A power system reactive power optimization control system and method based on event triggering

Abstract

The present invention proposes a reactive power optimization control system and method for an electric power system based on event triggering. Multiple nodes are selected in a transmission network model to incorporate new energy sources; the active power fluctuation at each moment of the transmission network model is calculated, and if it is greater than the active power fluctuation trigger threshold, global optimization is performed; a multi-moment reactive power optimization model is established, and the optimized reactive power of each node in the transmission network model at multiple future moments is obtained by solving the interior point method; the time interval between two adjacent moments is evenly divided into multiple small time intervals, and the active power fluctuation at each small time interval of each node is calculated, and if it is greater than the node active power fluctuation trigger threshold, local optimization is performed; the reactive power regulation amount of the fluctuation time interval of the fluctuation node is calculated, and the local controller performs reactive power control of the fluctuation time interval according to the reactive regulation amount. The present invention can realize rapid reactive power optimization of an electric power system with significant power fluctuations, reduce system network losses, and prevent system voltage from exceeding the limit.

Description

Technical Field

[0001] The present invention belongs to the field of power system optimization control, and in particular relates to a power system reactive power optimization control system and method based on event triggering. Background Art

[0002] Renewable energy has developed rapidly due to its environmental friendliness. With the diversification of electricity users, many random loads are connected to the power system. The continuous increase in the penetration rate of renewable energy and random loads has brought huge challenges to the safety and economy of power system operation.

[0003] Reactive power optimization is a crucial tool for maintaining the normal operation of power systems. By scheduling various reactive devices, multiple objective functions, such as generator costs, network losses, voltage deviation, and reactive power regulation costs, are minimized. Traditional centralized control algorithms require a central server to monitor the status of each device and solve for optimal power flow to achieve optimal system control. As power systems continue to expand, this burden will incur a significant computational burden. To overcome the shortcomings of centralized algorithms, some decentralized algorithms, such as the multiplier alternating direction method, divide large power systems into multiple subregions for optimization calculations. However, during the iterative process of decentralized optimization algorithms, the coordination role of the centralized controller cannot be completely eliminated. Several distributed optimization methods have been proposed to balance the computational load across the entire network in multi-agent systems. Distributed algorithms do not require a central server and instead achieve the optimal operating state through coordination among multiple agents. While this reduces computational complexity and communication costs, these algorithms also suffer from high local communication requirements and slow convergence. Given the limited computing power and communication bandwidth of the system, traditional reactive power optimization methods typically perform optimal control of the system at regular intervals. However, with the increasing penetration of renewable energy and random loads, significant short-term power fluctuations within a period will reduce the accuracy of reactive power optimization control. To adapt to power fluctuations, many algorithms require frequent optimization procedures, which significantly increases the amount of computation and communication data.

[0004] Event-triggered algorithms are widely used in systems with limited computing power and bandwidth. For example, in multi-agent systems, event-triggered algorithms can reduce the communication resource requirements between agents, reduce the number of algorithm iterations, and thus alleviate the computational burden on the agents. In the power system field, event-triggered algorithms are commonly used in frequency control, economic dispatch, and other applications. As the scale of modern power systems continues to expand, the uncertainty of grid access increases, requiring the real-time collection and processing of large amounts of data. Event-triggered algorithms have become one of the feasible solutions for improving the operational efficiency of power systems. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention proposes an event-triggered power system reactive power optimization control system and method, which is used to achieve rapid reactive power optimization of power systems with significant power fluctuations and reduce the computing burden of the central server and the communication burden of the system network.

[0006] The technical solution of the system of the present invention is a power system reactive power optimization control system based on event triggering, comprising:

[0007] Central server, multiple local controllers;

[0008] The central server is connected to the multiple local controllers in sequence;

[0009] Deploy local controllers at each node in the transmission network model;

[0010] The central server selects multiple nodes in the transmission network model and integrates new energy sources; the central server calculates the active power fluctuation at each moment of the transmission network model, and performs global optimization if it is greater than the active power fluctuation trigger threshold; establishes a multi-moment reactive power optimization model, and obtains the optimized reactive power of each node in the transmission network model at multiple future moments through the interior point method; the time interval between two adjacent moments is evenly divided into multiple small time intervals, and the active power fluctuation at each small time interval of each node is calculated, and performs local optimization if it is greater than the node active power fluctuation trigger threshold; calculates the reactive power regulation amount of the fluctuation time interval of the fluctuation node, and the local controller of each node in the transmission network model performs reactive power control at the fluctuation time interval according to the reactive regulation amount.

[0011] The technical solution of the method of the present invention is a power system reactive power optimization control system based on event triggering, comprising the following steps:

[0012] Step 1: Build a transmission network model and select multiple nodes in the transmission network model to integrate new energy sources;

[0013] Step 2: The central server obtains the active power of each node in the transmission network model at each moment in real time, and calculates the active power fluctuation of the transmission network model at each moment. If the active power fluctuation of the transmission network model at each moment is greater than the transmission network active power fluctuation trigger threshold, the process jumps to step 3, i.e., performs global optimization, and the transmission network model state is a fluctuating state. Otherwise, continue to step 2.

