Optimization regulation and control method for distributed resources in ring

Abstract

The invention discloses an optimization regulation and control method for distributed resources in a ring, and belongs to the technical field of power system operation control. The method comprises the following steps: acquiring a distributed photovoltaic active power total down-regulation amount, a node voltage total deviation amount, a network loss total amount, and electrical parameters of the distributed photovoltaic, each node and a network; constructing a target optimization function according to the total active power down-regulation amount, the total voltage deviation and the total network loss amount; constructing a target constraint condition according to the electrical parameters of the distributed photovoltaic, each node and the network; and analyzing an optimization model constructed by the target optimization function and the target constraint condition according to an optimization algorithm to generate an optimal regulation and control strategy. Therefore, through mutual cooperation among the optimization algorithm, the target optimization function and the target constraint condition, a more reasonable optimal regulation and control strategy is generated.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, and more specifically, to an optimized control method for distributed resources within a ring. Background Technology

[0002] With the high proportion of renewable energy access and the development of intelligent distribution networks, the large-scale access of distributed generation (DG) and flexible resources, and the interweaving of new single-phase and three-phase loads, the problems of three-phase imbalance, large voltage deviation and high loss in distribution networks have become more serious.

[0003] Existing centralized control methods suffer from drawbacks such as three-phase imbalance in distribution networks, large voltage deviations, and high losses, making it difficult to cope with the rapid fluctuations in new distributed source-load characteristics. While local protection strategies respond quickly, they often come at the cost of sacrificing the system's economic operating indicators. At the same time, existing distributed control methods suffer from problems such as insufficient convergence, lack of global optimization capabilities, and failure to consider the impact of inter-phase flexibility resources, making it impossible to rationally schedule distributed resources within the loop.

[0004] In view of this, the present invention is hereby proposed. Summary of the Invention

[0005] In view of the above problems, this invention proposes an optimized control method for distributed resources within a ring, so as to achieve optimized scheduling of distributed resources within a ring.

[0006] The technical solution of this invention is:

[0007] This invention provides an optimized control method for distributed resources within a ring network. The method includes: obtaining the total reduction in active power of distributed photovoltaic (PV) power, the total voltage deviation of nodes, the total network loss, and the electrical parameters of the distributed PV, each node, and the network; constructing a target optimization function based on the total reduction in active power, the total voltage deviation, and the total network loss; constructing target constraints based on the electrical parameters of the distributed PV, each node, and the network; and analyzing the optimization model constructed from the target optimization function and the target constraints using an optimization algorithm to generate the optimal control strategy.

[0008] Furthermore, obtaining the total reduction in active power of distributed photovoltaic power includes: obtaining the sum of the reduction in active power of all distributed photovoltaic power in phase A, phase B, and phase C; and adding the sums of the reductions in active power of phase A, phase B, and phase C to determine the total reduction in active power of distributed photovoltaic power.

[0009] Further, the acquisition of the total node voltage deviation includes: acquiring the voltage deviation of phase A node, the voltage deviation of phase B node, and the voltage deviation of phase C node; determining the total node voltage deviation based on the voltage deviations of phase A node, phase B node, and phase C node; wherein the acquisition methods for the voltage deviations of phase A node, phase B node, and phase C node are the same, specifically: acquiring the actual voltage, reference voltage, upper voltage limit, and lower voltage limit of each node in each of the S phase nodes; determining the voltage deviation of each node in the S phase based on the difference between the actual voltage and the reference voltage; determining the allowable voltage range of each node in the S phase based on the difference between the upper voltage limit and the lower voltage limit; normalizing the voltage deviation to the allowable voltage range, and determining the sum of the squares of the results as the voltage deviation of the S phase node; wherein S∈{A,B,C}, A represents phase A, B represents phase B, and C represents phase C.

[0010] Furthermore, the expression for the objective optimization function is:

[0011] ;

[0012] in, This is the solution to the optimal objective function. This represents the weight of the total reduction in active power output of distributed photovoltaic systems. This represents the total reduction in active power output of distributed photovoltaic systems. This is a per-unit reference value for the total reduction in active power of distributed photovoltaic systems. The weight of the total deviation of the node voltage. This represents the total deviation of the node voltage. This is the per-unit reference value for the total deviation of node voltage. As a weight of the total network loss, Total network loss This serves as a standardized reference value for the total network loss.

[0013] Furthermore, the electrical parameters of the distributed photovoltaic system include the power parameters of the distributed photovoltaic system; the electrical parameters of each node include the operating parameters, voltage parameters, and power parameters of each node; the electrical parameters of the network include the power parameters of the interphase transfer lines, and the weights corresponding to the total reduction in active power of the distributed photovoltaic system, the total voltage deviation of the nodes, and the total network loss; the target constraints include line transmission constraints, power output regulation constraints, and interphase control constraints.

