Optimized scheduling method, device and equipment for green hydrogen comprehensive energy system

Abstract

The invention discloses an optimal scheduling method, device and equipment for a green hydrogen comprehensive energy system, and relates to the technical field of energy optimal scheduling. According to the method, a multi-time scale framework of'day-ahead optimization-intra-day adjustment 'is applied, multi-objective optimization considering efficiency and distributed robust optimization are effectively integrated, high-quality operation of the system is realized, and meanwhile, the capability of coping with uncertainty is improved; wherein for the day-ahead optimization, a hub model is constructed based on an energy hub model of the green hydrogen comprehensive energy system, so that an optimization model for minimizing the operation cost and maximizing the efficiency of the system is established, and the high-quality operation of the system is realized; for intra-day adjustment, a fuzzy set based on a Wasserstein distance is applied to depict uncertainty of renewable energy sources, a distributed robust optimization model of the system is established, and optimality and robustness are balanced; according to the method, the cooperation of economy, high quality and robustness of the green hydrogen comprehensive energy system is realized.

Description

Technical Field

[0001] This invention relates to the field of energy optimization and scheduling technology, and in particular to an optimization and scheduling method, apparatus and equipment for a green hydrogen integrated energy system. Background Technology

[0002] Energy efficiency refers to the ratio of energy effectively utilized by a system to energy actually consumed; it is a comprehensive indicator reflecting the level of energy utilization. Currently, in Integrated Energy System (IES) research, the most common indicators for measuring energy efficiency include energy conversion efficiency and primary energy utilization rate. However, these traditional indicators only consider changes in the "quantity" of energy and fail to take into account differences in the "quality" of energy.

[0003] The "quality" attribute of energy reflects its potential to be converted into useful work. Different forms of energy have different qualities. For example, electrical energy can be almost completely converted into mechanical work, while thermal energy is limited by Carnot efficiency, resulting in a limited conversion capacity. Therefore, the quality of electrical energy is higher than that of thermal energy. If indicators that only consider the "quantity" of energy are used as the basis for judging the overall energy utilization efficiency, conclusions that violate thermodynamic laws may be drawn. For instance, electric boilers typically have higher energy conversion efficiency than gas-fired boilers, but judging electric boilers as superior solely based on this ignores the fact that electrical energy has a higher quality. In contrast, gas-fired boilers directly utilize low-quality natural gas for heating, avoiding the waste of "high-quality energy used for low-quality purposes." Energy efficiency, a concept based on the second law of thermodynamics, takes into account both the "quantity" and "quality" attributes of energy, and can more scientifically and comprehensively reflect the overall energy utilization efficiency of an energy system (IES). However, current research on the application of energy efficiency to IES mostly treats it as a post-hoc evaluation indicator to measure the merits of scheduling schemes, failing to fully leverage its potential to guide the system to high-quality operation during the decision-making process.

[0004] Renewable energy sources offer good energy quality, and a high proportion of renewable energy connected to the Integrated System Environment (IES) can improve the overall system efficiency. However, their inherent randomness and volatility can make operating strategies that solely pursue high efficiency less robust. To cope with fluctuations in renewable energy output, the system must perform costly power adjustments during real-time operation, resulting in actual total costs far exceeding day-ahead expectations. Furthermore, these emergency adjustments often rely on activating high-loss backup equipment, causing a significant drop in the actual system efficiency and making it impossible to maintain the high-quality targets set day-ahead. Traditional stochastic optimization and robust optimization methods both have inherent limitations when dealing with uncertainty: stochastic optimization heavily relies on pre-defined probability distributions and is computationally complex, and its resulting operating schemes may perform poorly when the actual distribution deviates; while robust optimization aims to ensure safe operation under worst-case scenarios, often at the expense of economic efficiency, resulting in overly conservative operating strategies.

[0005] In summary, there is an urgent need for an optimized scheduling method that can achieve economical, high-quality, and robust operation of the system. Summary of the Invention

[0006] Therefore, it is necessary to provide an optimized scheduling method, device, and equipment for a green hydrogen integrated energy system to address the aforementioned technical problems.

[0007] The present invention adopts the following technical solution: This invention provides an optimized scheduling method for a green hydrogen integrated energy system, comprising: Based on the energy hub model of the green hydrogen integrated energy system and the energy quality coefficients of various energy sources, a hub model is constructed to characterize the coupling relationship between the input energy and output energy of the green hydrogen integrated energy system, and an energy efficiency expression is obtained. The energy hub model characterizes the distribution, conversion, integration and energy storage processes of various energy sources by each energy production and storage device in the green hydrogen integrated energy system. Using the day-ahead scheduling plan of energy production and storage equipment as the optimization variable, and energy hubs, energy supply and demand balance, energy production and storage equipment constraints, and energy hubs as constraints, a day-ahead optimization objective function that minimizes the day-ahead planning cost and maximizes energy efficiency is constructed and solved to obtain the day-ahead scheduling plan of energy production and storage equipment. Based on the sample set of renewable energy output deviation, the empirical probability distribution of renewable energy output deviation is determined, and the fuzzy set of the true probability distribution of renewable energy output deviation is determined based on the Wasserstein distance between the empirical probability distribution and the true probability distribution. Using the intraday scheduling plan of energy production and storage equipment as the optimization variable, and energy hub, power adjustment balance, adjustment amount limit, and energy production and storage equipment limit as constraints, based on the day-ahead scheduling plan, an objective function is constructed according to fuzzy set to minimize the intraday adjustment cost under the worst renewable energy output deviation and solved to obtain the intraday scheduling plan of energy production and storage equipment; Based on the multi-timescale scheduling scheme of the day-ahead and intraday scheduling plans of energy production and storage equipment, the energy production and storage equipment of the green hydrogen integrated energy system are optimized for scheduling.

