Optimal configuration method, device, equipment and medium for power grid CPS

Abstract

The present invention provides a method, device, equipment and medium for optimizing the configuration of a power grid CPS. The optimization configuration method includes: obtaining a first correlation between the construction cost of the power grid CPS and the equipment cost of the equipment of the power grid CPS; obtaining a second correlation between the disaster losses caused by disasters within the construction period of the power grid CPS and the disaster parameters and the equipment technical parameters of the equipment; adjusting the equipment configuration data of the power grid CPS according to the first correlation, the second correlation and a given boundary parameter until the sum of the construction cost and the disaster loss is minimized, wherein the boundary parameter includes the equipment cost, the disaster parameter and the equipment technical parameter. The technical solution of the present invention can more reasonably optimize the configuration of the communication network of the power grid information-physical system.

Description

Technical Field

[0001] The present invention relates to the field of power transmission technology, and in particular to a method, device, equipment and medium for optimizing configuration of a CPS of a power grid. Background Art

[0002] In the related art, the difference between the power grid CPS (Cyber-Physical Systems) and the ordinary power grid is that there are backup transfer lines and control devices on them. Based on this, the power grid CPS can provide a transfer path when the original power supply path fails, thereby reducing system risks.

[0003] The power grid CPS usually includes an electrical network and a communication network. The structure of the electrical network is relatively fixed, while the communication network is generally a hierarchical structure. The configuration of the switch directly connected to the FTU (Feeder Terminal Unit) at the electrical node has a great impact on the economic and safety of the operation of the power grid CPS. Currently, there is no good way to configure the access layer switch.

[0004] Application Contents

[0005] The present invention provides a method, device, electronic device and non-transient computer calibration storage medium for optimizing configuration of a power grid CPS, so as to solve the defect that the prior art cannot optimize configuration of the power grid CPS well, so as to optimize configuration of the power grid CPS well.

[0006] In a first aspect, the present invention provides a method for optimizing the configuration of a power grid CPS, comprising: obtaining a first correlation between the construction cost of the power grid CPS and the equipment cost of the equipment of the power grid CPS; obtaining a second correlation between disaster losses caused by disasters within the construction period of the power grid CPS and disaster parameters, and equipment technical parameters of the equipment; adjusting the equipment configuration data of the power grid CPS according to the first correlation, the second correlation and given boundary parameters until the sum of the construction cost and the disaster loss is minimized, wherein the boundary parameters include the equipment cost, the disaster parameters and the equipment technical parameters.

[0007] According to an optimization configuration method for a power grid CPS provided by the present invention, before adjusting the device configuration data of the power grid CPS according to the first association relationship, the second association relationship and the given boundary parameters, the optimization configuration method also includes: acquiring historical data of the boundary parameters; inputting the historical data into a preset time series-based prediction model to obtain the boundary parameters within a set time period, and using them as the given boundary parameters to adjust the device configuration data of the power grid CPS.

[0008] According to a method for optimizing the configuration of a power grid CPS provided by the present invention, the prediction model based on time series includes a differential autoregressive moving average model, and the inputting of the historical data into a preset prediction model based on time series includes: inputting the historical data into the following stationary sequence u t The difference autoregressive moving average model of: t =c+φ1u t-1 +…+φ p u t-p +ε t +θ1ε t-1 +…+θ q ε t-q Among them, u t From the sequence y t It is formed after d times of difference, p is the order of the autoregressive model, q is the order of the moving average, d, p and q are all natural numbers, φ1,…φ p are the autoregressive coefficients, θ1,…,θ q is the moving average coefficient, ε t is a white noise process.

[0009] According to a method for optimizing configuration of a power grid CPS provided by the present invention, in the process of adjusting the device configuration data of the power grid CPS according to the first association relationship, the second association relationship and a given boundary parameter, the single disaster loss is calculated according to the following formula:

[0010]

[0011] Among them, Ω l is the load node set, C i represents the loss caused by the unit power loss of node i, W i is the power supply of node i in a unit time interval, is the failure probability of node i at time t, t∈[1,t n ].

[0012] According to a method for optimizing configuration of a CPS of a power grid provided by the present invention, the loss caused by the unit power loss is obtained by dividing the current gross national product by the corresponding power consumption.

