Resilience preventive dispatching method for hydrogen-containing integrated energy system

Abstract

The present application provides a kind of toughness preventive scheduling method for hydrogen-containing comprehensive energy system, the method is based on the preventive scheduling model of adaptive robust optimization framework and is prevented scheduling, the preventive scheduling model includes: the uncertainty impact caused by natural disaster to three kinds of infrastructure of power transmission line, natural gas pipeline and traffic road is quantified;Integrating power hydrogen production and hydrogen transportation into two kinds of scheduling measures;Two-stage optimization structure is used to generate active resource deployment scheme before disaster occurs.By building a two-stage preventive scheduling model based on adaptive robust optimization, the model first systematically incorporates the uncertainty damage caused by natural disasters to three key infrastructures of power transmission lines, natural gas pipelines and traffic roads into a unified framework;By introducing P2HH and HT scheduling, the purpose of flexible resource operation scheduling in the system is achieved.

Description

Technical Field

[0001] This invention belongs to the field of energy technology, and in particular relates to a resilient prevention and dispatching method for hydrogen-containing integrated energy systems. Background Technology

[0002] In recent years, with the rapid development of hydrogen energy production, transportation, and storage technologies, a hydrogen-integrated integrated energy system that deeply integrates hydrogen energy systems with traditional power, natural gas, and heat systems has become a research hotspot in the energy field. Hydrogen-integrated energy systems (HIES), by introducing hydrogen energy as a flexible energy carrier, demonstrate significant potential compared to traditional energy systems in terms of improved operational economy, flexibility, and environmental benefits. For example, ELZs can be used to convert fluctuating renewable energy electricity on-site into hydrogen for storage, achieving cross-seasonal energy storage and improving the spatiotemporal flexibility of energy utilization. This facilitates temporal transfer and cross-seasonal supply and demand balance.

[0003] Meanwhile, global climate change has led to increasingly frequent natural disasters such as hurricanes and freezing rains, posing a serious threat to the infrastructure security of large-scale energy systems. Enhancing the resilience of energy systems to withstand these high-impact, low-probability events has become a key focus of energy security today. Among numerous resilience enhancement measures, preventative dispatch, as a means of proactively adjusting system operation before disasters occur, has attracted significant attention due to its economic efficiency and flexibility.

[0004] Despite some progress in research on the resilience of energy systems, existing technologies still have the following significant shortcomings and technological gaps when dealing with complex integrated energy systems, including hydrogen energy:

[0005] Research on resilient scheduling of HIES is still in its infancy. Existing studies mostly focus on the microgrid level and are typically conducted under deterministic scenarios. These studies often simplify the interactions between different energy systems or assume that internal system facilities (such as generators and energy storage) are not damaged in disasters due to their small size. Therefore, these methods are not applicable to large-scale HIES at the transmission grid level, where the uncertainty of large-scale infrastructure damage needs to be considered.

[0006] Traditional preventative dispatch methods for power systems cannot be directly applied to HIES (Hydrogen Electrolyzer Systems). Existing preventative dispatch methods primarily focus on the dispatch of traditional generating units. However, in HIES, large-scale centralized ELZs are not only important power loads, but their hydrogen production is also a critical energy carrier in the system, directly affecting the supply of heat, electricity, gas, and other components. Therefore, traditional methods neglect the necessity of preventative dispatch for core HIES equipment such as electrolyzers. Furthermore, although there is considerable research on mobile emergency resources (MER), their capacity and application scenarios are mainly geared towards distribution networks, making it difficult to support the energy demands of HIES at the transmission network level under disaster conditions.

[0007] Existing robust optimization models fail to adequately account for the real-world impact of disasters on HIES (Higher Intensity Sequence). While ARO (Advanced Optimization Route) is an effective tool for handling uncertainty, current research applied to HIES often neglects the uncertainty of damage caused by disasters to the system's physical infrastructure (power lines, natural gas pipelines, transportation routes). This leads to overly optimistic scheduling schemes that may fail to guarantee the safe operation of the system in the face of real physical shocks.

[0008] The potential of critical flexibility resources in resilient scheduling has not been fully explored. This is specifically reflected in two aspects:

[0009] P2HH technology: P2HH technology can recover waste heat from the electrolysis process, which has advantages in improving the overall energy efficiency of the system. However, most existing studies focus on its role in improving system flexibility under normal operating scenarios, and its potential as a reliable backup heat source and to improve system resilience under disaster scenarios has not been studied and modeled in depth.