[0008] Optionally, the construction of a hub model characterizing the coupling relationship between the input and output quantities of the green hydrogen integrated energy system, based on the energy hub model of the green hydrogen integrated energy system and the energy quality coefficients of various energy sources, specifically includes: Based on the energy hub model of the green hydrogen integrated energy system and the energy quality coefficients of various energy sources, a hub model characterizing the coupling relationship between the input energy and output energy of the green hydrogen integrated energy system is constructed using the following formula: ; in, The output of the green hydrogen integrated energy system, A diagonal matrix representing the energy quality coefficients of various input energy sources for the green hydrogen integrated energy system. This is a diagonal matrix representing the energy quality coefficients of various energy sources output by the green hydrogen integrated energy system. Assign the transfer matrix of the layer to the energy hub model. For the transfer matrix of the energy hub model transformation layer, The transfer matrix of the integration layer in the energy hub model. The transfer matrix of the energy storage layer in the energy hub model. The energy storage power of the energy hub model. This is the input quantity for the green hydrogen integrated energy system.

[0009] Optionally, the step of constructing a day-ahead optimization objective function that minimizes day-ahead planning cost and maximizes day-ahead efficiency based on the hub model specifically includes: Based on the energy purchase cost, equipment storage operation and maintenance cost, and wind curtailment cost corresponding to the day-ahead dispatch plan, determine the day-ahead plan cost expression corresponding to the day-ahead dispatch plan; Substituting the optimization variables into the efficiency expression of the green hydrogen integrated energy system, we obtain the efficiency expression corresponding to the day-ahead scheduling plan. Based on the day-ahead scheduling cost expression and efficiency expression corresponding to the day-ahead scheduling plan, construct the day-ahead optimization objective function that minimizes the day-ahead scheduling cost and maximizes the efficiency; The equipment operation and maintenance costs include the operation and maintenance costs of energy production equipment and energy storage equipment.

[0010] Optionally, the fuzzy set of the true probability distribution for determining the renewable energy output deviation based on the Wasserstein distance between the empirical probability distribution and the true probability distribution specifically includes: The fuzzy set of the true probability distribution of renewable energy output deviation is determined by the following formula based on the Wasserstein distance between the empirical probability distribution and the true probability distribution: , , , ; in, A fuzzy set representing the true probability distribution of renewable energy output deviation. The empirical probability distribution of the output deviation of renewable energy. The true probability distribution of renewable energy output deviation. The Wasserstein distance between the empirical probability distribution and the true probability distribution. For in the space of random variables The complete probability distribution of , It is the infimal function. and Obey respectively and Distribution of variables, for and The joint distribution It is a 1-norm. Let be the radius of the probability sphere corresponding to the fuzzy set. For confidence level, As an intermediate auxiliary variable, The total number of samples in the renewable energy output deviation sample set. The first sample set of renewable energy output deviation One sample, The sample mean of the sample set of renewable energy output deviations.

[0011] Optionally, the objective function for minimizing the intraday adjustment cost under the worst-case renewable energy output deviation, based on the day-ahead scheduling plan and constructed according to the fuzzy set, specifically includes: Based on the day-ahead dispatch plan, the expression for the intraday adjustment cost corresponding to the intraday dispatch plan is determined according to the electricity purchase adjustment cost, gas purchase adjustment cost, intraday stage energy storage power adjustment cost, and wind curtailment adjustment penalty cost corresponding to the intraday dispatch plan. Based on the intraday adjustment cost expression, the true probability distributions of all renewable energy output deviations within the fuzzy set are iterated to determine the distribution that maximizes the expected intraday adjustment cost as the worst-case renewable energy output deviation scenario. This allows for the construction of an objective function that minimizes the intraday adjustment cost under the worst-case renewable energy output deviation. This invention provides an optimized scheduling device for a green hydrogen integrated energy system, comprising: The module is used to construct a hub model that characterizes the coupling relationship between the input energy and output energy of the green hydrogen integrated energy system based on the energy hub model of the green hydrogen integrated energy system and the energy quality coefficients of various energy sources, and to obtain the energy efficiency expression; the energy hub model characterizes the distribution, conversion, integration and energy storage processes of various energy sources by each energy production and storage device in the green hydrogen integrated energy system. The first optimization module is used to construct and solve a day-ahead optimization objective function that minimizes day-ahead planning costs and maximizes efficiency, taking the day-ahead scheduling plan of energy production and storage equipment as optimization variables and energy hubs, energy supply and demand balance, energy production and storage equipment constraints, and energy hubs as constraints, so as to obtain the day-ahead scheduling plan of energy production and storage equipment. The deviation modeling module is used to determine the empirical probability distribution of renewable energy output deviation based on the sample set of renewable energy output deviation, and to determine the fuzzy set of the true probability distribution of renewable energy output deviation based on the Wasserstein distance between the empirical probability distribution and the true probability distribution. The second optimization module is used to take the daily scheduling plan of energy production and storage equipment as the optimization variable, and energy hub, power adjustment balance, adjustment amount limit, and energy production and storage equipment limit as constraints. Based on the day-ahead scheduling plan, it constructs and solves the objective function of minimizing the daily adjustment cost under the worst renewable energy output deviation according to the fuzzy set, and obtains the daily scheduling plan of energy production and storage equipment. The scheduling module is used to optimize the scheduling of energy production and storage equipment in the green hydrogen integrated energy system based on multi-timescale scheduling schemes, including the day-ahead and intraday scheduling plans of the energy production and storage equipment.

[0012] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described optimized scheduling method for the green hydrogen integrated energy system.

[0013] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned optimized scheduling method for the green hydrogen integrated energy system.