[0013] According to a method for optimizing configuration of a power grid CPS provided by the present invention, the boundary parameters include at least one of the following: unit cost of communication lines, unit cost of switch equipment, unit cost of energy storage equipment, loss caused by unit power loss, maximum line output degree of the switch, maximum energy storage time and maximum communication line length.

[0014] According to a method for optimizing configuration of a power grid CPS provided by the present invention, the device configuration data of the power grid CPS is adjusted according to the first association relationship, the second association relationship and given boundary parameters, including adjusting the device configuration data of the power grid CPS according to a genetic algorithm and the first association relationship, the second association relationship and given boundary parameters.

[0015] In a second aspect, the present invention provides an optimization configuration device for a power grid information-physical system CPS, comprising: a first acquisition unit, used to obtain a first correlation relationship between the construction cost of the power grid CPS and the equipment cost of the equipment of the power grid CPS; a second acquisition unit, used to obtain a second correlation relationship between disaster losses caused by disasters within the construction period of the power grid CPS and disaster parameters, and equipment technical parameters of the equipment; an adjustment unit, used to adjust the equipment configuration data of the power grid CPS according to the first correlation relationship, the second correlation relationship and given boundary parameters until the sum of the construction cost and the disaster loss is minimized, wherein the boundary parameters include the equipment cost, the disaster parameters and the equipment technical parameters.

[0016] According to an optimization configuration device for a power grid CPS provided by the present invention, the optimization configuration device also includes a prediction unit for obtaining historical data of the boundary parameters, and inputting the historical data into a preset time series-based prediction model to obtain boundary parameters within a set time period, so as to adjust the equipment configuration data of the power grid CPS as the given boundary parameters.

[0017] According to the optimization configuration device of a power grid CPS provided by the present invention, the prediction model based on time series includes a differential autoregressive moving average model, and the prediction unit is further used to: input the historical data into the following stationary sequence u t The difference autoregressive moving average model of :

[0018] u t =c+φ1u t-1 +…+φ p u t-p +ε t +θ1ε t-1 +…+θ q ε t-q

[0019] Among them, u t From the sequence y t It is formed after d times of difference, p is the order of the autoregressive model, q is the order of the moving average, d, p and q are all natural numbers, ε t is a white noise process.

[0020] According to a power grid CPS optimization configuration device provided by the present invention, the adjustment unit is also used to calculate the single disaster loss according to the following formula:

[0021]

[0022] Among them, C i represents the loss caused by the unit power loss of node i, W i is the power supply of node i in unit time interval Δt, For device i The failure probability at time t, t∈[1, t n ].

[0023] According to the device for optimizing configuration of a power grid CPS provided by the present invention, the adjustment unit is further used to adjust the equipment configuration data of the power grid CPS according to the genetic algorithm and the first association relationship, the second association relationship and a given boundary parameter.

[0024] In a third aspect, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for optimizing configuration of a power grid CPS as described above are implemented.

[0025] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described methods for optimizing configuration of a power grid CPS.

[0026] The method, device, electronic device and non-transitory computer calibrated storage medium for optimizing configuration of the power grid CPS provided by the present invention adjust the equipment configuration data of the power grid CPS according to given boundary parameters so as to minimize the sum of the construction cost and the disaster loss, thereby taking into account both the construction cost and the loss during the construction process of the power grid CPS, ensuring maximum economic benefits and achieving optimized configuration of the power grid CPS. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 It is a flow chart of the optimization configuration method of the power grid CPS provided by the present invention;

[0029] Figure 2 It is a schematic diagram of the CPS morphology evolution process provided by the present invention;

[0030] Figure 3 It is a schematic diagram of the annual gross regional product forecast of a certain city provided by the present invention;

[0031] Figure 4 It is a schematic diagram of the annual electricity consumption forecast of a certain city provided by the present invention.

[0032] Figure 5 It is a schematic diagram of the prediction of the number of incoming typhoons provided by the present invention;

[0033] Figure 6 It is a structural schematic diagram of the optimized configuration device of the power grid CPS provided by the present invention;

[0034] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0036] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0037] like Figure 1 FIG. 1 is a flow chart of a method for optimizing configuration of a power grid CPS according to an embodiment of the present invention. The method provided by the embodiment of the present invention can be executed by any electronic device with computer processing capability, such as a terminal device and / or a server. Figure 1 As shown, the optimization configuration method of the power grid CPS includes:

[0038] Step 102: Obtain a first correlation between the construction cost of the power grid CPS and the equipment cost of the equipment of the power grid CPS.

