Energy storage combined configuration method and system based on composite rural power grid disaster susceptibility grading

Abstract

The application provides a kind of based on composite rural power grid disaster-prone classification energy storage combined configuration method and system, the method is aimed at composite rural power grid, first construct the fixed-mobile energy storage combined configuration model with the minimum system comprehensive power supply cost as target, the model considers the influence of multiple disaster scenarios on rural power line, road and load change, fixed energy storage and mobile energy storage are configured through disaster-prone classification, then the model is solved, and the fixed-mobile energy storage combined configuration scheme of composite rural power grid is obtained.The application can accurately identify weak nodes of power grid relying on disaster-prone classification, realize the differentiated layout of fixed energy storage and mobile energy storage two types of equipment, minimize the system comprehensive power supply cost on the premise of meeting the global power supply safety bottom line, and improve the whole life cycle utilization efficiency of energy storage assets.

Description

Technical Field

[0001] This invention belongs to the field of power system planning technology, specifically relating to a method and system for the joint configuration of energy storage based on the disaster susceptibility classification of composite rural power grids. Background Technology

[0002] The composite rural power grid is a rural distribution network system that integrates the power grid with the rural road network and covers diverse and characteristic agricultural loads. It possesses core characteristics such as bidirectional power-road coupling, differentiated load types, and complex and diverse operating scenarios. Compared to urban distribution networks, the composite rural power grid has a dispersed network layout, lower infrastructure disaster resistance standards, and a deep integration of power grid supply links with rural roads. The road network's accessibility directly affects the efficiency of power grid repair and emergency resource transfer. Furthermore, its coverage area includes both basic residential electricity consumption and various agricultural production loads. Different loads exhibit significant differences in their tolerance for power supply continuity and quality. Conventional distribution network planning theories are insufficient to adapt to its complex operating characteristics of power-traffic coupling and the coexistence of multiple types of agricultural loads. Therefore, targeted energy storage planning is urgently needed to adapt to its unique network structure and load characteristics.

[0003] Currently, research on the optimal configuration of energy storage in composite rural power grids often fails to classify disaster severity levels, leading to significant resource misallocation issues in this energy storage planning model. Insufficient energy storage capacity and equipment quantity in highly vulnerable disaster areas cannot support the load under extreme conditions; conversely, excessive deployment of energy storage equipment in low-risk areas results in continuous redundant costs related to equipment purchase, maintenance, and idle losses.

[0004] Furthermore, existing disaster economic loss assessments typically rely solely on power outage losses, which has significant limitations: Rural agricultural production is heavily dependent on electricity supply; power outages not only incur basic electricity loss costs but also trigger irreversible production losses in livestock and crop farming. Different agricultural sectors have vastly different requirements for outage duration and power supply reliability thresholds, and the mechanisms of yield reduction and crop / livestock damage caused by power outages differ fundamentally across various production scenarios. Therefore, these methods cannot accurately reflect the comprehensive economic costs of complex rural power grid outages. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a method and system for the combined configuration of energy storage based on the disaster susceptibility classification of composite rural power grids.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for the combined configuration of energy storage based on the disaster vulnerability classification of composite rural power grids, comprising:

[0008] S1. For complex rural power grids, a fixed-mobile energy storage joint configuration model is constructed with the goal of minimizing the overall power supply cost of the system. This model configures fixed and mobile energy storage based on the disaster vulnerability classification of each power grid node under multiple disaster scenarios. The disaster vulnerability classification is based on the vulnerability of power distribution lines, roads, and fixed energy storage at the power grid node and the severity of load loss caused by disasters.

[0009] S2. Solve the fixed-mobile energy storage joint configuration model to obtain the fixed-mobile energy storage joint configuration scheme of the composite rural power grid.

[0010] The comprehensive power supply cost includes the configuration cost of stationary and mobile energy storage, the cost of purchasing electricity from the upper-level grid, and the cost of load loss.

[0011] The constraints of the fixed-mobile energy storage joint configuration model include disaster vulnerability classification constraints, energy storage configuration and operation constraints.

[0012] The objective function of the fixed-mobile energy storage joint configuration model includes:

[0013] ;

[0014] ;

[0015] ;

[0016] In the above formula, , , These include the configuration costs of stationary and mobile energy storage, the cost of purchasing electricity from the upper-level grid, and the cost of load loss. , Respectively, power grid nodes The number of fixed and mobile energy storage units configured at the location. , These are the unit prices for stationary energy storage and mobile energy storage, respectively. For the discount rate, For the investment period, , Scenes Downlink Node During the period The upstream power grid supply capacity and other basic loads, The duration of a unit of time period Scenes within one year The expected duration of days, For time period The electricity price is calculated for scenarios including conventional scenarios, flood scenarios, ice storm scenarios, and high-temperature disaster scenarios.

[0017] The vulnerability classification constraints include:

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] In the above formula, For power grid nodes The overall vulnerability of power distribution lines to disasters. For the scene Downlink Node The vulnerability index of power distribution lines to disasters is used to characterize the scenario. The proportion affecting the transmission capacity of the line For the scene The probability of its occurrence, For power grid nodes Overall road vulnerability For the scene off the road The vulnerability index, which is used to characterize a scenario The proportion affecting road traffic. For power grid nodes With roads The coupling relationship variables, To connect power grid nodes The number of roads, For power grid nodes The overall vulnerability of fixed energy storage to disasters. For the scene Downlink Node The vulnerability index of the built-in stationary energy storage to disasters is used to characterize the scenario. The proportion affecting the storage capacity of stationary energy storage For power grid nodes The severity index of disaster load loss. , Respectively, power grid nodes Peak values ​​of uninterruptible load and interruptible load. , These are the load loss cost coefficients for uninterruptible loads and interruptible loads, respectively. For power grid nodes The vulnerability classification variable, when it takes the value 1, is the power grid node. For vulnerable nodes, take the power grid node value at time 0. As nodes less susceptible to disasters, For the large M constant, , , These are the critical vulnerability indices for power distribution lines, roads, and stationary energy storage, respectively. This is the critical severity index.

