Comprehensive evaluation method, system, equipment and medium for operation level of electricity-hydrogen storage integrated system

Abstract

The invention discloses a comprehensive evaluation method, system, equipment and medium for the operation level of an electricity-hydrogen storage integrated system, and the method comprises the steps: obtaining static basic data and dynamic real-time data of the electricity-hydrogen storage integrated system, and carrying out the preprocessing of the static basic data and the dynamic real-time data; based on the preprocessed static basic data and dynamic real-time data, constructing a planning model of the electricity-hydrogen storage integrated system, solving the planning model through an optimization algorithm to obtain an optimal equipment capacity combination, and outputting a planning scheme; performing dynamic operation optimization according to the optimal equipment capacity combination in combination with the dynamic real-time data to obtain operation parameters; based on the operation parameters, an operation level index is calculated from multiple dimensions, and a feedback optimization mechanism is triggered according to the operation level index; according to the planning scheme, a multi-dimensional evaluation index system is constructed, a comprehensive score is calculated, and an optimal planning scheme is obtained through screening, so that the overall efficiency and adaptability of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of renewable energy consumption and hybrid energy storage optimization, in particular to a kind of electric hydrogen storage integrated system operation level comprehensive evaluation method, system, equipment and medium. BACKGROUND

[0002] The core objective of the prior art is to solve the problem of abandoned wind and light in the scenario of high proportion of wind and light power generation. By mixed configuration of electrochemical energy storage and hydrogen energy storage, the optimal capacity combination of the energy storage system is determined with the optimization target of minimizing the net present value cost of the whole life cycle, and the economic demand of multiple application scenarios such as power generation side and power grid side is adapted. Its technical logic is highly consistent with the core demand of the present application to improve the renewable energy consumption capacity through equipment optimization configuration, and both take wind and light power generation and energy storage as the basic framework and take economy as the core optimization direction.

[0003] The prior art builds a power generation-energy storage-regulation architecture, but does not consider the core dynamic and physical property constraints of the electric hydrogen storage system, resulting in that the theoretical optimization result cannot be directly applied. Specific problems include: the hydrogen storage energy subsystem capacity configuration is only based on the simple matching of the rated power of the fuel cell and the running time, and a real-time hydrogen balance model is not established. The matching of the hydrogen production rate of the electrolyzer and the hydrogen charging rate of the hydrogen storage tank, and the influence of the pressure dynamic change of the hydrogen storage tank on the hydrogen storage amount are not considered, and the coupling relationship between the hydrogen consumption rate of the fuel cell and the power generation power is not quantified, resulting in the disconnection of the hydrogen production-storage-use links. The prior art only considers static constraints such as equipment rated capacity and SOC safety range, without taking into account dynamic characteristics such as equipment start-stop loss and operating efficiency change. The start-stop cost of the electrolyzer and the fuel cell is ignored, the dynamic change of the battery charge-discharge efficiency is not considered, and the prediction error constraint of wind power and photovoltaic output is not included, resulting in a large deviation between the optimization result and the actual operation cost loss. The prior art mainly focuses on minimizing the net present cost (NPC) and averaging the energy cost (COE), lacks a comprehensive economic-reliability-environmental evaluation system, and cannot fully reflect the system performance. The system power supply guarantee capability and hydrogen supply stability are not quantified, such as the lack of load satisfaction rate and hydrogen supply guarantee rate indicators, and the impact of equipment failure on system reliability is not considered. The environmental friendliness is only indirectly reflected through pollution penalty fees, and a direct environmental index system is not established, such as not quantifying the annual carbon emission reduction amount, ignoring the environmental loss of the hydrogen energy storage whole chain, and not directly assessing the wind and light curtailment rate. The prior art adopts a static scheduling strategy, preferentially using lithium batteries and hydrogen energy storage, but does not achieve hourly dynamic optimization, making it difficult to adapt to the random fluctuations of wind and light output and load. The scheduling strategy lacks dynamic adjustment mechanism and does not optimize according to real-time electricity price, wind and light output fluctuation and energy storage state. For example, the real-time electricity price difference is not used to optimize the charging and discharging time, the hydrogen production time is not adjusted according to the wind and light output fluctuation, and the fuel cell operation is not optimized in combination with the hydrogen storage tank state. The time scale granularity is insufficient, and the prior art calculates the optimization strategy at the hourly level, which cannot adapt to the minute-level source and load fluctuation, and the electric hydrogen balance calculation precision is low. These defects result in poor flexibility of the existing technology operation strategy, and the actual operation wind and light curtailment rate and grid interaction cost is higher than the theoretical calculation value. The prior art mentions multiple scenarios, such as power generation side, grid side and financing lease mode, but only adjusts the economic index weight to adapt, without optimizing the technical scheme according to the core demand of each scenario, such as not focusing on maximizing the consumption of wind and light curtailment. The prior art does not optimize the hydrogen storage tank capacity to match the peak of wind and light curtailment, and does not design a priority mechanism for hydrogen production from wind and light curtailment. It does not adapt to the peak regulation and frequency modulation demand. The prior art does not optimize the response speed of the battery, and does not quantify the peak regulation capacity of the hydrogen energy storage system. The prior art does not combine the user side heat load, and does not consider the direct utilization demand of the user side hydrogen. These defects limit the adaptability of the prior art scheme, and there is no way to meet the individualized needs of different scenarios, and the application range is limited.

