Power distribution network flexible mutual supply control method and system considering load uncertainty

Abstract

The invention discloses a power distribution network flexible mutual supply control method considering load uncertainty, and relates to the technical field of power system control. The method includes: acquiring historical operation data of a power distribution network, and establishing a robust optimization model; based on the robust optimization model, a flexible mutual supply control strategy is designed, and an adaptive control mechanism is introduced; evaluating a system state, and performing distributed coordination control based on the evaluated system state; carrying out robust stability analysis according to the characteristics of the distributed control system; and evaluating the index system. According to the invention, the robust optimization model is established to effectively cope with load fluctuation; a flexible mutual supply control strategy is designed based on the model, power distribution is optimized by combining a hierarchical architecture and a self-adaptive control mechanism, and the response capability of the system to sudden changes is enhanced; an improved Kalman filtering algorithm is used for evaluating the system state, distributed coordination control is implemented, and the communication cost is reduced; a Lyapunov direct method is adopted to carry out robust stability analysis, and stable operation of the system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system control, and in particular to a method and system for controlling flexible mutual supply of distribution networks taking load uncertainty into consideration. Background Art

[0002] With the large-scale integration of distributed power sources such as renewable energy and electric vehicles, the operating environment of distribution networks has undergone significant changes. Traditional distribution networks have relatively stable load characteristics, relying primarily on centralized generation and deterministic load forecasting, with control strategies often employing static designs. However, in modern distribution networks, the intermittent and unpredictable nature of renewable energy, coupled with the random nature of electric vehicle charging loads, has resulted in a high degree of uncertainty in load characteristics.

[0003] In the existing technology, most of the mutual supply control methods of distribution networks are based on deterministic load forecasting models, which show obvious limitations when faced with load fluctuations. For example, when the power generation of renewable energy fluctuates greatly due to weather changes, or the demand for electric vehicle charging suddenly increases, the control strategy based on deterministic prediction cannot be adjusted in time, resulting in reduced system operating efficiency and may even cause problems such as voltage limit violations and power imbalance. In addition, most of the existing mutual supply control strategies adopt static solutions and lack consideration of the dynamic characteristics of the system. When faced with sudden load changes, static control strategies are difficult to respond quickly, which can easily cause system instability and may even lead to local or global power outages. In this case, the reliability and economy of the distribution network are seriously challenged, and traditional methods are difficult to meet the requirements of modern distribution networks for flexibility and robustness. Summary of the Invention

[0004] Therefore, the problem to be solved by the present invention is to address the problems existing in the flexible mutual supply control of distribution networks, such as large load forecasting errors, inflexible control strategies, and low system reliability. A flexible mutual supply control method that takes load uncertainty into consideration is proposed, and the operating reliability and economy of the system are improved through robust optimization theory and adaptive control strategies.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for controlling flexible mutual supply of a distribution network considering load uncertainty, which includes obtaining historical operating data of the distribution network and establishing a robust optimization model;

[0007] Based on the robust optimization model, a flexible mutual supply control strategy is designed and an adaptive control mechanism is introduced;

[0008] Evaluate system status and perform distributed coordinated control based on the evaluated system status;

[0009] Conduct robust stability analysis based on the characteristics of distributed control systems;

[0010] Evaluation indicator system.

[0011] As a preferred solution of the distribution network flexible mutual supply control method considering load uncertainty described in the present invention, the process of establishing the robust optimization model is as follows:

[0012] First, a load uncertainty model is established through the interval prediction method. The load uncertainty is expressed as the superposition of the predicted value and the bounded error term, and the error limit is dynamically adjusted according to the time and space characteristics.

[0013] Based on the load uncertainty model, a robust optimization control model in the form of min-max is constructed, which takes into account the system operation constraints under multiple factors and optimizes the economy and reliability of the system under the worst case scenario;

[0014] Considering the influence of load forecast error, the load uncertainty model can be expressed as:

[0015] P load (t) = P pred (t)+ΔP(t),|ΔP(t)|≤ε(t)

[0016] Among them, P load (t) is the load power at time t, P pred (t) is the predicted load, ΔP(t) is the prediction error, and ε(t) is the error bound;

[0017] The objective function of the robust optimization control model is expressed as:

[0018] min max |ΔP|≤ε {f(x,u,ΔP)}

[0019] Where x is the system state variable, including node voltage, phase angle, etc., u is the control variable, including mutual power supply, regulating device gear, etc., ΔP is the load forecast error vector, ε is the Lagrange multiplier;

[0020] The system operation constraints are expressed as:

[0021]

[0022] As a preferred solution of the distribution network flexible mutual supply control method considering load uncertainty of the present invention, the step of designing the flexible mutual supply control strategy is:

[0023] Based on a robust optimization model, the optimal power mutual assistance solution is determined by real-time calculation of the power supply margin and power demand between feeders and combining line capacity constraints.

