A method for resiliency optimization of an electricity-cooling system considering district cooling

Abstract

The application discloses a kind of electric-cool system toughness optimization method considering regional cooling, belong to energy system optimization scheduling and comprehensive energy toughness promotion technical field.First, the full-dimensional parameters of electric-cool coupling system are collected, and a semi-physical linearization regional cooling pipe network model is constructed.A temperature safety dynamic model is established to distinguish pipe network cooling load and direct supply low-temperature load.A space-time collaborative scheduling model of mobile cold storage vehicle is built.Coupled with power distribution network power flow, distributed power supply, mobile energy storage power consumption constraints, a weighted optimization objective is constructed, giving priority to toughness, and taking economy into account.The system toughness is weighted represented by electric load service continuity rate and cooling load temperature safety rate.The scheduling scheme is solved by mixed integer programming.The application also restores the physical properties of the pipe network, ensures the key cooling load of data center, hospital and cold chain, collaborates with fixed cold storage and mobile electric cold resource to provide cross-regional emergency support, reduces the amount of fault load shedding and load temperature overrun, and improves the comprehensive toughness of electric-cool coupling system under extreme high temperature conditions.

Description

Technical Field

[0001] This invention relates to the field of energy system optimization scheduling and comprehensive energy resilience enhancement technology, and in particular to a method for optimizing the resilience of an electric-cooling system that takes into account district cooling. Background Technology

[0002] Under extreme heat, cooling loads from building air conditioning, data centers, hospitals, cold chain warehouses, and pharmaceutical cold storage facilities increase rapidly, significantly increasing the pressure on the power distribution network. Simultaneously, extreme heat can cause distributed photovoltaic modules to overheat, reducing their actual usable output and potentially leading to overheating faults in electrical equipment. This can reduce the connectivity of local areas to the main grid, even creating power islands. In such situations, relying solely on power balance for post-fault recovery scheduling is insufficient to accurately characterize the impact of cooling energy supply, pipeline transmission, end-point temperature safety, and the thermal inertia of cooling loads on system resilience.

[0003] As a crucial link between cold sources and cooling loads, the operational status of district cooling networks directly determines the actual cooling quality received by the cooling load. Existing optimization methods for electricity-cooling systems often simplify the cooling network to a single cooling balance node, neglecting physical characteristics such as water supply mass flow rate, water supply temperature transmission, pipeline cooling losses, pump power consumption, and terminal heat exchange capacity. This results in optimization results that fail to reflect the decline in cooling network delivery quality under extreme high temperatures.

[0004] On the other hand, different types of cooling loads have significantly different requirements for temperature safety. Critical cooling loads such as data centers, hospitals, cold chain warehouses, and pharmaceutical cold storage facilities typically have strict temperature boundaries; commercial buildings, hotels, office buildings, and residential areas have different service periods and thermal inertia characteristics. If only the amount of cooling load reduction or supply loss is used to evaluate the cold-side resilience, it is difficult to describe the dynamic process of the cooling load temperature gradually deviating from the safe range.

[0005] Therefore, it is necessary to propose a resilience optimization method for electric-cooling systems that considers the temperature safety of district cooling networks and multiple types of cooling loads under extreme high temperatures. This method can simultaneously characterize the cold network transmission process, the temperature dynamics of multiple types of cooling loads, and the constraints of electric-cooling coupling operation, thereby improving the service continuity and overall resilience of electric-cooling systems under extreme high-temperature fault scenarios. Summary of the Invention

[0006] The purpose of this invention is to propose a resilience optimization method for an electric-cooling system that considers district cooling. It establishes a complete linearized constraint model for the electric-cooling coupling across all dimensions, quantifies the degree of temperature degradation of different levels of cooling load, and coordinates fixed cold sources, mobile cold storage, mobile energy storage, and distributed power sources to carry out joint optimization scheduling, so as to achieve coordinated scheduling that prioritizes system resilience and keeps operating costs under extreme high-temperature fault scenarios.

[0007] To achieve the above objectives, the present invention provides a method for optimizing the resilience of an electric-cooling system considering district cooling, comprising the following steps: Step S1: Construct a data input model for the electric-cooling coupled system, and uniformly collect all basic operating parameters of the power distribution network, regional cooling pipe network, cooling source equipment, various types of cooling loads, mobile energy storage and cold storage resources, and environmental disturbances; Step S2: Construct a semi-physical linearized model of the district cooling network to fully characterize the physical characteristics of hydraulic transmission of the pipeline, temperature change of chilled water along the pipeline, heat exchange at the end, cooling loss of pipeline, and power consumption of circulating water pump. Use the source-end equivalent return water cooling balance method to eliminate the nonlinear constraints of the return water mixing link. Step S3: Construct a dynamic model for temperature safety of multiple types of cooling loads, distinguish between two types of cooling demand: cooling loads directly supplied by the pipeline network and low-temperature loads directly supplied by independent units. Based on the thermal inertia of the load building or storage space, establish a time-by-time temperature evolution equation and introduce a quantification of the degree of degradation of cooling service quality when the temperature exceeds the limit. Step S4: Construct a collaborative scheduling model for fixed cold source equipment and mobile cold storage resources, and establish spatiotemporal location integer constraints for mobile cold storage equipment, charging and discharging state constraints for sub-nodes, dynamic update constraints for on-board cold storage medium temperature, and upper limit constraints for heat exchange power. Step S5: Construct a power-cooling joint balance constraint that couples the power distribution network operation constraints, distributed power output model, mobile energy storage charging and discharging constraints, and the electricity load of the district cooling system, and uniformly include all electricity demands of electric chillers, fixed cold storage charging and discharging, pipeline circulating water pumps, and mobile energy storage charging and discharging. Step S6: Construct an optimization objective function that prioritizes resilience while also considering operating costs. The core optimization objective is to maximize the overall system resilience index, with system operating costs as a secondary constraint. The solution is obtained using mixed-integer linear programming or mixed-integer quadratic programming algorithms. The output is a coordinated scheduling scheme for the electric-cooling system under extreme high-temperature fault conditions.

[0008] Preferably, in step S1, the basic operating parameters collected specifically include: distribution network node set, regional cooling pipe network node set, cold source node set, cooling load node set, cooling pipe set, time-of-use electricity load demand, time-varying cooling load demand, ambient temperature for each time period, light intensity, power line impedance parameters, cooling pipe parameters, rated operating parameters of electric chillers and fixed cold storage equipment, capacity and charging / discharging efficiency parameters of mobile energy storage vehicles, heat exchange medium parameters of mobile cold storage vehicles, and upper and lower limits of allowable temperature for various cooling loads; Define the distribution network node identifier as The district cooling network node is identified as follows: District cooling network node set Divided into cold source node sets Set of cooling load nodes intermediate transmission node set The cooling pipe assembly is denoted as Any non-cold source node within a radiant district cooling network The upstream parent node is denoted as The set of downstream child nodes is denoted as ; The various types of cooling loads are divided into two categories: ①Pipeline cooling load: Relying on the regional cooling network to uniformly transport chilled water for cooling, including commercial complexes, office buildings, residential areas, data centers, hospitals, and industrial parks; ② Independent direct supply of low-temperature load: Equipped with a dedicated low-temperature refrigeration unit for independent cooling, with strict temperature constraints, including cold chain storage warehouses and pharmaceutical cold storage warehouses.

