Security calculation method and device for source-storage-load collaborative scheduling strategy

Abstract

The invention provides a security calculation method and device for a source, storage and load cooperative scheduling strategy. The method comprises the steps that a source storage load cooperative scheduling strategy of a power grid is acquired, constraint conditions of all operation nodes of the power grid are determined, and the constraint conditions comprise the maximum charging and discharging power constraint of an energy storage device of the power grid, the charge state constraint of the energy storage device, the charging and discharging balance constraint of the energy storage device, the safety constraint condition considering the out-of-limit risk and the power flow constraint; determining the Euclidean distance between each operation node of the power grid and the constraint condition according to a source storage load cooperative scheduling strategy; and according to the Euclidean distance, determining the security of the source-storage-load cooperative scheduling strategy. Based on the calculation of the Euclidean distance, an index with relatively high accuracy for evaluating the safety can be provided for the source storage load collaborative scheduling strategy, the stability of the power grid cannot be threatened under the condition that a new scheduling strategy is ensured, and the problem that the accuracy for evaluating the safety of the power grid collaborative scheduling strategy is relatively low in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of secure computing technology for source-storage-load collaborative scheduling strategies, and more specifically, to a secure computing method, device, computer-readable storage medium, and electronic device for source-storage-load collaborative scheduling strategies. Background Art

[0002] With the continuous growth of various new loads and the large-scale access of new energy sources, load fluctuations in urban power grids threaten the safe operation and reliable power supply of power grids. As one of the important means to improve the regulation capacity of power systems, source-storage-load coordinated dispatching helps power grids to make full use of massive distributed resources and improve the safe operation level of power grids. At present, from the perspective of national strategy and local layout, the construction of energy storage power stations is being accelerated, and local power grids are accelerating the construction of load management systems. However, the new loads, outputs and topological structures after coordinated dispatching will have unpredictable effects on the safety of the power grid, and even cause the risk of exceeding the limit. Therefore, it is necessary to make corresponding safety assessments for each coordinated dispatching strategy.

[0003] The existing technology has the problem of low accuracy in evaluating the safety of power grid coordinated dispatching strategies. Summary of the invention

[0004] The main purpose of this application is to provide a safe calculation method, device, computer-readable storage medium and electronic device for source-storage-load collaborative scheduling strategy, so as to at least solve the problem of low accuracy in evaluating the safety of power grid collaborative scheduling strategy in the prior art.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a safety calculation method for a source-storage-load collaborative scheduling strategy is provided, including: obtaining the source-storage-load collaborative scheduling strategy of the power grid, and determining the constraints of each operating node of the power grid, wherein the constraints include the maximum charging and discharging power constraints of the energy storage device of the power grid, the charge state constraints of the energy storage device, the charging and discharging balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the flow constraints; according to the source-storage-load collaborative scheduling strategy, determining the Euclidean distance between each operating node of the power grid and the constraint conditions; determining the safety of the source-storage-load collaborative scheduling strategy based on the Euclidean distance.

[0006] Optionally, according to the source-storage-load collaborative scheduling strategy, the Euclidean distance between each operating node of the power grid and the constraint condition is determined, including: according to the source-storage-load collaborative scheduling strategy, combined with the topological structure of the power grid, using a particle swarm algorithm to determine the maximum transmission power of each branch in the topological structure; according to the maximum transmission power, determining the Euclidean distance between each operating node of the power grid and the constraint condition, wherein the operating node is the energy storage device in the power grid, and the operating node includes power data of the energy storage device.

[0007] Optionally, determining the Euclidean distance between each operating node of the power grid and the constraint condition includes: using a Euclidean distance formula: Determine the Euclidean distance between each of the operating nodes and the constraint condition, where P i，max is the maximum transmission capacity of branch i, N n is the number of running nodes, C k-i is the impact factor of the unit injected power change of the operating node k on the power flow of branch i, M SD，i is the Euclidean distance of the branch.

[0008] Optionally, using a particle swarm algorithm to determine the maximum transmission power of each branch in the topology structure includes: using a particle swarm algorithm formula: Determine the maximum transmission power of each branch in the power grid topology, where: is the maximum transmission power of the branch as a particle in the dth dimension in the k+1th iteration, d is the dimension, is the historical optimal transmission capacity of the particle in the dth dimension in the kth iteration, is the transmission capacity of the particle in the dth dimension at the kth iteration.

[0009] Optionally, determining the constraint conditions of each operating node of the power grid includes: according to a first formula: Determine the power flow constraints of each operating node of the power grid, where P k (t) is the active power of k operating node, Q k (t) is the reactive power of the k operating node, u k (t) represents the voltage of the operating node k during period t, g ks is the conductance between the k running node and the s running node, b ks is the susceptance between the k running node and the s running node, δ ks (t) is the voltage phase angle difference between the k operating node and the s operating node.

