Hydrogen storage energy planning method and system considering uncertainty

Abstract

The application discloses a shared hydrogen energy storage planning method and system considering uncertainty, and belongs to the technical field of power system planning. The shared hydrogen energy storage planning method considering uncertainty comprises the following steps: obtaining historical load data of each park and adopting a time series decomposition method to obtain multiple load components; constructing a load demand uncertainty description model matching the characteristics of the components based on the decomposition results; establishing a long-time hydrogen energy storage planning model containing energy balance constraints, device operation boundary constraints and operation safety constraints; according to the time scales corresponding to the load components, the long-time hydrogen energy storage planning model is divided into a hierarchical planning model of a long-term trend capacity configuration layer and a short-term periodicity and randomness regulation layer; and taking the annualized configuration cost of flexible resources and the annual operation cost of the system as targets, a layered coordination optimization solving method is adopted to determine the capacity configuration scheme of the long-time hydrogen energy storage of multiple parks and the operation strategy of each unit. The application realizes optimal configuration of hydrogen energy storage capacity of multiple parks and reasonable distribution of cost and benefits.

Description

Technical Field

[0001] This invention belongs to the field of power system planning technology, specifically relating to a shared hydrogen energy storage planning method and system that takes into account uncertainties. Background Technology

[0002] The statements herein provide only background information in relation to this invention and do not necessarily constitute prior art.

[0003] Industrial parks, as important carriers of energy consumption and energy conversion, have gradually formed an operational pattern characterized by multiple parks coexisting, diversified energy structures, and significant differences in load characteristics. With the continuous increase in the penetration rate of renewable energy, the energy systems of industrial parks generally face problems such as strong fluctuations in renewable energy output, uneven spatial and temporal distribution, and complex supply and demand coupling relationships.

[0004] Hydrogen energy storage boasts advantages such as large capacity and strong cross-timescale regulation capabilities, effectively enabling time-series conversion between electricity and hydrogen, and has gradually become an important long-term energy storage form in integrated energy systems for industrial parks. However, current hydrogen energy storage planning research largely focuses on single-park or single-system scales, typically employing centralized or static configuration methods for capacity planning. This makes it difficult to fully consider the differences in energy resource endowment, load structure, and operational objectives among different parks. In multi-park scenarios, if independent planning or simple overlay methods are still used, it can easily lead to problems such as redundant hydrogen energy storage resource allocation and insufficient cross-park coordination capabilities.

[0005] The inventors discovered that existing technologies, in the process of configuring long-term hydrogen energy storage capacity, have a rather crude modeling consideration of uncertainties such as load fluctuations and renewable energy output, neglecting uncertainties at different time scales in the optimized configuration of long-term hydrogen energy storage, making it difficult to guarantee the robustness of the planning results under actual operating conditions. Furthermore, in multi-park shared hydrogen energy storage scenarios, there is a significant cooperative game relationship among energy operators in the energy storage resource utilization stage, and there is a lack of scientifically effective technical solutions for issues such as the rational allocation of energy storage capacity and the sharing of usage costs. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a shared hydrogen energy storage planning method and system that takes into account uncertainty. By decoupling the load, handling differentiated uncertainty and hierarchical optimization, the solution efficiency and economy are improved while ensuring the robustness of the planning, and the optimal configuration of hydrogen energy storage capacity in multiple parks and the reasonable allocation of cost and benefit are realized.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution: In a first aspect, the technical solution of the present invention provides a shared hydrogen energy storage planning method that takes into account uncertainties, including: Historical load data of each park within the planning time scale were obtained and decomposed using a time series decomposition method to obtain multiple load components. Based on the multiple load components obtained from the decomposition, a load demand uncertainty description model matching the characteristics of each component is constructed. Based on the load demand uncertainty description model, a long-term hydrogen energy storage planning model is established, which includes energy balance constraints, equipment operation boundary constraints, and operation safety constraints. According to the time scale corresponding to the load components, the long-term hydrogen energy storage planning model is divided into a hierarchical planning model of long-term trend capacity configuration layer and short-term periodic and stochastic adjustment layer. With the annualized allocation cost of flexible resources and the annual operating cost of the system as optimization objectives, a hierarchical and coordinated optimization solution method is adopted to determine the capacity configuration scheme of long-term hydrogen energy storage in multiple parks and the operation strategy of each unit.

