Active power distribution network new energy bearing capacity model prediction control method based on FMSS

Abstract

The invention provides an active power distribution network new energy bearing capacity model prediction control method based on an FMSS, and belongs to the technical field of power operation control, and the method comprises the following steps: S1, constructing an active power distribution network architecture containing the FMSS; s2, establishing a voltage-line loss prediction model; s3, constructing an MPC rolling optimization model; and S4, implementing rolling optimization and dynamic adjustment. According to the active power distribution network new energy bearing capacity model prediction control method based on the FMSS, the problems of power distribution network voltage instability and line loss rising caused by distributed new energy output randomness and load fluctuation are solved; by dynamically adjusting the transmission power of the FMSS, the loss of the power distribution network is reduced, the voltage drop is reduced, and the voltage fluctuation is stabilized, so that the bearing capacity of new energy is improved.

Description

Technical Field

[0001] This invention relates to the field of power operation control technology, and in particular to a predictive control method for the active distribution network renewable energy carrying capacity model based on FMSS. Background Technology

[0002] With the transformation of the global energy structure and the advancement of sustainable development strategies, the penetration rate of new energy sources such as photovoltaic and wind power in distribution networks is constantly increasing. However, the randomness and volatility of new energy output pose serious challenges to the stable operation of distribution networks, especially power quality issues such as voltage exceeding limits and increased line losses.

[0003] With the large-scale integration of new energy sources into the distribution network, voltage control and line loss management have become key factors restricting the stable operation and economic efficiency of the power grid. Traditional methods struggle to cope with the randomness and volatility of new energy output, leading to significant increases in voltage exceedances and line losses. While existing FMSS technology provides a means of rapid power regulation, it lacks dynamic collaborative optimization with the overall operating state of the power grid, making it difficult to achieve precise control of voltage and line losses. Existing distribution network control strategies often rely on high-speed communication networks for remote monitoring and regulation. However, the construction and maintenance costs of high-speed communication networks are high, and they are susceptible to external interference and attacks in actual operation. Furthermore, with the expansion and increasing complexity of the distribution network, the real-time requirements for control strategies are becoming increasingly stringent. As a complex system engineering project, the distribution network has a close relationship between global optimization and local regulation. Existing technologies often focus on the design and implementation of local regulation strategies, neglecting coordination and cooperation with the global optimization goals. This results in local regulation measures failing to achieve the expected results in actual operation, or even adversely affecting the overall operating state.

[0004] In order to effectively address these issues and enhance the power distribution network's capacity to support new energy sources, it is urgent to develop advanced control technologies and methods. Summary of the Invention

[0005] The purpose of this invention is to provide a predictive control method for the carrying capacity of renewable energy in distribution networks based on FMSS, which solves the problems of voltage instability and line loss increase in distribution networks caused by the randomness of distributed renewable energy output and load fluctuations. By dynamically adjusting the FMSS transmission power, the method reduces distribution network losses, minimizes voltage drops, and smooths voltage fluctuations, thereby improving the carrying capacity of renewable energy.

[0006] To achieve the above objectives, this invention provides a predictive control method for the renewable energy carrying capacity model of an active distribution network based on FMSS, comprising the following steps: S1. Construct an active distribution network architecture with FMSS: Integrate FMSS into key nodes of the distribution network. Its series part is equivalent to a controlled voltage source, and its parallel part is equivalent to a controlled current source. S2. Establish a voltage-line loss prediction model: Based on the distribution network topology parameters and long-term measurement data, combined with the calculation of the voltage-line loss correlation matrix, the influence coefficient of voltage fluctuations at each node on the line loss of the entire network is quantified, and a voltage-line loss prediction model is established. S3. Constructing the MPC rolling optimization model: Based on the rolling optimization idea, construct a dual-objective MPC controller, taking voltage deviation and line loss as optimization objectives, and establish a multivariate optimization model that includes FMSS active / reactive power regulation, node voltage safety constraints, and equipment capacity limitation constraints. S4. Implement rolling optimization and dynamic adjustment: Dynamically adjust the FMSS transmission power according to the optimization model, introduce prediction error information to dynamically correct the prediction model, and make targeted corrections to the line loss sensitivity based on the changes in the FMSS working status when the distribution network is running smoothly.