[0014] Furthermore, the construction of target constraints based on the electrical parameters of distributed photovoltaics, each node, and the network includes: constructing line transmission constraints based on the operating parameters, voltage parameters, and power parameters of each node; constructing output regulation constraints based on the power parameters of distributed photovoltaics; and constructing interphase regulation constraints based on the power parameters of the interphase transfer lines and the weights corresponding to the total reduction in active power of distributed photovoltaics, the total deviation of node voltage, and the total network loss.

[0015] Furthermore, the line transmission constraints include a first line transmission constraint, a second line transmission constraint, and a third line transmission constraint; the step of constructing the line transmission constraints based on the operating parameters, voltage parameters, and power parameters of each node includes: constructing the first line transmission constraint based on the operating parameters of each node; and / or, constructing the second line transmission constraint based on the voltage parameters of each node; and / or, constructing the third line transmission constraint based on the power parameters of each node.

[0016] Furthermore, the interphase control constraints include a first interphase control constraint, a second interphase control constraint, and a third interphase control constraint; the power parameters of the interphase transfer lines include a first power parameter and a second power parameter; the construction of interphase control constraints based on the power parameters of the interphase transfer lines and the weights corresponding to the total reduction in active power of distributed photovoltaic power, the total deviation of node voltage, and the total grid loss includes: constructing a first interphase control constraint based on the first power parameter; and / or, constructing a second interphase control constraint based on the second power parameter; and / or, constructing a third interphase control constraint based on the weights corresponding to the total reduction in active power of distributed photovoltaic power, the total deviation of node voltage, and the total grid loss.

[0017] The optimization model constructed based on the objective optimization function and objective constraints is analyzed using the optimization algorithm to generate the optimal control strategy, which includes: generating an initial population based on the chaotic Halton sequence, the initial population being constrained by the objective constraints; during the iteration process, dynamically adjusting the fusion weight of individual historical optimal information and group collaborative guidance information through an adaptive position search mechanism to make the population search towards the extreme value of the objective optimization function; after each iteration, implementing a dual perturbation strategy for the individuals in the population, recalculating the fitness values ​​of all individuals, and updating the globally optimal individual; when the fitness value of the globally optimal individual meets the convergence condition, the current globally optimal individual is taken as the optimal control strategy.

[0018] The beneficial effects of this invention are as follows: On the one hand, this invention constructs a target optimization function based on the total reduction in active power, the total voltage deviation, and the total network loss; on the other hand, it constructs target constraints based on the electrical parameters of distributed photovoltaics, each node, and the network, respectively; on this basis, through the mutual cooperation between the gorilla swarm optimization algorithm, the target optimization function, and the target constraints, a more reasonable optimal control strategy is generated to improve problems such as three-phase imbalance, large voltage deviation, and large losses in the distribution network. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments and drawings obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Figure 1 This is a flowchart illustrating an optimized control method for distributed resources within a ring, as provided in an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of a process for determining the center position, provided as an embodiment of the present invention.

[0023] Figure 3 This is a structural schematic diagram of a three-phase load distribution diagram provided in an embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram of a network loss comparison chart before and after optimization, provided in an embodiment of the present invention.

[0025] Figure 5 This is a schematic diagram of the structure of an optimization and control device for distributed resources within a ring, provided in an embodiment of the present invention.

[0026] Figure 6 This is a schematic diagram of the device provided in an embodiment of the present invention.

[0027] Figure 7 This is a schematic diagram of the structure of a computer-readable storage medium provided in an embodiment of the present invention.

[0028] Figure 8 A prediction graph of the distributed photovoltaic power output curve for an implementation example.

[0029] Figure 9 The diagram shows the optimization results of three imbalances provided for an example of the present invention.

[0030] Figure 10 The diagram shows the network loss optimization results provided for an example of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0032] In a first aspect, the present invention provides an optimized control method for distributed resources within a ring network. The method includes: obtaining the total reduction in active power of distributed photovoltaic (PV) power, the total voltage deviation of nodes, the total network loss, and the electrical parameters of the distributed PV, each node, and the network; constructing a target optimization function based on the total reduction in active power, the total voltage deviation, and the total network loss; constructing target constraints based on the electrical parameters of the distributed PV, each node, and the network; and analyzing the optimization model constructed from the target optimization function and the target constraints using an optimization algorithm to generate the optimal control strategy.

[0033] Therefore, by combining the gorilla population optimization algorithm, the objective optimization function, and the objective constraints, a more reasonable optimal control strategy can be generated.

[0034] Please see Figure 1 , Figure 1 This diagram illustrates a flowchart of an optimized control method for distributed resources within a ring, as provided in an embodiment of the present invention. Figure 1 As shown, the method may include steps 110 to 140.

[0035] In step 110, the total reduction in active power of distributed photovoltaic power, the total deviation of node voltage, the total network loss, and the electrical parameters of distributed photovoltaic power, each node, and the network are obtained.