[0010] Hydrogen transport fleets are a crucial means of providing cross-regional energy support. However, most existing studies assume that road traffic conditions are constant. This assumption clearly fails in the context of natural disasters, as road damage or disruptions can severely impact hydrogen transport efficiency and even disrupt emergency energy supply chains. Existing models generally lack consideration for the uncertainty in transport times caused by road damage. Summary of the Invention

[0011] In view of the problems existing in the prior art, the present invention provides a resilient prevention scheduling method for hydrogen-containing integrated energy systems, which at least partially solves the problem of insufficient resource operation scheduling flexibility in the prior art.

[0012] This disclosure provides a resilient preventive dispatch method for hydrogen-containing integrated energy systems.

[0013] The method performs preventative scheduling based on a preventative scheduling model constructed using an adaptive robust optimization framework. The preventative scheduling model includes:

[0014] Quantify the uncertain impact of natural disasters on three types of infrastructure: power transmission lines, natural gas pipelines, and transportation roads;

[0015] Integrating two scheduling measures: electricity-based hydrogen production and heating, and hydrogen transportation;

[0016] A two-stage optimized structure is used to generate proactive resource deployment plans before disasters occur.

[0017] Hydrogen transportation scheduling measures include: constructing a hydrogen transportation scheduling model that considers the impact of natural disasters on roads; and a cost function for hydrogen transportation scheduling based on transportation costs, hydrogen refueling / discharging costs, fleet refueling / discharging costs, road network nodes at time steps, the amount of hydrogen refueling / discharging by the fleet, fleet transportation costs, and fleet status on roads. The construction of travel time on the platform.

[0018] Optionally, the two-stage optimized structure for generating proactive resource deployment schemes before a disaster occurs includes: the first stage aims to minimize the unit commitment costs of coal-fired power generators and electrolyzers by making preventative start-up and shutdown decisions.

[0019] Optionally, the first-stage model includes an objective function for the first-stage decision and a formula function that constrains the state of the corresponding device and the minimum start-stop interval time, wherein the state of the corresponding device includes starting or stopping.

[0020] The objective function of the first-stage decision is to minimize the combined unit cost of coal-fired power generators and electrolyzers, including start-up / shutdown costs and no-load operating costs.

[0021] Optionally, the two-stage optimization structure for generating proactive resource deployment schemes before a disaster occurs includes: in the second stage, constructing a max-min optimization problem to find the worst-case scenario that leads to the highest system operation and load reduction costs among all disaster scenarios, and performing optimal scheduling under this scenario.

[0022] Optionally, the mathematical model for the max-min optimization problem is constructed based on random variables of facility damage status caused by disasters, power transmission lines, gas pipelines, roads, continuous variables in the second stage, and binary variables;

[0023] The second phase objectives include worst-case scenario identification and emergency dispatch, constrained by uncertainties in facility damage scenarios and the uncertainty set of the system operation model.

[0024] Optional, the indeterminate set is:

[0025] ,

[0026] in, and These represent the transmission line interruption and irreversibility at time t and time t+1, respectively, where l, p, and r represent the transmission line, gas pipeline, and road, respectively. , , These are the uncertain budgets for the disruption status of power lines, natural gas pipelines, and roads within the affected area. , , A collection of potentially damaged power lines, natural gas pipelines, and roads.

[0027] Optionally, the power-to-hydrogen and power-to-heat scheduling measures include:

[0028] Waste heat from the electrolytic hydrogen production process can be recovered by establishing a thermoelectric coupling model.

[0029] The operating characteristics and limiting functions of power-to-hydrogen heating are constructed based on the coefficients of the linear operating region of power-to-hydrogen heating, the electrolyzer temperature, the output heat power of electrolysis, the heat energy recovered from power-to-hydrogen heating, the input power of the electrolyzer unit, the hydrogen power of the electrolyzer, the thermal resistance / heat capacity of the electrolyzer, the ambient temperature, the conversion efficiency coefficient, and the input power of the electrolyzer unit.

[0030] By linearly linking the heat generation power with the operating parameters, effective support for the system's thermal load can be achieved. The operating parameters include the input electrical power and the electrolytic cell temperature.

[0031] Optionally, the vehicle position and driving status in the hydrogen transportation scheduling model are constrained by a formula consisting of the following parameters;

[0032] The team in time Arrival / Proceed to Waypoint binary variables, The total travel time of the fleet's current journey, road travel time, and binary variables related to equipment damage are all present. The remaining travel time of the current journey of the convoy, the convoy's time and The driving state binary variables and from Click Standard travel time for each point.

[0033] Optionally, the formula for the preventative scheduling model is:

[0034] ,

[0035] in, , These are the binary variable vectors from the first and second stages, respectively. It is the vector of continuous variables in the second stage, and u is the vector of uncertain variables. This is the second phase of constraints. , , This is the constant coefficient matrix in the first stage. , , , , , , It is the constant coefficient matrix in the second stage. It is an uncertain set.