[0027] The energy storage configuration and operation constraints include configuration constraints for stationary energy storage and mobile energy storage, and operation constraints for stationary energy storage and mobile energy storage.

[0028] The configuration constraints for stationary and mobile energy storage include:

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] In the above formula, , Respectively, power grid nodes The configuration decision variables for fixed energy storage and mobile energy storage, For the large M constant, For power grid nodes The overall vulnerability of fixed energy storage to disasters. The critical vulnerability index for stationary energy storage. , Respectively, power grid nodes The number of fixed and mobile energy storage units configured at the location. , These refer to the capacities of a single stationary energy storage unit and a mobile energy storage unit, respectively. , Respectively, power grid nodes Peak values ​​of uninterruptible load and interruptible load. The duration of a unit of time period This represents the upper limit for the energy storage configuration ratio. For power grid nodes The vulnerability classification variable, when it takes the value 1, is the power grid node. For vulnerable nodes, take the power grid node value at time 0. These are nodes less susceptible to disasters.

[0035] The operational constraints of the stationary and mobile energy storage include:

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] ;

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] In the above formula, For the scene Downlink mobile energy storage from grid nodes to grid node The time consumed For roads The usual passage time, For power grid nodes , With roads Related variables, roads Connecting power grid nodes and hour Select 1 if the value is 1, otherwise select 0. For the scene Mobile energy storage on roads The passage time coefficient on the above For the scene Downlink Node to grid nodes Decision variables for deploying mobile energy storage This is the upper limit for the arrival time of mobile energy storage. For the scene Downlink Node to grid nodes Decision variables for deploying mobile energy storage For the scene Downlink Node to grid nodes The number of mobile energy storage devices deployed. This is a collection of disaster scenarios, including flood scenarios, ice storm scenarios, and high-temperature disaster scenarios. For power grid nodes Any node within the service coverage area, , Scenes Downlink Node The lower and upper limits of the energy storage capacity. For the scene Downlink Node The initial amount of energy stored. , Scenes Downlink Node Energy storage during the period Discharge and charge power, for At any time during the period, For the last period of a typical day, This represents the initial state of charge of the energy storage. , These are the lower and upper limits of the energy storage state of charge, respectively. , These are the upper limits of discharge and charging power for a single energy storage unit, respectively. This is a collection of common scenarios. , Scenes Downlink Node During the period Discharge and charge operating state variables.

[0053] The cost of load loss The calculation formula is as follows:

[0054] ;

[0055] In the above formula, , , The total losses were for fishponds, livestock farms, and greenhouse crops, respectively.

[0056] The constraints of the fixed-mobile energy storage joint configuration model also include multi-type load differential demand constraints, which include load demand constraints for fish ponds, livestock farms, and greenhouses.

[0057] The load demand constraints of the fishpond include:

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] In the above formula, , Scenes Downlink Node Oxygenation equipment in fish ponds during certain periods Operating power and operating status variables, The oxygen enrichment coefficient is the oxygenation coefficient. , These are saturated dissolved oxygen and critical oxygen content, respectively. For the scene Downlink Node fishpond during the period oxygen content For the scene Downlink Node fishpond during the period The net oxygen consumption rate, Energy consumed per unit of oxygen production. The duration of a unit of time period For the scene Downlink Node Oxygenation equipment in fish ponds during certain periods The loss state variable, For the large M constant, For power grid nodes The total output value of the fishpond For the scene Loss coefficient when fish are in oxygen-deficient ponds Scenes within one year The expected duration in days.

[0064] The load demand constraints of the livestock farm include:

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] In the above formula, For the scene Next period Variables of hot and cold operating status of temperature control facilities in livestock farms. For the large M constant, , Scenes Next period The outdoor temperature inside, and the target indoor temperature of livestock farms. , Scenes Downlink Node Temperature control facilities in livestock farms during specific time periods The cooling and heating load demand, , These are the cooling and heating power demand coefficients per unit temperature difference and per unit space, respectively. For power grid nodes The space for livestock breeding factories , Scenes Downlink Node Temperature control facilities in livestock farms during specific time periods The actual cooling and heating power, For the scene Downlink Node Temperature control facilities in livestock farms during specific time periods Temperature control effect variable, For power grid nodes The total output value of the livestock breeding factory For the scene Loss coefficient when temperature control in livestock farms fails to meet standards Scenes within one year The expected duration in days.

[0074] The load demand constraints of the greenhouse include:

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] ;

[0083] ;

[0084] ;

[0085] In the above formula, For the scene Next period The cold and hot operating status variables of the temperature control facilities in the greenhouse. For the large M constant, , Scenes Next period The outdoor temperature inside and the target temperature inside the greenhouse. , Scenes Downlink Node The greenhouse temperature control facilities during the specified time period The cooling and heating load demand, , These are the cooling and heating power demand coefficients per unit temperature difference and per unit space, respectively. For power grid nodes The space of the greenhouse, , Scenes Downlink Node The greenhouse temperature control facilities during the specified time period The actual cooling and heating power, For the scene Downlink Node The greenhouse temperature control facilities during the specified time period Temperature control effect variable, For the scene Downlink Node The energy demand for drainage in a typical greenhouse within a day For power grid nodes The overall vulnerability of fixed energy storage to disasters. For power grid nodes The area of ​​the greenhouse, The energy demand for drainage per unit area of ​​greenhouse. Scenes of flooding For the scene Downlink Node The drainage facilities of the greenhouse are in use during certain periods. The actual power For the scene Downlink Node The drainage facilities of the greenhouse are in use during certain periods. The variable of drainage effect, For power grid nodes The total output value of crops grown in greenhouses For the scene The comprehensive loss coefficient when greenhouse temperature control or drainage fails to meet standards. Scenes within one year The expected duration in days.

