Configuration method and device for optical storage capacity of power distribution network and computer program product

Abstract

The invention provides a power distribution network optical storage capacity configuration method and device and a computer program product, and the method comprises the steps: obtaining power quality parameters of a power distribution network, constructing a power quality evaluation system of the power distribution network, and enabling the power quality parameters to at least comprise one of voltage deviation, voltage fluctuation and harmonic voltage; constructing a configuration model of the optical storage capacity at least according to the electric energy quality; and solving the configuration model according to an optimization algorithm, and configuring the optical storage capacity of the power distribution network according to the solution of the configuration model. The method not only can effectively improve the power quality of the power distribution network, but also has economical efficiency, and solves the problems that a power quality optimization model in the prior art has limitation and the power quality level of the power distribution network is low.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic storage capacity configuration of a distribution network, and more specifically, to a method for configuring photovoltaic storage capacity of a distribution network, a configuration device, a computer-readable storage medium, and a computer program product. Background Art

[0002] In view of the power quality problem after the distributed power generation is connected to the grid, the optimization model constructed with a single power quality as the goal may lead to the reduction of other indicators of the distributed power generation connection and the overall performance of the power grid; the optimization model is constructed with power quality such as node voltage deviation, three-phase imbalance, harmonics as constraints. With the large increase of distributed energy grid connection, the power quality of the power grid continues to decline. Moreover, the results obtained by only using power quality as a constraint to construct the optimization model can no longer meet the requirements of the distribution network system. The existing theory takes the node voltage as the model constraint and considers a single power quality as the objective function, which cannot give full play to the regulation and control capabilities of the distribution network and cannot maximize the power quality level of the system. At the same time, with the increase of distributed photovoltaic capacity access, the power quality problem of the distribution network has gradually emerged. It is difficult to meet the power quality requirements by optimizing only from the perspective of distributed photovoltaics, and it is difficult to meet the requirements of the photovoltaic storage capacity of the distribution network photovoltaic storage system. Summary of the invention

[0003] The main purpose of the present application is to provide a method for configuring the photovoltaic storage capacity of a distribution network, a configuration device, a computer-readable storage medium and a computer program product, so as to at least solve the problem that the power quality optimization model in the prior art has limitations, the power quality level of the distribution network is low, and the photovoltaic storage capacity of the photovoltaic storage system of the distribution network is poor.

[0004] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for configuring the photovoltaic storage capacity of a distribution network is provided, comprising: obtaining power quality parameters of the distribution network, and constructing a power quality assessment system for the distribution network, wherein the power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of nodes of the distribution network; constructing a configuration model of the photovoltaic storage capacity based on at least the power quality; solving the configuration model based on an optimization algorithm, and configuring the photovoltaic storage capacity of the distribution network based on the solution of the configuration model.

[0005] Optionally, the voltage deviation is obtained according to a first formula, an actual voltage and a nominal voltage, wherein the first formula is: Where i represents the distributed photovoltaic access node i of the distribution network, U i,pv represents the actual voltage of the photovoltaic access node i, U i,Nrepresents the nominal voltage of the photovoltaic access node i; and obtaining the voltage fluctuation according to at least a second formula and the voltage fluctuation of the distribution network, wherein the second formula is: in: represents the voltage fluctuation caused by the distributed photovoltaic access at the i-th photovoltaic access node; k is the number of the i-th photovoltaic access node of the distribution network; R i represents the grid line resistance before the i-th photovoltaic access node; represents the maximum output of the distributed photovoltaic; α represents the ratio of the instantaneous change amplitude of the output power of the distributed photovoltaic to the rated output power of the photovoltaic, is the reference voltage of the distribution network; and obtaining the harmonic voltage at least according to a third formula and the fundamental voltage, wherein the third formula is: Among them, U H is the total harmonic content of the voltage at the grid-connected point of the distributed photovoltaic system, U h is the hth harmonic content of the voltage at the grid-connected point, THD and U1 are the total voltage distortion rate and fundamental voltage of the grid-connected point respectively.

[0006] Optionally, the construction of the power quality evaluation system of the distribution network includes: constructing an evaluation function of the power quality according to the power quality parameters and the evaluation index, wherein: the evaluation function is: F i =λ1ΔU i,pv +λ2ρ pv.i +λ3THD, where λ i is the weight coefficient of the evaluation index, F i is the evaluation value of the power quality at the photovoltaic access node i.

[0007] Optionally, constructing a configuration model of the photovoltaic storage capacity at least according to the power quality includes: constructing an upper configuration model according to the abandoned photovoltaic power of the distribution network and the annual investment and operation cost of the photovoltaic storage power generation system of the distribution network, wherein the abandoned photovoltaic power rate of the distribution network is: in, is the maximum power emitted by the photovoltaic access node i of the distribution network at time t, represents the actual power of the photovoltaic access node i at the time t, M pv is the number of grid-connected points of distributed photovoltaics, T is the total operating time of the distributed photovoltaics; the annual investment and operating cost of the photovoltaic storage power generation system is: in, is the annual photovoltaic installation and maintenance cost of the photovoltaic access node i, is the annual installation and operation cost of the energy storage system of the distribution network of the photovoltaic access node i, c tis the price of electricity per unit power of the distribution network at time t, is the power required to be purchased by the photovoltaic access node i at the time t, M ES is the number of grid-connected nodes of the energy storage system, M pv is the number of photovoltaic access nodes, T is the total operating time of the distributed photovoltaic, and M is the number of nodes in the distribution network; according to the network loss and power quality assessment value, a lower layer configuration model is constructed, wherein the network loss is: Wherein, M is the number of nodes in the distribution network; G ij is the conductance between the photovoltaic access node i and the photovoltaic access node j of the distribution network; P ij,t and Q ij,t is the active power and reactive power flowing between the photovoltaic access node i and the photovoltaic access node j at time t; U ij,t is the voltage between the photovoltaic access node i and the photovoltaic access node j at the time t, M i is the set of branches connected to the photovoltaic access node i, T is the total operation time of the distributed photovoltaic, and M is the number of nodes in the distribution network; the power quality assessment value is: Among them, F i,t is the power quality assessment value of the distributed photovoltaic access node i at the time t.

[0008] Optionally, solving the configuration model according to the optimization algorithm includes: a first determination step: determining the initial particle population of the upper configuration model according to the abandoned light of the distribution network and the annual investment and operation cost of the photovoltaic storage power generation system of the distribution network; an initialization step: initializing the position, speed, group extreme value and individual extreme value of the initial particle population according to the constraints of the initial particle population and the upper configuration model; a second determination step: using tent mapping to perform chaotic processing on the global optimal particles with group extreme values ​​to obtain a new particle population, and updating the group extreme value and individual extreme value after chaotic processing; a third determination step: according to the group extreme value and the individual extreme value after chaotic processing and the fourth formula and the fifth formula, obtaining the position and speed of the optimized particle population, and according to the optimized position and speed, obtaining the fitness of the particle population, and determining the group extreme value and the individual extreme value of the updated particle population according to the fitness value; a fourth determination step: updating the particle population according to the genetic algorithm, and according to the updated The position and speed of the particle population are determined to obtain the fitness of the particle population; the fifth determination step: comparing the fitness value of the offspring particles in the updated particle population with the fitness of the parent particles in the particle population before the update, replacing the parent particles with offspring particles whose fitness is greater than that of the parent particles, regenerating a new particle population, and calculating the fitness of the new particle population again; the judgment step: judging whether the number of iterative calculations in the fifth determination step reaches the maximum number of iterative calculations, and re-executing the third determination step when judging that the number of iterative calculations does not meet the maximum number of iterative calculations; the sixth determination step: judging whether the configuration model meets the lower configuration model when judging that the current number of iterative calculations meets the maximum number of iterative calculations, and recording the current result as the upper configuration model when judging that the configuration model does not meet the lower configuration model, and bringing the network loss and the power quality assessment value into the first determination step, and outputting the optimal solution of the configuration model when judging that the configuration model meets the lower configuration model.

