Power distribution network light storage capacity configuration method, device and computer program product

Abstract

This application provides a method, apparatus, and computer program product for configuring photovoltaic (PV) and energy storage (ESS) capacity in a distribution network. The method includes: acquiring power quality parameters of the distribution network; constructing a power quality assessment system for the distribution network, wherein the power quality parameters include at least one of voltage deviation, voltage fluctuation, and harmonic voltage; constructing a configuration model for PV and ESS capacity based on at least the power quality parameters; solving the configuration model using an optimization algorithm; and configuring the PV and ESS capacity of the distribution network based on the solution of the configuration model. This method effectively improves the power quality of the distribution network while also being economical, solving the problems of limitations in existing power quality optimization models and low power quality levels in distribution networks.

Description

Technical Field

[0001] This application relates to the field of photovoltaic and energy storage capacity configuration in distribution networks. Specifically, it relates to a method, device, computer-readable storage medium, and computer program product for configuring photovoltaic and energy storage capacity in distribution networks. Background Technology

[0002] To address power quality issues after distributed generation (DG) is integrated into the grid, constructing optimization models solely based on power quality as the objective may lead to a decrease in other grid performance indicators and an overall reduction in grid performance. Constructing optimization models with power quality constraints such as node voltage deviation, three-phase imbalance, and harmonics also suffers from a continuous decline in grid power quality as DG integration increases. Furthermore, using only power quality as a constraint in optimization models no longer meets the requirements of distribution network systems. Existing theories, which use node voltage as a model constraint and consider only power quality as the objective function, fail to fully utilize the control capabilities of the distribution network and cannot maximize the system's power quality level. Simultaneously, with the increase in distributed photovoltaic (PV) capacity, distribution network power quality issues are becoming increasingly apparent. Optimization solely from the perspective of distributed PV is insufficient to meet power quality requirements, and consequently, fails to meet the capacity requirements of PV-storage systems within the distribution network. Summary of the Invention

[0003] The main objective of this application is to provide a method, device, computer-readable storage medium, and computer program product for configuring the photovoltaic and energy storage capacity of a distribution network, 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 thus the photovoltaic and energy storage capacity of the distribution network photovoltaic and energy storage system is poor.

[0004] To achieve the above objectives, according to one aspect of this application, a method for configuring photovoltaic and energy storage capacity in a distribution network is provided, comprising: obtaining power quality parameters of the distribution network; 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 in the distribution network; constructing a configuration model for photovoltaic and energy storage capacity based at least on the power quality; solving the configuration model according to an optimization algorithm; and configuring the photovoltaic and energy storage capacity of the distribution network based on the solution of the configuration model.

[0005] Optionally, the voltage deviation is obtained according to the 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 distribution network, U i,pv U represents the actual voltage of the photovoltaic access node i. i,NThe nominal voltage of the photovoltaic access node i is represented; the voltage fluctuation is obtained at least according to the second formula and the voltage fluctuation of the distribution network, wherein the second formula is: in: This 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 in the distribution network; R i This represents the resistance of the power grid line before the i-th photovoltaic access node; This represents the maximum output power of the distributed photovoltaic system; α represents the ratio of the instantaneous change in the output power of the distributed photovoltaic system to the rated output power of the photovoltaic system. The reference voltage of the distribution network is used; the harmonic voltage is obtained at least according to the third formula and the fundamental voltage, wherein the third formula is: Among them, U H U represents the total harmonic distortion (THD) of the voltage at the grid connection point of the distributed photovoltaic system. h The h-th harmonic content of the voltage at the grid connection point is denoted as THD, and THD and U1 are the total voltage distortion rate and fundamental voltage of the grid connection point, respectively.

[0006] Optionally, constructing the power quality assessment system for the distribution network includes: constructing an assessment function for the power quality based on the power quality parameters and assessment indicators, wherein the assessment function is: F i =λ1ΔU i,pv +λ2ρ pv.i +λ3THD, where λ i F represents the weighting coefficient of the evaluation index. i The power quality assessment value at the photovoltaic access node i.

