A collaborative optimization method considering power support capability of hydrogen-containing integrated energy system and energy demand of lifeline load under extremely cold disaster

Abstract

A collaborative optimization method considering the power support capability of a hydrogen-based integrated energy system and the energy demand of lifeline loads in a distribution network under extreme cold disasters is disclosed, relating to the field of distribution network power optimization technology. The method includes: acquiring environmental parameters of extreme cold disasters and operating parameters of key equipment in the hydrogen-based integrated energy system; constructing a dynamic response characteristic model of key equipment considering the impact of low temperatures based on the environmental parameters; constructing a power support capability assessment model of the hydrogen-based integrated energy system based on the dynamic response characteristic model; acquiring lifeline load data of rural distribution networks under extreme cold disasters and constructing an energy demand model oriented towards lifeline loads; and constructing a master-slave-Nash collaborative optimization model based on the power support capability assessment model of the hydrogen-based integrated energy system and the energy demand model oriented towards lifeline loads, solving for the optimal power support strategy of the hydrogen-based integrated energy system for the distribution network. This application can accurately support the grid load and improve the resilience of rural distribution networks.

Description

Technical Field

[0001] This application relates to the field of power optimization technology for distribution networks, and in particular to a collaborative optimization method that considers the power support capability of hydrogen-containing integrated energy systems and the energy demand of lifeline loads in distribution networks under extreme cold disasters, for optimizing the power support of hydrogen-containing integrated energy systems for the guaranteed loads of rural distribution networks under extreme cold disasters. Background Technology

[0002] For rural areas with weak infrastructure and low disaster resilience, extreme cold weather has become a key risk factor threatening the power supply security of rural power distribution networks, endangering residents' lives and basic livelihoods. On the one hand, energy conversion and consumption equipment in rural power distribution networks is highly sensitive to low temperatures. Under extreme cold conditions, it is prone to significant efficiency reduction, deterioration of dynamic response characteristics, and even freezing damage, significantly increasing the probability of power outages. On the other hand, the persistence and destructiveness of extreme cold weather are more pronounced in rural environments, leading not only to a sharp increase in energy demand for heating, agricultural and livestock insulation loads, but also significant changes in the load importance levels. Therefore, ensuring a continuous energy supply for lifeline loads under the background of extreme cold weather is of great significance for enhancing the disaster resilience of rural power distribution networks.

[0003] Previously, one method for configuring hydrogen energy storage in an integrated energy system to enhance the resilience of power distribution networks involved constructing operational models of a hydrogen-containing integrated energy system under normal and extreme scenarios, and utilizing a Nash equilibrium model to seek a balance between economic efficiency and resilience, thus achieving optimized configuration of hydrogen energy storage. Furthermore, another method for enhancing the resilience of power distribution networks based on optimized scheduling of integrated energy systems divides the resilience enhancement process into pre-disaster preparation and post-disaster defense phases, improving power supply recovery capabilities by optimizing the state of charge of electrical / thermal energy storage and the output of various power sources.

[0004] The methods described above enhance the resilience of the power distribution network to some extent by optimizing operational strategies or resource allocation. However, most of these methods are based on modeling the operating characteristics of equipment under standard operating conditions, failing to fully consider the nonlinear attenuation of the output capacity of key equipment such as hydrogen fuel cells, gas turbines, and batteries in extreme low-temperature environments. Furthermore, they fail to consider the actual situation of weak infrastructure and low disaster resistance in rural areas. As a result, when extreme cold disasters actually occur, the actual support capacity of the system deviates significantly from the expectation, making it difficult to accurately support the power grid load. Summary of the Invention

[0005] The purpose of this application is to propose a method and system for optimizing the power support of a hydrogen-containing integrated energy system for the load protection of the distribution network under extreme cold disasters, so as to accurately support the grid load and improve the resilience of the rural distribution network in the event of extreme cold disasters.

[0006] Firstly, a collaborative optimization method considering the power support capacity of a hydrogen-containing integrated energy system and the energy demand of the lifeline load of the distribution network under extreme cold disasters is disclosed, including: To obtain environmental parameters of extreme cold disasters and operating parameters of key equipment in hydrogen-containing integrated energy systems; among which, key equipment includes hydrogen fuel cells, gas turbines and batteries; Based on environmental parameters, dynamic response characteristic models of key equipment considering the effects of low temperatures were constructed. Specifically, the hydrogen fuel cell model was configured to: adjust the Nethers thermodynamic voltage, activation overvoltage, ohmic overvoltage, and concentration overvoltage of the fuel cell according to ambient temperature to determine the mapping relationship between the output power of the hydrogen fuel cell and temperature; the gas turbine model was configured to: adjust the air mass flow rate entering the compressor and the isentropic efficiency of the turbine according to ambient temperature to determine the mapping relationship between the power generation of the gas turbine and temperature; and the battery model was configured to: adjust the actual capacity and charge / discharge efficiency of the battery according to ambient temperature. Based on the dynamic response characteristic model, and with the goal of maximizing comprehensive benefits, a power support capability assessment model for hydrogen-containing integrated energy systems is constructed. This study acquires lifeline load data for rural power distribution networks under extreme cold weather conditions. Based on this data, energy demand models oriented towards lifeline loads are constructed. These lifeline load models include models for basic livelihood security, public services, and agricultural and livestock security. The basic livelihood security model is configured to determine the heat output of the heating system based on the lifeline heating temperature, building heat transfer coefficient, and indoor-outdoor temperature difference. Then, based on the coupling relationship between heat output and electricity consumption, the electricity demand for basic livelihood security is determined. The public service lifeline load model is configured to determine the additional electricity demand of medical institutions based on the lifeline temperature, area, and power of electric heating equipment in key areas within medical institutions. The agricultural and livestock security lifeline load model is configured to determine the electricity demand for facility-based agriculture or animal husbandry based on the lifeline temperature, the warming effect of insulation materials, and the electricity demand of supplemental lighting equipment. Based on the power support capability assessment model of the hydrogen-containing integrated energy system and the energy demand model for lifeline loads, a master-slave-Nash collaborative optimization model is constructed. With the rural distribution network as the leader and the hydrogen-containing integrated energy system as the follower, the optimal power support strategy of the hydrogen-containing integrated energy system for the distribution network is obtained.

