Configuration method and device of hydrogen energy storage device for improving toughness of island power distribution network, electronic equipment and storage medium

Abstract

The invention discloses a configuration method and device for a hydrogen energy storage device for improving the toughness of an island power distribution network, electronic equipment and a storage medium, and belongs to the technical field of electric energy storage. Constructing an economic index calculation model for evaluating the economical efficiency of the configuration scheme of the hydrogen energy storage device; according to the disaster risk parameters, the typical output curve of the fan and the typical load curve, constructing a toughness index calculation model for evaluating the toughness of the configuration scheme of the hydrogen energy storage device; and by taking the configuration scheme of the hydrogen energy storage device as a decision variable, cooperatively and iteratively solving the economic index calculation model and the toughness index calculation model, and generating an optimal configuration scheme of the hydrogen energy storage device. By implementing the invention, the problem of insufficient accuracy of the configuration scheme of the hydrogen energy storage device in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the field of power energy storage technology, specifically to a configuration method, device, electronic equipment, and storage medium for hydrogen energy storage devices aimed at improving the resilience of island power distribution networks. Background Technology

[0002] Today, renewable energy sources, such as wind and solar power, are increasingly penetrating the power system. However, the intermittency and volatility of renewable energy pose challenges to the safe and stable operation of the power grid. Hydrogen energy storage, as a technology with large-scale, long-cycle energy storage potential, can effectively mitigate renewable energy fluctuations, participate in peak shaving and valley filling of the power grid, and play a crucial role in improving power grid reliability and energy security. Therefore, scientifically and rationally planning and configuring hydrogen energy storage devices—that is, determining their optimal construction capacity and installation location—is of paramount importance for maximizing their technical and economic value and ensuring the safe and stable operation of the power grid.

[0003] Existing technologies for configuring hydrogen energy storage devices have two main shortcomings. First, existing methods typically focus on optimizing a single objective, either prioritizing economic efficiency under normal operating conditions or excessively pursuing reliability under extreme disasters. They lack a unified decision-making framework that can quantify and balance the inherently contradictory objectives of economic efficiency and resilience. Second, in resilience assessments, existing methods mostly employ single, fixed typical failure scenarios for analysis. This deterministic approach cannot effectively address the random uncertainties in the type, duration, and extent of damage in real-world disasters, resulting in insufficient robustness of the final configuration. Summary of the Invention

[0004] This invention provides a configuration method, device, electronic equipment, and storage medium for hydrogen energy storage devices aimed at improving the resilience of island power distribution networks, which can solve the problem of insufficient accuracy in the configuration schemes of hydrogen energy storage devices in the prior art.

[0005] An embodiment of the present invention provides a configuration method for a hydrogen energy storage device for improving the resilience of island power distribution networks, comprising:

[0006] Obtain typical load curves, typical wind turbine output curves, cost-benefit parameters, hardware parameters of hydrogen energy storage devices, and disaster risk parameters for typical disaster days on the island's power distribution network;

[0007] Based on the typical load curve, the typical output curve of the wind turbine, the hardware parameters, and the cost-benefit parameters, an economic index calculation model is constructed to evaluate the economic efficiency of hydrogen energy storage device configuration schemes.

[0008] Based on the disaster risk parameters, the typical output curve of the wind turbine, and the typical load curve, a resilience index calculation model is constructed to evaluate the resilience of hydrogen energy storage device configuration schemes.

[0009] With the goal of minimizing net loss and maximizing emergency power support, and using the configuration scheme of hydrogen energy storage devices as decision variables, the economic index calculation model and the resilience index calculation model are solved iteratively to generate the optimal configuration scheme of hydrogen energy storage devices.

[0010] The process of evaluating candidate configurations for a hydrogen energy storage device in each collaborative iteration includes:

[0011] Based on the economic indicator calculation model, the current candidate configuration scheme is simulated under normal operating conditions within the planning period based on a preset normal operating strategy, and the corresponding economic indicators are calculated and generated based on the normal operating conditions.

[0012] Based on the resilience index calculation model, and based on the preset extreme operation strategy, the current candidate configuration scheme is simulated under extreme operating conditions in each extreme scenario in the preset extreme scenario set, and the corresponding resilience index is calculated and generated based on the extreme operating conditions.

[0013] Based on the economic indicators and the resilience indicators, the comprehensive benefit indicators corresponding to the current candidate configuration schemes are calculated and generated.

[0014] Furthermore, the disaster risk parameters include disaster type, recovery period duration, and node failure probability;

[0015] The extreme scenario set can be obtained through the following methods:

[0016] Based on the disaster type, recovery period, and node failure probability corresponding to each typical disaster day, multiple extreme scenarios are randomly generated using the Monte Carlo sampling method.

[0017] The multiple extreme scenarios are combined to generate an extreme scenario set.

[0018] Furthermore, the economic indicator calculation model includes an economic indicator objective function;

[0019] The objective function for the economic indicators is specifically as follows:

[0020]

[0021] In the formula, F C For economic indicators; C in C represents the total investment cost; om Total operating and maintenance cost; C cW C is the total cost of wind curtailment penalties.FC The total revenue generated by fuel cells; Total revenue generated from the direct sale of hydrogen; K EL，in P represents the unit capacity investment cost of the electrolytic cell; EL The configuration capacity of the electrolytic cell; K FC，in The unit capacity investment cost of fuel cells; P FC For the configuration capacity of the fuel cell; K HST，in Q represents the unit capacity investment cost of the hydrogen storage tank; HST The configuration capacity of the hydrogen storage tank; K site For installation site costs; N site K represents the number of nodes configured for the energy storage device. EL，om K represents the annual operating and maintenance cost per unit capacity of the electrolytic cell. FC，om The annual operating and maintenance cost per unit capacity of a fuel cell; K HST，om The annual operating and maintenance cost per unit capacity of the hydrogen storage tank; K cW The penalty price per unit of wind curtailment; Q cW Total wind curtailment; T0 is the planning period duration; K q K is the discount factor for year q; FC1，q The unit price for providing electricity services to fuel cells in year q; Q FC1，q The total amount of electricity provided by fuel cells in year q; K FC2，q The unit price for providing heat services for fuel cells in year q; Q FC2，q The total amount of heat service provided by fuel cells in year q; Total revenue generated from the direct sale of hydrogen; This refers to the unit selling price of hydrogen. This represents the total hydrogen sales volume.

[0022] Furthermore, the economic indicator calculation model also includes configuration constraints, network constraints, and network operation reliability constraints;

[0023] The configuration constraints are specifically as follows:

[0024]

[0025] In the formula, P G (t) represents the power supplied by the main power grid via the submarine cable at time t; P WT (t) represents the output of the wind turbine at time t; P FC1，out (t) represents the output power of the fuel cell at time t; P L (t) represents the total electrical load demand of the island at time t; P EL，in (t) represents the input electrical power of the electrolytic cell at time t; ΔP l (t) represents the line network loss power at time t; U i，minU is the minimum allowable voltage for node i; i (t) represents the voltage at node i at time t; U i，max M is the maximum allowable voltage at point i; j M represents the configuration capacity of the j-th type of device; j，max P represents the maximum allowable configuration capacity of the j-th type of device; j Let j be the real-time operating value of the j-th device at a certain moment;

[0026] The network constraints are specifically as follows:

[0027]

[0028] In the formula, P i，G (t) represents the active power injected by the main grid at node i at time t; P i，HES (t) represents the active power injected into the hydrogen energy storage system at node i at time t, where fuel cell power generation is positive and electrolyzer hydrogen production is negative; P i，WT (t) represents the active power injected by the wind turbine at node i at time t; P i，L (t) represents the active power consumed by the load at node i at time t; P i，loss (t) represents the active power loss from node i to other nodes at time t; Q i，G (t) represents the reactive power injected by the main grid at node i at time t; Q i，HES (t) represents the reactive power injected into the hydrogen energy storage system at node i at time t; Q i，WT (t) represents the reactive power injected by the wind turbine at node i at time t; Q i，L (t) represents the reactive power consumed by the load at node i at time t; Q i，loss (t) represents the reactive power loss from node i to other nodes at time t; S ij Let be the power flowing through the line between node i and node j; This represents the minimum power transmission limit for line ij. U represents the maximum power transmission limit of line ij; i Let be the voltage at node i; This represents the minimum allowable voltage for node i; P represents the maximum allowable voltage at node i. cable P represents the real-time transmission power of the submarine cable. cable，max This represents the maximum permissible transmission power of the submarine cable.