[0039] Step 104 , obtaining a second correlation between the disaster loss caused by the disaster within the construction period of the power grid CPS and the disaster parameters and the equipment technical parameters of the equipment.

[0040] Step 106, adjusting the equipment configuration data of the power grid CPS according to the first association relationship, the second association relationship and given boundary parameters until the sum of the construction cost and the disaster loss is minimized, wherein the boundary parameters include equipment cost, disaster parameters and equipment technical parameters.

[0041] In the embodiment of the present invention, the power grid CPS may be referred to as CPS, and the energy storage device may be referred to as energy storage.

[0042] The technical solution of the embodiment of the present invention mainly studies the optimal configuration of the CPS access layer switch, that is, given the topology and parameters of the CPS electrical network, the relevant parameters of the switch and energy storage device, the switch and energy storage configuration solution is solved. The switch and energy storage configuration solution includes the location of all switches, the connection of each switch to the FTU, and the energy storage configuration.

[0043] In the embodiment of the present invention, it is assumed that the power grid security can be simply described by the power outage loss. The CPS optimization configuration goal is to minimize the sum of the construction cost of the communication system and the power outage loss during the construction period, that is, to minimize the following objective function, that is, formula (1):

[0044]

[0045] Specifically, the device configuration data of the CPS when formula (1) has the minimum value is the device configuration data of the optimized power grid CPS. The optimized device configuration data in the technical solution of the present invention can take into account both the economy and safety of the construction and operation of the power grid.

[0046] In formula (1), h represents a configuration scheme, which includes the location of all switches in the CPS, the connection of switches to FTUs, and the duration of energy storage. H is the set of all possible configuration schemes, and C(h) is the construction cost under configuration scheme h. The construction cost includes the cost of the communication line connecting the switch and FTU, the cost of the switch equipment, and the cost of energy storage. That is, the construction cost of C(h) is the following formula (2):

[0047] C(h)=C line (h)+C swc (h)+C bat (h)

[0048] =p cline ·L(h)+p swc ·S(h)+p bat ·B(h) (2)

[0049] Among them, p cline represents the unit cost of the communication line, p swc Indicates the unit cost of the switch equipment, p bat represents the unit cost of the energy storage device, where the unit cost of the energy storage device refers to the price of the energy storage device per unit duration, L(h) represents the total length of the communication line under solution h, S(h) represents the total number of switches under solution h, and B(h) represents the total energy storage duration under solution h.

[0050] In the related art, the energy storage devices configured in the same CPS will adopt the same specifications, that is, the energy storage duration of each energy storage device is the same, and the energy storage is only configured at the switch connected to the tie switch FTU of the transfer line, so the following formula (3) can be obtained:

[0051] B(h)=S c (h)·T(h) (3)

[0052] Substituting formula (3) into formula (2), we can obtain the following formula (4):

[0053] C(h)=p cline ·L(h)+p swc ·S(h)+p bat ·S c (h)·T(h) (4)

[0054] Among them, S c (h) represents the total number of switches connected to the tie switch FTU, and T(h) is the energy storage duration of the energy storage device under scheme h. Therefore, for scheme h, the total length of the communication line, the total number of switches, and the energy storage duration need to be paid attention to in the construction cost part.

[0055] The second term of formula (1) represents the disaster loss during the construction period of the CPS communication system. Assume that there are r disasters in the construction period, and the duration and intensity of these r disasters may be different. R i (h) represents the loss caused by the i-th disaster under scheme h, which can be calculated based on the duration of each disaster and the probability of line failure caused by the disaster impact within that duration.