[0004] Therefore, this invention proposes a comprehensive evaluation method, system, equipment, and medium for the operation level of an integrated electric-hydrogen-storage system to solve the above problems. Summary of the Invention

[0005] In view of the above-mentioned existing problems, the present invention provides a method, system, equipment and medium for comprehensive evaluation of the operation level of an integrated electric-hydrogen-storage system.

[0006] This invention provides a comprehensive evaluation method, system, equipment, and medium for the operation level of an integrated electric-hydrogen-storage system, addressing the problems of insufficient completeness, single-dimensional evaluation system, coarse operation control precision, and insufficient depth of scenario adaptation in existing technical solutions.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a comprehensive evaluation method for the operational level of an integrated electric-hydrogen-storage system, comprising: Acquire static basic data and dynamic real-time data of the integrated electric-hydrogen-storage system, and preprocess the static basic data and dynamic real-time data; Based on the preprocessed static basic data and dynamic real-time data, a planning model for an integrated electric hydrogen storage system is constructed. The planning model is solved by an optimization algorithm to obtain the optimal equipment capacity combination and output the planning scheme. Dynamic operation optimization is performed based on the optimal equipment capacity combination and the dynamic real-time data to obtain operating parameters; Based on the operating parameters, an operating level index is calculated from multiple dimensions, and a feedback optimization mechanism is triggered based on the operating level index. Based on the proposed planning scheme, a multi-dimensional evaluation index system is constructed and a comprehensive score is calculated to select the optimal planning scheme.

[0008] As a preferred embodiment of the comprehensive evaluation method for the operation level of an integrated electric-hydrogen-storage system described in this invention, the method includes: solving the planning model using an optimization algorithm, which includes: The first optimization algorithm is used to perform a global search to quickly locate the optimal solution range for device capacity. Based on the optimal solution interval, a second optimization algorithm is used to perform a local precise search and output the optimal combination of equipment capacity.

[0009] As a preferred embodiment of the comprehensive evaluation method for the operation level of the integrated electro-hydrogen storage system described in this invention, the dynamic operation optimization combined with the dynamic real-time data includes: Construct an operation optimization model with the objective function of minimizing daily operating costs; Based on the optimal equipment capacity constraints determined by the planning model, and combined with dynamic real-time data, the operating status of the equipment is dynamically adjusted to obtain hourly operating parameters.

[0010] The beneficial effects of this preferred technical solution are that, through dynamic optimization scheduling, the present invention can adjust the equipment operating status in real time, effectively reduce daily operating costs, and improve system flexibility and energy utilization efficiency.

[0011] As a preferred embodiment of the comprehensive evaluation method for the operation level of an integrated electro-hydrogen storage system described in this invention, the calculation of the operation level index includes: Based on the operating parameters, corresponding quantitative indicators are calculated from multiple performance dimensions. By integrating quantitative indicators from multiple performance dimensions, a comprehensive operational level index is generated.

[0012] As a preferred embodiment of the comprehensive evaluation method for the operation level of an integrated electro-hydrogen storage system described in this invention, the operation level index triggers a feedback optimization mechanism including: When the operational level index falls below the first threshold, short-term feedback is triggered to adjust the real-time operational strategy of the devices within the system. When the operating level index remains below the second preset threshold for a predetermined period of time, long-term feedback is triggered to re-optimize the equipment capacity configuration of the integrated electric hydrogen storage system.

[0013] The beneficial effects of this preferred technical solution are that, through multi-dimensional quantitative evaluation and feedback optimization, the operating strategy can be adjusted in real time, thereby improving the stability and economy of system operation.

[0014] As a preferred embodiment of the comprehensive evaluation method for the operation level of the integrated electric-hydrogen-storage system described in this invention, the planning model for constructing the integrated electric-hydrogen-storage system includes: The objective function is to minimize the total lifecycle cost of the system, which includes a comprehensive consideration of equipment investment costs, operation and maintenance costs, and penalty costs incurred due to failure to fully utilize renewable energy. The constraints include equipment capacity constraints, hydrogen balance constraints, and electrical balance constraints. Among them, the equipment capacity constraint includes limiting the operating power or capacity of each device to not exceed the corresponding rated configuration value; The hydrogen balance constraint includes the requirement that the hydrogen production of the electrolyzer equals the sum of the hydrogen filling capacity of the hydrogen storage tank and the hydrogen consumption of the fuel cell; The power balance constraint includes the sum of wind power, photovoltaic power output, and fuel cell power generation equaling the sum of load, electrolyzer power consumption, and battery charging and discharging.

[0015] The beneficial effects of this preferred technical solution are that, through multi-constraint optimization, the system's total lifecycle cost is minimized, thereby improving economic efficiency and resource utilization.

