New energy power grid cooperative control method and system based on dynamic coupling degree entropy

Abstract

The invention relates to the technical field of power grid cooperative control, and discloses a new energy power grid cooperative control method and system based on dynamic coupling degree entropy, and the method comprises the steps: obtaining real-time electrical parameters of a new energy power grid, and calculating the coupling strength between electrical equipment; constructing a dynamic coupling network model by taking the electrical equipment as a node and the coupling strength as an edge weight; calculating a dynamic coupling degree entropy based on an information entropy principle, calculating an entropy threshold by adopting a self-adaptive adjustment strategy according to historical electrical parameters, and determining a risk level of the new energy power grid according to a comparison relationship between the dynamic coupling degree entropy and the entropy threshold; setting a corresponding objective function according to the risk level; and solving by adopting a multi-objective optimization algorithm to obtain an optimal cooperative control strategy. According to the method, the complexity and the dynamism of the coupling relation between the power grid nodes are quantified through the dynamic coupling degree entropy, the targeted cooperative control strategy is generated through multi-objective optimization, and the robustness and the self-healing capability of the new energy power grid are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid cooperative control, in particular to a new energy power grid cooperative control method and system based on dynamic coupling degree entropy. BACKGROUND

[0002] With large-scale access of wind power, photovoltaic and other new energy to the power grid, the power system presents a high power electronic characteristic. The inherent strong fluctuation, weak anti-disturbance and multi-scale dynamic characteristics of new energy units lead to multiple challenges in power grid operation. On the one hand, the dynamic interaction mechanism of new energy units and traditional synchronous units is complex, which easily leads to wideband oscillation risk. On the other hand, the weak inertia characteristic of high proportion of new energy leads to significant decline in power grid frequency and voltage support capability. In addition, due to the rising proportion of weather-sensitive power sources, the probability of cascading failures caused by extreme weather events such as typhoons, hail and sandstorms is multiplied.

[0003] In order to ensure the stability and reliability of new energy power grid operation, the commonly used control method at present is mostly based on experience and fixed rules, and only the control strategy for protection and recovery of single device or system, but this mode based on single device protection is difficult to capture the cascading failure caused by dynamic coupling between devices, and the traditional static rules cannot adapt to the multi-dimensional time-varying characteristics of topology operation mode brought by minute-level fluctuation of new energy output, so as to lead to the inability to timely and effectively restore the normal operation of the power grid. SUMMARY

[0004] In order to solve the above technical problems, the present application provides a new energy power grid cooperative control method and system based on dynamic coupling degree entropy, which quantifies the complexity and dynamics of the coupling relationship between power grid nodes through dynamic coupling degree entropy, and generates a cooperative coping strategy combined with a multi-objective optimization algorithm, so as to achieve the technical effect of improving the robustness and self-healing ability of new energy power grid.

[0005] In the first aspect, the present application provides a new energy power grid cooperative control method based on dynamic coupling degree entropy, which comprises:

[0006] Obtaining real-time electrical parameters of the new energy power grid, calculating the coupling strength between each electrical device according to the real-time electrical parameters, the real-time electrical parameters including electrical distance, power interaction amount and information transmission frequency;

[0007] Taking the electrical devices in the new energy power grid as nodes and the coupling strength between each electrical device as edge weight, a dynamic coupling network model of the new energy power grid is constructed;

[0008] The dynamic coupling degree entropy of the dynamic coupling network model is calculated based on an information entropy principle, the entropy threshold is calculated by using an adaptive adjustment strategy according to historical electrical parameters of the new energy power grid, and the risk level of the new energy power grid is determined according to a comparison relationship between the dynamic coupling degree entropy and the entropy threshold.

[0009] According to the risk level, a corresponding target function is set, a collaborative control model is constructed according to the target function and a preset constraint condition, and the constraint condition includes a power flow constraint, an output constraint and a voltage constraint.

[0010] A multi-objective optimization algorithm is used to solve the collaborative control model to obtain an optimal collaborative control strategy.

[0011] Further, the step of calculating the coupling strength between each electrical device according to the real-time electrical parameters comprises:

[0012] The electrical distance, the power interaction amount and the information transmission frequency between each electrical device are weighted and summed according to a preset weight coefficient to obtain the coupling strength between each electrical device.

[0013] The coupling strength is represented by the following formula:

[0014]

[0015] In the formula, w ij (t) represents the coupling strength of node i and node j at time t, Z ij (t) represents the electrical distance of node i and node j at time t, P ij (t) represents the power interaction amount of node i and node j at time t, F ij (t) represents the information transmission frequency of node i and node j at time t, P base represents the reference power, a represents the first weight coefficient, β represents the second weight coefficient, and γ represents the third weight coefficient.

[0016] Further, the step of calculating the dynamic coupling degree entropy of the dynamic coupling network model based on the information entropy principle comprises:

[0017] The edge weight matrix of the dynamic coupling network model is normalized to obtain a probability distribution matrix.

[0018] The dynamic coupling degree entropy of the dynamic coupling network model is calculated according to the probability distribution matrix and the information entropy principle.