[0008] In at least one embodiment, the multiple load components include at least a long-term trend component, a periodic component, and a random fluctuation component; the time series decomposition method is implemented based on the STL decomposition method, and the load series is smoothed through local weighted regression; The core iterative formula for STL decomposition is specifically expressed as follows:

[0009]

[0010] In the formula, This represents the long-term trend component of electrical load; Represents the periodic components of electrical load; Indicates the number of iterations. hour, ; This represents the locally weighted regression fitting function; The width of the local window used to fit the long-term trend component of the load; The width of the local window used to fit the periodic components of the load; The order of the polynomial fitted to the long-term trend components of the load; The order of the polynomial fitted to the periodic components of the load; Indicates the period length of the load sequence; For time Perform periodic modulus to achieve cyclic fitting of periodic components.

[0011] In at least one embodiment, the periodic component of the load is represented by constructing a scenario probability representation uncertainty set, as follows:

[0012] In the formula, The set interval for the scenario probability distribution of the periodic component of the load, i.e. the confidence interval of the comprehensive norm; This represents the total number of scenes after clustering. For real-world scenarios after clustering The probability distribution.

[0013] In at least one embodiment, the load random fluctuation component is represented by constructing an error interval to describe the uncertainty set, specifically as follows:

[0014] In the formula, Represents the set of uncertain intervals for random fluctuation components; Indicates confidence level; This indicates the trend of electrical load output in a typical scenario; Indicates the magnitude of electrical load fluctuations; This indicates the hydrogen load output trend under typical scenarios; This indicates the range of hydrogen load fluctuations.

[0015] In at least one embodiment, the long-term hydrogen energy storage planning model aims to optimize the overall economic efficiency of the system operation, and the objective function is expressed as follows:

[0016] In the formula, Indicates different decomposition amounts Total cost below; Indicates investment cost; Indicates operating costs; These represent trend, period, and stochastic decomposition, respectively. This is a component identifier.

[0017] In at least one embodiment, the energy balance constraint includes multi-energy coupling balance constraints of electricity, hydrogen, and heat, for each park. At any given moment ,satisfy:

[0018] In the formula, and Indicates the output of photovoltaic and wind turbines; Indicates the park exist Power constantly interacting with the power grid; Indicates that the electrolytic cell is in The volumetric flow rate of hydrogen produced at any given time; Indicates the park At any moment Hydrogen load demand; express The volumetric flow rate of hydrogen consumed by the hydrogen fuel cell at any given time; express The volumetric flow rate of hydrogen stored in the hydrogen storage tank at any given time; Indicates the park exist The heat power requirement at any given time; and This indicates that hydrogen fuel cells are in The heat generation capacity at any given time.

[0019] In at least one embodiment, the equipment operating boundary constraints include electrolyzer constraints and hydrogen fuel cell constraints; Electrolytic cell constraints are represented as:

[0020]

[0021] In the formula, Indicates that the electrolytic cell is in The volumetric flow rate of hydrogen produced at any given time; Indicates that the electrolytic cell is in The electrical power consumed in the electrolysis of water to produce hydrogen at any given time; and The fitting coefficients representing the electro-hydrogen conversion relationship; This indicates the capacity of the electrolytic cell configured in the system; Indicates the maximum operating load rate of the electrolytic cell; The constraints of hydrogen fuel cells are expressed as follows:

[0022]

[0023]

[0024] In the formula, and These respectively represent the hydrogen fuel cell in Power generation and thermal power at any given time; and This represents the fitting coefficient between hydrogen consumption and power generation of a hydrogen fuel cell; and This represents the electro-thermal fitting coefficient of a hydrogen fuel cell; This indicates the upper limit of the power generation capacity of hydrogen fuel cells.

[0025] In at least one embodiment, for shared hydrogen energy storage, the annualized configuration cost of hydrogen energy storage is determined based on the proportion of new energy installed capacity and load with intermittent power generation characteristics in each park. final investment cost The Shapley value allocation method distributes the alliance's additional profits based on the marginal contributions of each participant, specifically as follows:

[0026] In the formula, Indicates the park In the case of independent energy storage configuration, the equipment Independent investment costs; This indicates that participants within the cooperative alliance are included in the collection. This indicates a cooperative alliance for the equipment. Total investment cost; Indicates comparison with each park In the fully independent configuration model, the investment cost savings achieved by each park jointly building a hydrogen energy storage system under the cooperative model are distributed among the parks based on their internal photovoltaic installed capacity and load. For the park medium equipment The cost allocation factor is calculated using the following formula:

[0027] In the formula, Indicates the park i The photovoltaic configuration capacity; Indicates the park i Wind power configuration capacity; This indicates the total installed capacity of new energy vehicles in each industrial park; and They represent the park At any moment The electrical load and hydrogen load.