[0029] The network operation reliability constraints are specifically as follows:

[0030]

[0031] In the formula, σ SLD σ represents the load loss rate;SLD，MAX The maximum allowable load power failure rate of the system; The power of the load at any given moment when power is lost; Let t be the total load demand power at time t.

[0032] Furthermore, the resilience index calculation model includes a resilience index objective function;

[0033] The objective function for the resilience index is as follows:

[0034]

[0035] In the formula, F R As a resilience indicator; This is the sum of resilience gains calculated across all simulated Type I extreme scenarios; T is the sum of resilience gains calculated across all simulated Type II extreme scenarios; Case T1 represents the total number of disasters occurring during the planning period; T2 represents the total number of simulations for the first type of extreme scenario; T3 represents the total number of simulations for the second type of extreme scenario.

[0036] The sum of resilience gains calculated in all simulated Type I extreme scenarios is obtained in the following way;

[0037]

[0038] In the formula, S1 is the set of the first type of extreme scenarios; N is the total number of nodes in the distribution network; Let be the probability that node i fails in scenario s1(r); In scenario s1(r), the economic losses recovered by providing support to node i; P represents the total emergency support power provided to node i at time t in scenario s1(r); i,HES (t) represents the supporting power provided by the hydrogen energy storage system at node i at time t; P i,WT (t) represents the supporting power provided by the wind turbine at node i at time i; is the recovery period of scenario s1(r); c0 is the power outage loss cost per unit load;

[0039] The sum of resilience gains calculated across all simulated Type II extreme scenarios is obtained as follows:

[0040]

[0041] In the formula, S2 represents the set of the second type of extreme scenarios; Let s2(r) be the total effective support power provided by the system for the entire island at time t. Let s2(r) be the total load demand of the entire island at time t. The recovery period for scenario s2(r) is .

[0042] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0043] One embodiment of the present invention provides a configuration device for a hydrogen energy storage device for improving the resilience of island power distribution networks, including: an island power distribution network data acquisition module, an economic index calculation model construction module, a resilience index calculation model construction module, and a model solving module;

[0044] The island power distribution network data acquisition module is used to acquire typical load curves, typical wind turbine output curves, cost-effectiveness parameters, hardware parameters of hydrogen energy storage devices, and disaster risk parameters for typical disaster days of the island power distribution network.

[0045] The economic index calculation model construction module is used to construct an economic index calculation model for evaluating the economic efficiency of hydrogen energy storage device configuration schemes based on the typical load curve, the typical output curve of the wind turbine, the hardware parameters, and the cost-benefit parameters.

[0046] The resilience index calculation model construction module is used to construct a resilience index calculation model for evaluating the resilience of hydrogen energy storage device configuration schemes based on the disaster risk parameters, the typical output curve of the wind turbine, and the typical load curve.

[0047] The model solving module is used to minimize net loss and maximize emergency support power, with the hydrogen energy storage device configuration scheme as the decision variable, to collaboratively and iteratively solve the economic index calculation model and the resilience index calculation model, and generate the optimal configuration scheme of the hydrogen energy storage device.

[0048] The process of evaluating candidate configurations for a hydrogen energy storage device in each collaborative iteration includes:

[0049] Based on the economic indicator calculation model, the current candidate configuration scheme is simulated under normal operating conditions within the planning period based on a preset normal operating strategy, and the corresponding economic indicators are calculated and generated based on the normal operating conditions.

[0050] Based on the resilience index calculation model, and based on the preset extreme operation strategy, the current candidate configuration scheme is simulated under extreme operating conditions in each extreme scenario in the preset extreme scenario set, and the corresponding resilience index is calculated and generated based on the extreme operating conditions.

[0051] Based on the economic indicators and the resilience indicators, the comprehensive benefit indicators corresponding to the current candidate configuration schemes are calculated and generated.

[0052] Based on the above method embodiments, the present invention provides corresponding electronic device embodiments.

[0053] One embodiment of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the configuration method of the hydrogen energy storage device for improving the resilience of island power distribution networks as described in any of the above-described method embodiments.

[0054] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments.

[0055] One embodiment of the present invention provides a storage medium storing a computer program thereon, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the configuration method of the hydrogen energy storage device for improving the resilience of island power distribution networks as described in any of the above-described method embodiments.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] This invention provides a method, apparatus, electronic equipment, and storage medium for configuring hydrogen energy storage devices to enhance the resilience of island power distribution networks. The method first obtains typical load curves and wind turbine output curves, cost-effectiveness parameters, device hardware parameters, and historical disaster risk parameters of the island power distribution network. Based on this, it constructs an economic index calculation model for evaluating the economic viability of the configuration scheme and a resilience index calculation model for evaluating the resilience of the configuration scheme. With the dual objectives of minimizing net loss and maximizing emergency support power, different configuration schemes are evaluated through a collaborative iterative solution process. In each evaluation, based on normal and extreme operating strategies, the normal operating conditions and extreme operating conditions of the configuration scheme are simulated respectively to calculate its corresponding economic and resilience indices. Based on the comprehensive benefit index of each configuration scheme, the optimal hydrogen energy storage device configuration scheme is determined.

[0058] This invention addresses the problem of insufficient accuracy in configuration schemes caused by existing technologies that rely on single, fixed scenario analysis by introducing disaster risk parameters and simulating system performance under multiple randomly generated extreme scenarios. Furthermore, this invention constructs two independent evaluation indicators—economic efficiency and resilience—and achieves a balance between the two through collaborative iterative solutions, overcoming the shortcomings of existing methods that prioritize a single objective, making it difficult to simultaneously achieve both economic efficiency and reliability in configuration schemes. Attached Figure Description

[0059] Figure 1 This is a schematic flowchart of a method for configuring a hydrogen energy storage device to improve the resilience of island power distribution networks, provided by an embodiment of the present invention.

[0060] Figure 2 This is a schematic diagram of the structure of a submarine power distribution network for a hydrogen-containing energy storage device provided in an embodiment of the present invention.

[0061] Figure 3 This is a schematic diagram of the configuration device of a hydrogen energy storage device for improving the resilience of island power distribution networks, provided by an embodiment of the present invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] like Figure 1 As shown, to address the problem of insufficient accuracy in the configuration schemes of hydrogen energy storage devices in existing technologies, an embodiment of the present invention provides a configuration method for hydrogen energy storage devices aimed at improving the resilience of island power distribution networks, comprising at least the following steps:

[0064] Step S1: Obtain the typical load curve, typical wind turbine output curve, cost-benefit parameters, hardware parameters of the hydrogen energy storage device, and disaster risk parameters for each typical disaster day of the island power distribution network;

[0065] Specifically, such as Figure 2 As shown, the island's power distribution network includes renewable energy sources, hydrogen storage devices, and submarine cables. The island is equipped with renewable energy sources (wind turbines are selected in this invention), electrical loads, thermal loads, hydrogen loads, and hydrogen storage devices. In addition, it is connected to the upstream power grid via submarine cables.