[0019] Further, the step of calculating the entropy threshold according to the historical electrical parameters of the new energy power grid by using the adaptive adjustment strategy comprises:

[0020] According to the historical electrical parameters of the new energy power grid, a historical dynamic coupling degree entropy sequence is obtained, and based on a sliding window mechanism, a moving average entropy value and an entropy value standard deviation are calculated;

[0021] According to the moving average entropy value and the entropy value standard deviation, an entropy threshold value is obtained;

[0022] The entropy threshold value is expressed by the following formula:

[0023] H th (t)=μ(t)+k·σ(t)

[0024] In the formula, H th (t) represents the entropy threshold value of the time period t, μ(t) represents the moving average entropy value of the time period t, σ(t) represents the entropy value standard deviation of the time period t, and k represents a safety factor.

[0025] Further, the step of determining the risk level of the new energy power grid according to the comparison relationship between the dynamic coupling degree entropy and the entropy threshold value comprises:

[0026] The dynamic coupling degree entropy and the entropy threshold value are compared, the change rate of the dynamic coupling degree entropy is calculated, and the change rate and a preset change rate threshold value are compared;

[0027] In response to the dynamic coupling degree entropy being less than the entropy threshold value, it is determined that the risk level of the new energy power grid is a low risk level;

[0028] In response to the dynamic coupling degree entropy being greater than or equal to the entropy threshold value and the change rate being less than the change rate threshold value, it is determined that the risk level of the new energy power grid is a medium risk level;

[0029] In response to the dynamic coupling degree entropy being greater than or equal to the entropy threshold value and the change rate being greater than or equal to the change rate threshold value, it is determined that the risk level of the new energy power grid is a high risk level.

[0030] Further, the step of setting a corresponding target function according to the risk level comprises:

[0031] In response to the risk level being a low risk level, power loss minimization is taken as the target function;

[0032] In response to the risk level being a medium risk level, power loss minimization and power grid recovery speed maximization are taken as the target functions;

[0033] In response to the risk level being a high risk level, power loss minimization, power grid recovery speed maximization and node load deviation minimization are taken as the target functions.

[0034] Further, before the step of determining the risk level of the new energy power grid according to the comparison relationship of the dynamic coupling degree entropy and the entropy threshold, the method further comprises:

[0035] obtaining a node voltage deviation and a system frequency deviation of the preset key node, comparing the node voltage deviation with a voltage deviation threshold, and comparing the system frequency deviation with a frequency deviation threshold;

[0036] in response to the node voltage deviation being greater than the voltage deviation threshold, or the system frequency deviation being greater than the frequency deviation threshold, determining that the risk level of the new energy power grid is an emergency risk level, and issuing an emergency response strategy to the new energy power grid.

[0037] Further, the step of calculating the weight coefficient comprises:

[0038] taking the mean square error between the dynamic coupling degree entropy calculation value based on the weight coefficient and the preset dynamic coupling degree entropy actual value as a loss function;

[0039] according to historical electrical parameters, using a gradient descent optimization algorithm to iteratively optimize the loss function, and obtaining an optimal weight coefficient.

[0040] Further, the loss function is expressed by the following formula:

[0041]

[0042] wherein, H j (t) represents the dynamic coupling degree entropy calculation value of the time period t, H s (t) represents the dynamic coupling degree entropy actual value of the time period t, T represents a time period set, λ represents a penalty coefficient, α represents a first weight coefficient, β represents a second weight coefficient, and γ represents a third weight coefficient.

[0043] In a second aspect, the present application provides a new energy power grid cooperative control system based on dynamic coupling degree entropy, which comprises:

[0044] a coupling strength calculation module, configured to obtain real-time electrical parameters of the new energy power grid, and calculate the coupling strength between each electrical device according to the real-time electrical parameters, wherein the real-time electrical parameters include electrical distance, power interaction amount and information transmission frequency;

[0045] a model construction module, configured to take the electrical devices in the new energy power grid as nodes, and take the coupling strength between each electrical device as edge weight, and construct a dynamic coupling network model of the new energy power grid;

[0046] The risk assessment module is used to calculate the dynamic coupling degree entropy of the dynamic coupling network model based on the principle of information entropy, calculate the entropy threshold using an adaptive adjustment strategy based on the historical electrical parameters of the new energy power grid, and determine the risk level of the new energy power grid based on the comparison between the dynamic coupling degree entropy and the entropy threshold.

[0047] The collaborative control module is used to set a corresponding objective function according to the risk level, and to construct a collaborative control model based on the objective function and preset constraints, including power flow constraints, output constraints and voltage constraints.

[0048] The cooperative control model is solved using a multi-objective optimization algorithm to obtain the optimal cooperative control strategy.

[0049] This invention provides a method and system for collaborative control of new energy power grids based on dynamic coupling entropy. This invention quantifies the complexity and dynamism of the coupling relationships between power grid nodes through dynamic coupling entropy, and accurately classifies risk levels through an adaptive entropy threshold mechanism. By generating targeted collaborative control strategies through multi-objective optimization, it achieves dynamic collaborative regulation of the power grid, improves the robustness and self-healing capability of the new energy power grid, and thus ensures the safety and reliability of the new energy power grid operation. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the collaborative control method for new energy power grids based on dynamic coupling degree entropy in an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of the structure of the new energy power grid collaborative control system based on dynamic coupling degree entropy in an embodiment of the present invention;

[0052] Figure label:

[0053] 10. Coupling strength calculation module; 20. Model building module; 30. Risk assessment module; 40. Collaborative control module. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figure 1The first embodiment of the application provides a new energy power grid cooperative control method based on dynamic coupling degree entropy, which comprises steps S10-S50.