[0009] Optionally, obtaining the position and velocity of the particle population according to the group extreme value and the individual extreme value after the chaos processing includes: obtaining the position of the particle population according to the fourth formula, the group extreme value and the individual extreme value, wherein the fourth formula includes: i (t+1)=x i (t)+v i (t+1); according to the fifth formula, the group extreme value and the individual extreme value, the velocity of the particle population is obtained, wherein the fifth formula includes: Among them, w is the inertia weight, t is the current iteration number, c1 and c2 are acceleration factors, r1 and r2 are random numbers on [0,1], and p best,i , g best is the individual extreme value and the group extreme value, v i (t), x i (t) is the speed and position of the current iteration.

[0010] Optionally, the constraint conditions of the configuration model of the photovoltaic storage capacity include at least one of the following: node photovoltaic output and capacity constraints, energy storage constraints, node power balance constraints, and system power flow constraints.

[0011] According to another aspect of the present application, a device for configuring the photovoltaic storage capacity of a distribution network is provided, comprising: a first acquisition module, used to acquire power quality parameters of the distribution network and construct a power quality assessment system for the distribution network, wherein the power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of nodes of the distribution network; a first construction module, used to construct a configuration model of the photovoltaic storage capacity based on at least the power quality; and a configuration module, used to solve the configuration model according to an optimization algorithm, and configure the photovoltaic storage capacity of the distribution network according to the solution of the configuration model.

[0012] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods for configuring the photovoltaic storage capacity of the distribution network.

[0013] According to another aspect of the present application, a computer program product is provided, comprising computer instructions, which, when executed by a processor, implement any one of the methods for configuring the photovoltaic storage capacity of a distribution network.

[0014] Applying the technical solution of the present application, the above-mentioned method for configuring the photovoltaic storage capacity of the distribution network first obtains the power quality parameters of the distribution network and constructs a power quality evaluation system for the distribution network, wherein the power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of the nodes of the distribution network; then, at least based on the power quality, a configuration model of the photovoltaic storage capacity is constructed; finally, according to the optimization algorithm, the configuration model is solved, and according to the solution of the configuration model, the photovoltaic storage capacity of the distribution network is configured. This method can not only effectively improve the power quality of the distribution network and provide a certain theoretical basis for the planning and construction of a new distribution network system, but also has economical efficiency, and solves the problems of limitations of the power quality optimization model in the prior art and low power quality level of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for executing a method for configuring the optical storage capacity of a distribution network provided in an embodiment of the present application is shown;

[0017] Figure 2 A schematic diagram of a flow chart of a method for configuring photovoltaic storage capacity of a distribution network provided according to an embodiment of the present application is shown;

[0018] Figure 3 A schematic diagram of a GA-CPSO algorithm according to an embodiment of the present application is shown;

[0019] Figure 4 A schematic diagram of an IEEE33 node system provided according to an embodiment of the present application is shown;

[0020] Figure 5 A schematic diagram of photovoltaic day power generation provided according to an embodiment of the present application is shown;

[0021] Figure 6 A schematic diagram of a peak-valley electricity price provided according to an embodiment of the present application is shown;

[0022] Figure 7 A schematic diagram of an external power grid output provided according to an embodiment of the present application is shown;

[0023] Figure 8 A schematic diagram of a system network loss provided according to an embodiment of the present application is shown;

[0024] Fig. 9 A schematic diagram of a 24-node energy storage charging and discharging power provided according to an embodiment of the present application is shown;

[0025] Fig.10 A schematic diagram of a 17-node energy storage charging and discharging power provided according to an embodiment of the present application is shown;

[0026] Fig.11 A structural block diagram of a device for configuring photovoltaic storage capacity in a distribution network provided according to an embodiment of the present application is shown.

[0027] The above drawings include the following reference numerals:

[0028] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0029] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] As introduced in the background technology, in view of the power quality problem after the distributed power generation is connected to the grid, constructing an optimization model with a single power quality as the goal may lead to the reduction of other indicators of the distributed power generation connected to the grid and the overall performance of the power grid; constructing an optimization model with power quality such as node voltage deviation, three-phase imbalance, harmonics as constraints, with the large increase of distributed energy connected to the grid, the power quality of the power grid continues to decline, and only using power quality as a constraint to construct an optimization model, the result can no longer meet the requirements of the distribution network system. The existing theory takes the node voltage as the model constraint and considers a single power quality as the objective function, which cannot give full play to the regulation and control capabilities of the distribution network and cannot maximize the power quality level of the system. At the same time, with the increase of distributed photovoltaic capacity access, the power quality problem of the distribution network gradually emerges. It is difficult to meet the power quality requirements by optimizing only from the perspective of distributed photovoltaics.

[0033] In order to solve the problem that the power quality optimization model in the prior art has limitations, the power quality level of the distribution network is low and thus the photovoltaic storage capacity of the photovoltaic storage system of the distribution network is poor, the embodiments of the present application provide a method for configuring the photovoltaic storage capacity of the distribution network, a configuration device, a computer-readable storage medium and a computer program product.

[0034] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0035] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for configuring the optical storage capacity of a distribution network according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0036] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the configuration method of the optical storage capacity of the distribution network in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0037] In this embodiment, a method for configuring the photovoltaic storage capacity of a distribution network running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] Figure 2 FIG. 1 is a flow chart of a method for configuring the photovoltaic storage capacity of a distribution network according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0039] Step S201, obtaining power quality parameters of the distribution network and constructing a power quality assessment system for the distribution network, wherein the power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of nodes of the distribution network;

[0040] Step S202, constructing a configuration model of the photovoltaic storage capacity at least according to the above power quality;

[0041] Step S203, solving the configuration model according to the optimization algorithm, and configuring the photovoltaic storage capacity of the distribution network according to the solution of the configuration model.

[0042] Through the configuration method of the photovoltaic storage capacity of the distribution network, the power quality parameters of the distribution network are first obtained, and the power quality evaluation system of the distribution network is constructed. The power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of the nodes of the distribution network; then, at least according to the power quality, a configuration model of the photovoltaic storage capacity is constructed; finally, according to the optimization algorithm, the configuration model is solved, and the photovoltaic storage capacity of the distribution network is configured according to the solution of the configuration model. This method can not only effectively improve the power quality of the distribution network and provide a certain theoretical basis for the planning and construction of a new distribution network system, but also has economical efficiency, and solves the problems of limitations of the power quality optimization model in the prior art and low power quality level of the distribution network.

