Global collaborative optimization scheduling method of main network UPFC (Unified Power Flow Controller) and distribution network flexible interconnection equipment and related device

Abstract

The invention provides a global collaborative optimization scheduling method and related device for a main network UPFC and a distribution network flexible interconnection device, and the method comprises the steps: respectively solving operation optimization models of a main network layer and a distribution network layer, and obtaining UPFC parameters and flexible interconnection device parameters; alternately updating a distribution network boundary variable or a main network boundary variable on the basis of UPFC parameters and flexible interconnection equipment parameters in a pre-constructed cooperative computing layer; and judging whether the boundary variables of the main network and the distribution network meet convergence conditions or not, and if not, alternately solving the operation optimization model of the main network or the distribution network and updating the boundary variables based on the boundary variables of the main network and the distribution network until the updated boundary variables of the main network and the distribution network meet the convergence conditions. According to the method, the main network operation optimization model and the distribution network operation optimization model are solved respectively, and the boundary variables are updated alternately based on the parameters, so that effective linkage of the main network UPFC and the distribution network flexible interconnection equipment is realized, the action frequency and loss of the main network equipment are reduced, and cross-level power interaction of new energy output fluctuation is adapted.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization and dispatching, and specifically relates to a global collaborative optimization and dispatching method and related devices for main grid UPFC and distribution grid flexible interconnection equipment. Background Art

[0002] With the continuous advancement of the construction of new power systems, the structure and operating characteristics of the power system have changed significantly. The coupling relationship between the main grid and the distribution network has become increasingly close, which has put forward higher requirements for the dispatching and control of the power system.

[0003] On the main grid, the Unified Power Flow Controller (UPFC), a key regulating device, has limitations in its traditional optimization model. On the distribution grid, flexible interconnect devices such as intelligent soft open points (SOPs) and phase-shifting controls (PSCs) can improve voltage quality, but their scheduling models present challenges. Furthermore, there is a lack of effective coordination mechanisms between the main and distribution grids.

[0004] The traditional main grid UPFC optimization model is limited to the local bus power balance of the transmission network and cannot effectively link with the flexible interconnection equipment of the distribution network. As a result, the main grid equipment operates frequently, the losses are increased, and it cannot adapt to the cross-level power interaction caused by the fluctuation of renewable energy output. Summary of the Invention

[0005] In view of this, the present invention provides a global collaborative optimization scheduling method and related devices for the main network unified power flow controller (UPFC) and the distribution network flexible interconnection equipment, aiming to solve the above-mentioned problems existing in the collaborative control of the main and distribution networks through a hierarchical optimization architecture.

[0006] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0007] In a first aspect, the present invention provides a global collaborative optimization scheduling method for a main network UPFC and a distribution network flexible interconnection device, comprising the following steps:

[0008] Solve the operation optimization models of the main grid layer and the distribution network layer respectively to obtain the UPFC parameters and flexible interconnection equipment parameters;

[0009] In the pre-built collaborative computing layer, the distribution network boundary variables or main network boundary variables are updated alternately based on the UPFC parameters and the flexible interconnection equipment parameters. The main network boundary variables and distribution network boundary variables are the interaction parameters of the main and distribution network connection points on the main network side and the distribution network side respectively.

[0010] Determine whether the main and distribution network boundary variables meet the convergence conditions. If so, perform global collaborative optimization scheduling of the main network UPFC and distribution network flexible interconnection equipment based on the main and distribution network boundary variables. If not, alternately solve the operation optimization model of the main network or distribution network and update the boundary variables based on the main and distribution network boundary variables until the updated main and distribution network boundary variables meet the convergence conditions.

[0011] Furthermore, in the step of alternately updating the distribution network boundary variables or the main network boundary variables based on the UPFC parameters and the flexible interconnection equipment parameters, the power reference values of the SOP in the UPFC and the flexible interconnection equipment are rolled over according to the minute-level prediction model for renewable energy output, and the reactive power of the distribution network is corrected according to the second-level dynamic correction model. The mathematical expression of the minute-level prediction model for renewable energy output is as follows:

[0012]

[0013] Where, Indicates at time The power reference value, Indicates at time The power reference value, Indicates time New energy output The amount of change, Represents new energy output Regarding the rate of change of time, and All represent prediction correction coefficients;

[0014] The mathematical expression of the second-level dynamic correction model is as follows:

[0015]

[0016] Where, Indicates reactive power correction amount, Indicates voltage deviation, are the proportional coefficient, integral coefficient, and differential coefficient respectively.

[0017] Furthermore, the mathematical expression of the main network layer operation optimization model is as follows:

[0018]

[0019] Where, and Both represent weight coefficients, Indicates the power flow deviation value calculated based on the power reference value and the power measurement value. represents the number of converters, Indicates the quantity index, and They represent the number of operations and the maximum allowed number of operations of the kth converter respectively.

[0020] Furthermore, the mathematical expression of the operation optimization model of the distribution network layer is as follows:

[0021]

[0022] Where, Indicates the optimization time period, 、 and Both represent weight coefficients, and represents the resistance and time of line ij The current, Indicates time The voltage deviation, and They represent the kth distribution network flexible interconnection equipment time The number of actions and the maximum allowed number of actions.