[0056] Assuming that there is a typical disaster that can represent the average level of all disasters during the construction period of the CPS communication system, and its risk under scheme h is recorded as R(h), the objective function can be equivalent to the following formula (5):

[0057] min h∈H C(h)+r·R(h) (5)

[0058] In the embodiment of the present invention, in the process of adjusting the device configuration data of the power grid CPS according to the first association relationship, the second association relationship and the given boundary parameter, the single disaster loss R(h), i.e., R, can be calculated according to the following formula (6):

[0059]

[0060] Among them, Ω l is the load node set, C i represents the loss caused by the unit power loss of node i, W iis the power supply of node i in a unit time interval, is the failure probability of node i at time t, t∈[1, t n ], and its calculation method is not limited in the present invention.

[0061] In the embodiment of the present invention, the loss caused by the unit power loss can be obtained by dividing the current gross national product by the corresponding power consumption.

[0062] Assuming that the electricity consumption loss caused by the unit power loss of all nodes in the system is the same, we estimate this loss by dividing the gross domestic product (GDP) by the electricity consumption. The estimation formula is formula (7):

[0063]

[0064] Here, the temporal and spatial scales of GDP and electricity consumption need to correspond, for example, both use the cumulative values ​​within a certain construction period of the region where the CPS is located.

[0065] Due to the limitation of technology level or construction budget, the outgoing degree of the switch is limited, that is, the number of FTUs that each switch can connect is limited. , Assume that only the same type of switch equipment is used in the CPS, and its outgoing degree upper limit is the maximum outgoing degree of the switch d max , and the number of FTUs actually connected to each switch under solution h is d i (h), i∈Ω S , where Ω swc is a set of switch devices, then a constraint condition of the above formula (5) is the following formula (9):

[0066] 0 <d i (h)≤d max , i∈Ω swc (9)

[0067] Due to the limitation of technology level or construction budget, the energy storage time of the energy storage equipment used in CPS is also limited. The upper limit of the energy storage time is set to the maximum energy storage time T max , it has been assumed that the system is equipped with energy storage devices of the same specification and its energy storage duration is T(h). Then another constraint condition of the above formula (5) is the following formula (10):

[0068] 0≤T(h)≤T max (10)

[0069] In addition, for the effectiveness of signal transmission, the general communication line should not be too long. The upper limit of the line length is set to the maximum communication line length l max , the communication line set under scheme h is Ω cline, then another constraint condition of the above formula (5) is the following formula (11):

[0070] 0 <l i (h)≤l max , i∈Ω cline (11)

[0071] Assume that all FTUs in a partition of a CPS can only be connected to the switches in the partition. If the CPS has only one partition, there is no restriction on the scope of the communication connection. In summary, the entire optimization problem can be expressed as the following equation system consisting of formulas (5), (9), (10) and (11):

[0072] min h∈H C(h)+r·R(h) (5)

[0073] 0 <d i (h)≤d max , i∈Ω swc (9)

[0074] 0≤T(h)≤T max (10)

[0075] 0 <l i (h)≤l max ,i∈Ω cline (11)

[0076] Furthermore, the solution h can be expressed by the following decision variables: [u, t], where u is a row vector, each element of which represents the switch position to which an electrical node FTU is connected, so the dimension of the u vector is equal to the number of electrical nodes n e =|Ω s ∪Ω l |, where Ω s is the power node set, and Ω1 is the load node set.

[0077] Ω s ∪Ω l All nodes in the are numbered from 1 to n. e , the element u in u i ,i∈[1,n e ], indicating the switch position to which the FTU at node i is connected, then the decision variable is an integer, and its value range is [1,n e ].

[0078] Due to the limitation of the switch's outgoing degree, the maximum number of repeated elements in u does not exceed the switch's maximum outgoing degree d max Due to the limitation of communication line length, node i and node u i The length of the communication line between them does not exceed the maximum communication line length lmax .

[0079] In addition, the t in the decision variable represents the energy storage duration, that is, t≤T max , where t is a natural number, T max The maximum energy storage duration. The energy storage duration of the energy storage device can be selected in multiple levels.

[0080] Thus, a detailed expression of the CPS optimization configuration model can be obtained. The CPS optimization configuration model is an integer programming problem with inequality constraints. The parameters involved include the unit cost of the communication line, the unit cost of the switch equipment, the unit cost of the energy storage equipment, the loss caused by the unit power loss, the maximum line degree of the switch, the maximum energy storage time and the maximum communication line length.