[0043] Step S201, obtaining power quality parameters of the distribution network and constructing a power quality assessment system for the distribution network, wherein the power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of nodes of the distribution network;

[0044] Specifically, the range of relevant indicators that reflect the power quality level in the distribution network is relatively wide, including voltage deviation, voltage fluctuation, voltage harmonics, frequency deviation, etc., but the selection of indicators needs to be based on the actual situation of distributed photovoltaic grid-connected. According to the randomness and volatility of distributed photovoltaic output, the incorporation of distributed photovoltaic capacity is prone to cause voltage fluctuations in the distribution network, generating voltage deviations and harmonics. Therefore, this application selects more targeted voltage deviations, fluctuations and harmonics as power quality indicators, thereby building a more accurate power quality assessment system.

[0045] The step of obtaining the power quality parameters of the power distribution network includes the following steps:

[0046] Step S301, obtaining the voltage deviation according to a first formula, the actual voltage and the nominal voltage, wherein the first formula is: Where i represents the distributed photovoltaic access node i of the above distribution network, U i,pv represents the actual voltage of the photovoltaic access node i, U i,N represents the nominal voltage of the above photovoltaic access node i;

[0047] Specifically, voltage deviation is an indicator to measure the normal operation of the power grid system. Obtaining voltage deviation parameters can timely avoid voltage over-limit problems and improve the power quality of the power grid. After distributed photovoltaics are connected to the distribution network, the voltage at the photovoltaic access node will increase with the increase of photovoltaic output. When the photovoltaic output exceeds the absorption capacity of the distribution network itself, the node voltage may exceed the voltage limit and other problems.

[0048] Step S302, obtaining the voltage fluctuation at least according to a second formula and the voltage fluctuation of the distribution network, wherein the second formula is: in: represents the voltage fluctuation caused by the above distributed photovoltaic access at the i-th photovoltaic access node; k is the number of the i-th photovoltaic access node of the above distribution network; R i represents the grid line resistance before the i-th photovoltaic access node; represents the maximum output of the above distributed photovoltaic; α represents the ratio of the instantaneous change amplitude of the output power of the above distributed photovoltaic to the rated output power of the photovoltaic. is the reference voltage of the above distribution network;

[0049] Specifically, distributed photovoltaic output is often affected by external sunlight cycles, cloud changes, etc., which not only cause voltage deviations, but also produce more obvious voltage fluctuations. Obtaining voltage fluctuation parameters is conducive to evaluating the stability and power quality of the power grid system, and provides a basis for building a power quality assessment system for the distribution network.

[0050] Among them, R in the second formula above irepresents the grid line resistance before the i-th photovoltaic access node. In a specific implementation, for example, if the photovoltaic access node i is 5, then R i Indicates the resistance value between the 1st, 2nd, 3rd, and 4th nodes.

[0051] Step S303, obtaining the harmonic voltage at least according to a third formula and the fundamental voltage, wherein the third formula is: Among them, U H is the total harmonic content of the voltage at the grid-connected point of the above distributed photovoltaic system, U h is the hth harmonic content of the voltage at the above-mentioned grid-connected point, THD and U1 are the total voltage distortion rate and fundamental voltage of the above-mentioned grid-connected point respectively.

[0052] Specifically, distributed photovoltaics are connected to the grid through inverters. Due to the characteristics of the inverter, it is easy to bring harmonic pollution to the system and have a negative impact on the loads and equipment in the distribution network. Therefore, selecting voltage harmonics as an electric energy indicator can not only evaluate its impact on the power grid system, but also protect the loads and equipment in the distribution network in a timely manner and improve the power quality.

[0053] The construction of the power quality evaluation system of the distribution network includes: constructing the power quality evaluation function according to the power quality parameters and evaluation indicators, wherein the evaluation function is: i =λ1ΔU i,pv +λ2ρ pv.i +λ3THD, where λ i is the weight coefficient of the above evaluation indicators, F i is the evaluation value of the power quality at the photovoltaic access node i.

[0054] Specifically, voltage deviation, voltage fluctuation and voltage harmonics are comprehensively considered to construct a comprehensive power quality evaluation function, the analytic hierarchy process is used to determine the weight coefficients of the three evaluation indicators, and a comprehensive power quality evaluation system is established to facilitate objective reliability analysis of power quality.

[0055] Step S202, constructing a configuration model of the photovoltaic storage capacity at least according to the above power quality;

[0056] Specifically, the distributed photovoltaic access distribution network photovoltaic storage capacity optimization configuration model established in this application takes into account the economy and power quality of the distribution network. The upper-level optimization model is constructed with the total abandonment rate of the distribution network and the annual investment and operation cost of the photovoltaic storage power generation system as the objective function, and the lower-level optimization model is constructed with network loss and power quality assessment value as the target.

[0057] Wherein, at least according to the above-mentioned power quality, constructing a configuration model of photovoltaic storage capacity includes the following steps:

[0058] Step S401, constructing an upper configuration model according to the abandoned light of the distribution network and the annual investment and operation cost of the photovoltaic power generation system of the distribution network, wherein the abandoned light rate of the distribution network is: in, is the maximum power generated by the photovoltaic access node i of the above distribution network at time t, represents the actual power of the photovoltaic access node i at the above time t, M pv is the number of grid-connected points of distributed photovoltaics, and T is the total operating time of the above distributed photovoltaics;

[0059] Step S402, the annual investment and operation cost of the above-mentioned photovoltaic power generation system is: in, is the annual photovoltaic installation and maintenance cost of the photovoltaic access node i, is the annual installation and operation cost of the energy storage system of the distribution network of the photovoltaic access node i, c t is the power purchase price per unit of the distribution network at time t, is the power required to be purchased by the photovoltaic access node i at the above time t, M ES is the number of grid-connected nodes of the above energy storage system, M pv is the number of the photovoltaic access nodes, T is the total operation time of the distributed photovoltaic, and M is the number of nodes in the distribution network;

[0060] Specifically, the annual investment and maintenance cost of photovoltaic It can be expressed as:

[0061]

[0062] in, and Respectively represent the annual photovoltaic investment, operation and maintenance and treatment costs per unit capacity, represents the photovoltaic installed capacity of photovoltaic access node i.

[0063] Annual investment and operation and maintenance costs of the above energy storage system Calculation formula:

[0064]

[0065] in, and They represent the annual investment, operation and maintenance, and treatment costs of energy storage per unit power capacity respectively; and They represent the annual investment, operation and maintenance, and treatment costs of energy storage per unit energy capacity respectively; and They represent the power installed capacity and energy installed capacity of the energy storage system at the photovoltaic access node i respectively.