[0086] Secondly, the present invention provides an energy storage joint configuration system based on the disaster susceptibility classification of composite rural power grids, including a joint configuration model construction module and a joint configuration model solving module;

[0087] The joint configuration model construction module is used to construct a fixed-mobile energy storage joint configuration model for composite rural power grids with the goal of minimizing the overall power supply cost of the system. This model configures fixed energy storage and mobile energy storage based on the disaster vulnerability classification of each power grid node under multiple disaster scenarios. The disaster vulnerability classification is based on the vulnerability of power distribution lines, roads, and fixed energy storage at the power grid node and the severity of load loss caused by disasters.

[0088] The joint configuration model solving module is used to solve the fixed-mobile energy storage joint configuration model to obtain the fixed-mobile energy storage joint configuration scheme of the composite rural power grid.

[0089] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0090] 1. This invention presents a fixed-mobile energy storage joint configuration model based on a composite rural power grid disaster susceptibility classification method. This model considers the impact of multiple disaster scenarios on rural power grid lines, roads, and load changes: floods affect road traffic capacity, ice storms affect road traffic efficiency and power grid supply, and high-temperature disasters lead to a surge in electricity load, increasing the pressure on power supply facilities and reducing the available capacity of the energy storage system. Based on the vulnerability of power grid nodes and the severity of load loss caused by disasters under multiple disaster scenarios, the model classifies disaster susceptibility and configures fixed and mobile energy storage according to the disaster susceptibility classification. This method relies on disaster susceptibility classification to accurately identify weak nodes in the power grid, distinguish the degree of disaster impact on different power grid nodes, and achieve differentiated layout of fixed and mobile energy storage devices. Under the premise of meeting the bottom line of power supply safety across the entire region, it minimizes the overall power supply cost of the system, avoids ineffective investment, and improves the utilization efficiency of energy storage assets throughout their entire life cycle.

[0091] 2. The present invention provides a method for the combined configuration of energy storage based on the disaster susceptibility classification of composite agricultural power grids. This method considers the differentiated power supply requirements of the unique loads (fish ponds, livestock farms, and greenhouses) of composite agricultural power grids. It proposes a method for calculating the cost of losses caused by load failure by taking the total loss of fish ponds, livestock farms, and greenhouse crops as the cost of loss. This method is not limited to electricity itself, but combines the production and operation logic of various agricultural loads, and incorporates the production chain damage caused by power outages into the quantitative system, which is more in line with the comprehensive economic losses of rural production. Attached Figure Description

[0092] Figure 1 This is a schematic diagram of the composite agricultural network architecture in this invention.

[0093] Figure 2 This is a schematic diagram of the composite agricultural network and transportation network topology in Example 1.

[0094] Figure 3 This is a distribution map of the travel time coefficients for each road under different disaster scenarios in Example 1.

[0095] Figure 4 This is a distribution diagram of the transmission capacity coefficients of each power distribution line under different disaster scenarios in Example 1.

[0096] Figure 5 This is a structural block diagram of the system described in Example 2.

[0097] Figure 2 N1-N13 are the grid node numbers. Figure 3 R1-R17 are road numbers. Figure 4L1-L12 are the distribution line numbers. Detailed Implementation

[0098] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0099] Example 1:

[0100] The architecture of the composite agricultural network in the multi-scenario scenarios addressed in this embodiment is as follows: Figure 1 As shown, the topology of the composite agricultural network and transportation network is as follows: Figure 2 As shown, the specific process of the energy storage joint configuration method based on the disaster vulnerability classification of composite rural power grids includes:

[0101] 1. Construct a joint configuration model for fixed and mobile energy storage. The goal of this model is to minimize the overall power supply cost while ensuring reliable power supply under both normal and disaster scenarios. It considers the impact of various disaster scenarios on rural power grid lines, roads, and load changes, and configures fixed and mobile energy storage according to the disaster vulnerability classification. The objective function of the model is:

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] In the above formula, The scene index includes four regular scenes corresponding to each season, a flood scene, an ice storm scene, and a high-temperature disaster scene. , , These include the configuration costs of stationary and mobile energy storage, the cost of purchasing electricity from the upper-level grid, and the cost of load loss. , Respectively, power grid nodes The number of fixed and mobile energy storage units configured at the location. , These are the unit prices for stationary energy storage and mobile energy storage, respectively. For the discount rate, For the investment period, , Scenes Downlink Node During the period The upstream power grid supply capacity and other basic loads, The duration of a unit of time period Scenes within one year The expected duration of days, For time period Electricity price, , , The total losses were for fish ponds, livestock farms, and greenhouse crops, respectively.

[0107] The model's constraints include disaster vulnerability classification constraints, energy storage configuration and operation constraints, and differentiated demand constraints for various load types.