[0066] The HES hydrogen energy storage system mainly consists of an electrolyzer (EL), hydrogen storage tanks (HST), and fuel cells (FC), offering flexible energy conversion capabilities. The electrolyzer converts surplus renewable energy output into hydrogen, significantly reducing power curtailment. The hydrogen storage tanks enable large-scale energy storage in hydrogen form and can also generate revenue by selling green hydrogen to transportation systems. The fuel cells provide continuous and stable power and can also generate revenue through heating.

[0067] It should be noted that, in order to implement the configuration method proposed in this invention, the operation process of the hydrogen energy storage device needs to be simulated. Therefore, it is necessary to first establish mathematical models of each core component in the device. Specifically, the hydrogen energy storage device includes an electrolyzer, a fuel cell, and a hydrogen storage tank, and their mathematical models are described below.

[0068] The electrolyzer is a proton exchange membrane electrolyzer with high energy efficiency, fast response, wide power adjustment range, and high gas production pressure.

[0069]

[0070] In the formula, V H n is the hydrogen production rate of the electrolyzer; EL η represents the number of electrolytic cells; F S is the Faraday efficiency coefficient; F is the Faraday constant, with a value of 96485 C / mol; F1 is the first relevant parameter of the Faraday efficiency coefficient, which is related to ambient temperature; F2 is the second relevant parameter of the Faraday efficiency coefficient, which is related to ambient temperature; S el I represents the electrode surface area; I represents the operating current.

[0071] For ease of capacity calculation, the hydrogen production unit is considered as a black box component, and only the relationship between energy conversion and loss is considered. The specific energy conversion relationship is as follows.

[0072]

[0073] In the formula, P EL，out (t) represents the hydrogen production power of the electrolyzer at time t; η EL P represents the conversion efficiency of the electrolyzer; EL，in (t) represents the input power of the electrolytic cell at time t; M EL (t) represents the hydrogen production rate of the electrolyzer at time t, in kg / h; HV H The higher heating value of hydrogen is taken as 39.72 kWh / kg; This is the lower limit of hydrogen production capacity of the electrolyzer; This represents the upper limit of hydrogen production capacity of the electrolyzer;

[0074] The fuel cell uses a proton exchange membrane fuel cell, which has fast start-up, high power generation efficiency, and a wide power adjustment range.

[0075]

[0076] In the formula, M FC (t) represents the hydrogen consumption rate of the fuel cell at time t, in kg / h; P FC,in (t) represents the operating power of the fuel cell at time t; HV L P represents the average calorific value of hydrogen, taken as 36.84 kWh / kg. FC1，out (t) represents the electrical power emitted by the fuel cell at time t; η FC1 P represents the electrical conversion efficiency of a fuel cell. FC2,out (t) represents the thermal power emitted by the fuel cell at time t; η FC2For the thermal conversion efficiency of fuel cells; This is the lower limit of the operating power of the fuel cell; This represents the upper limit of the operating power of the fuel cell;

[0077] Due to the abundant land resources on the island, HST chose a large 3MPa hydrogen storage tank with low cost and low loss.

[0078]

[0079] In the formula, E HST (t) represents the amount of hydrogen stored in the hydrogen storage tank at time t, in kg; E HST (t-1) represents the amount of hydrogen stored in the hydrogen storage tank at time t-1; M EL (t-1) represents the hydrogen production rate of the electrolyzer at time t, in kg / h; η HST1 The hydrogen filling efficiency of the hydrogen storage tank; M FC (t-1) represents the hydrogen consumption rate of the fuel cell at time t-1, in kg / h; η HST2 The hydrogen release efficiency of the hydrogen storage tank; Δt is XXX; SOHC(t) is the equivalent hydrogen charge state at time t; Q HST Configuration capacity of the hydrogen storage tank; SOHC min The equivalent hydrogen-loaded state limit; SOHC max This represents the upper limit of the equivalent hydrogen-loaded state.

[0080] In an optional embodiment, the acquired data includes typical load curves of the island's power distribution network in different seasons (e.g., peak and off-peak seasons) and typical output curves of wind turbines. These serve as the fundamental basis for system operation simulation and benefit analysis. Simultaneously, it is necessary to collect hardware parameters related to the hydrogen energy storage device. This mainly covers the core equipment constituting the hydrogen energy storage device: the electrolyzer, the hydrogen storage tank, and the fuel cell. For the electrolyzer, key parameters include its rated power and energy conversion efficiency; for the hydrogen storage tank, key parameters include its rated storage capacity and hydrogen charging / discharging efficiency; and for the fuel cell, parameters such as its power generation and electrothermal conversion efficiency are required.

[0081] Furthermore, comprehensive cost-benefit parameters are essential for conducting an economic assessment. Costs primarily include the unit capacity investment cost of each component of the hydrogen energy storage system (electrolyzer, fuel cell, hydrogen storage tank), annual unit operation and maintenance costs, and the unit price of wind curtailment penalties due to incomplete wind power utilization. Benefits include revenue from fuel cells participating in grid peak shaving or providing heat services, as well as revenue from directly selling green hydrogen produced through water electrolysis.

[0082] Finally, to accurately assess the effectiveness of the configuration scheme in improving grid resilience, it is essential to obtain disaster risk parameters for typical days of each disaster. These parameters specifically include the type of disaster, such as typhoons or submarine cable faults; the system recovery period and probability distribution corresponding to different disaster types; and the probability of failure at various nodes in the distribution network under the influence of a specific disaster. Comprehensive acquisition of these various data and parameters provides solid data support for the subsequent construction of accurate economic and resilience calculation models.

[0083] Step S2: Based on the typical load curve, the typical output curve of the wind turbine, the hardware parameters, and the cost-benefit parameters, construct an economic index calculation model for evaluating the economic efficiency of hydrogen energy storage device configuration schemes.

[0084] In a preferred embodiment, the economic indicator calculation model includes an economic indicator objective function;

[0085] The objective function for the economic indicators is specifically as follows:

[0086]

[0087] In the formula, F C For economic indicators; C in C represents the total investment cost; om Total operating and maintenance cost; C cW C is the total cost of wind curtailment penalties. FC The total revenue generated by fuel cells; Total revenue generated from the direct sale of hydrogen; K EL,in P represents the unit capacity investment cost of the electrolytic cell; EL The configuration capacity of the electrolytic cell; K FC,in The unit capacity investment cost of fuel cells; P FC For the configuration capacity of the fuel cell; K HST,in Q represents the unit capacity investment cost of the hydrogen storage tank; HST The configuration capacity of the hydrogen storage tank; K site For installation site costs; N site K represents the number of nodes configured for the energy storage device. EL，om K represents the annual operating and maintenance cost per unit capacity of the electrolytic cell. FC，om The annual operating and maintenance cost per unit capacity of a fuel cell; K HST，om The annual operating and maintenance cost per unit capacity of the hydrogen storage tank; K cW The penalty price per unit of wind curtailment; Q cW Total wind curtailment; T0 is the planning period duration; K q K is the discount factor for year q; FC1,q The unit price for providing electricity services to fuel cells in year q; Q FC1,qThe total amount of electricity provided by fuel cells in year q; K FC2,q The unit price for providing heat services for fuel cells in year q; Q FC2,q The total amount of heat service provided by fuel cells in year q; Total revenue generated from the direct sale of hydrogen; This refers to the unit selling price of hydrogen. This represents the total hydrogen sales volume.

[0088] In a preferred embodiment, the economic indicator calculation model further includes configuration constraints, network constraints, and network operation reliability constraints.