[0016] As a preferred embodiment of the comprehensive evaluation method for the operation level of an integrated electro-hydrogen storage system described in this invention, the method includes: constructing a multi-dimensional evaluation index system and calculating a comprehensive score, which includes: Construct a multi-dimensional evaluation indicator system that includes an objective layer, a criterion layer, and an indicator layer; A combined weighting method, combining the analytic hierarchy process (AHP) and the entropy weighting method, was used to determine the weight of each evaluation indicator. The planning schemes are comprehensively evaluated based on weights, and a comprehensive score is output. The planning scheme with the highest comprehensive score is the optimal one.

[0017] Secondly, the present invention provides a comprehensive evaluation system for the operational level of an integrated electric-hydrogen-storage system, comprising: The preprocessing module is used to acquire static basic data and dynamic real-time data of the integrated electric-hydrogen-storage system, and to preprocess the static basic data and dynamic real-time data. The planning model construction module is used to construct a planning model for an integrated electric-hydrogen-storage system based on preprocessed static basic data and dynamic real-time data. The planning model is solved by an optimization algorithm to obtain the optimal combination of equipment capacity and output the planning scheme. The operation optimization module is used to perform dynamic operation optimization based on the optimal equipment capacity combination and the dynamic real-time data to obtain operating parameters. The calculation module is used to calculate the operation level index from multiple dimensions based on the operation parameters, and trigger a feedback optimization mechanism based on the operation level index; The optimal planning scheme determination module is used to construct a multi-dimensional evaluation index system and calculate a comprehensive score based on the planning scheme, and then select the optimal planning scheme.

[0018] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the comprehensive evaluation method for the operation level of an integrated electric-hydrogen storage system.

[0019] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the comprehensive evaluation method for the operation level of an integrated electric-hydrogen storage system.

[0020] Compared with existing technologies, the beneficial effects of this invention are as follows: The planning scheme of this invention is transformed from theoretically optimal to practically feasible, reducing cost deviation rate. The entire hydrogen energy storage subsystem is coordinated, reducing hydrogen waste rate; accurate calculation of equipment lifecycle losses reduces start-up and shutdown frequency, and lowers the calculation deviation of battery charging and discharging efficiency. This invention achieves synergy between economy, reliability, and environmental protection, with multiple dimensions reaching industry-leading levels. The lifecycle cost (LCC) of existing technologies is lower than that of existing technologies, while increasing load fulfillment rate and annual carbon emission reduction. The dynamic optimization strategy of this invention adapts to source-load fluctuations, improving energy utilization efficiency. Enhanced adaptability to source-load fluctuations improves renewable energy absorption efficiency; energy utilization efficiency leaps, resulting in improved overall energy utilization efficiency. This invention achieves full-scenario adaptation from the generation side to the grid side to the user side, significantly expanding application boundaries. This invention doubles the wind and solar curtailment absorption capacity on the generation side; the grid side achieves peak-shaving and frequency regulation performance standards; the multi-energy complementarity value on the user side is highlighted, reducing overall energy costs. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the overall process logic of a comprehensive evaluation method for the operation level of an integrated electric-hydrogen-storage system provided in one embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figure 1 As an embodiment of the present invention, a comprehensive evaluation method for the operational level of an integrated electric-hydrogen-storage system is provided, comprising: S100: Acquire static basic data and dynamic real-time data of the integrated electric hydrogen storage system, and preprocess the static basic data and dynamic real-time data. Specifically, static basic data includes equipment parameters (photovoltaic / wind power installed capacity, electrolyzer hydrogen production efficiency, hydrogen storage tank rated pressure, battery charge and discharge efficiency, etc.), economic parameters (equipment unit investment cost, discount rate of 6%-8%, life cycle of 20 years, wind and solar curtailment penalty coefficient), and scenario parameters (power generation side curtailment target, grid side peak shaving response time, user side heat load ratio).

[0025] Dynamic real-time data includes hourly wind and solar power output forecast data, load demand data, and real-time electricity price data; minute-level battery SOC, hydrogen storage tank pressure / hydrogen storage capacity, and equipment operating status (start-up / overload status); ultra-short-term (15-minute level) wind and solar power output fluctuation data, and grid interaction power data.

[0026] Preprocessing of static basic data and dynamic real-time data includes filtering extreme abnormal data in wind and solar power output and load, such as sudden drops in output caused by sudden failures; filling missing hourly data with the average of adjacent time periods; and filling minute-level dynamic data with linear interpolation to ensure data continuity and accuracy, providing reliable support for subsequent model input.

[0027] It should be noted that by preprocessing data, filtering outliers and filling in missing data, the continuity and accuracy of the data are ensured, providing reliable input for system planning and operation optimization, and improving the stability and decision-making accuracy of the model.

[0028] S200: Based on preprocessed static basic data and dynamic real-time data, a planning model for an integrated electric hydrogen storage system is constructed. The planning model is solved by an optimization algorithm to obtain the optimal combination of equipment capacity and output the planning scheme. S300: Dynamically optimizes operation based on the optimal combination of equipment capacity and dynamic real-time data to obtain operating parameters; S400: Based on operating parameters, calculates the operating level index from multiple dimensions, and triggers a feedback optimization mechanism based on the operating level index; S500: Based on the planning scheme, construct a multi-dimensional evaluation index system and calculate the comprehensive score to select the optimal planning scheme.

