Cyber-physical power grid resilient energy storage planning method, device and equipment

Abstract

The application discloses a kind of information physical integrated power transmission network elasticity energy storage planning method, device, equipment, comprising: according to power transmission network characteristics and meteorological data under ice disaster scene, establish information physical integrated power transmission network elasticity basic model, and determine the probability that power transmission network appears ice disaster scene through information physical integrated power transmission network elasticity basic model;According to the probability that power transmission network appears ice disaster scene, establish the planning model of electrochemical energy storage station, to realize the siting of electrochemical energy storage station Determination of volume;Based on the planning model of electrochemical energy storage station and information physical integrated power transmission network elasticity basic model, establish information physical integrated elasticity evaluation model, and realize the simulation of the failure condition, response adjustment, overhaul recovery process of power transmission network through information physical integrated elasticity evaluation model.The application accurately reflects the failure and recovery characteristics of power transmission network, realizes the pre-disaster siting of energy storage station and determination of volume, and achieves the technical effect of improving the elasticity of power transmission network.

Description

Technical Field

[0001] This application relates to the field of power transmission networks, and in particular to a cyber-physical system-integrated energy storage planning method, device, and equipment for resilient power transmission networks. Background Technology

[0002] With global warming, extreme natural disasters are occurring more frequently, and the resilience (resistance and recovery capabilities) of power transmission systems under extreme disasters is receiving increasing attention. However, power transmission networks often transmit electricity via overhead conductors and use optical fiber-coupled overhead ground wire (OPGW) as the communication network medium. Therefore, the damage caused by extreme natural disasters to power transmission networks is not only a physical interruption of power transmission, but also an interruption of telemetry, remote signaling, remote control, and remote adjustment at the information layer. Information failures do not induce physical failures, but when physical layer failures occur, information layer failures, such as the loss of uploaded measurement information and the inability to deliver control strategies, can further amplify physical failures, thus having a greater impact on the resilience of the power transmission network.

[0003] For example, in 2016, Typhoon Meranti struck Xiamen, causing numerous 550kV and 220kV transmission towers to collapse. Although the optical transmission network employed active and backup routing mechanisms, Meranti's impact severely damaged the western power information network, disrupting communication between generators and the dispatch control center, further exacerbating the power outage. This demonstrates that existing technologies that only consider the resilience of physical-level faults, neglecting the indirect impact of information faults on the transmission network, often result in overly optimistic and unsatisfactory technical outcomes. Summary of the Invention

[0004] This application provides a cyber-physical system-integrated energy storage planning method, apparatus, and equipment for power grid resilience, in order to accurately reflect the fault and recovery characteristics of the power grid under ice storm scenarios, to rationally select and determine the location and capacity of energy storage stations under the premise of limited investment, and to achieve the technical effect of improving the resilience of the power grid.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] According to one aspect of this application, a cyber-physical system (CPS) transmission network resilient energy storage planning method is provided, applied to a transmission network using fiber-optic composite overhead ground wire (OPGW) as the communication network transmission medium, the method comprising:

[0007] Based on the characteristics of the power transmission network and meteorological data under ice storm scenarios, a cyber-physical fusion power transmission network resilience basic model is established, and the probability of ice storm scenarios occurring in the power transmission network is determined through the cyber-physical fusion power transmission network resilience basic model.

[0008] Based on the probability of ice storms occurring in the power grid, a planning model for electrochemical energy storage stations is established to enable the site selection and capacity determination of electrochemical energy storage stations.

[0009] Based on the planning model of the electrochemical energy storage station and the cyber-physical transmission network resilience model, a cyber-physical resilience assessment model is established, and the fault conditions, response adjustments, and maintenance and recovery processes of the transmission network are simulated through the cyber-physical resilience assessment model.

[0010] Optionally, the cyber-physical fusion transmission network resilience model includes: a transmission network model, an ice storm probability model, and a resilience assessment quantitative index model.

[0011] In the aforementioned cyber-physical converged transmission network resilience model, the method includes:

[0012] The power transmission network model divides the physical network and information network of the power transmission network into multiple one-to-one corresponding physical units and information units.

[0013] The ice thickness function of any physical unit or information unit in the power transmission network is determined by the ice disaster probability model, and a joint distribution function of ice disaster probability is established by the ice disaster probability model to determine the probability of ice disaster occurring in the power transmission network.

[0014] The elasticity assessment quantitative index model is used to determine the elasticity assessment quantitative index of the power transmission network.

[0015] Optionally, the quantitative indicators for resilience assessment include: R t Indicator, R r Indicator, R RICD Indicators, among which

[0016]

[0017]

[0018]

[0019] Where t represents any time, T0 represents the time when the extreme natural disaster arrives at the power grid, T1 represents the time when the power grid begins to reduce load, T2 represents the time when the power grid reduces load to its maximum, T3 represents the time when the power grid load begins to recover, T4 represents the time when the extreme natural disaster leaves the area, T5 represents the time when the power grid load recovers to its initial level, T6 represents the time when all components in the power grid are fully repaired, i(t) represents the power grid load curve without extreme natural disasters, and r(t) represents the power grid load curve under extreme natural disasters.

[0020] Optionally, establishing a planning model for the electrochemical energy storage station based on the probability of an ice storm in the transmission network, in order to achieve site selection and capacity determination for the electrochemical energy storage station, includes:

[0021] Construct an overall objective function for investment costs and operating costs.

[0022] The overall objective is to determine the minimum sum of the total construction cost of the electrochemical energy storage station and the operating cost of the power transmission network under ice storm scenarios by using the overall objective function of the investment cost and operating cost.

[0023] The overall objective function is expressed as:

[0024]

[0025] in, Ω represents the total construction cost of an electrochemical energy storage station. s Let represent the set of sampled ice storm scenarios s, and T represent the total duration of the ice storm. k represents the operating cost of the power transmission network under the ice storm scenario s at time t. g P represents the weighting coefficient. s This represents the probability of an ice storm scenario s occurring in the power transmission network.

[0026] Optionally, the cyber-physical convergence resilience assessment model includes a spatiotemporal fault model, a cyber-physical convergence response model, and a cyber-physical convergence maintenance model.

[0027] In the cyber-physical convergence resilience assessment model, the method further includes:

[0028] The fault probability of the physical units and / or information units of the power transmission network is calculated using the spatiotemporal fault model, and the fault condition of the power transmission network is determined.

[0029] The process of responding to the fault conditions of the physical units and / or information units of the power transmission network through the cyber-physical fusion response model and adjusting the operating state of the power transmission network in order to minimize the operating cost of the power transmission network.

[0030] The maintenance and recovery time of the physical units and / or information units of the power transmission network is determined by the maintenance time calculation model in the cyber-physical fusion maintenance model.

[0031] The method further includes:

[0032] The cyber-physical fusion resilience assessment model is used to solve for the resilience assessment quantification index, and the results of the resilience assessment quantification index are used to evaluate the resilience of the power transmission network.

[0033] Optionally, the process of responding to fault conditions of the physical units and / or information units of the power transmission network through the cyber-physical fusion response model and adjusting the operating state of the power transmission network to minimize the operating cost of the power transmission network includes:

[0034] The objective function for the system operating cost of the aforementioned cyber-physical fusion response model is constructed, and the operating cost of the transmission network is minimized using this objective function.