[0036] The total reduction in active power of distributed photovoltaic power, the total deviation of node voltage, the total network loss, and the electrical parameters of distributed photovoltaic power, each node, and the network can be obtained in real time through the distribution automation system, providing complete data support for the optimal control strategy of distributed resources within the ring.

[0037] In step 120, a target optimization function is constructed based on the total reduction in active power, the total voltage deviation, and the total network loss.

[0038] Specifically, in some implementations, obtaining the total reduction in the active power of distributed photovoltaic power includes the following steps:

[0039] (1) Obtain the total active power reduction of all distributed photovoltaic power in phase A, phase B and phase C;

[0040] (2) The total reduction in active power of distributed photovoltaic power is determined by adding the sum of the reduction in active power of phase A, phase B, and phase C.

[0041] In some implementations, the sum of the active power downsampling of all distributed photovoltaic systems in the S-phase can be determined using the following formula: .

[0042] in, This represents the sum of the active power reduction in the S-phase of all distributed photovoltaic systems. The number of distributed photovoltaic units. Let be the active power reduction of the Kth distributed photovoltaic power generation in phase S; where S∈{A,B,C}.

[0043] By substituting the relevant parameters of phases A, B, and C into the above method, the total active power reduction of all distributed photovoltaic systems in phase A, phase B, and phase C can be determined.

[0044] In some implementations, the reduction in active power of the Kth distributed photovoltaic power generation in phase S can be determined using the following formula:

[0045] .

[0046] in, Let K be the reduction in active power of the Kth distributed photovoltaic system in phase S. Let be the maximum active power that the Kth distributed photovoltaic power generation can produce in phase S. The actual active power output of the Kth distributed photovoltaic system in phase S.

[0047] Similarly, by substituting the relevant parameters of phases A, B, and C into the above method, the corresponding active power reduction amount for phases A, B, and C can be determined.

[0048] In a specific embodiment, the step "adding the sums of the active power reduction amounts of phase A, phase B, and phase C to determine the total reduction amount of active power for distributed photovoltaic power" can be expressed as:

[0049] .

[0050] in, This represents the total reduction in active power output of distributed photovoltaic systems. This represents the total reduction in active power for phase A. This represents the total reduction in active power for phase B. This represents the total reduction in active power of phase C.

[0051] Furthermore, in some embodiments, obtaining the total node voltage deviation includes the following steps:

[0052] (1) Obtain the voltage deviation of phase A node, phase B node and phase C node.

[0053] (2) Determine the total voltage deviation of the nodes based on the voltage deviation of the A-phase node, the voltage deviation of the B-phase node, and the voltage deviation of the C-phase node.

[0054] In one specific implementation, the total node voltage deviation is determined based on the voltage deviations of phase A, phase B, and phase C, and can be expressed as:

[0055] .

[0056] in, This represents the total deviation of the node voltage. The voltage deviation at phase A node. For the voltage deviation at phase B node, This refers to the voltage deviation at the C-phase node.

[0057] Furthermore, in some embodiments, the voltage deviations of phase A, phase B, and phase C are obtained in the same way, which may include the following steps:

[0058] (1) Obtain the actual voltage, reference voltage, upper voltage limit, and lower voltage limit of each node in phase S;

[0059] (2) Determine the voltage deviation of each node of phase S based on the difference between the actual voltage and the reference voltage;

[0060] (3) Determine the allowable voltage range for each node of phase S based on the difference between the upper and lower voltage limits;

[0061] (4) Normalize the voltage deviation to the allowable voltage range and sum the squares of the results to determine the voltage deviation of the S-phase node.

[0062] In one specific implementation, the voltage deviation is normalized to the allowable voltage range, and the sum of the squares of the results is determined as the S-phase node voltage deviation, which can be expressed as:

[0063] .

[0064] in, The voltage deviation at the S-phase node. For each node in phase S, the first The actual voltage of each node For each node in phase S, the first The reference voltage (i.e., the expected value) of each node. For phase S The upper limit of voltage at each node, For phase S The lower limit of the voltage at each node For phase S Voltage deviation at each node, For phase S The allowable voltage range for each node; This represents the number of nodes in the distribution network.

[0065] By substituting the relevant parameters of phases A, B, and C into the above formula for the voltage deviation of the S-phase node, the voltage deviations of phases A, B, and C can be determined.

[0066] Furthermore, in some embodiments, obtaining the total network loss includes the following steps:

[0067] (1) Obtain the network loss of phase A, phase B and phase C.

[0068] (2) The total three-phase network loss is determined by adding the network loss of phase A, phase B and phase C.

[0069] In one specific implementation, the total three-phase network loss is determined by adding the network losses of phase A, phase B, and phase C, and can be expressed as:

[0070] .

[0071] in, This represents the total loss of the three-phase network. For the network loss of phase A, For the network loss of phase B, This refers to the network loss in phase C.