[0066] Step S403: construct a lower layer configuration model according to the network loss and the power quality assessment value, wherein the network loss is: Where, M is the number of nodes in the above distribution network; G ij is the conductance between the photovoltaic access node i and the photovoltaic access node j of the above distribution network; P ij,t and Q ij,t is the active power and reactive power flowing between the photovoltaic access node i and the photovoltaic access node j at time t; U ij,t is the voltage between the photovoltaic access node i and the photovoltaic access node j at the time t, M i is the set of branches connected to the photovoltaic access node i, T is the total operation time of the distributed photovoltaic, and M is the number of nodes in the distribution network;

[0067] Step S404, the power quality evaluation value is: Among them, F i,t is the power quality assessment value of the distributed photovoltaic access node i at the time t.

[0068] Step S203, solving the configuration model according to the optimization algorithm, and configuring the photovoltaic storage capacity of the distribution network according to the solution of the configuration model.

[0069] Specifically, since the optimal configuration of photovoltaic storage capacity needs to comprehensively consider the economy and power quality of the distribution network, it is a multi-objective nonlinear integer programming problem. The prior art only uses the particle swarm algorithm to solve the model, which is prone to fall into the local optimal solution. The GA-CPSO algorithm (Genetic Algorithm-Chaotic Particle Swarm Optimization, which introduces the particle swarm algorithm of chaotic mapping of genetic algorithm) has better solution speed and accuracy. In order to effectively analyze nonlinear problems, this application uses the GA-CPSO algorithm to solve the model.

[0070] Wherein, according to the optimization algorithm, solving the above configuration model includes the following steps:

[0071] Step S501, a first determination step: determining an initial particle population of the upper configuration model according to the abandoned light of the distribution network and the annual investment and operation cost of the photovoltaic power generation system of the distribution network;

[0072] Step S502, initialization step: initializing the position, velocity, group extreme value and individual extreme value of the initial particle population according to the constraints of the initial particle population and the upper configuration model;

[0073] Step S503, second determination step: using tent mapping to perform chaotic processing on the global optimal particles with group extreme values ​​to obtain a new particle population, and updating the group extreme values ​​and individual extreme values ​​after chaotic processing;

[0074] Step S504, a third determination step: obtaining the optimized position and velocity of the particle population according to the group extreme value and the individual extreme value after the chaos processing and the fourth formula and the fifth formula, and obtaining the fitness of the particle population according to the optimized position and the velocity, and determining the updated group extreme value and the individual extreme value of the particle population according to the fitness value;

[0075] Wherein, obtaining the position and velocity of the particle population according to the group extreme value and the individual extreme value after the chaotic processing includes the following steps:

[0076] Step S5041, according to the fourth formula, the group extreme value and the individual extreme value, the position of the particle population is obtained, wherein the fourth formula includes: i (t+1)=x i (t)+v i (t+1);

[0077] Specifically, chaotic mapping is introduced to improve the population diversity and search range of the particle swarm algorithm, and chaotic tent mapping is used to generate an initial population with uniform distribution.

[0078] Step S5042: Obtain the velocity of the particle population according to the fifth formula, the group extreme value, and the individual extreme value, wherein the fifth formula includes: Among them, w is the inertia weight, t is the current iteration number, c1 and c2 are acceleration factors, r1 and r2 are random numbers on [0,1], and p best,i , g best is the individual extreme value and the group extreme value, v i (t), x i (t) is the speed and position of the current iteration number mentioned above.

[0079] Specifically, according to the particle swarm speed and position update formula, the position and speed of the current particle are optimized, and the particle fitness is calculated. According to the particle fitness value, the updated particle global optimum (group extreme value) and individual optimum (individual extreme value) are re-determined to form a new optimized particle population.

[0080] Step S505, fourth determination step: updating the particle population according to the genetic algorithm, and obtaining the fitness of the particle population according to the updated position and speed of the particle population;

[0081] Specifically, in the iterative process of the particle swarm algorithm, the genetic algorithm is introduced to use the crossover and genetic characteristics of the genetic algorithm to improve the information interaction between particles, improve the convergence speed of the algorithm and avoid falling into the local optimal solution. The calculation formulas for crossover and mutation are as follows:

[0082] x new =α·x i +(1-α)·x j ,

[0083] x new =x i +β·(x max -x min )·randn(),

[0084] Among them, α and β are crossover and genetic factors respectively, randn() is a random number in [0, 1], and x new is a new particle, x i and x j are the i-th and j-th particles in the above particle group, respectively, x max and x min are the maximum and minimum values ​​of the mutation rate, respectively.

[0085] Step S506, fifth determination step: compare the fitness value of the offspring particles in the updated particle population with the fitness of the parent particles in the particle population before the update, replace the parent particles with offspring particles whose fitness is greater than that of the parent particles, regenerate a new particle population, and calculate the fitness of the new particle population again;

[0086] Step S507, judging step: judging whether the number of iterative calculations in the fifth determining step reaches the maximum number of iterative calculations, and if it is judged that the number of iterative calculations does not meet the maximum number of iterative calculations, re-executing the third determining step;

[0087] Step S508, the sixth determination step: when it is determined that the current number of iterative calculations satisfies the maximum number of iterations, determine whether the configuration model satisfies the lower-layer configuration model; when it is determined that the configuration model does not satisfy the lower-layer configuration model, record the current result as the upper-layer configuration model, and bring the network loss and the power quality assessment value into the first determination step; when it is determined that the configuration model satisfies the lower-layer configuration model, output the optimal solution of the configuration model.

[0088] Specifically, when it is determined that the above configuration model does not satisfy the above lower-level configuration model, it indicates that the upper-level configuration model is being solved at this time, the solved upper-level configuration model is recorded, and then the parameters of the lower-level configuration model, that is, the above-mentioned network loss and the above-mentioned power quality assessment value are brought into the above-mentioned first determination step to calculate the lower-level configuration model, and finally the result of the lower-level configuration model is output.

[0089] Among them, the constraints of the configuration model of photovoltaic storage capacity include at least one of the following: node photovoltaic output and capacity constraints, energy storage constraints, node power balance constraints, and system power flow constraints.

[0090] Specifically, the photovoltaic output and capacity constraints of the above nodes are:

[0091]

[0092] in, is the installed capacity of distributed photovoltaic at photovoltaic access node i, is the maximum capacity that can be installed at the photovoltaic access node i, is the active power output of the photovoltaic at the photovoltaic access node i at time t, are the upper and lower limits of photovoltaic output at time t.

[0093] The above energy storage constraints are:

[0094]

[0095] in, They represent the energy storage charging and discharging states respectively. are the energy storage charging and discharging power, They are The maximum and minimum values ​​of energy storage charging and discharging power, They are The maximum and minimum values ​​of energy storage charging and discharging power, is the energy storage state at time t, is the energy storage state at time t+1, μ i ES,dis , μ i ES,cha are the charging and discharging efficiencies of energy storage, respectively.

[0096]

[0097] Among them, P i ES is the capacity power of photovoltaic access node i, is the maximum capacity power of photovoltaic access node i, The capacity of energy storage installed for PV access node i, The upper and lower limits of the energy storage capacity installed for PV access node i.

[0098] The above node power balance constraint is:

[0099]

[0100] in, is the power generation at the photovoltaic access node i; is the load power at the photovoltaic access node i.

