Capacity configuration method and device of distributed power supply

Abstract

The invention discloses a capacity configuration method and device for a distributed power supply. The method comprises the following steps: establishing a target function according to the maximum capacity of the distributed power supply which can be accessed to a power distribution network; establishing constraint conditions; the constraint conditions comprise a fault current constraint, a line voltage deviation constraint, a feeder voltage deviation constraint, a permeability constraint and a single-point grid-connected installed capacity constraint of the power distribution network under the access of the distributed power supply; and under the constraint condition, carrying out optimization solution on the target function by utilizing a particle swarm algorithm. Through the adaptive particle swarm algorithm, the maximum capacity boundary value of the multiple distributed power supplies is calculated, so that the capacity of the distributed power supplies is reasonably configured in the power distribution network, and the effectiveness and safety of a relay protection system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of capacity configuration of distributed power sources, and in particular to a capacity configuration method and device of distributed power sources. Background Art

[0002] With the low-cost and scalable development and utilization of renewable energy, distributed power sources are widely used in low-voltage distribution networks due to their clean, flexible and adjustable advantages. However, the access of high-density distributed power sources changes the grid topology and fault characteristics of the distribution network. When a system fault occurs, the distributed power sources may provide boost, drain and reverse short-circuit currents, causing the traditional three-stage current protection to malfunction or fail to operate. Therefore, how to reasonably configure the capacity of distributed power sources in low-voltage distribution networks and ensure the effectiveness and safety of relay protection systems is an important problem in current distribution network research. Summary of the invention

[0003] The present invention provides a method and device for configuring the capacity of a distributed power source, so as to reasonably configure the capacity of the distributed power source in a distribution network and ensure the effectiveness and safety of a relay protection system.

[0004] According to one aspect of the present invention, a method for configuring capacity of a distributed power source is provided, comprising:

[0005] The objective function is established based on the maximum capacity of distributed generation that can be connected to the distribution network;

[0006] Establishing constraint conditions; the constraint conditions include: fault current constraint of the distribution network under distributed power supply access, line voltage deviation constraint, feeder voltage deviation constraint, penetration rate constraint and single-point grid-connected installed capacity constraint;

[0007] Under the constraints, the objective function is optimized and solved using a particle swarm algorithm.

[0008] Optionally, the fault current constraint of the distribution network under the access of the distributed power source includes:

[0009] The fault current is greater than the maximum value of the current protection action and less than the minimum value of the current protection inaction;

[0010] The line voltage deviation meets:

[0011] Among them, ΔU DER.i is the line voltage deviation, Z L.i is the line impedance from the distributed generation access point to the busbar of the distribution network, U N is the rated voltage of the distribution network, S DER.i is the capacity of distributed power generation;

[0012] Feeder voltage deviation satisfies:

[0013] L is the total feeder length; P DER is the active power of all distributed power sources on the feeder; P L is the active power of all loads on the feeder; r and x are the equivalent resistance and reactance per unit distance of the feeder, respectively; is the rated power factor of the load;

[0014] The penetration rate is equal to the ratio of the total capacity of distributed generation to the total load of the load connected to the distribution network;

[0015] The single-point grid-connected installed capacity is less than or equal to 6MW.

[0016] Optionally, the optimizing and solving the objective function by using a particle swarm algorithm includes:

[0017] A group of power capacity particles are randomly generated based on the capacity of distributed power sources connected to each node in the distribution network line;

[0018] Initialize the position and velocity of each of the power capacity particles in the population;

[0019] The population is continuously iterated, and the inertia weight is updated after each iteration until the iteration end condition is met, and the optimal position of the population is output;

[0020] Each iteration process includes:

[0021] Calculate and update the speed and position of each of the power capacity particles;

[0022] Calculating the fitness value of each of the power capacity particles;

[0023] updating the individual optimal position and the population optimal position of the power capacity particles according to the fitness value of each of the power capacity particles;

[0024] Generate an evolution factor according to the fitness value of the optimal particle in the current iteration, the fitness value of the first preset particle and the fitness value of the second preset particle; the first preset particle is the particle with the largest fitness value, and the second preset particle is the particle with the smallest fitness value;

[0025] The inertia weight is adjusted according to the evolution factor.