[0010] Optionally, determining the constraint conditions of each operating node of the power grid includes: according to a second formula: Determine the safety constraint conditions of each operating node of the power grid considering the risk of over-limit, where P L0，k (t) is the load of k operating nodes before the reconstruction of the high-voltage distribution network in period t, P fig，k (t) is the load transferred from other operating nodes to operating node k through the reconstruction of the high-voltage distribution network during period t, P G，k (t) is the power injected into the running node k by the upper power supply, P PV，k (t) is the photovoltaic output at the operating node k during period t, P S，k(t) is the sum of the charging and discharging power of the energy storage device at the operating node k during period t, P C，k (t) represents the load shedding amount of the operating node k during period t.

[0011] Optionally, determining the safety of the source-storage-load collaborative scheduling strategy based on the Euclidean distance includes: determining whether the Euclidean distance is less than a preset distance; and determining that the source-storage-load collaborative scheduling strategy of the power grid is unsafe when the Euclidean distance is less than the preset distance.

[0012] According to another aspect of the present application, a safe computing device for a source-storage-load collaborative scheduling strategy is provided, comprising: an acquisition unit, used to acquire the source-storage-load collaborative scheduling strategy of a power grid, and determine the constraints of each operating node of the power grid, wherein the constraints include the maximum charging and discharging power constraints of the energy storage device of the power grid, the charge state constraints of the energy storage device, the charging and discharging balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the flow constraints; a first determination unit, used to determine the Euclidean distance between each operating node of the power grid and the constraint conditions according to the source-storage-load collaborative scheduling strategy; and a second determination unit, used to determine the safety of the source-storage-load collaborative scheduling strategy according to the Euclidean distance.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the security computing methods for source-storage-load collaborative scheduling strategies.

[0014] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a secure computing method for executing any one of the source-storage-load collaborative scheduling strategies.

[0015] Apply the technical solution of the present application to obtain the source-storage-load coordinated dispatching strategy of the power grid, and determine the constraints of each operating node of the power grid, wherein the constraints include the maximum charge and discharge power constraints of the energy storage device of the power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the flow constraints; according to the source-storage-load coordinated dispatching strategy, determine the Euclidean distance between each operating node of the power grid and the constraints; determine the safety of the source-storage-load coordinated dispatching strategy based on the Euclidean distance. Based on the calculation of the Euclidean distance, a high-accuracy indicator for evaluating the safety of the source-storage-load coordinated dispatching strategy can be provided to ensure that under the new dispatching strategy, there will be no threat to the stability of the power grid, solving the problem of low accuracy in evaluating the safety of the coordinated dispatching strategy of the power grid in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for executing a secure computing method for a source-storage-load collaborative scheduling strategy provided in an embodiment of the present application is shown;

[0018] Figure 2 A schematic flow chart of a secure calculation method for a source-storage-load collaborative scheduling strategy provided according to an embodiment of the present application is shown;

[0019] Figure 3 A structural block diagram of a secure computing device for a source-storage-load collaborative scheduling strategy provided according to an embodiment of the present application is shown.

[0020] The above drawings include the following reference numerals:

[0021] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] As introduced in the background technology, the prior art has a low accuracy in evaluating the security of power grid collaborative dispatching strategies. In order to solve the problem of low accuracy in evaluating the security of power grid collaborative dispatching strategies in the prior art, the embodiments of the present application provide a safe calculation method, device, computer-readable storage medium and electronic device for a source-storage-load collaborative dispatching strategy.

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0027] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal for a secure computing method for source-storage-load collaborative scheduling strategy according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1More or fewer components as shown, or with Figure 1 Different configurations shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the secure computing method for the source-storage-load collaborative scheduling strategy in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. The transmission device 106 is used to receive or send data via a network. The above network specific examples may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a secure calculation method for a source-storage-load collaborative scheduling strategy running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] Figure 2 It is a flowchart of a safe calculation method for a source-storage-load collaborative scheduling strategy according to an embodiment of the present application.

[0031] like Figure 2 As shown, the method comprises the following steps:

[0032] Step S201, obtaining the source-storage-load coordinated dispatching strategy of the power grid, and determining the constraints of each operating node of the above power grid, wherein the above constraints include the maximum charge and discharge power constraints of the energy storage device of the above power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the power flow constraints;

[0033] Among them, the source-storage-load coordinated scheduling strategy refers to a strategy in the energy and energy storage system that coordinates the scheduling of source energy and energy storage equipment to achieve efficient operation of the system and optimize energy utilization.

[0034] Among them, the maximum charge and discharge power constraint of the energy storage device is: P S，min ≤P S (t)≤P S，max , where P S (t) represents the output of the energy storage device in the t period. When charging, P S (t)<0, when discharging, P S (t)>0,P S，max and P S，min (P S，min <0) represent the maximum discharge power and maximum charging power of the energy storage device, respectively, and have different extreme values ​​according to the characteristics of different devices.

[0035] The state of charge constraint of the energy storage device is: SOC min ≤SOC(t)≤SOC max , where SOC(t) represents the state of charge of the energy storage device at the tth period, SOC min and SOC max They represent the upper and lower limits of the state of charge of the energy storage device. In order to extend the service life of the energy storage device, it is necessary to balance the energy of the energy storage system throughout the scheduling cycle, and introduce the charge and discharge balance constraint of the energy storage device.