[0035] The objective function for the system operating cost is expressed as:

[0036]

[0037] in, This means that for any time t within the total duration T of the ice storm, This represents the operating cost of the power transmission network under the ice storm scenario s at time t. This indicates the operating cost of an electrochemical energy storage station. This indicates the operating cost of thermal power units. Indicates load reduction costs; N ES c represents the collection of energy storage in the power transmission network. ES This represents the operating cost per unit power of energy storage. c represents the output power of the i-th energy storage unit at time t. g This represents the unit power operating cost of a thermal power unit. N represents the output power of the j-th thermal power unit at time t. g N represents the set of thermal power units in the power transmission network; d Represents the set of loads in the transmission network; c d This represents the cost per unit of power reduction in load. This represents the power reduction of the k-th load at time t.

[0038] Optionally, the method further includes:

[0039] Establish a two-stage optimization objective for the planning model of the electrochemical energy storage station and the cyber-physical fusion resilience assessment model;

[0040] The Monte Carlo method is used to conduct sampling simulations under ice disaster scenarios in order to solve the two-stage optimization objectives.

[0041] Optionally, the two-stage optimization objectives for establishing the planning model of the electrochemical energy storage station and the cyber-physical fusion resilience assessment model include:

[0042] The overall objective function for investment costs and operating costs is improved to obtain the improved overall objective function.

[0043] The improved overall objective function in the planning model of the electrochemical energy storage station and the objective function of system operating cost in the cyber-physical fusion resilience assessment model are optimized and solved; wherein, the improved overall objective function is expressed as:

[0044]

[0045] in, Ω represents the total construction cost of an electrochemical energy storage station. s Let represent the set of sampled ice storm scenarios s, and T represent the total duration of the ice storm. k represents the operating cost of the power transmission network under the ice storm scenario s at time t. g P represents the weighting coefficient. s This represents the probability of an ice storm scenario s occurring in the power transmission network.

[0046] According to a second aspect of this application, a cyber-physical system (CPS) transmission network resilient energy storage planning device is provided, applied to a transmission network using fiber optic composite overhead ground wire (OPGW) as the communication network transmission medium, characterized in that the device comprises:

[0047] The first module is used to establish a cyber-physical fusion transmission network resilience basic model based on the characteristics of the transmission network and meteorological data under ice disaster scenarios, and to determine the probability of ice disaster scenarios occurring in the transmission network through the cyber-physical fusion transmission network resilience basic model.

[0048] The second module is used to establish a planning model for the electrochemical energy storage station based on the probability of ice storms occurring in the power transmission network, so as to achieve site selection and capacity determination for the electrochemical energy storage station.

[0049] The third module is used to establish a cyber-physical system resilience assessment model based on the planning model of the electrochemical energy storage station and the cyber-physical system resilience basic model of the transmission network, and to simulate the fault conditions, response adjustments, and maintenance and recovery processes of the transmission network through the cyber-physical system resilience assessment model.

[0050] According to a third aspect of this application, an electronic device is provided, comprising:

[0051] A processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the method as described in any of the first aspects above.

[0052] According to a fourth aspect of this application, a computer-readable storage medium is provided that stores one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the methods described in any of the first aspects above.

[0053] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0054] By establishing a cyber-physical network resilience model for ice storm scenarios, multiple corresponding physical and information units are defined, solving the problem that the dispatch and control center cannot obtain real-time equipment status information and issue control commands when communication fails. By establishing a planning model for electrochemical energy storage stations, electrochemical energy storage stations are planned and configured at weak points in the transmission network, ensuring reasonable site selection and capacity allocation for energy storage stations before disasters, given limited investment. By establishing a cyber-physical network resilience assessment model, accurate simulations of transmission network fault conditions, response adjustments, and maintenance recovery processes are achieved. Finally, optimization and solutions are provided for the two-stage problems of the electrochemical energy storage station planning model and the cyber-physical network resilience assessment model. Therefore, this application simultaneously considers the faults of the transmission network's physical network and information network and their mutual coupling effects, overcoming the problem of overly idealistic resilience improvement results in existing technologies that only consider physical faults of the transmission network, and thus more accurately assessing the resilience of the transmission network. Attached Figure Description

[0055] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0056] Figure 1 This is a flowchart illustrating a cyber-physical fusion transmission network resilient energy storage planning method in one embodiment of this application;

[0057] Figure 2 This is a schematic diagram of the power grid resilience curve under extreme natural disasters in one embodiment of this application;

[0058] Figure 3 This is a schematic diagram of the monitoring and control of a cyber-physical fusion branch in one embodiment of this application;

[0059] Figure 4 This is a schematic diagram of an IEEE RTS-79 cyber-physical converged power grid in one embodiment of this application;

[0060] Figure 5 This is a schematic diagram of a rasterized IEEE RTS-79 test system in one embodiment of this application;

[0061] Figure 6 This is a schematic diagram of the power grid resilience curve in one embodiment of this application;

[0062] Figure 7This is a schematic diagram illustrating the operating cost of a power transmission network system in one embodiment of this application;

[0063] Figure 8 This is a schematic diagram of the time-sharing operation cost of the transmission network in example S6 of one embodiment of this application;

[0064] Figure 9 This is a schematic diagram of the time-sharing power output of the transmission network in example S6 of one embodiment of this application;

[0065] Figure 10 This is a schematic diagram of a cyber-physical fusion transmission network resilient energy storage planning device in one embodiment of this application;

[0066] Figure 11 This is a schematic diagram of the structure of an electronic device in one embodiment of this application;

[0067] Figure 12 This is a schematic diagram of the structure of a computer-readable storage medium in one embodiment of this application. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0069] As mentioned earlier, existing power transmission networks do not take into account issues such as the inability of dispatch and control centers to obtain real-time equipment status information and issue control commands due to communication failures. At the same time, the elasticity improvement technology that only considers physical faults ignores the indirect impact of information faults on the power transmission network. Therefore, the effects of existing technical solutions are often too idealistic and unsatisfactory.

[0070] Based on this, the embodiments of this application propose a cyber-physical system-integrated energy storage planning method for the resilience of the power grid, so as to accurately reflect the fault and recovery characteristics of the power grid under the ice disaster scenario, realize the reasonable pre-disaster site selection and capacity determination of energy storage stations, and achieve the technical effect of improving the resilience of the power grid.

[0071] The technical concept of this application is to simultaneously consider the faults of the physical network and the information network of the transmission network, as well as their mutual coupling and influence, in order to achieve a more accurate assessment of the resilience of the transmission network. By establishing a basic model of the cyber-physical integrated transmission network resilience, a planning model for electrochemical energy storage stations, and a cyber-physical integrated resilience assessment model, independent energy storage stations are rationally planned and configured for the transmission network before a disaster. By utilizing their real-time detection of frequency deviation at the grid connection point, an automatic power emergency support function for the cyber-physical integrated transmission network is realized, thereby achieving the effect of improving the resilience of the transmission network under cyber-physical collaborative faults.