[0024] At the same time, the control strategy adopts a hierarchical and layered architecture, with the upper layer responsible for global optimization and the lower layer performing real-time control;

[0025] The power mutual aid constraint is:

[0026]

[0027] in, is the transmission power of line ij at time t, For power supply margin, To meet electricity demand, is the line capacity;

[0028] Secondly, an adaptive control mechanism is introduced. The adaptive control law is designed through state feedback and feedforward compensation. The improved Kalman filter algorithm is combined to track the system state in real time to deal with sudden load changes or faults. The control law of the adaptive control mechanism is designed as follows:

[0029] u(t)=K(t)x(t)+v(t)

[0030] Where u(t) is the control input vector at time t, K(t) and x(t) represent the state feedback terms, and v(t) represents the feedforward compensation term;

[0031] Where K(t) also represents the feedback gain, and K(t) is updated in real time through the adaptive law. The parameter adaptive update is:

[0032]

[0033] in, is the time derivative of the gain matrix K, x T is the transpose of the state vector x, γ is the parameter update rate, e(t) is the system control error, and P is a positive definite symmetric matrix.

[0034] As a preferred solution of the distribution network flexible mutual supply control method considering load uncertainty of the present invention, wherein: the evaluation system state adopts an improved Kalman filter algorithm to achieve real-time tracking of the state through two steps of prediction and correction;

[0035] The state estimation equation is:

[0036]

[0037] in, is the estimated state value at time t+1, is the state estimate, A is the system state transfer matrix, B is the control input matrix, L is the observer gain matrix, y(t) is the system measurement output, and C is the output matrix;

[0038] The error dynamic equation is:

[0039] e(t+1)=(A-LC)e(t)+w(t)

[0040] Among them, e(t+1) is the estimation error at time t+1, e(t) is the state estimation error, and w(t) is the system process noise.

[0041] As a preferred solution of the flexible mutual supply control method for distribution networks considering load uncertainty described in the present invention, the process of performing distributed coordinated control based on the evaluated system state is to perform global optimization through local controllers and a consistency protocol on the premise of knowing the system state. The local controllers are designed as follows:

[0042]

[0043] Among them, u i (t) is the control output of the i-th controller, is the control weight matrix, is the input matrix of the i-th subsystem, P i is the solution of the Riccati equation, x i (t) is the state of the i-th subsystem;

[0044] Each local controller makes control decisions based on local information and limited communication, and achieves global optimization through a consistency protocol. The consistency protocol is expressed as:

[0045]

[0046] in, is the rate of change of the consistency variable, (ξ j -ξ i ) is the difference in consistency variables between neighbor node j and current node i, a ij is the adjacency matrix element of the communication topology, N i is the neighbor set of node i.

[0047] As a preferred solution of the distribution network flexible mutual supply control method considering load uncertainty described in the present invention, the process of performing robust stability analysis based on the characteristics of the distributed control system includes:

[0048] Lyapunov direct method is used for robust stability analysis;

[0049] Adopting hierarchical iterative strategy to optimize the solution algorithm;

[0050] Use real-time scheduling strategies to quickly respond to system changes;

[0051] The Lyapunov function is expressed as:

[0052]

[0053] Among them, V(x) is the Lyapunov function, x T Px is a quadratic function, where P is a positive definite symmetric matrix, is the parameter estimation error, is the parameter estimation error The transposed form of Γ i -1 Update the gain matrix for the parameters;

[0054] The stability condition is expressed as:

[0055]

[0056] in, is the time derivative of the Lyapunov function, and the parameters α and β represent the convergence rate and the final stable domain of the system, respectively.

[0057] As a preferred solution of the distribution network flexible mutual supply control method considering load uncertainty of the present invention, wherein: the indicators of the evaluation index system include reliability indicators and economic indicators;

[0058]

[0059] Among them, R sys represents the reliability index, λ i represents the equipment failure rate, τ i Indicates the repair time;

[0060]

[0061] Among them, C total represents the economic index, c op (t) represents the operating cost, c loss (t) represents the loss cost.