[0072] Specifically, the network loss for each phase can be determined using the following formula:

[0073] .

[0074] in, For the network loss of phase S, The number of busbars, The first phase of S Active power of each busbar This represents the number of distributed photovoltaic systems connected to the S-phase. Let be the maximum active power that the Kth distributed photovoltaic power generation can produce in phase S. Let K be the reduction in active power of the Kth distributed photovoltaic system in phase S. The number of distribution network nodes. For nodes The active power of the load. Let H be the power transferred from phase S to phase H, where H ≠ S.

[0075] It can be determined using the following formula:

[0076] .

[0077] in, The phase-to-phase power transfer efficiency coefficient. This represents the power that needs to be transferred in phase S.

[0078] Therefore, the total reduction in active power of distributed photovoltaic power can be obtained through the above methods. Total deviation of node voltage and total network loss Then, based on the total reduction in active power... Total deviation of node voltage and total network loss An objective optimization function is constructed to minimize the total reduction in active power, the total deviation of node voltage, and the total grid loss of distributed photovoltaic power. In a specific implementation, the expression of the objective optimization function is:

[0079] .

[0080] in, This is the solution to the optimal objective function. This represents the weight of the total reduction in active power output of distributed photovoltaic systems. This represents the total reduction in active power output of distributed photovoltaic systems. This is a per-unit reference value for the total reduction in active power of distributed photovoltaic systems. The weight of the total deviation of the node voltage. This represents the total deviation of the node voltage. This is the per-unit reference value for the total deviation of node voltage. As a weight of the total network loss, Total network loss This serves as a standardized reference value for the total network loss.

[0081] Thus, by constructing an objective optimization function that minimizes the total reduction in active power of distributed photovoltaic power, the total deviation of node voltage, and the total amount of network loss, the back-to-back phase-to-phase power transfer, bus power, and the output of distributed resources are optimized in a coordinated manner, thereby improving the economy and reliability of closed-loop operation.

[0082] In step 130, target constraints are constructed based on the electrical parameters of distributed photovoltaics, each node, and the network.

[0083] In some implementations, the electrical parameters of distributed photovoltaics include the power parameters of the distributed photovoltaics.

[0084] In some implementations, the electrical parameters of each node include the operating parameters, voltage parameters, and power parameters of each node.

[0085] In some implementations, the electrical parameters of the network include the power parameters of the transfer lines of the S-phase and H-phase, as well as the weights corresponding to the total reduction in active power of distributed photovoltaic power, the total deviation of node voltage, and the total network loss.

[0086] In some implementations, the target constraints include line transmission constraints, output regulation constraints, and phase-to-phase control constraints.

[0087] Specifically, in some implementations, the step of "constructing target constraints based on the electrical parameters of distributed photovoltaics, each node, and the network" may include the following steps:

[0088] (1) Based on the working parameters, voltage parameters and power parameters of each node, construct the line transmission constraints for the model operation;

[0089] (2) Based on the power parameters of distributed photovoltaic power generation, construct the output adjustment constraints for distributed photovoltaic power generation;

[0090] (3) Based on the power parameters of the transfer lines of phase S and phase H, the total reduction of active power of distributed photovoltaic power, the total deviation of node voltage and the total network loss, respectively, construct the inter-phase control constraints.

[0091] In some implementations, the operating parameters of each node may include the active power of the photovoltaic system connected to each node, the active power of the load on each node, the voltage of each node, and the conductance, impedance angle, and susceptance between nodes.

[0092] In some implementations, the voltage parameters of each node may include the voltage of each node, the minimum allowable voltage value of each node, and the maximum allowable voltage value of each node.

[0093] In some implementations, the power parameters of each node may include the apparent power, reactive power, and apparent power limit of the branches between nodes.

[0094] In some implementations, the line transmission constraints for model operation include a first line transmission constraint, a second line transmission constraint, and a third line transmission constraint.

[0095] In some implementations, the power parameters of distributed photovoltaics may include the active power of each photovoltaic unit, the maximum active power of each photovoltaic unit, and the reactive power of each photovoltaic unit.

[0096] In some implementations, the power parameters of the transfer line between phase S and phase H include a first power parameter and a second power parameter. In one specific implementation, the first power parameter may include the power transferred from phase S to phase H and the power received by phase H from phase S. In one specific implementation, the second power parameter may include the power transferred from phase S to phase H and the maximum power.

[0097] In some implementations, the interphase control constraints include a first interphase control constraint, a second interphase control constraint, and a third interphase control constraint.

[0098] Furthermore, in some implementations, the step "constructing line transmission constraints for model operation based on operating parameters, voltage parameters, and power parameters" may include the following steps:

[0099] (1) Construct the first line transmission constraint conditions based on the working parameters of each node in each node;

[0100] (2) and / or, construct a second line transmission constraint condition based on the voltage parameters of each node in each node;

[0101] (3) and / or, construct a third line transmission constraint based on the power parameters of each node in each node.

