Direct current power distribution network information physical cooperation active power control method and device for multi-microgrid access

Abstract

The application discloses a kind of active power control method and device design method of direct current distribution network information physical cooperation under the access of multiple microgrids, comprising: formulating information physical cooperation correlation analysis and collaborative active power regulation architecture;In upper layer, i.e. microgrid layer, the centralized control method of physical layer according to power flow constraint is designed;In lower layer, i.e. distributed resource layer, combination fault-tolerant control method is designed;The effectiveness of the method is verified by building experimental scene.The direct current distribution network under the access of multiple microgrids can effectively suppress the fluctuation of active power and realize data transmission path matching based on business requirements, with stronger active anti-interference capability, in consideration of the case that multiple microgrids are accessed.

Description

Technical Field

[0001] This invention relates to the field of smart grid control, specifically to a cyber-physical cooperative active power control method and device for DC distribution networks with multiple microgrid access. Background Technology

[0002] As a core component of smart grids, DC distribution networks possess advantages such as strong regulation flexibility and high efficiency, attracting increasing attention. Active power regulation is one of the key technologies in DC distribution networks, playing a crucial role in ensuring the safe and stable operation of the grid, improving grid operating efficiency, and optimizing energy utilization. In recent years, with the increasing proportion of microgrids and other aggregated distributed resources connected to the whole-station acceleration network, the flexible deployment of microgrid clusters and their internal controllable resources has become a research hotspot.

[0003] In traditional AC distribution networks, active power regulation is achieved by adjusting the output power of generators and the current of loads. However, in DC distribution networks, due to the unique direction of current and the fixed voltage on DC lines, active power regulation can only be achieved by adjusting the voltage on the DC side, resulting in a lack of flexibility in the active power regulation mechanism. In recent years, however, with the increasing proportion of distributed resource aggregations such as microgrids connected to distribution networks, researching the potential for flexible utilization of microgrids and their internal controllable resources has become a research hotspot. Furthermore, considering the inherent disorderly disturbance characteristics of numerous distributed resources, data acquisition and transmission links are often added to distribution networks to achieve real-time monitoring and analysis of key information such as operating status and load demand, improving the overall observability and controllability of the distribution network and microgrids. This is also causing distribution networks to gradually exhibit cyber-physical integration characteristics.

[0004] However, it cannot be ignored that in a cyber-physical convergence environment, physical layer uncertainties such as power output fluctuations and information layer uncertainties such as communication congestion will pose severe challenges to the active power regulation of DC distribution networks. On the one hand, the output fluctuations of distributed power sources connected to microgrids in DC distribution networks are significant. For example, renewable energy sources are highly volatile, experiencing sudden outages or increased output from time to time. This volatility directly affects the active power regulation of the DC distribution network. In this situation, traditional static power system dispatching methods may no longer be applicable, requiring more flexible and faster dispatching strategies to address the challenges posed by power output fluctuations. On the other hand, communication congestion is another significant challenge facing DC distribution networks. In the information transmission process, network congestion caused by large amounts of data transmission and communication can lead to delayed or lost transmission of active power regulation information, thereby affecting the real-time monitoring and control of the DC distribution network.

[0005] Therefore, this invention proposes a cyber-physical cooperative active power control method and device for DC distribution networks with multiple microgrid access, which can maintain the active power balance of the distribution network under multiple cyber-physical uncertainties. Summary of the Invention

[0006] To address the aforementioned issues, this invention discloses a cyber-physical collaborative correlation analysis and coordination control device that can maintain the active power balance of the distribution network from both cyber-physical and digital perspectives when active power fluctuations are caused by the access of multiple new energy sources to the distribution network.

[0007] To achieve the above objectives, the following technical solutions were adopted: For the upper layer, namely the microgrid layer, an active power support method for microgrids was proposed at the physical layer, and an optimal path matching method was proposed at the network layer; For the lower layer, namely the distributed resource layer, a multi-resource distributed fault-tolerant active power control method was proposed at the physical layer, and an event-triggered active defense method was proposed at the network layer.

[0008] The specific steps are as follows:

[0009] Step 1: Develop a cyber-physical collaboration correlation analysis and collaborative active power regulation architecture.

[0010] Step 2: Design a centralized control method based on power flow constraints at the upper level.

[0011] Step 3: Design a combined fault-tolerant control method at the lower level.

[0012] Step 4: Verify the effectiveness of the method by setting up an experimental scenario.