[0028] In at least one embodiment, the hierarchical coordination optimization solution method specifically includes: For the long-term trend component, a deterministic capacity planning sub-model is constructed to solve for the basic configuration capacity of the hydrogen energy storage system; Using the basic configuration capacity as prior information, and targeting the periodic component, a sub-model for sub-Brubar optimization is constructed under the uncertainty set of sub-Brubar optimization to optimize the system's operation strategy. For the random fluctuation component, under the interval uncertainty set, an interval optimization operation sub-model is constructed to solve the feasible interval of the operation strategy.

[0029] Secondly, the technical solution of the present invention also provides a shared hydrogen energy storage planning system that takes into account uncertainties, including: The data acquisition and decomposition module is configured to: acquire historical load data of each park within the planning time scale and decompose it using a time series decomposition method to obtain multiple load components; The uncertainty set construction module is configured to: construct a load demand uncertainty description model that matches the characteristics of each component based on multiple load components obtained from decomposition; The hierarchical planning module is configured to: establish a long-term hydrogen energy storage planning model based on the load demand uncertainty description model, which includes energy balance constraints, equipment operation boundary constraints and operation safety constraints, and divide the long-term hydrogen energy storage planning model into a hierarchical planning model with a long-term trend capacity configuration layer and a short-term periodic and stochastic adjustment layer according to the time scale corresponding to the load components. The optimization solution module is configured to: use the annualized allocation cost of flexible resources and the annual operating cost of the system as optimization objectives, and adopt a hierarchical and coordinated optimization solution method to determine the capacity configuration scheme of long-term hydrogen energy storage in multiple parks and the operation strategy of each unit.

[0030] The beneficial effects of the above-described technical solution of the present invention are as follows: This invention addresses the configuration redundancy problem caused by the insufficient flexibility of shared hydrogen energy storage regulation. The proposed hierarchical configuration method decomposes the load of multiple parks on a time scale and matches the form and capacity of hydrogen energy storage resources according to the characteristics of load components, effectively improving the configuration rationality and utilization efficiency of shared hydrogen energy storage resources. This invention, through a load time series decomposition → uncertainty modeling → hierarchical planning and hierarchical coordinated solution approach, can achieve the collaborative determination of long-term hydrogen energy storage capacity configuration and operation strategies across multiple parks. While improving the economic efficiency and robustness of the planning results, it significantly reduces the solution complexity of uncertain planning, transforming deterministic planning into hierarchical uncertainty optimization, thus enhancing the engineering robustness of the planning results. Attached Figure Description

[0031] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0032] Figure 1 This is a schematic diagram of the shared hydrogen energy storage planning method that takes into account uncertainties, as disclosed in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the energy flow in a multi-electric-hydrogen power supply park disclosed in Embodiment 1 of the present invention; Figure 3 This is a flowchart of the hierarchical optimization configuration of long-term hydrogen energy storage disclosed in Embodiment 1 of the present invention; Figure 4 These are statistical diagrams of the fitting errors of the electrolyzer and hydrogen fuel cell for electricity, hydrogen, and heat, as disclosed in Embodiment 1 of the present invention; wherein, (a) is a statistical diagram of the fitting error of the electro-hydrogen conversion of the electrolyzer, (b) is a statistical diagram of the fitting error of the hydrogen-electric conversion of the hydrogen fuel cell, and (c) is a statistical diagram of the fitting error of the fitting error of the electro-thermal conversion of the hydrogen fuel cell. Detailed Implementation

[0033] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0034] As described in the background section, the purpose of this invention is to overcome the shortcomings of the prior art and provide a shared hydrogen energy storage planning method and system that takes into account uncertainty. By decoupling the load, handling differentiated uncertainty, and hierarchical optimization, the solution efficiency and economy are improved while ensuring the robustness of the planning, and the optimal configuration of hydrogen energy storage capacity in multiple parks and the reasonable allocation of cost and benefit are achieved.