[0029] In one optional embodiment, constructing a multi-dimensional evaluation index system and calculating the comprehensive score can be achieved by combining principal component analysis and grey relational analysis. Each evaluation index is dimensionless, the covariance matrix between the indices is calculated, the correlation between the indices is analyzed, the principal components are solved from the covariance matrix, and the weights are determined. A reference sequence and a comparison sequence are determined, dimensionless, and the grey relational degree is calculated. Based on the grey relational degree, the weights of each index are determined. The obtained weights are then weighted and averaged to obtain the final combined weights. These combined weights are then used to sum the weights of each index to calculate the comprehensive score of the planning scheme.

[0030] In another optional embodiment, constructing a multi-dimensional evaluation index system and calculating a comprehensive score can also be achieved by combining the fuzzy comprehensive evaluation method with the entropy weight method. The evaluation indexes are standardized, the entropy value of each index is calculated, the weight is determined based on the entropy value, the actual value of each index is fuzzified with the preset evaluation level, a fuzzy evaluation matrix is ​​constructed, the membership function is determined, the weight of each index under different evaluation levels is determined based on the membership function, the weights obtained are weighted and averaged to obtain the final combined weight, and the combined weight is used to sum the weights of each index to calculate the comprehensive score of the planning scheme. In this embodiment of the invention, constructing a multi-dimensional evaluation index system and calculating a comprehensive score includes using the analytic hierarchy process (AHP) and entropy weighting method to combine weights, constructing a multi-dimensional evaluation index system, and quantitatively calculating the comprehensive score of the planning scheme.

[0031] Specifically, the multi-dimensional evaluation index system includes a target layer, a criterion layer, and an indicator layer. The indicators are standardized, and a combination of subjective weights from the analytic hierarchy process and objective weights from the entropy weight method is used to calculate the comprehensive score. The higher the score, the better the planning scheme.

[0032] It should be noted that incorporating dynamic constraints on hydrogen balance and dynamic characteristics of equipment into the whole life cycle cost optimization achieves a significant breakthrough in the planning scheme from theoretical calculation to engineering implementation. This not only improves the efficiency of hydrogen energy utilization but also ensures the economy and sustainability of the entire system. This invention constructs a multi-dimensional evaluation model encompassing economy, reliability, and environmental friendliness. By employing a combined weighting method of analytic hierarchy process (AHP) and entropy weighting, it effectively avoids performance imbalances that may result from a single indicator-driven approach, thus providing a comprehensive and balanced perspective for the overall evaluation of hydrogen energy systems. The hourly dynamic operation optimization strategy enables real-time adaptation to source-load fluctuations, effectively overcoming the limitations of existing static scheduling technologies, improving the flexibility and response speed of the entire system. Through customized adaptation schemes, it expands the application boundaries of the electric-hydrogen storage system, enabling it to better serve energy demands in different scenarios and enhancing the overall efficiency and adaptability of the system.

[0033] In this embodiment of the invention, step S200 includes the following sub-steps A1-A4; In A1: The objective function is to minimize the total lifecycle cost of the system, which includes the comprehensive consideration of equipment investment costs, operation and maintenance costs, and penalty costs incurred due to the failure to fully utilize renewable energy. In A1: the constraints include equipment capacity constraints, hydrogen balance constraints, and electrical balance constraints; In A2: Equipment capacity constraints include limiting the operating power or capacity of each device to no more than the corresponding rated configuration value; In A3: the hydrogen balance constraint includes the fact that the hydrogen production of the electrolyzer is equal to the sum of the hydrogen charge of the hydrogen storage tank and the hydrogen consumption of the fuel cell; In A4: The power balance constraint includes the sum of wind power, photovoltaic power output and fuel cell power generation equal to the sum of load, electrolyzer power consumption and battery charging and discharging.

[0034] Specifically, the objective function is the system's total lifecycle cost, including equipment investment cost, operation and maintenance cost, fuel cost (if including natural gas supplementary energy), wind and solar curtailment penalty cost, and equipment residual value recovery, expressed as: in, For equipment investment costs, For operation and maintenance costs, To reduce the penalty costs of curtailing wind and solar power, For fuel costs, To recover the residual value of the equipment; The equipment investment cost considers the initial investment in photovoltaic (PV), wind power (WT), electrolyzer (EC), hydrogen storage tank (H2T), hydrogen fuel cell (FC), and battery (Batt), calculated using a discount rate. Converted to life cycle Years are represented as: in, For equipment The unit investment cost For equipment Rated capacity, For the discount rate, Lifecycle (taken as 20 years); Operating and maintenance costs are annual maintenance costs calculated based on equipment investment. Proportional calculation, expressed as: in, , This refers to the proportion of equipment investment. The cost of curtailment of wind and solar power is calculated based on the amount of energy curtailed and the penalty factor. The calculation is expressed as: in, ; , Providing hourly power for wind and solar power, For electrical load, The power consumption of the electrolytic cell, To charge the battery, The penalty coefficient for wind and solar curtailment needs to be set in conjunction with policies and project scenarios.