[0089] The configuration constraints are specifically as follows:

[0090]

[0091] In the formula, P G (t) represents the power supplied by the main power grid via the submarine cable at time t; P WT (t) represents the output of the wind turbine at time t; P FC1,out (t) represents the output power of the fuel cell at time t; P L (t) represents the total electrical load demand of the island at time t; P EL，in (t) represents the input electrical power of the electrolytic cell at time t; ΔP l (t) represents the line network loss power at time t; U i,min U is the minimum allowable voltage for node i; i (t) represents the voltage at node i at time t; U i,max M is the maximum allowable voltage at point i; j M represents the configuration capacity of the j-th type of device; j，max P represents the maximum allowable configuration capacity of the j-th type of device; j Let j be the real-time operating value of the j-th device at a certain moment;

[0092] The network constraints are specifically as follows:

[0093]

[0094] In the formula, P i，G (t) represents the active power injected by the main grid at node i at time t; P i,HES (t) represents the active power injected into the hydrogen energy storage system at node i at time t, where fuel cell power generation is positive and electrolyzer hydrogen production is negative; P i，WT (t) represents the active power injected by the wind turbine at node i at time t; P i，L (t) represents the active power consumed by the load at node i at time t; P i，loss (t) represents the active power loss from node i to other nodes at time t; Qi,G (t) represents the reactive power injected by the main grid at node i at time t; Q i,HES (t) represents the reactive power injected into the hydrogen energy storage system at node i at time t; Q i,WT (t) represents the reactive power injected by the wind turbine at node i at time t; Q i，L (t) represents the reactive power consumed by the load at node i at time t; Q i,loss (t) represents the reactive power loss from node i to other nodes at time t; S ij Let be the power flowing through the line between node i and node j; This represents the minimum power transmission limit for line ij. U represents the maximum power transmission limit of line ij; i Let be the voltage at node i; This represents the minimum allowable voltage for node i; P represents the maximum allowable voltage at node i. cable P represents the real-time transmission power of the submarine cable. cable,max This represents the maximum permissible transmission power of the submarine cable.

[0095] The network operation reliability constraints are specifically as follows:

[0096]

[0097] In the formula, σ SLD σ represents the load loss rate; SLD,MAX The maximum allowable load power failure rate of the system; Let t be the power of the power-off load. Let t be the total load demand power at time t.

[0098] Specifically, after acquiring the basic data of the island's power distribution network, this invention constructs an economic index calculation model for evaluating the economic viability of hydrogen energy storage device configuration schemes. This model quantifies the economic value of configuration schemes based on typical load curves, wind turbine output curves, and detailed cost-benefit parameters.

[0099] Step S3: Based on the disaster risk parameters, the typical output curve of the wind turbine, and the typical load curve, construct a resilience index calculation model for evaluating the resilience of the hydrogen energy storage device configuration scheme.

[0100] In a preferred embodiment, the resilience index calculation model includes a resilience index objective function;

[0101] The objective function for the resilience index is as follows:

[0102]

[0103] In the formula, FR As a resilience indicator; This is the sum of resilience gains calculated across all simulated Type I extreme scenarios; T is the sum of resilience gains calculated across all simulated Type II extreme scenarios; Case T1 represents the total number of disasters occurring during the planning period; T2 represents the total number of simulations for the first type of extreme scenario; T3 represents the total number of simulations for the second type of extreme scenario.

[0104] The sum of resilience gains calculated in all simulated Type I extreme scenarios is obtained in the following way;

[0105]

[0106] In the formula, S1 is the set of the first type of extreme scenarios; N is the total number of nodes in the distribution network; Let be the probability that node i fails in scenario s1(r); In scenario s1(r), the economic losses recovered by providing support to node i; P represents the total emergency support power provided to node i at time t in scenario s1(r); i,HES (t) represents the supporting power provided by the hydrogen energy storage system at node i at time t; P i，WT (t) represents the supporting power provided by the wind turbine at node i at time i; is the recovery period of scenario s1(r); c0 is the power outage loss cost per unit load;

[0107] The sum of resilience gains calculated across all simulated Type II extreme scenarios is obtained as follows:

[0108]

[0109] In the formula, S2 represents the set of the second type of extreme scenarios; Let s2(r) be the total effective support power provided by the system for the entire island at time t. Let s2(r) be the total load demand of the entire island at time t. The recovery period for scenario s2(r) is .

[0110] Specifically, after constructing the economic evaluation model, this invention further constructs a resilience index calculation model for evaluating the resilience of hydrogen energy storage device configuration schemes based on the acquired disaster risk parameters, typical wind turbine output curves, and typical load curves. This model aims to quantify the contribution of a configuration scheme to ensuring power supply to the island and reducing power outage losses in the face of extreme disaster events. To effectively address the random uncertainty of disasters, the model generates multiple random extreme scenarios using methods such as Monte Carlo sampling and conducts evaluations under these scenarios.

[0111] In a preferred embodiment, the resilience index calculation model includes an objective function for calculating the overall resilience benefit. This resilience index represents the expected resilience benefit obtained by configuring the hydrogen energy storage system throughout the planning period. To comprehensively reflect the various risks that the island may face, the model categorizes extreme scenarios into two types for simulation and evaluation. The first type of extreme scenario is characterized by disasters directly damaging the island's power grid topology, potentially leading to line and node failures. The second type of extreme scenario specifically refers to a severe failure of the submarine cable connecting to the mainland, causing the island to operate as an isolated grid, while the island's power grid structure and power generation equipment remain intact. The overall resilience index is obtained by weighted summation of the resilience benefits calculated under these two scenarios. The weighting coefficients comprehensively consider the total estimated number of disasters occurring during the planning period and the total number of simulations for each scenario, thus reflecting the expected value of the resilience benefit.

[0112] For the first type of extreme scenario, the total resilience benefit is calculated by summing the economic losses recovered for each scenario across all simulated first-type extreme scenarios. In a single scenario, for each node in the distribution network, the recovered economic loss depends on the product of three factors: the probability of the node failing in that scenario, the outage cost per unit load, and the total emergency support power provided to the node over the entire recovery period. The total emergency support power is comprised of the support power provided by the hydrogen energy storage system and wind turbines at the same node.

[0113] For the second type of extreme scenario, the calculation of the total resilience benefit focuses on the island-wide load-satisfaction capacity under islanded grid operation. In all simulated second-type extreme scenarios, for each scenario, the model calculates the ratio of the total effective support power provided by the system to the total load demand of the island at each time point during the recovery period. This ratio reflects the degree of load satisfaction. By summing this ratio over all time points and all second-type scenarios, the total resilience benefit is finally obtained.

[0114] By constructing a resilience index calculation model that distinguishes disaster types and is based on a large number of random scenario simulations, this invention can scientifically and comprehensively quantify the actual effect of different hydrogen energy storage configuration schemes on improving the ability of island power distribution networks to cope with extreme disasters, providing an accurate evaluation method for maximizing resilience.

[0115] Step S4: With the goal of minimizing net loss and maximizing emergency support power, and with the hydrogen energy storage device configuration scheme as the decision variable, the economic index calculation model and the resilience index calculation model are solved iteratively to generate the optimal configuration scheme of the hydrogen energy storage device.

[0116] The process of evaluating candidate configurations for a hydrogen energy storage device in each collaborative iteration includes:

[0117] Based on the economic indicator calculation model, the current candidate configuration scheme is simulated under normal operating conditions within the planning period based on a preset normal operating strategy, and the corresponding economic indicators are calculated and generated based on the normal operating conditions.

[0118] Based on the resilience index calculation model, and based on the preset extreme operation strategy, the current candidate configuration scheme is simulated under extreme operating conditions in each extreme scenario in the preset extreme scenario set, and the corresponding resilience index is calculated and generated based on the extreme operating conditions.

[0119] Based on the economic indicators and the resilience indicators, the comprehensive benefit indicators corresponding to the current candidate configuration schemes are calculated and generated.

[0120] In an optional embodiment, the disaster risk parameters include disaster type, recovery period duration, and node failure probability;

[0121] The extreme scenario set can be obtained through the following methods:

[0122] Based on the disaster type, recovery period, and node failure probability corresponding to each typical disaster day, multiple extreme scenarios are randomly generated using the Monte Carlo sampling method.

