Distribution area three-phase imbalance optimization method and system based on distributed energy storage

Abstract

The invention discloses a distributed energy storage-based three-phase imbalance optimization method and system for a power distribution area, and the method comprises the steps: obtaining the operation parameters of the power distribution area containing distributed energy storage, calculating the three-phase imbalance degree of the power distribution area containing distributed energy storage according to the operation parameters, and building a three-phase imbalance power distribution system; establishing constraint conditions related to the three-phase unbalanced power distribution system, wherein the constraint conditions at least comprise a characteristic constraint of considering a PV type distributed power supply in load flow calculation and a charging and discharging characteristic constraint of considering distributed energy storage; and reconstructing an optimization model based on the three-phase unbalanced power distribution system and the constraint condition by using an improved particle swarm algorithm, and solving the optimization model. The method can effectively reduce the three-phase unbalance degree of a power distribution area, improves the quality of electric energy, optimizes the charging and discharging strategy of distributed energy storage, prolongs the service life of energy storage equipment, and improves the efficiency and accuracy of optimization calculation through improving the particle swarm optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of power system protection, control and automation, and in particular to a method and system for optimizing three-phase imbalance in a distribution station area based on distributed energy storage. Background Art

[0002] As society continues to progress, the requirements of power system power terminals for power quality are increasing. As a key link in directly supplying power to power terminals, the performance and stability of the distribution network are crucial. As the proportion of renewable energy in the power grid continues to increase, the uncertainty faced by the network also increases accordingly, which leads to more and more significant and complex power quality problems in the system. The three-phase imbalance problem in the distribution area is one of the problems that often occur in actual operation. There are many reasons for the three-phase imbalance, including load imbalance, uneven power equipment parameters, system failures and improper maintenance, and renewable energy power generation grid connection. The three-phase imbalance problem in the distribution area has an adverse effect on the power quality, and in severe cases may cause equipment damage and system instability. Common means of managing three-phase imbalance include redistributing single-phase loads by considering the timing characteristics of the load, or adjusting the load distribution by reconstructing the network. Balanced current distribution can also be achieved through balancing transformers, reactive compensation equipment, unbalanced current suppressors, etc., or real-time monitoring and automatic adjustment of intelligent systems can be used to prevent or reduce the impact of three-phase imbalance.

[0003] In recent years, energy storage equipment has attracted much attention due to its flexible operating characteristics and excellent voltage regulation capabilities. Energy storage systems have become an important means to effectively cope with power system fluctuations due to their unique two-way power regulation function, that is, they can charge and discharge. They can not only improve the system's ability to accept new energy, but also ensure the safe and stable operation of the entire power system. By reasonably setting parameters such as the access location and capacity of the distributed energy storage system and controlling the energy flow distribution within the distribution station area, the three-phase imbalance phenomenon can be reduced. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method and system for optimizing three-phase imbalance in a distribution station area based on distributed energy storage, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for optimizing three-phase imbalance in a distribution station area based on distributed energy storage, comprising:

[0009] Obtaining operating parameters of a distribution substation containing distributed energy storage, calculating the three-phase imbalance of the distribution substation containing distributed energy storage according to the operating parameters, and establishing a three-phase unbalanced power distribution system;

[0010] Establishing constraint conditions for the three-phase unbalanced power distribution system, wherein the constraint conditions at least include characteristic constraints of PV-type distributed power sources in power flow calculation and charging and discharging characteristic constraints of distributed energy storage;

[0011] An improved particle swarm algorithm is used to reconstruct an optimization model based on the three-phase unbalanced power distribution system and constraint conditions, and the optimization model is solved.

[0012] As a preferred solution of the method for optimizing the three-phase imbalance of a distribution station area based on distributed energy storage described in the present invention, the step of obtaining the operating parameters of the distribution station area containing the distributed energy storage and calculating the three-phase imbalance of the distribution station area of ​​the distributed energy storage according to the operating parameters includes:

[0013] The three-phase unbalance ρ is expressed as follows:

[0014] ρ=U0 / U1×100%

[0015] Among them, U0 is the zero-sequence component of the three-phase voltage of the distribution station area containing distributed energy storage; U1 is the positive-sequence component of the three-phase voltage of the distribution station area containing distributed energy storage.