[0035] Residual value recovery includes the remaining value of the equipment at the end of its life cycle, expressed as: in, This refers to the residual value rate.

[0036] Constraints include equipment capacity constraints: the rated capacity of each piece of equipment must meet the maximum output requirement, such as the capacity constraint of the electrolytic cell, which is expressed as: The capacity constraint of the hydrogen storage tank is expressed as: in, This refers to the hourly hydrogen storage capacity of the hydrogen storage tank. This represents the actual power of the electrolytic cell. This refers to the rated power of the electrolytic cell. This refers to the rated hydrogen storage capacity of the hydrogen storage tank.

[0037] Hydrogen balance constraints mean that the hydrogen production from the electrolyzer equals the hydrogen charge to the storage tank plus the hydrogen consumption of the fuel cell. in, For the efficiency of the electrolytic cell, For the power of the electrolytic cell, , This refers to the calorific value of hydrogen. , For fuel cell efficiency, Battery fuel power.

[0038] Electricity balance constraint, wind power + solar power output + fuel cell power generation = load + electrolyzer power consumption + battery charging and discharging (charging is positive, discharging is negative), can be expressed as: in, This refers to the battery discharge power. For fuel cell power generation, For wind power output, Contributing to photovoltaic power, For the total load demand of the system, This refers to the power consumption of the electrolytic cell. The charging power of the battery. This refers to the discharge power of the battery.

[0039] It should be noted that by constructing a full life cycle cost optimization model, comprehensively considering costs such as equipment investment, operation and maintenance, and wind and solar curtailment penalties, and combining equipment capacity, hydrogen balance, and electricity balance constraints, the economic efficiency and reliability of system planning can be optimized, effectively reducing the total system cost and improving resource utilization efficiency.

[0040] In this embodiment of the invention, after completing steps A1-A4, step S200 also includes steps A5-A6; In A5: The first optimization algorithm is used to perform a global search to quickly locate the optimal solution range for device capacity; In A6: Based on the optimal solution interval, a local exact search is performed using the second optimization algorithm to output the optimal combination of device capacities.

[0041] In one optional embodiment, the first optimization algorithm can be a differential evolution algorithm, which randomly generates an initial population, with each individual representing a potential solution. For each individual, three other distinct individuals are selected, and a mutant is generated through a differential operation. The mutant is then cross-crossed with the target individual to generate an experimental individual. The fitness of the experimental individual and the target individual is compared, and the better individual is selected to enter the next generation. The steps are repeated until a termination condition is met. In another alternative embodiment, the first optimization algorithm can also be a bee colony algorithm, randomly generating an initial population where each individual represents a potential solution. Hired bees search for new solutions within their neighborhood and update their positions based on fitness. Observer bees, based on information provided by the hired bees, select the better solution within their neighborhood for searching. If a solution is not improved in multiple iterations, it is randomly reinitialized by the scout bees. The above steps are repeated until a termination condition is met. In this embodiment of the invention, the first optimization algorithm includes an improved particle swarm optimization algorithm, comprising: Specifically, the particle swarm is initialized, with each particle representing a combination of device capacities. The number of particles is set to 50-80, and the number of iterations is set to 100.

[0042] With LCC as the core, combined with the compliance of scenario requirements, such as whether the wind curtailment rate is ≤5%, 1 point is awarded if the standard is met, and 0.2 points are deducted if the standard is not met. Adaptability = 1 / (LCC×(1-deduction coefficient)), and the larger the value, the better.

[0043] By adjusting the particle velocity and location ( This allows for the rapid location of the optimal solution interval.

[0044] In one alternative embodiment, the second optimization algorithm can be a simulated annealing algorithm. An initial solution is selected, and an initial temperature and cooling rate are set. A new solution is randomly generated in the neighborhood of the current solution. The objective function value of the new solution is calculated. The Metropolis criterion is used to decide whether to accept the new solution: if the new solution is better, it is accepted; if the new solution is worse, it is accepted with a certain probability. The temperature is reduced according to the cooling rate, and the above steps are repeated until the temperature is low enough or the maximum number of iterations is reached. In another alternative embodiment, the second optimization algorithm can also be the Chalk Algorithm, which selects an initial solution and initializes the tabu list, searches all possible solutions in the neighborhood of the current solution, selects the optimal solution from the neighborhood solutions, but the solution cannot be in the tabu list, adds the currently selected solution to the tabu list, updates the contents of the tabu list according to the rules, and repeats the above steps until the maximum number of iterations is reached or the stopping condition is met. In this embodiment of the invention, the second optimization algorithm includes a genetic algorithm; Specifically, the capacity range output by the particle swarm is encoded with a precision of 0.1MW to form chromosomes; a roulette wheel selection method is used to retain the top 30% of chromosomes in terms of fitness and eliminate inefficient combinations.

[0045] The crossover probability is set to 0.7, and the mutation probability is set to 0.05 (randomly adjusting individual capacity values ​​to avoid local optima); when the LCC change rate is <0.5% for 10 consecutive generations, the iteration stops, and the optimal combination of device capacities is output.