[0056] In step S10, real-time electrical parameters of the new energy power grid are obtained, coupling strength between each electrical device is calculated according to the real-time electrical parameters, and the real-time electrical parameters include electrical distance, power interaction amount and information transmission frequency.

[0057] In step S20, the electrical devices in the new energy power grid are taken as nodes, and the coupling strength between each electrical device is taken as edge weight to construct a dynamic coupling network model of the new energy power grid.

[0058] In step S30, the dynamic coupling degree entropy of the dynamic coupling network model is calculated based on the information entropy principle, the entropy threshold is calculated by using an adaptive adjustment strategy according to the historical electrical parameters of the new energy power grid, and the risk level of the new energy power grid is determined according to the comparison relationship between the dynamic coupling degree entropy and the entropy threshold.

[0059] In step S40, a corresponding target function is set according to the risk level, a cooperative control model is constructed according to the target function and a preset constraint condition, and the constraint condition includes power flow constraint, output constraint and voltage constraint.

[0060] In step S50, a multi-objective optimization algorithm is used to solve the cooperative control model to obtain an optimal cooperative control strategy.

[0061] The application performs dynamic correlation analysis on the whole new energy power grid, uses coupling strength based on electrical parameters to represent the correlation between each electrical device, takes the electrical devices in the new energy power grid as nodes, takes the coupling strength between each electrical device as edge weight, uses a complex network and a graph theory model to construct a dynamic coupling network model of the new energy power grid, and represents the electrical information interaction relationship between power grid devices through the dynamic coupling network model.

[0062] Specifically, the electrical devices in the new energy power grid are defined as nodes, and the nodes are divided into three parts of power generation nodes, transmission nodes and load nodes according to the device types, wherein the power generation nodes include new energy power generation devices such as wind turbines, photovoltaic arrays and energy storage systems, the transmission nodes include key power transmission devices such as transformers, converter stations and busbars, and the load nodes include power consumption terminals such as industrial loads and adjustable loads. Real-time electrical parameters between each node are obtained, the real-time electrical parameters mainly include electrical distance, power interaction amount and information transmission frequency, then the real-time electrical parameters are weighted and summed to obtain the coupling strength between two nodes, and the expression is as follows:

[0063]

[0064] wherein w ij (t) represents the coupling strength between node i and node j at time t, Z ij (t) represents the electrical distance between node i and node j at time t, P ij (t) represents the power interaction between node i and node j at time t, F ij (t) represents the information transmission frequency between node i and node j at time t, P base represents the reference power, a represents the first weight coefficient, β represents the second weight coefficient, and γ represents the third weight coefficient.

[0065] In the above formula, the electrical distance refers to the equivalent resistance between nodes, and the smaller the impedance, the stronger the electrical coupling. Therefore, the electrical coupling strength is represented by the reciprocal of the electrical distance in the formula; the power interaction refers to the power exchange value between nodes, and based on the different power flow directions between nodes, the power exchange value has positive and negative values. A positive value indicates that power flows from node i to node j, and a negative value indicates that power flows from node j to node i. The greater the power exchange value, the stronger the energy coupling between nodes. Therefore, the energy coupling strength is represented by the absolute value of the power interaction in the formula, and the reference power is used for power normalization to facilitate the unification of coupling strength with other parts; the information transmission frequency refers to the number of communications between nodes per unit time, i.e., the frequency of data exchange. The greater the information transmission frequency, the stronger the interaction coupling between nodes.

[0066] After obtaining the electrical distance, the power interaction, and the information transmission frequency, they are weighted and summed according to the preset weight coefficients to obtain the coupling strength between nodes, wherein the constraint conditions of the three weight coefficients are:

[0067] α+β+γ=1

[0068] Based on the node definition and coupling strength calculation, a dynamic coupling network model of the new energy power grid is constructed using complex network and graph theory model. The dynamic coupling network model can systematically represent the dynamic coupling relationship of the power grid to avoid the one-sidedness of isolated analysis and provide a global perspective for subsequent entropy calculation and risk assessment. At the same time, the dynamic coupling network model is dynamically updated based on real-time data, thereby ensuring that the subsequent entropy calculation, risk assessment, and strategy formulation are accurate and effective.

[0069] Based on the dynamic coupling network model, in order to quantify the complexity and uncertainty of the coupling relationship between nodes in the new energy power grid, the embodiment adopts a dynamic coupling degree entropy as a key indicator of the power grid to represent the stability and reliability of the power grid. The dynamic coupling degree entropy is calculated based on the edge weight of the dynamic coupling network model, and the specific calculation steps include:

[0070] normalizing the edge weight matrix of the dynamic coupling network model to obtain a probability distribution matrix;

[0071] According to the probability distribution matrix and the information entropy principle, the dynamic coupling degree entropy of the dynamic coupling network model is calculated.

[0072] In this embodiment, in order to make the coupling strength between different nodes comparable, the normalized coupling strength is used to convert the coupling strength parameters of different dimensions and orders of magnitude into dimensionless relative values, thereby eliminating the dimensional differences. Specifically, the edge weight matrix of the model is normalized into a probability distribution matrix P(t), and the elements in the matrix can be expressed as:

[0073]

[0074] In the formula, p ij (t) represents the element in the i-th row and the j-th column of the matrix, that is, the coupling strength proportion of node i and node j at time t, w ij (t) represents the coupling strength of node i and node j at time t, and N represents the total number of nodes.