[0016] As a preferred solution of the three-phase unbalanced power distribution area optimization method based on distributed energy storage described in the present invention, wherein: the establishment of a three-phase unbalanced power distribution system includes:

[0017] The three-phase unbalanced power distribution system includes a three-phase admittance matrix and a Jacobian matrix corresponding to a distribution station area containing distributed energy storage, and the three-phase admittance matrix includes:

[0018]

[0019] Where i and j are node numbers from 1 to n, p represents phase a, phase b, or phase c, m belongs to p and has the value of phase a, phase b, or phase c, I ia ,I ib ,I ic represents the three-phase current of node i, Y ij** represents the admittance between two phases from node i to node j, U ia, U ib , U ic represents the three-phase voltage at node i; ΔP i p , represents the change value of active power and reactive power at node i, P i p , Represents the active power and reactive power value at node i, V i p , represents the different phase voltage amplitudes at node i or j, Represents the admittance, reactance, and impedance angle of each phase between node i and node j.

[0020] As a preferred solution of the three-phase unbalanced power distribution area optimization method based on distributed energy storage described in the present invention, wherein: the establishment of a three-phase unbalanced power distribution system also includes:

[0021] The Jacobian matrix of the distribution station area containing distributed energy storage includes:

[0022]

[0023] Among them, H, N, J, and L are all matrices of order 3*3, n represents the number of nodes, and the final equation of the three-phase unbalanced power distribution system is expressed as:

[0024] ΔS=J*ΔV

[0025] Wherein, ΔV represents the correction value of the bus voltage, including the correction angle and amplitude.

[0026] As a preferred solution of the three-phase unbalanced optimization method of the distribution area based on distributed energy storage described in the present invention, the characteristic constraints of the PV type distributed power source in the power flow calculation include:

[0027] When considering the access of PV-type distributed power sources, it is necessary to delete the ΔQ of the corresponding phase of the access node, and at the same time delete the row and column vectors of the Jacobian matrix of the corresponding distribution station area containing distributed energy storage, so that the corresponding voltage correction can be calculated;

[0028] After each iteration, the phase angle of the node voltage and the reactive power are calculated. If the reactive power exceeds the limit, it is converted into a PQ type distributed power source with known PQ.

[0029] The limited range is Qmin and Qmax represent the minimum and maximum reactive power values.

[0030] As a preferred solution of the three-phase unbalanced optimization method of the distribution station area based on distributed energy storage described in the present invention, the charging and discharging characteristic constraints considering the distributed energy storage include:

[0031] The charging and discharging characteristic constraints of the distributed energy storage include energy storage charging and discharging power constraints, energy storage capacity constraints, and energy storage charging and discharging state constraints, which are specifically expressed as follows:

[0032] Energy storage charging and discharging power constraints:

[0033] P DESS,min ≤P DESS,t ≤P DESS,max

[0034] Energy storage capacity constraints:

[0035] E DESS,min ≤E DESS,t ≤E DESS,max

[0036] Energy storage charging and discharging state constraints:

[0037]

[0038] Among them, P DESS,t represents the charging and discharging power of the distributed energy storage device at time t, P DESS,min , P DESS,max represents the minimum and maximum charging and discharging power of the distributed energy storage device at time t, E DESS,t represents the remaining power of the distributed energy storage device at time t, E DESS,min 、E DESS,max represents the minimum and maximum value of the remaining power of the distributed energy storage device at time t, They represent the probability that the distributed energy storage device is discharging and charging at time t respectively.

[0039] As a preferred solution of the three-phase unbalanced power distribution area optimization method based on distributed energy storage described in the present invention, wherein: the use of the improved particle swarm algorithm to reconstruct the optimization model based on the three-phase unbalanced power distribution system and constraints, and solving the optimization model includes:

[0040] Input the distribution station area data, calculate the initial power flow, and use the input data to calculate the initial power flow of the entire system;

[0041] Initialize the particle swarm parameters and randomly assign the access nodes and capacity of the distributed energy storage as the particle initialization speed and position;

[0042] Calculate the power flow of the three-phase unbalanced system and the fitness of each particle;

[0043] Perform threshold judgment to determine whether the current particle fitness value meets the threshold. If not, reinitialize the particles.

[0044] If satisfied, update the speed and position of the corresponding particle, update the historical optimal solution and position of the individual particle, update the historical optimal solution and position of the population particle, and update the improved particle swarm algorithm parameters;

[0045] Determine whether the iteration end requirements are met. If so, complete the iteration and output the optimal solution. If not, recalculate the power flow of the three-phase unbalanced system, calculate the fitness of each particle, and re-judge the threshold.

[0046] In a second aspect, the present invention provides a three-phase unbalanced optimization system for a distribution station area based on distributed energy storage, comprising:

[0047] A system establishment module, used to obtain operating parameters of a distribution substation containing distributed energy storage, calculate the three-phase imbalance of the distribution substation containing distributed energy storage according to the operating parameters, and establish a three-phase unbalanced power distribution system;

[0048] A constraint establishment module, used to establish constraint conditions on the three-phase unbalanced power distribution system, wherein the constraint conditions at least include characteristic constraints of PV-type distributed power sources in power flow calculation and charging and discharging characteristic constraints of distributed energy storage;

[0049] The solution module is used to reconstruct the optimization model based on the three-phase unbalanced power distribution system and constraint conditions by using the improved particle swarm algorithm, and solve the optimization model.