[0046] If subsequent feedback optimization requires capacity adjustment (e.g., the operating level index remains <0.7), re-enter updated data (e.g., load growth, electricity price changes) and iteratively solve for the new optimal capacity.

[0047] It should be noted that by improving the particle swarm optimization algorithm for global search and combining it with the genetic algorithm for local optimization, the optimal solution interval can be quickly located and accurately solved, significantly improving optimization efficiency and accuracy, and ensuring the economy and adaptability of the system planning scheme.

[0048] In this embodiment of the invention, step S300 includes the following sub-steps B1-B2; In B1: Construct an operational optimization model with the objective function of minimizing daily operating costs; In B2: Based on the optimal equipment capacity constraints determined by the planning model and combined with dynamic real-time data, the operating status of the equipment is dynamically adjusted to obtain hourly operating parameters.

[0049] In this embodiment of the invention, the objective function is expressed as: in, For electricity sales revenue, For the benefits of hydrogen energy utilization, For grid interaction costs, For daily operation and maintenance costs, Costs for starting and stopping equipment.

[0050] Dynamic constraints include battery charge / discharge constraints, taking into account charge / discharge efficiency. , and The range is represented as: in, The rated capacity of the battery, , for Upper and lower limits, , ; Electrolyzer and fuel cell start-up and shutdown constraints: number of start-ups and shutdowns No more than the maximum number of times allowed per day , is represented as: Under the physical constraints of optimal equipment capacity, and combined with real-time dynamic data, such as hourly wind and solar power output, real-time electricity price, battery SOC, and real-time hydrogen storage capacity of hydrogen storage tanks, the operating status of the equipment is dynamically adjusted, and the hourly operating parameters, such as the power scheduling plan of each piece of equipment, are finally output, providing a precise operational basis for on-site operation.

[0051] It should be noted that by dynamically optimizing the model to adjust the equipment's operating status in real time, and combining this with real-time data for precise scheduling, operating costs can be effectively reduced, energy efficiency can be improved, and system operational flexibility can be enhanced.

[0052] In this embodiment of the invention, step S400 includes the following sub-steps C1-C2; In C1: Based on the running parameters, the corresponding quantitative indicators are calculated from multiple performance dimensions respectively; In C2: Quantitative indicators from multiple performance dimensions are integrated to generate a comprehensive operational level index.

[0053] In one alternative embodiment, the performance dimension can be environmentally friendly, and the quantitative indicators include annual carbon emission reduction, which is quantified by comparing the changes in carbon emissions before and after the system is in operation; and wind and solar curtailment rate, which measures the proportion of unutilized renewable energy. In another alternative embodiment, the performance dimension can also be system flexibility, with quantifiable indicators including peak shaving capacity, which measures the system's ability to adjust during peak and off-peak periods; and response speed, which measures the system's ability to respond quickly to load changes. In this embodiment of the invention, the performance dimension includes fluctuation mitigation indicators, and the quantitative indicators include safety indicators, economic indicators, and stability indicators. Specifically, the volatility mitigation indicator is calculated using volatility and is expressed as follows: in, For hourly grid interaction power, The standard deviation is the maximum value; the larger the value, the better the smoothing effect. A real-time operation evaluation model is constructed, and the operation level index is calculated from three dimensions: safety, economy, and stability, as follows: Safety indicator A includes equipment overload rate and hydrogen storage tank pressure deviation rate, expressed as: in, The rated pressure of the hydrogen storage tank; Economic indicator B includes the ratio of real-time operating cost to optimal cost, expressed as: in, This represents the theoretically optimal operating cost for the day. This refers to the actual operating cost; The stability index C includes the output fluctuation coefficient, which is expressed as: in, This represents the average daily output of the fuel cell.

[0054] The operational level index is calculated as follows: Index range To achieve excellent operational level, For good, The operating strategy needs to be adjusted.

[0055] It should be noted that by comprehensively evaluating the system's operational level through multi-dimensional quantitative indicators (security, economy, and stability), real-time monitoring and precise feedback are provided, effectively improving the system's operational efficiency and reliability, ensuring optimized adjustment of operational status, and guaranteeing stable and efficient system operation.

[0056] In this embodiment of the invention, after completing steps C1-C2, step S400 further includes steps C3-C4; In C3: When the operating level index falls below the first threshold, short-term feedback is triggered to adjust the real-time operating strategy of the devices in the system. In C4: When the operating level index remains below the second preset threshold for a predetermined period of time, long-term feedback is triggered to re-optimize the equipment capacity configuration of the integrated electric hydrogen storage system.

[0057] In this embodiment of the invention, the first threshold is 0.6 and the second threshold is 0.7; Specifically, if the operating level index is less than 0.6, short-term feedback (within the day) is triggered to provide real-time feedback to the operation optimization, and the minute-level scheduling strategy is adjusted. For example, when the battery SOC is too low, the discharge power is reduced and power is purchased from the grid first; when the hydrogen storage tank pressure is too high, the output of the fuel cell is increased and the power of the electrolyzer is reduced. If the operating level index is less than 0.7 for three consecutive days, long-term feedback is triggered and fed back to the system planning, which readjusts the equipment capacity configuration, such as increasing the battery capacity to improve peak shaving capability or optimizing constraints, such as reducing the start-up and shutdown costs of the electrolyzer, thus forming a closed loop.