[0023] Furthermore, the improved alternating direction multiplier method is used to update the distribution network boundary variables or the main network boundary variables. The mathematical expression of the update process is as follows:

[0024]

[0025] Where, and represent the boundary variables at the k+1th and kth iterations, respectively. represents the penalty factor at the kth iteration, and Represent the coefficient matrices of the main grid side and the distribution network side respectively, and They represent the state variable sets determined by the main grid side and the distribution network side based on the UPFC parameters and the flexible interconnection equipment parameters at the kth iteration respectively;

[0026] The convergence conditions are as follows:

[0027] ,in, is the convergence residual threshold.

[0028] Furthermore, it also includes:

[0029] The global network loss reduction rate and equipment action frequency balance are calculated to evaluate the global collaborative optimization scheduling effect.

[0030] Furthermore, it also includes adopting the main and distribution network interlocking strategy and rapid recovery mechanism to carry out collaborative self-healing in fault scenarios, including the following steps:

[0031] In response to the fault short-circuit current on the main grid side, an interlocking signal is triggered and the fault type is determined based on the voltage drop amplitude detected on the distribution grid side;

[0032] In response to the interlocking signal and fault type, trigger the UPFC control of the bypass reactor on the main grid side and the SOP control of the load transfer on the distribution grid side;

[0033] After performing timing calibration and redundant communication processing on the action times of UPFC control and SOP control, the corresponding UPFC control and SOP control are performed;

[0034] Obtain and determine whether the fault recovery evaluation parameters of the main distribution network after control meet the preset conditions. If not, extract the current control parameters;

[0035] With the goal of optimizing the fault recovery effect, the current control parameters are optimized to generate optimized control parameters;

[0036] UPFC control and SOP control are performed based on the optimized control parameters, and it is verified whether the fault recovery evaluation parameters meet the preset conditions. If not, the control parameters are re-optimized until they pass the verification.

[0037] In a second aspect, the present invention provides a global collaborative optimization scheduling device for a main network UPFC and a distribution network flexible interconnection device, comprising:

[0038] The model solving module is used to solve the operation optimization models of the main network layer and the distribution network layer respectively to obtain the UPFC parameters and flexible interconnection equipment parameters;

[0039] A collaborative update module is used to alternately update distribution network boundary variables or main network boundary variables based on UPFC parameters and flexible interconnection equipment parameters in the pre-built collaborative computing layer; the main network boundary variables and distribution network boundary variables are the interaction parameters of the main and distribution network connection points on the main network side and the distribution network side respectively;

[0040] The judgment and scheduling module is used to judge whether the boundary variables of the main and distribution networks meet the convergence conditions. If so, the global collaborative optimization scheduling of the main network UPFC and the distribution network flexible interconnection equipment is carried out based on the main and distribution network boundary variables; if not, the operation optimization model of the main network or the distribution network is solved alternately and the boundary variables are updated based on the main and distribution network boundary variables until the updated main and distribution network boundary variables meet the convergence conditions.

[0041] In a third aspect, the present invention provides a computer device, comprising a processor and a memory:

[0042] The memory is used to store computer programs and send instructions of the computer programs to the processor;

[0043] The processor executes a global collaborative optimization scheduling method for main network UPFC and distribution network flexible interconnection equipment according to the instructions of the computer program as described in the first aspect.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a global collaborative optimization scheduling method for a main network UPFC and a distribution network flexible interconnection equipment is implemented as in the first aspect.

[0045] In summary, the present invention provides a global collaborative optimization scheduling method and related devices for main network UPFC and distribution network flexible interconnection equipment, the method comprising respectively solving the operation optimization models of the main network layer and the distribution network layer to obtain UPFC parameters and flexible interconnection equipment parameters; in a pre-built collaborative calculation layer, the distribution network boundary variables or the main network boundary variables are alternately updated based on the UPFC parameters and the flexible interconnection equipment parameters; the main network boundary variables and the distribution network boundary variables are the interaction parameters of the main and distribution network connection points on the main network side and the distribution network side, respectively; it is judged whether the main and distribution network boundary variables meet the convergence conditions, and if so, the global collaborative optimization scheduling of the main network UPFC and the distribution network flexible interconnection equipment is performed based on the main and distribution network boundary variables; if not, the operation optimization model of the main network or the distribution network is alternately solved and the boundary variables are updated based on the main and distribution network boundary variables, until the updated main and distribution network boundary variables meet the convergence conditions. The present invention solves the main and distribution network operation optimization models separately and updates the boundary variables alternately based on parameters to achieve effective linkage between the main network UPFC and the distribution network flexible interconnection equipment, solves the problem that the traditional main network UPFC and the distribution network cannot effectively coordinate, reduces the frequency and loss of main network equipment operations, and adapts to the cross-level power interaction of new energy output fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A flowchart of a global collaborative optimization scheduling method for a main network UPFC and a distribution network flexible interconnection device provided by an embodiment of the present invention;

[0048] Figure 2 An overview of the technical architecture provided by an embodiment of the present invention;

[0049] Figure 3 A diagram of the main and distribution network collaborative architecture provided by an embodiment of the present invention;

[0050] Figure 4Optimization flow chart provided for an embodiment of the present invention;

[0051] Figure 5 A fault interlocking logic diagram provided by an embodiment of the present invention;

[0052] Figure 6 A flow chart of reinforcement learning parameter optimization provided by an embodiment of the present invention;

[0053] Figure 7 A block diagram of a global collaborative optimization scheduling device for a main network UPFC and distribution network flexible interconnection equipment provided by an embodiment of the present invention;

[0054] Figure 8 A block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purposes, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] The following first introduces some technical terms involved in the present invention.