[0035] Example 1 In a typical embodiment of the present invention, such as Figures 1 to 4 As shown, this embodiment discloses a shared hydrogen energy storage planning method that takes into account uncertainties, specifically including the following steps: S1. Obtain historical load data for each park within the planning time scale and decompose it using the time series decomposition method to obtain multiple load components; S2. Based on the multiple load components obtained from the decomposition, construct a load demand uncertainty description model that matches the characteristics of each component; S3. Based on the load demand uncertainty description model, establish a long-term hydrogen energy storage planning model that includes energy balance constraints, equipment operation boundary constraints and operation safety constraints. According to the time scale corresponding to the load components, the long-term hydrogen energy storage planning model is divided into a hierarchical planning model of long-term trend capacity configuration layer and short-term periodic and stochastic adjustment layer. S4. Taking the annualized allocation cost of flexible resources and the annual operating cost of the system as optimization objectives, a hierarchical and coordinated optimization solution method is adopted to determine the capacity configuration scheme of long-term hydrogen energy storage in multiple parks and the operation strategy of each unit.

[0036] The above-mentioned shared hydrogen energy storage planning method taking into account uncertainties will be described in detail below with reference to specific implementation methods.

[0037] S1. Obtain historical load data for each park within the planning time scale and decompose it using the time series decomposition method to obtain multiple load components.

[0038] Since hydrogen storage has superior economic efficiency and regulation capabilities over long time scales, this step uses time series decomposition to process the historical load data of each park in the multi-park complex in order to characterize the load change characteristics of the park over long time scales.

[0039] Specifically, historical load data and renewable energy output data for each park within the planned timescale (e.g., the past three years) are acquired. The acquired data undergoes preprocessing such as cleaning, normalization, and time alignment to form a standardized load time series. Historical load data includes electrical and thermal loads, while renewable energy output data includes photovoltaic and wind power output data.

[0040] The load time series of each park is decomposed using a time series decomposition method, decomposing the original load signal into multiple load components, including at least a long-term trend component, a periodic component, and a random fluctuation component. For the first... Taking the electrical load of each industrial park as an example, on the planning time scale... Within this timeframe, the electrical load time series can be represented as: (1) In the formula, It represents the long-term trend component of electrical load, which reflects the long-term evolution characteristics of energy demand in the park. It represents the periodic component of electrical load, which reflects the decomposition of quantities with a periodic trend of change; It represents the random fluctuation component of electrical load and is used to describe strong random disturbances.

[0041] In this embodiment, time series decomposition is implemented based on the STL method. Locally weighted regression is used to smooth the load series, revealing the variation patterns of load at different time scales without requiring prior assumptions about the load model structure. Specifically, the core iterative formula of STL decomposition is expressed as follows: (2) (3) In the formula, Indicates the number of iterations. hour, ; This represents the locally weighted regression fitting function; The width of the local window used to fit the long-term trend component of the load; The width of the local window used to fit the periodic components of the load; The order of the polynomial fitted to the long-term trend components of the load; The order of the polynomial fitted to the periodic components of the load; Indicates the period length of the load sequence; For time Perform periodic modulus to achieve cyclic fitting of periodic components.

[0042] The criterion for convergence of the iteration is: (4) In the formula, This represents the iteration convergence threshold.

[0043] S2. Based on the multiple load components obtained from the decomposition, construct a load demand uncertainty description model that matches the characteristics of each component.

[0044] In this step, based on the multiple load components obtained from step S1, energy storage resources with different regulation characteristics are configured for load components at different time scales, and a load demand uncertainty description model matching the characteristics of each component is constructed.

[0045] Among them, the long-term trend component is for deterministic decomposition trends. A deterministic optimization solution method is adopted to satisfy the approximate range of output.

[0046] For the periodic component of the load By constructing a scenario probability representation of the uncertainty set, specifically expressed as: (5) In the formula, For probability operators; The number of historical load data points; For discrete scenarios The initial probability value; This represents the total number of scenes after clustering. and They are respectively Norm and The limit of permissible deviation of probability under norm correspondence constraints.

[0047] For ease of representation, let , Let and represent the uncertainty confidence levels that the probability distribution needs to satisfy, respectively. The transformation expression then yields: (6) Then the periodic component of the load The confidence set that the probability distribution satisfies can be represented as: (7) In the formula, The set interval for the scenario probability distribution of the periodic component of the load, i.e. the confidence interval of the comprehensive norm; This represents the total number of scenes after clustering. For real-world scenarios after clustering The probability distribution of can be specifically expressed as: (8) In the formula, and These represent the positive and negative offsets of the probability distribution, respectively.