[0009] Preferably, in step S2, the semi-physical linearization model of the district cooling network includes 8 sets of linear constraints. The complete explanation of the constraint expressions and parameters for each set is as follows: ① Water supply network quality conservation and pipeline flow constraints: Non-source node traffic conservation: ; Mass flow rate of intermediate transmission nodes without local heat exchange branches: ; Pipeline flow rate limit constraint: ; In the formula: for Time-of-use pipeline Internal water supply mass flow rate; For load nodes Mass flow rate of heat exchange branch; For pipelines Maximum permissible mass flow rate; ② Pipeline water supply temperature transmission constraints: ; Operating temperature range of water supply at pipeline nodes: ; The outlet water temperature control range of the cold source equipment: ; In the formula: For nodes Time period Water supply temperature; for Ambient temperature during the period; The pipeline temperature retention coefficient is pre-calibrated based on pipeline length, insulation performance, and ambient heat exchange conditions. ③ Linearization constraints on the load-side heat transfer process: Original nonlinear heat transfer formula: ; Introducing auxiliary variables After performing product-free linearization, the transformed result is: ; For variables The McCormick linear outer envelope method is used to eliminate the nonlinearity caused by the multiplication of flow rate and temperature difference; In the formula: For nodes Actual cooling capacity of the regional pipe network; The specific heat capacity at constant pressure of the refrigerant; The temperature difference between the supply and return water of the load-side heat exchanger; ④ Limitations on the heat exchange capacity of the terminal heat exchanger: ; In the formula: For nodes Equivalent heat transfer coefficient of terminal heat exchanger; The internal temperature of the building or storage space corresponding to the load; For nodes Time period Total cooling load demand; ⑤ Constraints on cold energy transfer and loss in pipelines: Cold energy balance between pipeline inlet and outlet: ; The expression for linear flow rate and cold loss is: ; Pipe cooling capacity and flow rate matching range: ; Global cooling balance of non-source nodes: ; In the formula: , These represent the cooling capacity at the pipe inlet and outlet, respectively. This refers to the loss of cooling energy during pipeline transportation. The cold loss coefficient per unit mass flow rate of the pipeline; , The upper and lower limits of the supply and return water temperature difference in the pipeline network design standard; ⑥ Constraints on cold energy injection at cold source nodes: ; Breakdown of total cooling capacity from cold source: ; In the formula: Cold source node Total cooling output during the period; To fix the amount of cold released by the cold storage device; It directly supplies cooling capacity to electric refrigeration units; ⑦ Global balance constraint of equivalent return water cooling capacity at the source end: ; This constraint preserves the physical logic of return water recirculation cooling in closed-loop regional cooling networks, and avoids nonlinear multiplication and division operations caused by multi-node return water mixing temperature calculations. ⑧ Linear approximation constraint on power consumption of circulating water pumps in the pipeline network: ; In the formula: Total power consumption of water pumps in the entire area's cooling network; This refers to the unloaded base power of the water pump. For pipelines The power consumption coefficient of the water pump corresponding to the unit mass flow rate.

[0010] Preferably, in step S3, the multi-type cooling load temperature safety dynamic model includes three parts: load temperature dynamic evolution equation, type-specific cooling capacity supply equation, and temperature over-limit safety constraints. ① Dynamic update equation for internal temperature of the load: ; In the formula: To optimize the scheduling time step size; Equivalent heat capacity of the building or storage space; Equivalent thermal resistance of the building envelope; For nodes Time period Total cooling capacity; Internal thermal disturbances caused by indoor equipment, personnel, and lighting; ② Expressions for cooling capacity supply by load type: Total cooling capacity for piped cooling loads: ; Total cooling capacity for independent direct supply of low-temperature load: ; The coupling relationship between cooling capacity and power consumption of low-temperature refrigeration units: ; Unit operating limits: ; In the formula: For mobile cold storage vehicles to reach nodes Release cold energy; The coefficient of performance (COP) for cryogenic refrigeration units; This refers to the power consumption of the cryogenic unit; , These are the rated cooling capacity and rated input electrical power of the cryogenic unit, respectively. ③ Temperature limits and quantitative constraints: ; In the formula: , The upper and lower safety boundaries of the allowable operating temperature under load; The temperature limit is used to quantify the degree of degradation of cooling load service quality, which is different from the traditional method of evaluating cold side toughness by relying solely on load shedding.

[0011] Preferably, in step S4, the collaborative model between the cold source and mobile cold storage includes the spatiotemporal uniqueness constraint of the mobile cold storage vehicle, the charging and discharging location constraint of the sub-nodes, the temperature dynamics of the on-board cold storage medium, and the upper limit constraint of the cooling and heat exchange power. ①Unique temporal and spatial constraint for a single mobile refrigeration vehicle: ; In the formula: An integer variable, representing Refrigerated trucks docked at pipeline node ; An integer variable representing a vehicle. Time period In the pipe Transfer within a designated area; ②Constraints on the location of charging and discharging cooling: Cooling can only be completed at the cold source node: ; Cooling and replenishment are only permitted at load nodes: ; In the formula: , These are state variables, representing the vehicles respectively. Time period It is in the process of charging and discharging cooling. ③ Dynamic update equation for the temperature of the on-board cooling medium: ; Upper limit constraint on the temperature of the on-board medium: ; In the formula: For vehicle-mounted cooling medium period temperature; For the environmental heat loss and temperature rise during vehicle idling and transfer processes; For source-end cooling and temperature reduction; This is the cooling and heating term at the load end; This refers to the maximum permissible temperature of the vehicle-mounted cooling medium. ④ Power constraints of mobile cold storage vehicle for cooling and heat exchange: ; In the formula: The equivalent heat transfer coefficient of the vehicle-mounted heat exchanger; This refers to the return water temperature at the load node.

[0012] Preferably, in step S5, the power-side support and power-cold coupling constraint model includes distribution network power flow balance constraints, linearized voltage constraints, photovoltaic high-temperature derating output model, gas turbine power generation energy consumption model, mobile energy storage state of charge update, and comprehensive power load coupling equations for all network nodes. ① Active and reactive power balance equations at distribution network nodes: ; ; In the formula: For distribution network nodes Set of adjacent nodes; , For the line Time period The tide of merit and inmerit; , For nodes Distributed power sources have both active and reactive power outputs. ② Distribution network linearization voltage drop constraint: ; ; In the formula: For nodes Time period Voltage amplitude; , The lines are respectively Resistance, reactance; It is a constant; For the line Run on / off state variables; ③ Distributed photovoltaic power generation derating model under extreme high temperatures: ; In the formula: Rated power of photovoltaic modules; Real-time irradiance; Standard test irradiance; Photovoltaic power temperature coefficient; Photovoltaic module operating temperature; Standard test temperature; ④ Coupling relationship between gas turbine power generation and natural gas consumption: ; In the formula: Gas turbine power generation efficiency; Unit natural gas consumption; Natural gas has a low calorific value; ⑤ Dynamic update equation for the state of charge of mobile energy storage vehicles: ; In the formula: Energy storage vehicle Time period State of charge; Rated capacity of energy storage vehicle; , These are the charging and discharging efficiencies, respectively. , This refers to charging and discharging power. ⑥ Total electrical load equation for the electric-cold coupling node: ; In the formula: Represents a node Basic electrical load; Indicates the charging power of the mobile energy storage vehicle; This indicates the power consumption for charging a stationary cold storage device. Indicates the input electrical power of the electric chiller; This indicates the power consumption of the circulating water pumps in the district cooling network.