[0081] The various cost parameters such as the unit cost of the communication line p cline , Unit cost of switch equipment swc , Unit cost of energy storage equipment bat 、Loss caused by unit power loss C ele , typical disaster parameters and various equipment parameters such as the maximum line length of the switch d max , Maximum energy storage time T max , Maximum communication line length l max By inputting the known quantity into formula (5), the optimal configuration scheme and the corresponding optimal total investment, i.e., the minimum sum of construction cost and system loss, can be solved. Here, the equipment configuration data of the power grid CPS can be adjusted according to the black box optimizer such as genetic algorithm and the first association relationship, the second association relationship and the given boundary parameters.

[0082] According to the CPS optimization configuration model, given the boundary parameters including various cost parameters, disaster parameters and equipment technical parameters, the optimal configuration scheme of the CPS communication network can be solved. The evolution and development of CPS is actually to choose such an optimal scheme during planning and construction. Since the above boundary parameters are constantly changing with the changes in social and economic levels, science and technology, and the natural environment, the CPS form is also constantly evolving. Therefore, the study of the evolution of CPS form can be transformed into the study of the evolution of the above boundary parameters. The overall idea is as follows Figure 2 shown.

[0083] It is difficult to model the development trend of boundary parameters through the influence of external conditions. In the real-time example of the present invention, a prediction model based on time series can be adopted, that is, historical data of relevant boundary parameters are collected, and the classic time series prediction model is used to calculate the boundary parameters in the future period of time to describe the development trend of the boundary parameters.

[0084] Specifically, before step 106, historical data of boundary parameters are obtained; the historical data are input into a preset prediction model based on time series to obtain boundary parameters within a set time period, which are used as given boundary parameters to adjust the equipment configuration data of the power grid CPS.

[0085] The boundary parameters required in the CPS optimization configuration model include: the unit cost of the communication line p cline , Unit cost of replacement equipment swc , Unit cost of energy storage equipment bat 、Loss caused by unit power loss C ele , Typical disaster parameters, Maximum line length of the switch d max , Maximum energy storage time T max , Maximum communication line length l max These boundary parameters are closely related to the level of science and technology, and are also related to the budget constraints of power grid construction. These boundary parameters lack official and comprehensive historical statistics, so the authenticity and validity of the data will not be explored too much here.

[0086] The loss caused by the unit loss of electricity C ele It is defined as GDP divided by total electricity consumption. There are complete statistical data on GDP and total electricity consumption, which can truly reflect the role of electricity in the level of social and economic development, and thus can measure the economic losses caused by power grid failures.

[0087] In the embodiment of the present invention, the annual GDP and annual electricity consumption of a certain city are taken as an example for calculation. The typical disaster parameters select the national inbound typhoon data, which is sourced from the meteorological station data. In actual application, it is necessary to conduct specific statistics and analysis on the annual GDP and annual electricity consumption, typical disaster parameters and other data of the area where the CPS is located.

[0088] For time series prediction, we can simply use the classic Autoregressive Integrated Moving Average model (ARIMA) for prediction. The principle of ARIMA is briefly described as follows: Consider the sequence y t , if it can become a stationary series after d times of difference

[0089] u t =Δ d y t

[0090] Then we can calculate the stationary series u t Establish an ARMA(p,q) model:

[0091] u t =c+φ1u t-1 +…+φp u t-p +ε t +θ1ε t-1 +…+θ q ε t-q

[0092] The ARMA(p,q) model after d-order difference is also called the ARIMA(p,d,q) model, where p is the order of the autoregressive model, q is the order of the moving average, and ε t is a white noise process.

[0093] Specifically, when performing time series forecasting, historical data is input into the following stationary sequence u t The difference autoregressive moving average model of: t =c+φ1u t-1 +…+φ p u t-p +ε t +θ1ε t-1 +…+θ q ε t-q Among them, u t From the sequence y t It is formed after d times of difference, p is the order of the autoregressive model, q is the order of the moving average, d, p and q are all natural numbers, φ1,…φ p are the autoregressive coefficients, θ1,…,θ q is the moving average coefficient, ε t is a white noise process.