[0048] For load random fluctuation components The uncertainty set is represented by constructing an error interval, specifically as follows: (9) In the formula, Represents the set of uncertain intervals for random fluctuation components; Indicates confidence level; This indicates the trend of electrical load output in a typical scenario; Indicates the magnitude of electrical load fluctuations; This indicates the hydrogen load output trend under typical scenarios; This indicates the range of hydrogen load fluctuations.

[0049] S3. Based on the load demand uncertainty description model, establish a long-term hydrogen energy storage planning model that includes energy balance constraints, equipment operation boundary constraints, and operation safety constraints. According to the time scale corresponding to the load components, the long-term hydrogen energy storage planning model is divided into a hierarchical planning model of long-term trend capacity configuration layer and short-term periodic and stochastic adjustment layer.

[0050] In this step, based on the decomposition trend of different load characteristics, hydrogen energy storage devices of different capacities and specifications are configured to match diverse load demands. With the optimization objective of achieving optimal overall system operational economy, a hierarchical long-term hydrogen energy storage planning model coupled with multiple time scales is constructed, specifically as follows: (10) In the formula, Indicates different decomposition amounts Total cost below; Indicates investment cost; Indicates operating costs; These represent trend, period, and stochastic decomposition, respectively. This is the identifier for the decomposition quantity.

[0051] For shared hydrogen energy storage, the annualized configuration cost of hydrogen energy storage is determined based on the proportion of new energy installed capacity and load with intermittent power generation characteristics in each park. final investment cost The Shapley value allocation method distributes the alliance's additional profits based on the marginal contributions of each participant, specifically as follows: (11) In the formula, Indicates the park In the case of independent energy storage configuration, the equipment Independent investment costs; This indicates that participants within the cooperative alliance are included in the collection. This indicates a cooperative alliance for the equipment. Total investment cost; Indicates comparison with each park In the fully independent configuration model, the investment cost savings achieved by each park jointly building a hydrogen energy storage system under the cooperative model are distributed among the parks based on their internal photovoltaic installed capacity and load. For the park medium equipment The cost allocation factor is calculated using the following formula: (12) In the formula, Indicates the park i The photovoltaic configuration capacity; Indicates the park i Wind power configuration capacity; This indicates the total installed capacity of new energy vehicles in each industrial park; and They represent the park At any moment The electrical load and hydrogen load.

[0052] Taking into account load uncertainty, the annual operating cost of the system is... The minimum is the objective, expressed as: (13) In the formula, Indicates the park exist Power constantly interacting with the power grid; Indicates the park Electrolytic cell in The electrical power consumed in the electrolysis of water to produce hydrogen at any given time; This indicates the cost of electricity purchased by the power grid; This indicates the electricity price for hydrogen production via electrolysis.

[0053] For each park At any given moment The energy balance relationship between electricity, hydrogen, and heat can be specifically expressed as: (14) In the formula, and Indicates the output of photovoltaic and wind turbines; Indicates the park exist The power that constantly interacts with the power grid; Indicates the park Hydrogen fuel cells in Power generation at any given moment; Indicates that the electrolytic cell is in The volumetric flow rate of hydrogen produced at any given time; Indicates the park At any moment Hydrogen load demand; express The volumetric flow rate of hydrogen consumed by the hydrogen fuel cell at any given time; express The volumetric flow rate of hydrogen stored in the hydrogen storage tank at any given time; Indicates the park exist The heat power requirement at any given time; and This indicates that hydrogen fuel cells are in The heat generation capacity at any given time.

[0054] One of the key operating devices in the system for electro-hydrogen coupling—the electrolyzer—converts photovoltaic power and grid electricity into hydrogen. The established linear electro-hydrogen conversion relationship is specifically expressed as follows: (15) In the formula, Indicates that the electrolytic cell is in The volumetric flow rate of hydrogen produced at any given time; Indicates that the electrolytic cell is in The electrical power consumed in the electrolysis of water to produce hydrogen at any given time; and The fitting coefficient represents the relationship between the electro-hydrogen conversion.

[0055] The operating power of an electrolytic cell is limited by its configured capacity and maximum operating load rate, expressed as: (16) In the formula, This indicates the capacity of the electrolytic cell configured in the system; This indicates the maximum operating load rate of the electrolytic cell.