[0013] Preferably, in step S6, the objective function is optimized using a weighted single-objective form that prioritizes resilience maximization and secondarily considers operating cost. ; In the formula: To optimize the target value for the system as a whole; System overall resilience index; Total system operating cost over the entire scheduling cycle; The economic cost weighting coefficient is lowered for extreme high-temperature failure conditions. The numerical values ​​prioritize ensuring the system's cooling and power supply resilience. The overall system resilience index is a weighted coupling of resilience indices from the power side and the cooling load side: ; In the formula: , These are the resilience weights for the power side and the cold load side, respectively. Electrical load service continuity rate; Cooling load temperature safety rate.

[0014] Preferably, the complete calculation formulas for the electrical load service continuity rate and the cooling load temperature safety rate are as follows: ① Electricity load service continuity rate: ; In the formula: For nodes Weighting of electrical load importance; For time period node Shearing power; ② Cooling load temperature safety rate: ; In the formula: Weighting based on the importance of cooling load; This is a flag variable, representing the effective load temperature safety constraints during this period.

[0015] Preferably, the total system operating cost It consists of five categories of sub-costs: ; Cost expressions for each item: ① Cost of electricity purchased from the main grid: ; ② Gas turbine fuel consumption cost: ; ③ Electricity costs for refrigeration and cold storage equipment: ; ④ Operating electricity for district cooling network water pumps: ; ⑤ Transportation costs for mobile energy storage and mobile cold storage vehicles: ; In the formula: Time-of-use electricity purchase price; System power interaction with the upstream main network; Unit price of natural gas; Dispatch cost per unit time period for a single mobile vehicle; vehicle Time period Variables in transition state.

[0016] Preferably, the collaborative scheduling scheme output in step S6 includes all the operational decision variables of the electric-cooling system, specifically including: the cooling output of the electric chiller in each time period, the charging and discharging of the fixed cold storage device, the mass flow rate of each pipeline in the district cooling system, the water supply temperature of all network nodes, the indoor temperature of each type of cooling load in each time period, the spatiotemporal location and charging and discharging power of the mobile cold storage vehicle in all time periods, the charging and discharging power of the mobile energy storage vehicle, the power generation output of the gas turbine, the power absorption of distributed photovoltaic power, and the load shedding of each node in the distribution network.

[0017] Therefore, the present invention employs the above-described method for optimizing the resilience of an electric-cooling system considering district cooling, which has the following advantages: (1) The present invention retains key features such as mass conservation, water supply temperature transmission, pipeline cold loss, source-end equivalent return water cooling balance and water pump power consumption on the cold network side, avoiding oversimplification of the district cooling network into a single cooling balance node, and improving the physical interpretability of cold network modeling. (2) The present invention establishes a multi-type cooling load temperature dynamic and temperature limit model on the cooling load side, which can evaluate the cooling service quality degradation process according to the temperature boundary, service period and importance weight of different cooling loads, and can better reflect the cooling side toughness change under extreme high temperature than simply using the cooling load loss. (3) This invention integrates the power consumption of cold network water pumps, power consumption of chillers, cooling power of mobile cold storage vehicles, charging and discharging power of mobile energy storage vehicles, power load cut-off amount and distribution network operation constraints, thereby realizing the joint optimization scheduling of power resources and cold energy resources; (4) This invention constructs a comprehensive system resilience index by using the continuity rate of electrical load service and the temperature safety rate of cold load. It takes into account the system operating cost while prioritizing resilience, and is applicable to scenarios such as extreme high temperature, reduced power supply capacity, limited cold network transmission and supply of multiple types of cold loads.

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0019] Figure 1 This is an overall flowchart of a method for optimizing the resilience of an electric-cooling system considering district cooling, as described in an embodiment of the present invention. Figure 2 This is a topology diagram of the electro-cold coupling test system under extreme high-temperature fault scenarios in an embodiment of the present invention; Figure 3 This is a diagram showing the extreme high-temperature process and photovoltaic high-temperature derating results in an embodiment of the present invention; Figure 4 This is a comparison chart of load service continuity rates for various schemes in this invention embodiment; Figure 5 This is a comparison chart of the total load shedding amount for each scheme in the embodiments of the present invention; Figure 6 This is a graph showing the changes in transport capacity and cold loss of pipelines C10-C13 in an embodiment of the present invention; Figure 7 This is a temperature change curve diagram of a data center, an industrial park, and a hospital in an embodiment of the present invention; Figure 8 Temperature change curves for large commercial / industrial buildings, urban transportation and commercial complexes, hotels, Grade A office buildings, and residential areas in embodiments of the present invention; Figure 9 This is a temperature change curve diagram of cold chain 1, cold chain 2, and pharmaceutical refrigeration in an embodiment of the present invention; Figure 10 This is a phase change ratio curve diagram of two ice storage devices in an embodiment of the present invention; Figure 11 This is a diagram showing the cooling capacity curve before the failure in an embodiment of the present invention; Figure 12This is a diagram showing the cooling capacity curve after a fault in an embodiment of the present invention; Figure 13 The above are time-period charging and discharging power and spatial location scheduling curves of the mobile energy storage vehicle in this embodiment of the invention. Figure 14 The curves showing the charging and discharging power and spatiotemporal transport path of the mobile cold storage vehicle in this embodiment of the invention are shown. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0021] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0022] Example like Figures 1-14 As shown in the figure, this embodiment proposes a resilience optimization method for an electric-cooling system considering district cooling, and the specific steps are as follows: Step S1: Construct a data input model for the electric-cooling coupled system, and uniformly collect all basic operating parameters of the power distribution network, regional cooling pipe network, cooling source equipment, various types of cooling loads, mobile energy storage and cold storage resources, and environmental disturbances.