[0057] (1) Unified Power Flow Controller (UPFC): A flexible alternating current transmission system (FACTS) device used in power system transmission networks. It can simultaneously control the active power and reactive power of the transmission line, flexibly adjusting the power flow distribution of the power system. UPFC is usually composed of two voltage source converters (VSCs) connected through a common DC capacitor. One converter is connected in series with the transmission line to inject a controllable voltage to change the equivalent impedance of the line, thereby controlling the line power flow; the other converter is connected in parallel with the transmission line to provide or absorb reactive power to maintain bus voltage stability.

[0058] (2) Flexible interconnection equipment for distribution networks: This refers to a series of devices used in distribution networks that can flexibly adjust power parameters and optimize distribution network operations, such as smart soft switches (SOPs) and phase-commutation switches (PSCs). Taking smart soft switches (SOPs) as an example, they are based on power electronics technology and can quickly and flexibly control the power flow between different feeders in the distribution network to achieve optimal distribution of electric energy. Phase-commutation switches (PSCs) adjust the three-phase imbalance of the distribution network and optimize power quality by switching power sources with different phase sequences.

[0059] Secondly, the problems with the existing main and distribution network collaborative control are that the traditional main network UPFC optimization model is limited to the local bus power balance of the transmission network, and cannot be effectively linked with the flexible interconnected equipment of the distribution network, resulting in frequent operation of the main network equipment, increased losses and inability to adapt to the cross-level power interaction of fluctuating new energy output; the flexible interconnected equipment of the distribution network is independently scheduled, and is seriously separated from the main network control in terms of dynamic response and fault handling at the second level; the traditional solution has low recovery efficiency in the event of a main network short-circuit fault and there is a risk of cascading power outages; the existing centralized optimization framework is difficult to meet the real-time collaboration needs of massive heterogeneous equipment in the main and distribution networks.

[0060] Based on this, see Figure 1 The embodiment of the present invention first provides a global collaborative optimization scheduling method for the main network UPFC and the distribution network flexible interconnection equipment, including the following steps:

[0061] S11: Solve the operation optimization models of the main network layer and the distribution network layer respectively to obtain the UPFC parameters and flexible interconnection equipment parameters.

[0062] It should be noted that the operation optimization model is an optimization problem established based on the mathematical model of the power system (such as the power flow equation and the state equation). The goal can be economy (such as minimizing network losses), safety (such as voltage over-limit suppression) or reliability (such as power supply restoration), and it needs to be solved in combination with constraints (such as equipment capacity and voltage upper and lower limits).

[0063] In this embodiment, independent solutions are performed at the main grid and distribution grid levels, combining corresponding operational constraints with different optimization objectives (e.g., the main grid may focus on reducing network losses and improving transmission capacity, while the distribution grid may focus on improving voltage quality and balancing the output of distributed generation). Solving the main grid-level operational optimization model determines the optimal control parameters of the UPFC, such as the regulated voltage amplitude and phase angle. Solving the distribution grid-level operational optimization model yields the optimal operating parameters of the flexible interconnected equipment, such as the power regulation amount.

[0064] S12: In the pre-built collaborative computing layer, the distribution network boundary variables or the main network boundary variables are updated alternately based on the UPFC parameters and the flexible interconnection equipment parameters; the main network boundary variables and the distribution network boundary variables are the interaction parameters of the main distribution network connection point on the main network side and the distribution network side respectively.

[0065] It's important to note that the pre-built collaborative computing layer serves as the information exchange and collaborative computing hub between the main and distribution networks. After obtaining UPFC parameters and flexible interconnection equipment parameters, it alternately updates the distribution network boundary variables or the main network boundary variables based on these parameters. The main and distribution network boundary variables reflect the power interaction between the main and distribution networks. By alternating the boundary variable updates, the main and distribution networks gradually account for each other's operating status and parameter changes, achieving initial coordination between the main and distribution networks.

[0066] S13: Determine whether the boundary variables of the main and distribution networks meet the convergence conditions. If so, perform global collaborative optimization scheduling of the main network UPFC and the distribution network flexible interconnection equipment based on the boundary variables of the main and distribution networks; if not, alternately solve the operation optimization model of the main network or the distribution network and update the boundary variables based on the boundary variables of the main and distribution networks until the updated boundary variables of the main and distribution networks meet the convergence conditions.