[0007] Preferably, the voltage-line loss prediction model construction in S2 includes the following steps: S21. Collect key electrical quantities at the FMSS access point and load end; S22. Construct the node voltage-branch power sensitivity matrix and describe the dynamic characteristics of the system through state-space equations; S23. Using real-time measurement data and the distribution network loss value obtained from communication with the upper level, predict the distribution network voltage and line loss value at future times.

[0008] Preferably, the construction of the dual-objective MPC controller in S3 includes the following steps: S31. The objective function is to minimize the operating cost and network loss of FMSS, and the boundary conditions are node voltage safety constraints and equipment capacity limitations. S32. Use the quadratic programming algorithm to solve for the optimal control sequence for the future control cycle; S33. Only execute the first instruction of the control sequence, namely the active and reactive power adjustment at the current moment; S34. Based on the measured voltage value and prediction error, the sensitivity matrix is ​​corrected online, and the control strategy is dynamically adjusted.

[0009] Preferably, the FMSS adjustment process in S4 includes the following steps: S41. Through rapid power regulation, direct control of the voltage at the access point and adjacent nodes can be achieved; S42. Dynamically update the loss sensitivity matrix and voltage sensitivity matrix according to the changes in the operating status of the distribution network; S43. Introduce a feedback correction mechanism to use the error information generated by the previous round of control to correct the prediction model and improve the accuracy of the voltage prediction model.

[0010] Preferably, the expression for the node voltage-branch power sensitivity matrix in S22 is: ; in, Indicates the line resistance; Indicates the line reactance; Represents a node The set of nodes in the feeder branch; express Voltage amplitude at the node; For injection Active power of a node; For injection The reactive power of a node.

[0011] Preferably, in S23, the voltage and line loss are... The expression for the predicted value at time t is: ; in, Represents the predicted step size. It was obtained through real-time measurement. Voltage amplitude at any given moment; Received every 15 minutes via communication with the superior. Real-time distribution network loss values; for Always looking towards the future Predicted voltage amplitude at time; yes Predicting the future The value of loss at any given moment; yes Predicting the future Constantly control the increment of variables.

[0012] Preferably, the objective function expression in S31 is: ; in, It is the line loss coefficient; Represents the cost of line loss; Represents operating costs; Loss Costs Operating costs The expressions are as follows: ; ; in, It is achieved by adjusting the control cost weight of the FMSS active power; It is achieved by adjusting the control cost weight of FMSS reactive power; It is the weight of the loss cost of line loss; This refers to the change in active power transmitted by FMSS. This represents the change in reactive power transmitted at the FMSS series terminal. This refers to the change in reactive power transmitted at the parallel terminals of the FMSS.

[0013] Preferably, the constraints on FMSS in S3 include: ; in, This represents the maximum apparent power of FMSS. for Minimum voltage at any given moment; For The maximum voltage at any given moment; The reactive power transmitted at the FMSS series terminal; This refers to the reactive power transmitted at the parallel end of the FMSS.

[0014] Preferably, the corrected prediction model expression is: ; in, Represents the corrected voltage; This is the error correction factor.

[0015] Therefore, the present invention employs the above-mentioned predictive control method for the active distribution network renewable energy carrying capacity model based on FMSS, and the technical effects are as follows: 1. A predictive control optimization method based on FMSS for the carrying capacity of new energy sources in power grids is proposed. By precisely regulating the active and reactive power transmitted by FMSS, real-time optimization control of distribution network voltage is achieved, effectively solving the voltage instability problem caused by the randomness of new energy output, and significantly improving the new energy carrying capacity and operational stability of the distribution network.