[0013] Furthermore, in step 1, the main components of the DC distribution network can be divided into two layers: physical space and information space. The overall structure design is based on the concept of "cyber-physical collaboration, correlation analysis, and cooperation," where "cyber-physical collaboration" includes the network layer and the physical layer; "correlation analysis" means that the physical layer knows the network status, and the network layer knows the physical service requirements; "cooperation" means that the network layer should provide reliable communication support according to the service requirements of the physical layer; and the control method of the physical layer should be adaptively adjusted according to the support capabilities that the network layer can provide.

[0014] Furthermore, after completing the overall framework design in step 2, the next step is to design the upper-layer design based on cyber-physical correlation analysis and collaborative principles, specifically the matching strategy for the wired network architecture at the information layer and the aggregation and control strategy at the physical layer. The design process is as follows, focusing on both cyber-physical correlation analysis and collaborative principles.

[0015] Step 2-1, Cyber-physical correlation analysis

[0016] 1) Information layer analyzes physical layer business requirements

[0017] A model is constructed to investigate the correlation between the effectiveness of active power regulation and transmitted data. Furthermore, based on sensitivity analysis, the impact of changes in data transmission speed and accuracy on the effectiveness of active power regulation is quantified to determine the importance of different data points. When considering the business G, its relationship with relevant parameters can be modeled as follows:

[0018] G = f(x1,…,x) m (1)

[0019] Where x1,…,x m Let f be the dataset and f be the mapping relationship; then the changes in the transmission speed and accuracy of the i-th parameter will affect the performance of service G by means of sensitivity, overshoot and steady-state error.

[0020] 2) Physical layer correlation analysis of the network environment provided by the information layer

[0021] After network matching is complete, information regarding router status, network architecture, and transmission channel status is fed back to the physical layer. Based on this data, the physical layer can adjust its active power control strategy in real time. In this way, by adjusting the control strategy accordingly, the system can adapt more flexibly to different environments.

[0022] Step 2-2, Cyber-physical Collaboration and Coordination

[0023] 1) Centralized control dependent on power flow constraints

[0024] First, the supply and demand relationship and power flow were analyzed. In addition, for the source-grid-storage microgrid, control commands were formulated with the goal of minimizing regulation and load balancing, taking into account various factors.

[0025] 1-1) Optimization Model Construction

[0026] Given m microgrids with varying power fluctuations, for the i-th microgrid, the control command is set to a multi-objective (maximizing load balancing, rapidly suppressing power fluctuations, minimizing line losses, minimizing voltage deviation, etc.) optimization model is as follows:

[0027]

[0028] Where △P α / P α and △P β / P β These are the ratios of the active power changes to the capacity of the αth and βth microgrids; Speed1 and Speed m It is the response speed; △P α , △P β and △P m It is a change in active power; Ploss.α It is the line loss of the αth microgrid; △U α It is the voltage fluctuation between the αth bus; || abs Indicates taking the absolute value; || Nor This represents a normalization operation; ρ1+ρ2+ρ3+ρ4=1. The relevant constraint is power balance. Adjustable capacity limit: 0 ≤ ΔP α ≤△P α.max .

[0029] 1-2) Constraints

[0030] In this invention, some objectives are chosen to be transformed into constraints. For example,

[0031] Response time constraint: max(|△P1 / Speed1|) Nor ,…,|△P m / Speed m | Nor )≤T max (3)

[0032] Line loss constraint:

[0033] Node voltage limit: U α.min ≤U α ≤U α.max (5)

[0034] Where △P α It is the output active power of the αth microgrid; ΔP α.max It is the maximum adjustable power of the αth microgrid; ΔP α.loss P is the line loss of the αth microgrid; lineloss.max It is the set loss threshold; U α.min and U α.max These are the minimum and maximum voltages of the node; T max This is the maximum response time set.

[0035] 2) Demand-driven wired network matching

[0036] 2-1) Optimization Model Construction

[0037] First, sensitivity calculations should be performed in active power control. Assuming there are m buses, the sensor attitude equation for the power change injected into the α-th bus on target (2) is:

[0038]

[0039]

[0040] in It is the partial derivative of Z with respect to the output power of the αth microgrid in (2). After determining the sensitivity, the flow data to be transmitted is arranged in descending order of sensitivity to obtain...

[0041]

[0042] Furthermore, centralized tree networks can be planned according to the order in (7). The relevant objective function is constructed as follows:

[0043]

[0044] Where Sign is the sign function; Per αβ This refers to the performance (e.g., latency, length, and security risks) of the channel from the α-th router (connected to α microgrids) to the β-th router (connected to β microgrids); a αβ This relates to the relationship between the α-th and β-th routers. If the α-th router can send data to the β-th router, then a... αβ =1 or a αβ =0; otherwise, N α It is the set of neighboring routers of the α-th router.