[0014] Step 3: The central server predicts the active power of each node in the transmission network model at multiple historical moments using a linear regression prediction method to obtain the active power of each node in the transmission network model at multiple future moments; taking the minimization of the network loss of the transmission network model as the optimization goal, the power balance constraint equation, the state variable constraint equation, and the control variable constraint equation as constraints, the reactive power of each node in the transmission network model at multiple future moments as the decision variable, and the voltage and phase angle of each node in the transmission network model at multiple future moments as the state variables, a multi-moment reactive power optimization model is established and solved using the interior point method to obtain the optimized reactive power, optimized voltage, and optimized phase angle of each node in the transmission network model at multiple future moments; the central server sends the optimized reactive power, optimized voltage, and optimized phase angle of each node in the transmission network model at the next moment to the local controller of each node for reactive power control;

[0015] Step 4: The local controller of each node in the transmission network model evenly divides the time interval between two adjacent moments in step 2 into multiple small time intervals, collects the active power of each node in the transmission network model according to each small time interval, and calculates the active power fluctuation of each node in the transmission network model in each small time interval. If the active power fluctuation of each small time interval of the node in the transmission network model is greater than the node active power fluctuation trigger threshold, the corresponding node in the transmission network model is defined as a fluctuation node in the transmission network model, the corresponding small time interval is defined as a fluctuation time interval, and the process jumps to step 5, i.e., performs local optimization; otherwise, continue to execute step 4;

[0016] Step 5: The central server obtains the sensitivity matrix of the transmission network model by inverting the Jacobian matrix based on the optimized voltage and optimized phase angle of each node in the transmission network model at multiple future moments. The central server calculates the voltage of each node in the transmission network model corresponding to the fluctuating node in the transmission network model at the fluctuation time interval in combination with the sensitivity matrix of the transmission network model, and further calculates the reactive power regulation amount of the fluctuating node in the transmission network model at the fluctuation time interval. The local controller of the fluctuating node in the transmission network model performs reactive power control at the fluctuation time interval according to the reactive power regulation amount.

[0017] Preferably, each moment in step 2 is defined as the kth moment, k∈[1,K], where K represents the number of moments;

[0018] The calculation of the active power fluctuation of the transmission network model in step 2 is as follows:

[0019]

[0020] Among them, P i,k Represents the active power of the i-th node at the k-th moment in the transmission network model, W irepresents the active power of the i-th node in the last global optimization transmission network model, ΔP k represents the active power fluctuation at the kth moment of the transmission network model, and N represents the number of nodes in the transmission network model;

[0021] Preferably, the multiple historical moments in step 3 are specifically defined as follows:

[0022] Take the kth moment as the current moment;

[0023] The kLth moment, the k-L+1th moment, ..., the k-1th moment are regarded as L historical moments;

[0024] The multiple future moments described in step 3 are specifically defined as follows:

[0025] The k+1th moment, the k+2th moment, ..., the k+K1th moment are regarded as K1 future moments;

[0026] Preferably, the calculation of the active power fluctuation amount of each node in each hour interval in the transmission network model in step 4 is specifically defined as follows:

[0027] It is calculated by the absolute value of the difference between the active power of each node in each hour interval of the transmission network model and the active power of each node in each hour interval of the transmission network model in the previous local optimization;

[0028] Preferably, the voltage of each node in the transmission network model corresponding to the fluctuation node in the transmission network model at the fluctuation time interval is calculated in step 5, and the specific calculation is as follows:

[0029]

[0030] i=1,2…N,j=1,2…M

[0031] t b ∈[1,T]

[0032] b∈[1,B]

[0033] Among them, N represents the number of nodes in the transmission network model, M represents the number of fluctuating nodes in the transmission network model, T represents the number of small time intervals, B represents the number of fluctuating time intervals, and node j Represents the node in the transmission network model j The serial number of the node, j represents the serial number of the jth fluctuation node in the transmission network model, It represents the voltage of the bth fluctuation time interval of the i-th node in the transmission network model corresponding to the j-th fluctuation node in the transmission network model, that is, the voltage of the b-th fluctuation time interval of the i-th node in the transmission network model. j The tth node of the i-th node in the transmission network model corresponding to the nodeb The voltage at the hour interval, V 0,i represents the reference voltage of the i-th node in the transmission network model, It represents the voltage sensitivity coefficient of the i-th node in the transmission network model to the active power fluctuation of the b-th fluctuation time interval of the j-th fluctuation node in the transmission network model. Through the sensitivity matrix, It represents the active power fluctuation of the jth fluctuation node in the transmission network model at the bth fluctuation time interval, that is, it represents the active power fluctuation of the jth fluctuation node in the transmission network model. j The tth node b The active power fluctuation at the bth fluctuation time interval is calculated by the absolute value of the difference between the active power at the jth fluctuation node in the transmission network model at the bth fluctuation time interval and the active power at the corresponding fluctuation time interval of the jth fluctuation node in the transmission network model in the previous local optimization;