[0007] In another example, the power support capability assessment model of the hydrogen-containing integrated energy system takes maximizing the per-unit value of the comprehensive benefits of the hydrogen-containing integrated energy system before and after supporting the grid load under extreme cold disasters as the optimization objective, and the power adjustment amount at each time moment as the decision variable; wherein, the per-unit value of comprehensive benefits is determined based on the gas purchase cost, operating cost, load reduction cost and grid compensation cost of the hydrogen-containing integrated energy system before and after support.

[0008] In another example, the constraints of the power support capability assessment model for a hydrogen-containing integrated energy system include power balance constraints, load reduction constraints, energy storage device constraints, tie-line transmission constraints, equipment load rate constraints, and equipment ramp-up constraints. Among them, the load reduction constraints divide the loads within the hydrogen-containing integrated energy system into four levels according to the importance of energy supply: interruptible loads, general loads, relatively important loads, and important loads. Load reduction constraints are applied level by level in the order of cooling heating and depressurizing gas supply, interruptible load reduction, general load reduction, and relatively important load reduction.

[0009] In another example, in the master-slave Nash co-optimization model, the utility function of the distribution network is constructed based on Nash negotiations of economics and resilience. The goal is to maximize the Nash product of economics and resilience to determine the compensation electricity price paid to each hydrogen-containing integrated energy system. The utility function of the hydrogen-containing integrated energy system is constructed based on the per-unit value of comprehensive benefits before and after power support. Based on the received compensation electricity price, the optimal power support amount output to the rural distribution network is determined with the goal of maximizing its own per-unit value of comprehensive benefits.

[0010] In another example, the economic indicators of the rural distribution network are determined based on the power support compensation cost of the hydrogen-integrated energy system and the network loss cost of the distribution network; the resilience indicators of the rural distribution network are determined based on the power outage probability of each node, the load importance level, and the support power of the hydrogen-integrated energy system.

[0011] In another example, the environmental parameters for extreme cold disasters include at least ambient temperature, wind speed, and precipitation rate; the operating parameters for critical equipment include at least the equipment's rated power, efficiency curve, and temperature coefficient.

[0012] Secondly, a collaborative optimization system considering the power support capacity of a hydrogen-containing integrated energy system and the energy demand of the lifeline load of the distribution network under extreme cold disasters is disclosed, including: The data acquisition module is configured to acquire environmental parameters of extreme cold disasters and operating parameters of key equipment in hydrogen-containing integrated energy systems; among which, key equipment includes hydrogen fuel cells, gas turbines, and batteries; The first modeling module is configured to: construct dynamic response characteristic models of key equipment considering the effects of low temperatures based on environmental parameters; among them, the hydrogen fuel cell model is configured to: correct the Nectar thermodynamic voltage, activation overvoltage, ohmic overvoltage, and concentration overvoltage of the fuel cell according to the ambient temperature, so as to determine the mapping relationship between the output power of the hydrogen fuel cell and temperature; the gas turbine model is configured to: correct the air mass flow rate entering the compressor and the isentropic efficiency of the turbine according to the ambient temperature, so as to determine the mapping relationship between the power generation of the gas turbine and temperature; the battery model is configured to: correct the actual capacity and charge / discharge efficiency of the battery according to the ambient temperature. The second modeling module is configured to: construct a power support capability assessment model for a hydrogen-containing integrated energy system based on a dynamic response characteristic model and with the goal of maximizing comprehensive benefits; The third modeling module is configured to: acquire lifeline load data of rural power distribution networks under extreme cold disasters, and construct energy demand models oriented towards lifeline loads based on the lifeline load data; among them, the energy demand models oriented towards lifeline loads include lifeline load models for livelihood security, public service, and agricultural and livestock security; the lifeline load model for livelihood security is configured to: determine the heat production power of the heating system based on the lifeline heating temperature, building heat transfer coefficient, and indoor-outdoor temperature difference, and then determine the electricity demand for livelihood security based on the coupling relationship between heat production power and electricity consumption power; the lifeline load model for public service is configured to: determine the additional electricity demand of medical institutions based on the lifeline temperature, area, and power of electric heating equipment in key areas within medical institutions; the lifeline load model for agricultural and livestock security is configured to: determine the electricity demand for facility-based agriculture or facility-based animal husbandry based on the lifeline temperature, the warming effect of insulation materials, and the electricity demand of supplemental lighting equipment. The decision-making module is configured to: construct a master-slave-Nash collaborative optimization model based on the power support capability assessment model of the hydrogen-containing integrated energy system and the energy demand model for lifeline loads, with the rural distribution network as the leader and the hydrogen-containing integrated energy system as the follower, and solve for the optimal power support strategy of the hydrogen-containing integrated energy system for the distribution network.

[0013] Thirdly, an electronic device is disclosed, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the aforementioned collaborative optimization method.