[0007] Optionally, the step of constructing a configuration model for photovoltaic and energy storage capacity based at least on the power quality includes: constructing an upper-level configuration model based on the curtailment rate of the distribution network and the annual investment and operating costs of the photovoltaic and energy storage power generation system in the distribution network, wherein the curtailment rate of the distribution network is: in, Let be the maximum power output of the photovoltaic access node i in the distribution network at time t. M represents the actual power of the photovoltaic access node i at time t. pv Let T be the number of grid-connected points for the distributed photovoltaic system, and T be the total operating time of the distributed photovoltaic system. The annual investment and operating cost of the photovoltaic-storage power generation system is: in, The annual installation and maintenance cost of the photovoltaic system at the photovoltaic access node i is [value missing]. c represents the annual installation and operation cost of the energy storage system of the distribution network at the photovoltaic access node i. tLet be the unit power purchase price of the distribution network at time t. M represents the required power consumption of the photovoltaic access node i at time t. ES M represents the number of grid-connected nodes in the energy storage system. pv Let T be the number of photovoltaic access nodes, T be the total operating time of the distributed photovoltaic system, and M be the number of nodes in the distribution network. Based on network loss and power quality assessment values, a lower-level configuration model is constructed, where the network loss is: Where M is the number of nodes in the distribution network; G ij P is the electrical conductance between photovoltaic access node i and photovoltaic access node j in the distribution network; ij,t and Q ij,t Let U be the active power and reactive power flowing between photovoltaic access node i and photovoltaic access node j at time t; ij,t Let M be the voltage between photovoltaic access node i and photovoltaic access node j at time t. i Let T be the set of branches connected to the photovoltaic access node i, T be the total operating time of the distributed photovoltaic system, and M be the number of nodes in the distribution network; the power quality assessment value is: Among them, F i,t The power quality assessment value of the distributed photovoltaic access node i at 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-level configuration model based on the curtailment of solar power in the distribution network and the annual investment and operating cost of the solar-storage power generation system in the distribution network; an initialization step: initializing the position, velocity, population extreme value, and individual extreme value of the initial particle population based on the constraints of the initial particle population and the upper-level configuration model; a second determination step: using tent mapping to perform chaotic processing on the globally optimal particles with population extreme values ​​to obtain a new particle population, and updating the population extreme value and individual extreme value after chaotic processing; a third determination step: obtaining the optimized position and velocity of the particle population based on the population extreme value and individual extreme value after chaotic processing, as well as the fourth and fifth formulas, and obtaining the fitness of the particle population based on the optimized position and velocity, and determining the updated population extreme value and individual extreme value of the particle population based on the fitness value; a fourth determination step: updating the particle population according to a genetic algorithm, and determining the updated population extreme value and individual extreme value based on the updated position and velocity. The fifth determination step involves comparing the fitness values ​​of the child particles in the updated particle population with the fitness values ​​of the parent particles in the unupdated particle population. Child particles with fitness greater than their parent particles replace the parent particles, generating a new particle population, and recalculating the fitness of the new particle population. The sixth determination step involves determining whether the number of iterations in the fifth determination step has reached the maximum number of iterations. If the number of iterations does not meet the maximum number of iterations, the third determination step is re-executed. The seventh determination step involves determining whether the current number of iterations meets the maximum number of iterations, whether the configuration model satisfies the lower-level configuration model. If the configuration model does not meet the lower-level configuration model, the current result is recorded as the upper-level configuration model, and the network loss and power quality assessment values ​​are input into the first determination step. If the configuration model satisfies the lower-level configuration model, the optimal solution of the configuration model is output.

[0009] Optionally, obtaining the position and velocity of the particle population based on the chaotically processed population extrema and the individual extrema includes: obtaining the position of the particle population based on the fourth formula, the population extrema, and the individual extrema, wherein the fourth formula includes: x i (t+1)=x i (t)+v i (t+1); Based on the fifth formula, the population extreme value, and the individual extreme value, the velocity of the particle population is obtained, wherein the fifth formula includes: Where 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 For individual extreme values ​​and group extreme values, v i (t), x i (t) represents the velocity and position of the current iteration number.

[0010] Optionally, the constraints of the configuration model for photovoltaic and energy 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 this application, a configuration device for photovoltaic and energy storage capacity in a distribution network is provided, comprising: a first acquisition module, configured 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 in the distribution network; a first construction module, configured to construct a configuration model for photovoltaic and energy storage capacity based at least on the power quality; and a configuration module, configured to solve the configuration model according to an optimization algorithm and configure the photovoltaic and energy storage capacity of the distribution network according to the solution of the configuration model.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform any of the above-described methods for configuring the photovoltaic and energy storage capacity of the power distribution network.

[0013] According to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement any of the aforementioned methods for configuring the photovoltaic and energy storage capacity of the power distribution network.

[0014] Applying the technical solution of this application, the above-mentioned method for configuring photovoltaic and energy storage capacity in a distribution network first obtains the power quality parameters of the distribution network and constructs a power quality assessment system for the distribution network. The power quality parameters include at least one of the following: voltage deviation, voltage fluctuation, and harmonic voltage at the nodes of the distribution network. Then, based on at least the power quality, a configuration model for photovoltaic and energy storage capacity is constructed. Finally, the configuration model is solved using an optimization algorithm, and the photovoltaic and energy storage capacity of the distribution network is configured based on the solution of the configuration model. This method can effectively improve the power quality of the distribution network, providing a theoretical basis for the planning and construction of new distribution network systems. It is also economical and solves the problem of limitations in existing power quality optimization models and the low power quality level of distribution networks. Attached Figure Description

[0015] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for implementing a method for configuring the photovoltaic and energy storage capacity of a distribution network, according to an embodiment of this application, is shown.