[0034] Step 5 calculates the reactive regulation amount of the fluctuation time interval of the fluctuation node in the transmission network model, as follows:

[0035] If the voltage of each node in the transmission network model at the time of fluctuation is within the normal voltage range of the transmission network model, the reactive power regulation amount of the fluctuation node in the transmission network model at the time of fluctuation is calculated as follows:

[0036]

[0037] i∈[1,N],j∈[1,M],b∈[1,B]

[0038] in, It represents the optimal adjustment amount of the target reactive power of the system at the bth fluctuation time interval of the jth fluctuation node in the transmission network model, that is, it represents the optimal adjustment amount of the target reactive power of the system at the bth fluctuation time interval of the jth fluctuation node in the transmission network model. j The tth node b The reactive power optimization adjustment amount at hour intervals, k1 represents the first voltage safety factor and k1<1, V i,H represents the voltage upper limit of the i-th node in the transmission network model, The voltage sensitivity coefficient of the reactive power fluctuation of the jth fluctuation node in the transmission network model at the bth fluctuation time interval to the i-th node in the transmission network model is: Obtained through the sensitivity matrix, min(*) means taking the minimum value;

[0039] Will As the reactive regulation quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model;

[0040] If the voltage of any node in the transmission network model at the time of fluctuation exceeds the normal voltage range of the transmission network model, the reactive power regulation amount of the fluctuation time interval of the fluctuation node in the transmission network model is calculated as follows:

[0041]

[0042] i∈[1,N],j∈[1,M],b∈[1,B]

[0043] in, It represents the voltage over-limit elimination and reactive power optimization adjustment quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model, that is, it represents the voltage over-limit elimination and reactive power optimization adjustment quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model. j The tth node b The elimination voltage over-limit reactive power optimization adjustment amount at hour intervals, k2 represents the second voltage safety factor and k2>1, As the reactive regulation quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model.

[0044] The beneficial effects of the present invention are:

[0045] Because local reactive power optimization control utilizes sensitivity information, it eliminates the need for global coordination and centralized optimization, enabling rapid real-time reactive power optimization of entire power systems with significant power fluctuations. By enabling real-time control by local controllers without increasing the computational burden of the master controller or the communication burden of the entire network, the timescale for reactive power optimization is significantly shortened.

[0046] By optimizing the system on a shorter time scale and tracking the system's optimal operating point, more precise regulation of the system's reactive power can be achieved, preventing voltage over-limit and increased network losses that may occur due to significant fluctuations in the power of new energy and random loads, thereby improving the safety and economy of power system operation.

[0047] A multi-stage global optimization is carried out in a rolling manner, but only the optimization results of the first period are used, which is conducive to improving the robustness of system operation. The constructed global optimization model improves voltage safety by narrowing the voltage feasible region within the voltage constraint conditions.

[0048] The use of an event-triggered algorithm reduces unnecessary optimization operations during system optimization. When power system power fluctuations are small, global optimization and local reactive power optimization control are not required, which improves system optimization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 : A flow chart of a method according to an embodiment of the present invention;

[0050] Figure 2 : New energy and random load power curves according to an embodiment of the present invention;

[0051] Figure 3 : The time cost of method 1 and method 2 in the embodiment of the present invention;

[0052] Figure 4 : Implementation of the time-triggered global optimization according to an embodiment of the present invention;

[0053] Figure 5 : Schematic diagram of reactive power local optimization control according to an embodiment of the present invention;

[0054] Figure 6 : Method 3 of the embodiment of the present invention is based on the time-triggered optimization control execution;

[0055] Figure 7 : The time cost of the method 3 of the embodiment of the present invention for the on-site optimization control of reactive power;

[0056] Figure 8 : Wind turbine reactive power curves according to different optimization methods according to the embodiments of the present invention;

[0057] Figure 9 : Network loss curves of different optimization methods according to the embodiment of the present invention;

[0058] Figure 10 : A system voltage surface of a method 1 according to an embodiment of the present invention;

[0059] Figure 11 : Node voltage curves of different optimization methods according to the embodiments of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] In specific implementation, the method proposed in the technical solution of the present invention can be automatically run by those skilled in the art using computer software technology. System devices that implement the method, such as computer-readable storage media that store the corresponding computer program of the technical solution of the present invention and computer equipment that runs the corresponding computer program, should also be within the scope of protection of the present invention.