[0026] Optionally, generating the evolution factor according to the fitness value of the optimal particle in the current iteration, the fitness value of the first preset particle, and the fitness value of the second preset particle includes:

[0027] The ratio of the first difference to the second difference is used as the evolution factor; the first difference is the difference between the fitness value of the optimal particle and the fitness value of the second preset particle, and the second difference is the difference between the fitness value of the second preset particle and the fitness value of the first preset particle.

[0028] Optionally, adjusting the inertia weight according to the evolution factor includes:

[0029] The inertia weight ω satisfies:

[0030] f is the evolution factor.

[0031] Optionally, the speed of the i-th power capacity particle satisfies:

[0032] v id =w*v' id +c1r1(p id -x' id )+c2r2(p gd -x' id ), i = 1, 2, ..., N, N is the number of particles in the population, d = 1, 2, ..., D, D is the dimension of the power capacity particle, v' id is the speed of the i-th power capacity particle in the last iteration, x' id is the position of the i-th power capacity particle in the last iteration, c1 is the first learning factor, c2 is the second learning factor, and p id is the optimal position of the i-th power capacity particle up to the current iteration process, p gd is the optimal position of the population up to the current iteration process, r1 and r2 are uniform random numbers in the range of [0,1], and w is the inertia weight;

[0033] The position of the i-th power capacity particle satisfies:

[0034] x id =x' id +v' id .

[0035] Optionally, the population is iterated continuously and the inertia weight is updated after each iteration, and the following is also included:

[0036] After each iteration, the state set to which the evolution factor belongs is determined according to the size of the evolution factor; wherein, when the evolution factor is greater than or equal to the first threshold and less than or equal to the second threshold, it is determined that the evolution factor belongs to the third state set; when the evolution factor is greater than or equal to the second threshold and less than or equal to the third threshold, it is determined that the evolution factor belongs to the second state set; when the evolution factor is greater than or equal to the third threshold and less than or equal to the fourth threshold, it is determined that the evolution factor belongs to the first state set; when the evolution factor is greater than or equal to the fourth threshold and less than or equal to the fifth threshold, it is determined that the evolution factor belongs to the fourth state set; the first threshold to the fifth threshold increase in sequence;

[0037] When the evolution factor belongs to the first state set, increasing the first learning factor and decreasing the second learning factor;

[0038] When the evolution factor belongs to the second state set, increasing the first learning factor and decreasing the second learning factor;

[0039] When the evolution factor belongs to the third state set, increasing the first learning factor and increasing the second learning factor;

[0040] Among them, in the first state set, the increase of the first learning factor and the decrease of the second learning factor are both greater than or equal to 0.5 and less than or equal to 1, in the second state set, the increase of the first learning factor and the decrease of the second learning factor are both greater than or equal to 0 and less than or equal to 0.2, and in the third state set, the increase of the first learning factor and the decrease of the second learning factor are both greater than or equal to 0 and less than or equal to 0.2.

[0041] Optionally, the iteration end condition includes:

[0042] The evolution factor belongs to the fourth state set.

[0043] Optionally, calculating the fitness value of each of the power capacity particles includes:

[0044] The average value of the distance between each of the power capacity particles and other power capacity particles is used as the fitness value of the power capacity particle.

[0045] According to another aspect of the present invention, there is provided a capacity configuration device for a distributed power source, comprising:

[0046] An objective function establishment module is used to establish an objective function based on the maximum capacity of distributed power sources that can be connected to the distribution network;

[0047] A constraint condition establishment module is used to establish constraint conditions; the constraint conditions include: fault current constraint of the distribution network under distributed power supply access, line voltage deviation constraint, feeder voltage deviation constraint, penetration rate constraint and single-point grid-connected installed capacity constraint;

[0048] The solution module is used to optimize and solve the objective function under the constraints using a particle swarm algorithm.