[0017] Figure 2 A flowchart illustrating a method for configuring photovoltaic and energy storage capacity in a distribution network according to an embodiment of this application is shown.

[0018] Figure 3 A flowchart illustrating a GA-CPSO algorithm according to an embodiment of this application is shown;

[0019] Figure 4 A schematic diagram of an IEEE 33-node system according to an embodiment of this application is shown;

[0020] Figure 5 A schematic diagram of a photovoltaic power generation according to an embodiment of this application is shown;

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

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

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

[0024] Figure 9 A schematic diagram of a 24-node energy storage charge and discharge power provided according to an embodiment of this application is shown;

[0025] Figure 10 A schematic diagram of the charging and discharging power of a 17-node energy storage system according to an embodiment of this application is shown.

[0026] Figure 11 A structural block diagram of a power distribution network photovoltaic storage capacity configuration device provided according to an embodiment of this application is shown.

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

[0028] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0029] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0030] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort 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, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] As described in the background section, for power quality issues after distributed generation (DG) is connected to the grid, constructing an optimization model with power quality as the sole objective may lead to a decrease in other indicators of DG grid connection, resulting in a decline in overall grid performance. Constructing optimization models with power quality constraints such as node voltage deviation, three-phase imbalance, and harmonics also fails to meet the requirements of distribution network systems, as the number of DG grid connections increases significantly. Furthermore, using only power quality as a constraint in the optimization model yields results that are no longer sufficient for distribution network system requirements. Existing theories, which use node voltage as a model constraint and consider only power quality as the objective function, cannot fully utilize the control capabilities of the distribution network and cannot maximize the system's power quality level. Simultaneously, with the increase in distributed photovoltaic (PV) capacity, power quality problems in the distribution network are gradually becoming apparent, and optimization solely from the perspective of distributed PV is insufficient to meet power quality requirements.

[0033] To address the limitations of existing power quality optimization models and the resulting low power quality levels in distribution networks, which in turn lead to poor photovoltaic and energy storage capacity in distribution network photovoltaic and energy storage systems, embodiments of this application provide a method, apparatus, computer-readable storage medium, and computer program product for configuring photovoltaic and energy storage capacity in distribution networks.

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

[0035] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a method of configuring photovoltaic and energy storage capacity in a power distribution network, according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0036] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the configuration method of power distribution network photovoltaic storage capacity in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-described networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the above-described networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station 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] This embodiment provides a method for configuring the photovoltaic and energy storage capacity of a power distribution network that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0038] Figure 2 This is a flowchart of a method for configuring photovoltaic and energy storage capacity in a distribution network according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0039] Step S201: Obtain the power quality parameters of the above-mentioned distribution network and construct the power quality assessment system of the above-mentioned 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 above-mentioned distribution network.

[0040] Step S202: Based at least on the above power quality, construct a configuration model for photovoltaic and energy storage capacity;

[0041] Step S203: Solve the above configuration model according to the optimization algorithm, and configure the photovoltaic and energy storage capacity of the above distribution network according to the solution of the above configuration model.

[0042] This method for configuring photovoltaic (PV) and energy storage (ESS) capacity in a distribution network first obtains the power quality parameters of the distribution network and constructs a power quality assessment system. The power quality parameters include at least one of the following: voltage deviation, voltage fluctuation, and harmonic voltage at the nodes of the distribution network. Then, based on at least the power quality parameters, a configuration model for PV and ESS capacity is constructed. Finally, the configuration model is solved using an optimization algorithm, and the PV and ESS capacity of the distribution network is configured based on the solution. This method effectively improves the power quality of the distribution network, providing a theoretical basis for the planning and construction of new distribution network systems. It is also economical and addresses the limitations of existing power quality optimization models and the low power quality level of distribution networks.

[0043] Step S201: Obtain the power quality parameters of the above-mentioned distribution network and construct the power quality assessment system of the above-mentioned 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 above-mentioned distribution network.

[0044] Specifically, there is a wide range of relevant indicators that reflect the power quality level in the distribution network, including voltage deviation, voltage fluctuation, voltage harmonics, and frequency deviation. However, the selection of indicators needs to be based on the actual situation of distributed photovoltaic grid connection. Due to the randomness and volatility of distributed photovoltaic output, the connection of distributed photovoltaic capacity can easily cause voltage fluctuations, voltage deviations, and harmonics in the distribution network. Therefore, this application selects more targeted voltage deviation, fluctuations, and harmonics as power quality indicators to construct a more accurate power quality assessment system.