[0045] 2-2) Constraints

[0046] The constraints for uploading to the network are constructed as follows:

[0047] For the α-th router, the number of input channels and output channels should not exceed the allowed values:

[0048]

[0049] The number of connected channels is greater than or equal to the number of microgrids.

[0050]

[0051] Each transmission router has its output channel and input channel:

[0052]

[0053] Each microgrid must have at least one output channel:

[0054]

[0055] The microgrid control center should be connected to at least the same number of input channels as the microgrid:

[0056]

[0057] For the download network, constraints (12) and (13) are modified to the following equations, while other constraints remain unchanged:

[0058] Each MG has at least one input channel:

[0059]

[0060] The center has at least one down-load channel:

[0061]

[0062] Where Num out.α and Num in.α N is the number of output and input channels allowed for the α-th node; Tr It is a collection of transport routers. Once the optimization problem described above is resolved, the path can be obtained sequentially.

[0063] Furthermore, in step 3, after completing the upper-level design, the next step is to design the lower-level active power regulation strategy and dual-path optimization strategy that are capable of active fault tolerance.

[0064] 3-1) Design of Combined Fault-Tolerant Control Method

[0065] This invention, based on the control commands of the microgrid and the equal capacity ratio P, h-α / P h-α.cap =P h-β / P h-β.cap (Representing the ratio of the output command to the rated capacity of α / β distributed resources) and the control command of "virtual leader-follower consistency control," can adaptively adjust controller parameters, enabling resources to flexibly respond to microgrid commands. This invention illustrates this with an example; the control equation for the performance of the α-th resource is:

[0066] △P h-α =△P h-α.Ref (16)

[0067] Where △P h-α and △P h-β It refers to the active power adjustment of the α-th and β-th distributed resources in the h-th microgrid; P h-α.cap and P h-β.cap It represents the remaining capacity of the α-th and β-th resources; △P h-α.Ref and △P h-β.Ref It is achieved by controlling the inverse solution of the result (i.e., ΔP). h-α.Ref =γ h-α (t)·P h-α.cap and △P h-β.Ref =γ h-β (t)·P h-β.cap This is obtained by γ. In this case, γ is usually chosen. h-L (t) is , where P MGh This is the power required by the h-th microgrid. The relevant performance equations for distributed resources are:

[0068]

[0069] The output command is

[0070]

[0071] Where u h-1 (t) and u h-n (t) is the controller; k h-α1 k h-α2 k h-n2 and k h-n1 It is gain; a h-αβ This indicates the correlation between the α-th and β-th resources; b h-α This represents the relationship between the α-th resource and the capacity load. During the adjustment process, the deviation between the controlled variable and the setpoint of the capacity load is e. h =u h -γ h-L Under normal circumstances, the controlled deviation can be adjusted to 0 by selecting appropriate parameters. However, if the network switches, the resulting interference will negatively impact performance quality, requiring sliding mode control to suppress it.

[0072] Taking a system as an example, the affected controller u h ′ -1 (t)~u h ′ -n The equation for (t) is (19), where Δγ h-1 (t) and Δγ h-n (t) represents the introduced interference.

[0073]

[0074] Controllable error u′ -u for

[0075]

[0076] Where △u h-1 (t)=u h ′ -1 (t)-u h-1 (t). In this invention, the feedback matrix F is used to complete the sliding mode control, and it is designed as follows: Where F h-αβ =-K γh-αβ a h-αβ ;F h-αα It needs to be designed. Taking the α-th distributed resource as an example, the sliding surface is designed as S=K. P△γ h-α +K I ∫△γ h-α dt, where and It is a coefficient. The relevant controller is designed as F. h-αα =F h-αa +F h-αb .make and F h-α1 =F h-αa You can get

[0077]

[0078] Where F h-αa It is the solution to equation (21). Then, the Lyapunov function is taken as L = S 2 / 2, and make it satisfy Lyapunov stability, taking into account F h-αα =F h-αa +F h-αb You can get in This refers to the switching gain in the protocol. Therefore, by selecting appropriate parameters, the system's tolerance to congestion can be improved.