[0072] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0073] like Figure 1 As shown, the method includes the following steps S110 to S130:

[0074] Step S110: Based on the characteristics of the power transmission network and meteorological data under the ice disaster scenario, establish a cyber-physical fusion power transmission network resilience basic model, and determine the probability of the power transmission network experiencing an ice disaster scenario through the cyber-physical fusion power transmission network resilience basic model.

[0075] In one embodiment of this application, the cyber-physical fusion power grid resilience model includes: a power grid model, an ice storm probability model, and a resilience assessment quantitative index model.

[0076] The aforementioned power transmission network model refers to a power transmission network that uses optical fiber composite overhead ground wire (OPGW) as the communication network transmission medium. Because the physical transmission lines and the information optical fiber OPGW are constructed on the same tower, the physical network and the information network of this type of power transmission network have similar topologies.

[0077] In this embodiment, the power transmission network model divides the physical network and information network of the power transmission network into multiple one-to-one corresponding physical units and information units. The specific implementation process is as follows: Based on each substation corresponding to an information node, a corresponding physical network and information network are established; simultaneously, based on the actual geographical location of the power transmission network, it is rasterized, thereby dividing the physical network into multiple physical units and the information network into multiple information units. Therefore, this application simultaneously considers the faults of the power transmission network's physical network and information network, and their mutual coupling and influence, solving the problem that the dispatch control center cannot obtain real-time equipment status information and issue control commands when communication fails.

[0078] In one embodiment of this application, an ice disaster probability model is established. The ice disaster probability model can determine the ice thickness function of any physical unit or information unit in the power transmission network, and establish a joint distribution function of ice disaster probability to determine the probability of ice disaster occurring in the power transmission network.

[0079] Specifically, in this ice disaster probability model, the formula for calculating the ice thickness of a specific unit in the power transmission network at time t is as follows:

[0080]

[0081] Where R(t) is the ice thickness in cm; P(t) is the precipitation in mm / h; ρ i =0.9g / cm 3 The density of ice is ρ0 = 1 g / cm³ 3 Where is the density of water; v(t) is the wind speed in m / s; W(t) is the water content in the air, W(t) = 0.72P(t). 0.88 Those skilled in the art will understand that the above formulas have numerically equivalent operational relationships.

[0082] Furthermore, the formula for calculating wind speed v(t) in formula (1) is as follows:

[0083]

[0084] Among them, v max d(t) represents the maximum wind speed under ice storm conditions, in m / s; d(t) represents the distance between this location and the center of the ice storm, in km; r max The radius of the maximum wind circle is expressed in km.

[0085] Furthermore, d(t) in formula (2) can be calculated using the following formula:

[0086]

[0087] Where (x, y) are the coordinates of that position, (x...y ... c (t),y c (t) represents the coordinates of the center point of the ice storm.

[0088] Furthermore, the formula for calculating precipitation P(t) in formula (1) is as follows:

[0089]

[0090] Among them, P max This represents the maximum precipitation under an ice storm scenario, expressed in mm / h. It can be understood that an ice storm is caused by the parameter of maximum wind speed v.max Maximum wind circle radius r max Maximum precipitation P max Movement speed V m The probability P of an ice storm scenario s occurring in a specific power grid is determined by the direction of movement δ (in m / s), the landing coordinates (x0, y0), and the direction of movement δ (in radians). Therefore, in one embodiment of this application, the probability P of an ice storm scenario s occurring in a specific power grid is determined by the direction of movement δ (in radians) and the landing coordinates (x0, y0). s It can be represented by the following joint distribution function:

[0091] P s =P r (v s )P r (r s )P r (P s )P r (V s )P r (δ s )P r (x 0,s ,y 0,s (5)

[0092] Among them, P r (v s ) for v max =v s The probability, P r (r s ) is r max =r s The probability, P r (P s ) is P max =P s The probability, P r (V s ) is V m =V s The probability, P r (δ s ) is δ=δ s The probability, P r (x 0,s ,y 0,s ) is (x0,y0)=(x 0,s ,y 0,s The probability of ), and at the same time, P in the above formula r (v s ), P r (r s ), P r (P s ), P r (V s ), P r (δ s), P r (x 0,s ,y 0,s This can be obtained from historical meteorological statistics of the area where the transmission network is located. It can be understood that this joint distribution function can be used to describe the probability of an ice storm occurring under a specific parameter, and thus guide energy storage planning based on the probability distribution.

[0093] In one embodiment of this application, the method includes: determining a resilience assessment quantification index for the transmission network using the resilience assessment quantification index model, wherein the resilience assessment quantification index is used to evaluate the resilience of the transmission network, and the energy storage planning in this application is based on the results of this resilience assessment. Further, the resilience assessment quantification index includes, but is not limited to, the following three: R t Indicator, R r Indicator, R RICD The indicators, among which the calculation formulas for the above three indicators are as follows:

[0094]

[0095]

[0096]

[0097] Where t represents any time, T0 represents the time when the extreme natural disaster arrives at the power grid, T1 represents the time when the power grid begins to reduce load, T2 represents the time when the power grid reduces load to its maximum, T3 represents the time when the power grid load begins to recover, T4 represents the time when the extreme natural disaster leaves the area, T5 represents the time when the power grid load recovers to its initial level, T6 represents the time when all components in the power grid are fully repaired, i(t) represents the power grid load curve without extreme natural disasters, and r(t) represents the power grid load curve under extreme natural disasters.

[0098] Specifically, Figure 2 This paper illustrates a schematic diagram of the transmission network resilience curve under extreme natural disasters in an embodiment of this application. In the diagram, X1 represents the transmission network load without extreme natural disasters, X2 represents the transmission network load under extreme natural disasters, and A1, A2, A3, and A4 represent the areas of the corresponding portions. R... t R r R RICD The indicators can be represented by A1, A2, A3, and A4, as follows:

[0099]

[0100]

[0101]

[0102] Combination Figure 2 It can be seen that extreme natural disasters can be divided into three stages in terms of time: pre-disaster, during the disaster, and post-disaster. The load on the power transmission network changes with the duration of the disaster. It is understood that, based on the aforementioned elasticity curve, the elasticity index described in the embodiments of this application can be used to evaluate the power transmission network.

[0103] Step S120: Based on the probability of ice storms occurring in the power transmission network, establish a planning model for the electrochemical energy storage station to achieve site selection and capacity determination for the electrochemical energy storage station.

[0104] Specifically, in one embodiment of this application, the planning model for the electrochemical energy storage station refers to planning and configuring electrochemical energy storage stations at weak points in the power transmission network to achieve the overall goal of reducing energy storage investment and operating costs. In this embodiment, establishing a planning model for the electrochemical energy storage station based on the probability of ice storms occurring in the power transmission network to achieve site selection and capacity determination for the electrochemical energy storage station includes:

[0105] Construct an overall objective function for investment costs and operating costs.

[0106] The overall objective is to determine the minimum sum of the total construction cost of the electrochemical energy storage station and the operating cost of the power transmission network under ice storm scenarios by using the overall objective function of the investment cost and operating cost.