[0049] The technical solution of the embodiment of the present invention starts from the relay protection theory of low-voltage distribution network, comprehensively considers the control characteristics of distributed power sources and lines, loads, etc., and provides a theoretical basis for the subsequent distributed power source capacity configuration and protection setting adjustment. With the current protection, voltage quality, penetration rate, and single-point grid-connected capacity of the distribution network as constraints, and the maximum capacity of the accessible distributed power source as the objective function, the maximum capacity boundary value of multiple distributed power sources is calculated through the adaptive particle swarm algorithm, so as to reasonably configure the capacity of distributed power sources in the distribution network and ensure the effectiveness and safety of the relay protection system.

[0050] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 A distribution network topology diagram including distributed power sources provided in an embodiment of the present invention;

[0053] Figure 2 A flow chart of a capacity configuration method of a distributed power source provided by an embodiment of the present invention;

[0054] Figure 3 A flow chart of another distributed power supply capacity configuration method provided by an embodiment of the present invention;

[0055] Figure 4 A schematic structural diagram of a capacity configuration device for a distributed power source provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 creative work should fall within the scope of protection of the present invention.

[0057] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0058] Figure 1 A distribution network topology diagram including distributed power sources provided in an embodiment of the present invention, referring to Figure 1 , Figure 1 Where S is the power supply of the distribution network system, DG is the distributed power supply, and the distributed power supply DG is connected to the node C of the bus after being stepped up by transformer T. LoadE and LoadG are the equivalent loads of the system. Figure 1 It is only shown that the node C of each node A, B, C, D of the bus is connected to the distributed power source DG, and there is a feeder branch at node A, connected to the load LoadG, and there is a node F in the feeder branch. The current protections at nodes A, B, C, D, and F are respectively recorded as 1, 2, 3, 4, and 6. And there is also protection 5 between protection 4 and protection 6.

[0059] The relay protection of low-voltage distribution network is relatively simple. At present, it mainly adopts three-stage current protection, namely, current quick-break protection, time-limited current quick-break protection and time-limited overcurrent protection. Taking protection 1 as an example, the current protection setting value calculation formula is:

[0060]

[0061] In the formula, K' rel , K'' rel and K rel K is the reliability factor of current protection; Ms is the self-starting coefficient; K re is the return coefficient of the current relay; IL.max is the maximum load current that may appear on the line, Zs is the source impedance, Z1 is the impedance at protection point 1, Z2 is the impedance at protection point 2, and U is the line voltage.

[0062] The three-phase short circuit at the end of line AB is taken as the research object: when a short circuit occurs at the end of line AB, that is, bus B, the short circuit current is provided by the distributed power supply in addition to the system power supply. Among them, because the load at the end of the distribution network line containing distributed power supply is small, the influence of the load can be ignored during the fault. At this time:

[0063] 1) The short-circuit current flowing through protection 1 is entirely provided by the system power supply; therefore, when a short circuit occurs at the end of line AB, the access of the distributed power supply will not affect protection 1.

[0064] 2) Short-circuit current reverse flow protection 2, which is provided by distributed power sources, and E DG is the voltage of distributed power source DG, X DG , X T , X BC They are the reactance of the distributed generation DG, the reactance of the transformer T, and the reactance of the line between node B and node C.

[0065] The setting value of protection stage II is:

[0066] Z s , Z AB , Z BC , Z C D are the impedance of the power supply of the distribution network system, the impedance of the line between node A and node B, the impedance of the line between node B and node C, and the impedance of the line between node C and node D.

[0067] Therefore, when I k2 >I" dz2 When the current protection stage II of protection 2 is malfunctioning, the subsequent lines will form an island, and the distributed generation will supply power to the load LoadE alone.

[0068] When a short circuit occurs at the end of the BC line, the distributed generation will not affect the short-circuit current flowing through protection 1 and protection 2. At this time, the protection action of the distributed generation is required.

[0069] When a short circuit occurs at the end of the CD line, the short-circuit current injected by the distributed generation will drain the current flowing through protection 2, reducing its protection range. However, since the distributed generation is connected to the grid at bus C, its current protection section II is not affected by the distributed generation.

[0070] As for protection 3, during a short-circuit fault, the injected current of the distributed power source will increase the short-circuit current flowing through protection 3.