[0075] Then, the information entropy theory is used to calculate the dynamic coupling degree entropy, and the expression is:

[0076]

[0077] In the formula, H(t) represents the dynamic coupling degree entropy at time t, N represents the total number of nodes, p ij (t) represents the coupling strength proportion of node i and node j at time t.

[0078] In this embodiment, the dynamic coupling degree entropy is used as an index to measure the dynamic correlation and complexity between the parts of the system. By comparing the dynamic coupling degree entropy with the entropy threshold, the current risk level of the new energy power grid is evaluated. In order to improve the accuracy of the evaluation result, this embodiment uses an adaptive adjustment strategy to set the entropy threshold according to the historical electrical parameters of the new energy power grid. The specific setting steps include:

[0079] According to the historical electrical parameters of the new energy power grid, a historical dynamic coupling degree entropy sequence is obtained, and based on the sliding window mechanism, a sliding average entropy value and an entropy value standard deviation are calculated.

[0080] According to the sliding average entropy value and the entropy value standard deviation, the entropy threshold is obtained.

[0081] In this embodiment, first, the historical electrical parameters of the new energy power grid are obtained, i.e., the electrical distance, power interaction amount and information transmission frequency in the past period of time, and then a series of historical dynamic coupling degree entropies are calculated by constructing a historical dynamic coupling network model, so as to form a historical dynamic coupling degree entropy sequence. Based on a sliding window mechanism, the moving average entropy value and the entropy value standard deviation in the window are calculated. For example, if the time window length is set to 30 minutes, the moving average entropy value can be represented as:

[0082]

[0083] In the formula, μ(t) represents the moving average entropy value at time period t, n represents the time window length, and H(i) represents the dynamic coupling degree entropy at the i th minute.

[0084] The entropy value standard deviation σ(t) can be represented as:

[0085]

[0086] Based on the moving average entropy value and the entropy value standard deviation, the entropy threshold value corresponding to the current time t can be calculated:

[0087] H th (t) = μ(t) + k·σ(t)

[0088] In the formula, H th (t) represents the entropy threshold value at time period t, and k represents a safety factor.

[0089] The above entropy threshold value formula consists of two parts. The moving average entropy value μ(t) represents the average coupling complexity of the power grid in the recent period, and the entropy value standard deviation reflects the volatility of the entropy value in the window, which is used to quantify the stability of the power grid state. The product of the entropy value standard deviation and the safety factor is used as the volatility safety margin to avoid the influence of accidental noise on the accuracy of the evaluation. The safety factor is a preset value, and preferably, the Monte Carlo simulation can be used to simulate fault event scenarios to optimize the safety factor to balance the false negative rate and the false positive rate. Moreover, since the sliding window mechanism is used to set the entropy threshold value, the entropy threshold value can be adaptively adjusted in real time according to the change of the power grid operating state, thereby ensuring the accuracy of the subsequent risk assessment results.

[0090] After the dynamic coupling degree entropy and the corresponding entropy threshold value are calculated, the risk level of the new energy power grid can be determined according to the comparison relationship between the dynamic coupling degree entropy and the entropy threshold value. The specific steps include:

[0091] The dynamic coupling degree entropy and the entropy threshold value are compared, the change rate of the dynamic coupling degree entropy is calculated, and the change rate and the preset change rate threshold value are compared.

[0092] in response to the dynamic coupling degree entropy being less than the entropy threshold, determining that the risk level of the new energy power grid is a low risk level;

[0093] in response to the dynamic coupling degree entropy being greater than or equal to the entropy threshold and the change rate being less than the change rate threshold, determining that the risk level of the new energy power grid is a medium risk level;

[0094] in response to the dynamic coupling degree entropy being greater than or equal to the entropy threshold and the change rate being greater than or equal to the change rate threshold, determining that the risk level of the new energy power grid is a high risk level.

[0095] In the embodiment, the risk level of the new energy power grid is defined as three, namely a low risk level, a medium risk level and a high risk level, in order to improve the accuracy of the evaluation result, the judgment condition of the embodiment adopts the change rate of the dynamic coupling degree entropy as a judgment parameter in addition to the dynamic coupling degree entropy, and the change rate of the dynamic coupling degree entropy is determined by the difference value of the entropy values in the preset time length and the corresponding time length.

[0096] Specifically, for the low risk level, only the relationship between the dynamic coupling degree entropy and the entropy threshold needs to be judged, if the dynamic coupling degree entropy is less than the entropy threshold, it indicates that the power grid system has not reached the critical state of the system, therefore, the risk is low, for the medium risk level, it needs to meet the conditions that the dynamic coupling degree entropy is greater than or equal to the entropy threshold and the change rate is less than the change rate threshold, at this time, it indicates that the power grid system has reached the critical state of the system, but the coupling strength change trend fluctuates little, therefore, the risk has not reached the high level, if the dynamic coupling degree entropy is greater than or equal to the entropy threshold and the change rate is greater than or equal to the change rate threshold, it indicates that the power grid system has reached the high risk level.