[0050] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes a method and system for optimizing the three-phase imbalance of a distribution station area based on distributed energy storage, obtains the operating parameters of a distribution station area containing distributed energy storage, calculates the three-phase imbalance of the distribution station area of ​​the distributed energy storage according to the operating parameters, and establishes a three-phase unbalanced power distribution system; establishes constraints on the three-phase unbalanced power distribution system, the constraints at least include the characteristic constraints of the PV-type distributed power source in the flow calculation and the charging and discharging characteristic constraints of the distributed energy storage; uses the improved particle swarm algorithm to reconstruct the optimization model based on the three-phase unbalanced power distribution system and the constraints, and solves the optimization model. It can effectively reduce the three-phase imbalance of the distribution station area, improve the power quality, optimize the charging and discharging strategy of the distributed energy storage, extend the service life of the energy storage equipment, and improve the efficiency and accuracy of the optimization calculation by improving the particle swarm algorithm.

[0053] Specifically, the present invention ensures that the influence of PV-type distributed power sources on the distribution area can be accurately simulated in the power flow calculation by considering the characteristic constraints of PV-type distributed power sources, so as to more accurately calculate the three-phase imbalance. At the same time, by considering the charging and discharging characteristic constraints of distributed energy storage, the present invention can reasonably arrange the charging and discharging plan of the energy storage equipment, avoid damage to the equipment caused by excessive charging and discharging, and extend the service life of the equipment.

[0054] In addition, the present invention adopts an improved particle swarm algorithm to solve the optimization model, which effectively avoids the problem that the traditional particle swarm algorithm is prone to falling into the local optimal solution, improves the global search capability of the solution, and thus can find a better three-phase unbalanced optimization solution for the distribution station area.

[0055] In summary, the present invention can not only effectively reduce the three-phase imbalance in the distribution station area and improve the power quality, but also reasonably arrange the charging and discharging strategy of distributed energy storage equipment and extend the service life of the equipment. At the same time, the efficiency and accuracy of the optimization calculation are improved through the improved particle swarm algorithm, which has significant technical advantages and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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 labor. Among them:

[0057] Figure 1 A method flow chart of a method and system for optimizing three-phase imbalance in a distribution station area based on distributed energy storage provided by an embodiment of the present invention;

[0058] Figure 2 A detailed flow chart of an improved particle swarm algorithm for a three-phase unbalanced optimization method and system for a distribution station area based on distributed energy storage provided by an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of a three-phase unbalance optimization method and system for a distribution station area based on distributed energy storage provided by an embodiment of the present invention, where no energy storage is connected and node 18 has the highest unbalance degree;

[0060] Figure 4 A schematic diagram of a three-phase unbalanced optimization method and system for a distribution station area based on distributed energy storage provided by an embodiment of the present invention, wherein the C phase of node 18 is connected to 0.17MVA energy storage (charging);

[0061] Figure 5 A schematic diagram of a node 18 of a three-phase unbalanced optimization method and system for a distribution station area based on distributed energy storage provided by an embodiment of the present invention, with B phase connected to -0.33MVA (discharging) and C phase 0.1MVA (charging);

[0062] Figure 6 A method for optimizing three-phase imbalance in a distribution area based on distributed energy storage and a schematic diagram of the ABC phases of the nodes of the system being connected to energy storage (0.09MVA, -0.33MVA, 0.09MVA) respectively, provided by an embodiment of the present invention;

[0063] Figure 7 A schematic diagram of energy storage configuration after node 19 of a three-phase unbalanced optimization method and system for a distribution station area based on distributed energy storage provided by an embodiment of the present invention is connected to a 0.12MVA photovoltaic;

[0064] Figure 8 A schematic diagram of energy storage configuration after node 2 of a three-phase unbalanced optimization method and system for a distribution station area based on distributed energy storage provided by an embodiment of the present invention is connected to a 0.12MVA photovoltaic;

[0065] Fig. 9 An internal structural diagram of a computer device of a method and system for optimizing three-phase imbalance in a distribution station area based on distributed energy storage provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0067] Example 1

[0068] Reference Figure 1-Figure 9 , which is the first embodiment of the present invention, and provides a method and system for optimizing three-phase imbalance in a distribution station area based on distributed energy storage, comprising:

[0069] Before describing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity.