[0062] Secondly, an embodiment of the present invention provides a flexible mutual supply control system for a distribution network that takes into account load uncertainty. The control system includes four core functional modules: a data preprocessing module, a model solving module, a control execution module, and a performance evaluation and optimization module. The modules interact with each other and operate collaboratively through standardized interfaces.

[0063] The data preprocessing module performs comprehensive cleaning and standardization on the collected load data;

[0064] The system uses an outlier detection algorithm based on statistical characteristics to identify and correct deviations and outliers in the data, normalizes the cleaned data to eliminate dimensional effects, reconstructs missing data based on sliding time window technology to ensure data continuity and integrity, and extracts load change characteristics through statistical analysis of historical data, providing a basis for subsequent parameter initialization and model construction.

[0065] The model solving module includes converting the theoretical model into a solvable mathematical form using robust optimization solution;

[0066] An optimization model is constructed based on system characteristics, and a multi-objective function including economy and reliability is set. At the same time, various operating constraints such as voltage constraints and power constraints are considered. During the solution process, a hierarchical iterative strategy is adopted. The optimality of the solution is guaranteed by combining the solution of the main problem and the verification of the dual problem. Convergence tests are performed in each round of iteration to ensure the stability of the algorithm.

[0067] The control execution module uses an improved Kalman filter algorithm to achieve state estimation, accurately track the system operation status, update the controller's feedback gain based on the state estimation results, calculate the optimal control quantity, and send control instructions to each execution unit through a standardized interface to achieve real-time closed-loop control. The entire process adopts a distributed architecture to ensure the real-time and reliability of the control.

[0068] The performance evaluation and optimization are used for tasks such as system operation monitoring and effect evaluation;

[0069] By real-time monitoring of key system operating parameters, calculating performance indicators, and promptly detecting and reporting abnormal situations, we have established a multi-dimensional evaluation system covering voltage quality, economy, reliability, etc., and regularly generate performance analysis reports and optimization suggestions to provide a basis for continuous improvement of the system.

[0070] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the distribution network flexible mutual supply control method considering load uncertainty as described in the first aspect of the present invention are implemented.

[0071] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the distribution network flexible mutual supply control method considering load uncertainty as described in the first aspect of the present invention are implemented.

[0072] The beneficial effects of the present invention are as follows: by obtaining historical operating data of the distribution network and establishing a robust optimization model, a reliable basis is provided for control strategy design, which can effectively cope with the load uncertainty brought about by the access of renewable energy and electric vehicles, and significantly improve the adaptability and flexibility of the system; the flexible mutual supply control strategy designed based on the robust optimization model, combined with a hierarchical architecture and an adaptive control mechanism, realizes dynamic optimization of power distribution and mutual assistance, enhances the system's rapid response capability to load changes and emergencies, and further improves the reliability and economy of operation; the system state is evaluated in real time through an improved Kalman filter algorithm, and distributed coordinated control is implemented based on this, which optimizes the overall performance of the distribution network, reduces communication costs and computational complexity, and improves the operating efficiency and flexibility of the system; based on the characteristics of the distributed control system, the Lyapunov direct method is used for robust stability analysis, combined with a hierarchical iterative strategy and a real-time scheduling mechanism, to ensure the stable operation of the system in an uncertain environment and enhance the system's anti-interference capability and robustness; by establishing an evaluation system including reliability and economy indicators, a clear evaluation standard is provided for the operation optimization of the distribution network, facilitating the objective measurement and continuous improvement of the effect of the control strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0074] Figure 1 This is a flow chart of the flexible mutual supply control method for distribution networks considering load uncertainty;

[0075] Figure 2 Computer equipment diagram for the flexible mutual supply control method of distribution network considering load uncertainty;

[0076] Figure 3 Flowchart of the flexible mutual supply control system of distribution network considering load uncertainty. DETAILED DESCRIPTION

[0077] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0078] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0079] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0080] Reference Figures 1 and 2 This embodiment provides a distribution network flexible mutual supply control method considering load uncertainty, including:

[0081] S1: Obtain historical operating data of the distribution network and establish a robust optimization model;

[0082] The process of establishing a robust optimization model is as follows:

[0083] First, a load uncertainty model is established through the interval prediction method. The load uncertainty is expressed as the superposition of the predicted value and the bounded error term, and the error limit is dynamically adjusted according to the time and space characteristics.