[0014] The above-mentioned at least one technical solution adopted in this invention can achieve the following beneficial effects: This invention applies a multi-timescale framework of "day-ahead optimization-intraday adjustment," effectively integrating multi-objective optimization considering efficiency with sub-Bruker optimization. This achieves both economic efficiency and high-quality operation while enhancing the system's ability to cope with uncertainties. Specifically, for day-ahead optimization, an efficiency hub model is constructed based on the energy hub model of the green hydrogen integrated energy system, thus establishing an optimization model that minimizes operating costs and maximizes efficiency, achieving economic efficiency and high-quality operation. For intraday adjustment, the uncertainty of renewable energy is characterized using Wasserstein fuzzy sets, and a sub-Bruker optimization model of the system is established. This overcomes the shortcomings of traditional stochastic optimization relying on probabilistic assumptions and robust optimization being overly conservative, balancing optimality and robustness. This invention achieves economic, high-quality, and robust operation of the green hydrogen integrated energy system. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0016] Figure 1 A schematic diagram of the optimized scheduling method for a green hydrogen integrated energy system provided by the present invention; Figure 2 This invention provides a schematic diagram of a green hydrogen integrated energy system structure. Figure 3 A schematic diagram of a multi-timescale optimization scheduling framework considering efficiency and uncertainty provided by the present invention; Figure 4 A schematic diagram of an optimized scheduling device for a green hydrogen integrated energy system provided by the present invention; Figure 5 A schematic diagram of a computer device for implementing an optimized scheduling method for a green hydrogen integrated energy system, provided by the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0018] Currently, most research on the application of scheduling efficiency to IES treats it as a posterior evaluation metric to measure the merits of scheduling schemes, rather than embedding it directly into the decision-making model as an optimization objective. This fails to fully leverage its potential to guide the system to high-quality operation during the decision-making process. Furthermore, traditional stochastic optimization and robust optimization methods both have inherent limitations when dealing with uncertainties.

[0019] To address the above issues, this invention proposes a multi-timescale optimization scheduling strategy for a green hydrogen integrated energy system that considers efficiency and uncertainty. First, a hierarchical energy hub model is established for the studied green hydrogen IES to effectively handle multi-energy coupling and ensure supply-demand balance. Then, a multi-timescale optimization framework of "day-ahead-intra-day" is designed: In the day-ahead phase, an energy quality coefficient is introduced to construct the system's efficiency model and hub model, establishing a multi-objective optimization model with the goal of minimizing operating costs and maximizing system efficiency, thus obtaining an economical and optimal day-ahead scheduling strategy. In the intra-day phase, considering the uncertainty of wind power output, a fuzzy set based on Wasserstein distance is used to characterize the probability distribution uncertainty of prediction errors, establishing a sub-Bruker optimization model with the goal of minimizing the worst-case cost expectation, adjusting the day-ahead schedule. Through the division of labor and collaboration between "improving efficiency day-ahead" and "addressing uncertainty intra-day," the system achieves economical, high-quality, and robust operation.

[0020] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic diagram of the optimized scheduling method for a green hydrogen integrated energy system according to the present invention, which specifically includes the following steps: S101: Based on the energy hub model of the green hydrogen integrated energy system and the energy quality coefficients of various energy sources, construct an energy hub model that characterizes the coupling relationship between the input energy and output energy of the green hydrogen integrated energy system, so as to construct an energy efficiency expression; the energy hub model characterizes the distribution, conversion, integration and energy storage processes of various energy sources by each energy production and storage device in the green hydrogen integrated energy system.

[0022] S102: Using the day-ahead scheduling plan of energy production and storage equipment as the optimization variable, and energy hubs, energy supply and demand balance, energy production and storage equipment constraints, and energy hubs as constraints, construct and solve the day-ahead optimization objective function that minimizes the day-ahead planning cost and maximizes energy efficiency to obtain the day-ahead scheduling plan of energy production and storage equipment.

[0023] S103: Based on the sample set of renewable energy output deviations, determine the empirical probability distribution of renewable energy output deviations, and determine the fuzzy set of the true probability distribution of renewable energy output deviations based on the Wasserstein distance between the empirical probability distribution and the true probability distribution.

[0024] S104: Using the intraday scheduling plan of energy production and storage equipment as the optimization variable, and energy hub, power adjustment balance, adjustment amount limit, and energy production and storage equipment limit as constraints, based on the day-ahead scheduling plan, construct and solve the objective function of minimizing the intraday adjustment cost under the worst renewable energy output deviation, and obtain the intraday scheduling plan of energy production and storage equipment.

[0025] S105: Optimize the scheduling of energy production and storage equipment in the green hydrogen integrated energy system based on the multi-timescale scheduling scheme of the day-ahead and intraday scheduling plans of energy production and storage equipment.

[0026] For ease of explanation, the following description focuses solely on the server as the executing entity. The server mentioned in this invention can be a server set up on a business platform, or a device such as a desktop computer or laptop computer capable of executing the solution of this invention.

[0027] For the multi-energy coupling model of the green hydrogen integrated energy system, in one or more embodiments of the present invention, the green hydrogen integrated energy system mainly includes: wind turbine, electrolyzer, hydrogen fuel cell, methane reactor, gas turbine, gas boiler, waste heat boiler, energy storage device (electrochemical energy storage, thermal storage, gas storage), and multiple loads, etc., as shown in the schematic diagram of the system structure. Figure 2 As shown, Figure 2 This is a schematic diagram of a green hydrogen integrated energy system according to the present invention.

[0028] To facilitate analysis, this invention proposes a hierarchical green hydrogen integrated energy system energy hub model.