[0023] The basic operating parameters collected specifically include: distribution network node set, regional cooling pipe network node set, cold source node set, cooling load node set, cooling pipe set, time-of-use electricity load demand, time-varying cooling load demand, ambient temperature for each time period, light intensity, power line impedance parameters, cooling pipe parameters, rated operating parameters of electric chillers and fixed cold storage equipment, capacity and charge / discharge efficiency parameters of mobile energy storage vehicles, heat exchange medium parameters of mobile cold storage vehicles, and upper and lower limits of allowable temperature for various cooling loads; Define the distribution network node identifier as The district cooling network node is identified as follows: District cooling network node set Divided into cold source node sets Set of cooling load nodes intermediate transmission node set The cooling pipe assembly is denoted as Any non-cold source node within a radiant district cooling network The upstream parent node is denoted as The set of downstream child nodes is denoted as ; The various types of cooling loads are divided into two categories: ①Pipeline cooling load: Relying on the regional cooling network to uniformly transport chilled water for cooling, including commercial complexes, office buildings, residential areas, data centers, hospitals, and industrial parks; ② Independent direct supply of low-temperature load: Equipped with a dedicated low-temperature refrigeration unit for independent cooling, with strict temperature constraints, including cold chain storage warehouses and pharmaceutical cold storage warehouses.

[0024] Step S2: Construct a semi-physical linearized model of the district cooling network to fully characterize the physical characteristics of hydraulic transmission of the pipeline, temperature change of chilled water along the pipeline, heat exchange at the end, cooling loss of pipeline, and power consumption of circulating water pump. Use the source-end equivalent return water cooling balance method to eliminate the nonlinear constraints of the return water mixing link.

[0025] The semi-physical linearization model of the district cooling network includes eight sets of linear constraints. The complete explanations of the constraint expressions and parameters for each set are as follows: ① Water supply network quality conservation and pipeline flow constraints: Non-source node traffic conservation: ; Mass flow rate of intermediate transmission nodes without local heat exchange branches: ; Pipeline flow rate limit constraint: ; In the formula: for Time-of-use pipeline Internal water supply mass flow rate; For load nodes Mass flow rate of heat exchange branch; For pipelines Maximum permissible mass flow rate; ② Pipeline water supply temperature transmission constraints: ; Operating temperature range of water supply at pipeline nodes: ; The outlet water temperature control range of the cold source equipment: ; In the formula: For nodes Time period Water supply temperature; for Ambient temperature during the period; The pipeline temperature retention coefficient is pre-calibrated based on pipeline length, insulation performance, and ambient heat exchange conditions. ③ Linearization constraints on the load-side heat transfer process: Original nonlinear heat transfer formula: ; Introducing auxiliary variables After performing product-free linearization, the transformed result is: ; For variables The McCormick linear outer envelope method is used to eliminate the nonlinearity caused by the multiplication of flow rate and temperature difference; In the formula: For nodes Actual cooling capacity of the regional pipe network; The specific heat capacity at constant pressure of the refrigerant; The temperature difference between the supply and return water of the load-side heat exchanger; ④ Limitations on the heat exchange capacity of the terminal heat exchanger: ; In the formula: For nodes Equivalent heat transfer coefficient of terminal heat exchanger; The internal temperature of the building or storage space corresponding to the load; For nodes Time period Total cooling load demand; ⑤ Constraints on cold energy transfer and loss in pipelines: Cold energy balance between pipeline inlet and outlet: ; The expression for linear flow rate and cold loss is: ; Pipe cooling capacity and flow rate matching range: ; Global cooling balance of non-source nodes: ; In the formula: , These represent the cooling capacity at the pipe inlet and outlet, respectively. This refers to the loss of cooling energy during pipeline transportation. The cold loss coefficient per unit mass flow rate of the pipeline; , The upper and lower limits of the supply and return water temperature difference in the pipeline network design standard; ⑥ Constraints on cold energy injection at cold source nodes: ; Breakdown of total cooling capacity from cold source: ; In the formula: Cold source node Total cooling output during the period; To fix the amount of cold released by the cold storage device; It directly supplies cooling capacity to electric refrigeration units; ⑦ Global balance constraint of equivalent return water cooling capacity at the source end: ; This constraint preserves the physical logic of return water recirculation cooling in closed-loop regional cooling networks, and avoids nonlinear multiplication and division operations caused by multi-node return water mixing temperature calculations. ⑧ Linear approximation constraint on power consumption of circulating water pumps in the pipeline network: ; In the formula: Total power consumption of water pumps in the entire area's cooling network; This refers to the unloaded base power of the water pump. For pipelines The power consumption coefficient of the water pump corresponding to the unit mass flow rate.

[0026] Step S3: Construct a dynamic model for temperature safety of multiple types of cooling loads, distinguish between two types of cooling demand: cooling loads directly supplied by the pipeline network and low-temperature loads directly supplied by independent units. Based on the thermal inertia of the load building or storage space, establish a time-by-time temperature evolution equation, and introduce a quantifier to measure the degree of degradation of cooling service quality when the temperature exceeds the limit.

[0027] The multi-type cooling load temperature safety dynamic model consists of three parts: load temperature dynamic evolution equation, type-specific cooling capacity supply equation, and temperature over-limit safety constraints. ① Dynamic update equation for internal temperature of the load: ; In the formula: To optimize the scheduling time step size; Equivalent heat capacity of the building or storage space; Equivalent thermal resistance of the building envelope; For nodes Time period Total cooling capacity; Internal thermal disturbances caused by indoor equipment, personnel, and lighting; ② Expressions for cooling capacity supply by load type: Total cooling capacity for piped cooling loads: ; Total cooling capacity for independent direct supply of low-temperature load: ; The coupling relationship between cooling capacity and power consumption of low-temperature refrigeration units: ; Unit operating limits: ; In the formula: For mobile cold storage vehicles to reach nodes Release cold energy; The coefficient of performance (COP) for cryogenic refrigeration units; This refers to the power consumption of the cryogenic unit; , These are the rated cooling capacity and rated input electrical power of the cryogenic unit, respectively. ③ Temperature limits and quantitative constraints: ; In the formula: , The upper and lower safety boundaries of the allowable operating temperature under load; The temperature limit is used to quantify the degree of degradation of cooling load service quality, which is different from the traditional method of evaluating cold side toughness by relying solely on load shedding.

[0028] Step S4: Construct a collaborative scheduling model for fixed cold source equipment and mobile cold storage resources, and establish spatiotemporal location integer constraints for mobile cold storage equipment, charging and discharging state constraints for sub-nodes, dynamic update constraints for on-board cold storage medium temperature, and upper limit constraints for heat exchange power.

[0029] The collaborative model of cold source and mobile cold storage includes the spatiotemporal uniqueness constraint of the mobile cold storage vehicle, the charging and discharging location constraint of each node, the temperature dynamics of the on-board cold storage medium, and the upper limit constraint of the cooling and heat exchange power. ①Unique temporal and spatial constraint for a single mobile refrigeration vehicle: ; In the formula: An integer variable, representing Refrigerated trucks docked at pipeline node ; An integer variable representing a vehicle. Time period In the pipe Transfer within a designated area; ②Constraints on the location of charging and discharging cooling: Cooling can only be completed at the cold source node: ; Cooling and replenishment are only permitted at load nodes: ; In the formula: , These are state variables, representing the vehicles respectively. Time period It is in the process of charging and discharging cooling. ③ Dynamic update equation for the temperature of the on-board cooling medium: ; Upper limit constraint on the temperature of the on-board medium: ; In the formula: For vehicle-mounted cooling medium period temperature; For the environmental heat loss and temperature rise during vehicle idling and transfer processes; For source-end cooling and temperature reduction; This is the cooling and heating term at the load end; This refers to the maximum permissible temperature of the vehicle-mounted cooling medium. ④ Power constraints of mobile cold storage vehicle for cooling and heat exchange: ; In the formula: The equivalent heat transfer coefficient of the vehicle-mounted heat exchanger; This refers to the return water temperature at the load node.