[0045] Obtaining the power quality parameters of the aforementioned distribution network includes the following steps:

[0046] Step S301: Obtain the voltage deviation based on the first formula, the actual voltage, and the nominal voltage, wherein the first formula is: Where i represents the distributed photovoltaic access node i in the aforementioned distribution network, U i,pv U represents the actual voltage of the photovoltaic access node i mentioned above. i,N This represents the nominal voltage of the aforementioned photovoltaic access node i;

[0047] Specifically, voltage deviation is an indicator of the normal operation of a power grid system. Obtaining voltage deviation parameters can help prevent voltage exceedance issues in a timely manner and improve the power quality of the grid. After distributed photovoltaic (PV) power is connected to the distribution network, the voltage at the PV connection node will increase with the increase in PV output. When the PV output exceeds the absorption capacity of the distribution network itself, problems such as voltage exceedance may occur at the node.

[0048] Step S302: Obtain the voltage fluctuation based at least on the second formula and the voltage fluctuation of the distribution network, wherein the second formula is: in: This indicates the voltage fluctuation caused by the aforementioned distributed photovoltaic (PV) access at the i-th PV access node; k is the number of the i-th PV access node in the aforementioned distribution network; R i This represents the resistance of the power grid line before the i-th photovoltaic access node mentioned above; This represents the maximum output power of the aforementioned distributed photovoltaic system; α represents the ratio of the instantaneous change in the output power of the aforementioned distributed photovoltaic system to the rated output power of the photovoltaic system. This is the reference voltage for the aforementioned power distribution network;

[0049] Specifically, distributed photovoltaic power output is often affected by external factors such as solar cycle and cloud cover changes. In addition to causing voltage deviation, it also generates significant voltage fluctuations. Obtaining voltage fluctuation parameters is beneficial for assessing the stability and power quality of the power grid system, and provides a basis for constructing a power quality assessment system for the distribution network.

[0050] In the second formula above, R iRepresents the grid line resistance before the i-th photovoltaic access node. In one specific embodiment, for example, if photovoltaic access node i is 5, then R i This represents the resistance value between the 1st, 2nd, 3rd, and 4th nodes.

[0051] Step S303: Obtain the aforementioned harmonic voltage based at least on the third formula and the fundamental voltage, wherein the aforementioned third formula is: Among them, U H U represents the total harmonic distortion (THD) of the voltage at the grid connection point of the aforementioned distributed photovoltaic system. h Let H be the h-th harmonic content of the voltage at the above grid connection point, and THD and U1 be the total voltage distortion rate and fundamental voltage of the above grid connection point, respectively.

[0052] Specifically, distributed photovoltaic systems are connected to the grid via inverters. Due to the characteristics of inverters, they are prone to harmonic pollution, which can negatively impact loads and equipment in the distribution network. Therefore, voltage harmonics are selected as an energy indicator to assess their impact on the power grid system and to protect loads and equipment in the distribution network in a timely manner, thereby improving power quality.

[0053] The construction of the power quality assessment system for the aforementioned distribution network includes: constructing an assessment function for the aforementioned power quality based on the aforementioned power quality parameters and assessment indicators, wherein the assessment function is: F i =λ1ΔU i,pv +λ2ρ pv.i +λ3THD, where λ i F represents the weighting coefficient of the above evaluation indicators. i The power quality assessment value is given at the aforementioned photovoltaic access node i.

[0054] Specifically, taking into account voltage deviation, voltage fluctuation, and voltage harmonics, a comprehensive power quality assessment function is constructed. The weight coefficients of the three assessment indicators are determined using the analytic hierarchy process (AHP), and a comprehensive power quality assessment system is established to facilitate objective and reliable power quality analysis.

[0055] Step S202: Based at least on the above power quality, construct a configuration model for photovoltaic and energy storage capacity;

[0056] Specifically, the distributed photovoltaic grid-connected photovoltaic storage capacity optimization configuration model established in this application takes into account both the economic efficiency of the grid and the power quality. The upper-level optimization model is constructed with the total curtailment rate of the grid and the annual investment and operating cost of the photovoltaic storage power generation system as objective functions, while the lower-level optimization model is constructed with network loss and power quality assessment values ​​as objectives.

[0057] Based on the aforementioned power quality, at least a configuration model for photovoltaic and energy storage capacity is constructed, including the following steps:

[0058] Step S401: Based on the curtailment of solar power in the aforementioned distribution network and the annual investment and operating costs of the solar-storage power generation system in the aforementioned distribution network, construct an upper-level configuration model, wherein the curtailment rate of solar power in the aforementioned distribution network is: in, Let i be the maximum power output of the photovoltaic access node i in the aforementioned distribution network at time t. M represents the actual power of the photovoltaic access node i at time t. pv Where is the number of grid-connected points for distributed photovoltaic (PV) power generation, and T is the total operating time of the aforementioned distributed PV power generation.