[0097] Since different risk levels correspond to different power grid vulnerability levels, that is, correspond to different power grid states, in order to improve the decision efficiency and resource utilization, the embodiment sets different objective functions for different risk levels to construct a collaborative control model more in line with the current power grid state, the specific steps include:

[0098] in response to the risk level being a low risk level, taking power loss minimization as an objective function;

[0099] in response to the risk level being a medium risk level, taking power loss minimization and power grid recovery speed maximization as objective functions;

[0100] in response to the risk level being a high risk level, taking power loss minimization, power grid recovery speed maximization and node load deviation minimization as objective functions.

[0101] In the embodiment, the setting of the objective function is associated with the power grid risk level and the control measures. In the low risk level, the optimization objective should be simplified to reduce the calculation complexity, speed up the response speed, maintain the economic operation, and avoid resource waste caused by excessive control. Therefore, the objective function in the low risk level is set to minimize the power loss. In the medium risk level, in addition to the need to meet the economy of operation, the control objective also needs to meet the rapid recovery of the power grid state since the power grid is in a critical state. The power loss control and the rapid recovery demand are balanced to prevent risk spread. Therefore, the objective function in the medium risk level is set to minimize the power loss and maximize the power grid recovery speed. In the high risk level, in addition to the need to ensure power balance and rapid recovery, the node load rate also needs to be balanced to prevent overload. Therefore, the objective function in the high risk level is set to minimize the power loss, maximize the power grid recovery speed, and minimize the node load deviation. The embodiment quantifies the load balance of the nodes in the power grid by calculating the absolute deviation sum of the actual load and the rated load of all nodes.

[0102] Based on the above description, the power loss minimization, the power grid recovery speed maximization, and the node load deviation minimization are represented by F1, F2, and F3 respectively. The specific expressions are as follows:

[0103]

[0104]

[0105] wherein i represents the i-th node, N represents the total number of nodes, k represents a region, M represents the total number of regions, P loss,i represents the power loss of the i-th node, T re,k represents the recovery time of the k-th region, L i represents the actual load of the i-th node, L rated,i represents the rated load of the i-th node.

[0106] According to the above formula, in the embodiment, the power loss minimization is characterized by the minimization of the sum of the power losses of all nodes. The power grid recovery speed maximization is characterized by adding a negative sign in front of the sum of the recovery times of all regions to convert it into the minimization of the recovery time. The node load deviation minimization is characterized by the minimization of the absolute deviation sum of the actual load and the rated load of all nodes.

[0107] Therefore, at the low risk level, the objective function is F1, at the medium risk level, the objective function is F1 and F2, and at the high risk level, the objective function is F1, F2 and F3, although the objective functions set at different risk levels are different, the constraint conditions that the new energy power grid needs to meet at different risk levels are consistent, all of which include power flow constraints, output constraints and voltage constraints. The power flow constraints are expressed as:

[0108]

[0109] wherein, P g represents the active power of the gth generator, G represents the total number of generators, P d represents the active power of the dth load, D represents the total number of loads, V i and V j respectively represent the voltage amplitude of the ith node and the voltage amplitude of the jth node, N represents the total number of nodes, the nodes here are also electrical equipment, δ ij represents the voltage phase angle difference between the ith node and the jth node, R ij represents the real part of the admittance value between the ith node and the jth node in the node admittance matrix, I ij represents the imaginary part of the admittance value between the ith node and the jth node in the node admittance matrix.

[0110] The output constraints are expressed as:

[0111] P g,min ≤ P g ≤ P g,max

[0112] wherein, P g,min and P g,max are the minimum output and the maximum output of the generator, respectively.

[0113] The voltage constraints are expressed as:

[0114] V min ≤ V i ≤ V max

[0115] wherein, V min and V max are the lower limit and the upper limit of the node voltage, respectively.

[0116] According to the objective function and the constraint condition, a collaborative control model under different risk levels can be constructed, and by solving the collaborative control model, the optimal collaborative control strategy of the new energy power grid can be obtained. It can be seen that for the medium risk level and the high risk level, the collaborative control model is a multi-objective optimization model. In fact, the multi-objective optimization algorithm can also solve a single objective function. Therefore, in this embodiment, a multi-objective optimization algorithm is used to solve the collaborative control model under different risk levels to obtain the optimal collaborative control strategy.

[0117] In a preferred embodiment, the improved multi-objective genetic algorithm NSGA-III (Non-dominated Sorting Genetic Algorithm III) is used to solve the collaborative control model. Taking the collaborative control model under the high risk level as an example, the solving process of the model is described. First, initialize the population, take the wind turbine output, photovoltaic output, energy storage charging and discharging power, cuttable load, and circuit breaker switch state as decision variables, encode the decision variables, determine the value range of each variable by the physical limit of the equipment, randomly generate the initial population to ensure that all feasible solution spaces are covered. The constraint condition is converted into an objective function additional item by the penalty function method, and the population is divided into multiple levels according to the Pareto dominance relationship of individual objective values; the initial reference points are uniformly distributed on the hyperplane of the target space, and the reference point density is dynamically adjusted according to the current population distribution and the real-time state of the power grid, then the vertical distance of each individual to each reference point is calculated, the individual is associated to the nearest reference point, and the population selection is performed, the individuals associated with fewer reference points are preferentially retained to ensure the diversity of the population, and redundant individuals are eliminated; the decision variables of the parent individuals are crossed to generate offspring, and random disturbance is applied to the offspring to ensure the local search ability. According to the above steps, iterative optimization is performed until the iteration convergence condition is reached, the convergence condition includes reaching the maximum number of iterations or the population converging, so as to obtain the optimal solution, i.e., the optimal collaborative control strategy. The specific solving steps can refer to the solving steps of the NSGA-III algorithm, which will not be described here.