[0029] The model includes a distribution layer, a conversion layer, an integration layer, and an energy storage layer. The energy conversion and transfer relationships in each layer can be represented as a transfer matrix. In this model, the various levels are integrated through matrix operations, ultimately generating a coupling matrix. This coupling matrix comprehensively covers the multi-energy coupling characteristics between various stages of green hydrogen IES, including production capacity, energy storage, and distribution. Compared to traditional methods that build mathematical models for each individual device, this hierarchical energy hub modeling method not only significantly improves the clarity and mathematical simplicity of the model, but more importantly, it can be directly extended into a hub model to describe the system's current relationships without needing to build a current mathematical model for each device, thus improving modeling efficiency.

[0030] The transfer matrix of the distribution layer describes the distribution process of externally input energy among multiple energy production devices, such as natural gas being partially supplied to gas turbines and partially supplied to gas boilers.

[0031] (1) In the formula, This refers to the energy distribution coefficient of the wind turbine. This refers to the energy distribution coefficient of a combined heat and power (CHP) unit. This refers to the energy distribution coefficient of a gas-fired boiler. This is the energy distribution coefficient of the electrolytic cell; The energy distribution coefficient for hydrogen fuel cells; This represents the energy distribution coefficient for the methane reactor. The dimension of the distribution layer transfer matrix is ​​the number of input ports for the production equipment multiplied by the number of input energy types.

[0032] This is the transfer matrix of the conversion layer, used to describe the energy conversion process, such as a gas turbine converting natural gas into electrical and thermal energy.

[0033] (2) In the formula, For wind turbine energy conversion efficiency, For the power generation efficiency of combined heat and power units, For the heat production efficiency of combined heat and power units, For the energy conversion efficiency of waste heat boilers, For the energy conversion efficiency of gas-fired boilers, For the energy conversion efficiency of the electrolytic cell, For the power generation efficiency of hydrogen fuel cells, For the heat generation efficiency of hydrogen fuel cells, This represents the energy conversion efficiency of the methane reactor. The conversion layer transfer matrix dimension is the number of output ports of the production equipment multiplied by the number of input ports of the production equipment.

[0034] The transfer matrix of the integration layer is used to describe the sum of the output power of multiple devices belonging to a certain energy form. For example, the total electricity is equal to the sum of renewable energy generation, gas turbine generation, hydrogen fuel cell generation and the electricity purchased from the external power grid.

[0035] (3) In the formula, energies of the same form should be added together at this layer, while energies of different forms have no overlap at this layer. Therefore, the transfer matrix of this layer consists of two types of elements: 1 and 0. The dimension of the integrated layer transfer matrix is ​​the number of output energies × the number of output ports of the production equipment.

[0036] This is the transfer matrix of the energy storage layer, used to describe the energy storage process in green hydrogen IES, such as the impact of electrochemical energy storage charging or discharging on the supply and demand balance of electrical energy.

[0037] (4) in, , , , These represent the charging and discharging states of electrochemical energy storage, thermal storage tanks, gas storage equipment, and hydrogen storage equipment, respectively. 1 indicates that the energy is being discharged, and -1 indicates that the energy is being charged.

[0038] Set the following three vectors , , Represent t The input power, output power, and energy storage power of the green hydrogen IES during a given time period are as follows: (5) In the formula, To input the electrical power of the green hydrogen IES, To input natural gas power into the Green Hydrogen IES, To input renewable energy power into the Green Hydrogen IES, The electrical power output of the green hydrogen IES. The thermal power output of the green hydrogen IES. The natural gas power output of the Green Hydrogen IES The hydrogen power output of the green hydrogen IES. The charging and discharging power of electrochemical energy storage, The charging and discharging power of the thermal storage tank, The charging and discharging power of the gas storage equipment. The hydrogen charging and discharging power of the hydrogen storage device.

[0039] Therefore, the relationship between the total input and total output of the Green Hydrogen IES can be expressed as: (6) Equation (6) establishes the complete coupling relationship between the input and output of the Green Hydrogen IES.

[0040] This invention proposes a multi-timescale optimization scheduling strategy for a green hydrogen integrated energy system that considers efficiency and uncertainty, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of a multi-timescale optimization scheduling framework that considers efficiency and uncertainty in this invention.

[0041] In the day-ahead phase, system efficiency is introduced to provide a day-ahead high-quality energy supply dispatch plan for Green Hydrogen IES; in the intraday phase, based on the day-ahead dispatch plan and considering the uncertainty of renewable energy, a system partial Bruker optimization model is established to form an intraday dispatch plan that adjusts the day-ahead dispatch plan.

[0042] The term 㶲 (Exergy, Ex) refers to the maximum energy value that can theoretically be converted entirely into any other form of useful work when the system reversibly changes from any state to a state in equilibrium with a given environment. It can be used to describe energy quality; the higher the 㶲 value, the higher the energy quality.

[0043] If we could assign a coefficient to each form of energy that characterizes its energy quality... It can easily establish the energy value of a certain form of energy. With The relationship is: (7) in, The larger the value, the higher the energy quality; the energy quality coefficients of the green hydrogen IES involved in this invention are shown in Table 1.

[0044] Table 1. Schematic diagram of energy quality coefficient To quantify the operational quality of a system, the concept of "efficiency" based on the second law of thermodynamics is introduced. The efficiency of the green hydrogen IES can be defined as:

[0045] (8) in, The amount of energy input from the outside to the Green Hydrogen IES. This refers to the amount of green hydrogen IES output to the demand side.

[0046] Combining equation (7), the method for calculating efficiency can be further derived as follows: (9) in, , These are the sets of input and output energy forms, respectively; , The first Input energy value and energy quality coefficient; , The first Output energy value and energy quality coefficient.