[0056] The linear model of hydrogen consumption and electrical-thermal coupling characteristics of a hydrogen fuel cell can be expressed as: (17) In the formula, and These respectively represent the hydrogen fuel cell in Power generation and thermal power at any given time; and This represents the fitting coefficient between hydrogen consumption and power generation of a hydrogen fuel cell; and This represents the electro-thermal fitting coefficient of a hydrogen fuel cell.

[0057] A capacity configuration optimization model for shared hydrogen energy storage is constructed, which includes energy balance constraints, equipment operation boundary constraints, and operation safety constraints.

[0058] The operating status of the hydrogen storage device meets the following requirements: (18) In the formula, Indicates time The hydrogen storage state; Indicates time Hydrogen production from the electrolyzer; This indicates the amount of hydrogen consumed by a hydrogen fuel cell.

[0059] And satisfy the capacity constraint: (19) In the formula, and These represent the configuration capacity of the electrolyzer and the hydrogen fuel cell, respectively. and These represent the upper and lower limits of the electrolytic cell configuration capacity, respectively. and These represent the upper and lower limits of the configuration capacity of the hydrogen fuel cell, respectively.

[0060] The equipment operating boundary constraints are: (20) In the formula, This indicates the upper limit of electrical power consumed by the electrolyzer for producing hydrogen through water electrolysis; This indicates the upper limit of the power generation capacity of hydrogen fuel cells.

[0061] S4. Taking the annualized allocation cost of flexible resources and the annual operating cost of the system as optimization objectives, a hierarchical and coordinated optimization solution method is adopted to determine the capacity configuration scheme of long-term hydrogen energy storage in multiple parks and the operation strategy of each unit.

[0062] In this step, a capacity planning sub-model is constructed based on the long-term load trend components. Under the constraints of system energy balance, hydrogen storage state evolution, and equipment operating boundary conditions, the hydrogen storage capacity variables are optimized. The aforementioned uncertain optimization configuration model uses KKT condition functions to uniformly transform the model into a simple single-layer solution structure, and performs hierarchical optimization.

[0063] Long-term trend component for deterministic decomposition trend Based on the heterogeneous uncertainty characteristics of each decomposition term, highly adaptable optimization methods are selected. The trend term changes slowly over an annual timescale, exhibiting a stable and gradual change characteristic overall. The optimized position results are used as prior information to be transferred to the intraday planning stage, thereby effectively improving the solution efficiency. In this hierarchical optimization process, the optimization objectives are to minimize the overall economic efficiency of system operation, the annualized configuration cost of hydrogen storage, and the annual operating cost of the system. Under the conditions of system energy balance constraints, hydrogen storage state evolution constraints, and equipment operation boundary constraints, the hydrogen storage configuration results adapted to the long-term trend component decomposition term are obtained based on the Cplex solver.

[0064] For load periodic components exhibiting obvious diurnal periodic characteristics Using the probabilities of typical load scenarios as a reference benchmark, a PD set considering the uncertainty of the periodic term is constructed. The sub-Bruker optimization model on the periodic term can be expressed as: (twenty one) In the formula, The probability of scenario s occurring after clustering; The set interval for the probability distribution of the scenario is the confidence interval of the composite norm; This represents the total number of scenes after clustering. , These are the parameters representing the variables in the formula; This represents the configured capacity of each device in the hydrogen energy storage system. This represents the operational optimization variables for each unit.

[0065] For load random fluctuation components Unlike deterministic optimization models, its parameter set is transformed from a deterministic form to an interval uncertain form. To obtain interval solutions for the decision variables, the IO model is constructed as an optimization model that includes both optimistic and pessimistic scenarios, and its compact form is expressed as follows: (twenty two) (twenty three) (twenty four) In the formula, Uncertainty is represented by an interval; Indicates confidence level; , , and The variances of photovoltaic output, electrical load, and gas load represent the clustering reference values ​​for photovoltaic and wind power, respectively.

[0066] Figure 4These are statistical charts showing the fitting errors of the electrolyzer and hydrogen fuel cell for electricity, hydrogen, and heat, as disclosed in this embodiment. (a) is the statistical chart of the fitting error of the electro-hydrogen conversion in the electrolyzer, (b) is the statistical chart of the fitting error ... Figure 4 It can be seen that the electro-hydrogen and electro-thermal conversion relationships of the electrolyzer and hydrogen fuel cell were fitted based on the linear formulas (15) and (17), and the error was controlled within a very small range.