[0014] Fourthly, a computer-readable storage medium is disclosed, on which a computer program is stored, which, when executed by a processor, implements the aforementioned collaborative optimization method.

[0015] This application has the following beneficial effects: The embodiments of this application construct a device operation characteristic model of HIES under extreme cold disasters, considering the sensitivity of equipment such as hydrogen fuel cells and gas turbines to low-temperature environments, to accurately characterize the power support capability of HIES under extreme cold conditions, thereby quantifying the potential of HIES to support power supply in distribution networks under extreme cold conditions. Based on this, a priority system for rural power load under extreme cold disaster conditions is constructed to provide load classification basis for precise emergency dispatch and power security strategies in the process of improving distribution network resilience. Furthermore, a master-slave-Nash collaborative optimization model is used to optimize power resource allocation under extreme cold disasters. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of a collaborative optimization method for considering the power support capacity of a hydrogen-containing integrated energy system and the energy demand of the lifeline load of the distribution network under extreme cold disasters, according to an embodiment of this application. Figure 2 This is a schematic diagram of the topology of a power distribution system simulation example. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0019] A Hydrogen-Integrated Energy System (HIES) uses electricity and hydrogen as its primary energy carriers, integrating various energy conversion devices and energy storage units such as photovoltaic power generation, wind power generation, gas turbine generators, and waste heat boilers. Through technologies such as water electrolysis for hydrogen production and hydrogen fuel cell power generation, this system achieves combined energy production and supply, meeting users' diverse energy needs for electricity, heat, and cooling. Existing research, for example, has demonstrated the economical, flexible, and reliable operation of smart distribution networks based on hydrogen storage and renewable energy integrated energy systems.

[0020] This application considers utilizing a hydrogen-containing integrated energy system to support distribution network fault recovery through the synergistic conversion and complementary coupling of multiple energy sources, thereby enhancing the resilience of distribution networks, especially rural distribution networks, under extreme cold disasters. Unlike conventional applications, under extreme cold disasters, the operating conditions of hydrogen fuel cells, gas turbines, and batteries all change significantly. Coupled with the complexity of rural distribution network fault recovery, existing research is difficult to directly apply. Therefore, this application proposes an optimized method for power support of a hydrogen-containing integrated energy system for distribution network load protection under extreme cold disasters. Through in-depth research on the impact mechanism of low-temperature conditions on the operating characteristics of key equipment in the distribution network and reconstructing the importance level of rural loads, a multi-stage coordinated regulation and power support strategy based on the hydrogen energy system (HIES) is proposed to ensure the continuous energy supply to lifeline loads and enhance the disaster resilience of rural distribution networks.

[0021] Figure 1 This is a schematic diagram illustrating a collaborative optimization method based on an embodiment of this application, considering the power support capacity of a hydrogen-containing integrated energy system and the energy demand of the lifeline load of the distribution network under extreme cold disasters. Figure 1 As shown, the collaborative optimization method includes: Step S1: Obtain environmental parameters of the extreme cold disaster and operating parameters of key equipment in the hydrogen-containing integrated energy system; For example, environmental parameters for extreme cold disasters include ambient temperature, wind speed, and precipitation rate. Operating parameters for key equipment include rated power, efficiency curves, and temperature coefficients.

[0022] Step S2: Based on environmental parameters, construct a dynamic response characteristic model for key equipment that takes into account the effects of low temperature; 1. Hydrogen fuel cells Under extreme cold weather, hydrogen fuel cells face a dual challenge: First, the low temperature prevents the hydrogen fuel cell from starting normally, requiring necessary measures to rapidly heat the stack and coolant to ensure that the subsequent electrochemical reaction can proceed normally; second, the polarization loss of the hydrogen fuel cell increases, the operating voltage drops significantly, resulting in a substantial reduction in output power.

[0023] For example, the functional relationship between the output power of a hydrogen fuel cell and temperature is constructed as follows:

[0024] In the formula, This represents the total number of fuel cell stacks. For temperature Lower single-cell battery voltage; It has the lowest calorific value of hydrogen. For current; It is Faraday's constant; This refers to the battery temperature.

[0025] Fuel cell voltage and NEXT thermodynamic voltage Activation overvoltage Ohmic overvoltage Concentration overvoltage The relevant functional relationship is as follows:

[0026] (1) Nernst thermodynamic voltage

[0027] In the formula, Battery temperature The electromotive force under; It is the gas constant; , These represent the partial pressures of hydrogen and oxygen at the anode catalyst / gas interface, respectively. For reference temperature; For the change in Gibbs free energy of the battery reaction; It is the entropy change of the battery reaction.

[0028] (2) Activation overvoltage

[0029] In the formula, , , , These are empirical parameters; This represents the oxygen concentration.

[0030] (3) Ohmic overvoltage

[0031] In the formula, The contact resistance is negligible due to temperature variations. The temperature-dependent function of the proton exchange membrane resistance can be obtained from the following empirical formula:

[0032] In the formula, , , , These are the resistivity, thickness, effective area, and water content of the proton exchange membrane, respectively.

[0033] (4) Concentration overvoltage

[0034] In the formula, The mass transfer factor is related to temperature. This represents the growth rate of electrochemical reaction products in the catalyst layer.

[0035] 2. Gas turbine Under extreme cold disasters, on the one hand, the low temperature increases the air density, which in turn increases the mass flow rate of the air entering the compressor; on the other hand, the low temperature causes the temperature difference between the gas at the turbine inlet and the ambient temperature to widen, which is conducive to the gas expanding more fully in the turbine to do work, thereby improving the operating efficiency of the gas turbine.