[0058] It should be noted that by using short-term and long-term feedback mechanisms, the operating strategy is adjusted in real time and the system planning is optimized to ensure the efficient and stable operation of the system and improve its overall performance and adaptability.

[0059] In this embodiment of the invention, step S500 includes the following sub-steps D1-D3; In D1: Construct a multi-dimensional evaluation index system that includes an objective layer, a criterion layer, and an indicator layer; In D2: A combined weighting method combining the analytic hierarchy process (AHP) and the entropy weighting method is used to determine the weight of each evaluation indicator; In D3: Based on the weights, the planning schemes are comprehensively evaluated and a comprehensive score is output. The planning scheme with the highest comprehensive score is the optimal one.

[0060] In this embodiment of the invention, the target layer of the multi-dimensional evaluation index system is the comprehensive evaluation of the planning scheme; the criterion layer includes economy, environmental protection and reliability; and the index layer includes positive and negative indicators. For economic efficiency, a weight of 40% is set, and the indicators include total life cycle cost (negative), investment payback period (negative), and return on investment (positive); investment, operation and maintenance, residual value and other costs are calculated based on the optimal equipment capacity.

[0061] For environmental protection, a weight of 30% is set, and the indicators include annual carbon emission reduction (positive) and wind and solar curtailment rate (negative); the power supply capacity and load demand are calculated based on the equipment capacity.

[0062] For reliability, a weight of 30% is set, and the indicators include load fulfillment rate (positive) and hydrogen supply guarantee rate (positive); the calculation is based on the hydrogen storage tank capacity and the hydrogen demand of fuel cells.

[0063] The standardized positive metric (larger is better) is represented as follows: The standardized negative indicator (smaller is better) is expressed as: in, For the first The first scheme The original values ​​of each indicator This is the standardized value.

[0064] The subjective weights in the analytic hierarchy process (AHP) are calculated using a judgment matrix; the objective weights in the entropy weight method are calculated based on information entropy, and are expressed as follows: in, The number of schemes, For the number of indicators, Avoid logarithmic inconsistencies; Combined weights ( (weighting coefficients) The overall score is expressed as follows: in, For combined weights, Standardized values.

[0065] The higher the score, the better the planning scheme.

[0066] It should be noted that by constructing a multi-dimensional evaluation system that comprehensively considers economy, environmental protection and reliability, and by using a combination of the analytic hierarchy process and the entropy weight method to assign weights, the planning scheme is quantitatively evaluated, enabling scientific decision-making and improving the overall performance and sustainability of the system.

[0067] The above is a schematic scheme of a comprehensive evaluation method for the operation level of an integrated electric-hydrogen-storage system according to this embodiment. It should be noted that the technical solution of this comprehensive evaluation system for the operation level of the integrated electric-hydrogen-storage system belongs to the same concept as the technical solution of the aforementioned comprehensive evaluation method for the operation level of the integrated electric-hydrogen-storage system. Details not described in detail in the technical solution of the comprehensive evaluation system for the operation level of the integrated electric-hydrogen-storage system in this embodiment can be found in the description of the technical solution of the aforementioned comprehensive evaluation method for the operation level of the integrated electric-hydrogen-storage system.

[0068] The comprehensive evaluation system for the operation level of the integrated electro-hydrogen storage system in this embodiment includes: The preprocessing module is used to acquire static basic data and dynamic real-time data of the integrated electric-hydrogen-storage system, and to preprocess the static basic data and dynamic real-time data. The planning model construction module is used to construct a planning model for an integrated electric-hydrogen-storage system based on preprocessed static basic data and dynamic real-time data. The planning model is solved by an optimization algorithm to obtain the optimal combination of equipment capacity and output the planning scheme. The operation optimization module is used to perform dynamic operation optimization based on the optimal equipment capacity combination and the dynamic real-time data to obtain operating parameters. The calculation module is used to calculate the operation level index from multiple dimensions based on the operation parameters, and trigger a feedback optimization mechanism based on the operation level index; The optimal planning scheme determination module is used to construct a multi-dimensional evaluation index system and calculate a comprehensive score based on the planning scheme, and then select the optimal planning scheme.

[0069] This embodiment also provides a computer device suitable for the comprehensive evaluation of the operational level of an integrated electric-hydrogen storage system, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a comprehensive evaluation method for the operational level of an integrated electric-hydrogen-storage system as proposed in the above embodiments.

[0070] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a comprehensive evaluation method for the operation level of an integrated electric-hydrogen storage system as proposed in the above embodiment.