[0102] In one specific implementation, the first line transmission constraint can be expressed as:

[0103] .

[0104] in, For nodes The active power of the photovoltaic system connected to the site, For nodes The active power of the load. For nodes voltage, For nodes voltage ( and These are power grid operating parameters, which can be obtained through voltage measurement. For nodes With nodes The conductance of the branch between them For nodes With nodes The impedance angle of the branch between them. For nodes With nodes The susceptance value of the branch between them For each node The reactive power of the photovoltaic system connected to the site, For nodes The reactive power of the load.

[0105] In one specific implementation, the second line transmission constraint can be expressed as:

[0106] .

[0107] in, For nodes Minimum allowable voltage For nodes voltage, For nodes The maximum allowable voltage.

[0108] In one specific implementation, the expression for the third-line transmission constraint can be expressed as:

[0109] .

[0110] in, For nodes With nodes Apparent power of the branch between For nodes With nodes The active power of the branch circuits between them For nodes With nodes The reactive power of the branch circuits between them For nodes With nodes The apparent power limit of the branch between them.

[0111] The active power of the branches between nodes can be determined by the following equation:

[0112] .

[0113] in, For nodes With nodes The active power of the branch between them For nodes With nodes The conductance of the branch between them For nodes voltage, For nodes voltage, For nodes With nodes The impedance angle of the branch between them. For nodes With nodes The susceptance value of the branch between them For nodes With nodes The upper limit of active power of the branch circuit.

[0114] The reactive power of branches between nodes can also be determined using the following equation:

[0115] .

[0116] in, For nodes With nodes The reactive power of the branch circuits between them For nodes With nodes The susceptance value of the branch between them For nodes voltage, For nodes voltage, For nodes With nodes The conductance of the branch between them For nodes With nodes The impedance angle of the branch between them. For nodes With nodes The susceptance value of the branch circuit.

[0117] In one specific implementation, the output regulation constraint of distributed photovoltaic power can be expressed as:

[0118] .

[0119] in, For the first The active power of a distributed photovoltaic system. For the first The maximum active power of a distributed photovoltaic system. For the first The reactive power of a distributed photovoltaic system.

[0120] Furthermore, in some implementations, a first phase-to-phase control constraint is constructed based on the first power parameters of the transfer lines of phase S and phase H.

[0121] In one specific implementation, the first phase-to-phase control constraint can be expressed as:

[0122] .

[0123] in, This refers to the power transferred from phase S to phase H. The H phase receives the power from the S phase.

[0124] In some implementations, a second phase-to-phase control constraint is constructed based on the second power parameters of the transfer lines between phase S and phase H.

[0125] In one specific implementation, the second phase-to-phase control constraint can be expressed as:

[0126] .

[0127] in, This refers to the power transferred from phase S to phase H. This represents the maximum power transferred from the S phase to the H phase.

[0128] In some implementations, a third phase-to-phase control constraint is constructed based on the weights corresponding to the total reduction in active power of distributed photovoltaic power, the total deviation of node voltage, and the total grid loss.

[0129] In a specific implementation, the third-phase interphase control constraint can be expressed as:

[0130] .

[0131] in, This represents the weight of the total reduction in active power output of distributed photovoltaic systems. The weight of the total deviation of the node voltage. This is the weight of the total network loss.

[0132] Therefore, through the above implementation methods, target constraints are constructed based on the electrical parameters of distributed photovoltaic systems, each node, and the network. An optimization model is then built based on the target optimization function and the target constraints. Finally, the optimal control strategy is obtained by analyzing the optimization model. Specifically:

[0133] In step 140, the optimization model constructed from the objective optimization function and objective constraints is analyzed according to the improved gorilla population optimization algorithm to generate the optimal control strategy.

[0134] In this invention, an improved gorilla population optimization algorithm is introduced to analyze the optimization model and generate the optimal control strategy. This enhances global exploration capabilities and, combined with directional perturbations based on the center position of high-quality individuals, improves local development efficiency, effectively preventing the algorithm from getting trapped in local optima. This significantly improves the convergence speed and solution accuracy of the improved gorilla population optimization algorithm. Specifically:

[0135] Its main components include a sequence initialization population, an adaptive position search mechanism, and a dual perturbation strategy. For details, please refer to [link to relevant documentation]. Figure 2 , Figure 2 The diagram illustrates a process for determining the center position according to an embodiment of the present invention. In some implementations, the step "analyzing the optimization model constructed based on the objective optimization function and objective constraints using the gorilla population optimization algorithm to generate the optimal control strategy" may include the following steps:

[0136] (1) Generate an initial population based on the chaotic Halton sequence. The initial population is constrained by the objective constraint; where the population size dimension is... The population size is (i.e., the number of individual gorillas);

[0137] (2) During the iteration process, the fusion weight of individual historical optimal information and group collaborative guidance information is dynamically adjusted through an adaptive position search mechanism so that the population searches towards the extreme value of the objective optimization function;

[0138] (3) After each iteration, a double perturbation strategy is implemented on the individuals in the population to recalculate the fitness values ​​of all individuals and update the global best individual;

[0139] (4) When the fitness value of the global best individual satisfies the convergence condition, the current global best individual is taken as the optimal control strategy.