[0036] The resilient preventive scheduling method for hydrogen-containing integrated energy systems provided by this invention establishes a two-stage preventive scheduling model based on adaptive robust optimization. This model is the first to systematically incorporate the uncertain damage caused by natural disasters to three key infrastructures—transmission lines, natural gas pipelines, and transportation roads—into a unified framework. By introducing P2HH and HT scheduling, the method achieves the goal of flexible resource operation scheduling within the system.

[0037] Furthermore, by modeling the time of HT scheduling as a function related to road conditions, the modeling of the uncertain impact of road interruptions is achieved. By employing an ATC-C&CG-AOP hybrid decomposition algorithm, this complex optimization problem involving multidimensional uncertainty and large-scale integer variables is solved efficiently. This improves the problem of low model solution efficiency in existing technologies when dealing with large-scale HIES resilient scheduling, thereby proactively deploying resources before disasters occur and significantly enhancing the safety resilience and overall operational efficiency of the energy system. Attached Figure Description

[0038] The above and other objects, features and advantages of this disclosure will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0039] Figure 1 This is a schematic diagram of the energy hub structure provided in an embodiment of the present disclosure;

[0040] Figure 2 The topology of the test system and the disaster-affected area map provided in this embodiment of the disclosure;

[0041] Figure 3 A scheduling diagram of a coal-fired power generating unit and an electrolytic cell provided in an embodiment of this disclosure;

[0042] Figure 4An energy supply plan diagram provided for embodiments of this disclosure;

[0043] Figure 5 A hydrogen transportation scheduling plan diagram provided for embodiments of this disclosure;

[0044] Figure 6 Load reduction rate diagrams for different cases provided in embodiments of this disclosure. Detailed Implementation

[0045] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0046] It should be understood that the following specific examples illustrate the implementation of this disclosure, and those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0047] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0048] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The illustrations only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0049] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0050] This embodiment discloses a resilient preventive scheduling method for hydrogen-based integrated energy systems (HIES), aiming to address the increasingly frequent natural disasters caused by climate change and enhance the operational resilience of energy systems. The method includes the following steps: constructing a preventive scheduling model based on an adaptive robust optimization (ARO) framework, which can quantify and handle the uncertain impacts of natural disasters on energy system infrastructure (including transmission lines, natural gas pipelines, and transportation roads); integrating operational constraints of multiple types of facilities in the energy system and interactions between different energy systems in the model; incorporating power-to-hydrogen-and-heat (P2HH) scheduling and hydrogen transportation (HT) scheduling as flexible scheduling measures into the model to improve the system's energy conversion efficiency and emergency supply capacity; and employing a hybrid optimization algorithm to efficiently solve the model containing large-scale integer variables, thereby generating a preventive scheduling scheme capable of proactively responding to disaster risks.

[0051] A specific resilient preventive dispatch method for hydrogen-containing integrated energy systems includes:

[0052] Based on the ARO framework, a preventive scheduling model is constructed and solved. This model can comprehensively consider the uncertain impact of natural disasters on three different types of infrastructure: power transmission lines, natural gas pipelines, and transportation roads. It also integrates two flexible scheduling measures, P2HH and HT, to generate a scheduling scheme that can proactively deploy resources before a disaster occurs, thereby enhancing the resilience of the system.

[0053] The preventative scheduling model is a two-stage optimization model, wherein: the first stage aims to minimize the unit commitment costs of CGs and ELZs by making preventative start-up and shutdown decisions; the first-stage model is expressed as:

[0054]

[0055] st

[0056]

[0057] ,

[0058] in, For time step subscript, , , These are the labels for EHs, CGs, and CSs, respectively. This represents the maximum / minimum value. Startup / shutdown costs for ELZ (CG). The no-load operating cost of ELZ (CG); It is a binary variable; it is 1 if it is in running state, and 0 otherwise. This is a binary variable; it is 1 if the corresponding device is running and 0 if it is stopped. This is the minimum start-stop interval. The binary decision variables represent the unit combination in the first stage. Formula (1) represents the objective function of the first stage decision, which aims to minimize the unit combination cost of CGs and ELZs, including start-up / shutdown costs and no-load operation costs. Formula (3) constrains the start-up / shutdown status and minimum start-up / shutdown interval of the corresponding equipment.

[0059] The second stage involves constructing a max-min optimization problem to identify the worst-case scenario among all possible disaster scenarios that results in the highest costs for system operation and load reduction, and then performing optimal scheduling under this scenario. The mathematical model for this max-min optimization problem is as follows:

[0060]

[0061] The random variable represents the state of facility damage caused by a disaster, where L, P, and R are transmission lines (TLs), gas pipelines, and roads, respectively. It is an uncertain set. , represents the continuous variable and the binary variable in the second stage, respectively. The second stage objective shown in Equation (4) includes worst-case identification and emergency dispatch, and is constrained by the facility damage scenario in Equation (5) and the uncertainty set of the system operation model. Among them, the system operation model includes Energy Hubs (EHs), Transmission Network System (TNS), Gas Network System (GNS) and HT dispatch.