[0036] The charge and discharge balance constraint of the energy storage device is:

[0037] Step S202, determining the Euclidean distance between each of the operating nodes of the power grid and the constraint condition according to the source-storage-load coordinated scheduling strategy;

[0038] Specifically, each energy storage device in the power grid topology structure is used as the above-mentioned operating node.

[0039] Step S203: determining the safety of the source-storage-load collaborative scheduling strategy according to the Euclidean distance.

[0040] Through this embodiment, the source-storage-load collaborative dispatching strategy of the power grid is obtained, and the constraints of each operating node of the power grid are determined, wherein the constraints include the maximum charge and discharge power constraints of the energy storage device of the power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the flow constraints; according to the source-storage-load collaborative dispatching strategy, the Euclidean distance between each operating node of the power grid and the constraints is determined; according to the Euclidean distance, the safety of the source-storage-load collaborative dispatching strategy is determined. Based on the calculation of the Euclidean distance, a high-accuracy indicator for evaluating the safety of the source-storage-load collaborative dispatching strategy can be provided to ensure that under the new dispatching strategy, there will be no threat to the stability of the power grid, solving the problem of low accuracy in evaluating the safety of the collaborative dispatching strategy of the power grid in the prior art.

[0041] During the specific implementation process, according to the source-storage-load coordinated scheduling strategy, the Euclidean distance between each of the above-mentioned operating nodes of the above-mentioned power grid and the above-mentioned constraints is determined, including: according to the above-mentioned source-storage-load coordinated scheduling strategy, combined with the topological structure of the above-mentioned power grid, a particle swarm algorithm is used to determine the maximum transmission power of each branch in the above-mentioned topological structure; according to the above-mentioned maximum transmission power, the Euclidean distance between each of the above-mentioned operating nodes of the above-mentioned power grid and the above-mentioned constraints is determined, wherein the above-mentioned operating node is the above-mentioned energy storage device in the above-mentioned power grid, and the above-mentioned operating node includes the power data of the above-mentioned energy storage device.

[0042] More specifically, the particle swarm algorithm is used to determine the maximum transmission power of each branch in the above topology structure, including: using the particle swarm algorithm formula: Determine the maximum transmission power of each branch in the power grid topology, where: is the maximum transmission power of the above branch as a particle in the dth dimension in the k+1th iteration, where d is the dimension, is the historical optimal transmission capacity of the particle in the dth dimension in the kth iteration, is the transmission capacity of the particle in the dth dimension at the kth iteration.

[0043] Among them, the particle swarm algorithm is an optimization algorithm based on swarm intelligence, which can be used to solve complex optimization problems. When determining the maximum transmission power of each branch in the topology, the problem can be transformed into a multi-objective optimization problem, the goal of which is to maximize the transmission power of each branch while taking into account the constraints of the system, such as current, voltage and other restrictions. Through the particle swarm algorithm, the position and velocity of the particles can be continuously updated iteratively, so that the particles can find the optimal solution in the search space. In each iteration, by evaluating the fitness value of each particle, the solution with the maximum transmission power can be found. The final result can effectively optimize the transmission power of each branch in the topology, so that the performance of the system is improved. In general, the use of the particle swarm algorithm to determine the maximum transmission power of each branch in the topology can achieve better results, and can effectively optimize the performance of the system and meet the constraints.

[0044] Specifically, determining the Euclidean distance between each of the operating nodes of the power grid and the constraint condition includes: using the Euclidean distance formula: Determine the Euclidean distance between each of the above operating nodes and the above constraints, where P i，max is the maximum transmission capacity of branch i, N n is the number of the above running nodes, C k-i is the impact factor of the power change of the operating node k on the branch i power flow, M SD，i is the Euclidean distance of the above branch.

[0045] Further, determining the constraint conditions of each operating node of the above power grid includes: according to the first formula: Determine the power flow constraints of the above-mentioned operation nodes of the above-mentioned power grid, where P k (t) is the active power of k operating node, Q k (t) is the reactive power of the k operating node, u k (t) represents the voltage of the operating node k during period t, g ks is the conductance between the k running node and the s running node, b ks is the susceptance between the k running node and the s running node, δ ks (t) is the voltage phase angle difference between the k operating node and the s operating node.

[0046] Furthermore, determining the constraint conditions of each operating node of the above power grid includes: according to the second formula: Determine the safety constraints of the above-mentioned operation nodes of the above-mentioned power grid considering the risk of over-limit, where P L0，k (t) is the load of k operating nodes before the reconstruction of the high-voltage distribution network in period t, P fig，k (t) is the load transferred from other operating nodes to operating node k through the reconstruction of the high-voltage distribution network during period t, P G，k(t) is the power injected into the running node k by the upper power supply, P PV，k (t) is the photovoltaic output at the operating node k during period t, P S，k (t) is the sum of the charging and discharging power of the energy storage device at the operating node k during period t, P C，k (t) represents the load shedding amount of the operating node k during period t.