[0140] Assuming the search space is a two-dimensional Halton sequence, the process of optimizing the initial solution distribution of the improved gorilla population optimization algorithm by introducing the Halton sequence can be as follows: select two prime numbers as the base, which correspond to two dimensions respectively, and each dimension is continuously divided and roundaboutly selected according to the base, thereby forming non-repeating and uniform points.

[0141] In one specific implementation, the mathematical model of this segmentation process can be expressed as:

[0142] .

[0143] .

[0144] .

[0145] in, Let be the ordinal number of the Halton sequence. Let be the cardinality of the Halton sequence, and let its value be a prime number greater than or equal to 2. `a` is a non-negative integer used to determine the upper limit of the number of terms in the summation. For constant variables, For the defined sequence function, It is a two-dimensional uniform Halton sequence.

[0146] In some implementations, the weights of individual historical best information and group collaborative guidance information may include adaptive factors of the individual's own position on the iteration effect and adaptive factors of the influence of other gorilla individuals on the individual's search.

[0147] In an embodiment of the present invention, by dynamically adjusting the weights of individual historical best information and group collaborative guidance information, the gorilla group optimization algorithm can adaptively balance the relationship between global search and local exploitation. This process can be expressed as follows:

[0148] .

[0149] .

[0150] in, For gorilla individuals in the first Candidate positions in the next iteration The adaptive factor represents the weight of an individual's current position relative to its position in the next iteration. Let be the current position of the individual gorilla at the t-th iteration. For a random value that takes the value (0,1), The random value in (0,1) represents the weighting coefficient of the influence of the location of other gorillas on the current individual's search. For the first In the next iteration, the position of another gorilla individual randomly selected from the population. It serves as an intermediate variable (used in the balancing algorithm).

[0151] in, 1 can be determined using the following formula:

[0152] .

[0153] in, As an intermediate variable, This represents the current iteration number of the algorithm (which reflects the current optimization stage of the algorithm). The maximum number of iterations for which the optimization operation is performed on the algorithm (which is one of the critical conditions for the termination of the algorithm's iteration).

[0154] In some implementations, the calculation is performed after each iteration. The expression for the average fitness of an individual gorilla can be:

[0155] .

[0156] in, This represents the average fitness of the population. For individual fitness, This refers to the number of individual gorillas.

[0157] In some implementations, individuals in the population are divided into j inferior individuals with higher fitness values ​​than the average fitness value, and... -j high-quality individuals with fitness values ​​lower than the average, and calculate the fitness values ​​of j low-quality individuals respectively. The fitness change rate of -j high-quality individuals, and the average fitness change rate of high-quality individuals, can be expressed as:

[0158] .

[0159] in, The average fitness rate of change. The rate of change in an individual's fitness. This represents the magnitude of the change after the number of iterations.

[0160] In some implementations, the first layer of the dual-perturbation strategy is a Cauchy perturbation, and the probability density function of the one-dimensional standard Cauchy distribution can be expressed as:

[0161] .

[0162] The expression for the perturbation method can be:

[0163] .

[0164] in, 1 represents the value of the Cauchy perturbation function. Let the initial value be the Cauchy perturbation. 2 represents the Cauchy perturbation scaling factor (which determines the magnitude of the Cauchy operator perturbation). It follows the standard Cauchy distribution.

[0165] Among them, the Cauchy distribution random variable generating function for:

[0166] .

[0167] in, Let be a random variable that follows a uniform distribution on [0,1].

[0168] The second layer of the dual perturbation strategy is the center position perturbation strategy. First, calculate... The center position of -j high-quality individuals is used, and then this position is used to perturb the low-quality individuals. The expression for the perturbation method can be:

[0169] .

[0170] in, The new candidate positions generated for individual gorillas after perturbation of the central position; The current position of the individual gorilla before the disturbance; The central position of high-quality individuals These are the weighting coefficients.

[0171] Furthermore, the effectiveness of the method of the present invention was verified based on the IEEE 33-bus distribution network system:

[0172] Please see Figure 3 , Figure 3 This invention provides a comparison diagram of the three-phase load distribution before and after optimization. Figure 4 A comparison chart of network loss before and after optimization, provided by an embodiment of the present invention, is shown. From... Figure 3 and Figure 4 It can be seen that after an individual completes its location update, a double random perturbation is applied to the individual to help reduce the risk of the individual falling into a potential local optimum, and to encourage the individual to explore the location of the global optimum better. The three-phase load is more balanced after optimization than before optimization, and the network loss is reduced.