[0062] Uncertainty sets are used to model the impact of uncertainty on a system. The modeling of uncertainty impacts employs uncertainty budgeting, and the uncertainty set U is described as follows:

[0063]

[0064] in , , These are the uncertain budgets for the disruption status of power lines, natural gas pipelines, and roads within the affected area. , , This is a collection of potentially damaged power lines, gas pipelines, and roads. Once these facilities are damaged, repair is not considered within the remaining dispatch timeframe.

[0065] EH Model: The structure of EH in HIE is as follows Figure 1 As shown, the EH exchanges electricity and natural gas with the TNS and GNS respectively, employing energy production (i.e., ELZ with P2HH dispatch and gas turbines), fuel cells (FC) and methanation reactors (MR), electric storage (ES), hydrogen storage (HS) and thermal storage (TS)) and hydrogen transport chains to meet the region's electricity, heat, natural gas and hydrogen needs. The EH model is shown below:

[0066]

[0067] (7)

[0068] (8)

[0069] P2HH scheduling measures utilize a thermoelectric coupling model to recover waste heat from the hydrogen electrolysis process. The operating characteristics and limitations of P2HH are expressed as follows:

[0070] (9)

[0071] in , , , These are the coefficients of the P2HH linear operating region. This refers to the temperature of the electrolytic cell. This refers to the output thermal power of the electrolysis cell (EC). To recover the heat energy of P2HH. This is the input power of ELZ. This refers to the hydrogen power of the EC. This represents the limits of the upward / downward movement. To enable / disable ramp limits. / This represents the thermal resistance / heat capacity of EC. For time range. The ambient temperature. This is the conversion efficiency coefficient. The input power of ELZ. The bilinear term in equation (9) The large M method can be used for linearization. The model represented by formula (9) linearly correlates the heat generation power with the input electrical power, electrolytic cell temperature and other operating parameters, thereby achieving effective support for the system's thermal load.

[0072] (10)

[0073] (11)

[0074] Formula (6) represents the emergency dispatch cost of EH, including operating costs and load reduction costs. This refers to the operating cost of MR / FC. For MR / FC, the operating cost is [value]. For ELZ / GT / CG, the operating cost is [value]. Charging / discharging costs for ES / HS / TS / GS. EH / GT gas consumption. The hydrogen power is the input to MR. Charging power for ES / HS / TS. This refers to the discharge power of ES / HS / TS. This refers to the output power of the GT / FC. To reduce load costs. , , , This is to meet the demand for electricity, heat, hydrogen, and natural gas. Let be the load reduction rate. Constraint (7) gives the load reduction balance equations for power, hydrogen, natural gas and heat in EH. This is the input power of EH. This indicates that natural gas has a low calorific value. This is the conversion efficiency coefficient. This refers to the output thermal power of the GT / EC. The output hydrogen power of EC / HT. Constraint (8) represents the feasible operating area and slope limit of GT for the polygon set with feasible operating areas. The operational characteristics and limits of P2HH are shown in (9), where , , , These are the coefficients of the linear operation region of P2HH. Bilinear terms. Linearization can be achieved using the Big M method. The operating constraints of FC are shown in (10). Constraint (11) represents the charge / discharge limits of ES, HS, and TS, and the SOC operating range limit.

[0075] TNS Model: TNS is a DC power flow model that has been widely used in the study of resilient power systems. Emergency dispatch schemes (CGs) and power flow dispatching based on TNS are proposed. The TNS model is as follows:

[0076] (12)

[0077] (13)

[0078] (14)

[0079] (15)

[0080] As shown in Equation (12), the total cost includes the CG operating cost and the load reduction cost of the second-stage emergency dispatch. The output power of the CG. The up / down output and ramp rate of the CG are limited under constraints (13). Constraint (14) represents the power balance of the bus and the bus phase angle limit when there is a load reduction in the TNS. busbar The phase angle. This is the set of coupling indices for EHs under b. TL The power flow range of the system is shown in (15), and is forced to be 0 when the power is cut off due to a disaster. For TL The reactance.