[0030] Step S5: Construct a power-cooling joint balance constraint that couples the power distribution network operation constraints, distributed power output model, mobile energy storage charging and discharging constraints, and the power load of the district cooling system, and uniformly include all power demands of electric chillers, fixed cold storage charging and discharging, pipeline circulating water pumps, and mobile energy storage charging and discharging.

[0031] The power-side support and power-cold coupling constraint model includes distribution network power flow balance constraints, linearized voltage constraints, photovoltaic high-temperature derating output model, gas turbine power generation energy consumption model, mobile energy storage state of charge update, and comprehensive power load coupling equations for all network nodes. ① Active and reactive power balance equations at distribution network nodes: ; ; In the formula: For distribution network nodes Set of adjacent nodes; , For the line Time period The tide of merit and inmerit; , For nodes Distributed power sources have both active and reactive power outputs. ② Distribution network linearization voltage drop constraint: ; ; In the formula: For nodes Time period Voltage amplitude; , The lines are respectively Resistance, reactance; It is a constant; For the line Run on / off state variables; ③ Distributed photovoltaic power generation derating model under extreme high temperatures: ; In the formula: Rated power of photovoltaic modules; Real-time irradiance; Standard test irradiance; Photovoltaic power temperature coefficient; Photovoltaic module operating temperature; Standard test temperature; ④ Coupling relationship between gas turbine power generation and natural gas consumption: ; In the formula: Gas turbine power generation efficiency; Unit natural gas consumption; Natural gas has a low calorific value; ⑤ Dynamic update equation for the state of charge of mobile energy storage vehicles: ; In the formula: Energy storage vehicle Time period State of charge; Rated capacity of energy storage vehicle; , These are the charging and discharging efficiencies, respectively. , This refers to charging and discharging power. ⑥ Total electrical load equation for the electric-cold coupling node: ; In the formula: Represents a node Basic electrical load; Indicates the charging power of the mobile energy storage vehicle; This indicates the power consumption for charging a stationary cold storage device. Indicates the input electrical power of the electric chiller; This indicates the power consumption of the circulating water pumps in the district cooling network.

[0032] Step S6: Construct an optimization objective function that prioritizes resilience while also considering operating costs. The core optimization objective is to maximize the overall system resilience index, with system operating costs as a secondary constraint. The solution is obtained using mixed-integer linear programming or mixed-integer quadratic programming algorithms. The output is a coordinated scheduling scheme for the electric-cooling system under extreme high-temperature fault conditions.

[0033] The system's overall resilience index is obtained by weighted coupling of the electrical load service continuity rate and the cooling load temperature safety rate; extreme high-temperature fault conditions include complex disturbance scenarios such as ambient overheating, a surge in cooling load, high-temperature output reduction of photovoltaic modules, overheating faults in distribution network lines, and attenuation of the transport capacity of the regional cooling trunk pipeline.

[0034] The optimization objective function adopts a weighted single-objective form, with maximizing resilience as the primary objective and maximizing operating cost as a secondary objective. ; In the formula: To optimize the target value for the system as a whole; System overall resilience index; Total system operating cost over the entire scheduling cycle; The economic cost weighting coefficient is lowered for extreme high-temperature failure conditions. The numerical values ​​prioritize ensuring the system's cooling and power supply resilience. The overall system resilience index is a weighted coupling of resilience indices from the power side and the cooling load side: ; In the formula: , These are the resilience weights for the power side and the cold load side, respectively. Electrical load service continuity rate; Cooling load temperature safety rate.

[0035] The complete formulas for calculating the electrical load service continuity rate and the cooling load temperature safety rate are as follows: ① Electricity load service continuity rate: ; In the formula: For nodes Weighting of electrical load importance; For time period node Load shedding power; the numerator is the weighted total actual power supply, and the denominator is the weighted total power demand. ② Cooling load temperature safety rate: ; In the formula: Weighting based on the importance of cooling load; The variable is a marker, representing the effective load temperature safety constraint during this period; the numerator is the weighted cumulative temperature limit exceedance integral over the entire period, and the denominator is the weighted total safety capacity of the allowable load temperature range.

[0036] Total system operating cost It consists of five categories of sub-costs: ; Cost expressions for each item: ① Cost of electricity purchased from the main grid: ; ② Gas turbine fuel consumption cost: ; ③ Electricity costs for refrigeration and cold storage equipment: ; ④ Operating electricity for district cooling network water pumps: ; ⑤ Transportation costs for mobile energy storage and mobile cold storage vehicles: ; In the formula: Time-of-use electricity purchase price; System power interaction with the upstream main network; Unit price of natural gas; Dispatch cost per unit time period for a single mobile vehicle; vehicle Time period Variables in transition state.

[0037] The collaborative scheduling scheme output by step S6 includes all the operational decision variables of the electric-cooling system, specifically including: the cooling output of the electric chiller in each time period, the charging and discharging of the fixed cold storage device, the mass flow rate of each pipeline in the district cooling system, the water supply temperature of all network nodes, the indoor temperature of each type of cooling load in each time period, the spatiotemporal location and charging and discharging power of the mobile cold storage vehicle in all time periods, the charging and discharging power of the mobile energy storage vehicle, the power generation output of the gas turbine, the power absorption of distributed photovoltaic power, and the load shedding of each node in the distribution network.

[0038] One specific implementation method is as follows: This embodiment selects a 24-hour extreme high-temperature fault scenario as the example, and uses an improved IEEE 33-node power distribution system and a multi-regional cooling network to form an electric-cold coupling test system. The power distribution side is equipped with gas turbines, distributed photovoltaics, and mobile energy storage vehicles; the cooling side includes two district cooling networks, one set of direct-supply low-temperature loads, fixed cold storage resources, and mobile cold storage vehicles. The first cold network mainly serves critical cooling loads such as data centers, industrial parks, and hospitals; the second cold network mainly serves various types of loads such as large commercial / industrial buildings, urban transportation and commercial complexes, hotels, Grade A office buildings, and residential areas. In addition to the district cooling network loads, the system also sets up three types of direct-supply low-temperature loads: Cold Chain 1, Cold Chain 2, and pharmaceutical cold storage. Temperature constraints are in effect 24 hours a day and are jointly guaranteed by the direct-supply low-temperature refrigeration units and mobile cold storage vehicles.