[0036] For example, taking into account the performance changes of the compressor and turbine under cryogenic conditions, the following refined model is constructed to accurately reflect the variable operating characteristics of the gas turbine under extreme cryogenic conditions:

[0037] In the formula, For gas turbine t Power generation at any given moment; for t Air quality flow rate at any given time; , These are the specific heat capacities at constant pressure for fuel gas and air, respectively. , They are respectively t The inlet and exhaust temperatures of the turbine at all times; , They are respectively t The inlet and outlet temperatures of the compressor are constantly monitored. , These are the isentropic efficiencies of the turbine and compressor, respectively. The correction curve, which is related to the compressor inlet temperature and pressure, is provided by the equipment manufacturer. for t The heat production capacity of the gas turbine at any given time; for t Exhaust mass flow rate at any given time; This refers to the air mass flow rate under standard conditions. for t Fuel mass flow rate at any given time; The specific heat capacity at constant pressure of the gas turbine exhaust gas; for t The exhaust temperature of the gas turbine at all times; , They are respectively t Constant environmental and standard pressures; This is the standard temperature.

[0038] 3. Storage battery Batteries are extremely sensitive to temperature; therefore, for example, the relationship between the actual capacity of a battery and temperature under extreme cold weather conditions is constructed as follows:

[0039] In the formula, For the battery at an ambient temperature of The actual capacity at that time; This refers to the rated capacity of the battery under standard conditions. Temperature coefficient; Temperature under standard conditions.

[0040] Meanwhile, there is a complex nonlinear relationship between the charging and discharging efficiency of a battery and temperature. Based on known experimental data, the empirical relationship between the charging and discharging efficiency of a battery and temperature is obtained through data fitting as follows:

[0041] In the formula, , These are the charging and discharging efficiencies of the battery, respectively.

[0042] Step S3: Based on the dynamic response characteristic model, and with the goal of maximizing comprehensive benefits, construct a power support capability assessment model for a hydrogen-containing integrated energy system. Under extreme cold weather conditions, HIES can enhance the resilience of the distribution network by scheduling other types of internal energy sources and optimizing the output of coupled equipment. HIES aims to maximize the per-unit value of the comprehensive benefits of supporting grid load supply before and after extreme cold weather conditions, using power regulation at various times as decision variables to construct an evaluation model for HIES power support capabilities. For example, the objective function is:

[0043] In the formula, , To assess the comprehensive benefits of HIES in supporting power distribution network supply before and after; , , To support HIES in terms of gas purchase costs, operating costs, and load reduction costs before ensuring power supply to the distribution network; , , To cover the gas purchase cost, operating cost, and load reduction cost after HIES supports the distribution network to ensure gas supply; To compensate for the costs of the power grid.

[0044] in,

[0045] In the formula, The cost of gas per unit volume for HIES; for t HIES's gas purchase volume at any given time; , , The economic loss coefficient for the reduction of unit power electrical, heat, and gas load; , , for t The electrical, thermal, and gas load power reduced by HIES at any time; For HIES devices s Operating cost per unit power; for t Time device s Operating power; The duration of the extreme cold disaster; , , For different stages z ( z = 1, 2, 3 or 4 (details below) for the collection of electrical, heat and gas loads; .

[0046] For example, the constraints include: (1) Power balance constraint

[0047] In the formula, , , for t The actual transmission power of the HIES connection lines with the power grid, heating network, and gas network at any given time; , They are respectively t Time device s The power generation and power consumption; , They are respectively t The charging and discharging power of the battery at all times; , They are respectively t Time device s The heat generation and heat consumption power; , They are respectively t The charging and discharging power of the heat storage tank at all times; Let be the gas consumption of device s at time t; , , They are respectively t The electrical, thermal, and gas load requirements of HIES at all times.

[0048] (2) Load reduction constraints In supporting the load supply of the distribution network, the HIES (Heating, Heating, and Gas Systems) must simultaneously consider both economic efficiency and power supply reliability. Therefore, the electrical, heating, and gas loads within the HIES are divided into four levels based on their power supply importance: interruptible loads, general loads, relatively important loads, and important loads. Important loads must be guaranteed continuous power supply and do not participate in the load reduction process within the system. Meanwhile, considering the transmission time lag characteristics of the heating network and the fact that heating and gas loads can accept reduced power supply to a certain extent, these loads should be prioritized for regulation. Based on this, the load regulation of the HIES follows four progressively advancing stages: cooling and reducing heating and gas supply stage, interruptible load reduction stage, general load reduction stage, and relatively important load reduction stage. Each stage is initiated sequentially; that is, the next stage of regulation can only begin after the current stage's reduction target has been achieved.

[0049] Phase 1 ( z = 1): Cooling and heating and depressurizing gas supply stage

[0050] In the formula, To achieve maximum thermal power reduction under inertial conditions; This is the maximum reduction in gas power under the condition of allowing reduced gas supply pressure.

[0051] Phase 2 ( z = 2): Interruption of load reduction phase

[0052] In the formula, , , These represent the maximum power reduction for interruptible loads of electricity, heat, and gas, respectively.

[0053] Phase 3 ( z = 3): General load reduction phase

[0054] In the formula, , , These represent the maximum power reduction for general loads of electricity, heat, and gas, respectively.

[0055] Phase 4 ( z = 4): More important load reduction phase

[0056] In the formula, , , These represent the maximum power reduction for the most important loads: electricity, heat, and gas.