[0079] 3-2) Design of Dual Path Optimization Strategy

[0080] For distributed resources in a microgrid, a directed spanning tree must exist in the network to support distributed control. Therefore, relying on microgrid agents and routers, a dual optimization strategy is adopted, performing global optimization under alert conditions and local optimization under emergency conditions. Alert conditions refer to situations where inequality constraints are close to their limits, making it easy to transition to an emergency state; emergency conditions refer to situations where inequality constraints have been violated, making it difficult to satisfy equality constraints, and requiring emergency power support to suppress fluctuations. The design process is as follows:

[0081] First, when the microgrid is operating in alarm mode, the sensitivity between the controlled variables and relevant data should be calculated; when the system is operating in emergency mode, the sensitivity between the controlled variables and time should be calculated. Taking the α-th distributed resource as an example, the partial derivative between its controller and the data of the β-th distributed resource is... Its partial derivative with respect to time is

[0082]

[0083] Furthermore, the paths need to be updated. Upper-level optimization primarily addresses issues under alert conditions, reconstructing the directed spanning tree using global information based on the principle of matching path performance with data importance. When the α-th distributed resource needs to be connected to the directed tree, the transmission path should be matched with the data in sequence. The optimization objective adopts... The constraints are:

[0084] Number of channels in DER:

[0085]

[0086] There can be at most one connection channel between two routers:

[0087]

[0088] The controlled DER has only one input channel:

[0089]

[0090] Volumetric loads have at least one output channel and no input channels.

[0091]

[0092] Where P αβ Number is the security probability of the channel from the β-th router to the α-th router; DER It represents the number of distributed resources that need to be connected.

[0093] The lower-layer optimization primarily addresses path reconstruction in emergency situations. Based on the transmission network built in the upper layer, it first sets selected paths to 1, and unselectable and faulty paths to 0. Furthermore, based on the principle of matching transmission speed and data input, it uses distributed resources to quickly generate path reconstruction schemes using local information. The target model is... , where t αβ This is the transmission time from the α-th distributed resource to the β-th distributed resource. If the i-th distributed resource needs to connect to the network, the following constraints need to be considered in the optimization model;

[0094] Transmission security constraints:

[0095] ∏P αβ ·Sign(a αβ )≥Prob set (27)

[0096] Ensure that the i-th distributed resource is reconnected to the directed tree:

[0097]

[0098] Among them Prob set This refers to the required safety probability. If multiple sets of data need to be reconnected, the data should be matched with the channels sequentially.

[0099] Furthermore, in step 4, the effectiveness of the method is verified by setting up an experimental scenario.

[0100] Compared with the prior art, the present invention has the following advantages:

[0101] 1. The centralized control method of this invention generates optimal control commands based on power flow constraints, comprehensively considering factors such as remaining capacity, adjustment time, voltage constraints, and line losses. Compared with the traditional capacity ratio equalization method, it is more flexible and applicable to various operating scenarios.

[0102] 2. The path matching algorithm can calculate data sensitivity and match the optimal path, realizing data transmission path matching in a business-driven manner, which is superior to traditional methods.

[0103] 3. The combined fault-tolerant control method can achieve active power regulation using only a sparse communication network. Compared with common centralized control methods, the proposed method significantly reduces communication costs and computational burden.

[0104] 4. The dual optimization method can perform global and local optimizations to meet the needs of various business scenarios. Attached Figure Description

[0105] Figure 1 A diagram illustrating the cyber-physical collaboration and collaborative active power control architecture of a DC distribution network.

[0106] Figure 2 Schematic diagram of centralized control of microgrid clusters.

[0107] Figure 3 Schematic diagram of multi-source distributed coordinated regulation. Detailed Implementation

[0108] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. It should be noted that the terms "front," "rear," "left," "right," "up," and "down" used in the following description refer to directions in the accompanying drawings, and the terms "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0109] like Figure 1 As shown in the diagram, this embodiment designs a cyber-physical cooperation correlation analysis and collaborative active power control architecture for a DC distribution network, including an upper-layer wired network, a lower-layer wireless network, transmission lines, a microgrid control center, a microgrid cluster, and distributed resources. When active power fluctuations occur in the DC distribution network, the correlation mechanism is first clarified based on cyber-physical cooperation understanding. By designing hierarchical control strategies and reliable transmission schemes, the power control capabilities of the microgrid and its internal distributed resources are utilized in a cyber-physical cooperative manner to eliminate the fluctuations.

[0110] like Figure 2 and 3 As shown, this invention provides a cyber-physical cooperative active power control method for DC distribution networks with multiple microgrids, comprising:

[0111] S1. Develop a cyber-physical collaboration correlation analysis and collaborative active power regulation architecture.

[0112] The architecture for cyber-physical collaborative correlation analysis and coordinated active power control of DC distribution networks is designed based on the concept of "cyber-physical collaboration correlation analysis and cooperation". "Cyber-physical collaboration" includes the network layer and the physical layer; "correlation analysis" means that the physical layer knows the network status and the network layer knows the physical service requirements; "cooperation" means that the network layer should provide reliable communication support according to the service requirements of the physical layer; and the control method of the physical layer should be adaptively adjusted according to the support capabilities that the network layer can provide.

