hours per year is performed using the photovoltaic-hydrogen-storage multi-energy flow coupling modeling module. If the purchased electricity is greater than 0 at any given moment, a penalty function value is assigned to that individual. Otherwise, it will be based on the equipment carbon emission factor (photovoltaic modules 2500kg). 1500kg electrolytic cell 800kg hydrogen storage tank 120kg energy storage battery Calculate carbon emissions over the entire life cycle. ,in Let i be the installation capacity of the i-th type of equipment (i=1,2,3,4 correspond to photovoltaic array, electrolyzer, hydrogen storage tank, and energy storage battery, respectively). The life-cycle carbon emission factor per unit capacity of equipment of type i is calculated based on the unit cost of the equipment (photovoltaic 3000 yuan / kW, electrolyzer 5000 yuan / kW, hydrogen storage tank 2000 yuan / Nm³, energy storage battery 1500 yuan / kWh) and the operation and maintenance cost (2% of the initial investment per year, project cycle 20 years). Then, the population is non-dominated and orthogonalized, with 105 reference points evenly distributed in the target space. The vertical distance from each individual to a reference point is calculated and associated with the nearest reference point. Binary crossover (distribution index) is simulated. ) and multinomial variation (distribution index) A progeny population is generated. After merging the parent and progeny generations, the top 100 elite individuals are selected based on non-dominated sorting and reference point association to form a new population. The fitness calculation, non-dominated sorting, and elite retention process is repeated until 200 iterations are reached. Finally, a unique optimal capacity configuration scheme is selected from the Pareto optimal frontier using a fuzzy decision-making method (carbon emission weight 0.6, cost weight 0.4), and then passed to the dynamic control module.

[0086] In one embodiment, see Figure 1 and Figure 4 The core of the DC microgrid dynamic control module is dynamic optimization based on the MPC algorithm. First, the prediction time domain of MPC is set to P=24 (corresponding to 24 discrete time periods in the future) and the control time domain to M=4 (corresponding to 4 discrete time periods). The state vector is selected as follows: ,in The energy storage state of charge during time period k, Let k be the mass of hydrogen gas in the hydrogen storage tank during time period k; the control input vector is:

[0087] ;

[0088] The superscript T indicates the transpose operation, used for matrix representation in vector form; These correspond to the hydrogen production power of the electrolyzer, the power release of the fuel cell, and the charging and discharging power of the energy storage during time period k, respectively.

[0089] In each control cycle, the module is based on the state vector at the current moment. The photovoltaic output equation and charging load prediction model in the photovoltaic-hydrogen-storage multi-energy flow coupling modeling module are used. The photovoltaic output was predicted using an Autoregressive Integrated Moving Average (ARIMA(2,1,2) model, where parameters (2,1,2) represent the autoregressive order p=2, the difference order d=1, and the moving average order q=2, respectively. The charging load was predicted using a 7-day weighted average of historical data, with weights of 0.4, 0.25, 0.15, 0.1, 0.05, 0.03, and 0.02 (i.e., 0.4 for the day before the forecast, 0.25 for the two days before, 0.15 for the three days before, 0.1 for the four days before, 0.05 for the five days before, 0.03 for the six days before, and 0.02 for the seven days before). The weights satisfied the normalization condition and were larger in the near term and smaller in the far term, highlighting the impact of recent load trends on the prediction results. The photovoltaic output and load demand were predicted for the next 24 time periods. Then, an objective function was constructed to minimize the weighted deviation between purchased electricity and energy storage SOC in the prediction time domain.

[0090] ;

[0091] In the formula, To predict the square of the purchased power at step j in the time domain, The state of charge of energy storage in time period k+j is predicted based on information from time period k. This is a reference value for the State of Charge (SOC) of energy storage. The rate of change of the control variable is the difference vector of the control input between adjacent forecast periods; where, The control input vector for time period k+j is obtained by predicting the information for time period k. This is the control input vector for time period k+j-1 predicted based on information from time period k; the superscript T indicates vector transpose operation; weight coefficients Ensure that the hard constraint of zero carbon is met first.

[0092] The constraints include: power balance equation constraints:

[0093] ;

[0094] In the formula, The photovoltaic output power for time period k+j is predicted based on information from time period k. The fuel cell output power for time period k+j is predicted based on information from time period k. The charging and discharging power of the energy storage battery in time period k+j is predicted based on the information of time period k. The charging load demand power for time period k+j is predicted based on information from time period k. The input electrical power of the electrolyzer for time period k+j is predicted based on the information of time period k; The purchased power for time period k+j is predicted based on information from time period k.

[0095] Equipment output upper and lower limit constraints , , Energy storage SOC constraints Hydrogen storage constraints ; and zero-carbon hard constraints .