[0039] The service periods and temperature constraints for various cooling loads are shown in Table 1: Table 1. Cold-side load type, service period and temperature constraints

[0040] This embodiment presents a scenario of a combined electrical-cold coupling fault induced by extreme high temperatures. The scheduling cycle is 24 hours, with the ambient temperature gradually increasing during the day and reaching 42°C around noon. The extreme high temperatures lead to a rapid increase in regional cooling demand, increasing the pressure on the cold-side energy supply; on the other hand, they cause the temperature of photovoltaic modules to rise, reducing the available output of distributed photovoltaic systems. Simultaneously, due to the high-temperature, heavy-load operation of the upstream power grid, the supporting power that the distribution network can obtain from the main grid is reduced, further exacerbating the source-load imbalance in the local area.

[0041] like Figure 2 As shown, the electric-cold coupling test system consists of an improved distribution network, a multi-regional cooling network, distributed power sources, mobile energy storage vehicles, mobile cold storage vehicles, and various types of cooling loads. Figure 3 As shown, under extreme high temperature conditions, the actual usable output of photovoltaic power is lower than the theoretical output under the condition of considering only irradiance. This indicates that extreme high temperatures not only increase the demand for cooling load, but also weaken the power supply support capacity of local renewable energy and the upstream power grid for the fault area.

[0042] Under the combined effects of sustained high temperatures, increased cooling load, and reduced power supply from the upstream grid, local areas of the distribution network experienced source-load imbalance. In this embodiment, during time period 10, lines B5-B6 experienced a fault due to equipment overheating and were isolated, causing a disconnect between downstream areas and the main grid. After the fault isolation, areas connected to the main grid could still maintain power supply through the upstream grid, gas turbines, and distributed photovoltaic systems; the isolated areas relied primarily on gas turbines, photovoltaic systems, mobile energy storage vehicles, and an islanded microgrid composed of fixed cold storage resources and mobile cold storage vehicles for emergency support. Time periods 10-18 constituted the emergency recovery phase, and after time period 19, the lines were restored, and the system entered the power supply capacity recovery phase.

[0043] To demonstrate the impact of extreme high temperatures on the quality of cold network delivery, this embodiment sets up a second set of regional cold network main pipelines C10-C13 where delivery capacity decreases and cold loss increases during periods 10-18. The maximum delivery flow rate drops to 60% of normal, and the pipeline cold loss coefficient increases to 1.7 times the normal value. After period 19, as the main feeder resumes power supply and the system's operating status improves, the cold network delivery capacity returns to normal. This setup describes the combined effects of increased pipeline heat exchange losses, decreased pump delivery margin, and reduced available cooling capacity at distant locations under high-temperature conditions.

[0044] After optimization using the method of this invention, the comprehensive resilience evaluation results of the system are shown in Table 2. Table 2 shows that the system's electrical load service continuity rate is 0.9731, the cooling load temperature safety rate is 0.9947, and the comprehensive resilience index is 0.9839; the total load shedding is 3426.47 kWh, the total temperature exceedance is 7.5365 °C·h, and the minimum cold storage margin is 0.4668. These results indicate that even under the combined effects of main feeder tripping and reduced cold network transmission capacity, the system can still maintain a high level of service on both the electricity and cooling sides through the coordinated scheduling of gas turbines, photovoltaics, mobile energy storage vehicles, chillers, fixed cold storage resources, and mobile cold storage vehicles.

[0045] Table 2. System comprehensive toughness evaluation results using the method of the present invention.

[0046] The results of the electrical side optimization are as follows Figure 4 and Figure 5 As shown. By Figure 4 and Figure 5 It can be seen that after the thermal tripping of the B5-B6 main feeder in period 10, the power supply capacity of the downstream area decreased, and the system entered the fault emergency recovery phase. During periods 10-18, due to the combined effects of main feeder blocking, photovoltaic high-temperature derating, and increased demand for cooling electricity, the system experienced a certain scale of load shedding. After the lines were restored in period 19, the load shedding quickly dropped to zero, indicating that the power supply capacity on the power side was restored. Furthermore, the service results of different levels of loads show that the service rate of critical loads reached 1.0000, the service rate of high-priority loads reached a relatively high level, and ordinary loads underwent a certain service degradation when resources were scarce, indicating that differentiated load weights can guide the system to prioritize the protection of critical nodes.

[0047] The results of temperature changes under cooling load are as follows Figures 7 to 9 As shown. By Figure 7 It can be seen that the C6 data center and C8 hospital maintained temperatures near or below the upper limit throughout the entire service period, with no significant temperature exceedances. Although the C7 industrial park experienced temperatures exceeding the set upper limit during some non-service periods, its temperature constraints only apply during production operation and therefore are not included in the temperature exceedance evaluation. Figure 8 It can be seen that the C12 urban transportation and commercial complex maintained a generally safe temperature range during operating hours. The C14 hotel and C16 residential area, being located relatively far from the second cooling network and subject to comfort loads, experienced some temperature exceedances in the later stages of the malfunction. Figure 9 It can be seen that cold chain 1, cold chain 2 and pharmaceutical cold storage are jointly guaranteed by direct-supply low-temperature refrigeration units and mobile cold storage vehicles. Among them, pharmaceutical cold storage is always maintained within the allowable temperature range due to its high temperature safety weight.

[0048] Table 3 shows the temperature safety and cooling results for different cooling loads. As can be seen from Table 3, the C6 data center, C7 industrial park, C8 hospital, C11 large commercial / industrial building, C12 urban transportation and commercial complex, C15 Grade A office building, and pharmaceutical cold storage can all maintain temperatures within the safe range or have virtually no temperature exceedances; the C14 hotel, C16 residential area, and cold chain 1 and cold chain 2 showed limited temperature exceedances. These results indicate that the method of this invention can prioritize critical cooling loads under conditions of limited cold-side resources, and mainly limit temperature degradation to comfort loads and some low-temperature loads.

[0049] Table 3. Temperature safety and cooling results under different cooling loads

[0050] The optimization results for the cold source side and mobile resources are as follows: Figures 10 to 14 As shown. By Figures 10 to 12 It can be seen that before the failure, the two sets of fixed cold storage resources maintained a high cold storage state through pre-cold storage. After the failure, they entered the cold release support stage, reducing the immediate power demand of the chiller and ensuring the load of the cold network. Figure 13 and Figure 14 It is known that mobile energy storage vehicles and mobile cold storage vehicles can transfer and provide electrical or cold energy support between different nodes according to the power supply pressure and cold load temperature safety requirements during the fault stage, thereby alleviating local power shortages.

[0051] Comparison results of different schemes show that, compared with a unified cooling load model, the method of this invention can significantly reduce the total temperature exceedance; compared with the operating condition of disabling mobile energy storage vehicles and mobile cold storage vehicles, the method of this invention can reduce the total load shedding and temperature exceedance. These results demonstrate that multi-type cooling load temperature safety modeling and collaborative support of mobile electric-cooling resources can effectively improve the resilience of electric-cooling systems under extreme high-temperature faults.