[0108] The vulnerability classification constraints include:

[0109] Vulnerability constraints of rural power grid nodes to disasters:

[0110] ;

[0111] ;

[0112] ;

[0113] In the above formula, For power grid nodes The overall vulnerability of power distribution lines to disasters. For the scene Downlink Node The vulnerability index of power distribution lines to disasters is used to characterize the scenario. The proportion affecting the transmission capacity of power distribution lines is typically taken as 0 in normal scenarios. For the scene The probability of its occurrence, For power grid nodes Overall road vulnerability For the scene off the road The vulnerability index, which is used to characterize a scenario The proportion affecting road capacity is typically set to 0 in normal scenarios. For power grid nodes With roads Coupled variables, road With grid nodes A value of 1 indicates connectivity, while a value of 0 indicates disconnection. To connect power grid nodes The number of roads, For power grid nodes The overall vulnerability of stationary energy storage to disasters is significant. When the power distribution area is located in a low-lying area, stationary energy storage is easily damaged during disasters, not only failing to provide power support but also suffering hardware losses. For the scene Downlink Node The vulnerability index of the built-in stationary energy storage to disasters is used to characterize the scenario. The proportion affecting the storage capacity of fixed energy storage is taken as 0 in normal scenarios.

[0114] Constraints on the severity of load loss at rural power grid nodes due to disasters:

[0115] ;

[0116] In the above formula, For power grid nodes The severity index of disaster load loss. , Respectively, power grid nodes Peak values ​​of uninterruptible load and interruptible load. , These are the load loss cost coefficients for uninterruptible loads and interruptible loads, respectively.

[0117] Disaster vulnerability classification constraints for rural power grid nodes:

[0118] ;

[0119] ;

[0120] ;

[0121] ;

[0122] In the above formula, For power grid nodes The vulnerability classification variable, where a value of 1 indicates a power grid node. For vulnerable nodes, a value of 0 indicates a power grid node. As nodes less susceptible to disasters, For the large M constant, , , These are the critical vulnerability indices for power distribution lines, roads, and stationary energy storage, respectively. This is the critical severity index.

[0123] This constraint indicates that when any vulnerability index or severity index exceeds the corresponding critical value, the power grid node is considered a vulnerable node.

[0124] The energy storage configuration and operation constraints include configuration constraints for stationary energy storage and mobile energy storage, and operation constraints for stationary energy storage and mobile energy storage. The configuration constraints for stationary energy storage and mobile energy storage include:

[0125] Fixed energy storage configuration point constraints:

[0126] ;

[0127] ;

[0128] In the above formula, For power grid nodes The configuration decision variable for fixed energy storage, where a value of 1 indicates a grid node Fixed energy storage is configured at the location, and a value of 0 indicates a grid node. No fixed energy storage is configured at the location. For the large M constant, For power grid nodes The overall vulnerability of fixed energy storage to disasters. The critical vulnerability index for stationary energy storage. For power grid nodes The number of fixed energy storage units configured at the location. The capacity of a single stationary energy storage unit.

[0129] This constraint stipulates that fixed energy storage should not be installed in areas where the overall vulnerability of grid nodes to disasters is too high.

[0130] Configuration point constraints for mobile energy storage:

[0131] ;

[0132] In the above formula, For power grid nodes The number of mobile energy storage units configured at the location. For power grid nodes The configuration decision variable for mobile energy storage, where a value of 1 indicates a grid node At least one mobile energy storage unit should be configured at each location; a value of 0 indicates a grid node. No mobile energy storage is installed at this location.

[0133] Maximum capacity for stationary and mobile energy storage:

[0134] ;

[0135] In the above formula, For the capacity of a single mobile energy storage unit, , Respectively, power grid nodes Peak values ​​of uninterruptible load and interruptible load. The duration of a unit of time period This is the upper limit for the energy storage configuration ratio.

[0136] In conventional scenarios, mobile energy storage operates economically as a fixed energy storage access point.

[0137] Minimum number of stationary and mobile energy storage configurations:

[0138] ;

[0139] This constraint ensures that vulnerable nodes are equipped with at least one energy storage facility.

[0140] The operational constraints of the stationary and mobile energy storage include:

[0141] Disaster scenario scheduling constraints for mobile energy storage, which only consider dispatching mobile energy storage to nodes other than the deployment point during a disaster, further include:

[0142] Travel time constraints for mobile energy storage:

[0143] ;

[0144] In the above formula, For the scene Downlink mobile energy storage from grid nodes to grid node The time consumed For roads The usual passage time, For power grid nodes , With roads Related variables, roads Connecting power grid nodes and hour Select 1 if the value is 1, otherwise select 0. For the scene Mobile energy storage on roads The travel time coefficient is a factor that increases the travel time of mobile energy storage when disasters obstruct road traffic. This is a collection of disaster scenarios, including flood scenarios, ice storm scenarios, and high-temperature disaster scenarios. For power grid nodes Any node within the service coverage area.

[0145] Mobile energy storage dispatch location constraints:

[0146] ;

[0147] ;

[0148] ;

[0149] In the above formula, For the scene Downlink Node to grid nodes Decision variables for dispatching mobile energy storage, grid nodes to grid nodes Use 1 when dispatching mobile energy storage, otherwise use 0. This is the upper limit for the arrival time of mobile energy storage. For the scene Downlink Node to grid nodes Decision variables for dispatching mobile energy storage, grid nodes to grid nodes Use 1 when dispatching mobile energy storage, otherwise use 0.

[0150] This constraint restricts the dispatch of mobile energy storage to only mobile energy storage deployment points, and prohibits two deployment points from dispatching mobile energy storage to each other simultaneously.

[0151] Mobile energy storage deployment quantity constraints:

[0152] ;

[0153] ;

[0154] In the above formula, For the scene Downlink Node to grid nodes The number of mobile energy storage devices deployed.