[0070] PV distributed power: PV distributed power refers to photovoltaic distributed power, which is a device that directly converts solar energy into electrical energy. This type of power supply is usually installed on the user side, such as on the roof or other available surfaces, to generate electricity on site and directly supply it to users or feed it into the power grid. In the distribution system, PV distributed power can provide additional power support and help reduce dependence on traditional power grids.

[0071] Three-phase unbalance: Three-phase unbalance refers to the degree of voltage or current imbalance in a three-phase system. In an ideal three-phase system, the voltage or current of the three phases should be exactly the same, but in practical applications, various factors may cause three-phase imbalance. Unbalance is usually measured by calculating the ratio of the effective value of the zero-sequence component (U0) to the positive-sequence component (U1).

[0072] Three-phase unbalanced power distribution system: A three-phase unbalanced power distribution system refers to a power distribution system in which there is a significant difference in voltage or current between the three phases due to factors such as unbalanced load, system failure, improper maintenance, etc. This imbalance will affect the power quality and the overall stability of the system, and may cause equipment damage and other problems.

[0073] Improved Particle Swarm Optimization: Particle Swarm Optimization (PSO) is a heuristic search algorithm that simulates the foraging behavior of bird flocks. In this algorithm, each potential solution is called a "particle", and these particles move in the search space to find the optimal solution. The improved particle swarm algorithm is an improved version of the basic particle swarm algorithm. It usually introduces some new mechanisms or parameters to improve the convergence speed and optimization ability of the algorithm, such as dynamically adjusting the inertia weight and introducing mutation operations to prevent particles from falling into the local optimal solution too early.

[0074] In related technologies, there are some technical challenges, such as how to effectively reduce the three-phase imbalance in the distribution station area during the access of distributed power sources and the charging and discharging of energy storage devices.

[0075] The present application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to explain in detail how to implement the three-phase unbalance optimization method of the distribution station area based on distributed energy storage;

[0076] Figure 1 A method flow chart of a three-phase unbalanced optimization method and system for a distribution station area based on distributed energy storage is shown, including:

[0077] S101, obtaining operating parameters of a distribution substation containing distributed energy storage, calculating the three-phase imbalance of the distribution substation containing distributed energy storage according to the operating parameters, and establishing a three-phase unbalanced power distribution system;

[0078] In the embodiment of the present application, obtaining the operating parameters of the distribution station area containing the distributed energy storage, and calculating the three-phase imbalance of the distribution station area of ​​the distributed energy storage according to the operating parameters includes:

[0079] The three-phase unbalance ρ is expressed as follows:

[0080] ρ=U0 / U1×100%

[0081] Among them, U0 is the zero-sequence component of the three-phase voltage of the distribution station area containing distributed energy storage; U1 is the positive-sequence component of the three-phase voltage of the distribution station area containing distributed energy storage.

[0082] It should be noted that the reason for using the above three-phase unbalance solution method is that it can accurately reflect the degree of voltage or current imbalance in the three-phase system, thereby providing a reliable basis for optimization and adjustment. By calculating the ratio of the effective value of the zero-sequence component to the positive-sequence component, the difference between the phases in the system can be intuitively understood, and then corresponding measures can be taken to balance the three-phase load.

[0083] In an embodiment of the present application, establishing a three-phase unbalanced power distribution system includes:

[0084] The three-phase unbalanced power distribution system includes a three-phase admittance matrix and a Jacobian matrix corresponding to the distribution station area containing distributed energy storage. The three-phase admittance matrix includes:

[0085]

[0086]

[0087] Where i and j are node numbers from 1 to n, p represents phase a, phase b, or phase c, m belongs to p and has the value of phase a, phase b, or phase c, I ia ,I ib ,I ic represents the three-phase current of node i, Y ij** represents the admittance between two phases from node i to node j, U ia , Uib , U ic represents the three-phase voltage at node i; ΔP i p , represents the change value of active power and reactive power at node i, P i p , Represents the active power and reactive power value at node i, V i p , represents the different phase voltage amplitudes at node i or j, Represents the admittance, reactance, and impedance angle of each phase between node i and node j.

[0088] In the embodiment of the present application, establishing a three-phase unbalanced power distribution system further includes:

[0089] The Jacobian matrix of the distribution substation area with distributed energy storage includes:

[0090]

[0091] Among them, H, N, J, and L are all matrices of order 3*3, n represents the number of nodes, and the values ​​of their elements are given by the following formula:

[0092]

[0093] Among them, H, N, J, L are all matrices of order 3x3, and their elements represent the terms of the Jacobian matrix, which is used to describe the rate of change of various variables (such as voltage and power) in the system. The elements of the Jacobian matrix representing the distribution substation area containing distributed energy storage;

[0094] The final equation for the three-phase unbalanced distribution system is expressed as:

[0095] ΔS=J*ΔV

[0096] Wherein, ΔV represents the correction value of the bus voltage, including the correction angle and amplitude.