[0123] The multiple extreme scenarios are combined to generate an extreme scenario set.

[0124] It should be noted that, for the convenience of modeling and simulation in this application, the normal operation strategy is explained as follows:

[0125] In a typical scenario: due to the current low efficiency of the electricity-hydrogen-electricity conversion, fuel cell power generation has no economic advantage during periods of low demand, and therefore only participates in peak shaving during peak load periods. The process of selling green hydrogen and participating in heating is simplified, ignoring the transportation costs of hydrogen energy and the transmission losses and storage costs of heat energy, and both are sold at a price slightly lower than the market price.

[0126] The preset normal operation strategy is as follows:

[0127] Given the high investment required for hydrogen energy storage devices, maximizing economic benefits under normal operating conditions is crucial. For island power distribution networks, the primary methods for generating profits from hydrogen energy storage devices under normal operating conditions are to utilize renewable energy sources to produce hydrogen, reduce wind curtailment, and smooth load fluctuations. Hydrogen energy storage devices typically employ different operating strategies based on peak and off-peak load periods under normal operating conditions. Specifically:

[0128] (1) During peak load periods, the island's power supply is provided by fuel cells, wind turbines, and submarine cables, thus fully utilizing renewable energy while leveraging the peak-shaving function of hydrogen storage tanks. To avoid the hydrogen storage tanks reaching their lower limit, the amount of hydrogen stored should match the electricity consumption during peak load periods.

[0129] (2) During off-peak hours, the island's power supply is primarily provided by submarine cables, while wind turbines generate electricity to supply the electrolyzers. When the hydrogen storage tank capacity reaches its limit, the electrical load is primarily provided by wind turbines, and if the power supply is insufficient, it is supplemented by submarine cables.

[0130] (3) During the flat period, the island's power supply is primarily provided by submarine cables and wind turbines. If there is a surplus of wind power, it is supplied to the electrolyzer. If the power generation of cables and wind turbines is insufficient, it is supplemented by fuel cells.

[0131] It should be noted that, for the convenience of modeling and simulation of this application, the preset extreme operating strategies are explained as follows:

[0132] Extreme scenarios: Fuel cells can operate under short-term overload when necessary to maximize power supply. Hydrogen storage tanks disregard the impact of exceeding design pressure on lifespan, maximizing hydrogen supply or storage. Electrolyzers can briefly exceed rated power to produce hydrogen, maximizing the utilization of surplus wind power.

[0133] This invention reflects resilience indicators by measuring the difference in load loss under various typical extreme scenarios before and after the configuration of hydrogen energy storage devices. It addresses the uncertainties faced by island power distribution networks in extreme scenarios by using a Monte Carlo sampling method to generate a set of extreme scenarios.

[0134] The disaster risk parameters for typical days of each disaster include the recovery period duration and probability distribution of various extreme disasters. This invention constructs data-mechanism jointly driven distribution network fault probability models for different disasters to calculate the node failure rate under different extreme disasters.

[0135] The extreme scenarios that island power distribution networks may encounter can be divided into the following two categories.

[0136] (1) The first type of extreme scenario is characterized by the island being directly damaged by extreme disasters, which may not only break submarine cables but also destroy the original topology. The main types of disasters include typhoons, rainstorms, thunderstorms, and earthquakes.

[0137] (2) The second type of extreme scenario is characterized by severe damage to submarine cables due to typhoons, earthquakes, ship operations, natural corrosion, etc., resulting in a prolonged disconnection between the island and the mainland power grid, while the power grid structure and renewable energy sources remain undamaged. Since the power grid structure and renewable energy sources remain undamaged, the node failure probability in the second type of extreme scenario is 0.

[0138] In addition, to address the issues of lifespan differences and safety hazards caused by the varying operating conditions of different array electrolyzers during their rotation cycles in hydrogen energy storage devices, an electrolyzer array rotation strategy is introduced. This strategy ensures that the load and losses of the entire electrolyzer array are evenly distributed, thereby significantly extending the overall lifespan of the system and improving operational safety.

[0139] The preset extreme operating strategy is as follows:

[0140] The emergency control capability of hydrogen energy storage devices refers to the ability of these devices, in conjunction with undamaged power sources, to minimize power outage losses for critical loads (including Class I and Class II loads, as well as loads with significant power loss during outages) and achieve maximum power supply protection under extreme scenarios. The specific operational strategy is as follows:

[0141] (1) When the output of the fan exceeds the power demand of the critical load and far exceeds the safety margin, that is:

[0142]

[0143] In the formula, P WT (t) represents the wind turbine output at time t; k key For safety, the value is set to 1.1; The power of the critical load at time t under extreme scenarios;

[0144] At this point, the electrolyzer utilizes the remaining wind power to produce and store hydrogen, thus satisfying the critical load.

[0145] (2) When the fan output exceeds the critical load requirement but cannot meet the safety margin, i.e.:

[0146]

[0147] At this time, the fan output is used entirely to supply the critical load, and the electrolytic cell is not working.

[0148] (3) When the wind turbine output is less than the critical load demand, but the combined output of the fuel cell can meet the load demand, i.e.:

[0149]

[0150] In the formula, The output power of the fuel cell at time t under extreme conditions;

[0151] At this time, all wind power output is used for critical loads, and the fuel cell output is the difference between the critical load demand and the wind power output. The electrolyzer is not working at this time.

[0152] (4) When the sum of the wind turbine output and the maximum output of the fuel cell cannot meet the demand for critical loads, i.e.:

[0153]

[0154] At this point, the critical load recovery power is the sum of the wind turbine output and the fuel cell output. Any shortfall is calculated as a critical load power outage penalty.

[0155] In extreme scenarios, the degree of recovery of critical loads largely determines the severity of the power outage penalty. To achieve better economic efficiency, the hydrogen storage device in this invention employs the following operating strategy to determine the output of the fuel cell: When the hydrogen charge in the storage tank is greater than the safe hydrogen charge, and the fan can meet the power demand of critical loads, i.e.:

[0156]

[0157] In the formula: The safe range of hydrogen load in hydrogen storage tanks under extreme scenarios;

[0158] At this point, the excess power is used to restore non-critical loads.

[0159] The emergency control capability of hydrogen energy storage devices includes emergency control capacity and emergency support power. The former is determined by the hydrogen charge state of the hydrogen storage tank and the operation of the electrolyzer under extreme conditions, while the latter is determined by the operating characteristics of the fuel cell. The specific model is as follows:

[0160]

[0161] In the formula, This refers to the total supporting power of the hydrogen energy storage device under extreme scenarios. To provide fixed supporting power for hydrogen energy storage devices in extreme scenarios; To provide flexible power support for hydrogen energy storage devices in extreme scenarios; Q HST The configuration capacity of the hydrogen storage tank; This represents the lower limit of the hydrogen capacity of a hydrogen storage tank under extreme scenarios. This represents the input power of the electrolytic cell at time t under extreme conditions. Power support for hydrogen energy storage devices in extreme scenarios; This represents the lower limit of the electrical power output of a fuel cell under extreme conditions. This represents the upper limit of the electrical power output of a fuel cell under extreme conditions.

[0162] The above model is subject to the following constraints:

[0163]

[0164] In the formula, This represents the hydrogen production power of the electrolyzer at time t under extreme conditions. This represents the operating power of the fuel cell at time t under extreme conditions. This represents the lower limit of the input power of the electrolytic cell under extreme conditions. This represents the lower limit of the input power of the electrolytic cell under extreme conditions. This represents the electrical power emitted by the fuel cell at time t under extreme conditions. This is the lower limit of the operating power of the fuel cell; This refers to the upper limit of the operating power of a fuel cell; SOHC * (t) represents the equivalent hydrogen-loaded state at time t under extreme scenarios; This represents the upper limit of the equivalent hydrogen-loaded state under extreme scenarios.