[0096] In the formula, To input electrical power into the electrolytic cell, The rated power of the electrolyzer (output by the zero-carbon guided capacity optimization module); the lower limit of 0 indicates that the electrolyzer can be shut down, and the upper limit is the rated power to prevent equipment overload; this constraint ensures that the electrolyzer operates within a safe and efficient range, avoiding equipment damage caused by low efficiency under low load or operation beyond the rated capacity. For fuel cell output power, The rated power of the fuel cell (output by the zero-carbon guided capacity optimization module); the lower limit of 0 indicates that the fuel cell can be shut down, and the upper limit is the rated power to protect the stack life; this constraint prevents the fuel cell from being operated at excessive power, which could lead to catalyst poisoning or membrane electrode damage. The charging and discharging power of the energy storage battery, This is the rated maximum discharge power. This refers to the maximum rated charging power; the rated value is determined by the battery rate characteristics (e.g., at 0.5C rate). , This constraint prevents overcharging and over-discharging of the battery, extending its cycle life. SOC refers to the state of charge of the energy storage battery (dimensionless, ranging from 0 to 1); lower limit... Determined by the battery manufacturer's technical specifications, this prevents deep discharge from damaging the electrode materials; upper limit. The top space is reserved to cope with sudden charging needs and avoid the risk of lithium dendrite formation caused by full charging; this constraint ensures that the energy storage system is always in a healthy working state and maintains the system's regulation capability. The mass of hydrogen in the hydrogen storage tank, The minimum hydrogen storage capacity is typically 5% of the effective volume of the hydrogen storage tank corresponding to the mass of hydrogen, to maintain the minimum emergency reserve of the system. The maximum hydrogen storage capacity (i.e., the hydrogen mass corresponding to the effective volume of the hydrogen storage tank, output by the zero-carbon guided capacity optimization module) ensures that the hydrogen reserve always meets the operating requirements of the fuel cell and does not exceed the safe capacity of the hydrogen storage tank.

[0097] The constrained optimization problem described above is solved using a quadratic programming solver to obtain the optimal control sequence. In the formula, This is the optimal control sequence. The optimal control input vector for time period k is obtained by solving based on the information of time period k. The optimal control input vector for time period k+1 is obtained by solving based on the information of time period k. This is the optimal control input vector for time period k+3 obtained based on the information from time period k; the superscript * indicates the optimal solution, and the superscript T indicates vector transpose. After solving, only the first control command within the current control time domain is executed and sent to the controllers of the electrolyzer, fuel cell, and energy storage system (this is because the optimal control sequence solved by the MPC based on the prediction model has the highest reliability only in the first time period. As the prediction step size increases, the accumulated prediction errors of photovoltaic output and charging load lead to a decrease in the reliability of subsequent control commands. After executing the first command, the system re-acquires the latest real-time measurement data and re-solves the optimization problem in the next control cycle, forming a closed loop of 'prediction-optimization-execution-feedback', effectively dealing with random disturbances in photovoltaic output and charging load, and ensuring the real-time performance and robustness of the control strategy).

[0098] In the next control cycle, the module calculates the deviation between the actual state and the predicted state based on the latest real-time measurement data. If the deviation exceeds a preset threshold... Then, the photovoltaic temperature coefficient and energy storage degradation model parameters in the photovoltaic-hydrogen-storage multi-energy flow coupling modeling module are corrected, with a preset threshold value. The adjustable parameters are determined based on the system's requirements for prediction accuracy and measurement noise level, with an optimal range of [range to be specified]. .when Take the smaller value (e.g.) When the system requires higher model accuracy and more frequent feedback corrections, it is suitable for scenarios with drastic changes in lighting conditions; when Take the larger value (e.g.) When [the system] is in a stable operating condition, it has higher tolerance and reduces unnecessary parameter adjustments, making it suitable for scenarios with relatively stable operating conditions. In this embodiment... Pick As a balancing value, it takes into account both the sensitivity of model correction and computational stability.

[0099] By repeating the above-mentioned rolling optimization process of "prediction-optimization-execution-feedback", the scheduling strategy can be dynamically corrected, effectively addressing the volatility of photovoltaic power output and the randomness of charging load.

[0100] In summary, this application constructs a charging island system capable of self-sufficiency and zero-carbon operation throughout its entire lifecycle through the collaborative work of three major modules: photovoltaic-hydrogen-storage multi-energy flow coupling modeling, zero-carbon guided capacity optimization, and dynamic regulation of DC microgrids. This system fully utilizes renewable energy and ensures the reliability and stability of power supply through advanced control algorithms, making it of significant value in promoting the low-carbon transformation of the transportation sector.