[0084] Considering the influence of load forecast error, the load uncertainty model can be expressed as:

[0085] P load (t) = P pred (t)+ΔP(t),|ΔP(t)|≤ε(t)

[0086] Among them, P load (t) is the load power at time t, P pred (t) is the predicted load, ΔP(t) is the prediction error, and ε(t) is the error bound;

[0087] The actual load can be expressed as the sum of the predicted load and the prediction error, where the absolute value of the prediction error does not exceed the error bound. This modeling approach considers the dynamic nature of the load while ensuring the mathematical tractability of the model. The error bound can be obtained through statistical analysis of historical data and dynamically adjusted based on temporal and spatial characteristics. Based on this uncertainty modeling, a robust optimization model can be constructed to cope with system fluctuations.

[0088] Based on the load uncertainty model, a robust optimization control model in the form of min-max is constructed, which takes into account the system operation constraints under multiple factors and optimizes the economy and reliability of the system under the worst case scenario;

[0089] The objective function of the robust optimization control model is expressed as:

[0090] min max |ΔP|≤ε {f(x,u,ΔP)}

[0091] Where x is the system state variable, including node voltage, phase angle, etc., u is the control variable, including mutual power supply, regulating device gear, etc., ΔP is the load forecast error vector, ε is the Lagrange multiplier;

[0092] The model takes both economy and reliability into consideration and achieves a balance of multiple objectives through weight coefficients.

[0093] The system operation constraints are expressed as:

[0094]

[0095] System operation constraints include voltage constraints, power constraints, and equipment capacity constraints. These constraints must be satisfied within any allowable uncertainty range to ensure the robustness of the solution. To achieve this optimization goal, a corresponding flexible mutual supply control strategy needs to be designed.

[0096] S2: Based on the robust optimization model, a flexible mutual supply control strategy is designed and an adaptive control mechanism is introduced;

[0097] The steps for designing a flexible mutual supply control strategy are to determine the optimal power mutual supply scheme based on a robust optimization model by calculating the power supply margin and power demand between each feeder in real time and combining it with line capacity constraints.

[0098] At the same time, the control strategy adopts a hierarchical and layered architecture, with the upper layer responsible for global optimization and the lower layer performing real-time control;

[0099] The power mutual aid constraint is:

[0100]

[0101] in, is the transmission power of line ij at time t, For power supply margin, To meet electricity demand, is the line capacity;

[0102] The control strategy adopts a hierarchical architecture to ensure that the system can respond quickly when a sudden load change or failure occurs, achieving a dynamic balance between power supply and demand.

[0103] Secondly, in order to improve the adaptability of the control strategy, an adaptive control mechanism is introduced. The adaptive control law is designed through state feedback and feedforward compensation. The improved Kalman filter algorithm is combined to track the system state in real time to deal with sudden load changes or faults. The control law of the adaptive control mechanism is designed as follows:

[0104] u(t)=K(t)x(t)+v(t)

[0105] Where u(t) is the control input vector at time t, K(t) and x(t) represent the state feedback terms, and v(t) represents the feedforward compensation term;

[0106] Where K(t) also represents the feedback gain, and K(t) is updated in real time through the adaptive law. The parameter adaptive update is:

[0107]

[0108] in, is the time derivative of the gain matrix K, x T is the transpose of the state vector x, γ is the parameter update rate, e(t) is the system control error, and P is a positive definite symmetric matrix used to ensure Lyapunov stability.

[0109] Furthermore, parameter updates utilize a gradient descent method based on Lyapunov stability theory, with the update rate γ dynamically adjusted based on the system error e(t). This mechanism adapts to changes in system parameters and the operating environment, improving control robustness. It introduces a dynamic adjustment mechanism based on the adaptive law and avoids the limitations of traditional fixed-gain control.

[0110] S3: Evaluate the system status and perform distributed coordinated control based on the evaluated system status;

[0111] The improved Kalman filter algorithm is used to evaluate the system status, and the real-time tracking of the status is achieved through two steps: prediction and correction.

[0112] The state estimation equation is:

[0113]

[0114] in, is the estimated state value at time t+1, is the state estimate, A is the system state transfer matrix, B is the control input matrix, L is the observer gain matrix, y(t) is the system measurement output, and C is the output matrix;

[0115] The error dynamic equation is:

[0116] e(t+1)=(A-LC)e(t)+w(t)

[0117] Among them, e(t+1) is the estimation error at time t+1, e(t) is the state estimation error, and w(t) is the system process noise.

[0118] Taking into account the influence of system noise w(t), an adaptive gain matrix L is designed to minimize the estimation error e(t). This process improves the accuracy of state estimation by introducing uncertainty analysis of the measurement equation. The state assessment results directly influence control decisions and play a key role in the overall system performance.