[0165] In a preferred embodiment, the comprehensive benefit index is calculated using the following formula:

[0166] OBF = max(F R -d R )(d C -F C ) / (d R d C )

[0167] In the formula, OBF is the comprehensive benefit index; d R The negotiation breakdown point is set as a pre-defined resilience indicator; d C This became the point of breakdown in negotiations regarding economic indicators.

[0168] It should be noted that the Overall Benefit Factor (OBF) is the final score calculated using the Nash negotiation equilibrium model. Mathematically, it is the "Nash product" in Nash negotiation theory, representing the degree to which the conflicting objectives of economy and resilience are balanced. Nash negotiation primarily simulates the negotiation agreement process between two parties, taking into account both individual and collective interests. The Nash negotiation model treats the two objective functions as the two negotiating parties, and the final negotiation structure forms an equilibrium point on the Pareto frontier. The solution that maximizes the Nash product is... This is the equilibrium solution to the Nash negotiation game problem. The Nash equilibrium solution allows both parties to achieve Pareto optimal benefits.

[0169] In this invention, the resilience and economics of the island power distribution network are treated as two entities in a game, with the configuration capacity of the hydrogen production unit and fuel cell being the decision variables in the game, and the objective function being F. C F R During the negotiation process, d was obtained. C d R As the upper limit of economic efficiency and the lower limit of resilience, (d C ,d R ) is the point of breakdown in negotiations, in this invention (d) C ,d R ) = (2704,3777) million yuan. Where, d C Determined by overall economic benefits, including initial investment, operation and maintenance, and various revenues; d R The lower limit is determined by the demand of critical loads in the scenario, and is obtained when only the demand of Class I and Class II loads is met. It includes the critical load volume of Class I and Class II loads, the recovery period, and the power outage loss per unit load.

[0170] 1) Consider the extreme scenarios that may be encountered, and handle the uncertainty of extreme scenarios by constructing a failure probability model and Monte Carlo sampling.

[0171] 2) Use the difference in load loss in extreme island scenarios before and after HES configuration as a quantitative evaluation index of distribution network resilience.

[0172] 3) The economic efficiency is measured based on the investment cost, operation and maintenance cost, wind curtailment penalty and various benefits of HES during normal operation of the island distribution network.

[0173] 4) The configured capacity of EL, HST, and FC, along with the location of hydrogen storage, are used as independent variables. To facilitate the comparison of resilience and economic indicators in the model, Nash negotiations are used to conduct a game between the economic and resilience sides, yielding an equilibrium solution to the Nash negotiation game problem.

[0174] Furthermore, this paper compares several common optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). GA possesses strong global search capabilities, making it suitable for multi-objective optimization and complex constraint problems, but it is sensitive to initial conditions and has a slow convergence speed. PSO converges quickly and is less sensitive to initial conditions, making it suitable for continuous optimization problems, but it is prone to getting trapped in local optima, especially performing poorly in complex problems. Simulated Annealing effectively avoids local optima and is highly adaptable, but it has a slow convergence speed and high computational complexity. Considering the complexity of the hydrogen energy storage configuration problem and the need for multi-objective optimization, this invention selects the Genetic Algorithm and increases population diversity by randomizing various initial populations, reducing dependence on initial conditions, thereby improving global search capabilities and algorithm robustness.

[0175] Specifically, the core objective of this invention is to solve for and generate the optimal configuration scheme of a hydrogen energy storage device. This process is ultimately guided by minimizing net loss (economic objective) and maximizing emergency power support under extreme conditions (resilience objective). The optimization solution process uses the configuration scheme of the hydrogen energy storage device, i.e., the configuration capacity and installation location of each device in the system (electrolyzer, fuel cell, hydrogen storage tank), as decision variables. Since the economic and resilience objectives are inherently contradictory, this invention adopts a collaborative iterative solution method to jointly solve the aforementioned economic index calculation model and resilience index calculation model to seek the optimal balance between the two.

[0176] During each collaborative iterative solution process, the system comprehensively evaluates a candidate configuration scheme for a hydrogen energy storage device. This evaluation process consists of two levels: First, based on the economic index calculation model and a preset normal operation strategy, the system simulates the normal operating conditions of the candidate configuration scheme throughout the entire planning period and calculates its corresponding economic index; second, based on the resilience index calculation model and a preset extreme operation strategy, the system simulates the extreme operating conditions of the candidate configuration scheme in each scenario within a preset set of extreme scenarios, thereby calculating its corresponding resilience index.

[0177] To find an equilibrium between the conflicting objectives of economy and resilience, this invention introduces an equilibrium model based on Nash negotiation theory to integrate the two indicators. A comprehensive benefit index is calculated to uniformly evaluate each candidate configuration. In a preferred embodiment, the calculation of this comprehensive benefit index follows the principles of Nash negotiation, aiming to maximize the product of the gains from resilience exceeding its "negotiation breakpoint" and the gains from economy exceeding its "negotiation breakpoint." Here, the "negotiation breakpoint" can be understood as a preset acceptable lower limit for resilience and an upper limit for economic cost. By constructing such a comprehensive benefit index, the model's objective becomes finding a configuration that maximizes the product of excess gains in resilience and cost savings in economy. This configuration is considered the Pareto optimal solution, achieving the best balance between the two opposing objectives. By employing optimization algorithms such as genetic algorithms to maximize this comprehensive benefit index, the final optimal solution is the optimal configuration for the hydrogen energy storage device.

[0178] By implementing this collaborative iterative solution method oriented towards comprehensive benefit indicators, this invention can effectively solve the problem of balancing economy and reliability, ensuring that the final configuration scheme is economically reasonable and feasible, while also maximizing the survival and recovery capabilities of the island power distribution network under extreme disasters.

[0179] This invention provides an embodiment that illustrates in detail how to perform a comprehensive performance evaluation of a specific "candidate configuration scheme for hydrogen energy storage device" in one iteration of the collaborative iterative solution process.

[0180] First, a candidate configuration scheme is selected from the current population of the optimization algorithm for evaluation. This scheme is an entity containing specific parameters, and its decision variables are known. A candidate configuration scheme can be defined as: configuring a hydrogen energy storage device with {electrolyzer capacity: 1500kW; fuel cell capacity: 1000kW; hydrogen storage tank capacity: 2000kg} at node 7 of the island power distribution network.

[0181] Using the typical load curves of the island power distribution network, the typical output curves of the wind turbines, cost-benefit parameters, hardware parameters of the hydrogen energy storage device, and historical disaster risk parameters obtained in advance, the candidate configuration scheme is evaluated in terms of both economy and resilience.

[0182] Calculation of Economic Indicators: To calculate the economic indicators of this candidate configuration, a simulation of normal operating conditions covering the entire planning period (10 years) will be performed. This simulation is based on a preset normal operating strategy, which defines the "peak-valley-flat" periods and their corresponding hydrogen storage device operating modes according to typical load curves. At each time step (1 hour) of the simulation, the load and wind turbine output at that time will be read, and according to the strategy instructions, combined with the hardware parameters of the candidate configuration (such as the conversion efficiency and constraints of a 1500kW electrolyzer capacity and a 1000kW fuel cell capacity), the actual hydrogen production, power generation, changes in hydrogen storage status, wind curtailment, and energy exchange with the main grid will be calculated. After the long-term simulation is completed, based on all recorded normal operating condition data, and combined with cost-benefit parameters (such as equipment investment unit price, operation and maintenance costs, electricity and hydrogen sales prices, etc.), the total revenue and total cost of the candidate configuration within the planning period will be calculated, ultimately generating economic indicators that can measure its net loss.