[0107] The overall objective function is expressed as:

[0108]

[0109]

[0110] in, The total construction cost of the electrochemical energy storage station is expressed in yuan; Ω s s represents the set of sampled ice storm scenarios; T represents the total duration of the ice storm in hours. k represents the operating cost of the power transmission network system under the ice storm scenario s at time t, expressed in yuan. g C represents the weighting coefficient. fix The fixed construction cost of the electrochemical energy storage station is expressed in yuan; C cap The unit capacity construction cost of the electrochemical energy storage station is expressed in yuan; N represents the collection of busbars in the power transmission network. The 0 and 1 variables represent whether the bus is equipped with an energy storage station (1 indicates that it is equipped, and 0 indicates that it is not equipped); The capacity of the energy storage station configured for the bus is expressed in MWh.

[0111] Furthermore, the following constraints are set for the overall objective function:

[0112]

[0113]

[0114] Where, N ES,set This represents the upper limit for the number of electrochemical energy storage stations. This represents the upper limit of the capacity of a single electrochemical energy storage station, expressed in MWh.

[0115] It is understandable that by rationally planning and configuring independent energy storage stations for the power transmission network before a disaster, not only can the reasonable site selection and capacity determination of energy storage stations be achieved before a disaster, but it is also conducive to achieving the overall goal of minimizing total construction and operating costs. At the same time, by utilizing energy storage stations to detect frequency deviations at grid connection points in real time, an automatic emergency power support function for the power transmission network can be realized, thereby achieving the effect of improving the resilience of the power transmission network under cyber-physical fault conditions.

[0116] Step S130: Based on the planning model of the electrochemical energy storage station and the cyber-physical fusion transmission network resilience basic model, establish a cyber-physical fusion resilience assessment model, and simulate the fault conditions, response adjustments, and maintenance and recovery processes of the transmission network through the cyber-physical fusion resilience assessment model.

[0117] In one embodiment of this application, the cyber-physical fusion resilience assessment model includes a spatiotemporal fault model, a cyber-physical fusion response model, and a cyber-physical fusion maintenance model.

[0118] In the cyber-physical convergence resilience assessment model, the method further includes:

[0119] The spatiotemporal fault model is used to calculate the fault probability of the transmission network and determine the fault conditions of the transmission network. Specifically, the spatiotemporal fault model includes three types of faults: insulator flashover, line breakage, and tower collapse.

[0120] Among them, the probability of insulator flashover occurring in a physical unit. for:

[0121]

[0122] Where U represents the voltage level of the power transmission network; The insulator flashover voltage (can be expressed by the formula) (Calculation); A is a coefficient related to the degree of pollution, insulation material, and insulator type; R is the icing thickness in cm; c is the influence coefficient of icing thickness on flashover voltage; h is the dry arc distance in m.

[0123] The probability of a physical unit or information unit experiencing a disconnection for:

[0124]

[0125] in, The maximum rated icing thickness (in cm) for both physical and data transmission lines. The values ​​are also different.

[0126] The probability of a physical or information unit collapsing. for:

[0127]

[0128] in, The maximum rated ice thickness for transmission towers, in cm.

[0129] Those skilled in the art will understand that, for a physical branch in a power transmission network, a fault occurs when any physical unit under its jurisdiction experiences an insulator flashover fault and / or a line break fault and / or a tower collapse fault; similarly, for an information branch in a power transmission network, a fault occurs when any information unit under its jurisdiction experiences a line break fault or a tower collapse fault. Therefore, the aforementioned spatiotemporal fault model can determine the fault probability of any information unit or physical unit in the power transmission network and identify the relevant fault conditions.

[0130] In one embodiment of this application, the method further includes: responding to the fault conditions of the physical units and / or information units of the power transmission network through the cyber-physical fusion response model, and adjusting the operating state of the power transmission network to minimize the operating cost of the power transmission network.

[0131] Specifically, the cyber-physical fusion response model is a model designed to minimize the operating cost of the power transmission network, and includes the following two aspects:

[0132] First, the cyber-physical fusion response process. For example... Figure 3 As shown, both ends of the branch between physical network bus 1 and bus 2 are equipped with merging units and control units; the information network is established on the physical branch and has the same topology as the physical network. After measuring the electrical parameters, the CT and PT measurement signals in the merging unit are uploaded to the control center via the monitoring information stream.

[0133] Specifically, when a physical branch experiences a fault (such as a short-circuit fault or a branch power flow exceeding its limit), the relay protection device automatically disconnects the fault. Subsequently, the control center receives the reported fault information, calculates the response strategy, and simultaneously distributes the response strategy to the corresponding controlled objects via control information flow. When an information branch experiences a fault, the information fault does not directly lead to the occurrence of a physical fault or the triggering of the response strategy. However, in the presence of a physical fault in the transmission network, the information fault may further exacerbate the physical fault, resulting in more severe load shedding.

[0134] The integration of energy storage power stations can effectively improve system resilience, especially when a power deficit occurs in the system. In cyber-physical transmission networks, the causes of power deficits are numerous, such as the formation of physical islands, information failures preventing the control center from adjusting and responding to target objects, ramp constraints, power flow constraints, etc. Therefore, when the above situations occur, the energy storage station in this application can automatically fill the power deficit and provide emergency power support without communication.

[0135] Second, the process of handling information failures (including monitoring failures and control failures).

[0136] Monitoring Failure: Monitoring is a crucial means for the control center to promptly detect faults. It is worth noting that pre-disaster energy storage planning requires equivalent technical methods for disaster simulation, specifically including: (i) Due to the lack of hardware-in-the-loop, actual branch power flow measurements are obtained through equivalent power flow calculations. (ii) For branches that cannot be monitored due to information network failures, the branch power flow upper limit is set to infinity. This is only used to blind the control center from calculating fault response strategies when the branch power flow exceeds the limit. In reality, unmonitorable branches still have their original physical power flow upper limit. Furthermore, when a physical fault occurs, the failure of the monitoring function will not affect the normal operation of the relay protection device. (iii) The equivalent infinity setting for branch power flow is only used in the fault detection process. Once another monitorable branch exceeds its limit, the original branch power flow upper limit is still used in the control center's response strategy calculation.

[0137] Control failure: Remotely adjusting the opening and closing of circuit breakers on both sides of the thermal power unit and the load is the control center's response decision-making process after a fault occurs. If the information network between the controlled object and the control center fails, i.e., there is no connected network link, the adjustment communication process can only be carried out manually, such as by telephone, SMS, or email, which is more difficult in terms of control speed and complexity. Therefore, manual communication is generally not used to simulate fault response in resilience assessments. For objects with an interrupted information network with the control center, their operating status either remains the same as the previous moment or is automatically disconnected by protection devices.

[0138] Based on this, in one embodiment of this application, the process of responding to fault conditions of the physical units and / or information units of the power transmission network through the cyber-physical fusion response model and adjusting the operating state of the power transmission network to minimize the operating cost of the power transmission network includes: constructing a system operating cost objective function of the cyber-physical fusion response model, and using the system operating cost objective function to minimize the operating cost of the power transmission network. The system operating cost objective function is expressed as:

[0139]

[0140] in, For time t within the total duration T of the ice storm; This represents the operating cost of the power transmission network under the ice storm scenario s at time t, expressed in yuan. The operating cost of the electrochemical energy storage station is expressed in yuan. The operating cost of thermal power units is expressed in yuan. Cost reduction for load, unit is yuan; N ES c is the collection of energy storage in the power transmission network; ES The unit operating cost of energy storage is expressed in yuan / MW. N represents the output power of the i-th energy storage unit at time t, in MW. g c is the collection of thermal power units in the power transmission network; g The unit power operating cost of thermal power units is expressed in yuan / MW. N represents the output power of the j-th thermal power unit at time t, in MW. d c is the collection of loads in the transmission network. d Cost reduction per unit power of load, expressed in yuan / MW; Let be the power reduction of the k-th load at time t, in MW.