[0071] Similarly, the applicability of protection when short circuit occurs at the end of the AF line can be further analyzed. The short-circuit current provided by the distributed generation will flow in the reverse direction through protections 1 and 2, and will increase the short-circuit current flowing through protection 5.

[0072] Based on the above analysis, the present invention starts from the low-voltage distribution network relay protection theory, comprehensively considers the distributed power supply control characteristics and lines, loads, etc., and provides a theoretical basis for the subsequent distributed power supply capacity configuration and protection setting adjustment.

[0073] Figure 2 This is a flow chart of a method for configuring the capacity of a distributed power source provided by an embodiment of the present invention. This embodiment can be applied to configuring the capacity of distributed power sources connected to each node in a distribution network. The method can be executed by a capacity configuration device for a distributed power source, which can be implemented in the form of hardware and / or software. Figure 2 As shown, the method includes:

[0074] S110: Establishing an objective function based on the maximum capacity of distributed power sources that can be connected to the distribution network.

[0075] S120: Establish constraint conditions; the constraint conditions include: fault current constraint of the distribution network under distributed power generation access, line voltage deviation constraint, feeder voltage deviation constraint, penetration rate constraint and single-point grid-connected installed capacity constraint.

[0076] According to the analysis of the fault current of the distribution network under the access of distributed power generation, the distributed power generation mainly increases, decreases or reverses the fault current flowing through the protection. When the access capacity of the distributed power generation is large enough, the protection may malfunction or refuse to operate. When solving the problem, the fault current should be greater than the maximum value of the current protection action and less than the minimum value of the current protection inaction, so as to avoid the malfunction and refusal of the protection caused by the access of distributed power generation.

[0077] The access of distributed power sources to the distribution network will cause line voltage deviation, and the line voltage deviation satisfies:

[0078] Among them, ΔU DER.i is the line voltage deviation, Z L.i is the line impedance from the distributed generation access point to the busbar of the distribution network, U N is the rated voltage of the distribution network, S DER.i is the capacity of distributed power generation.

[0079] In addition to satisfying the single-point voltage deviation constraint, the load and distributed generation should also be considered comprehensively. The voltage deviation at the point with the most serious voltage deviation on the feeder is determined by the maximum voltage upper deviation constraint caused by the distributed generation and the maximum voltage lower deviation constraint caused by the load. The feeder voltage deviation satisfies:

[0080] L is the total feeder length; P DER is the active power of all distributed power sources on the feeder; P L is the active power of all loads on the feeder; r and x are the equivalent resistance and reactance per unit distance of the feeder, respectively; is the rated power factor of the load.

[0081] Under the premise of ensuring the quality of power, it is considered that the penetration rate of distributed power generation is not greater than 100% during normal power supply, and the penetration rate is equal to the ratio of the total capacity of distributed power generation to the total load of the load connected to the distribution network. That is, the ratio of the sum of the capacity of the distribution network connected to each node in the line to the total load of all loads connected to the line is taken as the penetration rate.

[0082] According to relevant regulations, small photovoltaic power generation facilities with a total installed capacity of no more than 6MW are allowed to generate and use their own surplus electricity and connect to the grid at a single point. Therefore, for photovoltaic power generation, its single-point grid-connected installed capacity is less than or equal to 6MW. That is, the grid-connected capacity of a single machine is: S DER.i ≤6MW.

[0083] S130: Under the constraints, the objective function is optimized and solved using a particle swarm algorithm.

[0084] According to the distributed power capacity requirements, a group of distributed power capacity particles are randomly generated, and the group of distributed power capacity particles are continuously iterated. During the iteration process, the advantages and disadvantages of the distributed power particles are evaluated by preset parameters such as fitness values, and the individual optimal position and the global optimal position are updated until the iteration ends. The optimal position of the population is obtained, and then the capacity of each node of the distributed power supply and the required maximum total capacity are obtained.