[0183] Implementation of resilience index calculation: To calculate the resilience index of this candidate configuration scheme, an extreme operating condition simulation covering all extreme scenarios (500 scenarios) will be performed. This process first initializes a total resilience index value of zero, and then iterates through each scenario in the preset set of extreme scenarios.

[0184] In a simulation of a single extreme scenario (a typhoon lasting 12 hours causing partial power outages), the "power accumulator" for that scenario is initialized to zero. The simulator then enters a time loop with hourly increments, continuing from the start to the end of the disaster. Each hour, the simulator determines the current power shortage by combining wind turbine output and load curves, and decides on the "theoretical emergency support power" that the fuel cell needs to provide based on a preset extreme operating strategy. This theoretical value is compared with the hardware parameters of the candidate configuration (i.e., the fuel cell's 1000kW capacity limit and the hydrogen storage tank's 2000kg capacity limit) to determine the actual emergency support power that can be provided within that hour. It should be noted that in the extreme scenario, the fuel cell's maximum operating power is its rated capacity (1000kW in this case), and the emergency control strategy allows for short-term overload operation to maximize power supply. The simulator multiplies this actual support power by one hour to obtain the emergency support power provided within that hour and adds it to the "power accumulator."

[0185] After the 12-hour simulation of a single scenario is completed, the value in the "Power Accumulator" represents the total emergency power support actually provided by the candidate solution in that scenario. This power is then multiplied by the unit outage loss cost and the probability of the scenario's occurrence to calculate the weighted resilience gain for that scenario, which is then added to the total resilience metric. After traversing all 500 extreme scenarios, the final accumulated total resilience metric is the final indicator that measures the resilience level of the candidate configuration.

[0186] After calculating the economic and resilience indicators for this candidate configuration, a corresponding comprehensive benefit indicator will be generated based on these two indicators. This comprehensive benefit indicator will serve as the final score for this candidate configuration and will be fed back to the collaborative iterative solution algorithm for comparison with other candidate configurations, guiding the next optimization step.

[0187] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0188] like Figure 3 As shown, an embodiment of the present invention provides a configuration device for a hydrogen energy storage device for improving the resilience of island power distribution networks, including: an island power distribution network data acquisition module, an economic index calculation model construction module, a resilience index calculation model construction module, and a model solving module;

[0189] The island power distribution network data acquisition module is used to acquire typical load curves, typical wind turbine output curves, cost-effectiveness parameters, hardware parameters of hydrogen energy storage devices, and disaster risk parameters for typical disaster days of the island power distribution network.

[0190] The economic index calculation model construction module is used to construct an economic index calculation model for evaluating the economic efficiency of hydrogen energy storage device configuration schemes based on the typical load curve, the typical output curve of the wind turbine, the hardware parameters, and the cost-benefit parameters.

[0191] The resilience index calculation model construction module is used to construct a resilience index calculation model for evaluating the resilience of hydrogen energy storage device configuration schemes based on the disaster risk parameters, the typical output curve of the wind turbine, and the typical load curve.

[0192] The model solving module is used to minimize net loss and maximize emergency support power, with the hydrogen energy storage device configuration scheme as the decision variable, to collaboratively and iteratively solve the economic index calculation model and the resilience index calculation model, and generate the optimal configuration scheme of the hydrogen energy storage device.

[0193] The process of evaluating candidate configurations for a hydrogen energy storage device in each collaborative iteration includes:

[0194] Based on the economic indicator calculation model, the current candidate configuration scheme is simulated under normal operating conditions within the planning period based on a preset normal operating strategy, and the corresponding economic indicators are calculated and generated based on the normal operating conditions.

[0195] Based on the resilience index calculation model, and based on the preset extreme operation strategy, the current candidate configuration scheme is simulated under extreme operating conditions in each extreme scenario in the preset extreme scenario set, and the corresponding resilience index is calculated and generated based on the extreme operating conditions.

[0196] Based on the economic indicators and the resilience indicators, the comprehensive benefit indicators corresponding to the current candidate configuration schemes are calculated and generated.

[0197] It should be noted that the embodiments of the devices described above correspond to the embodiments of the present invention described above, and can realize the configuration method of the hydrogen energy storage device for improving the resilience of island power distribution networks as described in any one of the above embodiments of the present invention. In addition, the embodiments of the devices described above are merely illustrative, and the modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network units.

[0198] Based on the above-described method embodiments of the present invention, a corresponding embodiment of an electronic device is provided.

[0199] An embodiment of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the configuration method of the hydrogen energy storage device for improving the resilience of island power distribution networks as described in any one of the present invention, or the processor implements the functions of each module in the above-described device embodiments.

[0200] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device.

[0201] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0202] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0203] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile phone, etc.

[0204] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments;

[0205] Another embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is running, the device where the storage medium is located is controlled to execute any of the above-described configuration methods of the hydrogen energy storage device for improving the resilience of island power distribution networks.

[0206] The aforementioned storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form.

[0207] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0208] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for configuring a hydrogen energy storage device to enhance the resilience of island power distribution networks, characterized in that, include: Obtain typical load curves, typical wind turbine output curves, cost-benefit parameters, hardware parameters of hydrogen energy storage devices, and disaster risk parameters for typical disaster days on the island's power distribution network; Based on the typical load curve, the typical output curve of the wind turbine, the hardware parameters, and the cost-benefit parameters, an economic index calculation model is constructed to evaluate the economic efficiency of hydrogen energy storage device configuration schemes. Based on the disaster risk parameters, the typical output curve of the wind turbine, and the typical load curve, a resilience index calculation model is constructed to evaluate the resilience of hydrogen energy storage device configuration schemes. With the goal of minimizing net loss and maximizing emergency power support, and using the configuration scheme of hydrogen energy storage devices as decision variables, the economic index calculation model and the resilience index calculation model are solved iteratively to generate the optimal configuration scheme of hydrogen energy storage devices. The process of evaluating candidate configurations for a hydrogen energy storage device in each collaborative iteration includes: Based on the economic indicator calculation model, the current candidate configuration scheme is simulated under normal operating conditions within the planning period based on a preset normal operating strategy, and the corresponding economic indicators are calculated and generated based on the normal operating conditions. Based on the resilience index calculation model, and based on the preset extreme operation strategy, the current candidate configuration scheme is simulated under extreme operating conditions in each extreme scenario in the preset extreme scenario set, and the corresponding resilience index is calculated and generated based on the extreme operating conditions. Based on the economic indicators and the resilience indicators, the comprehensive benefit indicators corresponding to the current candidate configuration schemes are calculated and generated.

2. The configuration method of the hydrogen energy storage device for improving the resilience of island power distribution networks as described in claim 1, characterized in that, The disaster risk parameters include disaster type, recovery period duration, and node failure probability; The extreme scenario set can be obtained through the following methods: Based on the disaster type, recovery period, and node failure probability corresponding to each typical disaster day, multiple extreme scenarios are randomly generated using the Monte Carlo sampling method. The multiple extreme scenarios are combined to generate an extreme scenario set.

3. The configuration method of the hydrogen energy storage device for improving the resilience of island power distribution networks as described in claim 2, characterized in that, The economic indicator calculation model includes an economic indicator objective function; The objective function for the economic indicators is specifically as follows: In the formula, F C For economic indicators; C in C represents the total investment cost. om Total operating and maintenance cost; C cW Total cost of wind curtailment penalty; C FC The total revenue generated by fuel cells; Total revenue generated from the direct sale of hydrogen; K EL,in P represents the unit capacity investment cost of the electrolytic cell; EL The configuration capacity of the electrolytic cell; K FC,in The unit capacity investment cost of fuel cells; P FC For the configuration capacity of the fuel cell; K HST,in Q represents the unit capacity investment cost of the hydrogen storage tank; HST The configuration capacity of the hydrogen storage tank; K site For installation site costs; N site K represents the number of nodes configured for the energy storage device. EL，om K represents the annual operating and maintenance cost per unit capacity of the electrolytic cell. FC,om The annual operating and maintenance cost per unit capacity of a fuel cell; K HST，om The annual operating and maintenance cost per unit capacity of the hydrogen storage tank; K cW The penalty price per unit of wind curtailment; Q cW Total wind curtailment; T0 is the planning period duration; K q K is the discount factor for year q; FC1,q The unit price for providing electricity services to fuel cells in year q; Q FC1,q The total amount of electricity provided by fuel cells in year q; K FC2,q The unit price for providing heat services for fuel cells in year q; Q FC2,q The total amount of heat service provided by fuel cells in year q; Total revenue generated from the direct sale of hydrogen; This refers to the unit selling price of hydrogen. This represents the total hydrogen sales volume.