[0118] After obtaining the optimal collaborative control strategy, it is converted into corresponding electrical equipment control instructions and issued to corresponding electrical equipment, so as to realize the collaborative control of the new energy power grid and ensure the safety and stability of the new energy power grid. Further, after the instructions are issued, the running data of the power grid is obtained in real time through the monitoring device to evaluate the execution effect of the strategy. If the dynamic coupling degree entropy of the power grid has not yet decreased to the safe range, a secondary optimization will be triggered. In the secondary optimization, the length of the time window set for the entropy threshold is shortened to speed up the optimization frequency, and the strategy is adjusted in time to ensure that the power grid resumes stable operation as soon as possible.

[0119] The application is to evaluate the risk level of new energy power grid by dynamic coupling degree entropy, and the accuracy of evaluation is directly related to the calculation accuracy of dynamic coupling degree entropy. It can be understood that the closer the calculated power grid coupling strength is to the actual power grid operating state, the more accurate the risk evaluation result is. According to the coupling strength calculation formula, the coupling strength is related to the weight coefficient of each parameter in addition to the electrical parameters, therefore, the setting of the weight coefficient will also affect the accuracy and reliability of the risk evaluation result. In order to improve the accuracy and reliability of the evaluation, in a preferred embodiment, the application also provides a weight coefficient calculation method, and the specific steps include:

[0120] The mean square error between the dynamic coupling degree entropy calculation value based on the weight coefficient and the preset dynamic coupling degree entropy actual value is taken as the loss function;

[0121] According to the historical electrical parameters, the gradient descent optimization algorithm is adopted to iteratively optimize the loss function, and the optimal weight coefficient is obtained.

[0122] In this embodiment, the historical data is used to iteratively optimize the weight coefficient to improve the rationality of the weight coefficient setting. Specifically, the three weight coefficients are initialized first, such as setting their initial values according to uniform distribution. Based on the initialized weight coefficients, the coupling strength and entropy value are calculated to obtain the dynamic coupling degree entropy calculation value. The mean square error between the dynamic coupling degree entropy calculation value and the dynamic coupling degree entropy actual value is taken as the loss function, and a constraint term generated based on the constraint condition of the weight coefficient is added to construct the loss function. The constraint condition of the weight coefficient is that the sum of the three weight coefficients is equal to 1, therefore, the expression of the loss function is:

[0123]

[0124] In the formula, H j (t) represents the dynamic coupling degree entropy calculation value of time period t, H s (t) represents the dynamic coupling degree entropy actual value of time period t, T represents a set of time periods, λ represents a penalty coefficient, α represents a first weight coefficient, β represents a second weight coefficient, and γ represents a third weight coefficient.

[0125] Then the loss function is iteratively optimized based on the historical data of the new energy power grid, including historical electrical parameters and dynamic coupling degree entropy actual values. In this embodiment, the dynamic coupling degree entropy actual values are calculated based on the historical electrical parameters according to fixed weight coefficients, which can be set by professionals according to the actual power grid state. The dynamic coupling degree entropy actual values are used as label data to optimize the current weight coefficients, that is, by minimizing the difference between the calculated value and the true value, the gradient descent method is used to optimize the weight coefficients in reverse, so that the entropy value expression can more accurately represent the dynamic characteristics of the power grid, thereby improving the accuracy and reliability of the power grid risk assessment results.

[0126] In another preferred embodiment, before the risk level assessment according to the dynamic coupling degree entropy, it is necessary to determine whether the current power grid is in an emergency risk level. The emergency risk level is another risk level in addition to the three risk levels based on the dynamic coupling degree entropy. The emergency risk level is higher than the high risk level, so this level needs to be determined simply and quickly to avoid system collapse by emergency treatment. In this embodiment, the determination steps of the emergency risk level include:

[0127] Obtaining the node voltage deviation and system frequency deviation of the preset key node, comparing the node voltage deviation with the voltage deviation threshold, and comparing the system frequency deviation with the frequency deviation threshold;

[0128] In response to the node voltage deviation being greater than the voltage deviation threshold, or the system frequency deviation being greater than the frequency deviation threshold, determining that the risk level of the new energy power grid is an emergency risk level, and issuing an emergency response strategy to the new energy power grid.

[0129] In the embodiment, hub nodes in the power grid topology are selected as key nodes, such as new energy grid connection points, main transmission nodes, load center nodes, etc. The voltage deviation of the key nodes is monitored, and the system frequency deviation of the power system is monitored at the same time. If the voltage deviation of the key nodes exceeds a threshold value, such as more than ±10%, an emergency risk state is triggered. If the system frequency deviation exceeds a threshold value, such as more than ±0.5Hz, an emergency risk state is also triggered. In addition, regional voltage monitoring can also be used for collaborative evaluation. Specifically, if more than a preset proportion of non-key nodes in the same region have a voltage deviation exceeding a second voltage deviation threshold value, such as more than 30% of non-key nodes in the same region having a voltage deviation exceeding ±15%, it is determined that the regional voltage is unstable, and an emergency risk state is triggered at this time. When the new energy power grid is determined to be in an emergency risk level, an emergency response strategy needs to be immediately executed, such as topology reconstruction and removal of part of the load, to prevent system collapse. At the same time, the corresponding collaborative control model is solved according to the high risk level, to generate the corresponding collaborative control strategy, to optimize the power grid control. The embodiment cooperates the emergency risk level with the risk level based on the dynamic coupling degree entropy, to realize timely and effective dynamic collaborative control, and further ensures the reliability of the new energy power grid operation.