[0085] The technical solution of the embodiment of the present invention starts from the relay protection theory of low-voltage distribution network, comprehensively considers the control characteristics of distributed power sources and lines, loads, etc., and provides a theoretical basis for the subsequent distributed power source capacity configuration and protection setting adjustment. With the current protection, voltage quality, penetration rate, and single-point grid-connected capacity of the distribution network as constraints, and the maximum capacity of the accessible distributed power source as the objective function, the maximum capacity boundary value of multiple distributed power sources is calculated through the adaptive particle swarm algorithm, so as to reasonably configure the capacity of distributed power sources in the distribution network and ensure the effectiveness and safety of the relay protection system.

[0086] Figure 3A flow chart of another distributed power capacity configuration method provided by an embodiment of the present invention, referring to Figure 3 , the method comprising:

[0087] S111: Establish an objective function based on the maximum capacity of distributed power sources that can be connected to the distribution network.

[0088] S121: Establish constraint conditions; the constraint conditions include: fault current constraint of the distribution network under distributed power generation access, line voltage deviation constraint, feeder voltage deviation constraint, penetration rate constraint and single-point grid-connected installed capacity constraint.

[0089] S131: randomly generate a group of power capacity particles based on the capacity of distributed power sources connected to each node in the line of the distribution network.

[0090] Assume that in a D-dimensional target search space, there are N power capacity particles forming a community, where the i-th power capacity particle is represented as a D-dimensional vector:

[0091] x i =(x i1 ,x i2 ,...,x iD ),i=1,2,...,N; D can represent the number of lines in the distribution network;

[0092] Among them, the flight speed of the i-th power capacity particle is also a D-dimensional vector, recorded as:

[0093] V i =(v i1 ,v i2 ,...,v iD ),i=1,2,...,N.

[0094] S141: Initialize the position and velocity of each power capacity particle in the population.

[0095] The population is continuously iterated, and the inertia weight is updated after each iteration until the iteration end condition is met.

[0096] Each iteration process includes:

[0097] S151: Calculate and update the speed and position of each power capacity particle.

[0098] For the ith power capacity particle, according to the speed of the ith power capacity particle in the previous iteration, the inertia weight of the current iteration, the optimal position of the ith power capacity particle up to the current iteration, the position of the ith power capacity particle in the previous iteration, and the optimal position of the population up to the current iteration, determine the speed of the ith power capacity particle in the current iteration.

[0099] According to the position of the ith power capacity particle in the previous iteration and the speed of the ith power capacity particle in the current iteration, the position of the ith power capacity particle in the current iteration is determined.

[0100] Specifically, the speed of the i-th power capacity particle satisfies:

[0101] v id =w*v' id +c1r1(p id -x' id )+c2r2(p gd -x' id ), i = 1, 2, ..., N, N is the number of particles in the population, d = 1, 2, ..., D, D is the dimension of the power capacity particle, v' id is the speed of the i-th power capacity particle in the last iteration, x' id is the position of the i-th power capacity particle in the last iteration, c1 is the first learning factor, c2 is the second learning factor, and p id is the optimal position of the i-th power capacity particle up to the current iteration process, p gd is the optimal position of the population up to the current iteration process, r1 and r2 are uniform random numbers in the range of [0,1], and w is the inertia weight of the current iteration process;

[0102] The position of the i-th power capacity particle satisfies:

[0103] x id =x' id +v' id .

[0104] S161: Calculate the fitness value of each power capacity particle.

[0105] The average value of the distance between each power capacity particle and other power capacity particles is taken as the fitness value of the power capacity particle. Specifically for the i-th power capacity particle, the average value of its distance relative to other power capacity particles is:

[0106]

[0107] S171: Update the individual optimal position and population optimal position of the power capacity particle according to the fitness value of each power capacity particle.

[0108] The optimal position searched so far by the i-th power capacity particle is also called the individual extreme value and is recorded as:

[0109] p best =(p i1 ,pi2 ,...,p iD ),i=1,2,...,N;

[0110] The optimal position searched by the entire particle swarm so far is also called the global extreme value, which is recorded as:

[0111] g best =(p g1 ,p g2 ,...,p gD );

[0112] In step S141, in addition to initializing the position and speed of the power capacity particle, the optimal position of each power capacity particle and the optimal position of the population are also initialized. For the i-th power capacity particle, if the fitness value in the current iteration is less than the fitness value in the previous iteration, the optimal position of the i-th power capacity particle is updated to the position in the current iteration, otherwise the optimal position in the previous iteration remains unchanged. And the optimal position of the population is updated according to the positions of each power capacity particle updated in the current iteration, where the optimal position of the population is the position corresponding to the power capacity particle with the smallest fitness value as of the current iteration.