[0101] The power flow constraints of the above system are:

[0102]

[0103] Among them, P pv,i,t , P L,i,t are the active power injected by photovoltaic access node i and the active power consumed by load at time t; Q pv,i,t , Q L,i,t are the reactive power injected by photovoltaic access node i and the reactive power consumed by load at time t; P ES,i,t , Q C,i,t are the energy storage active power and capacitor reactive power injected into photovoltaic access node i at time t; G ij , B ij ,θ ij are the conductance, admittance and power angle between photovoltaic access node i and photovoltaic access node j respectively; U i.t , U j.t are the actual voltage of photovoltaic access node i at time t; M i is the number of branches connected to PV access node i.

[0104] For example, Figure 3As shown, first input the parameters of the upper configuration model, initialize the speed and position of the population particles, calculate the fitness, initialize the individual and the global optimum; use Tent mapping to chaotically transform the global optimal particles to generate a chaotic population; update the individual and global optimum of the population particles; update the position and speed of the particles, recalculate the fitness of the particles, and update the individual and global optimal particles; according to the crossover and mutation calculation of the genetic algorithm, retain half of the high fitness offspring particles to enter the next generation; calculate the fitness value, update the individual optimum and the global optimum of the particles; determine whether the current calculation iteration number is greater than the maximum iteration number, and if the first judgment result indicates greater than, determine whether the output result of the calculation is the lower configuration model, and if the second judgment result indicates yes, record the lower optimization result and output the optimal solution of the model; if the second judgment result indicates no, input the parameters of the lower configuration model to perform the above calculation until the solution of the lower configuration model is obtained; if the first judgment result indicates not greater than, return to the steps of updating the position and speed of the ion, recalculating the fitness of the particle, updating the individual and the global optimal particle, and recalculating.

[0105] In some specific embodiments, to verify the effectiveness of the method proposed by the present invention, Figure 4 As shown in the figure, a typical IEEE-33 node system is used, including nodes 1 to 33. The larger the node load, the larger the distributed power capacity that can be connected to the node. Therefore, the end of each branch is selected as the photovoltaic grid-connected access point, with a total of 4 photovoltaic access points (distributed photovoltaic No. 1, distributed photovoltaic No. 2, distributed photovoltaic No. 3 and distributed photovoltaic No. 4), and energy storage is added in 2 of the photovoltaics (distributed photovoltaic No. 2 and distributed photovoltaic No. 3). The cost parameters of each power generation unit are shown in Table 1:

[0106] Table 1 Cost parameters of each power generation unit

[0107]

[0108] Using the typical daily output of photovoltaic power generation and peak and valley electricity prices in a certain area, Figure 5 and Figure 6 As shown by Figure 5 (Time - Unit PV output power) It can be seen that the actual PV output is mainly concentrated in 05:00-19:00; Figure 6 (Time - Peak and Valley Electricity Prices) It can be known that the peak and valley electricity prices, ignoring the fluctuations in electricity prices, adopt the 10kV peak and valley electricity prices in a certain area. The peak electricity price is 0.73 yuan / (kW·h), the valley electricity price is 0.32 yuan / (kW·h), and the normal electricity price is 0.51 yuan / (kW·h).

[0109] At the same time, in order to ensure the reliability and solution speed of the photovoltaic storage capacity configuration results of the present invention, under the conditions of satisfying various constraints, the maximum photovoltaic output and configuration capacity limits of each grid-connected point (photovoltaic access node) are calculated as shown in Table 2.

[0110] Table 2 Node capacity limits

[0111]

[0112] This application adopts the GA-CPSO algorithm to solve the photovoltaic storage capacity optimization configuration model, and the optimal solution shown in Table 3 is obtained through iterative calculation of the algorithm.

[0113] Table 3 Light storage capacity configuration

[0114]

[0115] When the algorithm is used to calculate the optimal configuration results of the photovoltaic storage as shown in Table 3, the distribution network is connected to the operation. The operation of the distribution network system before and after the photovoltaic storage is connected is as follows Figure 7 (time - external power grid output) and Figure 8 (Time - System Network Loss). The access of the photovoltaic storage system greatly reduces the demand of the distribution network for external power purchases. The access of the photovoltaic storage system reduces the operating cost of the power grid system and shows good economic benefits. At the same time, with the access of the photovoltaic storage system, the system network loss is greatly improved. Under the condition of maximum photovoltaic output, the system network loss is reduced by more than 60%, which improves the power quality of the distribution network to a certain extent.

[0116] Table 4 Comparison of PV-storage access results

[0117]

[0118]

[0119] The GA-CPSO algorithm is used to solve the photovoltaic storage capacity optimization configuration model. The solution results of each objective function are shown in Table 4. With the access of distributed photovoltaic storage system, the overall system network loss is reduced by about 47% compared with that before access, and the system network loss has a significant decrease; and compared with the time when distributed photovoltaic storage is not connected, the annual investment and operation and maintenance cost of the system is reduced by nearly 3 million yuan, which has good economic benefits; although the power quality assessment value is slightly lower than when photovoltaic storage is not connected, its value is still at an excellent level of power quality.

[0120] Fig. 9 (Time - Energy Storage Charge and Discharge Power) represents the energy storage charge and discharge power at 24 nodes. Fig.10 (time - energy storage charging and discharging power) represents the energy storage charging and discharging power at node 17, which is given by Fig. 9 and Fig.10It can be seen that energy storage plays a positive role in reducing the power purchase of distribution networks. Energy storage can be charged when photovoltaic power overflows and discharged when photovoltaic output is insufficient, playing the role of peak shaving and valley filling.

[0121] When the photovoltaic storage system is connected to the grid with the above optimal capacity, the voltage fluctuations at the grid connection point caused by each photovoltaic storage system are shown in Table 5. It can be seen that the photovoltaic storage grid connection has little effect on the voltage fluctuations of each node under the optimal capacity. It can be seen that when the photovoltaic grid connection capacity is small, the node voltage fluctuations of nodes 21 and 32 are also low; nodes 17 and 24 have large grid connection capacity and are not line end nodes, and are far away from the distribution network bus, but the maximum voltage fluctuation is only 1.73%. Compared with the photovoltaic only connection scenario, the simultaneous connection of photovoltaic storage has well suppressed the voltage fluctuations of the grid connection nodes, showing the role of energy storage in smoothing the photovoltaic output fluctuations, and reflecting the role of the photovoltaic storage capacity optimization planning method proposed in the present invention in improving the power quality.

[0122] Table 5 Voltage fluctuations at grid-connected nodes

[0123]

[0124] In order to verify the effectiveness of the method proposed in this application in considering the improvement of power quality and the economy of distribution network, the following situations are compared and analyzed respectively:

[0125] 1. Only consider the optimal power quality and configure the photovoltaic storage capacity.

[0126] 2. Only consider the system investment and operating costs and configure the optical storage capacity.

[0127] 3. Adopt this application model, comprehensively consider the power quality and system investment and operation costs, and configure the photovoltaic storage capacity.