[0155] Energy storage capacity constraints in disaster scenarios:

[0156] ;

[0157] ;

[0158] ;

[0159] ;

[0160] ;

[0161] ;

[0162] In the above formula, , Scenes Downlink Node The lower and upper limits of the energy storage capacity. For the scene Downlink Node The initial amount of energy stored. , Scenes Downlink Node Energy storage during the period Discharge and charge power, for At any time during the period, For the last period of a typical day, This represents the initial state of charge of the energy storage. , These are the lower and upper limits of the energy storage state of charge, respectively. , These are the upper limits of discharge and charging power for a single energy storage unit, respectively.

[0163] Energy storage power constraints in typical scenarios:

[0164] ;

[0165] In the above formula, This is a collection of common scenarios.

[0166] Energy storage operation constraints in all scenarios:

[0167] ;

[0168] In the above formula, , Scenes Downlink Node During the period The discharge and charge operating state variables. During discharge... Take 1, Take 0, during charging Take 0, Take 1.

[0169] This constraint limits energy storage to operating in only one state at any given time.

[0170] Energy storage charging and discharging power constraints in all scenarios:

[0171] ;

[0172] .

[0173] The differentiated demand constraints for various load types primarily consider the specific electricity needs of three main rural sectors: aquaculture, factory farming, and greenhouses. Specifically: aquaculture requires highly reliable oxygen supply equipment, as interruptions could lead to fish mortality; factory farming's main electricity load is temperature control; and greenhouses' main electricity loads are temperature control and drainage loads during floods. These load types exhibit different characteristics under different disaster scenarios. The constraints include load demand constraints for fishponds, livestock farms, and greenhouses, as well as power constraints.

[0174] The load demand constraints of the fishpond include:

[0175] Oxygen content range constraints in fishponds:

[0176] ;

[0177] ;

[0178] ;

[0179] In the above formula, For the scene Downlink Node Oxygenation equipment in fish ponds during certain periods Operating power Scenes Downlink Node Oxygenation equipment in fish ponds during certain periods The operating status variable is set to 1, indicating that the fishpond oxygenation equipment is in operation, and 0, indicating that the fishpond oxygenation equipment is in shutdown. The oxygen enrichment coefficient is the oxygenation coefficient. , These are saturated dissolved oxygen and critical oxygen content, respectively. For the scene Downlink Node fishpond during the period The oxygen content.

[0180] Critical oxygen content constraints in fishponds:

[0181] ;

[0182] ;

[0183] In the above formula, For the scene Downlink Node fishpond during the period The net oxygen consumption rate, Energy consumed per unit of oxygen production. The duration of a unit of time period For the scene Downlink Node Oxygenation equipment in fish ponds during certain periods The loss state variable is defined as follows: a value of 1 indicates that a loss has occurred, and a value of 0 indicates that no loss has occurred. For the large M constant, For power grid nodes The total output value of the fishpond For the scene Loss coefficient when fish are in oxygen-deficient ponds Scenes within one year The expected duration in days.

[0184] This constraint stipulates that when the oxygen content falls below a critical oxygen level, it will lead to fish mortality and thus losses.

[0185] The load demand constraints of the livestock farm include:

[0186] Operating constraints of temperature control facilities in livestock farms:

[0187] ;

[0188] In the above formula, For the scene Next period The variable representing the cooling and heating operation of the temperature control facilities in livestock farms is defined as follows: a value of 1 indicates that the facilities are operating in cooling mode, and a value of 0 indicates that they are operating in heating mode. For the large M constant, , Scenes Next period The outdoor temperature inside and the target indoor temperature of livestock farms.

[0189] Cooling power constraints of temperature control facilities in livestock farms:

[0190] ;

[0191] ;

[0192] In the above formula, For the scene Downlink Node Temperature control facilities in livestock farms during specific time periods The cooling load demand, This is the cooling power demand coefficient per unit space per unit temperature difference. For power grid nodes The space for livestock breeding factories.

[0193] Heating power constraints of temperature control facilities in livestock farms:

[0194] ;

[0195] ;

[0196] In the above formula, For the scene Downlink Node Temperature control facilities in livestock farms during specific time periods The heating load demand, It is the heating power demand coefficient per unit space per unit temperature difference.

[0197] Operating status constraints of temperature control facilities in livestock farms:

[0198] ;

[0199] ;

[0200] ;

[0201] In the above formula, , Scenes Downlink Node Temperature control facilities in livestock farms during specific time periods The actual cooling and heating power, For the scene Downlink Node Temperature control facilities in livestock farms during specific time periods The temperature control effect variable is set to 1, indicating that the temperature control facilities in the livestock farm have met the temperature control standards and no losses have occurred. A value of 0 indicates that the temperature control facilities in the livestock farm have failed to meet the temperature control standards, resulting in losses. For power grid nodes The total output value of the livestock breeding factory For the scene Loss coefficient when temperature control in livestock farms fails to meet standards Scenes within one year The expected duration in days.

[0202] The load demand constraints of the greenhouse include:

[0203] Operating constraints of greenhouse temperature control facilities:

[0204] ;

[0205] ;

[0206] ;

[0207] ;

[0208] ;

[0209] ;

[0210] ;

[0211] In the above formula, For the scene Next period The variable represents the cooling and heating operating status of the greenhouse temperature control system. A value of 1 indicates that the greenhouse temperature control system is operating in cooling mode, and a value of 0 indicates that the greenhouse temperature control system is operating in heating mode. For the large M constant, , Scenes Next period The outdoor temperature inside and the target temperature inside the greenhouse. , Scenes Downlink Node The greenhouse temperature control facilities during the specified time period The cooling and heating load demand, , These are the cooling and heating power demand coefficients per unit temperature difference and per unit space, respectively. For power grid nodes The space of the greenhouse, , Scenes Downlink Node The greenhouse temperature control facilities during the specified time period The actual cooling and heating power, For the scene Downlink Node The greenhouse temperature control facilities during the specified time period The temperature control effect variable is set to 1, which means that the greenhouse temperature control facilities have reached the temperature control standard and no loss has occurred. When it is set to 0, it means that the greenhouse temperature control facilities have not reached the temperature control standard and have caused loss.