[0081] The GNS model is:

[0082] (16)

[0083] (17)

[0084] (18)

[0085] (19)

[0086] (20)

[0087] (twenty one)

[0088] Equation (16) represents the total cost, which includes the purchase of gas from the gas well (GW), GS dispatch, and gas unloading. The transaction cost for natural gas. (17)-(18) take into account the balance constraints of nodes with load shedding in GNS. For pipelines Mass flow rate of injected gas at the start / end node. For the node The set of start / end pipeline indexes. This is the efficiency coefficient of the storage unit. for The EHs coupling index set below. Let GS be the mass flow rate of the charging / discharging gas. Constraint (19) represents the limit of the gas mass flow transmission capacity considering pipeline damage. The operating constraints of GS and GW are shown in (20) and (21), respectively. This indicates that the output gas mass flow rate is GW. .

[0089] HT Model: Due to natural disasters causing pipeline outages, users will face long-term energy shortages. By requisitioning and dispatching HT fleets with large storage capacity, hydrogen can be transported from energy-surplus EHs to energy-deficient EHs for emergency dispatch, where hydrogen can be converted into other forms of energy. Existing popular MER routing spatiotemporal network models are difficult to model for HT dispatch with road impact uncertainties in the second stage due to the constraint number being related to travel time. Therefore, a hydrogen transport dispatch model considering the impact of natural disasters on roads is proposed. The HT dispatch cost is:

[0090] (twenty two)

[0091] The first and second items are transportation costs and hydrogen refill / release costs, respectively. For HT team The charging / discharging cost. In time step road network nodes HT team The amount of hydrogen to be charged / discharged. For HT team The transportation cost. In a road network, the travel time between two nodes is represented as a symmetric matrix with diagonal elements of 0. The travel time between any two points not connected by roads is set to a very large number (e.g., slightly larger than the total scheduling step T). Indicates the team On the road Travel time on the road network. The travel time between two nodes in the road network is represented as a symmetric matrix with diagonal elements of 0. The travel time between any two points not connected by roads is set to a very large number (e.g., slightly larger than the total scheduling step T). Travel time changes when roads are disrupted by disasters. Fleet position status. and driving status Constrained by formulas (24)-(27). The second line of formula (25) indicates that the travel time for damaged roads needs to be multiplied by a coefficient. Formula (27) represents the hydrogen charging / discharging limits and SOC operating range for the fleet, which can only be charged / discharged when the EHs are shut down.

[0092] (twenty three)

[0093] (twenty four)

[0094] (25)

[0095] (26)

[0096] (27)

[0097] in For binary variables, if the HT team In time Arrival / Proceed to Waypoint If it is 1, then it is 1; otherwise, it is 0. for The total travel time for the HT team's current journey. This refers to the time spent traveling on the road. This is a binary variable; it is 0 if the device is damaged, and 1 otherwise. for The remaining travel time for the HT team's current journey. From Click Standard travel time for each point. For a binary variable, if the HT team In time and If the vehicle is in motion, the value is 1; otherwise, it is 0. For the SOC of the storage unit, EH(m) represents the EH number corresponding to the road network node m.

[0098] Furthermore, a compact form of ARO-based preventative measures is proposed:

[0099] (28)

[0100] in , These are the binary variable vectors in the first and second stage optimization models, respectively. It is a continuous variable vector in the second-stage model. It is the constraint of the second stage defined in formula (5). , , This is the constant coefficient matrix in the first-stage optimization model. , , , , , , It is the constant coefficient matrix from the second-stage model.

[0101] For optimization solutions, there are generally several decomposition methods for solving ARO models with inner integer variables, such as the nested column and constraint generation (nested-C&CG) algorithm. However, the nested-C&CG algorithm cannot directly solve the proposed model. The reasons are as follows: First, the lower-level problem contains a large number of integer variables, which reduces the efficiency of enumeration-based internal iterations. Second, by creating new variables in each iteration to solve the upper-level problem, feasible solutions to any scenarios added to the lower-level problem are obtained. As the system size increases, computational performance degrades. Therefore, an ATC-C&CG-AOP algorithm is proposed to solve the preventative scheduling model to find approximate optimal solutions. First, the C&CG-AOP algorithm is introduced to improve the computational efficiency of the lower-level problem. Then, the ATC algorithm is combined with the AOP process to improve the computational performance of the upper-level problem in a distributed manner.

[0102] The C&CG-AOP algorithm is an iterative algorithm based on C&CG. The main difference is that the lower-level problems are solved by heuristic alternating optimization of binary and continuous variables, without the need for original cut generation. The two-stage preventive scheduling model in Equation (28) is decomposed into MP and SP. SP involves SSP and SMP, which are given in (29)-(31) respectively.

[0103] (29)

[0104] (30)

[0105] (31)

[0106] The superscript * indicates the optimal value. This represents the dual variable of the second-stage constraint. Indicates the relative error threshold. The counter representing the outer loop. The indices for the external and internal iterations, respectively, and the bilinear term. The Big M method can be used for linearization. The worst-case disaster scenario is identified by iteratively solving subproblems, and this scenario is then added back to the main problem as a new constraint.