[0113] S181: Generate an evolution factor according to the fitness value of the optimal particle in the current iteration, the fitness value of the first preset particle, and the fitness value of the second preset particle; the first preset particle is the particle with the largest fitness value, and the second preset particle is the particle with the smallest fitness value. The evolution factor is greater than or equal to 0 and less than or equal to 1.

[0114] In an optional implementation, the ratio of the first difference to the second difference is used as the evolution factor; the first difference is the difference between the fitness value of the optimal particle and the fitness value of the second preset particle, and the second difference is the difference between the fitness value of the second preset particle and the fitness value of the first preset particle, that is, the evolution factor f satisfies:

[0115] Among all power capacity particles, the global optimal particle is selected according to the constraints, d g Represents the average distance between the optimal particle and other particles in the current iteration; d max is the fitness value of the first preset particle, d min The fitness value of the second preset particle.

[0116] S191: Determine whether the iteration end condition is met based on the evolution factor. If yes, execute S201; if no, execute S211.

[0117] Whether the iteration end condition is met can be determined based on the size of the evolution factor. For example, if the evolution factor is greater than or equal to 0.7 and less than or equal to 1, it can be considered that the iteration end condition is met.

[0118] S201: Output the optimal position of the population.

[0119] When the iterative constraints are met, the optimal position of the population is output, and the maximum capacity of the distributed power source connected to the line at each node can be obtained, and then the maximum capacity of the distributed power source that can be connected to the distribution network is obtained according to the sum of the maximum values ​​of the capacities of each distributed power source.

[0120] S211: Adjust the inertia weight according to the evolution factor and re-execute S151.

[0121] If the iteration end condition is not met, the inertia weight will continue to be adjusted to avoid local convergence during the iteration process.

[0122] Optionally, the inertia weight ω satisfies:

[0123] f is the evolution factor.

[0124] The population is continuously iterated, and in addition to updating the inertia weight after each iteration, the first learning factor and the second learning factor are also adjusted after each iteration.

[0125] In an optional implementation, after each iteration, the state set to which the evolution factor belongs is determined according to the size of the evolution factor; wherein, when the evolution factor is greater than or equal to the first threshold and less than or equal to the second threshold, it is determined that the evolution factor belongs to the third state set; when the evolution factor is greater than or equal to the second threshold and less than or equal to the third threshold, it is determined that the evolution factor belongs to the second state set; when the evolution factor is greater than or equal to the third threshold and less than or equal to the fourth threshold, it is determined that the evolution factor belongs to the first state set; when the evolution factor is greater than or equal to the fourth threshold and less than or equal to the fifth threshold, it is determined that the evolution factor belongs to the fourth state set; the first threshold, the second threshold, the third threshold, the fourth threshold, to the fifth threshold increase in sequence;

[0126] When the evolution factor belongs to the first state set, the first learning factor is increased and the second learning factor is decreased; when the evolution factor belongs to the second state set, the first learning factor is increased and the second learning factor is decreased; when the evolution factor belongs to the third state set, the first learning factor is increased and the second learning factor is increased.

[0127] Among them, in the first state set, the increase of the first learning factor and the decrease of the second learning factor are both greater than or equal to 0.5 and less than or equal to 1, in the second state set, the increase of the first learning factor and the decrease of the second learning factor are both greater than or equal to 0 and less than or equal to 0.2, and in the third state set, the increase of the first learning factor and the decrease of the second learning factor are both greater than or equal to 0 and less than or equal to 0.2.

[0128] The first state set, the second state set, the third state set and the fourth state set represent the states of exploration, utilization, convergence and exit, respectively. In the first state set, the first learning factor is increased by an arbitrary number between 0.5 and 1.0, and the second learning factor is decreased by an arbitrary number between 0.5 and 1.0. In the second state set, the first learning factor is increased by an arbitrary number between 0 and 0.2, and the second learning factor is decreased by an arbitrary number between 0 and 0.2. In the third state set, the first learning factor is increased by an arbitrary number between 0 and 0.2, and the second learning factor is increased by an arbitrary number between 0 and 0.2.