[0047] To describe the flow relationship of the system, the obtained hierarchical energy hub model is extended into a hub model as follows.

[0048] The amount of energy input to the Green Hydrogen IES from the outside and the amount of energy output to the demand side by the Green Hydrogen IES can be expressed as: (10) in, This is a diagonal matrix composed of the energy quality coefficients of various types of energy input to the Green Hydrogen IES from the outside. This is a diagonal matrix composed of the energy quality coefficients of each energy of the green hydrogen IES. and The diagonal elements are not 0, that is and Reversible. From equations (6) and (10), the hub model can be obtained as follows:

[0049] (11) After deriving the efficiency expression, in one or more embodiments of the present invention, the server can determine the day-ahead planning cost expression corresponding to the day-ahead scheduling plan based on the energy purchase cost, equipment operation and maintenance cost, and wind curtailment cost corresponding to the day-ahead scheduling plan; then, it substitutes the optimization variables into the efficiency expression of the green hydrogen integrated energy system to obtain the efficiency expression corresponding to the day-ahead scheduling plan; thereby, based on the day-ahead planning cost expression and efficiency expression corresponding to the day-ahead scheduling plan, a day-ahead optimization objective function that minimizes the day-ahead planning cost and maximizes the efficiency is constructed; wherein, the equipment operation and maintenance cost includes the operation and maintenance cost of energy production equipment and the operation and maintenance cost of energy storage equipment.

[0050] For example, for a current-day optimization objective function, the objective function can be expressed as follows: (12) in, Costs planned for the day ahead; x For variables to be optimized in the current day (such as equipment scheduling plan); , These are the weighting coefficients.

[0051] The method for calculating planned costs is as follows: (13) in, For energy purchase costs; For energy storage operation and maintenance costs; Cost of wind curtailment.

[0052] The specific calculation methods for each cost are as follows: Energy purchase cost: (14) in, , They are respectively Electricity and gas prices during specific time periods; , They are respectively Electricity and gas purchases during specific time periods.

[0053] Energy storage operation and maintenance costs: (15) in, For the first The unit power operation and maintenance cost of energy storage devices; , They are respectively Time period The charging and discharging power of energy storage devices.

[0054] Cost of wind curtailment: (16) in, The cost of wind curtailment penalty per unit; for Wind curtailment power during a given time period.

[0055] Substituting each optimization variable into equation (9), the specific calculation method for efficiency is as follows: (17) When solving the objective function, considering the specific application scenarios of Green Hydrogen IES, certain constraints under these scenarios should be followed. Specifically, current optimization constraints may include: (1) Energy hub constraints: The energy input, conversion, storage and output processes of Green Hydrogen IES must satisfy the coupling relationship described by the hierarchical energy hub model, that is, satisfy the energy hub constraints established by equations (1) to (6).

[0056] (2) Energy supply and demand balance constraints: The energy output of the green hydrogen IES should be greater than or equal to the total energy demand on the load side.

[0057] (18) In the formula, This represents the total energy demand on the load side.

[0058] (3) Constraints on energy production equipment: Although the various energy production equipment in the Green Hydrogen IES have different characteristics, there are still some common constraints, such as upper and lower power limits and ramping constraints.

[0059] , (19) , (20) In the formula, For the first Lower limit of power for energy production equipment for Time period Actual power of energy production equipment For the first Upper limit of power for energy production equipment It is a collection of all energy production equipment. For the first Lower limit of ramp rate for energy production equipment For the first Upper limit of ramp rate for energy production equipment.

[0060] (4) Constraints of energy storage devices: This invention considers four types of energy storage devices: electrochemical energy storage, thermal storage tanks, gas storage devices, and hydrogen storage devices. They all adhere to the following common constraints: (twenty one) In the formula, , The first Type of energy storage equipment Charging and discharging power during the time period; For the first The maximum charge and discharge power of this type of energy storage device during a single charge / discharge cycle; , All are binary variables, respectively the first... Type of energy storage equipment Time period charging and discharging status parameters, , This indicates that it is in a charging state. , This indicates that the device is in a state of energy release. , The first The charging and discharging efficiency of various energy storage devices; for Time period The capacity of this type of energy storage device; , The first The upper and lower limits of the capacity of various energy storage devices; It is the collection of all energy storage devices.

[0061] (5) Hub constraints: The current relationship of the green hydrogen IES must satisfy the hub model, that is, the current input-output relationship described by equation (11).

[0062] Based on the above constraints, the server can solve the day-ahead optimization objective function to obtain the day-ahead scheduling plan for energy production and storage equipment.

[0063] Considering the randomness and volatility of renewable energy sources in the input energy, this invention employs robust optimization within the intraday phase. For the intraday robust optimization model, the uncertainty of renewable energy output is first modeled to obtain the true probability distribution of renewable energy output deviation using the Wasserstein fuzzy set. This invention does not limit the specific type of renewable energy source; wind power will be used as an example in the following explanation.

[0064] Uncertainty in wind power output is a key factor affecting the scheduling decisions of Green Hydrogen IES. This invention employs the Distributed Robust Optimization (DRO) method to address this uncertainty and utilizes Wasserstein fuzzy sets to characterize the probability distribution of wind power output prediction errors, thereby overcoming the dependence of traditional stochastic optimization on probability distribution assumptions and the conservatism of robust optimization.

[0065] Let the sample set of wind power output deviation be represented as The predicted value of wind power output is Actual output is Then the prediction error for: (twenty two) In actual industrial production scenarios, wind power output deviation True probability distribution It is difficult to calculate accurately, but its empirical probability distribution is... It can be by To characterize the true distribution. With experience distribution To determine the discrepancy between the two sets, Wasserstein distance is introduced to construct a fuzzy set.