[0113] S2. A centralized control method based on power flow constraints is designed at the upper level.

[0114] like Figure 2 As shown, after completing the cyber-physical collaboration correlation analysis and collaborative active power regulation architecture, the upper-layer design is carried out. The specific steps are as follows:

[0115] First, the supply and demand relationship and power flow were analyzed. In addition, for the source-grid-storage microgrid, control commands were formulated with the goal of minimizing regulation and load balancing, taking into account various factors.

[0116] 1-1) Optimization Model Construction

[0117] Given m microgrids with varying power fluctuations, for the i-th microgrid, the control command is set to a multi-objective (maximizing load balancing, rapidly suppressing power fluctuations, minimizing line losses, minimizing voltage deviation, etc.) optimization model is as follows:

[0118]

[0119] Where △P α / P α and △P β / P β These are the ratios of the active power changes to the capacity of the αth and βth microgrids; Speed1 and Speed m It is the response speed; △P α , △P β and △P m It is a change in active power; P loss.α It is the line loss of the αth microgrid; △U α It is the voltage fluctuation between the αth bus; || abs Indicates taking the absolute value; || NorThis represents a normalization operation; ρ1+ρ2+ρ3+ρ4=1. The relevant constraint is power balance. Adjustable capacity limit: 0 ≤ ΔP α ≤△P α.max .

[0120] 1-2) Constraints

[0121] In this invention, some objectives are chosen to be transformed into constraints. For example,

[0122] Response time constraint: max(|△P1 / Speed1|) Nor ,…,|△P m / Speed m | Nor )≤T max (3)

[0123] Line loss constraint:

[0124] Node voltage limit: U α.min ≤U α ≤U α.max (5)

[0125] Where △P α It is the output active power of the αth microgrid; ΔP α.max It is the maximum adjustable power of the αth microgrid; ΔP α.loss P is the line loss of the αth microgrid; lineloss.max It is the set loss threshold; U α.min and U α.max These are the minimum and maximum voltages of the node; T max This is the maximum response time set.

[0126] 2) Demand-driven wired network matching

[0127] 2-1) Optimization Model Construction

[0128] First, sensitivity calculations should be performed in active power control. Assuming there are m buses, the sensor attitude equation for the power change injected into the α-th bus on target (2) is:

[0129]

[0130] in It is the partial derivative of Z with respect to the output power of the αth microgrid in (2). After determining the sensitivity, the flow data to be transmitted is arranged in descending order of sensitivity to obtain...

[0131]

[0132] Furthermore, centralized tree networks can be planned according to the order in (7). The relevant objective function is constructed as follows:

[0133]

[0134] Where Sign is the sign function; Per αβ This refers to the performance (e.g., latency, length, and security risks) of the channel from the α-th router (connected to α microgrids) to the β-th router (connected to β microgrids); a αβ This relates to the relationship between the α-th and β-th routers. If the α-th router can send data to the β-th router, then a... αβ =1 or a αβ =0; otherwise, N α It is the set of neighboring routers of the α-th router.

[0135] 2-2) Constraints

[0136] The constraints for uploading to the network are constructed as follows:

[0137] For the α-th router, the number of input channels and output channels should not exceed the allowed values:

[0138]

[0139] The number of connected channels is greater than or equal to the number of microgrids.

[0140]

[0141] Each transmission router has its output channel and input channel:

[0142]

[0143] Each microgrid must have at least one output channel:

[0144]

[0145] The microgrid control center should be connected to at least the same number of input channels as the microgrid:

[0146]

[0147] For the download network, constraints (12) and (13) are modified to the following equations, while other constraints remain unchanged:

[0148] Each MG has at least one input channel:

[0149]

[0150] The center has at least one download channel:

[0151]

[0152] Where Num out.α and Num in.α N is the number of output and input channels allowed for the α-th node; Tr It is a collection of transport routers. Once the optimization problem described above is resolved, the path can be obtained sequentially.

[0153] S3. Design a combined fault-tolerant control method at the lower level.