[0173] Please see Figure 5 , Figure 5 The diagram illustrates the structure of an optimization and control device for distributed resources within a ring according to an embodiment of the present invention. The optimization and control device 200 for distributed resources within a ring includes: an acquisition module 210, a first construction module 220, a second construction module 230, and a determination module 240. Specifically:

[0174] The acquisition module 210 is used to acquire the total reduction in active power of distributed photovoltaic power, the total deviation of node voltage, the total network loss, and the electrical parameters of distributed photovoltaic power, each node, and the network.

[0175] The first construction module 220 is used to construct a target optimization function based on the total reduction in active power, the total voltage deviation, and the total network loss.

[0176] The second construction module 230 is used to construct target constraints based on the electrical parameters of distributed photovoltaic, each node, and the network, respectively.

[0177] The determination module 240 is used to analyze the optimization model constructed based on the objective optimization function and objective constraints according to the gorilla group optimization algorithm, and generate the optimal control strategy.

[0178] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0179] In the several embodiments provided by the present invention, the coupling or direct coupling or communication connection between the modules shown or discussed may be an indirect coupling or communication connection through some interface, device or module, and may be electrical, mechanical or other forms.

[0180] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0181] Please see Figure 6 , Figure 6 The schematic diagram of the device provided in the embodiment of the present invention shows that the device 300 in the present invention may include one or more of the following components: processor 310, memory 320 and one or more application programs, wherein the one or more application programs may be stored in memory 320 and configured to be executed by one or more processors 310, and the one or more programs are configured to execute the optimization and control method of distributed resources within the ring as described in the foregoing method embodiment.

[0182] Processor 310 may include one or more processing cores. Processor 310 connects to various parts within device 300 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 320, and by calling data stored in memory 320. Optionally, processor 310 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 310 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into processor 310 and may be implemented separately using a communication chip.

[0183] The memory 320 may include random access memory (RAM) or read-only memory (ROM). The memory 320 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 320 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described below, etc. The data storage area may also store data created by the device 300 during use.

[0184] Please see Figure 7 , Figure 7 A schematic diagram of a computer-readable storage medium provided in an embodiment of the present invention is shown. The computer-readable storage medium 400 stores program code, which can be called by a processor to execute the control method described in the above method embodiment.

[0185] The computer-readable storage medium 400 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 400 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 400 has storage space for program code 410 that performs any of the method steps described above. This program code can be read from or written to one or more computer program devices. The program code 410 may be compressed, for example, in a suitable form.

[0186] See Figure 8 , Figure 8 The distributed photovoltaic (PV) output curve prediction diagram provided in the embodiment of the present invention is shown. The diagram uses 24 hours as the horizontal axis and active power as the vertical axis, and displays the power change curves of PV1 (node ​​6), PV2 (node ​​13), PV3 (node ​​25) and the total PV output. It is used to analyze the output characteristics of distributed PV and provide data support for the planning, scheduling and operation of distribution networks containing distributed PV.

[0187] See Figure 9 and Figure 10 , Figure 9 and Figure 10 The optimization result diagram provided by the embodiment of the present invention is shown. After optimization, the three imbalances of the system and the network loss are significantly reduced.

[0188] 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing regulation of distributed resources within a ring, characterized in that, The method comprises: obtaining the total active power reduction amount of the distributed photovoltaic, the total voltage deviation amount of the nodes, the total network loss amount, and the electrical parameters of the distributed photovoltaic, the nodes, and the network; constructing a target optimization function according to the total active power reduction amount, the total voltage deviation amount, and the total network loss amount; constructing a target constraint condition according to the electrical parameters of the distributed photovoltaic, the nodes, and the network; analyzing an optimization model constructed by the optimization algorithm on the target optimization function and the target constraint condition to generate an optimal control strategy.

2. The method of claim 1, wherein, The obtaining of the total active power reduction amount of the distributed photovoltaic comprises: obtaining the total active power reduction amount of the distributed photovoltaic in phase A, the total active power reduction amount of the distributed photovoltaic in phase B, and the total active power reduction amount of the distributed photovoltaic in phase C; adding the total active power reduction amount of the distributed photovoltaic in phase A, the total active power reduction amount of the distributed photovoltaic in phase B, and the total active power reduction amount of the distributed photovoltaic in phase C to determine the total active power reduction amount of the distributed photovoltaic.