[0066] The Wasserstein distance is defined as follows: (twenty three) in, and Obey respectively and Variables of distribution; for and The joint distribution; It is a 1-norm; It is the infimum function.

[0067] The obtained fuzzy set can be viewed as an empirical distribution Centered on, by real distribution The radius formed around it is The sphere contains the true distribution of any possible wind power prediction error. Therefore, the Wasserstein fuzzy set can be defined as:

[0068] (twenty four) in, The probability sphere is constructed based on the Wasserstein distance, and its center is the empirical distribution. , radius is ; For in the space of random variables The total probability distribution of wind power prediction is typically represented by upper and lower bounds of the prediction error. Radius This determines the level of conservatism in the model: The model degenerates into stochastic optimization. The model tends to be robustly optimized.

[0069] radius The solution can be obtained using the following method: (25) (26) in, For confidence level, As an intermediate auxiliary variable, This is the sample mean of wind power output deviation. The confidence level should be adjusted appropriately. This allows control over the degree of conservatism in the optimization results of the Bruker optimization model. The total number of samples in the sample set for renewable energy output deviation.

[0070] Based on the fuzzy set of the true probability distribution of renewable energy output deviation, the server can further use the intraday scheduling plan of energy production and storage equipment as optimization variables, and the constraints of energy hub, power adjustment balance, adjustment amount limit, and energy production and storage equipment limit as constraints. Based on the day-ahead scheduling plan, the server constructs an objective function to minimize the intraday adjustment cost under the worst renewable energy output deviation.

[0071] Specifically, the server can determine the intraday adjustment cost expression corresponding to the intraday scheduling plan based on the day-ahead scheduling plan, the corresponding electricity purchase adjustment cost, gas purchase adjustment cost, intraday stage energy storage power adjustment cost, and wind curtailment adjustment penalty cost. Then, based on the intraday adjustment cost expression, it can traverse the true probability distribution of all renewable energy output deviations in the fuzzy set and determine the distribution that maximizes the expected intraday adjustment cost as the worst-case renewable energy output deviation case, so as to construct the objective function of minimizing the intraday adjustment cost under the worst-case renewable energy output deviation case.

[0072] For example, for the objective function of intraday adjustment costs, the intraday phase optimizes the variables before the current day. Based on this, and addressing wind power prediction errors The objective function for rescheduling can be expressed as follows: (27) in, Expressing expectations; It is a supremum function; For intraday optimization variables (such as equipment rescheduling power); The worst-case expected rescheduling cost, i.e., in the fuzzy set Iterate through all possible distributions to find a single distribution. This maximizes the expected cost of intraday adjustments. Faced with this worst-case scenario, optimize intraday adjustment decisions. Minimizing the expected intraday adjustment cost in the worst-case scenario is the core manifestation of the model's robustness.

[0073] Intraday adjustment costs are calculated as follows: (28) in, Adjusting costs for electricity purchases; Cost of gas turbine rescheduling; Costs associated with the rescheduling of gas-fired boilers; Adjusting costs for energy storage devices; Adjust the penalty cost for wind curtailment.

[0074] The specific calculation methods for each cost are as follows: Adjusted electricity purchase costs: (29) in, for Time-of-use electricity pricing Within the day Adjusted power consumption for purchasing electricity from the grid during specific time periods.

[0075] Gas purchase adjustment costs: (30) (31) in, for Gas prices during certain time periods , intraday Adjustments to the amount of natural gas consumed by gas turbines and gas boilers during specific time periods.

[0076] Intraday energy storage capacity adjustment costs: (32) in, For the first The unit power operation and maintenance cost of various energy storage devices , intraday Time period The charging and discharging power adjustment of this type of energy storage device.

[0077] Wind curtailment adjustment penalty costs (33) in, The cost coefficient for wind curtailment penalties. Within the day Adjustments to wind curtailment during specific time periods.

[0078] Of course, solving the objective function for intraday adjustment costs also requires following certain constraints under the Green Hydrogen IES application scenario. Specifically, intraday adjustment constraints may include: Energy hub constraints: During the daytime phase, all devices in the Green Hydrogen IES must also meet the energy hub constraints established by equations (1) to (6).

[0079] Power adjustment balance constraints: The intraday adjustment variables must maintain a real-time balance with the wind power forecast error. Considering power balance, the adjustment amount and the wind power forecast error must satisfy the following formula:

[0080] (34) In the formula, for Adjustments to the amount of hydrogen consumed by the hydrogen fuel cell during different time periods.

[0081] Adjustment amount upper and lower limit constraints: The adjustments made to each piece of equipment during the day must meet its physical operating limitations.

[0082] (35) In the formula, for Time period Adjustments to energy production equipment For the first time in a day Lower limit of adjustment for energy production equipment For the first time in a day Upper limit for adjustments to energy production equipment.

[0083] Equipment actual output constraints: After intraday adjustments, the total output of each piece of equipment must not exceed its upper or lower operating limits.

[0084] ， (36) Energy storage device adjustment constraints: (37) In the formula, for Time period Energy storage device charging adjustment amount for Time period Energy release adjustment amount of energy storage equipment.

[0085] Based on the above constraints, the server can solve the objective function of intraday adjustment costs to obtain the intraday scheduling plan for energy production and storage equipment. Then, based on the multi-timescale scheduling scheme of the daily and intraday scheduling plans of energy production and storage equipment, the server can optimize the scheduling of energy production and storage equipment in the green hydrogen integrated energy system.

[0086] By establishing a multi-timescale optimization model for the green hydrogen integrated energy system that considers efficiency and uncertainty, the system aims to achieve high-quality energy supply in the near term and improve its ability to cope with the uncertainties of renewable energy sources in the long term. This will enable the economical, high-quality, and robust operation of the green hydrogen integrated energy system. Both objective models can be solved using genetic algorithms when solving the objective function.