[0141] Furthermore, the objective function for the system's operating cost is subject to the following constraints:

[0142] F(S(t))=A(S(t))(P ES (t)+P g (t)-P d (t)+ΔP d (t)) (20)

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154] Where F(S(t)) is the branch power flow vector at time t; A(S(t)) is the power transmission distribution factor matrix of the transmission network at time t, used to describe the relationship between the branch power flow F(S(t)) and the bus injected power; P ES (t) represents the output power vector of the energy storage power station at time t; P g (t) represents the output power vector of the thermal power unit at time t; P d (t) represents the load power vector at time t, ΔP d (t) represents the load reduction power vector at time t; Let be the output power of the i-th energy storage at time t, in MW; Let be the output power of the j-th thermal power unit at time t, in MW; Let be the k-th load power at time t, in MW; Let be the power reduction of the k-th load at time t, in MW; The maximum output power planned for the energy storage power station Let i be the output power of the energy storage unit at time t; Let i be the SOC value of energy storage at time t. Let t be the SOC value of energy storage i at time t-1, and Δt be the simulation time interval in hours. N represents the capacity of energy storage i, expressed in MWh. ES It is a collection of energy storage facilities in the power transmission network;

[0155] The minimum SOC limit for energy storage i, The maximum SOC limit for energy storage i; x(t) is a 0-1 variable, representing the start-up and shutdown status of the thermal power unit at time t (1 indicates start-up, 0 indicates shutdown); The minimum technical output of thermal power unit j, in MW; The maximum technical output of thermal power unit j, expressed in MW; Let be the output power of the j-th thermal power unit at time t, in MW. Let be the set of controllable thermal power units at time t; Let x(t-1) be the output power of the j-th thermal power unit at time t-1, in MW. x(t-1) is a 0-1 variable, representing the start-up and shutdown status of the thermal power unit at time t-1 (1 indicates start-up, 0 indicates shutdown). The unit is the upward climbing capacity of a thermal power unit j per unit time, expressed in MW. y(t) represents the downward ramping capability of thermal power unit j per unit time, in MW; y(t) is a 0-1 variable, representing the state of the uncontrollable thermal power unit at time t (1 indicates maintaining the output power at time t-1, 0 indicates being disconnected from the grid by the relay protection device); Let be the set of uncontrollable thermal power units at time t; Let z(t) be the set of controllable loads at time t; z(t) is a 0-1 variable, representing the state of uncontrollable loads at time t (1 represents the power maintained at time t-1, and 0 represents the load being disconnected from the grid by the relay protection device). Let F be the set of uncontrollable loads at time t; l (S(t)) represents the line power flow of branch l, in MW; N represents the upper limit of power flow for branch l, in MW; b It is a collection of power transmission network branches.

[0156] In one embodiment of this application, the method further includes: determining the interaction between the information network and the physical network of the power transmission network through the interaction influence model in the cyber-physical fusion maintenance model. For example, for the sake of the personal safety of maintenance personnel, when the information unit is under maintenance, the physical unit must be shut down.

[0157] Of course, the above examples are not intended to limit this application. Those skilled in the art can formulate corresponding control decisions for the relevant faults of physical units or information units based on the characteristics of the coupling effects between the various units and in combination with specific application scenarios.

[0158] Furthermore, in this embodiment, the maintenance recovery time of the physical units and / or information units of the transmission network is determined by the maintenance time calculation model in the cyber-physical fusion maintenance model. Specifically, the maintenance time calculation model is as follows:

[0159]

[0160]

[0161] in, The total time required to repair faulty unit b, in hours; The time spent on maintenance work for faulty unit b, in hours; The time (in hours) for faulty unit b to wait for an available maintenance team. is the total maintenance time for fault unit b under normal weather conditions, in hours; k is the weather impact coefficient under extreme disasters, which is related to the icing thickness R and is dimensionless. The time for maintenance work to be carried out on fault unit b under normal weather conditions is expressed in hours. The round-trip time of the maintenance team under normal weather conditions for carrying out maintenance work on faulty unit b is expressed in hours.

[0162] Meanwhile, it is worth noting that the cyber-physical fusion resilience assessment model can be used to solve the quantitative indicators of resilience assessment in the aforementioned resilience basic model, and the calculation results can be used to quantitatively assess the resilience of the power transmission network system.

[0163] In a preferred embodiment of this application, the method further includes:

[0164] A two-stage optimization objective is established for the planning model of the electrochemical energy storage station and the cyber-physical fusion resilience assessment model; the Monte Carlo method is used to conduct sampling simulations under ice disaster scenarios to solve the two-stage optimization objective.

[0165] Furthermore, the two-stage optimization objectives for establishing the planning model of the electrochemical energy storage station and the cyber-physical fusion resilience assessment model include:

[0166] The overall objective function for investment costs and operating costs, i.e., formula (12) in this application, is improved to obtain the improved overall objective function;

[0167] The improved overall objective function in the planning model of the electrochemical energy storage station and the objective function of system operating cost in the cyber-physical fusion resilience assessment model are optimized and solved; wherein, the improved overall objective function is expressed as:

[0168]

[0169] in, Ω represents the total construction cost of an electrochemical energy storage station. s Let represent the set of sampled ice storm scenarios s, and T represent the total duration of the ice storm. k represents the operating cost of the power transmission network under the ice storm scenario s at time t. g P represents the weighting coefficient. s This represents the probability of an ice storm scenario s occurring in the power transmission network.

[0170] It can be understood that the first "min" in formula (34) refers to the goal of minimizing the total cost. In order to calculate the most severe moment t in various ice disaster scenarios, the second min in formula (34) refers to achieving the lowest operating cost at the most severe moment t.

[0171] Furthermore, the process of establishing the two-stage optimization objectives is as follows:

[0172]

[0173]

[0174] It is understandable that, in order to solve the optimization problems of Stage 1 and Stage 2 mentioned above, on the one hand, this application uses the Monte Carlo method to conduct sampling simulation under the ice disaster scenario, that is, to sample and simulate formula (6) in the ice disaster probability model of the power transmission network and formulas (16-18) in the spatiotemporal fault model, thereby realizing the equivalent simulation of the probability of ice disaster occurrence. On the other hand, since the above optimization problem belongs to the mixed integer linear programming problem, it has an optimal solution in mathematics. Therefore, this application can use a mathematical programming solver to solve it. Of course, the above optimization and solution methods are not intended to limit this application.

[0175] The following will describe in detail application embodiments using the method described in this application.