[0067] It should be noted that during the process of alternating updates to the main and distribution network boundary variables, it is necessary to determine whether the boundary variables meet the convergence conditions. Convergence conditions are key indicators for ensuring that the calculation results are stable, reasonable, and consistent with actual operating conditions. When the boundary variables meet the convergence conditions, it indicates that the power interaction relationship between the main and distribution networks and the equipment operating parameters have reached a relatively stable state. At this time, based on these stable main and distribution network boundary variables, global collaborative optimization scheduling of the main network UPFC and the distribution network flexible interconnection equipment can be carried out to achieve the coordinated operation of the main and distribution network equipment and achieve the overall optimization goal. If the convergence conditions are not met, based on the current main and distribution network boundary variables, the operation optimization model of the main network or distribution network and the boundary variable update are alternately performed again. Through continuous iterative calculations, the operating parameters and boundary variables of the main and distribution network equipment are gradually adjusted until the boundary variables meet the convergence conditions, thereby achieving effective coordination and global optimization between the main and distribution networks.

[0068] This embodiment provides a global collaborative optimization scheduling method for the main network UPFC and the distribution network flexible interconnection equipment. This method establishes an effective connection between the main network UPFC and the distribution network flexible interconnection equipment by constructing a hierarchical optimization method for the main network and the distribution network, interactively updating boundary variables in the collaborative computing layer, and realizing global collaborative optimization scheduling based on convergence conditions. This method can fully consider the different operating characteristics and control objectives of the main network and the distribution network, realize cross-level power interaction optimization, and adapt to the complex power changes caused by fluctuations in the output of new energy. Through collaborative optimization scheduling, the frequent actions and additional losses caused by the isolated operation of the main network equipment are reduced, the economy and reliability of the power system operation are improved, and new technical means and solutions are provided for the efficient and stable operation of the power system.

[0069] See also Figure 2 , Figure 2This article provides an overview of the technical architecture of a global collaborative optimization scheduling method for main grid UPFCs and distribution network flexible interconnected equipment, designed based on the aforementioned embodiments. The main grid optimization layer acquires voltage, phase angle, and other information through SCADA data collection. This information is then processed by a main grid optimization algorithm (mixed integer programming) after UPFC equivalent modeling. The distribution network control layer utilizes PMU data feedback, which is then dispatched and dynamically corrected through the SOP / PSC. The collaborative computing engine, serving as the core hub, incorporates fault interlocking logic. Using the ADMM (Alternating Direction Method of Multipliers) solution module and dynamic penalty factor adjustment, it enables interactive coordination between the main grid and distribution network based on data such as power and voltage. Interlocking commands can also be issued based on fault signals, ensuring the coordinated operation of the main grid UPFC and distribution network flexible interconnected equipment. The following describes this technical architecture in conjunction with some embodiments of the present invention.

[0070] See also Figure 3 , Figure 3 This paper presents a collaborative architecture for the main and distribution networks. This architecture achieves standardized coupling and real-time interaction of cross-level parameters by building a unified data model and dynamic interaction mechanism for the main and distribution networks. The main network first constructs a UPFC equivalent model, introducing virtual nodes to modify and reconstruct the node admittance matrix and determine the interaction form of boundary variables. The distribution network defines SOP / PSC constraints to construct an optimization model and clarify boundary variable interactions. In the middle, based on the dynamic interaction mechanism, boundary variables of the main and distribution networks are updated through the ADMM collaborative channel, achieving collaborative optimization of the main and distribution networks. Boundary variables are continuously updated iteratively, resulting in collaborative optimization of the main and distribution network operating parameters and achieving global collaborative goals.

[0071] For the equivalent modeling of the main grid UPFC, an equivalent injection power model is established based on the physical characteristics of the UPFC series transformer and parallel converter:

[0072]

[0073] Where, and They represent the active power and reactive power injected into the system by UPFC, represents the self-conductance of node i, and denote the voltage amplitudes at nodes i and j respectively, represents the mutual conductance between nodes i and j, represents the voltage phase angle difference between node i and node j, represents the self-susceptance of node i, represents the mutual susceptance between node i and node j; is the node admittance matrix element.

[0074] In order to enhance the dynamic regulation capability of UPFC, the virtual node impedance correction parameter is introduced as follows:

[0075]

[0076] Where, represents the virtual node impedance, represents the virtual node resistance, represents the imaginary unit, represents the virtual node reactance;

[0077] Reconstruct the node admittance matrix sensitivity model:

[0078]

[0079] Where, represents the modified node admittance matrix, represents the original node admittance matrix, It represents the admittance correction value, reflecting the dynamic impact of UPFC on the main grid current. represents the node impedance;

[0080] For distribution network flexible equipment modeling, define the capacity constraints of SOP:

[0081]

[0082] in, is the maximum apparent power (MVA) of the SOP.

[0083] Construct PSC phase switching logic:

[0084]

[0085] in, is the phase switching state (0 means locked, 1 means turned on), is the node load power (MW).

[0086] For the dynamic interaction mechanism design, in one embodiment, an improved alternating direction multiplier method (ADMM) is used to construct a distributed solution framework:

[0087]

[0088] Where, and represent the boundary variables at the k+1th and kth iterations, respectively. represents the penalty factor at the kth iteration, and Represent the coefficient matrices of the main grid side and the distribution network side respectively, and They represent the state variable sets determined by the main grid side and the distribution network side based on the UPFC parameters and the flexible interconnection equipment parameters at the kth iteration respectively.