[0059] Step S402, the annual investment and operating cost of the above photovoltaic-storage power generation system is: in, The annual installation and operation and maintenance cost of the photovoltaic grid at the aforementioned photovoltaic access node i is... c represents the annual installation and operation cost of the energy storage system for the aforementioned distribution network at the aforementioned photovoltaic access node i. t The unit power purchase price of the distribution network at time t is given above. M represents the required power consumption of the photovoltaic access node i at time t. ES M represents the number of grid-connected nodes in the aforementioned energy storage system. pv Where T is the number of photovoltaic access nodes, T is the total operating time of the distributed photovoltaic system, and M is the number of nodes in the distribution network.

[0060] Specifically, the aforementioned annual investment and operation and maintenance costs for photovoltaic power generation... It can be represented as:

[0061]

[0062] in, and These represent the annual investment, operation and maintenance, and processing costs per unit capacity of photovoltaic power. This represents the photovoltaic installation capacity of photovoltaic access node i.

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

[0064]

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

[0066] Step S403: Based on the network loss and power quality assessment values, construct the lower-level configuration model, wherein the aforementioned network loss is: Where M is the number of nodes in the aforementioned distribution network; G ij P represents the electrical conductance between photovoltaic access node i and photovoltaic access node j in the aforementioned distribution network. ij,t and Q ij,t Let U be the active power and reactive power flowing between the aforementioned photovoltaic access node i and the aforementioned photovoltaic access node j at time t; ij,t Let M be the voltage between photovoltaic access node i and photovoltaic access node j at time t. i The set of branches connected to the aforementioned photovoltaic access node i, where T is the total operating time of the aforementioned distributed photovoltaic system, and M is the number of nodes in the aforementioned distribution network;

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

[0068] Step S203: Solve the above configuration model according to the optimization algorithm, and configure the photovoltaic and energy storage capacity of the above distribution network according to the solution of the above configuration model.

[0069] Specifically, since the optimal configuration of photovoltaic and energy storage capacity requires comprehensive consideration of the economics and power quality of the distribution network, it is a multi-objective nonlinear integer programming problem. In the existing technology, only the particle swarm algorithm is used to solve the model, which is prone to getting trapped in local optima. The GA-CPSO algorithm (Genetic Algorithm-Chaotic Particle Swarm Optimization, which introduces chaotic mapping of the genetic algorithm) has better solution speed and accuracy. In order to effectively analyze nonlinear problems, this application adopts the GA-CPSO algorithm to solve the model.

[0070] The optimization algorithm is used to solve the above configuration model, which includes the following steps:

[0071] Step S501, First determination step: Based on the curtailment of solar power in the above distribution network and the annual investment and operating cost of the solar-storage power generation system in the above distribution network, determine the initial particle population of the above upper-level configuration model;

[0072] Step S502, Initialization step: Based on the constraints of the initial particle population and the upper-level configuration model, initialize the position, velocity, population extreme value and individual extreme value of the initial particle population.

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

[0074] Step S504, Third determination step: Based on the above population extreme value and above individual extreme value after chaos processing, as well as the fourth formula and the fifth formula, obtain the optimized position and velocity of the above particle population, and obtain the fitness of the above particle population based on the optimized position and above velocity, and determine the updated population extreme value and above individual extreme value of the above particle population based on the fitness value.

[0075] The process of obtaining the position and velocity of the particle population based on the chaotic population extreme values ​​and the individual extreme values ​​includes the following steps:

[0076] Step S5041: Based on the fourth formula, the population extreme value, and the individual extreme value, the position of the particle population is obtained, wherein the fourth formula includes: x 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. Chaotic Tent mapping is used to generate a uniformly distributed initial population.

[0078] Step S5042: Based on the fifth formula, the population extreme value, and the individual extreme value described above, the velocity of the particle population is obtained, wherein the fifth formula includes: Where 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 For individual extreme values ​​and group extreme values, v i (t), x i (t) represents the velocity and position of the current iteration.

[0079] Specifically, based on the particle swarm velocity and position update formula, the position and velocity of the current particles are optimized, and the particle fitness is calculated. Based on the particle fitness value, the updated global optimum (population extreme value) and individual optimum (individual extreme value) of the particles are re-determined to form an optimized new particle population.

[0080] Step S505, Fourth determination step: Update the above particle population according to the genetic algorithm, and obtain the fitness of the above particle population according to the position and velocity of the updated particle population.

[0081] Specifically, in the iterative process of the particle swarm optimization algorithm, a genetic algorithm is introduced. The crossover and mutation characteristics of the genetic algorithm are used to improve information interaction between particles, thereby increasing the convergence speed of the algorithm and avoiding getting trapped in local optima. 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] Where α and β are the crossover and genetic factors, respectively, randn() is a random number in [0, 1], and x new For the new particle, x i and x j x represents the i-th and j-th particles in the aforementioned particle swarm, respectively. max and x min These represent the maximum and minimum values ​​of the mutation rate, respectively.