[0107] Solving MP using the ATC-AOP algorithm: To improve the computational performance of the algorithm in solving MP, the ATC algorithm based on augmented Lagrangian relaxation was adopted. In each iteration, MP (30) is decomposed into HIES scheduling subproblems (IES-MP) and HT scheduling subproblems (HT-MPs). The charging / releasing of hydrogen will float (i.e., in (4) ) is set as a coupling variable. The decomposed IES-MP and HT-MPs formulas are:

[0108] (32)

[0109] (33)

[0110] Among the symbols Let represent the Hadamard product. For IES-MP and HT-MPS, the constant coefficient matrix in (29) is adjusted to the appropriate dimension using subscripts 1 and 2, respectively. and denoted as linear penalty multipliers in IES-MP and HT-MPS, respectively. and denoted as the second-order penalty multiplier in IES-MP and HT-MPS, respectively. and Let represent the coupling variables in IES-MP and HT-MP, respectively. The variables obtained in equation (4) and This represents the worst-case scenario where the facility fails during the k-th iteration. This is a consensus vector that defines feasible search directions. Then, the problem is solved alternately by updating the Lagrange multipliers until the coupling variables of each subproblem converge.

[0111] The effectiveness of the proposed resilient preventative scheduling scheme was verified on an improved 6-bus, 6-node, 6-point system and an improved New England 39 bus system, along with a Belgian 20-node gas system and a 12-point traffic system. These models were programmed in Matlab and solved by GUROBI on a workstation with an Intel I7 3.7 GHz CPU and 16GB RAM. The MIP gap was set to 0.1%. The relative error thresholds for MP and SPs were both set to 1%. Hydrogen power was calculated using a lower calorific value (LHVH2 = 241.98 kJ / mol). The time range T was 24 hours. To ensure the safety of hydrogen transportation, it was assumed that the affected roads were completely disrupted. Therefore, the time coefficient was... Let t be the time. Assuming the HT fleet shares the same travel time, to simplify the problem, calculate the travel time on a single RN road. The travel time on each road is 1 hour. The cost of unloading electricity, heat, and hydrogen is $1000 / megawatt, and the cost of unloading gas is $10 / cubic meter. The transportation cost is calculated as $100 / hour. Each fleet has a capacity of 100 megawatts.

[0112] Among them, the improved 6-bus 6-node 6-point system is as follows: Figure 2 As shown, the shaded areas represent the affected regions. The test system consists of 3 CGs, 2 EHs, 2 GWs, and 1 GS. The two EHs serving as coupling points between the TNS and GNS are located at points 1 and 6 in the RN. Two HT fleets are pre-deployed at points 1 and 6, respectively, without initial hydrogen storage. To illustrate the rationality and effectiveness of the proposed prevention scheme, a deterministic facility damage scenario under extreme conditions was studied, with the following settings: TLs 1-3, 2-3, 4-3, 4-5, and 4-6 are interrupted sequentially every hour starting at 2:00; pipeline 4-2 is interrupted at two points; and pipelines 2-3, 2-6, 2-5, and 1-3 are interrupted sequentially every hour starting at 2:00.

[0113] Without considering facility damage, the UC scheduling of CGs and ELZs is compared with the scheduling results of the proposed preventative scheduling model under a deterministic facility damage scenario. Figure 3 As shown. CG1 and CG3 are always operational, with or without damage, while CG2, with its higher power generation costs, remains shut down except for producing hydrogen for high-temperature power generation during emergency dispatch. In a deterministic scenario, ELZ2 remains operational from 1:00 AM to 6:00 AM. Due to the disconnection of lines 4-6, TNS was ultimately divided into two island zones. The area where ELZ2 is located experienced power shortages due to the capacity of CG3.

[0114] like Figure 4Tables (a)-(d) show the energy supply schedules for electricity, heat, hydrogen, and natural gas, respectively. Hydrogen is transferred to EH2 via HT and converted into electricity via FC, as shown in Figure 1. Figure 4 As shown in (a) and (c) in the figure. Figure 4 As can be seen in (b), the P2HH dispatching system provides approximately half of the thermal power, leaving extra natural gas for both power generation and residential gas demand in the event of a natural gas shortage. Figure 4 In (d) of this study, natural gas is primarily supplied by the GW due to the capacity of the pipeline and GS. Considering the overall cost, only a portion of the hydrogen is converted into natural gas.