[0089] In addition, in one embodiment, the ADMM collaborative channel update also includes rolling updates of the power reference values of the SOPs in the UPFC and the flexible interconnected equipment based on the new energy output minute-level prediction model. The mathematical expression of the new energy output minute-level prediction model is as follows:

[0090]

[0091] Where, Indicates at time The power reference value, Indicates at time The power reference value, Indicates time New energy output The amount of change, Represents new energy output Regarding the rate of change of time, and Both represent the prediction correction coefficient. Second-level dynamic correction is based on Lyapunov stability theory:

[0092]

[0093] in, are the proportional, integral, and differential coefficients respectively.

[0094] Finally, the accuracy of the UPFC equivalent model is verified through sensitivity analysis: If the error exceeds the limit (such as ), then adjust the virtual node impedance .

[0095] See also Figure 4 , Figure 4 An optimization process is shown, which realizes the global optimal allocation of control resources of the main distribution network through a hierarchical optimization architecture and a dynamic solution algorithm. Among them, the main network side inputs SCADA data to build an optimization model, and the UPFC parameters are solved by mixed integer programming; the distribution network side inputs PMU data to build an optimization model, and the SOP / PSC parameters are solved by the MPSO algorithm; the two types of parameters are sent to the collaborative layer, and the main distribution network boundary variables are updated by ADMM. If convergence is judged by the convergence criterion, the control instructions are issued and executed by UPFC and SOP / PSC, realizing the hierarchical collaborative optimization and dynamic solution of the main distribution network. Specifically, the above process includes the following steps:

[0096] Step 1: Connect to the bus voltage of the main grid SCADA system , phase angle difference and UPFC converter parameters (including series transformer impedance , parallel converter capacity ), and synchronously obtain the flexible interconnection equipment operation data of the distribution network PMU (such as SOP power , PSC phase switching state , new energy output ). Normalize heterogeneous data through standardized interfaces: .in, is the voltage reference value (kV), is the rated power of renewable energy (MW).

[0097] Step 2: Optimize the main grid UPFC parameters. In one embodiment, a multi-objective operation optimization model for the main grid layer is proposed, combining the objectives of minimizing power flow deviation and balancing equipment losses, as follows:

[0098]

[0099] Where, and Both represent weight coefficients, Indicates the power flow deviation value calculated based on the power reference value and the power measurement value. represents the number of converters, Indicates the quantity index, and They represent the number of operations and the maximum allowed number of operations of the kth converter respectively.

[0100] Step 3: Flexible equipment scheduling for distribution network. In one embodiment, a distribution network optimization model is proposed with the goal of minimizing the weighted values of network loss, voltage deviation, and equipment life as follows:

[0101]

[0102] Where, Indicates the optimization time period, 、 and Both represent weight coefficients, and represents the resistance and time of line ij The current, Indicates time The voltage deviation, and They represent the kth distribution network flexible interconnection equipment time The number of actions and the maximum allowed number of actions.

[0103] Step 4: Global collaborative iterative solution. Update the main distribution network boundary variables alternately through the ADMM algorithm In one embodiment, a residual threshold criterion is designed: .in, is the convergence residual threshold (usually ).

[0104] Step 5: Real-time dynamic correction execution. Triggering second-level correction instructions based on the voltage over-limit probability prediction model:

[0105] , combined with Kalman filter state estimation to compensate for measurement noise: .in, is the state transition matrix, is the Kalman gain, is the observation matrix.

[0106] Step 6: Multi-time scale coordinated control. Update power reference value in minutes (cycle 15 minutes), perform dynamic correction in seconds (cycle 1 second), respond to fault events in milliseconds (response time ), forming a “prediction-optimization-correction” closed-loop control link.

[0107] Step 7: Verify the optimization effect. In one embodiment, the global network loss reduction rate and the device action frequency balance are calculated to evaluate the global collaborative optimization scheduling effect.

[0108] Calculate the global network loss reduction rate:

[0109]

[0110] Statistical equipment action frequency balance:

[0111]

[0112] in, The total number of devices, For the The frequency of each device's operation, is the mean value of the equipment action frequency, The standard deviation is the balance degree of equipment action frequency, which is used to calculate the discrete degree of equipment action frequency. The smaller the standard deviation, the more balanced the equipment action frequency is and the more stable the system operation is. is the network loss before optimization (unit: MW), is the optimized network loss (unit: MW), is the global network loss reduction rate (unit: %).

[0113] See also Figure 5 , Figure 5This paper demonstrates a fault interlocking logic for achieving coordinated self-healing in fault scenarios through a main and distribution network interlocking strategy and a rapid recovery mechanism. The main network monitors short-circuit current, triggering bypass reactor operation and signaling the time of operation. The distribution network monitors voltage drops, triggering load transfer and also signaling the time of operation. The timing calibration module verifies the consistency of main and distribution network operations, ensuring signal reliability through redundant communication. If synchronization is successful, the bypass reactor takes effect and load recovery is complete, achieving coordinated self-healing in fault scenarios.

[0114] In one embodiment, a main distribution network interlocking strategy and a rapid recovery mechanism are proposed, including the following steps:

[0115] S21: In response to the fault short-circuit current on the main grid side, an interlocking signal is triggered and the fault type is determined according to the voltage drop amplitude detected on the distribution grid side.