[0129] Optionally, the iteration constraint condition includes: the evolution factor belongs to the fourth state set. In an optional implementation, the first threshold to the fifth threshold are 0, 0.2, 0.4, 0.7, and 1.0 respectively.

[0130] Initialize the parameters by giving the original data such as distribution network parameters and protection setting values. Randomly generate a group of distributed power capacity particles according to the distributed power capacity requirements. In each iteration, update the velocity vector and capacity vector of the power capacity particles. Calculate the fitness value of the power capacity particles, evaluate the quality of the particles, and update the individual optimal position and the global optimal position. When the iteration end condition is not met, continuously calculate and update the velocity vector and capacity vector of the power capacity particles, continuously adopt the corresponding adaptive strategy, adjust the inertia weight, and finally output the capacity of each node of the distributed power supply and the required maximum total capacity.

[0131] The embodiment of the present invention further provides a capacity configuration device for a distributed power source. Figure 4 A schematic diagram of a distributed power supply capacity configuration device provided by an embodiment of the present invention, referring to Figure 4 , the device comprises:

[0132] An objective function establishing module 10 is used to establish an objective function based on the maximum capacity of distributed power sources that can be connected to the distribution network;

[0133] The constraint condition establishment module 11 is used to establish the constraint conditions; the constraint conditions include: fault current constraint of the distribution network under the access of distributed power sources, line voltage deviation constraint, feeder voltage deviation constraint, penetration rate constraint and single-point grid-connected installed capacity constraint;

[0134] The solution module 12 is used to optimize and solve the objective function under the constraint conditions by using the particle swarm algorithm.

[0135] The beneficial effects of the capacity configuration device of the distributed power source are the same as the beneficial effects of the capacity configuration method of the distributed power source, which will not be repeated here.

[0136] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0137] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A capacity configuration method for a distributed power source, characterized in that: include: The objective function is established based on the maximum capacity of distributed generation that can be connected to the distribution network; Establish constraints; The constraints include: fault current constraints of the distribution network under distributed power generation access, line voltage deviation constraints, feeder voltage deviation constraints, penetration rate constraints and single-point grid-connected installed capacity constraints; Under the constraints, the objective function is optimized and solved using a particle swarm algorithm.

2. The capacity configuration method of distributed power source according to claim 1, characterized in that: The fault current constraints of the distribution network under the access of the distributed power source include: The fault current is greater than the maximum value of the current protection action and less than the minimum value of the current protection inaction; The line voltage deviation meets: Among them, ΔU DER.i is the line voltage deviation, Z L.i is the line impedance from the distributed generation access point to the busbar of the distribution network, U N is the rated voltage of the distribution network, S DER.i is the capacity of distributed power generation; Feeder voltage deviation satisfies: L is the total feeder length; P DER is the active power of all distributed power sources on the feeder; P L is the active power of all loads on the feeder; r and x are the equivalent resistance and reactance per unit distance of the feeder, respectively; is the rated power factor of the load; The penetration rate is equal to the ratio of the total capacity of distributed generation to the total load of the load connected to the distribution network; The single-point grid-connected installed capacity is less than or equal to 6MW.

3. The capacity configuration method of distributed power source according to claim 1, characterized in that: The optimizing and solving the objective function by using the particle swarm algorithm comprises: A group of power capacity particles are randomly generated based on the capacity of distributed power sources connected to each node in the distribution network line; Initialize the position and velocity of each of the power capacity particles in the population; The population is continuously iterated, and the inertia weight is updated after each iteration until the iteration end condition is met, and the optimal position of the population is output; Each iteration process includes: Calculate and update the speed and position of each of the power capacity particles; Calculating the fitness value of each of the power capacity particles; updating the individual optimal position and the population optimal position of the power capacity particles according to the fitness value of each of the power capacity particles; Generate an evolution factor according to the fitness value of the optimal particle in the current iteration, the fitness value of the first preset particle and the fitness value of the second preset particle; the first preset particle is the particle with the largest fitness value, and the second preset particle is the particle with the smallest fitness value; The inertia weight is adjusted according to the evolution factor.