[0016] 2. Only key electrical quantities at the FMSS access end and load end need to be collected. The voltage of key nodes is dynamically adjusted in each cycle of model predictive control. Through local optimization, the overall carrying capacity of new energy in the whole network is improved. At the same time, the model is continuously optimized by combining feedback correction mechanism, which improves control accuracy, reduces voltage fluctuations, and ensures that the system voltage operates within the ideal range. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the active distribution network structure of the FMSS of the present invention; Figure 2This is a schematic diagram of the MPC optimization process of the present invention; Figure 3 This is a schematic diagram of a 33-node system including FMSS according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the distribution network voltage distribution before and after control in an embodiment of the present invention; Figure 4 (a) Voltage distribution diagram before optimization; Figure 4 (b) shows the optimized voltage distribution. Figure 5 This is a schematic diagram of the voltage change at the end node 18 in an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the changes in distribution network losses after optimization in an embodiment of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0020] Example 1 This invention provides a predictive control method for the carrying capacity of new energy sources in an active distribution network based on FMSS, comprising the following steps: S1. Construct an active distribution network architecture including FMSS: such as Figure 1 As shown, the FMSS is integrated into a key node of the distribution network. The FMSS is considered a controllable unit integrated into the key node of the distribution network. Its series part is equivalent to a controlled voltage source used to compensate for the load-side voltage; the parallel part is equivalent to a controlled current source used to compensate for harmonics and reactive power components in the load current. Its core function is to achieve direct control of the voltage at the connection point and adjacent nodes through rapid power regulation.

[0021] S2. Establish a voltage-line loss prediction model: Based on the distribution network topology parameters and long-term measurement data, combined with the calculation of the voltage-line loss correlation matrix, the influence coefficient of voltage fluctuations at each node on the line loss of the entire network is quantified, and a voltage-line loss prediction model is established. For the active distribution network topology and operating status, key carrying capacity indicators, including grid voltage and line loss, are constructed. Furthermore, a carrying capacity prediction model is built, which, after adjusting the FMSS transmission power at a certain moment, predicts future... Predict the voltage and line loss of the distribution network at any given time.

[0022] Voltage and line loss prediction models in The control variable at time is defined as follows Its expression is: ; in, Represents the active power transmitted by FMSS. and These represent the reactive power transmitted on the series and parallel sides of the FMSS, respectively.

[0023] Control variables for distribution network voltage and line loss The effect expression is: ; in, This represents the sensitivity matrix of node voltage to control variables. This represents the sensitivity matrix of line loss to control variables.

[0024] Assuming higher-order error terms are ignored and the power flow direction of the distribution network remains constant, a small fluctuation in the injected power at a node in the distribution network will only affect the power flow distribution and voltage of that branch. It is assumed that the voltage at each node remains at its rated value. Based on the line voltage drop formula, the sensitivity expression of node voltage to changes in injected power can be derived as follows: ; in, Indicates the line resistance; Indicates the line reactance; Represents a node The set of nodes in the feeder branch; express Voltage amplitude at the node; For injection Active power of a node; For injection The reactive power of a node.

[0025] The expression for line losses on power distribution lines is: ; in, The resistance of the circuit. The active power flowing through the line. The reactive power flowing through the line. It is the line voltage, approximated by the voltage of the upstream node of the line. This is a collection of distribution network lines. Since the node voltage can be approximated as an invariant under small power disturbances, the expression for line losses on the distribution network power lines is expanded using a first-order Taylor series and its partial derivatives are taken. The resulting expression for the sensitivity of line losses to changes in active and reactive power is: ; in, , These represent the active power and reactive power injected into the node, respectively.

[0026] In summary, voltage and line loss are... The expression for the predicted value at time t is: ; in, Represents the predicted step size. It was obtained through real-time measurement. Voltage amplitude at any given moment; Received every 15 minutes via communication with the superior. Real-time distribution network loss values; for Always looking towards the future Predicted voltage amplitude at time; yes Predicting the future The value of loss at any given moment; yes Predicting the future Constantly control the increment of variables.

[0027] S3. Constructing the MPC rolling optimization model: Based on the rolling optimization idea, construct a dual-objective MPC controller, taking voltage deviation and line loss as optimization objectives, and establish a multivariate optimization model that includes FMSS active / reactive power regulation, node voltage safety constraints, and equipment capacity limitation constraints. Under the premise of ensuring that the voltages at both ends of the node meet the operating standards, the main objective of the rolling optimization stage is to minimize the FMSS operating cost and distribution network losses. A real-time optimization objective function based on model predictive control is constructed, specifically as follows: ; in, It is the line loss coefficient; Represents the cost of line loss; This represents operating costs.