[0097]

[0098] Assuming that there are n buses in the system, including m photovoltaic buses and one balancing node, there are 3n-3 phase angles and 3n-3m-3 voltage amplitudes, which can be solved iteratively using the above modified equation.

[0099] It should be noted that the benefit of the above content is that it can provide an efficient and accurate method to optimize the three-phase imbalance problem in the distribution area. By establishing a three-phase unbalanced distribution system and calculating the three-phase imbalance, the degree of imbalance in the system can be accurately identified and quantified. This provides a reliable basis for subsequent optimization and adjustment, ensuring the stable operation of the distribution system and the long-term safety of the equipment.

[0100] S102, establishing constraints on the three-phase unbalanced power distribution system, the constraints at least including characteristic constraints of PV-type distributed power sources in power flow calculation and charging and discharging characteristic constraints of distributed energy storage;

[0101] In the embodiment of the present application, the characteristic constraints of the PV type distributed power source in the power flow calculation include:

[0102] When considering the access of PV-type distributed power sources, it is necessary to delete the ΔQ of the corresponding phase of the access node, and at the same time delete the row and column vectors of the Jacobian matrix of the corresponding distribution station area containing distributed energy storage, so that the corresponding voltage correction can be calculated;

[0103] After each iteration, the phase angle of the node voltage and the reactive power are calculated. If the reactive power exceeds the limit, it is converted into a PQ type distributed power supply with known PQ:

[0104]

[0105] In the embodiments of the present application, the range is limited to Qmin and Qmax represent the minimum and maximum reactive power values.

[0106] In the embodiment of the present application, the charging and discharging characteristic constraints of the distributed energy storage are considered to include:

[0107] The charging and discharging characteristic constraints of distributed energy storage include energy storage charging and discharging power constraints, energy storage capacity constraints, and energy storage charging and discharging state constraints, which are specifically expressed as follows:

[0108] Energy storage charging and discharging power constraints:

[0109] P DESS,min ≤P DESS,t ≤P DESS,max

[0110] Energy storage capacity constraints:

[0111] E DESS,min ≤E DESS,t ≤E DESS,max

[0112] Energy storage charging and discharging state constraints:

[0113]

[0114] Among them, P DESS,t represents the charging and discharging power of the distributed energy storage device at time t, P DESS,min , P DESS,max represents the minimum and maximum charging and discharging power of the distributed energy storage device at time t, E DESS,t represents the remaining power of the distributed energy storage device at time t, E DESS,min 、E DESS,max represents the minimum and maximum value of the remaining power of the distributed energy storage device at time t, They represent the probability that the distributed energy storage device is discharging and charging at time t respectively.

[0115] It should be noted that the reason for selecting the above constraints is to ensure the stability and reliability of the distribution system, and reasonable constraints must be placed on the operation of distributed power sources and energy storage equipment. By considering the power flow calculation characteristic constraints of PV-type distributed power sources and the charging and discharging characteristic constraints of distributed energy storage, problems such as system overload and voltage instability can be effectively avoided, thereby improving the operating efficiency and safety of the entire distribution system.

[0116] It should be noted that the benefit of selecting the above constraints is that it can ensure that the distribution system can maintain stable operation when accepting distributed power sources and energy storage equipment. By reasonably setting the constraints of PV-type distributed power sources and distributed energy storage, problems such as system overload and voltage instability can be effectively avoided, thereby improving the operating efficiency and safety of the entire distribution system.

[0117] S103, using an improved particle swarm algorithm to reconstruct an optimization model based on the three-phase unbalanced power distribution system and constraint conditions, and solving the optimization model.

[0118] In the embodiment of the present application, the improved particle swarm algorithm is used to reconstruct the optimization model based on the three-phase unbalanced power distribution system and the constraint conditions, and the optimization model is solved including:

[0119] Input the distribution station area data, calculate the initial power flow, and use the input data to calculate the initial power flow of the entire system;

[0120] Initialize the particle swarm parameters and randomly assign the access nodes and capacity of the distributed energy storage as the particle initialization speed and position;

[0121] Calculate the power flow of the three-phase unbalanced system and the fitness of each particle;

[0122] Perform threshold judgment to determine whether the current particle fitness value meets the threshold. If not, reinitialize the particles.