[0212] Constraints on drainage power requirements of greenhouses in flood scenarios:

[0213] ;

[0214] ;

[0215] In the above formula, For the scene Downlink Node The energy demand for drainage in a typical greenhouse within a day For power grid nodes The overall vulnerability of fixed energy storage to disasters. For power grid nodes The area of ​​the greenhouse, The energy demand for drainage per unit area of ​​greenhouse. Scenes of flooding For the scene Downlink Node The drainage facilities of the greenhouse are in use during certain periods. The actual power For the scene Downlink Node The drainage facilities of the greenhouse are in use during certain periods. The variable representing the drainage effect is 1, which indicates that the drainage effect meets the standard and does not lead to losses, and 0 indicates that the drainage effect does not meet the standard and causes losses.

[0216] Greenhouse load loss constraint:

[0217] ;

[0218] In the above formula, For power grid nodes The total output value of crops grown in greenhouses For the scene The comprehensive loss coefficient when greenhouse temperature control or drainage fails to meet standards. Scenes within one year The expected duration in days.

[0219] The power constraint includes:

[0220] Node power balance constraints:

[0221] ;

[0222] In the above formula, For the scene Downlink Node During the period Other base load power, For the scene Downlink Node During the period The power supply capacity of the upstream power grid, For the scene Downlink Node During the period The actual output of new energy.

[0223] Power transmission constraints of power distribution lines:

[0224] ;

[0225] ;

[0226] In the above formula, For the scene Sub-distribution network lines During the period Power transmitted For the scene Sub-distribution network lines With grid nodes The correlation variable, where a value of 1 indicates a distribution line With grid nodes When connected, a value of 0 indicates a power distribution line. With grid nodes No connection. For power distribution lines Maximum conveying capacity For the scene Sub-distribution lines The transmission capacity coefficient indicates that the transmission capacity of the line may be limited in the event of a disaster. For power grid nodes The installed capacity of new energy sources configured at the location.

[0227] Actual output constraints of new energy sources:

[0228] ;

[0229] In the above formula, This is the output characteristic coefficient of new energy sources.

[0230] In this embodiment, the parameters involved in the model are set as follows:

[0231] The discount rate is 0.05; the investment period is 10 years; the unit time is 1 hour; the unit prices of stationary energy storage and mobile energy storage are RMB 1800 per kWh and RMB 2500 per kWh, respectively; the load loss cost coefficients for uninterruptible loads and interruptible loads are RMB 12 per kWh and RMB 3 per kWh, respectively; the critical vulnerability indices for power distribution lines, roads, and stationary energy storage are 0.65, 0.7, and 0.6, respectively; the critical severity index is 0.75; the capacities of a single unit of stationary energy storage and mobile energy storage are 500 kWh and 300 kWh, respectively; the upper limit for energy storage configuration ratio is 0.85; and the upper limit for mobile energy storage arrival time is 4 hours. The initial state of charge (SOC) of the energy storage is 0.8; the lower and upper limits of the SOC are 0.1 and 0.9, respectively; the upper limits of the discharge and charging power of a single energy storage unit are 100 kW and 80 kW, respectively; the energy consumption per unit of oxygen production is 0.025 kWh per milligram; the oxygen enrichment coefficient is 1.35; the saturated dissolved oxygen is 8.2 mg / L; the critical oxygen content is 4.0 mg / L; the cooling and heating power demand coefficients per unit space per unit temperature difference are 0.006 kW / m³ / °C and 0.004 kW / m³ / °C, respectively; the energy demand for drainage per unit area of ​​greenhouse is 0.12 kWh per square meter. The values ​​of various loss coefficients are shown in Table 1, and the vulnerability index distribution of each road, route, and node is shown in Table 2. The travel time coefficients of each road under different disaster scenarios are shown in Table 2. Figure 3 As shown, the transmission capacity coefficients of various power distribution lines under different disaster scenarios are as follows: Figure 4 As shown.

[0232] Table 1. Values ​​of Loss Coefficients

[0233] .

[0234] Table 2 Vulnerability Index Values

[0235] .

[0236] 2. Based on the MATLAB / CPLEX platform (hardware parameters: Intel Core i7-9750H, 32G RAM, 2.6GHz), the fixed-mobile energy storage joint configuration model is solved to obtain the fixed-mobile energy storage joint configuration scheme of the composite rural power grid.

[0237] To verify the effectiveness of the method described in this invention, the configuration scheme obtained by solving the composite rural power grid system described in Example 1 using the fixed energy storage single configuration method was used as a comparative example. The costs of the comparative example and the fixed-mobile energy storage combined configuration scheme obtained in Example 1 were compared, and the results are shown in Table 3:

[0238] Table 3. Comparison of overall power supply costs between the comparative example and Example 1

[0239] .

[0240] The comparison shows that Example 1 reduces energy storage configuration costs by 43.57%, electricity purchase costs by 4.58%, load loss costs by 81.38%, and overall power supply costs by 27.50% compared to the comparative example. This result demonstrates that the method proposed in this invention can effectively reduce the overall power supply cost of the system.