[0028] A voltage optimization model based on model predictive control is proposed, which can comprehensively consider the changes in active power injected at nodes. and reactive power changes The impact on the operating status of the distribution network, and the loss cost in the objective function are optimized. Operating costs The expression is: ; ; in, It is achieved by adjusting the control cost weight of the FMSS active power; It is achieved by adjusting the control cost weight of FMSS reactive power; It is the weight of the loss cost of line loss; This refers to the change in active power transmitted by FMSS. This represents the change in reactive power transmitted at the FMSS series terminal. This refers to the change in reactive power transmitted at the parallel terminals of the FMSS.

[0029] Given that FMSS is highly efficient, we assume that... The relationship between the nodes and the control strategy is as follows: To ensure that the voltage at both ends of the FMSS operates within the specified range, the control strategy is changed according to the different states of the nodes. This is achieved by altering the line loss coefficient in the objective function. The value determines the switching of control measures, i.e., voltage deviation. Larger line loss coefficient This will reduce the weight of the line loss term in the objective function. The specific implementation method is as follows: ; in, The weighting coefficient is a positive integer.

[0030] The formula for calculating the predicted rolling voltage is: ; Since the node voltages at both ends of the FMSS have upper and lower bounds and the FMSS operation has a maximum capacity limit, the following constraints are established for the FMSS: ; in, This represents the maximum apparent power of FMSS. for Minimum voltage at any given moment; For The maximum voltage at any given moment; The reactive power transmitted at the FMSS series terminal; This refers to the reactive power transmitted at the parallel end of the FMSS.

[0031] Considering that the sensitivity matrix is ​​constructed based on long-term historical measurement data, before starting a new round of prediction, it is necessary to update the power injection terms corresponding to the connected nodes in light of changes in the FMSS operating conditions, thereby generating updated loss sensitivity and voltage sensitivity matrices. On the other hand, since the operating state of the distribution network is time-varying, to achieve more accurate real-time control, error information generated by the previous round of predictive control can be introduced to dynamically correct the current prediction model, thereby improving the accuracy of grid voltage and line loss predictions. When the distribution network operation tends to be stable and the influence of fluctuations in the state of other nodes is negligible, the line loss sensitivity can be specifically corrected based solely on changes in the FMSS operating state, and its expression is: ; The actual control process requires adding a feedback correction mechanism in the voltage predictive control stage, and using the measured voltage value of the distribution network as the starting condition for a new round of rolling optimization scheduling, thereby constructing a voltage closed-loop control system. To effectively narrow the gap between the predictive control results and the actual control effect, the prediction model is corrected based on the error generated in the previous control round, thereby improving the accuracy of the voltage prediction model. The corrected prediction model expression is as follows: ; in, Represents the corrected voltage; This is the error correction factor.

[0032] S4. Implement rolling optimization and dynamic adjustment: Dynamically adjust the FMSS transmission power according to the optimization model, introduce prediction error information to dynamically correct the prediction model, and make targeted corrections to the line loss sensitivity based on the changes in the FMSS working status when the distribution network is running smoothly.

[0033] like Figure 2 As shown, this invention proposes a model predictive control (MMC) capacity enhancement strategy. This method constructs a multivariate optimization model that includes FMSS active / reactive power regulation, node voltage constraints, and equipment capacity limitations, thereby achieving dynamic adjustment of FMSS transmission power and adaptive updating of the sensitivity matrix.