[0123] If satisfied, update the speed and position of the corresponding particle, update the historical optimal solution and position of the individual particle, update the historical optimal solution and position of the population particle, and update the improved particle swarm algorithm parameters;

[0124] Determine whether the iteration end requirements are met. If so, complete the iteration and output the optimal solution. If not, recalculate the power flow of the three-phase unbalanced system, calculate the fitness of each particle, and re-judge the threshold.

[0125] In the embodiment of the present application, an improved particle swarm algorithm is used to optimize the distributed energy storage optimization configuration, with the minimum three-phase imbalance as the optimization goal. The fitness of the particles (the location and capacity of the energy storage access) is first initialized within a set range, and the upper limit of the energy storage is set. The fitness of each particle is calculated, and the local optimal solution and the global optimal solution are compared. The next iteration is performed according to the position and speed update until convergence and the "global optimum" is found.

[0126] Initially, assume that the search space has D dimensions, with N particles, and the moving distance, direction (speed), and position of the i-th particle. The local historical optimal solution and the global historical optimal solution are defined as:

[0127] X id =(x i1 ,x i2 ,...,x iD )

[0128] V id =(v i1 ,v i2 ,...,v iD )

[0129] P best,id =(p i1 ,p i2 ,...,p iD )

[0130] G gbest,id =(g i1 ,g i2 ,...,g iD )

[0131] The iterative process of velocity and position is:

[0132]

[0133] Among them, ω is the inertia weight; c1 is the individual learning factor, c2 is the social learning factor; r1 and r2 are random numbers between (0,1); p id is the d-th dimension component of the historical optimal position vector of the ith particle at time t; p gdis the d-th dimension component in the optimal position vector of the population at time t.

[0134] Furthermore, the calculation of power flow and energy storage site selection and sizing includes both continuous and discrete variables, and dynamic penalty functions and thresholds are added to adapt to the optimal solution problem.

[0135] F(x)=f(x)+c(k)H(x)

[0136]

[0137] Where f(x) is the original objective function; F(x) is the constructed new objective function; k is the number of iterations; c(k) is the coefficient that changes with the number of iterations; H(x) is the penalty part; m is the number of equality constraints; n is the number of inequality constraints; pun i (x) is the out-of-bounds condition function of the constraint condition; θ(pun i (x)) function with pun i (x) changes the value; γ(pun i (x)) is the exponentiation of the out-of-bounds function, and also follows pun i (x)Change.

[0138]

[0139] Where D is the threshold; Gbest is the global optimal solution of the particle swarm; n is the number of particles in the swarm; Pid represents the individual historical optimal solution of the i-th particle in the d dimension. In order to avoid falling into the local optimum, the threshold D is set. max (0<D max ≤1), when D≥D max When , some particles are reinitialized, so that the particles will not fall into the local optimum and be unable to move, thus searching for a better global optimal solution.

[0140] It should be noted that the benefit of the above step S103 is that the power distribution system can be optimized and configured by improving the particle swarm algorithm, thereby improving the overall performance of the system. By continuously iterating and updating the speed and position of the particles, the optimal access location and capacity of the distributed energy storage device can be effectively found to minimize the three-phase imbalance. This optimization method can not only improve the operating efficiency of the power distribution system, but also ensure the stability and safety of the system.

[0141] In practical applications, the solution process of the optimization model needs to consider many factors, such as the type of distributed power source, the capacity of energy storage equipment, the load demand of the system, etc. By setting reasonable constraints, it can be ensured that the optimization process can meet the actual needs while avoiding problems such as system overload and voltage instability. In addition, by introducing dynamic penalty functions and thresholds, the adaptability and flexibility of the optimization model can be further improved to cope with various complex distribution system scenarios.

[0142] The advantages of the improved particle swarm algorithm are that it is simple to implement, converges quickly, and has good global search capabilities. By reasonably setting the particle swarm parameters, such as inertia weight, individual learning factor, and social learning factor, the exploration and development capabilities of the algorithm can be effectively balanced, thereby increasing the probability of finding the global optimal solution. At the same time, by introducing a threshold mechanism, particles can be prevented from falling into a local optimal solution, further improving the optimization effect.

[0143] In summary, the present invention proposes a three-phase unbalanced optimization method for a distribution station area based on distributed energy storage, obtains the operating parameters of the distribution station area containing distributed energy storage, calculates the three-phase unbalance of the distribution station area of ​​the distributed energy storage according to the operating parameters, and establishes a three-phase unbalanced distribution system; establishes constraints on the three-phase unbalanced distribution system, the constraints at least include considering the characteristic constraints of PV-type distributed power sources in the flow calculation and considering the charging and discharging characteristic constraints of distributed energy storage; uses an improved particle swarm algorithm to reconstruct an optimization model based on the three-phase unbalanced distribution system and the constraints, and solves the optimization model. It can effectively reduce the three-phase imbalance of the distribution station area, improve the quality of electric energy, optimize the charging and discharging strategy of distributed energy storage, extend the service life of energy storage equipment, and improve the efficiency and accuracy of the optimization calculation by improving the particle swarm algorithm.