[0062] The technical solution of the system of the embodiment of the present invention is a power system reactive power optimization control system based on event triggering, including:

[0063] Central server, multiple local controllers;

[0064] The central server is connected to the multiple local controllers in sequence;

[0065] Deploy local controllers at each node in the transmission network model;

[0066] The central server selects multiple nodes in the transmission network model and integrates new energy sources; the central server calculates the active power fluctuation at each moment of the transmission network model, and performs global optimization if it is greater than the active power fluctuation trigger threshold; establishes a multi-moment reactive power optimization model, and obtains the optimized reactive power of each node in the transmission network model at multiple future moments through the interior point method; the time interval between two adjacent moments is evenly divided into multiple small time intervals, and the active power fluctuation at each small time interval of each node is calculated, and performs local optimization if it is greater than the node active power fluctuation trigger threshold; calculates the reactive power regulation amount of the fluctuation time interval of the fluctuation node, and the local controller of each node in the transmission network model performs reactive power control at the fluctuation time interval according to the reactive regulation amount.

[0067] The model of the central server is IBM server;

[0068] The model of the local controller is an RTU controller;

[0069] The following combination Figures 1 to 11 The technical solution of the method of the embodiment of the present invention is a method for controlling reactive power optimization in a power system based on event triggering, including:

[0070] This embodiment utilizes a proposed event-triggered power system reactive power optimization control method to combine global optimization with local reactive power optimization control, thereby reducing unnecessary system operations, lowering the system's computing and communication burdens, reducing system network losses, and preventing voltage over-limit.

[0071] like Figure 1 Shown is a flow chart of the method of the present invention.

[0072] Step 1: Build a transmission network model and select multiple nodes in the transmission network model to integrate new energy sources;

[0073] Step 2: The central server obtains the active power of each node in the transmission network model at each moment in real time, and calculates the active power fluctuation of the transmission network model at each moment. If the active power fluctuation of the transmission network model at each moment is greater than the transmission network active power fluctuation trigger threshold, the process jumps to step 3, i.e., performs global optimization, and the transmission network model state is a fluctuating state. Otherwise, continue to step 2.

[0074] Each moment in step 2 is defined as the kth moment, k∈[1,K], where K represents the number of moments;

[0075] The calculation of the active power fluctuation of the transmission network model in step 2 is as follows:

[0076]

[0077] Among them, P i,k Represents the active power of the i-th node at the k-th moment in the transmission network model, W i represents the active power of the i-th node in the last global optimization transmission network model, ΔP k represents the active power fluctuation at the kth moment of the transmission network model, and N = 39 represents the number of nodes in the transmission network model;

[0078] In this embodiment, a 30-minute simulation experiment was conducted on an IEEE-39 power system. A photovoltaic station was connected to node 33, a random load was connected to node 34, and a wind farm was connected to nodes 36 and 38. Power fluctuations followed a normal distribution over multiple time periods, with a standard deviation of 25 MW in the first period. The correlation coefficient between the two wind farms was 0.5, with a confidence level of β = 0.95. The minimum and maximum system voltages were 0.9 pu and 1.1 pu, respectively. An event-triggered, multi-period global optimization was performed every 5 minutes, with each period lasting 1 minute. Event-triggered, local reactive power optimization control was performed every 5 seconds. Because the time intervals for multi-period optimization in this embodiment are relatively short, the discrete variables were assumed to remain constant over the multiple periods. The event trigger thresholds ζ and γ were 5 MW. Safety factors k1 and k2 were 0.95 and 1.05, respectively. SVGs of sufficient capacity were installed at nodes {30, 34, 36, 38}. In order to verify the feasibility of the proposed algorithm, different optimization methods are compared. The introduction of different methods is shown in Table 1. Method 1 performs global optimization every minute and shrinks the voltage feasible domain in multi-period optimization to improve system safety. Method 2 performs global optimization every 5 seconds and does not shrink the voltage feasible domain in multi-period optimization because the uncertainty of new energy or random load within 5 seconds can be ignored. Method 3 performs global optimization every minute based on event triggering and shrinks the voltage feasible domain in multi-period optimization to improve system safety, and then performs local reactive power optimization control based on event triggering every 5 seconds.

[0079] Table 1: Introduction to different optimization methods in embodiments of the present invention

[0080]

[0081] The actual active power of nodes {33, 34, 36, 38} is as follows Figure 2 The solid line in shows that the active power fluctuation is measured around the time period.