[0094] Taking the GDP and annual electricity consumption of a certain city as an example, the prediction results are as follows: Figure 3 and Figure 4 The forecast of the number of typhoons entering the country each year is as follows: Figure 5 As shown, if the construction period is assumed to be 5 years, the number of typhoons within 5 years needs to be summed up.

[0095] In the real-time example of the present invention, the CPS evolution model can be applied to a 62-node example. Since the example is not an actual power grid and has no relevant regional statistical information, the CPS evolution model can be applied to a 62-node example. ele The typical disaster parameters will use the sample data above. The length of the communication line is only limited by the partition, and the unit cost of the communication line is p cline , Unit cost of replacement equipment swc , Unit cost of energy storage equipment bat 、Loss caused by unit power loss C ele , Maximum line length of the switch d max , Maximum energy storage time T maxThe boundary parameters will be predicted according to the overall trend of market prices. For example, the data shown in Table 1 below can be obtained, which has no strict statistical and practical significance.

[0096] Table 1 Historical and predicted values ​​of boundary parameters

[0097]

[0098]

[0099] In this way, the CPS optimization configuration solutions can be solved separately. For example, the solution for the 2020 data is as follows. A total of 11 access layer switches are configured in this CPS, and the node FTUs connected to each switch are shown in Table 2.

[0100] Table 2 Nodes connected to the switch

[0101]

[0102] In order to understand the optimized configuration solution more intuitively, the indicators shown in Table 3 below are selected for statistics. The key point selection indicator is that the switch line output degree reaches the upper limit. It can be seen that the key point numbers in the CPS are 2, 24, and 44. The power supply guarantee of these key nodes should be strengthened during construction.

[0103] Table 3 Statistics of CPS optimization configuration solutions

[0104]

[0105]

[0106] In addition, we can see that the proportion of power outage losses during the construction period is gradually increasing. This is firstly because we predict that the number of natural disasters will increase year by year. On the other hand, we predict that the GDP that can be created by a unit of electricity is also increasing year by year. That is, if the power supply of the power grid can be guaranteed, then the value created will be greater and the losses caused by power outages will be greater. Therefore, we should pay more attention to the safety of power grid operation. The test results also show that it is not reliable enough to ensure the security of the communication network only through energy storage. We should further explore the impact of configuring energy storage at the load, changing the power grid topology to increase transfer, and strengthening emergency repair and recovery on the security of the power grid.

[0107] The method for optimizing the configuration of the power grid CPS of an embodiment of the present invention adjusts the equipment configuration data of the power grid CPS according to given boundary parameters to minimize the sum of the construction cost and the disaster loss, thereby taking into account both the construction cost and the loss during the construction process of the power grid CPS, ensuring maximum economic benefits and achieving optimized configuration of the power grid CPS.

[0108] The following is a description of the optimized configuration device of the power grid CPS provided by the present invention. The optimized configuration device of the power grid CPS described below and the optimized configuration method of the power grid CPS described above can correspond to each other.

[0109] like Figure 6 As shown, the power grid CPS optimization configuration device provided by the embodiment of the present invention includes:

[0110] The first acquisition unit 602 is used to acquire a first correlation between the construction cost of the power grid CPS and the equipment cost of the equipment of the power grid CPS.

[0111] The second acquisition unit 604 is used to acquire a second correlation relationship between the disaster loss caused by the disaster within the construction period of the power grid CPS and the disaster parameters and the equipment technical parameters of the equipment.

[0112] The adjustment unit 606 is used to adjust the equipment configuration data of the power grid CPS according to the first association relationship, the second association relationship and the given boundary parameters until the sum of the construction cost and the disaster loss is minimized, wherein the boundary parameters include equipment cost, disaster parameters and equipment technical parameters.

[0113] The technical solution of the embodiment of the present invention mainly studies the optimal configuration of the CPS access layer switch, that is, given the topology and parameters of the CPS electrical network, the relevant parameters of the switch and energy storage device, the switch and energy storage configuration solution is solved. The switch and energy storage configuration solution includes the location of all switches, the connection of each switch to the FTU, and the energy storage configuration.