[0144] This embodiment also provides a three-phase unbalanced optimization system for a distribution station area based on distributed energy storage, including:

[0145] A system establishment module, used to obtain the operating parameters of the distribution substation containing distributed energy storage, calculate the three-phase imbalance of the distribution substation containing distributed energy storage according to the operating parameters, and establish a three-phase unbalanced distribution system;

[0146] A constraint establishment module, used to establish constraint conditions on the three-phase unbalanced power distribution system, wherein the constraint conditions at least include characteristic constraints of PV-type distributed power sources in power flow calculation and charging and discharging characteristic constraints of distributed energy storage;

[0147] The solution module is used to reconstruct the optimization model based on the three-phase unbalanced power distribution system and constraint conditions by using the improved particle swarm algorithm, and solve the optimization model.

[0148] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0149] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a three-phase unbalanced optimization method for a distribution station area based on distributed energy storage is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0150] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0151] Obtaining operating parameters of a distribution substation containing distributed energy storage, calculating the three-phase imbalance of the distribution substation containing distributed energy storage according to the operating parameters, and establishing a three-phase unbalanced power distribution system;

[0152] Establishing constraints on the three-phase unbalanced power distribution system, the constraints at least include considering the characteristic constraints of PV-type distributed power sources in power flow calculation and considering the charging and discharging characteristic constraints of distributed energy storage;

[0153] The improved particle swarm algorithm is used to reconstruct the optimization model based on the three-phase unbalanced distribution system and constraints, and the optimization model is solved.

[0154] Example 2

[0155] Reference Figure 3-Figure 8 , which is an embodiment of the present invention, provides a three-phase unbalanced optimization method and system for a distribution station area based on distributed energy storage. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0156] Figure 3 The three-phase power flow calculation of IEEE33 nodes is performed in the 18-node system, and the unbalance degree of node 18 is the lowest. When the 18-node system is connected to energy storage in single-phase, two-phase and three-phase conditions, the energy storage system is connected to the 18-node system. Figure 4 , 5 ,6, we can see that the imbalance is reduced, and when the improved particle swarm algorithm is used to access any node, Figure 7 It shows that the optimal access point and capacity can be found, and the improvement in three-phase imbalance can be found by comparing the case where 18 nodes are connected to energy storage and the case where no energy storage is connected. Figure 8 When the distributed power generation area is connected, the system imbalance can also be alleviated.

[0157] It is worth noting that the positive and negative values ​​of energy storage do not mean that the energy storage device is always in a single charging or discharging state. Due to the limitations of the capacity and remaining power of the energy storage device, in the actual operation process, the energy storage charging and discharging can be autonomously controlled by adjusting the connected phase sequence, thereby improving the three-phase imbalance.

[0158] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0159] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0160] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0161] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0162] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0163] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0164] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A three-phase unbalanced optimization method for distribution station area based on distributed energy storage, characterized in that: include: Obtaining operating parameters of a distribution substation containing distributed energy storage, calculating the three-phase imbalance of the distribution substation containing distributed energy storage according to the operating parameters, and establishing a three-phase unbalanced power distribution system; Establishing constraint conditions for the three-phase unbalanced power distribution system, wherein the constraint conditions at least include characteristic constraints of PV-type distributed power sources in power flow calculation and charging and discharging characteristic constraints of distributed energy storage; An improved particle swarm algorithm is used to reconstruct an optimization model based on the three-phase unbalanced power distribution system and constraint conditions, and the optimization model is solved.

2. The method for optimizing three-phase imbalance in a distribution station area based on distributed energy storage according to claim 1, characterized in that: The obtaining of operating parameters of a distribution station area containing distributed energy storage, and calculating the three-phase imbalance of the distribution station area of ​​the distributed energy storage according to the operating parameters comprises: The three-phase unbalance ρ is expressed as follows: ρ=U0 / U1×100% Among them, U0 is the zero-sequence component of the three-phase voltage of the distribution station area containing distributed energy storage; U1 is the positive-sequence component of the three-phase voltage of the distribution station area containing distributed energy storage.