[0128] Table 6 shows the optimization results of the photovoltaic storage capacity under the above three situations. Considering only the improvement of power quality, according to the power quality evaluation value in the table, the power quality is excellent, the system network loss is low, the abandoned light rate is 0, but the investment and operation cost is high; considering only the investment and operation cost, it can be seen from the power quality evaluation value that the power quality is reduced, the abandoned light rate and the system network loss increase; considering the power quality and investment and operation cost comprehensively, the system power quality level is excellent, the investment and operation cost is reduced, and the system network loss and abandoned light rate are low, which meets the requirements. By comparison, it can be concluded that the photovoltaic storage capacity optimization method proposed in this application that takes into account both power quality and investment and operation cost can effectively improve the economy of the system, while ensuring the power quality of the system, which proves the effectiveness of the model proposed in this application.

[0129] Table 6 Analysis of grid connection results under different conditions

[0130] Different situations Power quality System network loss / MW·h Cost / million yuan Abandoned light rate / % 1 102.671 1.361 11.964 0 2 137.934 1.763 9.637 7.35％ 3 107.734 1.395 10.375 1.27％

[0131] Comprehensively considering the economic efficiency and power quality of the distribution network, the upper optimization model is constructed with the total abandoned light rate of the distribution network and the annual investment and operation cost of the photovoltaic storage power generation system as the objective function; the lower optimization model is constructed with the network loss power quality assessment value as the target; the GA-CPSO algorithm is used to solve the model to improve the power quality of the power grid while ensuring the economy. Through case analysis, the method proposed in this invention takes into account both the economic efficiency and power quality of the distribution network. The access of the photovoltaic storage system has a positive effect on improving the power quality and new energy consumption of the distribution network, and has a certain reference significance for the photovoltaic storage access planning of the distribution network.

[0132] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0133] The embodiment of the present application also provides a device for configuring the photovoltaic storage capacity of a distribution network. It should be noted that the device for configuring the photovoltaic storage capacity of a distribution network in the embodiment of the present application can be used to execute the configuration method for the photovoltaic storage capacity of a distribution network provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0134] The following introduces the configuration device of the photovoltaic storage capacity of the distribution network provided in the embodiment of the present application.

[0135] Fig.11 Schematic diagram of a control device for configuring a distribution network photovoltaic storage capacity according to an embodiment of the present application. Fig.11 As shown, the device includes: a first acquisition module 10, a first construction module 20 and a configuration module 30. The first acquisition module 10 is used to acquire the power quality parameters of the above-mentioned distribution network and construct the power quality evaluation system of the above-mentioned distribution network. The above-mentioned power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of the nodes of the above-mentioned distribution network; the first construction module 20 is used to construct a configuration model of the photovoltaic storage capacity based on at least the above-mentioned power quality; the configuration module 30 is used to solve the above-mentioned configuration model according to the optimization algorithm, and configure the photovoltaic storage capacity of the above-mentioned distribution network according to the solution of the above-mentioned configuration model.

[0136] The above-mentioned configuration device of the photovoltaic storage capacity of the distribution network of the present application includes: a first acquisition module, a first construction module and a configuration module. The first acquisition module is used to obtain the power quality parameters of the distribution network and construct a power quality evaluation system of the distribution network. The power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of the nodes of the distribution network; the first construction module is used to construct a configuration model of the photovoltaic storage capacity based on at least the power quality; the configuration module is used to solve the configuration model according to the optimization algorithm, and configure the photovoltaic storage capacity of the distribution network according to the solution of the configuration model. This method can not only effectively improve the power quality of the distribution network and provide a certain theoretical basis for the planning and construction of a new distribution network system, but also has economical efficiency, and solves the problem that the power quality optimization model in the prior art has limitations, the power quality level of the distribution network is low, and then leads to the problem of poor photovoltaic storage capacity of the photovoltaic storage system of the distribution network.

[0137] In some examples, the first acquisition module includes a first sub-acquisition module, a second sub-acquisition module and a third sub-acquisition module, and the first sub-acquisition module is used to acquire the voltage deviation according to a first formula, an actual voltage and a nominal voltage, wherein the first formula is: Where i represents the distributed photovoltaic access node i of the above distribution network, U i,pv represents the actual voltage of the photovoltaic access node i, U i,N represents the nominal voltage of the photovoltaic access node i; the second sub-acquisition module is used to obtain the voltage fluctuation according to at least the second formula and the voltage fluctuation of the distribution network, wherein the second formula is: in, represents the voltage fluctuation caused by the above distributed photovoltaic access at the i-th photovoltaic access node, k is the number of the i-th photovoltaic access node in the above distribution network, R i represents the grid line resistance before the i-th photovoltaic access node, P i pv represents the maximum output of the above distributed photovoltaic, α represents the ratio of the instantaneous change amplitude of the output power of the above distributed photovoltaic to the rated output power of the photovoltaic, is the reference voltage of the distribution network; and the third sub-acquisition module is used to obtain the harmonic voltage according to at least a third formula and the fundamental voltage, wherein the third formula is: Among them, U H is the total harmonic content of the voltage at the grid-connected point of the above distributed photovoltaic system, U h is the hth harmonic content of the voltage at the above-mentioned grid-connected point, THD and U1 are the total voltage distortion rate and fundamental voltage of the above-mentioned grid-connected point respectively.

[0138] In some examples, the first acquisition module further includes a first sub-construction module, and the first sub-construction module is used to construct the evaluation function of the power quality according to the power quality parameters and the evaluation index, wherein the evaluation function is: i =λ1ΔU i,pv +λ2ρ pv.i +λ3THD, where λ i is the weight coefficient of the above evaluation indicators, F i is the evaluation value of the power quality at the photovoltaic access node i.

[0139] In some examples, the first construction module includes a second sub-construction module, a fourth sub-acquisition module, a third sub-construction module and a fifth sub-acquisition module, and the second sub-construction module is used to construct an upper-layer configuration model according to the abandoned light of the above-mentioned distribution network and the annual investment and operation cost of the photovoltaic storage power generation system of the above-mentioned distribution network, wherein the abandoned light rate of the above-mentioned distribution network is: in, is the maximum power generated by the photovoltaic access node i of the above distribution network at time t, represents the actual power of the photovoltaic access node i at the above time t, M pv is the number of grid-connected points of distributed photovoltaics, and T is the total operating time of the above distributed photovoltaics; the fourth sub-acquisition module is used to obtain the annual investment and operating cost of the above photovoltaic storage power generation system: in, is the annual photovoltaic installation and maintenance cost of the photovoltaic access node i, is the annual installation and operation cost of the energy storage system of the distribution network of the photovoltaic access node i, c t is the power purchase price per unit of the distribution network at time t, is the power required to be purchased by the photovoltaic access node i at the above time t, M ES is the number of grid-connected nodes of the above energy storage system, M pv is the number of the photovoltaic access nodes, T is the total operation time of the distributed photovoltaic, and M is the number of nodes of the distribution network; the third sub-construction module is used to construct a lower-layer configuration model according to the network loss and power quality assessment value, wherein the network loss is: Where, M is the number of nodes in the above distribution network; G ij is the conductance between the photovoltaic access node i and the photovoltaic access node j of the above distribution network; P ij,t and Q ij,t is the active power and reactive power flowing between the photovoltaic access node i and the photovoltaic access node j at time t; U ij,t is the voltage between the photovoltaic access node i and the photovoltaic access node j at the time t, M iis the set of branches connected to the photovoltaic access node i, T is the total operation time of the distributed photovoltaic, and M is the number of nodes in the distribution network; the fifth sub-acquisition module is used to obtain the power quality assessment value: Among them, F i,t is the power quality assessment value of the distributed photovoltaic access node i at the time t.