[0052] In summary, this embodiment demonstrates that the present invention can effectively characterize the decline in the delivery capacity of the district cooling network, the temperature safety degradation of multiple types of cooling loads, and the collaborative support process of electric and cooling resources under extreme high-temperature fault scenarios. Compared with solutions that ignore the physical constraints of the cooling network, ignore the temperature dynamics of multiple types of cooling loads, or disable mobile electric and cooling resources, the present invention can improve the overall resilience of the system, reduce the risk of critical cooling load temperature exceeding limits, and take into account the economic efficiency of system operation while ensuring resilience.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the resilience of an electric-cooling system considering district cooling, characterized in that, Includes the following steps: Step S1: Construct a data input model for the electric-cooling coupled system, and uniformly collect all basic operating parameters of the power distribution network, regional cooling pipe network, cooling source equipment, various types of cooling loads, mobile energy storage and cold storage resources, and environmental disturbances; Step S2: Construct a semi-physical linearized model of the district cooling network to fully characterize the physical characteristics of hydraulic transmission of the pipeline, temperature change of chilled water along the pipeline, heat exchange at the end, cooling loss of pipeline, and power consumption of circulating water pump. Use the source-end equivalent return water cooling balance method to eliminate the nonlinear constraints of the return water mixing link. Step S3: Construct a dynamic model for temperature safety of multiple types of cooling loads, distinguish between two types of cooling demand: cooling loads directly supplied by the pipeline network and low-temperature loads directly supplied by independent units. Based on the thermal inertia of the load building or storage space, establish a time-by-time temperature evolution equation and introduce a quantification of the degree of degradation of cooling service quality when the temperature exceeds the limit. Step S4: Construct a collaborative scheduling model for fixed cold source equipment and mobile cold storage resources, and establish spatiotemporal location integer constraints for mobile cold storage equipment, charging and discharging state constraints for sub-nodes, dynamic update constraints for on-board cold storage medium temperature, and upper limit constraints for heat exchange power. Step S5: Construct a power-cooling joint balance constraint that couples the power distribution network operation constraints, distributed power output model, mobile energy storage charging and discharging constraints, and the electricity load of the district cooling system, and uniformly include all electricity demands of electric chillers, fixed cold storage charging and discharging, pipeline circulating water pumps, and mobile energy storage charging and discharging. Step S6: Construct an optimization objective function that prioritizes resilience while also considering operating costs. The core optimization objective is to maximize the overall system resilience index, with system operating costs as a secondary constraint. The solution is obtained using mixed-integer linear programming or mixed-integer quadratic programming algorithms. The output is a coordinated scheduling scheme for the electric-cooling system under extreme high-temperature fault conditions.

2. The method for optimizing the resilience of an electric-cooling system considering district cooling as described in claim 1, characterized in that: In step S1, the basic operating parameters collected specifically include: distribution network node set, regional cooling pipe network node set, cold source node set, cooling load node set, cooling pipe set, time-of-use electricity load demand, time-varying cooling load demand, ambient temperature for each time period, light intensity, power line impedance parameters, cooling pipe parameters, rated operating parameters of electric chillers and fixed cold storage equipment, capacity and charging / discharging efficiency parameters of mobile energy storage vehicles, heat exchange medium parameters of mobile cold storage vehicles, and upper and lower limits of allowable temperature for various cooling loads. Define the distribution network node identifier as The district cooling network node is identified as follows: District cooling network node set Divided into cold source node sets Set of cooling load nodes intermediate transmission node set The cooling pipe assembly is denoted as Any non-cold source node within a radiant district cooling network The upstream parent node is denoted as The set of downstream child nodes is denoted as ; The various types of cooling loads are divided into two categories: ①Pipeline cooling load: Relying on the regional cooling network to uniformly transport chilled water for cooling, including commercial complexes, office buildings, residential areas, data centers, hospitals, and industrial parks; ② Independent direct supply of low-temperature load: Equipped with a dedicated low-temperature refrigeration unit for independent cooling, with strict temperature constraints, including cold chain storage warehouses and pharmaceutical cold storage warehouses.

3. The method for optimizing the resilience of an electric-cooling system considering district cooling, as described in claim 2, is characterized in that: In step S2, the semi-physical linearization model of the district cooling network contains 8 sets of linear constraints. The complete explanation of the constraint expressions and parameters for each set is as follows: ① Water supply network quality conservation and pipeline flow constraints: Non-source node traffic conservation: ； Mass flow rate of intermediate transmission nodes without local heat exchange branches: ; Pipeline flow rate limit constraint: ; In the formula: for Time-of-use pipeline Internal water supply mass flow rate; For load nodes Mass flow rate of heat exchange branch; For pipelines Maximum permissible mass flow rate; ② Pipeline water supply temperature transmission constraints: ； Operating temperature range of water supply at pipeline nodes: ; The outlet water temperature control range of the cold source equipment: ; In the formula: For nodes Time period Water supply temperature; for Ambient temperature during the period; The pipeline temperature retention coefficient is pre-calibrated based on pipeline length, insulation performance, and ambient heat exchange conditions. ③ Linearization constraints on the load-side heat transfer process: Original nonlinear heat transfer formula: ; Introducing auxiliary variables After performing product-free linearization, the transformed result is: ; For variables The McCormick linear outer envelope method is used to eliminate the nonlinearity caused by the multiplication of flow rate and temperature difference; In the formula: For nodes Actual cooling capacity of the regional pipe network; The specific heat capacity at constant pressure of the refrigerant; The temperature difference between the supply and return water of the load-side heat exchanger; ④ Limitations on the heat exchange capacity of the terminal heat exchanger: ； In the formula: For nodes Equivalent heat transfer coefficient of terminal heat exchanger; The internal temperature of the building or storage space corresponding to the load; For nodes Time period Total cooling load demand; ⑤ Constraints on cold energy transfer and loss in pipelines: Cold energy balance between pipeline inlet and outlet: ; The expression for linear flow rate and cold loss is: ; Pipe cooling capacity and flow rate matching range: ; Global cooling balance of non-source nodes: ; In the formula: , These represent the cooling capacity at the pipe inlet and outlet, respectively. This refers to the loss of cooling energy during pipeline transportation. The cold loss coefficient per unit mass flow rate of the pipeline; , The upper and lower limits of the supply and return water temperature difference in the pipeline network design standard; ⑥ Constraints on cold energy injection at cold source nodes: ； Breakdown of total cooling capacity from cold source: ; In the formula: Cold source node Total cooling output during the period; To fix the amount of cold released by the cold storage device; It directly supplies cooling capacity to electric refrigeration units; ⑦ Global balance constraint of equivalent return water cooling capacity at the source end: ； This constraint preserves the physical logic of return water recirculation cooling in closed-loop regional cooling networks, and avoids nonlinear multiplication and division operations caused by multi-node return water mixing temperature calculations. ⑧ Linear approximation constraint on power consumption of circulating water pumps in the pipeline network: ； In the formula: Total power consumption of water pumps in the entire area's cooling network; This refers to the unloaded base power of the water pump. For pipelines The power consumption coefficient of the water pump corresponding to the unit mass flow rate.