[0047] Specifically, determining the safety of the source-storage-load collaborative scheduling strategy based on the Euclidean distance includes: determining whether the Euclidean distance is less than a preset distance; and determining that the source-storage-load collaborative scheduling strategy of the power grid is unsafe when the Euclidean distance is less than the preset distance.

[0048] Among them, the preset distance can be set to 0. When it is determined that the Euclidean distance is less than 0, the source-storage-load collaborative scheduling strategy of the operating node is considered unsafe.

[0049] This method represents all the operating nodes that meet the safety constraints with a two-dimensional polygon. The data representing the operating point includes the power of the node, and its boundary represents all the constraints, including branch flow constraints, power source active output constraints, energy storage power station charging and discharging power constraints, etc. The specific meaning of the boundary point is the maximum / minimum transmission power that meets various constraints. Because the constraints meet the continuity, the safety margin can be represented by the Euclidean geometric distance. SD is the shortest Euclidean distance from the current operating point to the boundary. If the safety margin is a negative value, it means that the current operating point does not meet the constraints, and the current operation state of the power grid has a great risk. In order to characterize the operation safety of the 220kV power grid, the safety distance is defined as the safety margin indicator. Compared with other indicators, this indicator not only considers the remaining transmission capacity of the branch, but also takes the branch location into account, that is, the topological structure of the network. When this indicator is used as the safety margin evaluation indicator, the network safety margin is determined by the branch with the smallest safety distance, which can better reflect the impact of the branch topological position difference on the operation safety of the 220kV transmission network. The reconstruction of the high-voltage distribution network and the charging and discharging behavior of the energy storage power station will change the power injection of the 220kV system node, causing changes in the branch flow. However, each node has different degrees of influence on the flow deviation of different branches. Therefore, in order to fully consider the impact of all nodes on the branch flow.

[0050] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the safe calculation method for the source-storage-load collaborative scheduling strategy of the present application will be described in detail below in combination with specific embodiments.

[0051] This embodiment relates to a specific safety calculation method for source-storage-load coordinated scheduling strategy, which specifically includes the following contents:

[0052] First, we need to make calculations based on the current dispatching method: currently, the price-responsive load only participates in the day-ahead dispatching, while the frequency-responsive load can participate in both the day-ahead dispatching and the intraday dispatching. Since the flexible load is scattered, the dispatching center controls the load aggregator and the energy storage control center, and then the load aggregator and the energy storage control center control the flexible load and energy storage.

[0053] The energy storage power station includes a power dispatching center and multiple battery systems with adjustable charging and discharging power, where each battery system Aa (a=1, 2, ..., n) is independent of each other and consists of a power storage converter (PCS), a battery management system (BMS) and a battery stack (BP). The main function of the PCS is to realize the bidirectional conversion of the AC and DC states of electric energy, and send charging and discharging commands to the battery according to the instructions of the power dispatching center of the energy storage power station; the BMS monitors the operating status of the battery by collecting the battery's voltage, current and other status information; the BP is used to store electric energy and charge or discharge each battery according to the dispatching task.

[0054] The following constraints need to be met for the day-ahead optimal scheduling of energy storage devices.

[0055] 1) Maximum charge and discharge power constraints of energy storage devices:

[0056] P S，min ≤P S (t)≤P S，max , where: P S (t) represents the output of the energy storage device in the t period. When charging, P S (t)<0, when discharging, P S (t)>0;P S，max and P S，min (P S，min <0) represent the maximum discharge power and maximum charging power of the energy storage device, respectively, and have different extreme values ​​according to the characteristics of different devices.

[0057] 2) Constraints on the state of charge of energy storage devices:

[0058] SOC min ≤SOC(t)≤SOC max , where: SOC(t) represents the state of charge of the energy storage device at the tth time period; SOC min and SOC max They represent the upper and lower limits of the state of charge of the energy storage device. In order to extend the service life of the energy storage device, it is necessary to balance the energy of the energy storage system throughout the scheduling cycle, and introduce the charge and discharge balance constraint of the energy storage device.

[0059] 3) Energy storage device charge and discharge balance constraints:

[0060] 4) Safety constraints considering the risk of exceeding the limit:

[0061] Power balance constraint: The sum of all injected powers of the nodes of the 220kV system is kept balanced at any time. The power equation is as follows:

[0062]

[0063] P L0，k (t) is the load of node k before the reconstruction of the high-voltage distribution network in period t; P fig，k (t) is the load transferred from other nodes to node k through the reconstruction of the high-voltage distribution network during period t; P G，k (t) is the power injected into node k by the upper power source; P PV，k (t) is the photovoltaic output at node k during period t; P s，k (t) is the sum of the charging and discharging power of the energy storage station at node k during period t; P C，k (t) represents the load shedding amount in period t. S，a，d (t) and P S，a，c (t) are the discharge power and charge power of battery stack Aa during period t respectively.

[0064] 5) Flow constraints:

[0065] P k (t) = u k (t)∑ s∈k u s (t)(g ks cosδ ks (t)+b ks sinδ ks (t));

[0066] Q k (t) = u k (t)∑ s∈k u s (t)(g ks sinδ ks (t)-b ks cosδ ks (t));

[0067] u k (t) represents the voltage of node k during period t; g ks and b ks denote the conductance and susceptance between node k and node s respectively; δ ks (t) represents the voltage phase difference between node k and node s during period t, P k (t) is the active power of node k, Q k (t) is the reactive power of node k.