[0140] In some instances, the configuration module includes a first determination module, an initialization module, a second determination module, a third determination module, a fourth determination module, a fifth determination module, a judgment module and a sixth determination module, wherein the first determination module is used to execute a first determination step, and determine the initial particle population of the upper configuration model according to the abandoned light of the distribution network and the annual investment and operation cost of the photovoltaic storage power generation system of the distribution network; the initialization module is used to execute an initialization step, and initialize the position, speed, group extreme value and individual extreme value of the initial particle population according to the constraints of the initial particle population and the upper configuration model; the second determination module is used to The second determination step is executed, and the global optimal particles with group extreme values ​​are chaotically processed by using tent mapping to obtain a new particle population, and the group extreme value and the individual extreme value after the chaotic processing are updated; the third determination module is used to execute the third determination step, and the optimized position and speed of the particle population are obtained according to the group extreme value and the individual extreme value after the chaotic processing and the fourth formula and the fifth formula, and the fitness of the particle population is obtained according to the optimized position and the speed, and the updated group extreme value and the individual extreme value of the particle population are determined according to the fitness value; the fourth determination module is used to execute the third determination step, and the group extreme value and the individual extreme value of the particle population are obtained according to the fourth formula and the fifth formula. The fourth determination step is to update the particle population according to the genetic algorithm, and obtain the fitness of the particle population according to the position and speed of the updated particle population; the fifth determination module is used to execute the fifth determination step, compare the fitness value of the offspring particles in the updated particle population with the fitness of the parent particles in the particle population before the update, replace the parent particles with the offspring particles whose fitness is greater than the parent particles, regenerate a new particle population, and calculate the fitness of the new particle population again; the judgment module is used to execute the judgment step to judge whether the number of iterative calculations in the fifth determination step has reached the maximum iteration number times, and when it is judged that the number of iterative calculations does not meet the maximum number of iterations, re-execute the third determination step; the sixth determination module is used to execute the sixth determination step, and when it is judged that the current number of iterative calculations meets the maximum number of iterations, judge whether the configuration model meets the lower-layer configuration model, and when it is judged that the configuration model does not meet the lower-layer configuration model, record the current result as the upper-layer configuration model, and bring the network loss and the power quality assessment value into the first determination step, and when it is judged that the configuration model meets the lower-layer configuration model, output the optimal solution of the configuration model.

[0141] In some examples, the fourth determination module further includes a first sub-determination module and a second sub-determination module, wherein the first sub-determination module is used to obtain the position of the particle population according to the fourth formula, the group extreme value and the individual extreme value, wherein the fourth formula includes: i (t+1)=x i(t)+v i (t+1); the second sub-determination module is used to obtain the velocity of the particle population according to the fifth formula, the group extreme value and the individual extreme value, wherein the fifth formula includes: Among them, w is the inertia weight, t is the current iteration number, c1 and c2 are acceleration factors, r1 and r2 are random numbers on [0,1], and p best,i , g best is the individual extreme value and the group extreme value, v i (t), x i (t) is the speed and position of the current iteration number mentioned above.

[0142] In some instances, the configuration module includes constraints of the configuration model of the above-mentioned photovoltaic storage capacity, including at least one of the following: node photovoltaic output and capacity constraints, energy storage constraints, node power balance constraints, and system power flow constraints.

[0143] The above-mentioned configuration device for the photovoltaic storage capacity of the distribution network includes a processor and a memory, and the above-mentioned first acquisition module and the like are stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in any combination.

[0144] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the power quality optimization model in the prior art has limitations, and the power quality level of the distribution network is low.

[0145] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0146] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method for configuring the photovoltaic storage capacity of the distribution network.

[0147] An embodiment of the present invention provides a processor, which is used to run a program, wherein the method for configuring the photovoltaic storage capacity of the distribution network is executed when the program is run.

[0148] An embodiment of the present invention provides a device, the device including a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0149] Step S201, obtaining power quality parameters of the distribution network and constructing a power quality assessment system for the distribution network, wherein the power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of nodes of the distribution network;

[0150] Step S202, constructing a configuration model of the photovoltaic storage capacity at least according to the above power quality;

[0151] Step S203, solving the configuration model according to the optimization algorithm, and configuring the photovoltaic storage capacity of the distribution network according to the solution of the configuration model.

[0152] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0153] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0154] Step S201, obtaining power quality parameters of the distribution network and constructing a power quality assessment system for the distribution network, wherein the power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of nodes of the distribution network;

[0155] Step S202, constructing a configuration model of the photovoltaic storage capacity at least according to the above power quality;

[0156] Step S203, solving the configuration model according to the optimization algorithm, and configuring the photovoltaic storage capacity of the distribution network according to the solution of the configuration model.

[0157] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0158] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0159] 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 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0160] 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.

[0161] 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 The steps for the functions specified in one or more boxes.

[0162] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0163] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0164] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0165] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0166] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0167] 1) The configuration method of the photovoltaic storage capacity of the distribution network of the present application first obtains the power quality parameters of the distribution network and constructs a power quality evaluation system for the distribution network. The power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of the nodes of the distribution network; then, at least according to the power quality, a configuration model of the photovoltaic storage capacity is constructed; finally, according to the optimization algorithm, the configuration model is solved, and the photovoltaic storage capacity of the distribution network is configured according to the solution of the configuration model. This method can not only effectively improve the power quality of the distribution network and provide a certain theoretical basis for the planning and construction of new distribution network systems, but also has economical efficiency, and solves the problems of limitations of power quality optimization models in the prior art and low power quality level of the distribution network.

[0168] 2) The configuration device of the photovoltaic storage capacity of the distribution network of the present application includes: a first acquisition module, a first construction module and a configuration module. The first acquisition module is used to acquire the power quality parameters of the distribution network and construct a power quality evaluation system of the distribution network. The power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of the nodes of the distribution network; the first construction module is used to construct a configuration model of the photovoltaic storage capacity based on at least the power quality; the configuration module is used to solve the configuration model according to the optimization algorithm, and configure the photovoltaic storage capacity of the distribution network according to the solution of the configuration model. This method can not only effectively improve the power quality of the distribution network and provide a certain theoretical basis for the planning and construction of a new distribution network system, but also has economical efficiency and solves the problems of limitations of the power quality optimization model in the prior art and low power quality level of the distribution network.

[0169] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for configuring the photovoltaic storage capacity of a distribution network, characterized in that: The method comprises: Acquire power quality parameters of the distribution network and construct a power quality assessment system for the distribution network, wherein the power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of nodes of the distribution network; constructing a configuration model of photovoltaic storage capacity based at least on the power quality; The configuration model is solved according to the optimization algorithm, and the photovoltaic storage capacity of the distribution network is configured according to the solution of the configuration model.