[0176] In one embodiment of this application, the IEEE RTS-79 transmission network test system is used as the simulation object. Specifically, this test system has 32 thermal power units, an initial system load of 2850MW, and includes 24 busbars and 38 transmission lines. Its corresponding physical network and information network are as follows: Figure 4 As shown (Bus represents a bus node, Node represents an information node). For most buses, each bus represents a substation and corresponds to an information node; buses connected to transformers, such as Bus3 and Bus24, belong to the same information node because they are geographically close and belong to the same substation. Information node Node8 is the control center of the test system, responsible for monitoring the operating status of the transmission network system and sending response adjustment commands.

[0177] Furthermore, such as Figure 5 As shown, in order to analyze the impact of ice storms, the IEEE RTS-79 cyber-physical fusion power grid was gridded into 1600×1800=2,400,000 physical units and 2,400,000 information units, each with an area of ​​500m×500m. The gray circles in the figure represent ice storms, and the gray arrows represent possible ice storm paths.

[0178] During the simulation, the relevant parameters were set as follows:

[0179] The time interval Δt is set to 0.01h, and the number of Monte Carlo simulations is 1000.

[0180] The parameters of the ice storm probability model are set to follow a normal distribution, specifically: V max ~N(12,1 2 ), r max ~N(100,0.5) 2 ), P max ~N(35,1 2 V m ~N(20,0.5) 2 ), δ~N(π / 4,0.1 2 ), (x0, y0)=(x,670-x), x~U(220,240).

[0181] The relevant parameters for the planning model of the electrochemical energy storage station are: weighting coefficient k. g =10 8 Maximum capacity of a single energy storage station The energy storage station uses 0.125C cells, and the fixed construction cost of the energy storage station is C. fix =10 6 Yuan, the unit capacity construction cost of the energy storage station C cap =1.5×10 6 Yuan.

[0182] The relevant parameters for setting the spatiotemporal fault model are as follows: the operating voltage level of the transmission network U = 220kV; the insulator model is FXBW-110 / 100, with parameters A = 408.5, c = 0.49, h = 3; the transmission conductor model is LGJ-630 / 45, with parameters... The OPGW model is OPGW-150, and its parameters are as follows: The transmission tower model is ZB.

[0183] The relevant parameters for setting the cyber-physical fusion response model are: the unit power operating cost of energy storage, c. ES =600 yuan / MW, operating cost per unit power of thermal power unit c g =400 yuan / MW, unit power reduction cost of load c d = 10,000 yuan / MW.

[0184] The relevant parameters for setting the cyber-physical fusion maintenance model are: the time required to carry out maintenance work on fault unit b under normal weather conditions. Fault unit b: Maintenance work to be carried out under normal weather conditions; round-trip time of the maintenance team. v = 60km / h, D is the distance between fault unit b and the maintenance center (located on bus 3), in km.

[0185] To highlight the advantages of this application, six comparative embodiments S1, S2, S3, S4, S5, and S6 will be used below to illustrate the technical effects achieved by this application.

[0186] Specifically, as shown in Table 1, S6 is a system resilience assessment example under a cyber-physical transmission network that contains energy storage stations and for which energy storage stations have been planned. S1, S2, S3, S4, and S5 are comparative examples set from three dimensions: whether the transmission network considers cyber-physical characteristics, whether the transmission network contains energy storage stations, and whether energy storage stations have been planned if they are included.

[0187] Table 1 Configuration Table of Comparative Examples

[0188]

[0189] The simulation results for Phase 1 are as follows:

[0190] In S3 and S5, the electrochemical energy storage station is configured on the bus with the largest load; in S4 and S6, the energy storage station is planned and configured according to the planning scheme described in this application.

[0191] The allocation results and the costs of the first phase are shown in Table 2.

[0192] In this table,

[0193] Table 2 Energy Storage Configuration Results

[0194]

[0195] The above results indicate that S4 and S6 have lower [performance] compared to S3 and S5. and According to the probability model of ice storms, buses 14, 19, and 24 in S4 and S6 have a high probability of being islanded. Therefore, planning energy storage on these buses can effectively reduce the load reduction of the transmission network system.

[0196] In S3 and S5, configuring energy storage on buses 13, 15, and 18 not only leads to energy waste but also has a limited impact on improving the resilience of the transmission network. Furthermore, when energy storage is configured on the same bus, calculations considering only physical faults have smaller... For example, S3S4 Smaller than S5S6

[0197] The simulation results for Phase 2 are as follows:

[0198] Simulations were performed on the six examples in Table 1, such as... Figure 6 As shown, the corresponding comparative analysis is as follows.

[0199] First, regarding the cyber-physical integration characteristics: comparing the relevant curves of S1 and S2, S3 and S4, and S5 and S6 respectively, it can be seen that when the transmission network considers the cyber-physical integration characteristics, the system's resilience becomes worse and the load reduction becomes more severe.

[0200] Secondly, regarding the inclusion of energy storage: Comparing the relevant curves of S1 and S3, and S2 and S5, it can be seen that the resilience of the transmission network is improved when cyber-physical integration characteristics are taken into account in the simulation process. That is, when network failures are not considered, all thermal power units are connected to the control center, and control commands issued by the control center can be executed. In this case, because the unit power output cost of energy storage is higher than that of thermal power units, under the condition of minimum system operating cost, energy storage has no room for power generation, which leads to the load curves of S1 and S3 being almost identical. However, for the S2 and S5 examples considering cyber-physical integration characteristics, the inclusion of energy storage improves the resilience of the transmission network.

[0201] Third, regarding whether to consider energy storage planning: Comparing the relevant curves of S3 and S4, and S5 and S6, it can be seen that when energy storage is configured on the appropriate bus, the resilience of the transmission network is significantly improved. For example, the load curve of S4 is higher than that of S3 in the 4-17h period, and the same is true for S6 and S5.

[0202] Furthermore, it can be seen that the energy storage configuration delays the start time of system load shedding (as can be seen by comparing S4S6 with S1S2S3S5). At 15 hours, when the energy storage is depleted, load shedding occurs in the transmission network because the fault has not been completely repaired. It is worth noting that at 15 hours, the load shedding in S6 is more severe than in S5. This is because in the early stages of the ice storm (3-10 hours), there was no large-scale load shedding, causing thermal power units to maintain high power output. When the energy storage station is depleted, the thermal power units lack the ramp-up capability, leading to a larger load shedding.

[0203] Furthermore, the resilience assessment index R t R r R RICD The calculation results are shown in Table 3.

[0204] Table 3 Calculation results of elasticity index

[0205] Calculation example <![CDATA[R t ]]> <![CDATA[R r ]]> <![CDATA[R RICD ]]> Calculation example <![CDATA[R t ]]> <![CDATA[R r ]]> <![CDATA[R RICD ]]> <![CDATA[S1]]> 6416 0.872 0.397 <![CDATA[S4]]> 2622 0.918 0.424 <![CDATA[S2]]> 10554 0.836 0.307 <![CDATA[S5]]> 7948 0.881 0.317 <![CDATA[S3]]> 6455 0.871 0.397 <![CDATA[S6]]> 4653 0.917 0.334

[0206] Based on the above results, it can be understood that R t The lower the minimum performance, the better. r The closer to 1, the better the performance.RICD The closer to 0, the better the performance. Therefore, the calculation results of the above indicators in the embodiments of this application can reflect the necessity of considering the characteristics of cyber-physical convergence and the elastic improvement effect of reasonable energy storage planning on cyber-physical converged transmission networks, which will not be elaborated here.