4. The capacity configuration method of distributed power source according to claim 3, characterized in that: The step of generating the evolution factor according to the fitness value of the optimal particle in the current iteration, the fitness value of the first preset particle, and the fitness value of the second preset particle includes: The ratio of the first difference to the second difference is used as the evolution factor; the first difference is the difference between the fitness value of the optimal particle and the fitness value of the second preset particle, and the second difference is the difference between the fitness value of the second preset particle and the fitness value of the first preset particle.

5. The method according to claim 4, characterized in that The adjusting the inertia weight according to the evolution factor comprises: The inertia weight ω satisfies: f is the evolution factor.

6. The capacity configuration method of distributed power source according to claim 3, characterized in that: The speed of the i-th power capacity particle satisfies: v id =w*v′ id +c1r1(p id -x′ id )+c2r2(p gd -x′ id ), i = 1, 2, ..., N, N is the number of particles in the population, d = 1, 2, ..., D, D is the dimension of the power capacity particle, v′ id is the speed of the i-th power capacity particle in the last iteration, x′ id is the position of the i-th power capacity particle in the last iteration, c1 is the first learning factor, c2 is the second learning factor, and p id is the optimal position of the i-th power capacity particle up to the current iteration process, p gd is the optimal position of the population up to the current iteration process, r1 and r2 are uniform random numbers in the range of [0,1], and w is the inertia weight; The position of the i-th power capacity particle satisfies: x id =x′ id +v′ id 。 7. The capacity configuration method of distributed power source according to claim 6, characterized in that: Continuously iterate the population and update the inertia weight after each iteration, and also include: After each iteration, the state set to which the evolution factor belongs is determined according to the size of the evolution factor; wherein, when the evolution factor is greater than or equal to the first threshold and less than or equal to the second threshold, it is determined that the evolution factor belongs to the third state set; when the evolution factor is greater than or equal to the second threshold and less than or equal to the third threshold, it is determined that the evolution factor belongs to the second state set; when the evolution factor is greater than or equal to the third threshold and less than or equal to the fourth threshold, it is determined that the evolution factor belongs to the first state set; when the evolution factor is greater than or equal to the fourth threshold and less than or equal to the fifth threshold, it is determined that the evolution factor belongs to the fourth state set; the first threshold to the fifth threshold increase in sequence; When the evolution factor belongs to the first state set, increasing the first learning factor and decreasing the second learning factor; When the evolution factor belongs to the second state set, increasing the first learning factor and decreasing the second learning factor; When the evolution factor belongs to the third state set, increasing the first learning factor and increasing the second learning factor; Among them, in the first state set, the increase of the first learning factor and the decrease of the second learning factor are both greater than or equal to 0.5 and less than or equal to 1, in the second state set, the increase of the first learning factor and the decrease of the second learning factor are both greater than or equal to 0 and less than or equal to 0.2, and in the third state set, the increase of the first learning factor and the decrease of the second learning factor are both greater than or equal to 0 and less than or equal to 0.

2.

8. The capacity configuration method of distributed power source according to claim 7, characterized in that: The iteration end conditions include: The evolution factor belongs to the fourth state set.

9. The capacity configuration method of a distributed power source according to any one of claims 3 to 8, characterized in that: Calculating the fitness value of each of the power capacity particles includes: The average value of the distance between each of the power capacity particles and other power capacity particles is used as the fitness value of the power capacity particle.

10. A capacity configuration device for a distributed power source, characterized in that: include: An objective function establishment module is used to establish an objective function based on the maximum capacity of distributed power sources that can be connected to the distribution network; A constraint condition establishment module, used to establish constraint conditions; The constraints include: fault current constraints of the distribution network under distributed power generation access, line voltage deviation constraints, feeder voltage deviation constraints, penetration rate constraints and single-point grid-connected installed capacity constraints; The solution module is used to optimize and solve the objective function under the constraints using a particle swarm algorithm.

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