[0067] Example 2 In a typical embodiment of the present invention, this embodiment discloses a shared hydrogen energy storage planning system that takes into account uncertainties, comprising: The data acquisition and decomposition module is configured to: acquire historical load data of each park within the planning time scale and decompose it using a time series decomposition method to obtain multiple load components; The uncertainty set construction module is configured to: construct a load demand uncertainty description model that matches the characteristics of each component based on multiple load components obtained from decomposition; The hierarchical planning module is configured to: establish a long-term hydrogen energy storage planning model based on the load demand uncertainty description model, which includes energy balance constraints, equipment operation boundary constraints and operation safety constraints, and divide the long-term hydrogen energy storage planning model into a hierarchical planning model with a long-term trend capacity configuration layer and a short-term periodic and stochastic adjustment layer according to the time scale corresponding to the load components. The optimization solution module is configured to: use the annualized allocation cost of flexible resources and the annual operating cost of the system as optimization objectives, and adopt a hierarchical and coordinated optimization solution method to determine the capacity configuration scheme of long-term hydrogen energy storage in multiple parks and the operation strategy of each unit.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A shared hydrogen energy storage planning method that takes into account uncertainties, characterized in that, include: Historical load data of each park within the planning time scale were obtained and decomposed using a time series decomposition method to obtain multiple load components. Based on the multiple load components obtained from the decomposition, a load demand uncertainty description model matching the characteristics of each component is constructed. Based on the load demand uncertainty description model, a long-term hydrogen energy storage planning model is established, which includes energy balance constraints, equipment operation boundary constraints, and operation safety constraints. According to the time scale corresponding to the load components, the long-term hydrogen energy storage planning model is divided into a hierarchical planning model of long-term trend capacity configuration layer and short-term periodic and stochastic adjustment layer. With the annualized allocation cost of flexible resources and the annual operating cost of the system as optimization objectives, a hierarchical and coordinated optimization solution method is adopted to determine the capacity configuration scheme of long-term hydrogen energy storage in multiple parks and the operation strategy of each unit.

2. The shared hydrogen energy storage planning method considering uncertainties as described in claim 1, characterized in that, The multiple load components include at least long-term trend components, periodic components, and random fluctuation components; the time series decomposition method is based on the STL decomposition method, and the load series is smoothed through local weighted regression. The core iterative formula for STL decomposition is specifically expressed as follows: In the formula, This represents the long-term trend component of electrical load; Represents the periodic components of electrical load; Indicates the number of iterations. hour, ; This represents the locally weighted regression fitting function; The width of the local window used to fit the long-term trend component of the load; The width of the local window used to fit the periodic components of the load; The order of the polynomial fitted to the long-term trend components of the load; The order of the polynomial fitted to the periodic components of the load; Indicates the period length of the load sequence; For time Perform periodic modulus to achieve cyclic fitting of periodic components.

3. The shared hydrogen energy storage planning method considering uncertainties as described in claim 1, characterized in that, The periodic component of the load is represented by a set of uncertainties through the construction of a scenario probability representation, as follows: In the formula, The set interval for the scenario probability distribution of the periodic component of the load, i.e. the confidence interval of the comprehensive norm; This represents the total number of scenes after clustering. For real-world scenarios after clustering The probability distribution.

4. The shared hydrogen energy storage planning method considering uncertainties as described in claim 3, characterized in that, The random fluctuation component of the load is represented by an error interval to describe the uncertainty set, specifically as follows: In the formula, Represents the set of uncertain intervals for random fluctuation components; Indicates confidence level; This indicates the trend of electrical load output in a typical scenario; Indicates the magnitude of electrical load fluctuations; This indicates the hydrogen load output trend under typical scenarios; This indicates the range of hydrogen load fluctuations.

5. The shared hydrogen energy storage planning method considering uncertainties as described in claim 1, characterized in that, The long-term hydrogen energy storage planning model aims to optimize the overall economic efficiency of the system. The objective function is expressed as: In the formula, Indicates different decomposition amounts Total cost below; Indicates investment cost; Indicates operating costs; These represent trend, period, and stochastic decomposition, respectively. This is a component identifier.