[0114] In the embodiment of the present invention, it is assumed that the power grid security can be simply described by the power outage loss. The CPS optimization configuration goal is to minimize the sum of the construction cost of the communication system and the power outage loss during the construction period, that is, to minimize the following objective function, that is, formula (1):

[0115]

[0116] Specifically, the device configuration data of the CPS when formula (1) has the minimum value is the device configuration data of the optimized power grid CPS. The optimized device configuration data in the technical solution of the present invention can take into account both the economy and safety of the construction and operation of the power grid.

[0117] In an embodiment of the present invention, the optimization configuration device also includes a prediction unit for acquiring historical data of boundary parameters, and inputting the historical data into a preset time series-based prediction model to obtain boundary parameters within a set time period, so as to adjust the equipment configuration data of the power grid CPS as given boundary parameters.

[0118] In the embodiment of the present invention, the prediction model based on the time series includes a differential autoregressive moving average model, and the prediction unit is further used to: input the historical data into the following stationary sequence u t The difference autoregressive moving average model of :

[0119] u t =c+φ1u t-1 +…+φ p u t-p +ε t +θ1ε t-1 +…+θ q ε t-q

[0120] Among them, u t From the sequence y t It is formed after d times of difference, p is the order of the autoregressive model, q is the order of the moving average, d, p and q are all natural numbers, ε t is a white noise process.

[0121] In the embodiment of the present invention, the adjustment unit 606 is further configured to calculate the single disaster loss according to the following formula:

[0122]

[0123] Among them, C i represents the loss caused by the unit power loss of node i, W i is the power supply of node i in unit time interval Δt, For device i The failure probability at time t, t∈[1, t n ].

[0124] In the embodiment of the present invention, the adjustment unit 606 is further configured to adjust the device configuration data of the power grid CPS according to the genetic algorithm, the first association relationship, the second association relationship and the given boundary parameters.

[0125] It can be understood that the above-mentioned power grid CPS optimization configuration device can implement the various steps of the power grid CPS optimization configuration method provided in the aforementioned embodiment. The relevant explanations on the power grid CPS optimization configuration method are applicable to the power grid CPS optimization configuration device and will not be repeated here.

[0126] The power grid CPS optimization configuration device of the embodiment of the present invention adjusts the equipment configuration data of the power grid CPS according to given boundary parameters to minimize the sum of the construction cost and the disaster loss, thereby taking into account both the construction cost and the loss during the construction process of the power grid CPS, ensuring maximum economic benefits and achieving optimal configuration of the power grid CPS.

[0127] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communication interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the optimization configuration method of the power grid CPS, the method comprising: obtaining a first correlation relationship between the construction cost of the power grid CPS and the equipment cost of the equipment of the power grid CPS; obtaining a second correlation relationship between the disaster loss caused by the disaster during the construction period of the power grid CPS and the disaster parameters and the equipment technical parameters of the equipment; adjusting the equipment configuration data of the power grid CPS according to the first correlation relationship, the second correlation relationship and the given boundary parameters until the sum of the construction cost and the disaster loss is minimized, wherein the boundary parameters include the equipment cost, the disaster parameters and the equipment technical parameters.

[0128] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0129] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the optimization configuration method of the power grid CPS provided by the above-mentioned methods, and the method includes: obtaining a first correlation relationship between the construction cost of the power grid CPS and the equipment cost of the equipment of the power grid CPS; obtaining a second correlation relationship between disaster losses caused by disasters within the construction period of the power grid CPS and disaster parameters and equipment technical parameters of the equipment; adjusting the equipment configuration data of the power grid CPS according to the first correlation relationship, the second correlation relationship and given boundary parameters until the sum of the construction cost and the disaster loss is minimized, wherein the boundary parameters include the equipment cost, the disaster parameters and the equipment technical parameters.

[0130] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the above-mentioned methods for optimizing configuration of the power grid CPS, the methods comprising: obtaining a first correlation between the construction cost of the power grid CPS and the equipment cost of the equipment of the power grid CPS; obtaining a second correlation between disaster losses caused by disasters within the construction period of the power grid CPS and disaster parameters and equipment technical parameters of the equipment; adjusting the equipment configuration data of the power grid CPS according to the first correlation, the second correlation and given boundary parameters until the sum of the construction cost and the disaster loss is minimized, wherein the boundary parameters include the equipment cost, the disaster parameters and the equipment technical parameters.