[0057] (3) Constraints of energy storage equipment

[0058] In the formula, ; The self-discharge rate for energy storage; , It is a variable of integers from 0 to 1; , These are the maximum charging and discharging power of the energy storage, respectively. , These represent the charging and discharging efficiencies of energy storage, respectively.

[0059] (4) Transmission constraints of tie lines HIES interacts with external power grids, heating networks, and gas networks via tie lines, and the power of this interaction is limited by the transmission capacity of the tie lines.

[0060] In the formula, , , These represent the maximum permissible transmission power of the HIES connection lines to the electrical, thermal, and gas networks, respectively.

[0061] (5) Equipment load rate constraint

[0062] In the formula, The scheduling factor for the device represents t Moments HIES h medium equipment s Whether it is put into operation; For HIES h Installation capacity , HIES h medium equipment s Maximum and minimum load rates; for t Moments HIES h medium equipment s The active power.

[0063] (6) Equipment climbing force constraint

[0064] In the formula, and HIES h medium equipment s Maximum power increase and maximum power decrease limits per unit time.

[0065] Step S4: Obtain lifeline load data of rural power distribution networks under extreme cold disasters, and construct an energy demand model oriented towards lifeline loads based on the lifeline load data; Extreme cold disasters are characterized by their long duration and high severity. In the process of enhancing the resilience of power distribution networks, the reliable power supply of lifeline loads, as critical power loads, is of paramount importance.

[0066] According to the embodiments of this application, a lifeline load model under extreme cold disasters is constructed based on three types of loads: livelihood security, public services, and agricultural and livestock security. During the load supply process, the priority of these loads is adjusted, for example, by prioritizing them over general important loads, thereby realizing the reconstruction of the load priority system.

[0067] 1. The burden on the lifeline of people's livelihood security During periods of sustained low temperatures caused by extreme cold disasters, ensuring the power supply to centralized heating systems becomes a core task for grid operation, in order to maintain indoor temperatures above the prescribed critical threshold. The heat generation process of centralized heating systems relies on the coordinated operation of boiler equipment, thermal circulation systems, and other auxiliary equipment.

[0068] According to the embodiments of this application, taking into account the characteristics of building heat load and heating demand under extreme cold disasters, and based on the coupling relationship between the heat generation and electricity consumption of the heating system, the energy demand model for the lifeline load of people's livelihood is constructed as follows:

[0069] In the formula, , They are respectively t The power consumption and heat output of the heating system at all times; , These are respectively the heating area and the building heat transfer area; Provide heating temperature for the lifeline; , They are respectively t Real-time indoor and outdoor temperatures for residential users; The heat power required to increase the temperature of a unit heating area by 1°C; The heat transfer coefficient of the building structure.

[0070] 2. Public service lifeline load Medical institutions bear core social functions such as saving lives and maintaining public health security, and their power supply should be given the highest priority during extreme cold disasters. During extreme cold disasters, in order to maintain the necessary thermal comfort in medical spaces and ensure the normal operation of medical equipment, medical institutions often need to rely on electric heating equipment to meet additional heating needs beyond the regular power load, resulting in a significant increase in electricity consumption.

[0071] According to an embodiment of this application, based on the sensitivity of medical institutions to ambient temperature, a lifeline load energy demand model under extreme cold disasters is constructed as follows:

[0072] In the formula, , , They are respectively t The electricity demand of medical institutions at all times, the routine electricity demand of medical institutions, and the additional electricity demand of medical institutions; , They are respectively t Additional energy needs of life support and treatment areas and special patient care areas in medical institutions at all times; , , , These are the lifeline temperatures for the intensive care unit, operating room, neonatal intensive care unit, and burn ward, respectively. , , , They are respectively t Monitor the internal temperature of the intensive care unit, operating room, neonatal intensive care unit, and burn ward at all times; , , , These are the areas of the intensive care unit, operating room, neonatal intensive care unit, and burn ward, respectively. The power consumed to increase the temperature per unit area of ​​an electric heating device by 1°C.

[0073] 3. Supporting the lifeline of planting and breeding. (1) Energy demand for the lifeline of facility-based agriculture The load of greenhouse agriculture mainly includes four categories: temperature control load, irrigation load, lighting load, and environmental control load. During extreme cold disasters, in order to minimize crop losses, avoid frost damage to plants, and ensure their normal growth, it is necessary to comprehensively adopt methods such as electric heating and external covering insulation materials (such as cotton quilts) to maintain the internal temperature of the greenhouse above 10℃, which is the critical temperature for safe crop growth. At the same time, it is necessary to activate the crop supplemental lighting system for no less than 4 hours a day to compensate for the impact of insufficient sunlight on photosynthesis.

[0074] According to the embodiments of this application, based on the above-mentioned heat balance and light compensation requirements, the energy demand model for the lifeline load of facility-based agriculture under extreme cold disasters is constructed as follows:

[0075] In the formula, for t The electricity demand of facility agriculture at all times; This refers to the area of ​​facility agriculture planting; To increase the electricity demand of electric heating equipment by 1°C per unit area; Temperature is the lifeline of facility agriculture; for t The greenhouse temperature was constantly raised by covering the greenhouse with cotton quilts; Let t be the power demand per unit area of ​​the supplementary lighting.

[0076] (2) Energy demand for the lifeline load of facility-based aquaculture The load on facility-based livestock farming mainly includes three categories: temperature control load, ventilation load, and automatic feeding / watering load. During extreme cold weather, priority must be given to ensuring the power supply to these critical equipment to sustain livestock survival. Different types of livestock have varying temperature requirements: piglet farrowing sheds need to be maintained between 32-34℃, chick brooding sheds need to be controlled within the range of 25-30℃, and lactating cattle and sheep sheds need to be maintained between 4-16℃.