[0119] The process of distributed coordinated control based on the evaluated system state is to perform global optimization through local controllers and consistency protocols based on the knowledge of the system state. The local controllers are designed as follows:

[0120]

[0121] Among them, u i (t) is the control output of the i-th controller, is the control weight matrix, is the input matrix of the i-th subsystem, P i is the solution of the Riccati equation, x i (t) is the state of the i-th subsystem;

[0122] Each local controller makes control decisions based on local information and limited communication, and achieves global optimization through a consistency protocol. The consistency protocol is expressed as:

[0123]

[0124] in, is the rate of change of the consistency variable, (ξ j -ξ i ) is the difference in consistency variables between neighbor node j and current node i, a ij is the adjacency matrix element of the communication topology, N i is the neighbor set of node i.

[0125] The controller is designed using optimal control theory, and the weight matrix R i Each control area is designed independently according to its characteristics. In order to ensure the stability of the distributed control system, a rigorous robust stability analysis is required.

[0126] S4: Conduct robust stability analysis based on the characteristics of distributed control systems;

[0127] According to the characteristics of distributed control systems, the process of robust stability analysis includes:

[0128] Lyapunov direct method is used for robust stability analysis;

[0129] The robust stability analysis uses the Lyapunov direct method, and the constructed Lyapunov function contains the state term x TPx and parameter error terms, Lyapunov function is expressed as:

[0130]

[0131] Among them, V(x) is the Lyapunov function, x T Px is a quadratic function, where P is a positive definite symmetric matrix, is the parameter estimation error, is the parameter estimation error The transposed form of Γ i -1 Update the gain matrix for the parameters;

[0132] By analyzing the derivative of the Lyapunov function, we can obtain the sufficient conditions for the stability of the system. The stability condition is expressed as:

[0133]

[0134] in, is the time derivative of the Lyapunov function, and the parameters α and β represent the convergence rate and final stable region of the system, respectively. On the basis of ensuring the stability of the system, it is necessary to design an efficient optimization solution algorithm.

[0135] Adopting hierarchical iterative strategy to optimize the solution algorithm;

[0136] In order to realize the engineering application of theoretical analysis results, a hierarchical iterative strategy is adopted to solve the optimization problem. The main problem is solved by linear programming method, and the dual problem is constructed to verify the optimality of the solution. The solution of the main problem is expressed as:

[0137] min x,y {c T x+d T y:Ax+By≤b}

[0138] Among them, c T is the objective function coefficient vector, d T is the auxiliary variable coefficient vector, A is the inequality constraint matrix, B is the auxiliary variable constraint matrix, and b is the right-hand side constraint vector;

[0139] The solution to the dual problem is expressed as:

[0140] max λ≥0 {min x,y L(x,y,λ)}

[0141] The Lagrange multiplier λ is updated using the subgradient method to ensure the convergence of the algorithm. The efficient implementation of the algorithm provides a computational basis for the execution of real-time scheduling strategies.

[0142] Use real-time scheduling strategies to quickly respond to system changes;

[0143] Based on an efficient optimization algorithm, the real-time scheduling strategy can quickly respond to system changes. The scheduling goal is to minimize system losses and voltage deviations. The scheduling goal is expressed as:

[0144]

[0145] Among them, J optimizes the objective function value, P loss (t) is the system loss at time t, ΔV(t) is the voltage deviation at time t, and the weight coefficients α and β in the objective function are determined by the multi-objective optimization method.

[0146] The voltage constraint is expressed as:

[0147]

[0148] Among them, V i (t) is the voltage of each node;

[0149] Voltage constraints ensure that the voltage at each node V i (t) Maintaining it within the permissible range and improving the power supply quality, a complete evaluation index system has been established to comprehensively evaluate the dispatching effect.

[0150] S5: Evaluation indicator system

[0151] The indicators of the evaluation index system include reliability indicators and economic indicators;

[0152]

[0153] Among them, R sys represents the reliability index, λ i represents the equipment failure rate, τ i Indicates the repair time;

[0154]

[0155] Among them, C total represents the economic index, c op (t) represents the operating cost, c loss (t) represents the loss cost.

[0156] As the effectiveness test mechanism of the entire control system, the evaluation index system considers the equipment failure rate λ through the reliability index i and repair time τ i The impact of, and considering the operating cost through economic indicators c op (t) and loss cost c loss (t) influence, providing an important basis for optimizing the algorithm implementation process.