[0068] According to the three-point estimation method, for a certain operating condition of random sampling, the particle swarm algorithm is used to calculate the maximum transmission power of each branch according to the above constraints. According to the maximum transmission power, the minimum Euclidean geometric distance between the operating point and the safe operating boundary can be calculated to calculate the safety index.

[0069] Three-point estimation method: Assume that the photovoltaic output and node load are represented by random variables X = [X1, X2, ..X3]. Each random variable x h Take three sampling values ​​respectively. h Take the sampling value x according to the following formula h，p When , the remaining random variables take the mean value to form a sampling sample. The purpose is to make the sampling result obey the probability of normal distribution, x h，p =u h +δ h，p σ h ; Where: u h , σ h are random variables x h The mean and standard deviation of h，p For x h The position coefficient is calculated as follows:

[0070] p=1,2,δ h，p =0, p=3; where γ h，3 , γ h，4 are the skewness and kurtosis of the random sample.

[0071] By using the particle swarm algorithm, starting from a random solution, the quality of the solution is evaluated by fitness, and the global optimal solution is found by following the currently searched optimal value. The safety distance is used as the fitness value in the algorithm, and the variance and node power are used as the position and speed attributes in the algorithm. The direction is changed according to the local optimal solution, and the final gathering point is the global optimal solution.

[0072] The particle swarm algorithm starts from a random solution and searches for the optimal solution through iteration. It also evaluates the quality of the solution through fitness. It searches for the global optimum by following the currently searched optimal value. In the particle swarm algorithm, the solution of each optimization problem is a bird in the search space, which we call a "particle". All particles have a fitness value determined by the optimized function, and each particle also has a speed that determines the direction and distance they fly. Then the particles follow the current optimal particle to search in the solution space.

[0073] The particle swarm algorithm is initialized as a group of random particles (random solutions), and then finds the optimal solution through iteration. In each iteration, the particles update themselves by tracking two "extreme values". The first is the optimal solution found by the particle itself, which is called the individual extreme value. The other extreme value is the optimal solution currently found by the entire population, which is the global extreme value.

[0074] Particles have only two properties: speed and position. Speed ​​represents the speed of movement, and position represents the direction of movement. The position of particle i in N-dimensional space is represented by vector Xi = (x1, x2, ..., xN), and the flight speed is represented by vector Vi = (v1, v2, ..., vN). Each particle has a fitness value determined by the objective function, and knows the best position it has found so far (pbest) and the current position Xi. This can be regarded as the particle's own flight experience. In addition, each particle also knows the best position (gbest) found by all particles in the entire group so far (gbest is the best value in pbest), which can be regarded as the experience of the particle's companions. The particle decides the next movement based on its own experience and the best experience of its companions, and updates the existing speed and direction.

[0075]

[0076] in, d is the dimension, here d = 2, is the d-th dimension velocity vector of the particle in the k-th iteration. is the historical optimal position of the particle in the dth dimension in the kth iteration.

[0077] Definition of safety index: All the operating points that meet the safety constraints are represented by a two-dimensional polygon. The data representing the operating points include the power of the nodes. The boundary represents all the constraints, including branch flow constraints, power source active output constraints, energy storage power station charging and discharging power constraints, etc. The specific meaning of the boundary point is the maximum / minimum transmission power that meets various constraints. Because the constraints meet the continuity, the safety margin can be represented by the Euclidean geometric distance. SD is the shortest Euclidean distance from the current operating point to the boundary. If the safety margin is a negative value, it means that the current operating point does not meet the constraints and the current operation state of the power grid has a great risk. In order to characterize the operation safety of the 220kV power grid, the safety distance is defined as the safety margin indicator. Compared with other indicators, this indicator not only considers the remaining transmission capacity of the branch, but also takes the branch location into account, that is, the topological structure of the network. When this indicator is used as the safety margin evaluation indicator, the network safety margin is determined by the branch with the smallest safety distance, which can better reflect the impact of the branch topological position difference on the operation safety of the 220kV transmission network. The high-voltage distribution network reconstruction and the charging and discharging behavior of the energy storage station will change the injected power of the 220kV system node, causing the branch power flow to change. However, each node has different influences on the power flow deviation of different branches. Therefore, in order to fully consider the influence of all nodes on the branch power flow, the Euclidean distance calculation formula of branch i is: Among them, P i，max is the maximum transmission capacity of branch i; P i is the current transmission capacity of branch i; N n is the number of nodes; C k-i is the impact factor of the unit injected power change of node k on the power flow of branch i, which is calculated as: Among them, ΔP k is the injection power change at node k, ΔP i is the power flow change of branch i, X lk , X mk Respectively represent the lth row and mth column and the mth row and kth column of the power grid reactance matrix. Reflects the influence of the change of node injection power on the remaining transmission capacity of branch i. The maximum transmission power of the line should be determined by the maximum P under the above constraints.