[0082] Step 3: The central server predicts the active power of each node in the transmission network model at multiple historical moments using a linear regression prediction method to obtain the active power of each node in the transmission network model at multiple future moments; taking the minimization of the network loss of the transmission network model as the optimization goal, the power balance constraint equation, the state variable constraint equation, and the control variable constraint equation as constraints, the reactive power of each node in the transmission network model at multiple future moments as the decision variable, and the voltage and phase angle of each node in the transmission network model at multiple future moments as the state variables, a multi-moment reactive power optimization model is established and solved using the interior point method to obtain the optimized reactive power, optimized voltage, and optimized phase angle of each node in the transmission network model at multiple future moments; the central server sends the optimized reactive power, optimized voltage, and optimized phase angle of each node in the transmission network model at the next moment to the local controller of each node for reactive power control;

[0083] The multiple historical moments described in step 3 are specifically defined as follows:

[0084] Take the kth moment as the current moment;

[0085] The kLth moment, the k-L+1th moment, ..., the k-1th moment are regarded as L historical moments;

[0086] The multiple future moments described in step 3 are specifically defined as follows:

[0087] The k+1th moment, the k+2th moment, ..., the k+K1th moment are regarded as K1 future moments;

[0088] The time cost of global optimization of method 1 is as follows Figure 3 As shown in (a), the power system only needs to be optimized once per minute, and the computing burden and network communication burden of the central server are relatively low. However, method 1 cannot solve the impact of short-term power fluctuations (i.e., second-level fluctuations) on the power system. The time cost of optimizing the power system by method 2 is as follows: Figure 3 As shown in (b), since method 2 optimizes the power system on a shorter time scale, its system control accuracy is higher. However, frequent global optimization brings a lot of computational and communication burdens to the system. The average time cost of each execution of the optimization program by method 2 is close to 5s. Adding the time for collecting system status and issuing instructions, in actual operation, it is difficult for method 2 to achieve system optimization with a time scale of 5s. Method 3 performs global optimization in each time period (i.e. 1 minute), so the computational burden of the central server and the communication cost of the network are relatively low. Figure 4As shown, global optimization in time periods {11, 13, 16, 19, 21, 25} is avoided because their system power deviation is less than the threshold ζ, i.e., 5 MW. Their previous global optimization results (i.e., time periods {10, 12, 15, 18, 20, 24}) will continue to be used in time periods {11, 13, 16, 19, 21, 25}. Statistics show that 20% of global optimizations were exempted in the 30-minute simulation experiment.

[0089] Step 4: The local controller of each node in the transmission network model evenly divides the time interval between two adjacent moments in step 2 into multiple small time intervals, collects the active power of each node in the transmission network model according to each small time interval, and calculates the active power fluctuation of each node in the transmission network model in each small time interval. If the active power fluctuation of each small time interval of the node in the transmission network model is greater than the node active power fluctuation trigger threshold, the corresponding node in the transmission network model is defined as a fluctuation node in the transmission network model, the corresponding small time interval is defined as a fluctuation time interval, and the process jumps to step 5, i.e., performs local optimization; otherwise, continue to execute step 4;

[0090] The schematic diagram of reactive power local optimization control is as follows: Figure 5 As shown in the figure, the black solid arrows represent the active power fluctuation at the moment, while the black empty arrows represent the real-time reactive power regulation. Taking node {33, 34} as an example, the execution of its real-time power deviation and local reactive power optimization control is shown in the figure. Figure 6 As shown in Table 2, only when the real-time power deviation is greater than the threshold value, the local reactive power optimization control is executed. As shown in Table 2, 67.99% of the reactive power local optimization control is avoided. Therefore, the introduction of the event trigger algorithm reduces the calculation and control burden of the local controller. The time cost of the reactive power local optimization control of method 3 of this embodiment is as follows: Figure 7 As shown in Figure 3, the time cost of each optimization control is at the ms level. From the statistics in Table 3, we can see that the optimization control time scale of method three is much smaller than that of method two.

[0091] Table 2: Statistical table of the execution of the third method of the optimization control in the embodiment of the present invention

[0092]

[0093] Table 3: Time cost statistics of the optimization control of Method 2 and Method 3 according to the embodiment of the present invention

[0094]

[0095] Step 5: The central server obtains the sensitivity matrix of the transmission network model by inverting the Jacobian matrix based on the optimized voltage and optimized phase angle of each node in the transmission network model at multiple future moments. The central server calculates the voltage of each node in the transmission network model corresponding to the fluctuating node in the transmission network model at the fluctuation time interval in combination with the sensitivity matrix of the transmission network model, and further calculates the reactive power regulation amount of the fluctuating node in the transmission network model at the fluctuation time interval. The local controller of the fluctuating node in the transmission network model performs reactive power control at the fluctuation time interval according to the reactive power regulation amount.

[0096] Step 5 calculates the voltage of each node in the transmission network model corresponding to the fluctuation node in the transmission network model at the fluctuation time interval. The specific calculation is as follows:

[0097]

[0098] i=1,2…N,j=1,2…M

[0099] t b ∈[1,T]

[0100] b∈[1,B]