[0085] Step S506, Fifth Determination Step: Compare the fitness values ​​of the offspring particles in the updated particle population with the fitness values ​​of the parent particles in the unupdated particle population, replace the parent particles with offspring particles whose fitness values ​​are greater than those of the parent particles, regenerate a new particle population, and recalculate the fitness values ​​of the new particle population.

[0086] Step S507, Judgment step: Determine whether the number of iterations in the fifth determination step above has reached the maximum number of iterations. If the number of iterations does not meet the maximum number of iterations, re-execute the third determination step above.

[0087] Step S508, the sixth determination step: If the current iteration calculation number satisfies the maximum iteration number, determine whether the configuration model satisfies the lower-level configuration model. If the configuration model does not satisfy the lower-level configuration model, record the current result as the upper-level configuration model, and bring the network loss and power quality assessment value into the first determination step. If the configuration model satisfies the lower-level configuration model, output the optimal solution of the configuration model.

[0088] Specifically, if 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, namely the above network loss and the above power quality assessment value, are substituted into the above first determination step to calculate the lower-level configuration model, and finally the result of the lower-level configuration model is output.

[0089] The constraints of the configuration model for photovoltaic and energy 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 at the aforementioned nodes are as follows:

[0091]

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

[0093] The above energy storage constraints are:

[0094]

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

[0096]

[0097] Among them, P i ES Let i be the capacity power of the photovoltaic access node. The maximum capacity power of photovoltaic access node i. The capacity for installing energy storage at photovoltaic access node i, The upper and lower limits of the energy storage capacity to be installed at photovoltaic access node i.

[0098] The above node power balance constraints are:

[0099]

[0100] in, The power generation at photovoltaic access node i; Let be the load power at 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 Let Q be the active power injected by the photovoltaic grid at photovoltaic node i and the active power consumed by the load at time t, respectively. pv,i,t Q L,i,t P represents the reactive power injected by the photovoltaic grid at photovoltaic node i and consumed by the load at time t, respectively. ES,i,t Q C,i,t These represent the active power of energy storage and the reactive power of the capacitor injected into photovoltaic access node i at time t, respectively; G ij B ij θ ij These represent the conductance, admittance, and power angle between photovoltaic access node i and photovoltaic access node j, respectively; U i.t U j.t The actual voltage of photovoltaic access node i at time t is M. i This represents the number of branches connected to photovoltaic access node i.

[0104] For example, such as Figure 3As shown, the process begins by inputting the parameters of the upper-level configuration model, initializing the velocity and position of the population particles, calculating fitness, and initializing individual and global optimum particles. The global optimum particle is then chaoticated using a Tent mapping to generate a chaotic population. The individual and global optimum particles are updated. The particle positions and velocities are updated, the particle fitness is recalculated, and the individual and global optimum particles are updated again. Based on crossover and mutation calculations using a genetic algorithm, half of the offspring particles with high fitness are retained for the next generation. Fitness values ​​are calculated, and the individual and global optimum particles are updated. The current iteration count is checked against the maximum iteration count. If the first check indicates a greater number of iterations, the output is checked against the lower-level configuration model. If the second check indicates a greater number of iterations, the lower-level optimization result is recorded, and the optimal solution is output. If the second check indicates a negative number of iterations, the lower-level configuration model parameters are input, and the above calculations are performed until the solution for the lower-level configuration model is obtained. If the first check indicates a less than or equal number of iterations, the process returns to updating the particle positions and velocities, recalculating the particle fitness, updating the individual and global optimum particles, and recalculating.

[0105] In some specific embodiments, to verify the effectiveness of the method proposed in this invention, such as... Figure 4 As shown, a typical IEEE-33 node system is adopted, 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 connection point, with a total of 4 photovoltaic connection points (distributed photovoltaic PV 1, distributed photovoltaic PV 2, distributed photovoltaic PV 3, and distributed photovoltaic PV 4). Energy storage is installed in 2 of the photovoltaic PV units (distributed photovoltaic PV 2 and distributed photovoltaic PV 3). The cost parameters of each power generation unit are shown in Table 1.

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

[0107]

[0108] Using the typical daily power output and peak-valley electricity price of a certain region, such as Figure 5 and Figure 6 As shown, by Figure 5 (Time - Unit Photovoltaic Output Power) shows that the actual photovoltaic output is mainly concentrated between 05:00 and 19:00; from Figure 6 The peak and valley electricity prices can be determined by (time-peak-valley electricity price). Ignoring price fluctuations, the peak and valley electricity prices of a certain region's 10kV are used. The peak price is 0.73 yuan / (kW·h), the valley price is 0.32 yuan / (kW·h), and the normal price is 0.51 yuan / (kW·h).