[0078] Probabilistic estimation is adopted, and the three-point estimation method is used to sample the power of each node according to the probability density based on the normal distribution. At the same time, the expected value of the remaining nodes is used to judge the degree of influence of the power on the node.

[0079] Based on the correct establishment of models of different loads and energy storage stations, this embodiment proposes a method of using a particle swarm algorithm to calculate the maximum power under existing constraints and perform a safety assessment based on this power; based on the calculation of the safety distance, it can provide an indicator for evaluating the safety of the source-storage-load coordinated scheduling strategy, ensuring that under the new scheduling strategy, there will be no threat to the stability of the power grid.

[0080] The embodiments of the present application also provide a secure computing device for a source-storage-load collaborative scheduling strategy. It should be noted that the secure computing device for a source-storage-load collaborative scheduling strategy in the embodiments of the present application can be used to execute the secure computing method for a source-storage-load collaborative scheduling strategy provided in the embodiments of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0081] The following introduces a secure computing device for source-storage-load collaborative scheduling strategy provided in an embodiment of the present application.

[0082] Figure 3 It is a schematic diagram of a secure computing device for a source-storage-load collaborative scheduling strategy according to an embodiment of the present application.

[0083] like Figure 3 As shown, the device comprises:

[0084] An acquisition unit 31 is used to acquire a source-storage-load coordinated dispatching strategy of a power grid, and determine the constraints of each operating node of the above power grid, wherein the above constraints include the maximum charge and discharge power constraints of the energy storage device of the above power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the power flow constraints;

[0085] A first determining unit 32 is used to determine the Euclidean distance between each of the operating nodes of the power grid and the constraint condition according to the source-storage-load coordinated scheduling strategy;

[0086] The second determining unit 33 is used to determine the safety of the source-storage-load coordinated scheduling strategy according to the Euclidean distance.

[0087] In this embodiment, the acquisition unit is used to acquire the source-storage-load collaborative dispatching strategy of the power grid, and determine the constraints of each operating node of the power grid, wherein the constraints include the maximum charge and discharge power constraints of the energy storage device of the power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the flow constraints; the first determination unit is used to determine the Euclidean distance between each operating node of the power grid and the constraints according to the source-storage-load collaborative dispatching strategy; the second determination unit is used to determine the safety of the source-storage-load collaborative dispatching strategy according to the Euclidean distance. Based on the calculation of the Euclidean distance, a high-accuracy indicator for evaluating the safety of the source-storage-load collaborative dispatching strategy can be provided to ensure that under the new dispatching strategy, there will be no threat to the stability of the power grid, which solves the problem of low accuracy in evaluating the safety of the collaborative dispatching strategy of the power grid in the prior art.

[0088] As an optional scheme, the first determination unit includes a first determination module and a second determination module; the first determination module is used to determine the maximum transmission power of each branch in the above-mentioned topology structure by using a particle swarm algorithm based on the above-mentioned source-storage-load collaborative scheduling strategy and the topology structure of the above-mentioned power grid; the second determination module is used to determine the above-mentioned Euclidean distance between each of the above-mentioned operating nodes of the above-mentioned power grid and the above-mentioned constraint conditions based on the above-mentioned maximum transmission power, wherein the above-mentioned operating node is the above-mentioned energy storage device in the above-mentioned power grid, and the above-mentioned operating node includes the power data of the above-mentioned energy storage device.

[0089] In an optional solution, the first determination module includes a determination submodule, which is used to adopt a particle swarm algorithm formula: Determine the maximum transmission power of each branch in the power grid topology, where: is the maximum transmission power of the above branch as a particle in the dth dimension in the k+1th iteration, where d is the dimension, is the historical optimal transmission capacity of the particle in the dth dimension in the kth iteration, is the transmission capacity of the particle in the dth dimension at the kth iteration.

[0090] In an optional solution, the second determining unit includes a third determining module, which is used to adopt the Euclidean distance formula: Determine the Euclidean distance between each of the above operating nodes and the above constraints, where P i，max is the maximum transmission capacity of branch i, N n is the number of the above running nodes, C k-i is the impact factor of the power change of the operating node k on the branch i power flow, M SD，i is the Euclidean distance of the above branch.

[0091] In an optional solution, the acquisition unit includes a fourth determination module, which is used to determine the value of the first formula: Determine the power flow constraints of the above-mentioned operation nodes of the above-mentioned power grid, where P k (t) is the active power of k operating node, Q k (t) is the reactive power of the k operating node, u k (t) represents the voltage of the operating node k during period t, g ks is the conductance between the k running node and the s running node, b ks is the susceptance between the k running node and the s running node, δ ks (t) is the voltage phase angle difference between the k operating node and the s operating node.