[0101] Among them, N represents the number of nodes in the transmission network model, M represents the number of fluctuating nodes in the transmission network model, T represents the number of small time intervals, B represents the number of fluctuating time intervals, and node j Represents the node in the transmission network model j The serial number of the node, j represents the serial number of the jth fluctuation node in the transmission network model, It represents the voltage of the bth fluctuation time interval of the i-th node in the transmission network model corresponding to the j-th fluctuation node in the transmission network model, that is, the voltage of the b-th fluctuation time interval of the i-th node in the transmission network model. j The tth node of the i-th node in the transmission network model corresponding to the node b The voltage at the hour interval, V 0,i represents the reference voltage of the i-th node in the transmission network model, It represents the voltage sensitivity coefficient of the i-th node in the transmission network model to the active power fluctuation of the b-th fluctuation time interval of the j-th fluctuation node in the transmission network model. Through the sensitivity matrix, It represents the active power fluctuation of the jth fluctuation node in the transmission network model at the bth fluctuation time interval, that is, it represents the active power fluctuation of the jth fluctuation node in the transmission network model. j The tth node bThe active power fluctuation at the bth fluctuation time interval is calculated by the absolute value of the difference between the active power at the jth fluctuation node in the transmission network model at the bth fluctuation time interval and the active power at the corresponding fluctuation time interval of the jth fluctuation node in the transmission network model in the previous local optimization;

[0102] Step 5 calculates the reactive regulation amount of the fluctuation time interval of the fluctuation node in the transmission network model, as follows:

[0103] If the voltage of each node in the transmission network model at the time of fluctuation is within the normal voltage range of the transmission network model, the reactive power regulation amount of the fluctuation node in the transmission network model at the time of fluctuation is calculated as follows:

[0104]

[0105] i∈[1,N],j∈[1,M],b∈[1,B]

[0106] in, It represents the optimal adjustment amount of the target reactive power of the system at the bth fluctuation time interval of the jth fluctuation node in the transmission network model, that is, it represents the optimal adjustment amount of the target reactive power of the system at the bth fluctuation time interval of the jth fluctuation node in the transmission network model. j The tth node b The reactive power optimization adjustment amount at hour intervals, k1 represents the first voltage safety factor and k1<1, V i,H represents the voltage upper limit of the i-th node in the transmission network model, The voltage sensitivity coefficient of the reactive power fluctuation of the jth fluctuation node in the transmission network model at the bth fluctuation time interval to the i-th node in the transmission network model is: Obtained through the sensitivity matrix, min(*) means taking the minimum value;

[0107] Will As the reactive regulation quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model;

[0108] If the voltage of any node in the transmission network model at the time of fluctuation exceeds the normal voltage range of the transmission network model, the reactive power regulation amount of the fluctuation time interval of the fluctuation node in the transmission network model is calculated as follows:

[0109]

[0110] i∈[1,N],j∈[1,M],b∈[1,B]

[0111] in, It represents the voltage over-limit elimination and reactive power optimization adjustment quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model, that is, it represents the voltage over-limit elimination and reactive power optimization adjustment quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model. jThe tth node b The elimination voltage over-limit reactive power optimization adjustment amount at hour intervals, k2 represents the second voltage safety factor and k2>1, As the reactive regulation quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model.

[0112] This paper assumes that local controllers can rapidly adjust the reactive power output of converters by controlling them. Each local controller, when performing local reactive power optimization control at each moment within a time period, assumes that the power of other nodes remains stable. In other words, the local controller responds only to fluctuations in its own active power by adjusting its own reactive power. The linear superposition principle shows that if all nodes in the system containing renewable energy and random loads simultaneously execute local reactive power optimization control, system-wide reactive power optimization will be achieved.

[0113] Under different optimization methods, the reactive power curves of nodes {33, 34, 36, 38} are as follows: Figure 8 As shown. For method one, since the reactive power remains unchanged during the time period, the voltage feasible region is shrunk, and its reactive power deviates from the optimal reactive power, especially for nodes 36 and 38. Among them, the optimal reactive power is obtained by method two, but for method two, executing the global optimization every 5 seconds will bring a large computational and communication burden to the power system. The reactive power of method three is closer to the optimal reactive power, which shows that the proposed algorithm can effectively track the optimal reactive power while considering the impact of short-term power fluctuations. For method three, the event-triggered global optimization is executed once a minute, and the event-triggered reactive local optimization control is executed every 5 seconds, and its computational and communication burden is not large.

[0114] like Figure 9 As shown, the system loss obtained by Method 3 is closer to the optimal system loss obtained by Method 2 than that obtained by Method 1. Table 4 shows that the proposed algorithm reduces the system loss by 0.17% compared to Method 1, while only exceeding the optimal system loss by 0.085%. Therefore, compared to Method 1, the proposed algorithm can account for short-term power fluctuations and effectively reduce system losses.

[0115] Table 4: Network loss statistics of different optimization methods according to the embodiment of the present invention

[0116]

[0117] The system voltage surface obtained by method 1 is as follows Figure 10 As shown in Figure 2, due to a large short-term power fluctuation at node 38, a voltage over-limit phenomenon occurs at nodes {25, 26}, which are electrically closer to node 38, from time 307 to 312. The voltage curves of nodes {25, 26, 36, 38} under different methods are shown in Figure 2. Figure 11As shown in the figure, during the entire 30-minute simulation experiment, the voltage curve of method three always remains within the allowable range. Therefore, the proposed algorithm can effectively prevent voltage exceeding the limit that may be caused by significant short-term power fluctuations.