[0077] According to the embodiments of this application, based on the above-mentioned differentiated thermal environment control requirements, the energy demand model for the lifeline load of facility-based aquaculture under extreme cold disasters is constructed as follows:

[0078] In the formula, for t Electricity demand for aquaculture facilities at all times; , , They are respectively t The electricity demand for temperature control, ventilation equipment, and automatic feeding / drinking systems in the facility-based aquaculture industry is measured in real time. , , They are respectively t Real-time temperature control load power demand of pigsties, chicken houses, and cattle sheds; , , , , , These are the floor area and heat transfer area of ​​the pigsty, chicken coop, and cattle shed, respectively. , , These are the lifeline temperatures for pigsties, chicken coops, and cattle sheds, respectively. , , They are respectively t Monitor the internal temperature of pigsties, chicken coops, and cattle sheds at all times; , , The electricity demand per unit area of ​​pigsties, chicken coops, and cattle sheds is increased by 1°C.

[0079] In summary, the total energy demand for lifeline loads at various times during extreme cold weather (model) is as follows:

[0080] Step S5: Based on the power support capability assessment model of the hydrogen-containing integrated energy system and the energy demand model for lifeline loads, construct a master-slave-Nash collaborative optimization model. With the rural distribution network as the leader and the hydrogen-containing integrated energy system as the follower, solve for the optimal power support strategy of the hydrogen-containing integrated energy system for the distribution network.

[0081] 1. Distribution network model (1) Utility function The distribution network aims to achieve optimal balance between economic efficiency and resilience. However, economic efficiency and resilience are two heterogeneous indicators with different dimensions, magnitudes, and trends. Therefore, according to the embodiments of this application, a distribution network utility function considering multi-objective equilibrium is constructed using Nash negotiation.

[0082] In the formula, , These are the sub-objectives of economic efficiency and resilience; , These represent the maximum possible payouts for the economic and resilience game players, i.e., the point at which negotiations break down.

[0083] 1) Economic indicators

[0084] In the formula, The unit power compensation price for the power distribution network; for t Moments HIES h Power supporting the power distribution network; Price per unit of grid loss; , Branch roads ij Active power and reactive power; branch road ij The resistance; For nodes j The voltage.

[0085] 2) Resilience Indicators

[0086] In the formula, For nodes i The probability of power failure; For nodesi The load importance level; For HIES h Support distribution network nodes i The power; A 0-1 integer variable used to represent HIES h Restore node? i A fault in the power supply line to the load.

[0087] (2) Constraints 1) Lifeline load supply requirements The lifeline load for extreme cold disasters must ensure a continuous and reliable energy supply.

[0088] In the formula, for t The lifeline of power supply must always meet the actual load power demand.

[0089] 2) Distflow branch power flow constraints

[0090] In the formula, , They are nodes j Active power and reactive power; branch road ij The square of the current; For nodes i The square of the voltage; , Branch roads ij Resistance and reactance; collection , respectively with j The set of the first and last nodes of the branches of the terminal node, with j It is the set of end nodes of the branches of the first end node.

[0091] 3) Branch power constraints

[0092] In the formula, , Indicates a branch ij Maximum and minimum permissible transmission power.

[0093] 4) Node voltage constraints

[0094] In the formula, , Representing nodes respectively iThe upper and lower limits of the voltage it possesses.

[0095] 2. HIES Power Support Model (1) Utility function As mentioned above, each HIES uses the power support amount at each time moment as the decision variable and aims to maximize the per-unit value of the overall benefit, that is:

[0096] (2) Constraints 1) HIES power support capability constraints Each HIES power support amount must not exceed its power support capability range.

[0097] In the formula: For HIES h Real-time maximum power support capability.

[0098] 2) Power balance constraints, energy storage devices, tie line transmission power constraints, equipment load rate and ramp force constraints are as described above.

[0099] Taking an IEEE 33-bus distribution system containing 5 HIES stations as a simulation example, its topology is as follows: Figure 2 As shown in Tables 1 and 2, the relevant data for lifeline load nodes and important load nodes of the distribution network under extreme cold weather are shown in Table 3. Assume the extreme cold weather temperature is -40℃, and the distribution network disconnects from the main grid at 9:00 AM, lasting for 4 hours.

[0100] Table 1 Power Grid Lifeline Load Data

[0101] Table 2. Key Power Grid Load Data

[0102] Table 3. Unit Power Compensation Price for Distribution Network

[0103] Taking HIES1 as an example, this paper analyzes the impact of extreme cold weather on the assessment results of HIES power support capability. The assessment results show that under standard conditions (25℃), the support capability of HIES1 is 35.26% higher than that under extreme cold conditions (-40℃). This difference mainly stems from the fact that the modeling process under standard conditions did not fully consider the operating characteristics and efficiency degradation of HIES equipment under low-temperature conditions, leading to an overestimation of equipment output and thus an inflated assessment of its support capability. Therefore, in the process of rural power distribution network fault restoration, although the power support capability of HIES under standard conditions can theoretically meet the power demand of the distribution network, its actual control output is lower compared to the operating strategy that considers the impact of low temperatures, resulting in limited effective support and insufficient improvement in system resilience.

[0104] The optimized power distribution operation results are shown in Table 4: Table 4 Power Distribution Operation Results Data

[0105] It is evident that considering lifeline loads in enhancing the resilience of rural power distribution networks under extreme cold disasters can improve the power recovery rate of lifeline loads and enhance economic efficiency. Furthermore, prioritizing centralized heating loads as the most important lifeline load can improve the heating security rate for residents, thereby further enhancing the resilience of rural power distribution networks.