[0101] Figure 5 This paper presents the Pareto front distribution obtained by the NSGA-III algorithm in the bi-objective optimization space, where the horizontal axis represents total lifecycle carbon emissions and the vertical axis represents total lifecycle cost. Each non-dominated solution on the Pareto front corresponds to a specific set of capacity configuration schemes. These schemes achieve an optimal trade-off between the two conflicting objectives of minimizing carbon emissions and minimizing costs; that is, no scheme can improve either objective without worsening the other. The left endpoint of the front curve corresponds to an excessively redundant configuration with extremely low carbon emissions but high costs, typically manifested as photovoltaic arrays and hydrogen storage tanks having rated capacities significantly exceeding actual needs. The right endpoint corresponds to a compact configuration with the lowest cost but higher carbon emissions, which may lead to occasional external power purchases due to insufficient capacity, thus violating the zero-carbon hard constraint. The inflection point where the front curvature is maximum represents the optimal balance range between the marginal substitution rate of carbon emissions and costs. Near this point, the incremental cost required for a unit reduction in carbon emissions is minimal, making it a preferred configuration region in engineering practice. After comprehensive evaluation using the fuzzy decision-making method with a carbon emission weight of 0.6 and a cost weight of 0.4, the unique optimal solution selected from the Pareto front is usually located in the region to the left of the inflection point. The rated capacity parameter corresponding to this solution is transmitted to the dynamic control module as the hardware operating boundary, thereby realizing a rigid connection between offline optimization and online control.

[0102] Figure 6This paper presents the power scheduling time-series curves of a DC microgrid dynamic regulation strategy based on model predictive control algorithms under typical operating scenarios, covering multi-dimensional state variables such as photovoltaic output, charging load, electrolyzer hydrogen production power, fuel cell power release, energy storage battery charging and discharging power, purchased electricity power, energy storage state of charge, and hydrogen quality in the hydrogen storage tank. At peak photovoltaic output, surplus electricity drives the electrolyzer to operate at its rated power limit, converting excess electricity into hydrogen for long-term storage, reflecting the scheduling logic of "surplus hydrogen production and storage." If the electrolyzer power reaches its limit while photovoltaic power still has surplus, curtailment occurs, indicating a potential bottleneck in the electrolyzer's rated power configuration during capacity optimization. During the early morning peak charging load period, the energy storage battery initially addresses the transient power deficit with a millisecond-level response speed, followed by the fuel cell gradually increasing its output to maintain steady-state energy balance. This layered collaboration avoids the need for purchased electricity due to response delays in the hydrogen energy system. When photovoltaic output drops sharply due to cloud cover, the model predictive controller adjusts the energy storage discharge depth in advance based on the predictive model. If the deviation between the actual and predicted states exceeds a preset threshold, an online correction mechanism for the photovoltaic temperature coefficient and energy storage degradation model parameters is triggered, thereby enhancing the system's robustness to uncertainties. During nighttime operation, the hydrogen quality in the hydrogen storage tank continuously decreases. If it approaches the minimum hydrogen storage capacity constraint boundary, it indicates that the long-term hydrogen storage capacity configuration is too tight, and the seasonal hydrogen energy balance capacity needs to be evaluated in conjunction with 8760 hours of simulation data throughout the year. The local minimum inflection point in the energy storage state of charge curve reflects the regulatory effect of the energy storage reference state of charge traction term in the model predictive control objective function. This penalty term ensures that the energy storage system always maintains a healthy charge range, avoiding damage to electrode materials due to deep discharge or the risk of lithium dendrite formation due to full charge. In addition, the zero-crossing frequency of the energy storage battery charge and discharge power curve characterizes the smoothness of the control action. If the zero crossings are too frequent, the weight of the penalty term for the rate of change of control quantity needs to be increased in the objective function to suppress high-frequency oscillations and extend the battery cycle life. Figure 6 Zhongheng's zero-purchased power curve verifies from an operational perspective Figure 5 The feasibility of the capacity optimization scheme is assessed. If a non-zero period occurs, it indicates that the hardware configuration is insufficient to support the actual operating requirements. Feedback needs to be sent to the capacity optimization module for re-optimization, thus forming a closed-loop technical architecture.