[0131] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0132] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the configuration of a power grid cyber-physical system (CPS), characterized in that: include: Obtaining a first correlation between the construction cost of the power grid CPS and the equipment cost of the equipment of the power grid CPS, wherein the construction cost includes the cost of a communication line connecting the switch and the FTU, the cost of the switch equipment, and the cost of energy storage; Obtaining a second correlation relationship between disaster losses caused by disasters during the construction period of the power grid CPS and disaster parameters and equipment technical parameters of the equipment; Adjusting the equipment configuration data of the power grid CPS according to the first association relationship, the second association relationship and a given boundary parameter until the sum of the construction cost and the disaster loss is minimized, wherein the boundary parameter includes the equipment cost, the disaster parameter and the equipment technical parameter; Before adjusting the device configuration data of the power grid CPS according to the first association relationship, the second association relationship and the given boundary parameter, the optimization configuration method further includes: Obtaining historical data of the boundary parameters; The historical data is input into a preset time series-based prediction model to obtain boundary parameters within a set time period, which are used as the given boundary parameters to adjust the equipment configuration data of the power grid CPS.

2. The optimization configuration method according to claim 1, characterized in that: The prediction model based on time series includes a differential autoregressive moving average model, and the inputting of the historical data into the preset prediction model based on time series includes: Input the historical data into the following stationary sequence u t The difference autoregressive moving average model of : you t =c+φ1u t-1 +…+φ p you t-p +e t +θ1ε t-1 +…+θ q e t-q Among them, u t From the sequence y t It is formed after d times of difference, p is the order of the autoregressive model, q is the order of the moving average, d, p and q are all natural numbers, φ1,…φ p are the autoregressive coefficients, θ1,...,θ q is the moving average coefficient, ε t is a white noise process.

3. The optimization configuration method according to claim 1, characterized in that: In the process of adjusting the equipment configuration data of the power grid CPS according to the first association relationship, the second association relationship and the given boundary parameter, the single disaster loss is calculated according to the following formula: Among them, Ω l is the load node set, C i represents the loss caused by the unit power loss of node i, W i is the power supply of node i in a unit time interval, is the failure probability of node i at time t, t∈[1, t n ].

4. The optimization configuration method according to claim 3, characterized in that: The loss caused by the unit amount of power loss is obtained by dividing the current gross national product by the corresponding amount of power used.

5. The optimization configuration method according to claim 3, characterized in that: The boundary parameters include at least one of the following: unit cost of communication lines, unit cost of switch equipment, unit cost of energy storage equipment, loss caused by unit power loss, maximum line output degree of the switch, maximum energy storage time and maximum communication line length.

6. The optimization configuration method according to claim 5, characterized in that: The adjusting the device configuration data of the power grid CPS according to the first association relationship, the second association relationship and the given boundary parameters includes adjusting the device configuration data of the power grid CPS according to a genetic algorithm and the first association relationship, the second association relationship and the given boundary parameters.

7. An optimization configuration device for a power grid cyber-physical system CPS, characterized in that: include: A first acquisition unit is used to acquire a first correlation between the construction cost of the power grid CPS and the equipment cost of the equipment of the power grid CPS, wherein the construction cost includes the cost of the communication line connecting the switch and the FTU, the cost of the switch equipment, and the cost of energy storage; A second acquisition unit is used to acquire a second correlation relationship between the disaster loss caused by the disaster within the construction period of the power grid CPS and the disaster parameter and the equipment technical parameter of the equipment; an adjusting unit, configured to adjust the equipment configuration data of the power grid CPS according to the first association relationship, the second association relationship and a given boundary parameter until the sum of the construction cost and the disaster loss is minimized, wherein the boundary parameter includes the equipment cost, the disaster parameter and the equipment technical parameter; Before adjusting the device configuration data of the power grid CPS according to the first association relationship, the second association relationship and the given boundary parameter, the optimization configuration method further includes: Obtaining historical data of the boundary parameters; The historical data is input into a preset time series-based prediction model to obtain boundary parameters within a set time period, which are used as the given boundary parameters to adjust the equipment configuration data of the power grid CPS.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for optimizing configuration of a power grid CPS as described in any one of claims 1 to 6 are implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing configuration of a power grid CPS as claimed in any one of claims 1 to 6 are implemented.

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