[0106] According to another embodiment of this application, a collaborative optimization system considering the power support capability of a hydrogen-containing integrated energy system and the energy demand of the lifeline load of the distribution network under extreme cold disasters is also disclosed, including: The data acquisition module is configured to acquire environmental parameters of extreme cold disasters and operating parameters of key equipment in hydrogen-containing integrated energy systems; among which, key equipment includes hydrogen fuel cells, gas turbines, and batteries; The first modeling module is configured to: construct dynamic response characteristic models of key equipment considering the effects of low temperatures based on environmental parameters; among them, the hydrogen fuel cell model is configured to: correct the Nectar thermodynamic voltage, activation overvoltage, ohmic overvoltage, and concentration overvoltage of the fuel cell according to the ambient temperature, so as to determine the mapping relationship between the output power of the hydrogen fuel cell and temperature; the gas turbine model is configured to: correct the air mass flow rate entering the compressor and the isentropic efficiency of the turbine according to the ambient temperature, so as to determine the mapping relationship between the power generation of the gas turbine and temperature; the battery model is configured to: correct the actual capacity and charge / discharge efficiency of the battery according to the ambient temperature. The second modeling module is configured to: construct a power support capability assessment model for a hydrogen-containing integrated energy system based on a dynamic response characteristic model and with the goal of maximizing comprehensive benefits; The third modeling module is configured to: acquire lifeline load data of rural power distribution networks under extreme cold disasters, and construct energy demand models oriented towards lifeline loads based on the lifeline load data; among them, the energy demand models oriented towards lifeline loads include lifeline load models for livelihood security, public service, and agricultural and livestock security; the lifeline load model for livelihood security is configured to: determine the heat production power of the heating system based on the lifeline heating temperature, building heat transfer coefficient, and indoor-outdoor temperature difference, and then determine the electricity demand for livelihood security based on the coupling relationship between heat production power and electricity consumption power; the lifeline load model for public service is configured to: determine the additional electricity demand of medical institutions based on the lifeline temperature, area, and power of electric heating equipment in key areas within medical institutions; the lifeline load model for agricultural and livestock security is configured to: determine the electricity demand for facility-based agriculture or facility-based animal husbandry based on the lifeline temperature, the warming effect of insulation materials, and the electricity demand of supplemental lighting equipment. The decision-making module is configured to: construct a master-slave-Nash collaborative optimization model based on the power support capability assessment model of the hydrogen-containing integrated energy system and the energy demand model for lifeline loads, with the rural distribution network as the leader and the hydrogen-containing integrated energy system as the follower, and solve for the optimal power support strategy of the hydrogen-containing integrated energy system for the distribution network.

[0107] According to another embodiment of this application, an electronic device is also disclosed, the electronic device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the aforementioned collaborative optimization method.

[0108] According to another embodiment of this application, a computer-readable storage medium is also disclosed, on which a computer program is stored, which, when executed by a processor, implements the aforementioned cooperative optimization method.

[0109] The embodiments of this application construct a device operation characteristic model of HIES under extreme cold disasters, considering the sensitivity of equipment such as hydrogen fuel cells and gas turbines to low-temperature environments, to accurately characterize the power support capability of HIES under extreme cold conditions, thereby quantifying the potential of HIES to support power supply in distribution networks under extreme cold conditions. Based on this, a priority system for rural power load under extreme cold disaster conditions is constructed to provide load classification basis for precise emergency dispatch and power security strategies in the process of improving distribution network resilience. Furthermore, a master-slave-Nash collaborative optimization model is used to optimize power resource allocation under extreme cold disasters.

[0110] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A collaborative optimization method considering the power support capacity of a hydrogen-containing integrated energy system and the energy demand of the lifeline load of the distribution network under extreme cold disasters, comprising: To obtain environmental parameters of extreme cold disasters and operating parameters of key equipment in hydrogen-containing integrated energy systems; among which, key equipment includes hydrogen fuel cells, gas turbines and batteries; Based on environmental parameters, dynamic response characteristic models of key equipment considering the effects of low temperatures were constructed. Specifically, the hydrogen fuel cell model was configured to: adjust the Nethers thermodynamic voltage, activation overvoltage, ohmic overvoltage, and concentration overvoltage of the fuel cell according to ambient temperature to determine the mapping relationship between the output power of the hydrogen fuel cell and temperature; the gas turbine model was configured to: adjust the air mass flow rate entering the compressor and the isentropic efficiency of the turbine according to ambient temperature to determine the mapping relationship between the power generation of the gas turbine and temperature; and the battery model was configured to: adjust the actual capacity and charge / discharge efficiency of the battery according to ambient temperature. Based on the dynamic response characteristic model, and with the goal of maximizing comprehensive benefits, a power support capability assessment model for hydrogen-containing integrated energy systems is constructed. This study acquires lifeline load data for rural power distribution networks under extreme cold weather conditions. Based on this data, energy demand models oriented towards lifeline loads are constructed. These lifeline load models include models for basic livelihood security, public services, and agricultural and livestock security. The basic livelihood security model is configured to determine the heat output of the heating system based on the lifeline heating temperature, building heat transfer coefficient, and indoor-outdoor temperature difference. Then, based on the coupling relationship between heat output and electricity consumption, the electricity demand for basic livelihood security is determined. The public service lifeline load model is configured to determine the additional electricity demand of medical institutions based on the lifeline temperature, area, and power of electric heating equipment in key areas within medical institutions. The agricultural and livestock security lifeline load model is configured to determine the electricity demand for facility-based agriculture or animal husbandry based on the lifeline temperature, the warming effect of insulation materials, and the electricity demand of supplemental lighting equipment. Based on the power support capability assessment model of the hydrogen-containing integrated energy system and the energy demand model for lifeline loads, a master-slave-Nash collaborative optimization model is constructed. With the rural distribution network as the leader and the hydrogen-containing integrated energy system as the follower, the optimal power support strategy of the hydrogen-containing integrated energy system for the distribution network is obtained.