[0103] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A DC microgrid self-powered system, characterized in that, include: The multi-energy flow coupling modeling module is configured to construct a multi-physical quantity coupling simulation model, characterize the conversion efficiency and dynamic response of each energy link, and output real-time multi-energy flow state mapping data in real time; the real-time multi-energy flow state mapping data is used to quantify physical constraints. The capacity optimization module is configured to be coupled to the multi-energy flow coupling modeling module, receive the multi-energy flow state mapping data, and, with the full life cycle carbon footprint as a hard constraint, combine the charging load timing characteristics to collaboratively plan the optimal capacity of the photovoltaic array, hydrogen storage tank and energy storage battery pack, and obtain the optimized capacity parameters. as well as A dynamic control module is configured to couple the capacity optimization module and the multi-energy flow coupling modeling module. Using the received optimized capacity parameters as the hardware operating boundary, and based on real-time operating data, it formulates and dynamically corrects a multi-energy complementary scheduling strategy. The multi-energy complementary scheduling strategy includes: Photovoltaic power supply is prioritized for direct supply, surplus hydrogen is produced and stored, insufficient hydrogen is released for energy replenishment, and / or energy storage is discharged.

2. The system according to claim 1, characterized in that, The optimized capacity parameters include the rated power of the photovoltaic array, the rated power of the electrolyzer, the effective volume of the hydrogen storage tank, and the rated capacity of the energy storage battery.

3. The system according to claim 1, characterized in that, The optimization objectives of the capacity optimization module are to minimize total lifecycle carbon emissions and total lifecycle costs.

4. The system according to claim 1, characterized in that, The construction of the multi-physical quantity coupled simulation model adopts the time series discretization method, and the specific steps include: Divide the data into N discrete time periods of equal length, k=1,2,…,N; and collect the external environment input data, load demand data, and the current initial value data of each energy storage component within each discrete time period. A set of multi-physical quantity coupled equations is constructed to map the external environment input to photovoltaic power, the input electrical power to hydrogen production efficiency and hydrogen output, the hydrogen flow rate to changes in hydrogen storage and power generation, and the charging and discharging power to changes in state of charge. The power distribution relationship between each energy link is limited by power balance constraints. In each discrete time period, the initial state values ​​of each energy storage link collected at the beginning of the time period are known quantities. The multi-physical quantity coupled equation set is solved simultaneously. The power allocation in the time period is solved according to the preset scheduling logic. The intermediate solution results, including the power commands of each controllable device and the updated energy storage state quantities, are obtained. The energy supply and demand error in each time period is made to be less than the preset threshold through iteration. The solution results are used as the initial state values ​​of the next time period. After traversing all discrete time periods, the external environment input data, load demand data, initial state data, and intermediate solution results obtained for each time period are integrated and output to obtain multi-energy flow state mapping data. The multi-energy flow state mapping data is a full-dimensional time-series state trajectory dataset that includes source output time series, hydrogen production efficiency time series, energy storage state of charge change time series, hydrogen storage change time series, and fuel cell output time series.

5. The system according to any one of claims 1 to 3, characterized in that, The capacity optimization module uses the NSGA-Ⅲ algorithm for multi-objective optimization.

6. The system according to claim 1, characterized in that, The dynamic control module employs dynamic optimization based on the MPC algorithm, and the specific steps include: Construct an objective function that minimizes the weighted deviation between purchased electricity and energy storage SOC in the prediction time domain; Set constraints, including power balance equation constraints, upper and lower limits of equipment output constraints, energy storage SOC constraints, hydrogen storage constraints, and zero-carbon hard constraints. The constrained optimization problem is solved to obtain the optimal control sequence and generate control commands.

7. The system according to claim 6, characterized in that, The control command is the first control command in the current control time domain.

8. The system according to claim 6, characterized in that, The control commands are sent to the controllers of the electrolyzer, fuel cell, and energy storage system.

9. The system according to claim 6, characterized in that, The dynamic optimization steps based on the MPC algorithm also include: In the next control cycle, the deviation between the actual state and the predicted state is calculated based on real-time measurement data. If the deviation exceeds the preset threshold, the photovoltaic temperature coefficient and energy storage degradation model parameters are corrected.

10. A method for self-powering a DC microgrid, characterized in that, Includes the following steps: A multi-physical quantity coupled simulation model is constructed to characterize the conversion efficiency and dynamic response of each energy link, and to output real-time mapping data of multi-energy flow states in real time; the real-time mapping data of multi-energy flow states is used to quantify physical constraints. Based on multi-energy flow state mapping data, with the full life cycle carbon footprint as a hard constraint, and combined with the charging load time sequence characteristics, the optimal capacity of photovoltaic array, hydrogen storage tank and energy storage battery pack is planned in a coordinated manner to obtain the optimized capacity parameters. Using the optimized capacity parameters as the hardware operating boundary, and based on real-time operating data, a multi-energy complementary scheduling strategy is formulated and dynamically corrected; the multi-energy complementary scheduling strategy includes: photovoltaic priority direct supply, surplus hydrogen production and storage, insufficient hydrogen release for energy replenishment and / or energy storage discharge.

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