[0241] Example 2:

[0242] See Figure 5 A joint configuration system for energy storage based on the disaster susceptibility classification of composite rural power grids includes a joint configuration model construction module and a joint configuration model solution module.

[0243] The joint configuration model construction module is used to construct a fixed-mobile energy storage joint configuration model for complex rural power grids, aiming to minimize the overall power supply cost of the system. This model considers the impact of multiple disaster scenarios on rural power grid lines, roads, and load changes, and configures fixed and mobile energy storage based on disaster vulnerability classification. The disaster vulnerability classification is based on the vulnerability of power grid nodes and the severity of load loss caused by disasters under multiple disaster scenarios. The objective function and constraints of this model are described in step 1 of Example 1.

[0244] The joint configuration model solving module is used to solve the fixed-mobile energy storage joint configuration model to obtain the fixed-mobile energy storage joint configuration scheme of the composite rural power grid.

Claims

1. A method for the combined configuration of energy storage based on the disaster vulnerability classification of composite rural power grids, characterized in that: The method includes: S1. For complex rural power grids, a fixed-mobile energy storage joint configuration model is constructed with the goal of minimizing the overall power supply cost of the system. This model configures fixed and mobile energy storage based on the disaster vulnerability classification of each power grid node under multiple disaster scenarios. The disaster vulnerability classification is based on the vulnerability of power distribution lines, roads, and fixed energy storage at the power grid node and the severity of load loss caused by disasters. S2. Solve the fixed-mobile energy storage joint configuration model to obtain the fixed-mobile energy storage joint configuration scheme of the composite rural power grid.

2. The method for combined energy storage configuration based on the disaster susceptibility classification of composite rural power grids according to claim 1, characterized in that: The comprehensive power supply cost includes the configuration cost of stationary and mobile energy storage, the cost of purchasing electricity from the upper-level grid, and the cost of load loss. The constraints of the fixed-mobile energy storage joint configuration model include disaster vulnerability classification constraints, energy storage configuration and operation constraints.

3. The method for combined energy storage configuration based on the disaster susceptibility classification of composite rural power grids according to claim 2, characterized in that: The objective function of the fixed-mobile energy storage joint configuration model includes: ； ； ； In the above formula, , , These include the configuration costs of stationary and mobile energy storage, the cost of purchasing electricity from the upper-level grid, and the cost of load loss. , Respectively, power grid nodes The number of fixed and mobile energy storage units configured at the location. , These are the unit prices for stationary energy storage and mobile energy storage, respectively. For the discount rate, For the investment period, , Scenes Downlink Node During the period The upstream power grid supply capacity and other basic loads, The duration of a unit of time period Scenes within one year The expected duration of days, For time period The electricity price is calculated for scenarios including conventional scenarios, flood scenarios, ice storm scenarios, and high-temperature disaster scenarios.

4. The method for combined energy storage configuration based on the disaster susceptibility classification of composite rural power grids according to claim 2, characterized in that: The vulnerability classification constraints include: ； ； ； ； ； ； ； ； In the above formula, For power grid nodes The overall vulnerability of power distribution lines to disasters. For the scene Downlink Node The vulnerability index of power distribution lines to disasters is used to characterize the scenario. The proportion affecting the transmission capacity of power distribution lines For the scene The probability of its occurrence, For power grid nodes Overall road vulnerability For the scene off the road The vulnerability index, which is used to characterize a scenario The proportion affecting road capacity For power grid nodes With roads The coupling relationship variables, To connect power grid nodes The number of roads, For power grid nodes The overall vulnerability of fixed energy storage to disasters. For the scene Downlink Node The vulnerability index of the built-in stationary energy storage to disasters is used to characterize the scenario. The proportion affecting the storage capacity of stationary energy storage For power grid nodes The severity index of disaster load loss. , Respectively, power grid nodes Peak values ​​of uninterruptible load and interruptible load. , These are the load loss cost coefficients for uninterruptible loads and interruptible loads, respectively. For power grid nodes The vulnerability classification variable, when it takes the value 1, is the power grid node. For vulnerable nodes, take the power grid node at time 0. As nodes less susceptible to disasters, For the large M constant, , , These are the critical vulnerability indices for power distribution lines, roads, and stationary energy storage, respectively. This is the critical severity index.

5. The method for combined energy storage configuration based on the disaster susceptibility classification of composite rural power grids according to claim 2, characterized in that: The energy storage configuration and operation constraints include configuration constraints for stationary energy storage and mobile energy storage, and operation constraints for stationary energy storage and mobile energy storage. The configuration constraints for stationary and mobile energy storage include: ； ； ； ； ； In the above formula, , Respectively, power grid nodes The configuration decision variables for fixed energy storage and mobile energy storage For the large M constant, For power grid nodes The overall vulnerability of fixed energy storage to disasters. The critical vulnerability index for stationary energy storage. , Respectively, power grid nodes The number of fixed and mobile energy storage units configured at the location. , These refer to the capacities of a single stationary energy storage unit and a mobile energy storage unit, respectively. , Respectively, power grid nodes Peak values ​​of uninterruptible load and interruptible load. The duration of a unit of time period This represents the upper limit for the energy storage configuration ratio. For power grid nodes The vulnerability classification variable, when it takes the value 1, is the power grid node. For vulnerable nodes, take the power grid node at time 0. These are nodes less susceptible to disasters. The operational constraints of the stationary and mobile energy storage include: ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； In the above formula, For the scene Downlink mobile energy storage from grid nodes to grid node The time consumed For roads The usual passage time, For power grid nodes , With roads Related variables, roads Connecting power grid nodes and hour Select 1 if the value is 1, otherwise select 0. For the scene Mobile energy storage on roads The passage time coefficient on the above For the scene Downlink Node to grid nodes Decision variables for deploying mobile energy storage This is the upper limit for the arrival time of mobile energy storage. For the scene Downlink Node to grid nodes Decision variables for deploying mobile energy storage For the scene Downlink Node to grid nodes The number of mobile energy storage devices deployed. This is a collection of disaster scenarios, including flood scenarios, ice storm scenarios, and high-temperature disaster scenarios. For power grid nodes Any node within the service coverage area, , Scenes Downlink Node The lower and upper limits of the energy storage capacity. For the scene Downlink Node The initial amount of energy stored. , Scenes Downlink Node Energy storage during the period Discharge and charge power, for At any time during the period, For the last period of a typical day, This represents the initial state of charge of the energy storage. , These are the lower and upper limits of the energy storage state of charge, respectively. , These are the upper limits of discharge and charging power for a single energy storage unit, respectively. This is a collection of common scenarios. , Scenes Downlink Node During the period Discharge and charge operating state variables.