[0130] Referring to Figure 2 , based on the same inventive concept, the second embodiment of the present application proposes a new energy power grid collaborative control system based on dynamic coupling degree entropy, comprising:

[0131] The coupling strength calculation module 10 is used to obtain real-time electrical parameters of the new energy power grid, and calculate the coupling strength between each electrical device according to the real-time electrical parameters. The real-time electrical parameters include electrical distance, power interaction amount and information transmission frequency.

[0132] The model construction module 20 is used to construct a dynamic coupling network model of the new energy power grid, taking electrical devices in the new energy power grid as nodes and taking the coupling strength between each electrical device as edge weight.

[0133] The risk assessment module 30 is used to calculate the dynamic coupling degree entropy of the dynamic coupling network model based on the information entropy principle, calculate the entropy threshold value by using an adaptive adjustment strategy according to the historical electrical parameters of the new energy power grid, and determine the risk level of the new energy power grid according to the comparison relationship between the dynamic coupling degree entropy and the entropy threshold value.

[0134] The collaborative control module 40 is used to set a corresponding target function according to the risk level, construct a collaborative control model according to the target function and a preset constraint condition, and the constraint condition includes power flow constraint, output constraint and voltage constraint.

[0135] The multi-objective optimization algorithm is used to solve the cooperative control model to obtain an optimal cooperative control strategy.

[0136] The technical features and technical effects of the new energy power grid cooperative control system based on dynamic coupling degree entropy proposed in the embodiments of the application are the same as those of the method proposed in the embodiments of the application, which will not be repeated here. Each module in the new energy power grid cooperative control system based on dynamic coupling degree entropy can be realized by software, hardware, or a combination thereof, in whole or in part. Each module can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0137] In summary, the new energy power grid cooperative control method and system based on dynamic coupling degree entropy proposed in the embodiments of the application, the method obtains real-time electrical parameters of the new energy power grid, calculates the coupling strength between each electrical device according to the real-time electrical parameters, and the real-time electrical parameters include electrical distance, power interaction amount, and information transmission frequency; takes the electrical device in the new energy power grid as a node and takes the coupling strength between each electrical device as an edge weight to construct a dynamic coupling network model of the new energy power grid; based on the information entropy principle, calculates the dynamic coupling degree entropy of the dynamic coupling network model, calculates the entropy threshold value by using an adaptive adjustment strategy according to the historical electrical parameters of the new energy power grid, and determines the risk level of the new energy power grid according to the comparison relationship between the dynamic coupling degree entropy and the entropy threshold value; according to the risk level, sets a corresponding objective function, constructs a cooperative control model according to the objective function and a preset constraint condition, and the constraint condition includes power flow constraint, output constraint, and voltage constraint; the multi-objective optimization algorithm is used to solve the cooperative control model to obtain an optimal cooperative control strategy. The application quantifies the complexity and dynamics of the coupling relationship between grid nodes through dynamic coupling degree entropy, and realizes accurate risk level division through the adaptive mechanism of the entropy threshold value, generates a targeted cooperative control strategy through multi-objective optimization, realizes dynamic cooperative regulation of the power grid, improves the robustness and self-healing ability of the new energy power grid, and thus ensures the safety and reliability of the operation of the new energy power grid.

[0138] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0139] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A new energy power grid collaborative control method based on dynamic coupling entropy, characterized in that, include: The system acquires real-time electrical parameters of the new energy power grid and calculates the coupling strength between various electrical devices based on these parameters. The real-time electrical parameters include electrical distance, power interaction, and information transmission frequency. A dynamic coupled network model of the new energy power grid is constructed, using electrical equipment in the new energy power grid as nodes and the coupling strength between each electrical device as the edge weight. Based on the principle of information entropy, the dynamic coupling degree entropy of the dynamic coupling network model is calculated. According to the historical electrical parameters of the new energy power grid, an adaptive adjustment strategy is adopted to calculate the entropy threshold. Based on the comparison relationship between the dynamic coupling degree entropy and the entropy threshold, the risk level of the new energy power grid is determined. Based on the risk level, a corresponding objective function is set, and a collaborative control model is constructed based on the objective function and preset constraints, including power flow constraints, output constraints, and voltage constraints. The cooperative control model is solved using a multi-objective optimization algorithm to obtain the optimal cooperative control strategy.

2. The new energy power grid collaborative control method based on dynamic coupling entropy according to claim 1, characterized in that, The step of calculating the coupling strength between various electrical devices based on the real-time electrical parameters includes: According to the preset weighting coefficients, the electrical distance, power interaction and information transmission frequency between each electrical device are weighted and summed to obtain the coupling strength between each electrical device. The coupling strength is expressed by the following formula: In the formula, w ij Z(t) represents the coupling strength between node i and node j at time t. ij (t) represents the electrical distance between node i and node j at time t, P ij F(t) represents the power interaction between node i and node j at time t. ij (t) represents the information transmission frequency between node i and node j at time t, P base Let α represent the base power, β represent the first weighting coefficient, and γ represent the second weighting coefficient.