[0109] Meanwhile, in order to ensure the reliability and solution speed of the photovoltaic storage capacity configuration results of this invention, under the condition of satisfying various constraints, the maximum photovoltaic output and configuration capacity limits of each grid connection point (photovoltaic access node) were calculated as shown in Table 2.

[0110] Table 2 Node Capacity Limits

[0111]

[0112] This application uses the GA-CPSO algorithm to solve the photovoltaic storage capacity optimization configuration model, and the optimal solution is shown in Table 3 after algorithm iteration.

[0113] Table 3 Photovoltaic and Storage Capacity Configuration

[0114]

[0115] When the optimal configuration results of the photovoltaic and energy storage system, as shown in Table 3, are obtained by using the algorithm and connected to the distribution network, the operation of the distribution network system before and after the photovoltaic and energy storage connection is as follows: Figure 7 (Time - External Power Grid Output) and Figure 8 As shown in (Time - System Network Loss). The integration of photovoltaic and energy storage systems significantly reduces the demand for external power purchases in the distribution network, lowers the operating costs of the power grid, and demonstrates good economic benefits. At the same time, with the integration of photovoltaic and energy storage systems, system network losses are greatly improved. Under the maximum output of photovoltaic power, system network losses are reduced by more than 60%, which improves the power quality of the distribution network to a certain extent.

[0116] Table 4 Comparison of Photovoltaic and Energy Storage Connection Results

[0117]

[0118]

[0119] The GA-CPSO algorithm was used to solve the photovoltaic-storage capacity optimization configuration model. The results of solving each objective function are shown in Table 4. With the access of the distributed photovoltaic-storage system, the overall network loss of the system is reduced by about 47% compared with the unconnected system, and the network loss of the system has decreased significantly. Moreover, compared with the unconnected distributed photovoltaic-storage system, 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 that when the photovoltaic-storage system is not connected, its value is still at the excellent level of power quality.

[0120] Figure 9 (Time-energy storage charge / discharge power) represents the energy storage charge / discharge power at node 24. Figure 10 (Time - Energy Storage Charge / Discharge Power) represents the energy storage charge / discharge power at node 17, derived from... Figure 9 and Figure 10It is evident that energy storage plays a positive role in reducing the electricity purchase cost of the distribution network. Energy storage can charge when photovoltaic power overflows and discharge when photovoltaic output is insufficient, thus playing a role in peak shaving and valley filling.

[0121] When the photovoltaic and energy storage systems are connected to the grid at the optimal capacity, the voltage fluctuations at the grid connection points caused by each system are shown in Table 5. It can be seen that under the optimal capacity, the grid connection of photovoltaic and energy storage has a relatively small impact on the voltage fluctuations of each node. Specifically, nodes 21 and 32 show low voltage fluctuations even when the photovoltaic grid connection capacity is small. Nodes 17 and 24, with larger grid connection capacities and not being at the end of the line, are far from the distribution network bus, yet their maximum voltage fluctuation is only 1.73%. Compared to scenarios where only photovoltaic systems are connected, simultaneous photovoltaic and energy storage effectively suppresses voltage fluctuations at the grid connection nodes, demonstrating the role of energy storage in mitigating photovoltaic output fluctuations and showcasing the effectiveness of the proposed photovoltaic-energy storage capacity optimization planning method in improving power quality.

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

[0123]

[0124] To verify the effectiveness of the method proposed in this application in considering both power quality improvement and distribution network economy, comparative analyses are conducted under the following conditions:

[0125] 1. Only consider optimal power quality when configuring photovoltaic and energy storage capacity.

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

[0127] 3. Using the model in this application, the photovoltaic and energy storage capacity is configured by comprehensively considering power quality and system investment and operating costs.

[0128] Table 6 shows the optimization results of photovoltaic and energy storage capacity under the three scenarios mentioned above. Considering only the improvement of power quality, according to the power quality assessment values ​​in the table, the power quality is excellent, the system network loss is low, and the curtailment rate is 0, but the investment and operating costs are high. Considering only the investment and operating costs, the power quality assessment values ​​show that the power quality decreases, and the curtailment rate and system network loss increase. Taking into account both power quality and investment and operating costs, the system power quality level is excellent, the investment and operating costs are reduced, and the system network loss and curtailment rate are low, meeting the requirements. The comparison shows that the photovoltaic and energy storage capacity optimization method proposed in this application, which takes into account both power quality and investment and operating costs, can effectively improve the system's economic efficiency, while ensuring the system's power quality, proving the effectiveness of the model proposed in this application.