3. The method for optimizing three-phase imbalance in a distribution station area based on distributed energy storage according to claim 2, characterized in that: The establishment of the three-phase unbalanced power distribution system comprises: The three-phase unbalanced power distribution system includes a three-phase admittance matrix and a Jacobian matrix corresponding to a distribution station area containing distributed energy storage, and the three-phase admittance matrix includes: Where i and j are node numbers from 1 to n, p represents phase a, phase b, or phase c, m belongs to p and has the value of phase a, phase b, or phase c, I ia ,I ib ,I ic represents the three-phase current of node i, Y ij** represents the admittance between two phases from node i to node j, U ia , U ib , U ic represents the three-phase voltage at node i; ΔP i p , represents the change value of active power and reactive power at node i, P i p , Represents the active power and reactive power value at node i, V i p , represents the different phase voltage amplitudes at node i or j, Represents the admittance, reactance, and impedance angle of each phase between node i and node j.

4. The method for optimizing three-phase imbalance in a distribution station area based on distributed energy storage as claimed in claim 3, characterized in that: The establishment of the three-phase unbalanced power distribution system also includes: The Jacobian matrix of the distribution station area containing distributed energy storage includes: Among them, H, N, J, and L are all matrices of order 3*3, n represents the number of nodes, and the final equation of the three-phase unbalanced power distribution system is expressed as: ΔS=J*ΔV Wherein, ΔV represents the correction value of the bus voltage, including the correction angle and amplitude.

5. The method for optimizing three-phase imbalance in a distribution station area based on distributed energy storage according to claim 4, characterized in that: The characteristic constraints of the PV type distributed power source in the power flow calculation include: When considering the access of PV-type distributed power sources, it is necessary to delete the ΔQ of the corresponding phase of the access node, and at the same time delete the row and column vectors of the Jacobian matrix of the corresponding distribution station area containing distributed energy storage, so that the corresponding voltage correction can be calculated; After each iteration, the phase angle of the node voltage and the reactive power are calculated. If the reactive power exceeds the limit, it is converted into a PQ type distributed power source with known PQ. The limited range is Qmin and Qmax represent the minimum and maximum reactive power values.

6. The method for optimizing three-phase imbalance in a distribution station area based on distributed energy storage according to claim 5, characterized in that: The charging and discharging characteristic constraints of the distributed energy storage include: The charging and discharging characteristic constraints of the distributed energy storage include energy storage charging and discharging power constraints, energy storage capacity constraints, and energy storage charging and discharging state constraints, which are specifically expressed as follows: Energy storage charging and discharging power constraints: P DESS,min ≤P DESS,t ≤P DESS,max Energy storage capacity constraints: AND DESS,min ≤E DESS,t ≤E DESS,max Energy storage charging and discharging state constraints: Among them, P DESS,t represents the charging and discharging power of the distributed energy storage device at time t, P DESS,min , P DESS,max represents the minimum and maximum charging and discharging power of the distributed energy storage device at time t, E DESS,t represents the remaining power of the distributed energy storage device at time t, E DESS,min 、E DESS,max represents the minimum and maximum value of the remaining power of the distributed energy storage device at time t, They represent the probability that the distributed energy storage device is discharging and charging at time t respectively.

7. The method for optimizing three-phase imbalance in a distribution station area based on distributed energy storage according to claim 6, characterized in that: The method of reconstructing the optimization model based on the three-phase unbalanced power distribution system and constraint conditions by using the improved particle swarm algorithm and solving the optimization model includes: Input the distribution station area data, calculate the initial power flow, and use the input data to calculate the initial power flow of the entire system; Initialize the particle swarm parameters and randomly assign the access nodes and capacity of the distributed energy storage as the particle initialization speed and position; Calculate the power flow of the three-phase unbalanced system and the fitness of each particle; Perform threshold judgment to determine whether the current particle fitness value meets the threshold. If not, reinitialize the particles. If satisfied, update the speed and position of the corresponding particle, update the historical optimal solution and position of the individual particle, update the historical optimal solution and position of the population particle, and update the improved particle swarm algorithm parameters; Determine whether the iteration end requirements are met. If so, complete the iteration and output the optimal solution. If not, recalculate the power flow of the three-phase unbalanced system, calculate the fitness of each particle, and re-judge the threshold.

8. A three-phase unbalanced optimization system for distribution stations based on distributed energy storage, characterized in that: include: A system establishment module, used to obtain operating parameters of a distribution substation containing distributed energy storage, calculate the three-phase imbalance of the distribution substation containing distributed energy storage according to the operating parameters, and establish a three-phase unbalanced power distribution system; A constraint establishment module, used to establish constraint conditions on the three-phase unbalanced power distribution system, wherein the constraint conditions at least include characteristic constraints of PV-type distributed power sources in power flow calculation and charging and discharging characteristic constraints of distributed energy storage; A solution module is used to reconstruct an optimization model based on the three-phase unbalanced power distribution system and constraint conditions by using an improved particle swarm algorithm, and solve the optimization model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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