[0034] The specific implementation steps are as follows: 1. Construct a sensitivity matrix for node voltage and network loss by combining real-time measurement data, form the basic parameter set for the rolling optimization prediction model, construct a voltage-loss joint prediction model with FMSS active and reactive power increments as control variables, and describe the dynamic characteristics of the system through state-space equations; 2. The optimization process uses the system's current active power. and reactive power Using the initial values, update the state space matrix and weight matrix, and start iterative calculation; 3. Taking the minimization of FMSS operating cost and network loss as the objective function, and node voltage safety constraints and equipment capacity limitations as boundary conditions, a rolling optimization model is established, and a quadratic programming algorithm is used to solve for the future... Optimal control sequence for each control cycle ; 4. Only execute the first instruction in the control sequence, namely the active and reactive power adjustment at the current moment. ; 5. Determine if the optimization cycle has ended. If the cycle has ended, exit the program; otherwise, proceed with... Real-time system measured voltage value As the initial state for the next optimization cycle, the sensitivity matrix is ​​corrected online using the prediction error according to the change in the FMSS operating state.

[0035] To verify the effectiveness of the proposed method, the following methods were used: Figure 3 The IEEE 33-node distribution network example shown analyzes and verifies the proposed real-time voltage optimization method for distribution networks. The FMSS series terminal is connected to node 33, and the parallel terminal is connected to node 18, in order to improve the power quality of the distribution network. Figure 3 The connection locations and capacities of the photovoltaic power plants are shown in Table 1 below.

[0036] Table 1. Location and Capacity of Photovoltaic Power Plants

[0037] The voltage distribution of the distribution network before and after control by the load-bearing capacity model predictive control method is as follows: Figure 4 As shown, the voltage change at the load end connected to the FMSS is as follows: Figure 5 As shown.

[0038] contrast Figure 4 (a) and Figure 4 (b) It can be seen that without optimization, the minimum voltage of the distribution network system nodes is lower than 0.91 (pu), while the voltage of the optimized FMSS connection node is basically maintained in the range of [0.98pu, 1.02pu], which meets the given ideal voltage operating range index. The voltage deviation of the end node 18 is significantly reduced after the FMSS is connected, indicating that the FMSS connection significantly improves the power quality of the distribution network and verifies that the proposed method can effectively deal with voltage over-limit and anomalies.

[0039] Furthermore, after optimization using FMSS, the changes in distribution network losses are obtained as follows: Figure 6 As shown, the distribution network losses are significantly reduced, greatly improving the economic efficiency of the distribution network system. This is achieved by introducing a network voltage deviation index. Total network loss The optimization level of the distribution network can be evaluated using the following expression: ; ; in, and It specifies the upper and lower limits of voltage; n To optimize the number of sampling points in the process; Sampling time, in seconds; The total system loss during the operating time is expressed in kW·h. Table 2 shows a comparison of distribution network operation indicators before and after optimization. After optimization using FMSS, network loss decreased by 68.4%, and network voltage deviation decreased by 83.95%, verifying the economy and effectiveness of the proposed method.

[0040] Table 2 Comparison of distribution network operation indicators before and after optimization

[0041] Therefore, this invention adopts the above-mentioned predictive control method for the carrying capacity of new energy in active distribution networks based on FMSS. By collecting key electrical quantities such as voltage amplitude, phase angle, and power flow at the FMSS access point and load end, a node voltage-branch power sensitivity matrix is ​​constructed. In each control cycle of the model predictive control, the voltage of key nodes is dynamically adjusted, and the overall voltage quality of the entire network is improved through the optimization of local voltage. At the same time, a feedback correction link is introduced to continuously optimize the model parameters, thereby improving the accuracy and robustness of the model optimization process.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for model predictive control of active power distribution network based on FMSS new energy carrying capacity, characterized in that, The method comprises the following steps: S1, constructing a main distribution network architecture containing FMSS: integrating FMSS at key nodes of the distribution network, the series part of which is equivalent to a controlled voltage source, and the parallel part of which is equivalent to a controlled current source; S2, establishing a voltage-line loss prediction model: based on the topological structure parameters and long-term measurement data of the distribution network, combining the calculation of the voltage-line loss correlation matrix, quantifying the influence coefficient of voltage fluctuation of each node on the line loss of the whole network, and establishing a voltage-line loss prediction model; S3, constructing an MPC rolling optimization model: based on the rolling optimization idea, constructing a double-objective MPC controller, taking voltage deviation and line loss as optimization objectives, and establishing a multivariable optimization model containing FMSS active / reactive power regulation, node voltage safety constraints, and device capacity limitation constraints; S4, implementing rolling optimization and dynamic adjustment: dynamically adjusting the transmission power of FMSS according to the optimization model, introducing prediction error information to dynamically correct the prediction model, and when the distribution network runs smoothly, correcting the line loss sensitivity according to the change of the working state of FMSS.