[0092] In an optional solution, the acquisition unit further includes a fifth determination module, configured to determine, according to the second formula: Determine the safety constraints of the above-mentioned operation nodes of the above-mentioned power grid considering the risk of over-limit, where P L0，k (t) is the load of k operating nodes before the reconstruction of the high-voltage distribution network in period t, P fig，k (t) is the load transferred from other operating nodes to operating node k through the reconstruction of the high-voltage distribution network during period t, P G，k (t) is the power injected into the running node k by the upper power supply, P PV，k (t) is the photovoltaic output at the operating node k during period t, P S，k (t) is the sum of the charging and discharging power of the energy storage device at the operating node k during period t, P C，k (t) represents the load shedding amount of the operating node k during period t.

[0093] An optional scheme, the second determination unit includes a sixth determination module and a seventh determination module, the sixth determination module is used to determine whether the above-mentioned Euclidean distance is less than the preset distance; the seventh determination module is used to determine that the above-mentioned source-storage-load collaborative scheduling strategy of the above-mentioned power grid is unsafe when the above-mentioned Euclidean distance is less than the above-mentioned preset distance.

[0094] The above-mentioned secure computing device for source-storage-load collaborative scheduling strategy includes a processor and a memory. The above-mentioned acquisition unit, the first determination unit, the second determination unit, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to implement corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in any combination.

[0095] The processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and the problem of low accuracy in evaluating the safety of the power grid coordinated dispatching strategy in the prior art is solved by adjusting the kernel parameters.

[0096] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0097] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned safe calculation method for the source-storage-load collaborative scheduling strategy.

[0098] Specifically, the safe calculation methods for the source-storage-load coordinated scheduling strategy include:

[0099] Step S201, obtaining the source-storage-load coordinated dispatching strategy of the power grid, and determining the constraints of each operating node of the above power grid, wherein the above constraints include the maximum charge and discharge power constraints of the energy storage device of the above power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the power flow constraints;

[0100] Step S202, determining the Euclidean distance between each of the operating nodes of the power grid and the constraint condition according to the source-storage-load coordinated scheduling strategy;

[0101] Step S203: determining the safety of the source-storage-load collaborative scheduling strategy according to the Euclidean distance.

[0102] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes the above-mentioned safe computing method for the source-storage-load collaborative scheduling strategy when running.

[0103] Specifically, the safe calculation methods for the source-storage-load coordinated scheduling strategy include:

[0104] Step S201, obtaining the source-storage-load coordinated dispatching strategy of the power grid, and determining the constraints of each operating node of the above power grid, wherein the above constraints include the maximum charge and discharge power constraints of the energy storage device of the above power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the power flow constraints;

[0105] Step S202, determining the Euclidean distance between each of the operating nodes of the power grid and the constraint condition according to the source-storage-load coordinated scheduling strategy;

[0106] Step S203: determining the safety of the source-storage-load collaborative scheduling strategy according to the Euclidean distance.

[0107] An embodiment of the present invention provides an electronic device, the device comprising a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0108] Step S201, obtaining the source-storage-load coordinated dispatching strategy of the power grid, and determining the constraints of each operating node of the above power grid, wherein the above constraints include the maximum charge and discharge power constraints of the energy storage device of the above power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the power flow constraints;

[0109] Step S202, determining the Euclidean distance between each of the operating nodes of the power grid and the constraint condition according to the source-storage-load coordinated scheduling strategy;

[0110] Step S203: determining the safety of the source-storage-load collaborative scheduling strategy according to the Euclidean distance.

[0111] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0112] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0113] Step S201, obtaining the source-storage-load coordinated dispatching strategy of the power grid, and determining the constraints of each operating node of the above power grid, wherein the above constraints include the maximum charge and discharge power constraints of the energy storage device of the above power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the power flow constraints;

[0114] Step S202, determining the Euclidean distance between each of the operating nodes of the power grid and the constraint condition according to the source-storage-load coordinated scheduling strategy;

[0115] Step S203: determining the safety of the source-storage-load collaborative scheduling strategy according to the Euclidean distance.

[0116] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0121] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0122] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0123] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0124] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0125] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0126] 1) A safety calculation method for the source-storage-load collaborative scheduling strategy of the present application includes: obtaining the source-storage-load collaborative scheduling strategy of the power grid, and determining the constraints of each operating node of the power grid, wherein the constraints include the maximum charging and discharging power constraints of the energy storage device of the power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the flow constraints; according to the source-storage-load collaborative scheduling strategy, determining the Euclidean distance between each operating node of the power grid and the constraints; determining the safety of the source-storage-load collaborative scheduling strategy according to the Euclidean distance. Based on the calculation of the Euclidean distance, a high-accuracy indicator for evaluating the safety of the source-storage-load collaborative scheduling strategy can be provided to ensure that under the new scheduling strategy, there will be no threat to the stability of the power grid, solving the problem of low accuracy in evaluating the safety of the power grid collaborative scheduling strategy in the prior art.