[0154] like Figure 3 As shown, the lower-level design employs a grouping and fault-tolerant control method, with the specific steps as follows:

[0155] This invention, based on the control commands of the microgrid and the equal capacity ratio P, h-α / P h-α.cap =P h-β / P h-β.cap (Representing the ratio of the output command to the rated capacity of α / β distributed resources) and the control command of "virtual leader-follower consistency control" can adaptively adjust controller parameters, enabling resources to flexibly respond to microgrid commands. In this embodiment, the control equation for the performance of the α-th resource is:

[0156] △P h-α =△P h-α.Ref (16)

[0157] Where △P h-α and △P h-β It refers to the active power adjustment of the α-th and β-th distributed resources in the h-th microgrid; P h-α.cap and P h-β.cap It represents the remaining capacity of the α-th and β-th resources; △P h-α.Ref and △P h-β.Ref It is achieved by controlling the inverse solution of the result (i.e., ΔP). h-α.Ref =γ h-α (t)·P h-α.cap and △P h-β.Ref =γ h-β (t)·P h-β.cap This is obtained by γ. In this case, γ is usually chosen. h-L (t) is , where P MGh This is the power required by the h-th microgrid. The relevant performance equations for distributed resources are:

[0158]

[0159] The output command is

[0160]

[0161] Where u h-1 (t) and u h-n (t) is the controller; k h-α1 k h-α2 k h-n2 and k h-n1 It is gain; a h-αβ This indicates the correlation between the α-th and β-th resources; b h-α This represents the relationship between the α-th resource and the capacity load. During the adjustment process, the deviation between the controlled variable and the setpoint of the capacity load is e. h =u h -γ h-L Under normal circumstances, the controlled deviation is adjusted to zero by selecting appropriate parameters. However, if the network switches, the resulting interference will negatively impact performance quality, requiring sliding mode control to suppress it. Taking a system as an example, the affected controller u′ h-1 (t)~u′ h-n The equation for (t) is (19), where Δγ h-1 (t) and Δγ h-n (t) represents the introduced interference.

[0162]

[0163] The controllable error u′-u is

[0164]

[0165] Where △u h-1 (t)=u h ′ -1 (t)-u h-1 (t). In this invention, the feedback matrix F is used to complete the sliding mode control, and it is designed as follows: Where F h-αβ =-K γh-αβ a h-αβ ;F h-αα It needs to be designed. Taking the α-th distributed resource as an example, the sliding surface is designed as S=K. P △γ h-α +K I ∫△γ h-α dt, where and It is a coefficient. The relevant controller is designed as F. h-αα =F h-αa +F h-αb .make and F h-α1 =F h-αa You can get

[0166]

[0167] Where F h-αa It is the solution to equation (21). Then, the Lyapunov function is taken as L = S 2 / 2, and make it satisfy Lyapunov stability, taking into account F h-αα =F h-αa +F h-αb You can get in This refers to the switching gain in the protocol. Therefore, by selecting appropriate parameters, the system's tolerance to congestion can be improved.

[0168] The design process of the dual optimization path is as follows:

[0169] First, when the microgrid is operating in alarm mode, the sensitivity between the controlled variables and relevant data should be calculated; when the system is operating in emergency mode, the sensitivity between the controlled variables and time should be calculated. Taking the α-th distributed resource as an example, the partial derivative between its controller and the data of the β-th distributed resource is... Its partial derivative with respect to time is

[0170]

[0171] Furthermore, the paths need to be updated. Upper-level optimization primarily addresses issues under alert conditions, reconstructing the directed spanning tree using global information based on the principle of matching path performance with data importance. When the α-th distributed resource needs to be connected to the directed tree, the transmission path should be matched with the data in sequence. The optimization objective adopts... The constraints are:

[0172] Number of channels in DER:

[0173]

[0174] There can be at most one connection channel between two routers:

[0175]

[0176] The controlled DER has only one input channel:

[0177]

[0178] Volumetric loads have at least one output channel and no input channels.

[0179]

[0180] Where P αβNumber is the security probability of the channel from the β-th router to the α-th router; DER It represents the number of distributed resources that need to be connected.

[0181] The lower-layer optimization primarily addresses path reconstruction in emergency situations. Based on the transmission network built in the upper layer, it first sets selected paths to 1, and unselectable and faulty paths to 0. Furthermore, based on the principle of matching transmission speed and data input, it uses distributed resources to quickly generate path reconstruction schemes using local information. The target model is... , where t αβ This is the transmission time from the α-th distributed resource to the β-th distributed resource. If the i-th distributed resource needs to connect to the network, the following constraints need to be considered in the optimization model;

[0182] Transmission security constraints:

[0183] ∏P αβ ·Sign(a αβ )≥Prob set (27)

[0184] Ensure that the i-th distributed resource is reconnected to the directed tree:

[0185]

[0186] Among them Prob set This refers to the required safety probability. If multiple sets of data need to be reconnected, the data should be matched with the channels sequentially.