[0207] Figure 7 This embodiment illustrates the operating costs of the power transmission network system, such as... Figure 7 As shown, the total system operating cost C system and Figure 6 There is a corresponding relationship between the system load reduction amount in Table 3 and the elasticity index in Table 3. system The larger the value, the worse the operating condition of the transmission network. Additionally, the figure also shows C. g 10C ES C d As a result, since the majority of the load on the power grid is still provided by thermal power units most of the time, the difference in operating costs between thermal power units is not significant.

[0208] Furthermore, Figure 8 This is a schematic diagram illustrating the time-sharing operation cost of the transmission network in example S6. Figure 9 This is a schematic diagram of time-sharing power output, such as... Figure 8 and Figure 9 As shown, at the start of the power grid simulation, all loads are supplied by thermal power units. As a fault occurs, energy storage power stations begin to output power and fill the system load gap. When the energy storage is depleted, its output power rapidly drops to zero, causing a large-scale load reduction in the system. The load reduction cost C at this point is... d Rapidly rising; as the maintenance process was completed, P g Gradually increase, C d The rate gradually decreases. Therefore, it is evident that the resilience assessment method and energy storage planning method proposed in this scheme achieve the technical effect of enhanced resilience.

[0209] This application also provides an embodiment of a cyber-physical system-integrated power grid resilient energy storage planning device 1000, such as... Figure 10 As shown, the device includes:

[0210] The first module 1010 is used to establish a cyber-physical fusion transmission network resilience basic model based on the characteristics of the transmission network and meteorological data under the ice disaster scenario, and to determine the probability of the transmission network experiencing an ice disaster scenario through the cyber-physical fusion transmission network resilience basic model.

[0211] The second module 1020 is used to establish a planning model for the electrochemical energy storage station based on the probability of an ice storm in the power transmission network, so as to achieve the site selection and capacity determination of the electrochemical energy storage station.

[0212] The third module 1030 is used to establish a cyber-physical system resilience assessment model based on the planning model of the electrochemical energy storage station and the cyber-physical system resilience basic model of the transmission network, and to simulate the fault conditions, response adjustments, and maintenance and recovery processes of the transmission network through the cyber-physical system resilience assessment model.

[0213] It should be noted that the above-mentioned cyber-physical network resilient energy storage planning device can realize all the steps of the cyber-physical network resilient energy storage planning method provided in the foregoing embodiments. The relevant explanations of the cyber-physical network resilient energy storage planning device are applicable to the cyber-physical network resilient energy storage planning method, and will not be repeated here.

[0214] In summary, the technical solution of this application achieves at least the following technical effects:

[0215] By establishing a cyber-physical system (CPS) model of the transmission network resilience under ice storm scenarios, multiple corresponding physical and information units are defined, solving the problem that the dispatch and control center cannot obtain real-time equipment status information and issue control commands when communication fails. By establishing a planning model for electrochemical energy storage stations, electrochemical energy storage stations are planned and configured at weak points in the transmission network, ensuring reasonable site selection and capacity determination for energy storage stations before disasters, under the premise of limited investment. By establishing a CPS resilience assessment model, accurate simulation of the transmission network's fault conditions, response adjustments, and maintenance recovery processes is achieved. Finally, the planning model for electrochemical energy storage stations and the CPS resilience assessment model are optimized and solved in two stages. Therefore, this application simultaneously considers the faults of the transmission network's physical network and information network and their mutual coupling effects, overcoming the problem of overly idealistic resilience improvement results in existing technologies that only consider physical faults of the transmission network, and is more conducive to accurately assessing the resilience of the transmission network.

[0216] It should be noted that:

[0217] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. Various general-purpose devices can also be used in conjunction with the teachings herein. The required structure for constructing such devices is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0218] It should be understood that, in order to simplify this application and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0219] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0220] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0221] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the cyber-physical converged transmission grid resilient energy storage planning device according to the embodiments of this application. This application can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0222] For example, Figure 11 A schematic diagram of an electronic device according to an embodiment of this application is shown. The electronic device 1100 includes a processor 1110 and a memory 1120 arranged to store computer-executable instructions (computer-readable program code). The memory 1120 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. The memory 1120 has a storage space 1130 for storing computer-readable program code 1131 for performing any of the method steps described above. For example, the storage space 1130 for storing computer-readable program code may include various computer-readable program codes 1131 respectively for implementing the various steps in the methods described above. The computer-readable program code 1131 can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. Such computer program products are typically, for example... Figure 11 The computer-readable storage medium shown.

[0223] Figure 12 A schematic diagram of a computer-readable storage medium according to an embodiment of this application is shown. The computer-readable storage medium 1200 stores computer-readable program code 1131 for performing the method steps according to this application, which can be read by the processor 1110 of an electronic device 1100. When the computer-readable program code 1131 is executed by the electronic device 1100, it causes the electronic device 1100 to perform the various steps of the method described above. Specifically, the computer-readable program code 1131 stored in the computer-readable storage medium can perform the methods shown in any of the above embodiments. The computer-readable program code 1131 can be compressed in a suitable form.

[0224] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. A cyber-physical system-integrated power transmission network resilient energy storage planning method, applied to a power transmission network using fiber-optic composite overhead ground wire (OPGW) as the communication network transmission medium, characterized in that, The method includes: Based on the characteristics of the power transmission network and meteorological data under ice storm scenarios, a cyber-physical fusion power transmission network resilience basic model is established, and the probability of ice storm scenarios occurring in the power transmission network is determined through the cyber-physical fusion power transmission network resilience basic model. Based on the probability of ice storms occurring in the power grid, a planning model for electrochemical energy storage stations is established to enable the site selection and capacity determination of electrochemical energy storage stations. Based on the planning model of the electrochemical energy storage station and the cyber-physical transmission network resilience model, a cyber-physical resilience assessment model is established, and the fault conditions, response adjustments, and maintenance and recovery processes of the transmission network are simulated through the cyber-physical resilience assessment model. The cyber-physical fusion transmission network resilience foundation model includes: a transmission network model, an ice storm probability model, and a resilience assessment quantitative index model. In the aforementioned cyber-physical converged transmission network resilience model, the method includes: The power transmission network model divides the physical network and information network of the power transmission network into multiple one-to-one corresponding physical units and information units. The ice thickness function of any physical unit or information unit in the power transmission network is determined by the ice disaster probability model, and a joint distribution function of ice disaster probability is established by the ice disaster probability model to determine the probability of ice disaster occurring in the power transmission network. The elasticity assessment quantitative index model is used to determine the elasticity assessment quantitative index of the power transmission network. The step of establishing a planning model for the electrochemical energy storage station based on the probability of an ice storm in the power transmission network, in order to achieve site selection and capacity determination for the electrochemical energy storage station, includes: Construct an overall objective function for investment costs and operating costs. The overall objective is to determine the minimum sum of the total construction cost of the electrochemical energy storage station and the operating cost of the power transmission network under ice storm scenarios by using the overall objective function of the investment cost and operating cost. The overall objective function is expressed as: in, This indicates the total construction cost of the electrochemical energy storage station. This represents a scene of ice storm sampling. The set, Indicates the total duration of the ice storm. Indicates the power transmission network in Ice disaster scene Operating costs below Indicates the weighting coefficient. This indicates that the power transmission network is experiencing an ice storm. The probability of; Establish a two-stage optimization objective for the planning model of the electrochemical energy storage station and the cyber-physical fusion resilience assessment model; The Monte Carlo method was used to conduct sampling simulations under ice disaster scenarios in order to solve the two-stage optimization objectives. The two-stage optimization objectives for establishing the planning model of the electrochemical energy storage station and the cyber-physical fusion resilience assessment model include: The overall objective function for investment costs and operating costs is improved to obtain the improved overall objective function. The improved overall objective function in the planning model of the electrochemical energy storage station and the objective function of system operating cost in the cyber-physical fusion resilience assessment model are optimized and solved; wherein, the improved overall objective function is expressed as: in, This indicates the total construction cost of the electrochemical energy storage station. This represents a scene of ice storm sampling. The set, Indicates the total duration of the ice storm. Indicates the power transmission network in Ice disaster scene Operating costs below Indicates the weighting coefficient. This indicates that the power transmission network is experiencing an ice storm. The probability of.