[0115] HT fleet's route planning and charging / discharging status are as follows: Figure 5 As shown. Taking Fleet 1 as an example. Fleet 1 refuels with hydrogen at EH1 from 1:00 to 3:00, then travels along the shortest path 1-5-6 to reach the upcoming peak hydrogen demand period of 10:00-16:00 at 6:00. Once the hydrogen is fully released, the fleet immediately returns to EH1 for the next transport. Since the shortest path 6-5-1 is interrupted at 3:00, fleet 1 returns to EH1 along another path 6-3-2-1.

[0116] To illustrate the effectiveness of the proposed prevention measures, the following five scenarios were considered: Scenario 1 (Baseline): UC only, ELZs operating without P2HH scheduling; Scenario 2: UC only, ELZs operating with P2HH scheduling; Scenario 3: One HT vehicle fleet is requisitioned, but the road is disaster-resistant. Other settings are the same as Scenario 2; Scenario 4: Roads may be disrupted due to a disaster. Other settings are the same as Scenario 3; Scenario 5: Two HT vehicle fleets are requisitioned. Other settings are the same as Scenario 4. Results are shown in Table 1 and... Figure 6 The values ​​are given in bold, where changes compared to the baseline are shown in bold.

[0117] Table 1 Comparison of different cases in Experiment 1

[0118]

[0119] from Figure 6As can be seen, with the improvement of the scheme's effectiveness, the load shedding period gradually becomes more concentrated, decreasing to the peak period. Table 1 shows that Case 5 has the lowest total cost and load shedding. The recovered heat, compared to Case 1 and Case 2, reduces various energy types in the HIES. In Case 2, heating and natural gas reductions directly benefit from P2HH dispatch, decreasing by 17.33% and 31.61% respectively compared to Case 1, while electricity and hydrogen reductions are relatively smaller. When the TNS is divided into two islanded systems in the experiment, it is difficult to allocate different types of energy to reduce system costs due to power exchange interruptions and pipeline capacity limitations. The results of Cases 3-5 demonstrate the importance of high-temperature dispatch in the proposed prevention scheme. In Cases 3-5, the total cost and load shedding are reduced by approximately two times compared to Case 2. In the cases of requisitioning one fleet (Case 3-4) and two fleets (Case 5), the power curtailment is reduced by approximately three times and five times respectively compared to Case 2. Compared to Case 4, a large amount of hydrogen was transported to energy-deficient regions, thus further reducing the electricity and hydrogen cuts in Case 3 and Case 5.

[0120] To verify the effectiveness and computational performance of the method, a large-scale 39-bus, 20-node, 12-point system was tested. The system consisted of 5 CGs, 5 EHs, 2 GSs, and 3 GWs. Four HT fleets were requisitioned and pre-deployed at locations 3, 11, 6, and 2, respectively.

[0121] Table 2 Comparison of different budgets in Test 2

[0122]

[0123] Table 2 shows the results of robust prevention programs with different budgets. Initial , , Let them be set to 5, 4, and 2 respectively. When When =6, in the worst-case scenario, fault lines 39-1, 4-3, 17-16, 16-15, 28-26, and 29-26 divide the TNS into four islanded systems. In this case, power generation resources become unevenly distributed across the four island systems. The island system where EH1 is located has no CG, while the region where EH4 is located has four CGs. Therefore, a reduction in power load occurs. With the occurrence of this power load reduction, total costs surge by 44%, leading to increased load shedding for heat, hydrogen, and natural gas. When When set to 5, in the worst-case scenario, pipelines 3-4, 4-8, 7-10, 8-11, and 11-12 will be damaged. Natural gas load reduction increases by 61%, while electricity, heat, and hydrogen load reductions increase by less than 10%, demonstrating HIES's adaptability to natural gas supply shortages. Regarding circuit breaks, when... Increasing by 1, with other budgets remaining unchanged, results in an average increase of 4% and 10% in total cost and average load reduction, respectively. The results indicate that road traffic has a significant impact on the HT scheduling efficiency of resilient HIES operations, which is also reflected in Table 1.

[0124] Table 3 Comparison of different solutions

[0125]

[0126] Table 3 illustrates the computational performance of the proposed prevention schemes solved using different methods. (Budget) , , The values ​​are set to 6, 4, and 3 respectively. When directly using nested C&CG, the solver gets stuck when solving SMP, causing day-ahead scheduling failure. When using AOP within the C&CG inner loop, a near-optimal solution with a 2% deviation from the C&CG-AOP algorithm solution is obtained within 25214 seconds. The MP and SP iterations for the two algorithms are 8 and 26, respectively. Compared with the C&CG-AOP algorithm, the solution time of the ATC-C&CG-AOP algorithm proposed in this embodiment is further reduced by 21%.