6. The shared hydrogen energy storage planning method considering uncertainties as described in claim 1, characterized in that, Energy balance constraints include multi-energy coupling balance constraints involving electricity, hydrogen, and heat, for each park. At any given moment ,satisfy: In the formula, and Indicates the output of photovoltaic and wind turbines; Indicates the park exist Power constantly interacting with the power grid; Indicates the park Hydrogen fuel cells in Power generation at any given moment; Indicates that the electrolytic cell is in The volumetric flow rate of hydrogen produced at any given time; Indicates the park At any moment Hydrogen load demand; express The volumetric flow rate of hydrogen consumed by the hydrogen fuel cell at any given time; express The volumetric flow rate of hydrogen stored in the hydrogen storage tank at any given time; Indicates the park exist The heat power requirement at any given time; and This indicates that hydrogen fuel cells are in The heat generation capacity at any given time.

7. The shared hydrogen energy storage planning method considering uncertainties as described in claim 1, characterized in that, Equipment operating boundary constraints include electrolyzer constraints and hydrogen fuel cell constraints; Electrolytic cell constraints are represented as follows: In the formula, Indicates that the electrolytic cell is in The volumetric flow rate of hydrogen produced at any given time; Indicates that the electrolytic cell is in The electrical power consumed in the electrolysis of water to produce hydrogen at any given time; and The fitting coefficients representing the electro-hydrogen conversion relationship; This indicates the capacity of the electrolytic cell configured in the system; Indicates the maximum operating load rate of the electrolytic cell; The constraints of hydrogen fuel cells are expressed as follows: In the formula, and These respectively represent the hydrogen fuel cell in Power generation and thermal power at any given time; and This represents the fitting coefficient between hydrogen consumption and power generation of a hydrogen fuel cell; and This represents the electro-thermal fitting coefficient of a hydrogen fuel cell; This indicates the upper limit of the power generation capacity of hydrogen fuel cells.

8. The shared hydrogen energy storage planning method considering uncertainties as described in claim 1, characterized in that, For shared hydrogen energy storage, the annualized configuration cost of hydrogen energy storage is determined based on the proportion of new energy installed capacity and load with intermittent power generation characteristics in each park. final investment cost The Shapley value allocation method distributes the alliance's additional profits based on the marginal contributions of each participant, specifically as follows: In the formula, Indicates the park In the case of independent energy storage configuration, the equipment Independent investment costs; This indicates that participants within the cooperative alliance are included in the collection. This indicates a cooperative alliance for the equipment. Total investment cost; Indicates comparison with each park In the fully independent configuration model, the investment cost savings achieved by each park jointly building a hydrogen energy storage system under the cooperative model are distributed among the parks based on their internal photovoltaic installed capacity and load. For the park medium equipment The cost allocation factor is calculated using the following formula: In the formula, Indicates the park i The photovoltaic configuration capacity; Indicates the park i Wind power configuration capacity; This indicates the total installed capacity of new energy vehicles in each industrial park; and They represent the park At any moment The electrical load and hydrogen load.

9. The shared hydrogen energy storage planning method considering uncertainties as described in claim 1, characterized in that, The hierarchical coordination optimization solution method specifically includes: For the long-term trend component, a deterministic capacity planning sub-model is constructed to solve for the basic configuration capacity of the hydrogen energy storage system; Using the basic configuration capacity as prior information, and targeting the periodic component, a sub-model for sub-Brubar optimization is constructed under the uncertainty set of sub-Brubar optimization to optimize the system's operation strategy. For the random fluctuation component, under the interval uncertainty set, an interval optimization operation sub-model is constructed to solve the feasible interval of the operation strategy.

10. A shared hydrogen energy storage planning system that takes into account uncertainties, characterized in that, include: The data acquisition and decomposition module is configured to: acquire historical load data of each park within the planning time scale and decompose it using a time series decomposition method to obtain multiple load components; The uncertainty set construction module is configured to: construct a load demand uncertainty description model that matches the characteristics of each component based on multiple load components obtained from decomposition; The hierarchical planning module is configured to: establish a long-term hydrogen energy storage planning model based on the load demand uncertainty description model, which includes energy balance constraints, equipment operation boundary constraints and operation safety constraints, and divide the long-term hydrogen energy storage planning model into a hierarchical planning model with a long-term trend capacity configuration layer and a short-term periodic and stochastic adjustment layer according to the time scale corresponding to the load components. The optimization solution module is configured to: use the annualized allocation cost of flexible resources and the annual operating cost of the system as optimization objectives, and adopt a hierarchical and coordinated optimization solution method to determine the capacity configuration scheme of long-term hydrogen energy storage in multiple parks and the operation strategy of each unit.

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