[0157] Furthermore, this embodiment also provides a distribution network flexible mutual supply control system that takes into account load uncertainty, including four core functional modules: a data preprocessing module, a model solving module, a control execution module, and a performance evaluation and optimization module. The modules achieve data interaction and collaborative operation through standardized interfaces.

[0158] The data preprocessing module comprehensively cleans and standardizes the collected load data; the system adopts an outlier detection algorithm based on statistical characteristics to identify and correct deviations and anomalies in the data, normalizes the cleaned data to eliminate dimensional effects, and reconstructs missing data based on sliding time window technology to ensure data continuity and integrity. It also extracts load change characteristics through statistical analysis of historical data, providing a basis for subsequent parameter initialization and model construction.

[0159] The model solving module includes using robust optimization to transform the theoretical model into a solvable mathematical form; constructing an optimization model based on system characteristics, setting a multi-objective function that includes economy and reliability, and considering various operating restrictions such as voltage constraints and power constraints. During the solving process, a hierarchical iterative strategy is adopted to ensure the optimality of the solution by combining the solution of the main problem and the verification of the dual problem. Convergence tests are performed in each round of iteration to ensure the stability of the algorithm.

[0160] The control execution module uses an improved Kalman filter algorithm to achieve state estimation, accurately track the system operation status, update the controller's feedback gain based on the state estimation results, calculate the optimal control quantity, and send control instructions to each execution unit through a standardized interface to achieve real-time closed-loop control. The entire process adopts a distributed architecture to ensure the real-time and reliability of the control.

[0161] Performance evaluation and optimization are used for tasks such as system operation monitoring and effect evaluation. By real-time monitoring of key system operating parameters, calculating performance indicators, and promptly discovering and reporting abnormal situations, an evaluation system with multiple dimensions including voltage quality, economy, and reliability is established. Performance analysis reports and optimization suggestions are generated regularly to provide a basis for continuous system improvement.

[0162] This embodiment also provides a computer device, which is suitable for the case of a flexible mutual supply control method for a distribution network considering load uncertainty, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the flexible mutual supply control method for a distribution network considering load uncertainty proposed in the above embodiment.

[0163] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0164] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the flexible mutual supply control method for distribution networks considering load uncertainty as proposed in the above embodiment.

[0165] In summary, the present invention addresses the problem of load uncertainty in distribution networks and proposes a flexible mutual supply control method. While improving the flexibility, adaptability, reliability and economy of distribution network operation, it provides an innovative technical solution for the efficient operation of modern distribution networks, and has significant practical value and broad application prospects.

[0166] The present invention achieves efficient, flexible and stable operation of the system through five key steps. First, a robust optimization model is established by acquiring historical operation data to provide a reliable basis for the control strategy and effectively cope with load fluctuations. Secondly, a flexible mutual supply control strategy is designed based on the model, combining a hierarchical architecture and an adaptive control mechanism to optimize power distribution, enhance the system's response to sudden changes, and improve reliability and economy. Third, an improved Kalman filter algorithm is used to evaluate the system status, and distributed coordinated control is implemented to reduce communication costs and improve operating efficiency. Fourth, the Lyapunov direct method is used to perform robust stability analysis on the distributed control system to ensure the stable operation of the system in an uncertain environment. Finally, an evaluation index system is established to quantify system performance and provide a basis for optimization. While improving the flexibility, adaptability, reliability and economy of the distribution network, the present invention provides an innovative solution for the efficient operation of modern distribution networks and has significant practical value.

[0167] Reference Figure 2-Figure 3This embodiment provides a flexible mutual supply control method for distribution networks considering load uncertainty. To fully verify the effectiveness of the method of the present invention, we selected the IEEE 33-node standard test system as the simulation platform and designed a series of typical test scenarios.

[0168] Under normal operating conditions, the random load fluctuation range was set to ±10% to verify the system's daily control capabilities. In the sudden load change scenario, an extreme situation of a 50% local load surge was simulated to test the system's dynamic response performance. For fault conditions, a line N-1 fault scenario was set to evaluate the system's self-healing control effectiveness. In extreme weather scenarios, the load fluctuation range was increased to ±30% to verify the system's extreme adaptability.