2. The method as described in claim 1, characterized in that, The quantitative indicators for elasticity assessment include: index, index, Indicators, among which , , in, Indicates any time. Indicates the moment when an extreme natural disaster reaches the power transmission network. This indicates the moment when the power grid begins to reduce load. This indicates the moment when the load reduction of the transmission network reaches its maximum. This indicates the moment when the power grid load begins to recover. Indicates the moment of departure from an extreme natural disaster. This indicates the time when the power grid load returns to its initial level. This indicates the moment when all components in the power transmission network are completely repaired. This represents the power grid load curve under conditions without extreme natural disasters. This represents the power grid load curve under extreme natural disasters.

3. The method as described in claim 2, characterized in that, The cyber-physical convergence resilience assessment model includes a spatiotemporal fault model, a cyber-physical convergence response model, and a cyber-physical convergence maintenance model. In the cyber-physical convergence resilience assessment model, the method further includes: The fault probability of the physical units and / or information units of the power transmission network is calculated using the spatiotemporal fault model, and the fault condition of the power transmission network is determined. The process of responding to the fault conditions of the physical units and / or information units of the power transmission network through the cyber-physical fusion response model and adjusting the operating state of the power transmission network in order to minimize the operating cost of the power transmission network. The maintenance and recovery time of the physical units and / or information units of the power transmission network is determined by the maintenance time calculation model in the cyber-physical fusion maintenance model. The method further includes: The cyber-physical fusion resilience assessment model is used to solve for the resilience assessment quantification index, and the results of the resilience assessment quantification index are used to evaluate the resilience of the power transmission network.

4. The method as described in claim 3, characterized in that, The process of responding to fault conditions of the physical units and / or information units of the power transmission network through the cyber-physical fusion response model and adjusting the operating state of the power transmission network to minimize the operating cost of the power transmission network includes: The objective function for the system operating cost of the aforementioned cyber-physical fusion response model is constructed, and the operating cost of the transmission network is minimized using this objective function. The objective function for the system operating cost is expressed as: in, This indicates the total duration of the ice storm. any time within , Indicates the power transmission network in Ice disaster scene Operating costs below This indicates the operating cost of an electrochemical energy storage station. This indicates the operating cost of thermal power units. This indicates the cost of load reduction; This represents the collection of energy storage in the power transmission network. This represents the operating cost per unit power of energy storage. express Time of the first The output power of the energy storage unit This represents the unit power operating cost of a thermal power unit. express Time of the first The output power of each thermal power unit This represents the set of thermal power units in the power transmission network; This represents the set of loads in the power transmission network. This represents the cost per unit of power reduction in load. express Time of the first Reduced power of each load.

5. A cyber-physical system-integrated power transmission network resilient energy storage planning device, applied to a power transmission network using fiber optic composite overhead ground wire (OPGW) as the communication network transmission medium, characterized in that, The device includes: The first module is used to establish a cyber-physical fusion transmission network resilience basic model based on the characteristics of the transmission network and meteorological data under ice disaster scenarios, and to determine the probability of ice disaster scenarios occurring in the transmission network through the cyber-physical fusion transmission network resilience basic model. The second module is used to establish a planning model for the electrochemical energy storage station based on the probability of ice storms occurring in the power transmission network, so as to achieve site selection and capacity determination for the electrochemical energy storage station. The third module is used to establish a cyber-physical system resilience assessment model based on the planning model of the electrochemical energy storage station and the cyber-physical system resilience basic model of the transmission network, and to simulate the fault conditions, response adjustments, and maintenance and recovery processes of the transmission network through the cyber-physical system resilience assessment model. The cyber-physical fusion transmission network resilience foundation model includes: a transmission network model, an ice storm probability model, and a resilience assessment quantitative index model. In the aforementioned cyber-physical converged power grid resilience basic model, The power transmission network model divides the physical network and information network of the power transmission network into multiple one-to-one corresponding physical units and information units. The ice thickness function of any physical unit or information unit in the power transmission network is determined by the ice disaster probability model, and a joint distribution function of ice disaster probability is established by the ice disaster probability model to determine the probability of ice disaster occurring in the power transmission network. The elasticity assessment quantitative index model is used to determine the elasticity assessment quantitative index of the power transmission network. The step of establishing a planning model for the electrochemical energy storage station based on the probability of an ice storm in the power transmission network, in order to achieve site selection and capacity determination for the electrochemical energy storage station, includes: Construct an overall objective function for investment costs and operating costs. The overall objective is to determine the minimum sum of the total construction cost of the electrochemical energy storage station and the operating cost of the power transmission network under ice storm scenarios by using the overall objective function of the investment cost and operating cost. The overall objective function is expressed as: in, This indicates the total construction cost of the electrochemical energy storage station. This represents a scene of ice storm sampling. The set, Indicates the total duration of the ice storm. Indicates the power transmission network in Ice disaster scene Operating costs below Indicates the weighting coefficient. This indicates that the power transmission network is experiencing an ice storm. The probability of; Establish a two-stage optimization objective for the planning model of the electrochemical energy storage station and the cyber-physical fusion resilience assessment model; The Monte Carlo method was used to conduct sampling simulations under ice disaster scenarios in order to solve the two-stage optimization objectives. The two-stage optimization objectives for establishing the planning model of the electrochemical energy storage station and the cyber-physical fusion resilience assessment model include: The overall objective function for investment costs and operating costs is improved to obtain the improved overall objective function. The improved overall objective function in the planning model of the electrochemical energy storage station and the objective function of system operating cost in the cyber-physical fusion resilience assessment model are optimized and solved; wherein, the improved overall objective function is expressed as: in, This indicates the total construction cost of the electrochemical energy storage station. This represents a scene of ice storm sampling. The set, Indicates the total duration of the ice storm. Indicates the power transmission network in Ice disaster scene Operating costs below Indicates the weighting coefficient. This indicates that the power transmission network is experiencing an ice storm. The probability of.

6. An electronic device, characterized in that, The electronic device includes: a processor; And a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the cyber-physical converged transmission network resilient energy storage planning method as described in any one of claims 1 to 4.

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