2. The configuration method according to claim 1, characterized in that: The obtaining of the power quality parameters of the distribution network includes: The voltage deviation is obtained according to a first formula, an actual voltage and a nominal voltage, wherein the first formula is: Where i represents the distributed photovoltaic access node i of the distribution network, U i,pv represents the actual voltage of the photovoltaic access node i, U i,N represents the nominal voltage of the photovoltaic access node i; The voltage fluctuation is obtained at least according to a second formula and the voltage fluctuation of the distribution network, wherein the second formula is: in: represents the voltage fluctuation caused by the distributed photovoltaic access at the i-th photovoltaic access node; k is the number of the i-th photovoltaic access node of the distribution network; R i represents the grid line resistance before the ith photovoltaic access node; P i pv represents the maximum output of the distributed photovoltaic; α represents the ratio of the instantaneous change amplitude of the output power of the distributed photovoltaic to the rated output power of the photovoltaic, is the reference voltage of the power distribution network; The harmonic voltage is obtained at least according to a third formula and the fundamental voltage, wherein the third formula is: Among them, U H is the total harmonic content of the voltage at the grid-connected point of the distributed photovoltaic system, U h is the hth harmonic content of the voltage at the grid-connected point, THD and U1 are the total voltage distortion rate and fundamental voltage of the grid-connected point respectively.

3. The configuration method according to claim 2, characterized in that: The power quality assessment system for constructing the distribution network includes: According to the power quality parameters and the evaluation index, the evaluation function of the power quality is constructed, wherein: the evaluation function is: F i =λ1ΔU i,pv +λ2ρ pv.i +λ3THD, Among them, λ i is the weight coefficient of the evaluation index, F i is the evaluation value of the power quality at the photovoltaic access node i.

4. The configuration method according to claim 1, characterized in that: The step of constructing a configuration model of the photovoltaic storage capacity at least according to the power quality comprises: According to the abandoned light of the distribution network and the annual investment and operation cost of the photovoltaic power generation system of the distribution network, an upper configuration model is constructed, wherein the abandoned light rate of the distribution network is: in, is the maximum power emitted by the photovoltaic access node i of the distribution network at time t, represents the actual power of the photovoltaic access node i at the time t, M pv is the number of grid-connected points of distributed photovoltaics, and T is the total operating time of the distributed photovoltaics; The annual investment and operation cost of the photovoltaic power generation system is: in, is the annual photovoltaic installation and maintenance cost of the photovoltaic access node i, is the annual installation and operation cost of the energy storage system of the distribution network of the photovoltaic access node i, c t is the price of electricity per unit power of the distribution network at time t, is the power required to be purchased by the photovoltaic access node i at the time t, M ES is the number of grid-connected nodes of the energy storage system, M pv is the number of photovoltaic access nodes, T is the total operation time of the distributed photovoltaic, and M is the number of nodes in the distribution network; According to the network loss and power quality assessment value, a lower layer configuration model is constructed, wherein the network loss is: Wherein, M is the number of nodes in the distribution network; G ij is the conductance between the photovoltaic access node i and the photovoltaic access node j of the distribution network; P ij,t and Q ij,t is the active power and reactive power flowing between the photovoltaic access node i and the photovoltaic access node j at time t; U ij,t is the voltage between the photovoltaic access node i and the photovoltaic access node j at the time t, M i is the set of branches connected to the photovoltaic access node i, T is the total operation time of the distributed photovoltaic, and M is the number of nodes in the distribution network; The power quality assessment value is: Among them, F i,t is the power quality assessment value of the distributed photovoltaic access node i at the time t.

5. The configuration method according to claim 4, characterized in that: Solving the configuration model according to the optimization algorithm includes: The first determination step: determining the initial particle population of the upper configuration model according to the abandoned light of the distribution network and the annual investment and operation cost of the photovoltaic power generation system of the distribution network; Initialization step: initializing the position, velocity, group extreme value and individual extreme value of the initial particle population according to the constraints of the initial particle population and the upper configuration model; The second determination step: using tent mapping to perform chaotic processing on the global optimal particles with group extreme values, obtaining a new particle population, and updating the group extreme values ​​and individual extreme values ​​after chaotic processing; The third determination step: obtaining the optimized position and velocity of the particle population according to the group extreme value and the individual extreme value after the chaos processing and the fourth formula and the fifth formula, and obtaining the fitness of the particle population according to the optimized position and the velocity, and determining the updated group extreme value and the individual extreme value of the particle population according to the fitness value; Fourth determination step: updating the particle population according to the genetic algorithm, and obtaining the fitness of the particle population according to the updated position and speed of the particle population; The fifth determination step: comparing the fitness value of the offspring particles in the updated particle population with the fitness of the parent particles in the particle population before the update, replacing the parent particles with offspring particles whose fitness is greater than that of the parent particles, regenerating a new particle population, and calculating the fitness of the new particle population again; Determining step: determining whether the number of iterative calculations in the fifth determining step reaches a maximum number of iterative calculations, and if it is determined that the number of iterative calculations does not meet the maximum number of iterative calculations, re-executing the third determining step; The sixth determination step: when it is determined that the current number of iterative calculations meets the maximum number of iterations, determine whether the configuration model meets the lower-level configuration model; when it is determined that the configuration model does not meet the lower-level configuration model, record the current result as the upper-level configuration model, and bring the network loss and the power quality assessment value into the first determination step; when it is determined that the configuration model meets the lower-level configuration model, output the optimal solution of the configuration model.

6. The configuration method according to claim 5, characterized in that: According to the group extreme value and the individual extreme value after the chaos processing, the position and speed of the particle population are obtained, including: According to the fourth formula, the group extreme value and the individual extreme value, the position of the particle population is obtained, wherein the fourth formula includes: i (t+1)=x i (t)+v i (t+1); According to the fifth formula, the group extreme value and the individual extreme value, the velocity of the particle population is obtained, wherein the fifth formula includes: v i (t+1)=w·v i (t)+c1·r1·(p best,i -x i (t))+c2·r2·(g best -x i (t)), Among them, w is the inertia weight, t is the current iteration number, c1 and c2 are acceleration factors, r1 and r2 are random numbers on [0,1], and p best,i , g best is the individual extreme value and the group extreme value, v i (t), x i (t) is the speed and position of the current iteration.

7. The configuration method according to claim 1, characterized in that: The constraint conditions of the configuration model of the photovoltaic storage capacity include at least one of the following: node photovoltaic output and capacity constraints, energy storage constraints, node power balance constraints, and system power flow constraints.

8. A device for configuring the photovoltaic storage capacity of a distribution network, characterized in that: include: A first acquisition module is used to acquire power quality parameters of the distribution network and construct a power quality evaluation system of the distribution network, wherein the power quality parameters include at least one of the following: voltage deviation, voltage fluctuation and harmonic voltage of nodes of the distribution network; A first construction module, configured to construct a configuration model of the photovoltaic storage capacity based at least on the power quality; A configuration module is used to solve the configuration model according to an optimization algorithm, and configure the photovoltaic storage capacity of the distribution network according to the solution of the configuration model.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for configuring the photovoltaic storage capacity of the distribution network according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the method for configuring the photovoltaic storage capacity of the distribution network according to any one of claims 1 to 7 is implemented.

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