[0127] This embodiment proposes an ARO-based resilient disaster prevention scheduling model, which considers the interactions between different energy systems and P2HH and HT scheduling in HIES to mitigate the impact of natural disasters. Road disruptions, as well as TLs and pipeline failures due to disasters, are all involved in the model. To solve the ARO model more efficiently, an ATC-C&CG-AOP algorithm is proposed. Case studies demonstrate the potential application of P2HH and HT scheduling in the resilient operation of HIES in response to natural disasters. Furthermore, the proposed solution method is validated, improving the computational efficiency for solving large-scale integer variable models.

[0128] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0129] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0130] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0131] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0132] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0133] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0134] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A resilient preventive dispatch method for hydrogen-containing integrated energy systems, characterized in that, The method performs preventative scheduling based on a preventative scheduling model constructed using an adaptive robust optimization framework. The preventative scheduling model includes: Quantify the uncertain impact of natural disasters on three types of infrastructure: power transmission lines, natural gas pipelines, and transportation roads; Integrating two scheduling measures: electricity-based hydrogen production and heating, and hydrogen transportation; A two-stage optimized structure is used to generate proactive resource deployment plans before disasters occur. Hydrogen transportation scheduling measures include: constructing a hydrogen transportation scheduling model that considers the impact of natural disasters on roads; and a cost function for hydrogen transportation scheduling based on transportation costs, hydrogen refueling / discharging costs, fleet refueling / discharging costs, road network nodes at time steps, the amount of hydrogen refueling / discharging by the fleet, fleet transportation costs, and fleet status on roads. The process of creating the itinerary time; The two-stage optimized structure generates a proactive resource deployment plan before a disaster occurs, including: the first stage aims to minimize the unit commitment cost of coal-fired power generators and electrolyzers by making preventative start-up and shutdown decisions; The first-stage model includes the objective function of the first-stage decision and the formula function that constrains the state of the corresponding equipment and the minimum start-stop interval time. The state of the corresponding equipment includes starting or stopping. The objective function of the first-stage decision is to minimize the combined cost of the coal-fired power generator and the electrolyzer, including start-up / shutdown costs and no-load operating costs. The two-stage optimization structure for generating proactive resource deployment schemes before disasters includes: the second stage, constructing a max-min optimization problem to find the worst-case scenario that leads to the highest system operation and load reduction costs among all disaster scenarios, and performing optimal scheduling under this scenario; The mathematical model for the max-min optimization problem is constructed based on random variables of facility damage states caused by disasters, power transmission lines, gas pipelines, roads, continuous variables and binary variables in the second stage; The second phase objectives include worst-case scenario identification and emergency dispatch, constrained by uncertainties in facility damage scenarios and uncertainties in the system operation model; The uncertain set is: ， in, and These represent the transmission line interruption and irreversibility at time t and time t+1, respectively, where l, p, and r represent the transmission line, gas pipeline, and road, respectively. , , These are the uncertain budgets for the disruption status of power lines, natural gas pipelines, and roads within the affected area. , , A collection of potentially damaged power lines, natural gas pipelines, and roads.

2. The resilient preventive dispatch method for hydrogen-containing integrated energy systems according to claim 1, characterized in that, The power-to-hydrogen and power-to-heat dispatching measures include: Waste heat from the electrolytic hydrogen production process can be recovered by establishing a thermoelectric coupling model. The operating characteristics and limiting functions of power-to-hydrogen heating are constructed based on the coefficients of the linear operating region of power-to-hydrogen heating, the electrolyzer temperature, the output heat power of electrolysis, the heat energy recovered from power-to-hydrogen heating, the input power of the electrolyzer unit, the hydrogen power of the electrolyzer, the thermal resistance / heat capacity of the electrolyzer, the ambient temperature, the conversion efficiency coefficient, and the input power of the electrolyzer unit. By linearly linking the heat generation power with the operating parameters, effective support for the system's thermal load can be achieved. The operating parameters include the input electrical power and the electrolytic cell temperature.

3. The resilient preventive dispatch method for hydrogen-containing integrated energy systems according to claim 1, characterized in that, The vehicle position and driving status in the hydrogen transportation scheduling model are constrained by a formula consisting of the following parameters. The team in time Arrival / Proceed to Waypoint binary variables, The total travel time of the fleet's current journey, road travel time, and binary variables related to equipment damage are all present. The remaining travel time of the current journey of the convoy, the convoy's time and The driving state binary variables and from Click Standard travel time for each point.

4. The resilient preventive dispatch method for hydrogen-containing integrated energy systems according to claim 3, characterized in that, The formula for the preventive scheduling model is: ， in, , These are the binary variable vectors from the first and second stages, respectively. It is the vector of continuous variables in the second stage, and u is the vector of uncertain variables. This is the second phase of constraints. , , This is the constant coefficient matrix in the first stage. , , , , , , It is the constant coefficient matrix in the second stage. It is an uncertain set.

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