2. The collaborative optimization method of claim 1, wherein, The power support capability assessment model of the hydrogen-containing integrated energy system aims to maximize the per-unit value of the comprehensive benefits of the hydrogen-containing integrated energy system before and after supporting the grid load under extreme cold disasters, and uses the power adjustment amount at each time moment as the decision variable. The per-unit value of comprehensive benefits is determined based on the gas purchase cost, operating cost, load reduction cost and grid compensation cost of the hydrogen-containing integrated energy system before and after support.

3. The collaborative optimization method of claim 2, wherein, The constraints of the power support capability assessment model for hydrogen-containing integrated energy systems include power balance constraints, load reduction constraints, energy storage device constraints, tie-line transmission constraints, equipment load rate constraints, and equipment ramp-up constraints. Among them, the load reduction constraint divides the loads within the hydrogen-containing integrated energy system into four levels according to the importance of energy supply: interruptible load, general load, relatively important load, and important load. Load reduction constraints are applied level by level in the order of cooling heating and depressurizing gas supply, interruptible load reduction, general load reduction, and relatively important load reduction.

4. The collaborative optimization method of claim 2, wherein, In the master-slave-Nash collaborative optimization model, the utility function of the distribution network is constructed based on Nash negotiation of economics and resilience. The compensation electricity price paid to each hydrogen-containing integrated energy system is determined with the goal of maximizing the Nash product of economics and resilience. The utility function of the hydrogen-containing integrated energy system is constructed based on the per-unit value of comprehensive benefits before and after power support. Based on the received compensation electricity price, the optimal power support amount output to the rural distribution network is determined with the goal of maximizing its own per-unit value of comprehensive benefits.

5. The collaborative optimization method of claim 4, wherein, The economic indicators of rural power distribution networks are determined based on the power support compensation cost of the hydrogen-integrated energy system and the network loss cost of the distribution network; the resilience indicators of rural power distribution networks are determined based on the power outage probability of each node, the load importance level, and the support power of the hydrogen-integrated energy system.

6. The collaborative optimization method of claim 1, wherein, Environmental parameters for extreme cold disasters include at least ambient temperature, wind speed, and precipitation rate; operating parameters for key equipment include at least the equipment's rated power, efficiency curve, and temperature coefficient.

7. A collaborative optimization system considering the power support capacity of a hydrogen-containing integrated energy system and the energy demand of the lifeline load of the distribution network under extreme cold disasters, comprising: The data acquisition module is configured to acquire environmental parameters of extreme cold disasters and operating parameters of key equipment in hydrogen-containing integrated energy systems; among which, key equipment includes hydrogen fuel cells, gas turbines, and batteries; The first modeling module is configured to: construct dynamic response characteristic models of key equipment considering the effects of low temperatures based on environmental parameters; among them, the hydrogen fuel cell model is configured to: correct the Nectar thermodynamic voltage, activation overvoltage, ohmic overvoltage, and concentration overvoltage of the fuel cell according to the ambient temperature, so as to determine the mapping relationship between the output power of the hydrogen fuel cell and temperature; the gas turbine model is configured to: correct the air mass flow rate entering the compressor and the isentropic efficiency of the turbine according to the ambient temperature, so as to determine the mapping relationship between the power generation of the gas turbine and temperature; the battery model is configured to: correct the actual capacity and charge / discharge efficiency of the battery according to the ambient temperature. The second modeling module is configured to: construct a power support capability assessment model for a hydrogen-containing integrated energy system based on a dynamic response characteristic model and with the goal of maximizing comprehensive benefits; The third modeling module is configured to: acquire lifeline load data of rural power distribution networks under extreme cold disasters, and construct energy demand models oriented towards lifeline loads based on the lifeline load data; among them, the energy demand models oriented towards lifeline loads include lifeline load models for livelihood security, public service, and agricultural and livestock security; the lifeline load model for livelihood security is configured to: determine the heat production power of the heating system based on the lifeline heating temperature, building heat transfer coefficient, and indoor-outdoor temperature difference, and then determine the electricity demand for livelihood security based on the coupling relationship between heat production power and electricity consumption power; the lifeline load model for public service is configured to: determine the additional electricity demand of medical institutions based on the lifeline temperature, area, and power of electric heating equipment in key areas within medical institutions; the lifeline load model for agricultural and livestock security is configured to: determine the electricity demand for facility-based agriculture or facility-based animal husbandry based on the lifeline temperature, the warming effect of insulation materials, and the electricity demand of supplemental lighting equipment. The decision-making module is configured to: construct a master-slave-Nash collaborative optimization model based on the power support capability assessment model of the hydrogen-containing integrated energy system and the energy demand model for lifeline loads, with the rural distribution network as the leader and the hydrogen-containing integrated energy system as the follower, and solve for the optimal power support strategy of the hydrogen-containing integrated energy system for the distribution network.

8. An electronic device, comprising: The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the collaborative optimization method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the collaborative optimization method according to any one of claims 1-6.

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