[0116] First, fault detection and classification are performed. Specifically, the main grid side monitors the short-circuit current in real time. , when satisfied The interlock signal is triggered when is the threshold coefficient (usually 1.2), is the UPFC rated current (kA). The voltage drop amplitude of the node synchronously detected on the distribution network side , determined to be a serious fault scenario ( Usually 15% is taken. is the rated voltage).

[0117] S22: In response to the interlocking signal and the fault type, triggering the UPFC control of the bypass reactor on the main grid side and the SOP control of the load transfer on the distribution grid side.

[0118] Dynamically adjust the UPFC bypass reactance to achieve rapid suppression on the main grid side as follows:

[0119]

[0120] in, is the initial bypass reactance (pu), is the attenuation coefficient (dimensionless), The strategy dynamically adjusts the bypass reactance through an exponential function to suppress the short-circuit current and prevent the fault from expanding.

[0121] The objective function of load transfer on the distribution network side is to maximize the weighted power distribution of key load nodes to ensure that key loads (such as hospitals, data centers, etc.) are given priority in power supply. The constraint condition is that the total power distribution of the SOP must not exceed its maximum capacity. Combined with the PSC phase switching logic, by optimizing the SOP power distribution and combining the logic of the PSC (phase switching controller), the power supply of key loads is restored first, thereby improving the reliability and stability of the system power supply. Based on the key load weight factor , optimize SOP power distribution: .in, is a collection of key load nodes, For the The weight factor of each key load node (unit: dimensionless), For the Power allocation of key load nodes. Constraints: .in, The maximum capacity of SOP.

[0122] S23: After performing timing calibration and redundant communication processing on the action times of UPFC control and SOP control, corresponding UPFC control and SOP control are performed.

[0123] Among them, the timing coordination calibration is to check the consistency of the main and distribution network actions by designing multi-level timing criteria and timing errors of the main and distribution network actions: .in, Main network UPFC action timestamp (ms), The timestamp of the SOP action for the distribution network (ms). The allowed timing error threshold (usually 50ms) needs to be adjusted according to the actual communication delay value. Redundant communication channels (dual-channel hot standby) are used to improve signal transmission reliability and communication success rate. 99.99%.

[0124] S24: Obtain and determine whether the fault recovery evaluation parameters of the main distribution network after control meet the preset conditions. If not, extract the current control parameters.

[0125] Evaluate the fault recovery effect and calculate the corresponding fault recovery evaluation parameters. For example, calculate the voltage recovery time:

[0126]

[0127] in, is the voltage safety factor (usually 0.95), is the rated voltage.

[0128] The power supply recovery rate of key loads can also be counted:

[0129]

[0130] in, is the critical load node set (dimensionless), is the restored power supply of node i at time t, is the original load power of node i at time t.

[0131] S25: With the goal of optimizing the fault recovery effect, the current control parameters are optimized to generate optimized control parameters.

[0132] S26: UPFC control and SOP control are performed based on the optimized control parameters, and the fault recovery evaluation parameters are verified to see whether they meet the preset conditions. If not, the control parameters are re-optimized until they pass the verification.

[0133] Verifying whether the preset condition is met can be to verify whether the voltage recovery time meets , whether the critical load recovery rate reaches 90%.

[0134] See also Figure 6 , Figure 6 This paper illustrates a reinforcement learning parameter optimization process. It involves first inputting the voltage recovery time and critical load recovery rate, extracting current control parameters such as the bypass reactance attenuation coefficient and voltage safety factor, then calculating the reward value based on factors such as the comprehensive recovery efficiency. The actor network outputs parameter adjustments, and the critic network evaluates the state value and updates the actor network parameters. The control parameters are then updated and input into the fault suppression module. The system then determines whether the voltage recovery time and critical load recovery rate meet the standards. If not, optimization continues. This allows for dynamic optimization of control parameters in fault scenarios, improving the adaptability of the collaborative self-healing strategy.

[0135] In one embodiment, a process for dynamically optimizing control parameters using a reinforcement learning algorithm is proposed.

[0136] In this embodiment, a library of typical fault scenarios (such as single-phase grounding, three-phase short circuit, and new energy grid disconnection) is constructed to test the synergistic suppression effect under different new energy penetration rates. The main steps of dynamically optimizing control parameters using a reinforcement learning algorithm include:

[0137] Step 1: State Observation: Input Voltage Recovery Time (Step 3.5) with critical load recovery rate (Step 3.5); Extract the current control parameters (bypass reactance attenuation coefficient , voltage safety factor ).

[0138] Step 2: Reward Function Design: Define Reward Value , comprehensive recovery efficiency, equipment loss and parameter stability:

[0139]

[0140] in, is the weight coefficient (the default is ), 、 is the parameter change.

[0141] Step 3: Policy update: Using the Actor-Critic framework: Actor network output parameter adjustment :

[0142]

[0143] in, For the Actor network strategy function, input state , output parameter adjustment amount, are network parameters, Evaluate the state value for the state vector critic network :

[0144]

[0145] in, is the Critic network discount factor (usually 0.99), and updates the Actor network parameters based on the policy gradient:

[0146]

[0147] in, is the advantage function, is the action-value function.

[0148] Step 4: Parameter adjustment and feedback, update control parameters: , and send the new parameters to the fault suppression module to form a closed-loop optimization link. is the bypass reactance attenuation coefficient, Parameter increment (dimensionless) generated for the policy network, newly added The penalty term can avoid drastic fluctuations in parameters.