[0187] S4. Verify the effectiveness of the method by setting up an experimental scenario.

[0188] Table 1. Channel Communication Performance Between Microgrids

[0189]

[0190] Example 2

[0191] Based on the active power control method for cyber-physical cooperation in DC distribution networks under multiple microgrid access described in Embodiment 1, this embodiment of the invention provides an active power control device for cyber-physical cooperation in DC distribution networks under multiple microgrid access, comprising:

[0192] The overall structural module is configured as follows: to develop an architecture for cyber-physical collaboration correlation analysis and collaborative active power regulation.

[0193] The upper-level design module is configured as follows: a centralized control method based on power flow constraints.

[0194] The lower-level design module is configured to design a combined fault-tolerant control method.

[0195] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0196] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0197] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0198] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0199] The technical means disclosed in this invention are not limited to those disclosed in the above embodiments, but also include technical solutions composed of any combination of the above technical features.

Claims

1. A cyber-physical cooperative active power control method for DC distribution networks with multiple microgrids, characterized in that, Specifically, the following steps are included: Step 1: Develop an architecture for cyber-physical collaboration correlation analysis and collaborative active power regulation; Step 2: Implement a centralized control method based on power flow constraints at the upper level design level; Step 3: Design a combined fault-tolerant control method at the lower level; Step 4: Verify the effectiveness of the active power control method by building an experimental scenario; in Step 2, the upper-level design was carried out from the perspectives of cyber-physical correlation analysis and collaboration, and the specific design methods are as follows: Step 21, Cyber-physical correlation analysis; Step 22: Cyber-physical collaboration and coordination; The specific design method in step 21 is as follows: Step 211: Information layer analyzes physical layer business requirements; A model is constructed to investigate the correlation between the effectiveness of active power regulation and transmitted data. Based on sensitivity analysis, the impact of changes in data transmission speed and accuracy on the effectiveness of active power regulation is quantified to determine the importance of different data. When considering the business G, the model is constructed to determine its relationship with relevant parameters. (1) in It is a dataset. If it is a mapping relationship, then the changes in the transmission speed and accuracy of the i-th parameter will affect the performance of service G by using sensitivity, overshoot and steady-state error. Step 212: The physical layer analyzes the network environment provided by the information layer; after network matching is completed, information about router status, network architecture, and transmission channel status will be fed back to the physical layer; based on the above data, the physical layer adjusts the active power control strategy in real time; in this way, the control strategy is adjusted accordingly. Step 22: Cyber-physical collaboration and coordination; specifically including the following: Step 221: Centralized control dependent on power flow constraints; specifically: Step 2211: Optimize model construction; Given m microgrids with varying power fluctuations, for the i-th microgrid, the control command is set to a multi-objective optimization model. (2) in and It is the first One and The ratio of active power variation to capacity of a microgrid; and It refers to response speed; , and It is the first , The active power changes of m microgrids; It is the first Voltage fluctuations between the buses; Indicates taking the absolute value; This indicates a standardized operation; The relevant limitation is power balance: Adjustable capacity limit: ; Step 2212: Optimize model construction; Set constraints, including response time constraints: (3) Line loss constraint: (4) Node voltage limits: (5) in It is the first The maximum adjustable power of a microgrid; It is the set loss threshold; and These are the minimum and maximum voltages of the node; This is the maximum response time set. Step 222; Demand-driven wired network matching.