[0127] 2) A safety calculation device for source-storage-load collaborative scheduling strategy of the present application includes: an acquisition unit, used to acquire the source-storage-load collaborative scheduling strategy of the power grid, and determine the constraints of each operating node of the power grid, wherein the constraints include the maximum charge and discharge power constraints of the energy storage device of the power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the flow constraints; a first determination unit, used to determine the Euclidean distance between each operating node of the power grid and the constraints according to the source-storage-load collaborative scheduling strategy; a second determination unit, used to determine the safety of the source-storage-load collaborative scheduling strategy according to the Euclidean distance. Based on the calculation of the Euclidean distance, a high-accuracy indicator for evaluating the safety of the source-storage-load collaborative scheduling strategy can be provided to ensure that under the new scheduling strategy, there will be no threat to the stability of the power grid, solving the problem of low accuracy in evaluating the safety of the power grid collaborative scheduling strategy in the prior art.

[0128] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A secure calculation method for source-storage-load collaborative scheduling strategy, characterized in that: include: Obtain the source-storage-load coordinated dispatching strategy of the power grid, and determine the constraints of each operating node of the power grid, wherein the constraints include the maximum charge and discharge power constraints of the energy storage device of the power grid, the state of charge constraints of the energy storage device, the charge and discharge balance constraints of the energy storage device, the safety constraints considering the risk of over-limit, and the power flow constraints; Determining the Euclidean distance between each operating node of the power grid and the constraint condition according to the source-storage-load coordinated scheduling strategy; The security of the source-storage-load collaborative scheduling strategy is determined according to the Euclidean distance.

2. The method according to claim 1, characterized in that: Determining the Euclidean distance between each operating node of the power grid and the constraint condition according to the source-storage-load coordinated scheduling strategy includes: According to the source-storage-load coordinated scheduling strategy and in combination with the topological structure of the power grid, a particle swarm algorithm is used to determine the maximum transmission power of each branch in the topological structure; The Euclidean distance between each operating node of the power grid and the constraint condition is determined according to the maximum transmission power, wherein the operating node is the energy storage device in the power grid, and the operating node includes power data of the energy storage device.

3. The method according to claim 2, characterized in that The particle swarm algorithm is used to determine the maximum transmission power of each branch in the topology structure, including: The particle swarm algorithm formula is used: Determine the maximum transmission power of each branch in the power grid topology, where: is the maximum transmission power of the branch as a particle in the dth dimension in the k+1th iteration, d is the dimension, is the historical optimal transmission capacity of the particle in the dth dimension in the kth iteration, is the transmission capacity of the particle in the dth dimension at the kth iteration.

4. The method according to claim 1, characterized in that: Determining the Euclidean distance between each operating node of the power grid and the constraint condition includes: Using the Euclidean distance formula: Determine the Euclidean distance between each of the operating nodes and the constraint condition, where P i,max is the maximum transmission capacity of branch i, N n is the number of running nodes, C k-i is the impact factor of the unit injected power change of the operating node k on the power flow of branch i, M SD,i is the Euclidean distance of the branch.

5. The method according to claim 1, characterized in that Determining the constraint conditions of each operating node of the power grid includes: According to the first formula: Determine the power flow constraints of each operating node of the power grid, where P k (t) is the active power of k operating node, Q k (t) is the reactive power of the k operating node, u k (t) represents the voltage of the operating node k during period t, g ks is the conductance between the k running node and the s running node, b ks is the susceptance between the k running node and the s running node, δ ks (t) is the voltage phase angle difference between the k operating node and the s operating node.

6. The method according to claim 1, characterized in that Determining the constraint conditions of each operating node of the power grid includes: According to the second formula: Determine the safety constraint conditions of each operating node of the power grid considering the risk of over-limit, where P L0,k (t) is the load of k operating nodes before the reconstruction of the high-voltage distribution network in period t, P fig,k (t) is the load transferred from other operating nodes to operating node k through the reconstruction of the high-voltage distribution network during period t, P G,k (t) is the power injected into the running node k by the upper power supply, P PV,k (t) is the photovoltaic output at the operating node k during period t, P S,k (t) is the sum of the charging and discharging power of the energy storage device at the operating node k during period t, P C,k (t) represents the load shedding amount of the operating node k during period t.

7. The method according to any one of claims 1 to 6, characterized in that: Determining the safety of the source-storage-load collaborative scheduling strategy according to the Euclidean distance includes: Determining whether the Euclidean distance is less than a preset distance; When the Euclidean distance is less than the preset distance, it is determined that the source-storage-load coordinated scheduling strategy of the power grid is unsafe.

8. A secure computing device for source-storage-load collaborative scheduling strategy, characterized in that: include: An acquisition unit, used to acquire a source-storage-load coordinated dispatching strategy of a power grid, and determine the constraints of each operating node of the power grid, wherein the constraints include a maximum charge and discharge power constraint of an energy storage device of the power grid, a state of charge constraint of an energy storage device, a charge and discharge balance constraint of an energy storage device, a safety constraint considering an over-limit risk, and a power flow constraint; A first determining unit, configured to determine the Euclidean distance between each operating node of the power grid and the constraint condition according to the source-storage-load coordinated scheduling strategy; The second determining unit is used to determine the safety of the source-storage-load collaborative scheduling strategy according to the Euclidean distance.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the secure computing method for the source-storage-load collaborative scheduling strategy as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a secure computing method for executing the source-storage-load collaborative scheduling strategy described in any one of claims 1 to 7.

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