2. The FMSS-based proactive power distribution network new energy carrying capacity model prediction control method according to claim 1, characterized in that, The voltage-line loss prediction model in S2 comprises the following steps: S21, collecting key electrical quantities at the access end and load end of FMSS; S22, constructing a node voltage-branch power sensitivity matrix, and describing the dynamic characteristics of the system through a state space equation; S23, using real-time measurement data and the distribution network loss value obtained through upper communication to predict the distribution network voltage and line loss value at the future time.

3. The FMSS-based proactive power distribution network new energy carrying capacity model prediction control method according to claim 1, characterized in that, The double-objective MPC controller in S3 comprises the following steps: S31, taking the minimum of FMSS operation cost and network loss as the objective function, and taking node voltage safety constraints and device capacity limitations as boundary conditions; S32, using a quadratic programming algorithm to solve the optimal control sequence of the future control period; S33, only executing the first instruction of the control sequence, i.e. the active and reactive power adjustment amount at the current time; S34, according to the measured voltage value and the prediction error, online correcting the sensitivity matrix and dynamically adjusting the control strategy.

4. The FMSS-based proactive power distribution network new energy carrying capacity model prediction control method according to claim 1, characterized in that, The adjustment process of FMSS in S4 comprises the following steps: S41, realizing direct control of the voltage of the access point and adjacent nodes through fast power regulation; S42, dynamically updating the loss sensitivity matrix and the voltage sensitivity matrix according to the change of the operation state of the distribution network; S43, introducing a feedback correction mechanism, using the error information generated in the last round of control to correct the prediction model, and improving the accuracy of the voltage prediction model.

5. The FMSS-based proactive power distribution network new energy carrying capacity model prediction control method according to claim 2, characterized in that, The expression of the node voltage-branch power sensitivity matrix in S22 is: ； wherein, represents the line resistance; represents the line reactance; represents the node wherein the node is located; represents the voltage magnitude of the node; is the active power injected into the node; is the reactive power injected into the node.

6. The FMSS-based proactive power distribution network new energy carrying capacity model prediction control method according to claim 2, characterized in that, The voltage and line loss in S23 are predicted in The prediction value expression of the time is: ； wherein, represents the prediction step, is obtained by real-time measurement the voltage amplitude at the time instant; is obtained every 15 minutes via superior communication the power distribution network loss value at the time instant; is the prediction result of the voltage amplitude at the time instant for the future time instant; is the predicted future loss value at the time instant time instant; is the predicted future increment of the control variable at the time instant time instant.

7. The FMSS-based proactive power distribution network new energy carrying capacity model prediction control method according to claim 3, characterized in that, The expression of the objective function in S31 is: ； wherein, is a line loss coefficient; represents a line loss cost; represents an operating cost; loss cost with operating cost The expressions are respectively: ； ； wherein, is a control cost weight for regulating the active power of the FMSS; is a control cost weight for regulating the reactive power of the FMSS; is a loss cost weight for line loss; is a change in the active power transmitted by the FMSS; is a change in the reactive power transmitted by the FMSS at the series end; is a change in the reactive power transmitted by the FMSS at the parallel end.

8. The FMSS-based proactive power distribution network new energy carrying capacity model prediction control method according to claim 1, characterized in that, The constraint conditions of FMSS in S3 include: ； wherein, is the maximum apparent power of the FMSS; is the is the minimum voltage at the moment; is the is the maximum voltage at the moment; is the reactive power transmitted at the series end of the FMSS; is the reactive power transmitted at the parallel end of the FMSS.

9. The FMSS-based proactive power distribution network new energy carrying capacity model prediction control method according to claim 1, characterized in that, The expression of the corrected prediction model is: ； wherein, represents the corrected voltage; is an error correction coefficient.

Images