4. The method for optimizing the resilience of an electric-cooling system considering district cooling, as described in claim 3, is characterized in that: In step S3, the multi-type cooling load temperature safety dynamic model includes three parts: load temperature dynamic evolution equation, type-specific cooling capacity supply equation, and temperature over-limit safety constraints. ① Dynamic update equation for internal temperature of the load: ； In the formula: To optimize the scheduling time step size; Equivalent heat capacity of the building or storage space; Equivalent thermal resistance of the building envelope; For nodes Time period Total cooling capacity; Internal thermal disturbances caused by indoor equipment, personnel, and lighting; ② Expressions for cooling capacity supply by load type: Total cooling capacity for piped cooling loads: ; Total cooling capacity for independent direct supply of low-temperature load: ; The coupling relationship between cooling capacity and power consumption of low-temperature refrigeration units: ; Unit operating limits: ; In the formula: For mobile cold storage vehicles to reach nodes Release cold energy; The coefficient of performance (COP) for cryogenic refrigeration units; This refers to the power consumption of the cryogenic unit; , These are the rated cooling capacity and rated input electrical power of the cryogenic unit, respectively. ③ Temperature limits and quantitative constraints: ； In the formula: , The upper and lower safety boundaries of the allowable operating temperature under load; The temperature limit is used to quantify the degree of degradation of cooling load service quality, which is different from the traditional method of evaluating cold side toughness by relying solely on load shedding.

5. The method for optimizing the resilience of an electric-cooling system considering district cooling as described in claim 4, characterized in that: In step S4, the collaborative model of cold source and mobile cold storage includes the spatiotemporal uniqueness constraint of the mobile cold storage vehicle, the charging and discharging location constraint of the sub-nodes, the temperature dynamics of the on-board cold storage medium, and the upper limit constraint of the cooling and heat exchange power. ①Unique temporal and spatial constraint for a single mobile refrigeration vehicle: ； In the formula: An integer variable, representing Refrigerated trucks docked at pipeline node ; An integer variable representing a vehicle. Time period In the pipe Transfer within a designated area; ②Constraints on the location of charging and discharging cooling: Cooling can only be completed at the cold source node: ; Cooling and replenishment are only permitted at load nodes: ; In the formula: , These are state variables, representing the vehicles respectively. Time period It is in the process of charging and discharging cooling. ③ Dynamic update equation for the temperature of the on-board cooling medium: ； Upper limit constraint on the temperature of the on-board medium: ; In the formula: For vehicle-mounted cooling medium period temperature; For the environmental heat loss and temperature rise during vehicle idling and transfer processes; For source-end cooling and temperature reduction; This is the cooling and heating term at the load end; This refers to the maximum permissible temperature of the vehicle-mounted cooling medium. ④ Power constraints of mobile cold storage vehicle for cooling and heat exchange: ； In the formula: The equivalent heat transfer coefficient of the vehicle-mounted heat exchanger; This refers to the return water temperature at the load node.

6. The method for optimizing the resilience of an electric-cooling system considering district cooling, as described in claim 5, is characterized in that: In step S5, the electric-side support and electric-cold coupling constraint model includes distribution network power flow balance constraints, linearized voltage constraints, photovoltaic high-temperature derating output model, gas turbine power generation energy consumption model, mobile energy storage state of charge update, and comprehensive power load coupling equations for all network nodes: ① Active and reactive power balance equations at distribution network nodes: ； ； In the formula: For distribution network nodes Set of adjacent nodes; , For the line Time period The tide of merit and inmerit; , For nodes Distributed power sources have both active and reactive power outputs. ② Distribution network linearization voltage drop constraint: ； ； In the formula: For nodes Time period Voltage amplitude; , The lines are respectively Resistance, reactance; It is a constant; For the line Run on / off state variables; ③ Distributed photovoltaic power generation derating model under extreme high temperatures: ； In the formula: Rated power of photovoltaic modules; Real-time irradiance; Standard test irradiance; Photovoltaic power temperature coefficient; Photovoltaic module operating temperature; Standard test temperature; ④ Coupling relationship between gas turbine power generation and natural gas consumption: ； In the formula: Gas turbine power generation efficiency; Unit natural gas consumption; Natural gas has a low calorific value; ⑤ Dynamic update equation for the state of charge of mobile energy storage vehicles: ； In the formula: Energy storage vehicle Time period State of charge; Rated capacity of energy storage vehicle; , These are the charging and discharging efficiencies, respectively. , This refers to charging and discharging power. ⑥ Total electrical load equation for the electric-cold coupling node: ； In the formula: Represents a node Basic electrical load; Indicates the charging power of the mobile energy storage vehicle; This indicates the power consumption for charging a stationary cold storage device. Indicates the input electrical power of the electric chiller; This indicates the power consumption of the circulating water pumps in the district cooling network.

7. The method for optimizing the resilience of an electric-cooling system considering district cooling as described in claim 6, characterized in that: In step S6, the objective function is optimized using a weighted single-objective form that prioritizes resilience maximization and secondarily considers operating cost: ； In the formula: To optimize the target value for the system as a whole; System overall resilience index; Total system operating cost over the entire scheduling cycle; The economic cost weighting coefficient is lowered for extreme high-temperature failure conditions. The numerical values ​​prioritize ensuring the system's cooling and power supply resilience. The overall system resilience index is a weighted coupling of resilience indices from the power side and the cooling load side: ； In the formula: , These are the resilience weights for the power side and the cold load side, respectively. Electrical load service continuity rate; Cooling load temperature safety rate.

8. The method for optimizing the resilience of an electric-cooling system considering district cooling as described in claim 7, characterized in that: The complete formulas for calculating the electrical load service continuity rate and the cooling load temperature safety rate are as follows: ① Electricity load service continuity rate: ； In the formula: For nodes Weighting of electrical load importance; For time period node Shearing power; ② Cooling load temperature safety rate: ； In the formula: Weighting based on the importance of cooling load; This is a flag variable, representing the effective load temperature safety constraints during this period.

9. A method for optimizing the resilience of an electric-cooling system considering district cooling, as described in claim 8, characterized in that: Total system operating cost It consists of five categories of sub-costs: ； Cost expressions for each item: ① Cost of electricity purchased from the main grid: ; ② Gas turbine fuel consumption cost: ; ③ Electricity costs for refrigeration and cold storage equipment: ; ④ Operating electricity for district cooling network water pumps: ; ⑤ Transportation costs for mobile energy storage and mobile cold storage vehicles: ; In the formula: Time-of-use electricity purchase price; System power interaction with the upstream main network; Unit price of natural gas; Dispatch cost per unit time period for a single mobile vehicle; vehicle Time period Variables in transition state.

10. A method for optimizing the resilience of an electric-cooling system considering district cooling, as described in claim 9, characterized in that: The collaborative scheduling scheme output by step S6 includes all the operational decision variables of the electric-cooling system, specifically including: the cooling output of the electric chiller in each time period, the charging and discharging of the fixed cold storage device, the mass flow rate of each pipeline in the district cooling system, the water supply temperature of all network nodes, the indoor temperature of each type of cooling load in each time period, the spatiotemporal location and charging and discharging power of the mobile cold storage vehicle in all time periods, the charging and discharging power of the mobile energy storage vehicle, the power generation output of the gas turbine, the power absorption of distributed photovoltaic power, and the load shedding of each node in the distribution network.

Images