3. The new energy power grid collaborative control method based on dynamic coupling entropy according to claim 2, characterized in that, The steps for calculating the dynamic coupling degree entropy of the dynamically coupled network model based on the principle of information entropy include: The edge weight matrix of the dynamically coupled network model is normalized to obtain the probability distribution matrix; Based on the probability distribution matrix and the principle of information entropy, the dynamic coupling degree entropy of the dynamic coupling network model is calculated.

4. The new energy power grid collaborative control method based on dynamic coupling entropy according to claim 1, characterized in that, The step of calculating the entropy threshold based on historical electrical parameters of the new energy power grid using an adaptive adjustment strategy includes: Based on the historical electrical parameters of the new energy power grid, the historical dynamic coupling degree entropy sequence is obtained, and the moving average entropy value and the standard deviation of the entropy value are calculated based on the sliding window mechanism. The entropy threshold is obtained based on the moving average entropy value and the standard deviation of the entropy value; The entropy threshold is represented by the following formula: H th (t)=μ(t)+k·σ(t) In the formula, H th μ(t) represents the entropy threshold for time period t, μ(t) represents the moving average entropy value for time period t, σ(t) represents the standard deviation of the entropy value for time period t, and k represents the safety factor.

5. The new energy power grid collaborative control method based on dynamic coupling entropy according to claim 1, characterized in that, The step of determining the risk level of the new energy power grid based on the comparison relationship between the dynamic coupling degree entropy and the entropy threshold includes: The dynamic coupling degree entropy is compared with the entropy threshold, the rate of change of the dynamic coupling degree entropy is calculated, and the rate of change is compared with a preset rate of change threshold. In response to the dynamic coupling degree entropy being less than the entropy threshold, the risk level of the new energy power grid is determined to be low risk. In response to the dynamic coupling degree entropy being greater than or equal to the entropy threshold and the rate of change being less than the rate of change threshold, the risk level of the new energy power grid is determined to be medium risk level; In response to the dynamic coupling degree entropy being greater than or equal to the entropy threshold and the rate of change being greater than or equal to the rate of change threshold, the risk level of the new energy power grid is determined to be high risk level.

6. The new energy power grid collaborative control method based on dynamic coupling entropy according to claim 5, characterized in that, The step of setting the corresponding objective function based on the risk level includes: In response to the risk level being low risk, the objective function is to minimize power loss; In response to the risk level being medium risk, the objective function is to minimize power loss and maximize grid recovery speed; In response to the risk level being high risk, the objective functions are to minimize power loss, maximize grid recovery speed, and minimize node load deviation.

7. The new energy power grid collaborative control method based on dynamic coupling entropy according to claim 1, characterized in that, Before the step of determining the risk level of the new energy power grid based on the comparison relationship between the dynamic coupling degree entropy and the entropy threshold, the method further includes: Obtain the node voltage deviation and system frequency deviation of preset key nodes, compare the node voltage deviation with a voltage deviation threshold, and compare the system frequency deviation with a frequency deviation threshold; In response to the node voltage deviation being greater than the voltage deviation threshold, or the system frequency deviation being greater than the frequency deviation threshold, the risk level of the new energy power grid is determined to be an emergency risk level, and an emergency response strategy is issued to the new energy power grid.

8. The new energy power grid collaborative control method based on dynamic coupling entropy according to claim 3, characterized in that, The calculation steps for the weighting coefficients include: The mean square error between the calculated value of dynamic coupling entropy based on weight coefficients and the preset actual value of dynamic coupling entropy is used as the loss function; Based on historical electrical parameters, the loss function is iteratively optimized using a gradient descent optimization algorithm to obtain the optimal weight coefficients.

9. The new energy power grid collaborative control method based on dynamic coupling entropy according to claim 8, characterized in that, The loss function is expressed by the following formula: In the formula, H j (t) represents the calculated value of the dynamic coupling entropy for time period t, H s (t) represents the actual value of the dynamic coupling entropy of time period t, T represents the set of time periods, λ represents the penalty coefficient, α represents the first weight coefficient, β represents the second weight coefficient, and γ represents the third weight coefficient.

10. A new energy power grid collaborative control system based on dynamic coupling degree entropy, characterized in that, include: The coupling strength calculation module is used to obtain real-time electrical parameters of the new energy power grid and calculate the coupling strength between various electrical devices based on the real-time electrical parameters. The real-time electrical parameters include electrical distance, power interaction amount and information transmission frequency. The model building module is used to construct a dynamic coupled network model of the new energy power grid, using electrical equipment in the new energy power grid as nodes and the coupling strength between each electrical device as the edge weight. The risk assessment module is used to calculate the dynamic coupling degree entropy of the dynamic coupling network model based on the principle of information entropy, calculate the entropy threshold using an adaptive adjustment strategy based on the historical electrical parameters of the new energy power grid, and determine the risk level of the new energy power grid based on the comparison between the dynamic coupling degree entropy and the entropy threshold. The collaborative control module is used to set a corresponding objective function according to the risk level, and to construct a collaborative control model based on the objective function and preset constraints, including power flow constraints, output constraints and voltage constraints. The cooperative control model is solved using a multi-objective optimization algorithm to obtain the optimal cooperative control strategy.

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