[0087] based on Figure 1The proposed optimization scheduling method for the green hydrogen integrated energy system utilizes a "day-ahead optimization-intraday adjustment" operational framework. This framework effectively integrates multi-objective optimization considering efficiency with sub-Bruker optimization, achieving both economic efficiency and high-quality operation while enhancing the system's ability to cope with uncertainties. Specifically, for day-ahead optimization, a hub model is constructed based on the hierarchical energy hub model of the green hydrogen integrated energy system, thus establishing an optimization model that minimizes operating costs and maximizes efficiency, achieving both economic efficiency and high-quality operation. For intraday adjustment, a Wasserstein fuzzy set-based approach is used to characterize the uncertainties of renewable energy, and a sub-Bruker optimization model is established, overcoming the shortcomings of traditional stochastic optimization relying on probabilistic assumptions and robust optimization being overly conservative, thus balancing optimality and robustness. This invention achieves economic, high-quality, and robust operation of the green hydrogen integrated energy system.

[0088] When applying the optimized scheduling method for the green hydrogen integrated energy system provided by this invention, it is not necessary to consider... Figure 1 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this invention does not impose any restrictions on it.

[0089] The above describes an optimized scheduling method for a green hydrogen integrated energy system provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding optimized scheduling device for a green hydrogen integrated energy system, such as... Figure 4 As shown.

[0090] Figure 4 A schematic diagram of an optimized scheduling device for a green hydrogen integrated energy system provided by the present invention includes: Module 201 is used to construct a hub model that characterizes the coupling relationship between the input energy and output energy of the green hydrogen integrated energy system based on the energy hub model of the green hydrogen integrated energy system and the energy quality coefficients of various energy sources, and to obtain the energy efficiency expression; the energy hub model characterizes the distribution, conversion, integration and energy storage processes of various energy sources by each energy production and storage device in the green hydrogen integrated energy system. The first optimization module 202 is used to construct and solve a day-ahead optimization objective function that minimizes the day-ahead planning cost and maximizes the efficiency, taking the day-ahead scheduling plan of energy production and storage equipment as the optimization variable and energy hub, energy supply and demand balance, energy production and storage equipment constraints and energy hub as constraints, so as to obtain the day-ahead scheduling plan of energy production and storage equipment. Uncertainty modeling module 203 is used to determine the empirical probability distribution of renewable energy output deviation based on the sample set of renewable energy output deviation, and to determine the fuzzy set of the true probability distribution of renewable energy output deviation based on the Wasserstein distance between the empirical probability distribution and the true probability distribution. The second optimization module 204 is used to take the daily scheduling plan of energy production and storage equipment as the optimization variable, and energy hub, power adjustment balance, adjustment amount limit, and energy production and storage equipment limit as constraints. Based on the day-ahead scheduling plan, it constructs and solves the objective function of minimizing the daily adjustment cost under the worst renewable energy output deviation according to the fuzzy set, and obtains the daily scheduling plan of energy production and storage equipment. The scheduling module 205 is used to optimize the scheduling of energy production and storage equipment in the green hydrogen integrated energy system based on a multi-timescale scheduling scheme that includes the day-ahead and intraday scheduling plans of the energy production and storage equipment.

[0091] Specific limitations regarding the optimization scheduling device for the green hydrogen integrated energy system can be found in the limitations of the optimization scheduling method for the green hydrogen integrated energy system described above, and will not be repeated here. Each module in the aforementioned optimization scheduling device for the green hydrogen integrated energy system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0092] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The proposed optimization scheduling method for the green hydrogen integrated energy system.

[0093] The present invention also provides Figure 5 The schematic diagram of the computer device shown is as follows: Figure 5 As shown, at the hardware level, this computer device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above. Figure 1 The proposed optimization scheduling method for the green hydrogen integrated energy system.

[0094] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0095] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this invention.

Claims

1. An optimized scheduling method for a green hydrogen integrated energy system, characterized in that, include: Based on the energy hub model of the green hydrogen integrated energy system and the energy quality coefficients of various energy sources, a hub model is constructed to characterize the coupling relationship between the input energy and output energy of the green hydrogen integrated energy system, and an energy efficiency expression is obtained. The energy hub model characterizes the distribution, conversion, integration and energy storage processes of various energy sources by each energy production and storage device in the green hydrogen integrated energy system. Using the day-ahead scheduling plan of energy production and storage equipment as the optimization variable, and energy hubs, energy supply and demand balance, energy production and storage equipment constraints, and energy hubs as constraints, a day-ahead optimization objective function that minimizes the day-ahead planning cost and maximizes energy efficiency is constructed and solved to obtain the day-ahead scheduling plan of energy production and storage equipment. Based on the sample set of renewable energy output deviation, the empirical probability distribution of renewable energy output deviation is determined, and the fuzzy set of the true probability distribution of renewable energy output deviation is determined based on the Wasserstein distance between the empirical probability distribution and the true probability distribution. Using the intraday scheduling plan of energy production and storage equipment as the optimization variable, and energy hub, power adjustment balance, adjustment amount limit, and energy production and storage equipment limit as constraints, based on the day-ahead scheduling plan, an objective function is constructed according to fuzzy set to minimize the intraday adjustment cost under the worst renewable energy output deviation and solved to obtain the intraday scheduling plan of energy production and storage equipment; Based on the multi-timescale scheduling scheme of the day-ahead and intraday scheduling plans of energy production and storage equipment, the energy production and storage equipment of the green hydrogen integrated energy system are optimized for scheduling.