3. The method of claim 1, wherein the method further comprises: The obtaining of the total voltage deviation amount of the nodes comprises: obtaining the voltage deviation of the nodes in phase A, the voltage deviation of the nodes in phase B, and the voltage deviation of the nodes in phase C; determining the total voltage deviation amount of the nodes according to the voltage deviation of the nodes in phase A, the voltage deviation of the nodes in phase B, and the voltage deviation of the nodes in phase C; wherein the voltage deviation of the nodes in phase A, the voltage deviation of the nodes in phase B, and the voltage deviation of the nodes in phase C are obtained in the same way, specifically comprising: obtaining the actual voltage, the reference voltage, the upper limit of the voltage, and the lower limit of the voltage of each node in the nodes in phase S; determining the voltage deviation of each node in phase S according to the difference between the actual voltage and the reference voltage; determining the voltage allowable range of each node in phase S according to the difference between the upper limit of the voltage and the lower limit of the voltage; normalizing the voltage deviation into the voltage allowable range, and determining the sum of squares of the results as the voltage deviation of the nodes in phase S; wherein S ∈ {A, B, C}, A represents phase A, B represents phase B, and C represents phase C.

4. The method of claim 1-3, wherein, The expression of the target optimization function is: ； wherein, is a solution of the optimal target optimization function, is a weight of the total active power down-regulation of the distributed photovoltaic, is the total active power down-regulation of the distributed photovoltaic, is a normalized reference value of the total active power down-regulation of the distributed photovoltaic, is a weight of the total voltage deviation amount of the node, is the total voltage deviation amount of the node, is a normalized reference value of the total voltage deviation amount of the node, is a weight of the total network loss amount, is the total network loss amount, is a normalized reference value of the total network loss amount.

5. The method of claim 1, wherein, The electrical parameters of the distributed photovoltaic comprise the power parameters of the distributed photovoltaic; the electrical parameters of the nodes comprise the working parameters, the voltage parameters, and the power parameters of each node in the nodes; the electrical parameters of the network comprise the power parameters of the transfer lines between phases, and the weights corresponding to the total active power reduction amount of the distributed photovoltaic, the total voltage deviation amount of the nodes, and the total network loss amount, respectively; The target constraint condition comprises the line transmission constraint condition, the output adjustment constraint condition, and the inter-phase control constraint condition.

6. The method of claim 5, wherein, The construction of the target constraint condition according to the electrical parameters of the distributed photovoltaic, the nodes, and the network comprises: constructing the line transmission constraint condition according to the working parameters, the voltage parameters, and the power parameters of each node in the nodes; constructing the output adjustment constraint condition of the distributed photovoltaic according to the power parameters of the distributed photovoltaic; constructing the inter-phase control constraint condition according to the power parameters of the transfer lines between phases and the weights corresponding to the total active power reduction amount of the distributed photovoltaic, the total voltage deviation amount of the nodes, and the total network loss amount, respectively.

7. The method of claim 6, wherein the step of optimizing the distributed resources within the ring is performed by a distributed resource optimization module. The line transmission constraint condition comprises the first line transmission constraint condition, the second line transmission constraint condition, and the third line transmission constraint condition; The line transmission constraint condition is constructed according to the working parameter, the voltage parameter and the power parameter of each node in the nodes, and the line transmission constraint condition comprises: The first line transmission constraint condition is constructed according to the working parameter of each node in the nodes; And / or, the second line transmission constraint condition is constructed according to the voltage parameter of each node in the nodes; And / or, the third line transmission constraint condition is constructed according to the power parameter of each node in the nodes.

8. The method of claim 6, wherein the optimization and regulation of the in- ring distributed resources is performed by a resource optimization and regulation module. The phase-to-phase regulation constraint condition comprises the first phase-to-phase regulation constraint condition, the second phase-to-phase regulation constraint condition and the third phase-to-phase regulation constraint condition; The power parameter of the phase-to-phase transfer line comprises the first power parameter and the second power parameter; The phase-to-phase regulation constraint condition is constructed according to the power parameter of the phase-to-phase transfer line and the weights corresponding to the total active power reduction amount of the distributed photovoltaic, the total voltage deviation amount of the node and the total amount of network loss, and the phase-to-phase regulation constraint condition comprises: The first phase-to-phase regulation constraint condition is constructed according to the first power parameter; And / or, the second phase-to-phase regulation constraint condition is constructed according to the second power parameter; And / or, the third phase-to-phase regulation constraint condition is constructed according to the weights corresponding to the total active power reduction amount of the distributed photovoltaic, the total voltage deviation amount of the node and the total amount of network loss.

9. The method of claim 1, wherein, The optimal regulation strategy is generated by analyzing the optimization model constructed by the optimization algorithm on the target optimization function and the target constraint condition, and the optimal regulation strategy comprises: The initial population is generated according to the chaotic Halton sequence, and the initial population is limited to the target constraint condition; In the iteration process, the fusion weight of the individual historical optimal information and the group collaborative guidance information is dynamically adjusted through the adaptive position search mechanism, so that the population searches in the direction of the extreme value of the target optimization function; After each iteration, a double disturbance strategy is implemented on the population individuals, the fitness values of all individuals are recalculated, and the global optimal individual is updated; When the fitness value of the global optimal individual meets the convergence condition, the current global optimal individual is taken as the optimal regulation strategy.

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