[0169] The specific settings and data are shown in Table 1 and Table 2:

[0170] Table 1 Test scenario design

[0171] Scenario Type Specific settings Purpose of the test Normal operation Load fluctuation ±10% Verify daily control capabilities Load mutation Local load suddenly increases by 50% Testing dynamic response performance Fault recovery Line N-1 failure Evaluate the effectiveness of self-healing control extreme weather Severe load fluctuation ±30% Verify extreme adaptability

[0172] Table 2 Performance comparison of different methods

[0173]

[0174]

[0175] Through comparative experiments with traditional control methods, the method of the present invention has demonstrated significant performance advantages. In terms of voltage quality, the system voltage qualification rate increased from 92.5% to 97.8%, an increase of 5.3 percentage points; the voltage fluctuation amplitude was reduced by 35%, significantly enhancing the voltage stability of the system. In terms of economic indicators, the system loss rate was reduced from 4.8% to 3.2%, saving 12.5% ​​of operating costs, while the equipment utilization rate increased by 18%. In terms of reliability, the fault recovery time was shortened from 15 minutes to 8 minutes, the power supply reliability was improved by 8.5%, and the system's anti-disturbance capability was significantly enhanced. In terms of computational efficiency, the algorithm convergence time was reduced by 68%, and the memory usage was reduced by 45%, fully meeting the requirements of real-time control.

[0176] Experimental results fully demonstrate the superiority of the proposed method in multiple dimensions: First, adaptive control and distributed coordination mechanisms significantly improve the system's dynamic response and control accuracy; optimized scheduling strategies effectively reduce system losses and enhance economic benefits; second, robust optimization design enhances the system's anti-disturbance capabilities and improves power supply reliability; and finally, improved algorithms significantly increase computational efficiency and ensure real-time control. These experimental data provide strong support for the proposed method's engineering applications.

[0177] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A flexible mutual supply control method for distribution networks considering load uncertainty, characterized by: include, Obtain historical operating data of the distribution network and establish a robust optimization model; Based on the robust optimization model, a flexible mutual supply control strategy is designed and an adaptive control mechanism is introduced; Evaluate system status and perform distributed coordinated control based on the evaluated system status; Conduct robust stability analysis based on the characteristics of distributed control systems; Evaluation indicator system.

2. The method for controlling flexible mutual supply of distribution networks considering load uncertainty according to claim 1, characterized in that: The process of establishing the robust optimization model is as follows: First, a load uncertainty model is established through the interval prediction method. The load uncertainty is expressed as the superposition of the predicted value and the bounded error term, and the error limit is dynamically adjusted according to the time and space characteristics. Based on the load uncertainty model, a robust optimization control model in the form of min-max is constructed, which takes into account the system operation constraints under multiple factors and optimizes the economy and reliability of the system under the worst case scenario; Considering the influence of load forecast error, the load uncertainty model can be expressed as: P load (t)=P pred (t)+ΔP(t),|ΔP(t)|≤ε(t) Among them, P load (t) is the load power at time t, P pre d(t) is the predicted load, ΔP(t) is the prediction error, and ε(t) is the error bound; The objective function of the robust optimization control model is expressed as: min max |ΔP|≤ε {f(x,u,ΔP)} Where x is the system state variable, including node voltage, phase angle, etc., u is the control variable, including mutual power supply, regulating device gear, etc., ΔP is the load forecast error vector, ε is the Lagrange multiplier; The system operation constraints are expressed as:

3. The method for controlling flexible mutual supply of distribution networks considering load uncertainty according to claim 2, wherein: The steps of designing the flexible mutual supply control strategy are: Based on a robust optimization model, the optimal power mutual assistance solution is determined by real-time calculation of the power supply margin and power demand between feeders and combining line capacity constraints. At the same time, the control strategy adopts a hierarchical and layered architecture, with the upper layer responsible for global optimization and the lower layer performing real-time control; The power mutual aid constraint is: in, is the transmission power of line ij at time t, For power supply margin, To meet electricity demand, is the line capacity; Secondly, an adaptive control mechanism is introduced. The adaptive control law is designed through state feedback and feedforward compensation. The improved Kalman filter algorithm is combined to track the system state in real time to deal with sudden load changes or faults. The control law of the adaptive control mechanism is designed as follows: u(t)=K(t)x(t)+v(t) Where u(t) is the control input vector at time t, K(t) and x(t) represent the state feedback terms, and v(t) represents the feedforward compensation term; Where K(t) also represents the feedback gain, and K(t) is updated in real time through the adaptive law. The parameter adaptive update is: in, is the time derivative of the gain matrix K, x T is the transpose of the state vector x, γ is the parameter update rate, e(t) is the system control error, and P is a positive definite symmetric matrix.