4. The configuration method of the hydrogen energy storage device for improving the resilience of island power distribution networks as described in claim 3, characterized in that, The economic indicator calculation model also includes configuration constraints, network constraints, and network operation reliability constraints. The configuration constraints are specifically as follows: In the formula, P G (t) represents the power supplied by the main power grid via the submarine cable at time t; P WT (t) represents the output of the wind turbine at time t; P FC1，out (t) represents the output power of the fuel cell at time t; P L (t) represents the total electrical load demand of the island at time t; P EL，in (t) represents the input electrical power of the electrolytic cell at time t; ΔP l (t) represents the line network loss power at time t; U i，min U is the minimum allowable voltage for node i; i (t) represents the voltage at node i at time t; U i,max M is the maximum allowable voltage at point i; j M represents the configuration capacity of the j-th type of device; j，max P represents the maximum allowable configuration capacity of the j-th type of device; j Let j be the real-time operating value of the j-th device at a certain moment; The network constraints are specifically as follows: In the formula, P i,G (t) represents the active power injected by the main grid at node i at time t; P i,HES (t) represents the active power injected into the hydrogen energy storage system at node i at time t, where fuel cell power generation is positive and electrolyzer hydrogen production is negative; P i,WT (t) represents the active power injected by the wind turbine at node i at time t; P i,L (t) represents the active power consumed by the load at node i at time t; P i,loss (t) represents the active power loss from node i to other nodes at time t; Q i,G (t) represents the reactive power injected by the main grid at node i at time t; Q i,HES (t) represents the reactive power injected into the hydrogen energy storage system at node i at time t; Q i,WT (t) represents the reactive power injected by the wind turbine at node i at time t; Q i,L (t) represents the reactive power consumed by the load at node i at time t; Q i,loss (t) represents the reactive power loss from node i to other nodes at time t; S ij Let be the power flowing through the line between node i and node j; This represents the minimum power transmission limit for line ij; U represents the maximum power transmission limit of line ij; i Let be the voltage at node i; This represents the minimum allowable voltage for node i; P represents the maximum allowable voltage at node i. cable P represents the real-time transmission power of the submarine cable. cable，max This represents the maximum permissible transmission power of the submarine cable. The network operation reliability constraints are specifically as follows: In the formula, σ SLD σ represents the load loss rate; SLD,MAX The maximum allowable load power failure rate of the system; Let t be the power of the power-off load. Let t be the total load demand power at time t.

5. The configuration method of the hydrogen energy storage device for improving the resilience of island power distribution networks as described in claim 4, characterized in that, The resilience index calculation model includes a resilience index objective function; The objective function for the resilience index is as follows: In the formula, F R As a resilience indicator; This is the sum of resilience gains calculated across all simulated Type I extreme scenarios; T is the sum of resilience gains calculated across all simulated Type II extreme scenarios; Case This represents the total number of disasters occurring during the planning period. T1 represents the total number of simulations for the first type of extreme scenario; T2 represents the total number of simulations for the second type of extreme scenario. The sum of resilience gains calculated in all simulated Type I extreme scenarios is obtained in the following way; In the formula, S1 is the set of the first type of extreme scenarios; N is the total number of nodes in the distribution network; Let be the probability that node i fails in scenario s1(r); In scenario s1(r), the economic losses recovered by providing support to node i; P represents the total emergency support power provided to node i at time t in scenario s1(r); i，HES (t) represents the supporting power provided by the hydrogen energy storage system at node i at time t; P i,WT (t) represents the supporting power provided by the wind turbine at node i at time t; is the recovery period of scenario s1(r); c0 is the power outage loss cost per unit load; The sum of resilience gains calculated across all simulated Type II extreme scenarios is obtained as follows: In the formula, S2 represents the set of the second type of extreme scenarios; Let s2(r) be the total effective support power provided by the system for the entire island at time t. Let s2(r) be the total load demand of the entire island at time t. The recovery period is the duration of scenario s2(r).

6. The configuration method of the hydrogen energy storage device for improving the resilience of island power distribution networks as described in claim 5, characterized in that, The comprehensive benefit index is calculated using the following formula: OBF=max(F R -d R )(d C -F C ) / (d R the C ) In the formula, OBF is the comprehensive benefit index; d R The negotiation breakdown point is set as a pre-defined resilience indicator; d C This became the point of breakdown in negotiations regarding economic indicators.

7. A configuration device for a hydrogen energy storage device aimed at improving the resilience of island power distribution networks, characterized in that, include: The system includes a data acquisition module for island power distribution networks, a model construction module for calculating economic indicators, a model construction module for calculating resilience indicators, and a model solving module. The island power distribution network data acquisition module is used to acquire typical load curves, typical wind turbine output curves, cost-effectiveness parameters, hardware parameters of hydrogen energy storage devices, and disaster risk parameters for typical disaster days of the island power distribution network. The economic index calculation model construction module is used to construct an economic index calculation model for evaluating the economic efficiency of hydrogen energy storage device configuration schemes based on the typical load curve, the typical output curve of the wind turbine, the hardware parameters, and the cost-benefit parameters. The resilience index calculation model construction module is used to construct a resilience index calculation model for evaluating the resilience of hydrogen energy storage device configuration schemes based on the disaster risk parameters, the typical output curve of the wind turbine, and the typical load curve. The model solving module is used to minimize net loss and maximize emergency support power, with the hydrogen energy storage device configuration scheme as the decision variable, to collaboratively and iteratively solve the economic index calculation model and the resilience index calculation model, and generate the optimal configuration scheme of the hydrogen energy storage device. The process of evaluating candidate configurations for a hydrogen energy storage device in each collaborative iteration includes: Based on the economic indicator calculation model, the current candidate configuration scheme is simulated under normal operating conditions within the planning period based on a preset normal operating strategy, and the corresponding economic indicators are calculated and generated based on the normal operating conditions. Based on the resilience index calculation model, and based on the preset extreme operation strategy, the current candidate configuration scheme is simulated under extreme operating conditions in each extreme scenario in the preset extreme scenario set, and the corresponding resilience index is calculated and generated based on the extreme operating conditions. Based on the economic indicators and the resilience indicators, the comprehensive benefit indicators corresponding to the current candidate configuration schemes are calculated and generated.

8. A configuration device for a hydrogen energy storage device aimed at improving the resilience of island power distribution networks, characterized in that, Also includes: Extreme scenario collection acquisition module; The extreme scenario set acquisition module is used to randomly generate multiple extreme scenarios based on the Monte Carlo sampling method according to the disaster type, recovery period duration, and node failure probability corresponding to each typical disaster day; wherein, the disaster risk parameters include disaster type, recovery period duration, and node failure probability; and the multiple extreme scenarios are combined to generate an extreme scenario set.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the configuration method of the hydrogen energy storage device for improving the resilience of island distribution networks as described in any one of claims 1 to 6.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the storage medium to perform the configuration method of the hydrogen energy storage device for improving the resilience of island power distribution networks as described in any one of claims 1 to 6.

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