[0071] The storage medium proposed in this embodiment and the comprehensive evaluation method for the operation level of the integrated electric-hydrogen storage system proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0072] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computing device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A comprehensive evaluation method for the operational level of an integrated electricity-hydrogen-storage system, characterized in that, include: Acquire static basic data and dynamic real-time data of the integrated electric-hydrogen-storage system, and preprocess the static basic data and dynamic real-time data; Based on the preprocessed static basic data and dynamic real-time data, a planning model for an integrated electric hydrogen storage system is constructed. The planning model is solved by an optimization algorithm to obtain the optimal equipment capacity combination and output the planning scheme. Dynamic operation optimization is performed based on the optimal equipment capacity combination and the dynamic real-time data to obtain operating parameters; Based on the operating parameters, an operating level index is calculated from multiple dimensions, and a feedback optimization mechanism is triggered based on the operating level index. Based on the proposed planning scheme, a multi-dimensional evaluation index system is constructed and a comprehensive score is calculated to select the optimal planning scheme.

2. The comprehensive evaluation method for the operational level of an integrated electricity-hydrogen-storage system as described in claim 1, characterized in that, Solving the planning model using an optimization algorithm includes: The first optimization algorithm is used to perform a global search to quickly locate the optimal solution range for device capacity. Based on the optimal solution interval, a second optimization algorithm is used to perform a local precise search and output the optimal combination of equipment capacity.

3. The comprehensive evaluation method for the operational level of an integrated electro-hydrogen storage system as described in claim 2, characterized in that, Dynamic operation optimization based on the aforementioned dynamic real-time data includes: Construct an operation optimization model with the objective function of minimizing daily operating costs; Based on the optimal equipment capacity constraints determined by the planning model, and combined with dynamic real-time data, the operating status of the equipment is dynamically adjusted to obtain hourly operating parameters.

4. The comprehensive evaluation method for the operational level of an integrated electro-hydrogen storage system as described in claim 3, characterized in that, The calculation of the operational level index includes: Based on the operating parameters, corresponding quantitative indicators are calculated from multiple performance dimensions. By integrating quantitative indicators from multiple performance dimensions, a comprehensive operational level index is generated.

5. The method for comprehensive evaluation of the operational level of an integrated electro-hydrogen storage system as described in claim 4, characterized in that, The operational level index triggers a feedback optimization mechanism that includes: When the operational level index falls below the first threshold, short-term feedback is triggered to adjust the real-time operational strategy of the devices within the system. When the operating level index remains below the second preset threshold for a predetermined period of time, long-term feedback is triggered to re-optimize the equipment capacity configuration of the integrated electric hydrogen storage system.

6. A comprehensive evaluation method for the operational level of an integrated electro-hydrogen storage system as described in claim 1 or 2, characterized in that, The planning model for constructing an integrated electric-hydrogen-storage system includes: The objective function is to minimize the total lifecycle cost of the system, which includes a comprehensive consideration of equipment investment costs, operation and maintenance costs, and penalty costs incurred due to failure to fully utilize renewable energy. The constraints include equipment capacity constraints, hydrogen balance constraints, and electrical balance constraints. Among them, the equipment capacity constraint includes limiting the operating power or capacity of each device to not exceed the corresponding rated configuration value; The hydrogen balance constraint includes the requirement that the hydrogen production of the electrolyzer equals the sum of the hydrogen filling capacity of the hydrogen storage tank and the hydrogen consumption of the fuel cell; The power balance constraint includes the sum of wind power, photovoltaic power output, and fuel cell power generation equaling the sum of load, electrolyzer power consumption, and battery charging and discharging.

7. The comprehensive evaluation method for the operational level of an integrated electro-hydrogen storage system as described in claim 5, characterized in that, Constructing a multi-dimensional evaluation index system and calculating the comprehensive score includes: Construct a multi-dimensional evaluation indicator system that includes an objective layer, a criterion layer, and an indicator layer; A combined weighting method, combining the analytic hierarchy process (AHP) and the entropy weighting method, was used to determine the weight of each evaluation indicator. The planning schemes are comprehensively evaluated based on weights, and a comprehensive score is output. The planning scheme with the highest comprehensive score is the optimal one.

8. A comprehensive evaluation system for the operational level of an integrated electric-hydrogen-storage system, employing the comprehensive evaluation method for the operational level of an integrated electric-hydrogen-storage system as described in any one of claims 1-7, characterized in that, include: The preprocessing module is used to acquire static basic data and dynamic real-time data of the integrated electric-hydrogen-storage system, and to preprocess the static basic data and dynamic real-time data. The planning model construction module is used to construct a planning model for an integrated electric-hydrogen-storage system based on preprocessed static basic data and dynamic real-time data. The planning model is solved by an optimization algorithm to obtain the optimal combination of equipment capacity and output the planning scheme. The operation optimization module is used to perform dynamic operation optimization based on the optimal equipment capacity combination and the dynamic real-time data to obtain operating parameters. The calculation module is used to calculate the operation level index from multiple dimensions based on the operation parameters, and trigger a feedback optimization mechanism based on the operation level index; The optimal planning scheme determination module is used to construct a multi-dimensional evaluation index system and calculate a comprehensive score based on the planning scheme, and then select the optimal planning scheme.

9. A computer device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the comprehensive evaluation method for the operation level of an integrated electric-hydrogen storage system according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the steps of the comprehensive evaluation method for the operation level of an integrated electric-hydrogen storage system as described in any one of claims 1 to 7.

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