[0149] Based on the above-mentioned embodiments, a global collaborative optimization scheduling method for a unified power flow controller (UPFC) in the main grid and flexible interconnected equipment in the distribution network is proposed. By constructing a hierarchical optimization architecture, a dynamic interaction mechanism, and a fault interlocking strategy, this method addresses key bottlenecks in the coordinated control of the main and distribution networks. This method first establishes a multimodal equivalent model for the main grid UPFC and a multi-objective optimization function for the distribution network's flexible equipment. Dynamic constraints such as the UPFC's series transformer impedance and converter capacity, as well as distribution network equipment life balancing and voltage deviation penalty terms, are integrated into the unified optimization framework, breaking through the traditional serial optimization model. Secondly, a distributed solution architecture based on the modified alternating direction multiplier method (ADMM) is designed. By defining interactive power and voltage variables at the main and distribution network boundaries, cross-level control instructions are synchronized in milliseconds, significantly improving computational efficiency and coordination accuracy. A main and distribution network interlocking suppression strategy is proposed for fault scenarios: the main grid side dynamically adjusts the UPFC bypass reactance to rapidly suppress short-circuit currents. The distribution network side simultaneously triggers load transfer control for flexible equipment. Power distribution is optimized based on key load weighting factors, forming a time-series coordinated chain for fault isolation and recovery. Furthermore, a dynamic penalty factor and correlation matrix are introduced to ensure compatibility between the optimization of the main grid flow and the economic operation objectives of the distribution network. Through the organic integration of the above technical systems, the present invention can effectively reduce equipment losses, improve the capacity to absorb new energy, and achieve rapid self-healing of main and distribution network faults.

[0150] Based on the same inventive concept, embodiments of the present application also provide a device for global collaborative optimization scheduling of main network UPFC and distribution network flexible interconnected equipment, for implementing the aforementioned method for global collaborative optimization scheduling of main network UPFC and distribution network flexible interconnected equipment. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the embodiments of the device for global collaborative optimization scheduling of main network UPFC and distribution network flexible interconnected equipment provided below can be found in the aforementioned method for global collaborative optimization scheduling of main network UPFC and distribution network flexible interconnected equipment, and will not be repeated here.

[0151] See also Figure 7 The embodiment of the present invention further provides a global collaborative optimization scheduling device for a main network UPFC and a distribution network flexible interconnection device, comprising:

[0152] The model solving module is used to solve the operation optimization models of the main network layer and the distribution network layer respectively to obtain the UPFC parameters and flexible interconnection equipment parameters;

[0153] A collaborative update module is used to alternately update distribution network boundary variables or main network boundary variables based on UPFC parameters and flexible interconnection equipment parameters in the pre-built collaborative computing layer; the main network boundary variables and distribution network boundary variables are the interaction parameters of the main and distribution network connection points on the main network side and the distribution network side respectively;

[0154] The judgment and scheduling module is used to judge whether the boundary variables of the main and distribution networks meet the convergence conditions. If so, the global collaborative optimization scheduling of the main network UPFC and the distribution network flexible interconnection equipment is carried out based on the main and distribution network boundary variables; if not, the operation optimization model of the main network or the distribution network is solved alternately and the boundary variables are updated based on the main and distribution network boundary variables until the updated main and distribution network boundary variables meet the convergence conditions.

[0155] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0156] Reference Figure 8 An embodiment of the present invention also provides a computer device, including: a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, it implements the global collaborative optimization scheduling method of the main network UPFC and the distribution network flexible interconnection equipment as described in any one of the above methods.

[0157] The computer device may be a desktop computer, notebook computer, PDA, cloud server or other computing device. The computer device may include, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that Figure 8 The computer device is merely an example and does not constitute a limitation on the computer device. The computer device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, etc.

[0158] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0159] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped with the computer device. Furthermore, the memory may include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is about to be output.

[0160] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for global collaborative optimization scheduling of the main network UPFC and the distribution network flexible interconnection equipment as described in any one of the above methods is implemented.

[0161] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0162] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0163] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0164] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0165] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A global collaborative optimization scheduling method for main network UPFC and distribution network flexible interconnection equipment, characterized in that: The steps include: Solve the operation optimization models of the main grid layer and the distribution network layer respectively to obtain the UPFC parameters and flexible interconnection equipment parameters; In the pre-built collaborative computing layer, the distribution network boundary variables or the main network boundary variables are alternately updated based on the UPFC parameters and the flexible interconnection equipment parameters; the main network boundary variables and the distribution network boundary variables are interaction parameters of the main distribution network connection point on the main network side and the distribution network side respectively; Determine whether the main and distribution network boundary variables meet the convergence conditions. If so, perform global collaborative optimization scheduling of the main network UPFC and the distribution network flexible interconnection equipment based on the main and distribution network boundary variables; if not, alternately solve the operation optimization model of the main network or the distribution network and update the boundary variables based on the main and distribution network boundary variables until the updated main and distribution network boundary variables meet the convergence conditions.