[0118] This embodiment demonstrates the feasibility of the proposed event-triggered power system reactive power optimization control algorithm. The proposed method reduces unnecessary optimization operations in the system, balances system computing costs and communication burdens, reduces system network losses, and prevents system voltage from exceeding the limit.

[0119] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, or two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0120] Although the present invention frequently uses terms such as event triggering, new energy, power fluctuation, global optimization, local reactive power optimization control, network loss, voltage over-limit, central server, and local controller, the use of other terms is not excluded. These terms are used solely to more conveniently describe and explain the essence of the present invention; interpreting them as any additional limitations is contrary to the spirit of the present invention.

[0121] Although this article uses more terms such as visible light positioning base station and positioning terminal, it does not exclude the use of

[0122] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of protection of the patent of the present invention. Under the guidance of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested by the present invention shall be based on the attached claims.

Claims

1. An event-triggered power system reactive power optimization control system, characterized in that: include: Central server, multiple local controllers; The central server is connected to the multiple local controllers in sequence; Deploy local controllers at each node in the transmission network model; The central server selects multiple nodes in the transmission network model to incorporate new energy sources; The central server calculates the active power fluctuation at each moment of the transmission network model. If it is greater than the active power fluctuation trigger threshold, global optimization is performed; a multi-moment reactive power optimization model is established, and the optimized reactive power of each node in the transmission network model at multiple future moments is obtained by solving the interior point method; the time interval between two adjacent moments is evenly divided into multiple small time intervals, and the active power fluctuation at each small time interval of each node is calculated. If it is greater than the node active power fluctuation trigger threshold, local optimization is performed; the reactive power regulation amount of the fluctuation time interval of the fluctuation node is calculated, and the local controller of each node in the transmission network model performs reactive power control at the fluctuation time interval according to the reactive regulation amount.

2. A method for controlling reactive power optimization based on an event trigger of a power system using the power system reactive power optimization control system based on event trigger according to claim 1, characterized in that: The following steps are involved: Step 1: Build a transmission network model and select multiple nodes in the transmission network model to integrate new energy sources; Step 2: The central server obtains the active power of each node in the transmission network model at each moment in real time, calculates the active power fluctuation of the transmission network model at each moment, and jumps to step 3 if the active power fluctuation of the transmission network model at each moment is greater than the transmission network active power fluctuation trigger threshold. Otherwise, continue to step 2. Step 3: The central server uses a linear regression prediction method to predict the active power of each node in the transmission network model at multiple historical moments to obtain the active power of each node in the transmission network model at multiple future moments. A multi-moment reactive power optimization model is established and solved using the interior point method to obtain the optimized reactive power, optimized voltage, and optimized phase angle of each node in the transmission network model at multiple future moments, further achieving global optimization. Step 4: Collect the active power of each node in the transmission network model at each hourly interval, and calculate the active power fluctuation of each node in the transmission network model at each hourly interval. If the active power fluctuation of each hourly interval of the node in the transmission network model is greater than the node active power fluctuation trigger threshold, define the corresponding node in the transmission network model as a fluctuation node in the transmission network model, define the corresponding hourly interval as a fluctuation interval, and jump to step 5; otherwise, continue to execute step 4; Step 5: The central server obtains the sensitivity matrix of the transmission network model by inverting the Jacobian matrix based on the optimized voltage and optimized phase angle of each node in the transmission network model at multiple future moments. The central server calculates the voltage of each node in the transmission network model at the fluctuation time interval corresponding to the fluctuation node in the transmission network model in combination with the sensitivity matrix of the transmission network model, and further calculates the reactive regulation amount of the fluctuation time interval of the fluctuation node in the transmission network model to further achieve local optimization.

3. The method for controlling reactive power optimization in a power system based on event triggering according to claim 2, characterized in that: Each moment in step 2 is defined as the kth moment, k∈[1,K], where K represents the number of moments; The calculation of the active power fluctuation of the transmission network model in step 2 is as follows: Among them, P i,k Represents the active power of the i-th node at the k-th moment in the transmission network model, W i represents the active power of the i-th node in the last global optimization transmission network model, ΔP k represents the active power fluctuation at the kth moment of the transmission network model, and N represents the number of nodes in the transmission network model.

4. The method for controlling reactive power optimization in a power system based on event triggering according to claim 3, characterized in that: The multiple historical moments described in step 3 are specifically defined as follows: Take the kth moment as the current moment; The kLth moment, the k-L+1th moment, ..., the k-1th moment are regarded as L historical moments; The multiple future moments described in step 3 are specifically defined as follows: The k+1th moment, the k+2th moment, ..., the k+K1th moment are regarded as K1 future moments; Step 3 describes the establishment of a multi-time reactive power optimization model, as follows: The network loss minimization of the transmission network model is taken as the optimization objective, the power balance constraint equation, the state variable constraint equation and the control variable constraint equation are used as constraint conditions, the reactive power of each node in the transmission network model at multiple future moments is used as the decision variable, and the voltage and phase angle of each node in the transmission network model at multiple future moments is used as the state variables.