2. The optimized scheduling method for the green hydrogen integrated energy system as described in claim 1, characterized in that, The process involves constructing a hub model that characterizes the coupling relationship between the input and output quantities of the green hydrogen integrated energy system, based on the energy hub model of the green hydrogen integrated energy system and the energy quality coefficients of various energy sources. Specifically, this includes: Based on the energy hub model of the green hydrogen integrated energy system and the energy quality coefficients of various energy sources, a hub model characterizing the coupling relationship between the input energy and output energy of the green hydrogen integrated energy system is constructed using the following formula: ； in, The output of the green hydrogen integrated energy system, A diagonal matrix representing the energy quality coefficients of various input energy sources for the green hydrogen integrated energy system. This is a diagonal matrix representing the energy quality coefficients of various energy sources output by the green hydrogen integrated energy system. Assign the transfer matrix of the layer to the energy hub model. For the transfer matrix of the energy hub model transformation layer, The transfer matrix of the integration layer in the energy hub model. The transfer matrix of the energy storage layer in the energy hub model. The energy storage power of the energy hub model. This is the input quantity for the green hydrogen integrated energy system.

3. The optimized scheduling method for the green hydrogen integrated energy system as described in claim 1, characterized in that, The construction of the day-ahead optimization objective function that minimizes day-ahead planning cost and maximizes efficiency specifically includes: Based on the energy purchase cost, equipment operation and maintenance cost, and wind curtailment cost corresponding to the day-ahead scheduling plan, determine the day-ahead plan cost expression corresponding to the day-ahead scheduling plan. Substituting the optimization variables into the efficiency expression of the green hydrogen integrated energy system, we obtain the efficiency expression corresponding to the day-ahead scheduling plan. Based on the day-ahead scheduling cost expression and efficiency expression corresponding to the day-ahead scheduling plan, construct the day-ahead optimization objective function that minimizes the day-ahead scheduling cost and maximizes the efficiency; The equipment operation and maintenance costs include the operation and maintenance costs of energy production equipment and energy storage equipment.

4. The optimized scheduling method for the green hydrogen integrated energy system as described in claim 1, characterized in that, The fuzzy set for determining the true probability distribution of renewable energy output deviation based on the Wasserstein distance between the empirical probability distribution and the true probability distribution specifically includes: The fuzzy set of the true probability distribution of renewable energy output deviation is determined by the following formula based on the Wasserstein distance between the empirical probability distribution and the true probability distribution: ， ， ， ； in, A fuzzy set representing the true probability distribution of renewable energy output deviation. The empirical probability distribution of the output deviation of renewable energy. The true probability distribution of renewable energy output deviation. The Wasserstein distance between the empirical probability distribution and the true probability distribution. For in the space of random variables The complete probability distribution of , It is the infimal function. and Obey respectively and Distribution of variables, for and The joint distribution It is a 1-norm. Let be the radius of the probability sphere corresponding to the fuzzy set. For confidence level, As an intermediate auxiliary variable, The total number of samples in the renewable energy output deviation sample set. The first sample set of renewable energy output deviation One sample, The sample mean of the sample set of renewable energy output deviations.

5. The optimized scheduling method for the green hydrogen integrated energy system as described in claim 1, characterized in that, The objective function for minimizing intraday adjustment costs under the worst-case renewable energy output deviation, based on the day-ahead scheduling plan and constructed using fuzzy sets, specifically includes: Based on the day-ahead dispatch plan, the expression for the intraday adjustment cost corresponding to the intraday dispatch plan is determined according to the electricity purchase adjustment cost, gas purchase adjustment cost, intraday stage energy storage power adjustment cost, and wind curtailment adjustment penalty cost corresponding to the intraday dispatch plan. Based on the intraday adjustment cost expression, the true probability distribution of all renewable energy output deviations within the fuzzy set is traversed to determine the distribution that maximizes the expected intraday adjustment cost as the worst-case renewable energy output deviation scenario, thereby constructing an objective function that minimizes the intraday adjustment cost under the worst-case renewable energy output deviation scenario.

6. An optimized scheduling device for a green hydrogen integrated energy system, characterized in that, include: The module is used to construct a hub model that characterizes the coupling relationship between the input energy and output energy of the green hydrogen integrated energy system based on the energy hub model of the green hydrogen integrated energy system and the energy quality coefficients of various energy sources, and to obtain the energy efficiency expression; the energy hub model characterizes the distribution, conversion, integration and energy storage processes of various energy sources by each energy production and storage device in the green hydrogen integrated energy system. The first optimization module is used to construct and solve a day-ahead optimization objective function that minimizes day-ahead planning costs and maximizes efficiency, taking the day-ahead scheduling plan of energy production and storage equipment as optimization variables and energy hubs, energy supply and demand balance, energy production and storage equipment constraints, and energy hubs as constraints, so as to obtain the day-ahead scheduling plan of energy production and storage equipment. The uncertainty modeling module is used to determine the empirical probability distribution of renewable energy output deviation based on the sample set of renewable energy output deviation, and to determine the fuzzy set of the true probability distribution of renewable energy output deviation based on the Wasserstein distance between the empirical probability distribution and the true probability distribution. The second optimization module is used to take the daily scheduling plan of energy production and storage equipment as the optimization variable, and energy hub, power adjustment balance, adjustment amount limit, and energy production and storage equipment limit as constraints. Based on the day-ahead scheduling plan, it constructs and solves the objective function of minimizing the daily adjustment cost under the worst renewable energy output deviation according to the fuzzy set, and obtains the daily scheduling plan of energy production and storage equipment. The scheduling module is used to optimize the scheduling of energy production and storage equipment in the green hydrogen integrated energy system based on multi-timescale scheduling schemes, including the day-ahead and intraday scheduling plans of the energy production and storage equipment.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 5.

8. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of claims 1 to 5.

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