2. The global collaborative optimization scheduling method for main network UPFC and distribution network flexible interconnection equipment according to claim 1 is characterized in that: The step of alternately updating the distribution network boundary variables or the main network boundary variables based on the UPFC parameters and the flexible interconnection equipment parameters also includes rolling updating the power reference values of the SOP in the UPFC and the flexible interconnection equipment according to the new energy output minute-level prediction model, and correcting the distribution network reactive power according to the second-level dynamic correction model. The mathematical expression of the new energy output minute-level prediction model is as follows: Where, Indicates at time The power reference value, Indicates at time The power reference value, Indicates time New energy output The amount of change, Represents new energy output Regarding the rate of change of time, and All represent prediction correction coefficients; The mathematical expression of the second-level dynamic correction model is as follows: Where, Indicates reactive power correction amount, Indicates voltage deviation, are the proportional coefficient, integral coefficient, and differential coefficient respectively.

3. The global collaborative optimization scheduling method for the main network UPFC and distribution network flexible interconnection equipment according to claim 1 is characterized in that: The mathematical expression of the operation optimization model of the main network layer is as follows: Where, and Both represent weight coefficients, Indicates the power flow deviation value calculated based on the power reference value and the power measurement value. represents the number of converters, Indicates the quantity index, and They represent the number of operations and the maximum allowed number of operations of the kth converter respectively.

4. The global collaborative optimization scheduling method for main network UPFC and distribution network flexible interconnection equipment according to claim 1 is characterized in that: The mathematical expression of the operation optimization model of the distribution network layer is as follows: Where, Indicates the optimization time period, 、 and Both represent weight coefficients, and represents the resistance and time of line ij The current, Indicates time The voltage deviation, and They represent the kth distribution network flexible interconnection equipment time The number of actions and the maximum allowed number of actions.

5. The global collaborative optimization scheduling method for main network UPFC and distribution network flexible interconnection equipment according to claim 1 is characterized in that: The improved alternating direction multiplier method is used to update the distribution network boundary variables or the main network boundary variables. The mathematical expression of the update process is as follows: Where, and represent the boundary variables at the k+1th and kth iterations, respectively. represents the penalty factor at the kth iteration, and Represent the coefficient matrices of the main grid side and the distribution network side respectively, and represent the state variable sets determined by the main grid side and the distribution grid side based on the UPFC parameters and the flexible interconnection equipment parameters at the kth iteration respectively; The convergence conditions are as follows: ,in, is the convergence residual threshold.

6. The global collaborative optimization scheduling method for main network UPFC and distribution network flexible interconnection equipment according to claim 1 is characterized in that: Also includes: The global network loss reduction rate and equipment action frequency balance are calculated to evaluate the global collaborative optimization scheduling effect.

7. The global collaborative optimization scheduling method for main network UPFC and distribution network flexible interconnection equipment according to any one of claims 1 to 6, characterized in that: It also includes the use of main and distribution network interlocking strategies and rapid recovery mechanisms to carry out coordinated self-healing in fault scenarios, including the following steps: In response to the fault short-circuit current on the main grid side, an interlocking signal is triggered and the fault type is determined based on the voltage drop amplitude detected on the distribution grid side; In response to the interlocking signal and the fault type, triggering UPFC control of the bypass reactor on the main grid side and SOP control of the load transfer on the distribution grid side; After performing timing calibration and redundant communication processing on the action times of the UPFC control and the SOP control, the corresponding UPFC control and the SOP control are performed; Obtain and determine whether the fault recovery evaluation parameters of the main distribution network after control meet the preset conditions. If not, extract the current control parameters; With the goal of optimizing the fault recovery effect, the current control parameters are optimized to generate optimized control parameters; The UPFC control and the SOP control are performed based on the optimized control parameters, and it is verified whether the fault recovery evaluation parameters meet the preset conditions. If not, the control parameters are re-optimized until they pass the verification.

8. A global collaborative optimization scheduling device for main network UPFC and distribution network flexible interconnection equipment, characterized in that: include: The model solving module is used to solve the operation optimization models of the main network layer and the distribution network layer respectively to obtain the UPFC parameters and flexible interconnection equipment parameters; A collaborative update module is configured to alternately update distribution network boundary variables or main network boundary variables based on the UPFC parameters and the flexible interconnection equipment parameters in a pre-built collaborative computing layer; the main network boundary variables and the distribution network boundary variables are interaction parameters of the main distribution network connection point on the main network side and the distribution network side, respectively; The judgment and scheduling module is used to judge whether the boundary variables of the main and distribution networks meet the convergence conditions. If so, the global collaborative optimization scheduling of the main network UPFC and the distribution network flexible interconnection equipment is performed based on the main and distribution network boundary variables; if not, the operation optimization model of the main network or the distribution network is solved alternately and the boundary variables are updated based on the main and distribution network boundary variables until the updated main and distribution network boundary variables meet the convergence conditions.

9. A computer device, characterized in that: The device includes a processor and a memory: The memory is used to store the computer program and send instructions of the computer program to the processor; The processor executes a global collaborative optimization scheduling method for main network UPFC and distribution network flexible interconnection equipment according to any one of claims 1 to 7 according to the instructions of the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements a global collaborative optimization scheduling method for a main network UPFC and a distribution network flexible interconnection equipment according to any one of claims 1 to 7.

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