5. The method for controlling reactive power optimization in a power system based on event triggering according to claim 4, characterized in that: Step 3 implements global optimization, as follows: The central server sends the optimized reactive power, optimized voltage, and optimized phase angle of each node in the transmission network model at the next moment to the local controller of each node for reactive power control to achieve global optimization.

6. The method for controlling reactive power optimization in a power system based on event triggering according to claim 5, characterized in that: The specific processing process for each hour interval described in step 4 is as follows: The local controller of each node in the transmission network model evenly divides the time interval between two adjacent moments into multiple small time intervals; Step 4 calculates the active power fluctuation of each node in the transmission network model at each hour interval, which is specifically defined as follows: It is calculated by the absolute value of the difference between the active power of each node in each small time interval in the transmission network model and the active power of each node in each small time interval in the previous local optimization.

7. The method for controlling reactive power optimization in a power system based on event triggering according to claim 6, characterized in that: Step 5 calculates the voltage of each node in the transmission network model corresponding to the fluctuation node in the transmission network model at the fluctuation time interval. The specific calculation is as follows: i=1,2…N,j=1,2…M t b ∈[1,T] b∈[1,B] Among them, N represents the number of nodes in the transmission network model, M represents the number of fluctuating nodes in the transmission network model, T represents the number of small time intervals, B represents the number of fluctuating time intervals, and node j Represents the node in the transmission network model j The serial number of the node, j represents the serial number of the jth fluctuation node in the transmission network model, It represents the voltage of the bth fluctuation time interval of the i-th node in the transmission network model corresponding to the j-th fluctuation node in the transmission network model, that is, the voltage of the b-th fluctuation time interval of the i-th node in the transmission network model. j The tth node of the i-th node in the transmission network model corresponding to the node b The voltage at the hour interval, V 0,i represents the reference voltage of the ith node in the transmission network model, It represents the voltage sensitivity coefficient of the i-th node in the transmission network model to the active power fluctuation of the b-th fluctuation time interval of the j-th fluctuation node in the transmission network model. Through the sensitivity matrix, It represents the active power fluctuation of the jth fluctuation node in the transmission network model at the bth fluctuation time interval, that is, it represents the active power fluctuation of the jth fluctuation node in the transmission network model. j The tth node b The active power fluctuation at the bth fluctuation time interval is calculated by the absolute value of the difference between the active power of the jth fluctuation node in the transmission network model at the bth fluctuation time interval and the active power of the corresponding fluctuation time interval of the jth fluctuation node in the transmission network model in the previous local optimization.

8. The method for controlling reactive power optimization in a power system based on event triggering according to claim 7, characterized in that: Step 5 calculates the reactive regulation amount of the fluctuation time interval of the fluctuation node in the transmission network model, as follows: If the voltage of each node in the transmission network model at the time of fluctuation is within the normal voltage range of the transmission network model, the reactive power regulation amount of the fluctuation node in the transmission network model at the time of fluctuation is calculated as follows: i∈[1,N],j∈[1,M],b∈[1,B] in, It represents the optimal adjustment amount of the target reactive power of the system at the bth fluctuation time interval of the jth fluctuation node in the transmission network model, that is, it represents the optimal adjustment amount of the target reactive power of the system at the bth fluctuation time interval of the jth fluctuation node in the transmission network model. j The tth node b The reactive power optimization adjustment amount at hour intervals, k1 represents the first voltage safety factor and k1<1, V i,H represents the voltage upper limit of the i-th node in the transmission network model, The voltage sensitivity coefficient of the reactive power fluctuation of the jth fluctuation node in the transmission network model at the bth fluctuation time interval to the i-th node in the transmission network model is: Obtained through the sensitivity matrix, min(*) means taking the minimum value; Will As the reactive regulation quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model; If the voltage of any node in the transmission network model at the time of fluctuation exceeds the normal voltage range of the transmission network model, the reactive power regulation amount of the fluctuation time interval of the fluctuation node in the transmission network model is calculated as follows: i∈[1,N],j∈[1,M],b∈[1,B] in, It represents the voltage over-limit and reactive power optimization adjustment quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model, that is, it represents the voltage over-limit and reactive power optimization adjustment quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model. j The tth node b The elimination voltage over-limit reactive power optimization adjustment amount is set at hour intervals. k2 represents the second voltage safety factor and k2>1. As the reactive regulation quantity of the bth fluctuation time interval of the jth fluctuation node in the transmission network model; The local optimization described in step 5 is as follows The local controller of the fluctuating node in the transmission network model performs reactive power control at the fluctuation time interval according to the reactive power regulation amount.

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