6. The method for combined energy storage configuration based on the disaster susceptibility classification of composite rural power grids according to claim 3, characterized in that: The cost of load loss The calculation formula is as follows: ； In the above formula, , , The total losses were for fishponds, livestock farms, and greenhouse crops, respectively. The constraints of the fixed-mobile energy storage joint configuration model also include multi-type load differential demand constraints, which include load demand constraints for fish ponds, livestock farms, and greenhouses.

7. The method for combined energy storage configuration based on the disaster susceptibility classification of a composite rural power grid according to claim 6, characterized in that: The load demand constraints of the fishpond include: ； ； ； ； ； In the above formula, , Scenes Downlink Node Oxygenation equipment in fish ponds during certain periods Operating power and operating status variables, The oxygen enrichment coefficient is the oxygenation coefficient. , These are saturated dissolved oxygen and critical oxygen content, respectively. For the scene Downlink Node fishpond during the period oxygen content For the scene Downlink Node fishpond during the period The net oxygen consumption rate, Energy consumed per unit of oxygen production. The duration of a unit of time period For the scene Downlink Node Oxygenation equipment in fish ponds during certain periods The loss state variable, For the large M constant, For power grid nodes The total output value of the fishpond For the scene Loss coefficient when fish are in oxygen-deficient ponds Scenes within one year The expected duration in days.

8. The method for combined energy storage configuration based on the disaster vulnerability classification of composite rural power grids according to claim 6, characterized in that: The load demand constraints of the livestock farm include: ； ； ； ； ； ； ； ； In the above formula, For the scene Next period Variables of hot and cold operating status of temperature control facilities in livestock farms. For the large M constant, , Scenes Next period The outdoor temperature inside, and the target indoor temperature of livestock farms. , Scenes Downlink Node Temperature control facilities in livestock farms during specific time periods The cooling and heating load demand, , These are the cooling and heating power demand coefficients per unit temperature difference and per unit space, respectively. For power grid nodes The space for livestock breeding factories , Scenes Downlink Node Temperature control facilities in livestock farms during specific time periods The actual cooling and heating power, For the scene Downlink Node Temperature control facilities in livestock farms during specific time periods The temperature control effect variable, For power grid nodes The total output value of the livestock breeding factory For the scene Loss coefficient when temperature control in livestock farms fails to meet standards Scenes within one year The expected duration in days.

9. The method for combined energy storage configuration based on the disaster susceptibility classification of a composite rural power grid according to claim 6, characterized in that: The load demand constraints of the greenhouse include: ； ； ； ； ； ； ； ； ； ； In the above formula, For the scene Next period The cold and hot operating status variables of the temperature control facilities in the greenhouse. For the large M constant, , Scenes Next period The outdoor temperature inside and the target temperature inside the greenhouse. , Scenes Downlink Node The greenhouse temperature control facilities during the specified time period The cooling and heating load demand, , These are the cooling and heating power demand coefficients per unit temperature difference and per unit space, respectively. For power grid nodes The space in the greenhouse, , Scenes Downlink Node The greenhouse temperature control facilities during the specified time period The actual cooling and heating power, For the scene Downlink Node The greenhouse temperature control facilities during the specified time period The temperature control effect variable, For the scene Downlink Node The energy demand for drainage in a typical greenhouse within a day For power grid nodes The overall vulnerability of fixed energy storage to disasters. For power grid nodes The area of ​​the greenhouse, The energy demand for drainage per unit area of ​​greenhouse. Scenes of flooding For the scene Downlink Node The drainage facilities of the greenhouse are in use during certain periods. The actual power For the scene Downlink Node The drainage facilities of the greenhouse are in use during certain periods. The variable of drainage effect, For power grid nodes The total output value of crops grown in greenhouses For the scene The comprehensive loss coefficient when greenhouse temperature control or drainage fails to meet standards. Scenes within one year The expected duration in days.

10. A combined energy storage configuration system based on the disaster vulnerability classification of a composite rural power grid, characterized in that: The system includes a joint configuration model construction module and a joint configuration model solving module; The joint configuration model construction module is used to construct a fixed-mobile energy storage joint configuration model for composite rural power grids with the goal of minimizing the overall power supply cost of the system. This model configures fixed energy storage and mobile energy storage based on the disaster vulnerability classification of each power grid node under multiple disaster scenarios. The disaster vulnerability classification is based on the vulnerability of power distribution lines, roads, and fixed energy storage at the power grid node and the severity of load loss caused by disasters. The joint configuration model solving module is used to solve the fixed-mobile energy storage joint configuration model to obtain the fixed-mobile energy storage joint configuration scheme of the composite rural power grid.

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