4. The method for controlling flexible mutual supply of distribution networks considering load uncertainty according to claim 3, wherein: The evaluation system state adopts an improved Kalman filter algorithm to achieve real-time tracking of the state through two steps of prediction and correction; The state estimation equation is: in, is the estimated state value at time t+1, is the state estimate, A is the system state transfer matrix, B is the control input matrix, L is the observer gain matrix, y(t) is the system measurement output, and C is the output matrix; The error dynamic equation is: e(t+1)=(A-LC)e(t)+w(t) Among them, e(t+1) is the estimation error at time t+1, e(t) is the state estimation error, and w(t) is the system process noise.

5. The method for controlling flexible mutual supply of distribution networks considering load uncertainty according to claim 4, characterized in that: The process of distributed coordinated control based on the evaluated system state is to perform global optimization through local controllers and consistency protocols based on the knowledge of the system state. The local controllers are designed as follows: Among them, u i (t) is the control output of the i-th controller, is the control weight matrix, is the input matrix of the i-th subsystem, P i is the solution of the Riccati equation, x i (t) is the state of the i-th subsystem; Each local controller makes control decisions based on local information and limited communication, and achieves global optimization through a consistency protocol. The consistency protocol is expressed as: in, is the rate of change of the consistency variable, (ξ j -ξ i ) is the difference in consistency variables between neighbor node j and current node i, a ij is the adjacency matrix element of the communication topology, N i is the neighbor set of node i.

6. The method for controlling flexible mutual supply of distribution networks considering load uncertainty according to claim 5, characterized in that: The process of robust stability analysis based on the characteristics of distributed control systems includes: Lyapunov direct method is used for robust stability analysis; Adopting hierarchical iterative strategy to optimize the solution algorithm; Use real-time scheduling strategies to quickly respond to system changes; The Lyapunov function is expressed as: Among them, V(x) is the Lyapunov function, x T Px is a quadratic function, where P is a positive definite symmetric matrix, is the parameter estimation error, is the parameter estimation error The transposed form of Update the gain matrix for the parameters; The stability condition is expressed as: in, is the time derivative of the Lyapunov function, and the parameters α and β represent the convergence rate and the final stable domain of the system, respectively.

7. The method for controlling flexible mutual supply of distribution networks considering load uncertainty according to claim 6, characterized in that: The indicators of the evaluation index system include reliability indicators and economic indicators; Among them, R sys represents the reliability index, λ i represents the equipment failure rate, τ i Indicates the repair time; Among them, C total represents the economic index, c op (t) represents the operating cost, c loss (t) represents the loss cost.

8. A distribution network flexible mutual supply control system considering load uncertainty, based on the distribution network flexible mutual supply control method considering load uncertainty according to any one of claims 1 to 7, characterized in that: It also includes four core functional modules: data preprocessing module, model solving module, control execution module and performance evaluation and optimization module. The modules realize data interaction and collaborative operation through standardized interfaces. The data preprocessing module performs comprehensive cleaning and standardization on the collected load data; The system uses an outlier detection algorithm based on statistical characteristics to identify and correct deviations and outliers in the data, normalizes the cleaned data to eliminate dimensional effects, reconstructs missing data based on sliding time window technology to ensure data continuity and integrity, and extracts load change characteristics through statistical analysis of historical data, providing a basis for subsequent parameter initialization and model construction. The model solving module includes converting the theoretical model into a solvable mathematical form using robust optimization solution; An optimization model is constructed based on system characteristics, and a multi-objective function including economy and reliability is set. At the same time, various operating constraints such as voltage constraints and power constraints are considered. During the solution process, a hierarchical iterative strategy is adopted. The optimality of the solution is guaranteed by combining the solution of the main problem and the verification of the dual problem. Convergence tests are performed in each round of iteration to ensure the stability of the algorithm. The control execution module uses an improved Kalman filter algorithm to achieve state estimation, accurately track the system operation status, update the controller's feedback gain based on the state estimation results, calculate the optimal control quantity, and send control instructions to each execution unit through a standardized interface to achieve real-time closed-loop control. The entire process adopts a distributed architecture to ensure the real-time and reliability of the control. The performance evaluation and optimization are used for tasks such as system operation monitoring and effect evaluation; By real-time monitoring of key system operating parameters, calculating performance indicators, and promptly detecting and reporting abnormal situations, we have established a multi-dimensional evaluation system covering voltage quality, economy, reliability, etc., and regularly generate performance analysis reports and optimization suggestions to provide a basis for continuous improvement of the system.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distribution network flexible mutual supply control method considering load uncertainty according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distribution network flexible mutual supply control method considering load uncertainty according to any one of claims 1 to 7 are implemented.

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