2. The active power control method for cyber-physical cooperation in DC distribution networks under multi-microgrid access as described in claim 1, characterized in that, Step 222 specifically includes the following: Step 2221 Optimize model construction: First, sensitivity calculations should be performed in active power control; assuming there are m buses, the target (2) on the first The sensor attitude equation for power variation injected into a bus is: (6) in In (2), Z is the first... Partial derivative of the output power of a microgrid; After determining the sensitivity, the traffic data to be transmitted is sorted in descending order of sensitivity to obtain... (7) Furthermore, the centralized tree network is planned according to the order in (7); the relevant objective function is constructed as follows: (8) Where Sign is the sign function; From the first The router to the The performance of each router's channel; It is the first and the The relationship between the routers; If the The router sends to the first If a router sends data, then or ; It is the first A set of neighboring routers for each router; Step 2222 Constraints; The constraints for uploading to the network are constructed as follows: For the For each router, the number of input and output channels should not exceed the allowed values: and (9) Number of connected channels >= number of microgrids: （10） Each transmission router has its output channel and input channel: and (11) Each microgrid must have at least one output channel: （12） The microgrid control center should be connected to at least the same number of input channels as the microgrid: （13） For the download network, constraints (12) and (13) are modified to the following equations, while other constraints remain unchanged: Each MG has at least one input channel: （14） The center must have at least one download channel: （15） in and It is the first The number of output and input channels allowed per node; It is a collection of transmission routers; It is a set of neighboring nodes of the microgrid control center; once the above optimization problem is solved again, the path is obtained in sequence. In step 3, after completing the upper-level design, the next step is to design the lower-level active power regulation strategy and dual-path optimization strategy that can actively tolerate faults. Specifically, step 31 involves the design of a combined fault-tolerant control method: Based on the microgrid's control commands and the equal capacity ratio , Indicates output command and / The ratio of the rated capacity of distributed resources, the first The control equation for the performance of a resource is: （16） in and It is the h-th microgrid. and the Active power adjustment of distributed resources; and It is the first The and the first The remaining capacity of each resource; and It is achieved by controlling the inverse solution of the result, that is... and Obtained; in this case, usually selected for ,in This is the power required by the h-th microgrid; the relevant performance equations for distributed resources are: (17) The output command is (18) in and It is a controller; , , and It is gain; Indicates the first The and the first The correlation between resources; Indicates the first The relationship between individual resources and volumetric load; During the adjustment process, the deviation between the controlled variable and the setpoint of the volumetric load is: Under normal circumstances, the controlled deviation can be adjusted to 0 by selecting appropriate parameters. However, network switching can introduce interference that negatively impacts performance quality, requiring sliding mode control to mitigate it; the affected controller... ~ The equation is (19), where and It is introduced interference; (19) Controllable error for (20) in The feedback matrix F is used to complete sliding mode control; it is designed as follows: ,in ; It needs to be designed; in the first Taking a distributed resource as an example, the sliding surface is designed as ,in and These are coefficients; the relevant controller is designed as follows: ;make and ,get (21) in It is the solution to equation (21); then, the Lyapunov function is used as And make it satisfy Lyapunov stability, taking into account ,get ,in It refers to the switching gain in the protocol; Step 32 The design process of the dual-path optimization strategy is as follows: First, when the microgrid is operating in an alarm state, the sensitivity between the controlled variables and relevant data should be calculated; when the system is operating in an emergency state, the sensitivity between the controlled variables and time should be calculated; and so on. Taking a distributed resource as an example, its controller and the first The partial derivatives between the data of the distributed resources are: Its partial derivative with respect to time is (22) In addition, the path needs to be updated; upper-level optimization mainly addresses the problems under alert conditions, and reconstructs the directed spanning tree using global information based on the principle of matching path performance with data importance; when the... When a distributed resource needs to be connected to a directed tree, the transmission path should match the data in sequence; the optimization objective adopts... The constraints are: Number of channels in DER: （23） There can be at most one connection channel between two routers: （24） The controlled DER has only one input channel: （25） Volumetric loads have at least one output channel and no input channels. （26） in From the first The router to the The security probability of a router's channel; This refers to the number of distributed resources that need to be connected. The lower-layer optimization primarily addresses path reconstruction in emergency situations. Based on the transmission network built in the upper layer, it first sets selected paths to 1, and unselectable and faulty paths to 0. Furthermore, based on the principle of matching transmission speed and data input, it uses distributed resources to quickly generate path reconstruction schemes using local information. The target model is... ,in The passage is from the first The distributed resource to the first The transmission time of each distributed resource; if the i-th distributed resource needs to connect to the network; the following constraints need to be considered in the optimization model; Transmission security constraints: （27） Ensure that the i-th distributed resource is reconnected to the directed tree: （28） in It is the required safety probability; If multiple sets of data need to be reconnected, the data should be matched with the channels in sequence.

3. The active power control method for cyber-physical cooperation in DC distribution networks under multi-microgrid access as described in claim 1, characterized in that, In step 1, cyber-physical collaboration includes the network layer and the physical layer; correlation analysis means that the physical layer knows the network status and the network layer knows the physical service requirements; collaboration means that the network layer should provide reliable communication support according to the service requirements of the physical layer; the control method of the physical layer should be adaptively adjusted according to the support capabilities provided by the network layer.

4. A cyber-physical cooperative active power control device for DC distribution networks with multiple microgrid access, according to any one of claims 1-3, comprising: The overall structural module is configured as follows: to develop an architecture for cyber-physical collaboration correlation analysis and collaborative active power regulation; The upper-level design module is configured as follows: a centralized control method based on power flow constraints. The lower-level design module is configured to design a combined fault-tolerant control method.

Images