[0129] Table 6 Analysis of Grid Connection Results under Different Conditions

[0130] 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] Taking into account both the economic efficiency and power quality of the distribution network, an upper-level optimization model is constructed with the total curtailment rate of the distribution network and the annual investment and operating cost of the photovoltaic-storage power generation system as objective functions. A lower-level optimization model is constructed with the power quality assessment value of network loss as the objective. The GA-CPSO algorithm is used to solve the model, improving the power quality of the grid while ensuring economic efficiency. Through numerical examples, the proposed method balances the economic efficiency and power quality of the distribution network. The integration of photovoltaic-storage systems plays a positive role in improving the power quality of the distribution network and promoting the absorption of new energy sources, providing a certain reference for the planning of photovoltaic-storage integration in distribution networks.

[0132] It should be noted that the steps shown in the flowchart in 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 may be executed in a different order than that shown here.

[0133] This application also provides a configuration device for the photovoltaic and energy storage capacity of a distribution network. It should be noted that this configuration device can be used to execute the configuration method for photovoltaic and energy storage capacity of a distribution network provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0134] The following describes the configuration device for the photovoltaic and energy storage capacity of the distribution network provided in the embodiments of this application.

[0135] Figure 11 This is a schematic diagram of the control device for a distribution network photovoltaic-storage capacity configuration device according to an embodiment of this application. Figure 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 aforementioned distribution network and construct a power quality assessment system for the aforementioned distribution network. The aforementioned power quality parameters include at least one of the following: voltage deviation, voltage fluctuation, and harmonic voltage of the nodes of the aforementioned distribution network. The first construction module 20 is used to construct a configuration model of photovoltaic and energy storage capacity based at least on the aforementioned power quality. The configuration module 30 is used to solve the aforementioned configuration model according to an optimization algorithm and configure the photovoltaic and energy storage capacity of the aforementioned distribution network according to the solution of the aforementioned configuration model.

[0136] The aforementioned distribution network photovoltaic-storage capacity configuration device of this application includes: a first acquisition module, a first construction module, and a configuration module. The first acquisition module is used to acquire power quality parameters of the distribution network and construct a power quality assessment 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 in the distribution network. The first construction module is used to construct a configuration model for photovoltaic-storage capacity based at least on the power quality. The 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. This method can effectively improve the power quality of the distribution network, providing a certain theoretical basis for the planning and construction of new distribution network systems. It is also economical and solves the problem that the power quality optimization model in the existing technology has limitations, resulting in low power quality levels in the distribution network and consequently poor photovoltaic-storage capacity in the distribution network photovoltaic-storage system.

[0137] In some instances, the first acquisition module includes a first sub-acquisition module, a second sub-acquisition module, and a third sub-acquisition module. The first sub-acquisition module is used to acquire the voltage deviation mentioned above based on a first formula, the actual voltage, and the nominal voltage. The first formula is: Where i represents the distributed photovoltaic access node i in the aforementioned distribution network, U i,pv U represents the actual voltage of the photovoltaic access node i mentioned above. i,N The nominal voltage of the photovoltaic access node i is represented; the second sub-acquisition module is used to acquire the voltage fluctuation based at least on the second formula and the voltage fluctuation of the distribution network, wherein the second formula is: in, This represents the voltage fluctuation caused by the distributed photovoltaic (PV) access at the i-th PV access node, where k is the number of the i-th PV access node in the distribution network, and R... i P represents the grid line resistance before the i-th photovoltaic access node mentioned above. i pv This represents the maximum output power of the aforementioned distributed photovoltaic system, and α represents the ratio of the instantaneous change in the output power of the distributed photovoltaic system to the rated output power of the photovoltaic system. The reference voltage for the aforementioned distribution network; the third sub-acquisition module is used to acquire the aforementioned harmonic voltage based at least on the third formula and the fundamental voltage, wherein the aforementioned third formula is: Among them, U H U represents the total harmonic distortion (THD) of the voltage at the grid connection point of the aforementioned distributed photovoltaic system. h Let H be the h-th harmonic content of the voltage at the above grid connection point, and THD and U1 be the total voltage distortion rate and fundamental voltage of the above grid connection point, respectively.

[0138] In some instances, the first acquisition module further includes a first sub-construction module, which is used to construct the power quality evaluation function based on the aforementioned power quality parameters and evaluation indicators, wherein the evaluation function is: F i =λ1ΔU i,pv +λ2ρ pv.i +λ3THD, where λ i F represents the weighting coefficient of the above evaluation indicators. i The power quality assessment value is given at the aforementioned photovoltaic access node i.

[0139] In some instances, the first building module includes a second sub-building module, a fourth sub-acquisition module, a third sub-building module, and a fifth sub-acquisition module. The second sub-building module is used to construct an upper-level configuration model based on the curtailment rate of the aforementioned distribution network and the annual investment and operating costs of the photovoltaic-storage power generation system in the aforementioned distribution network. The curtailment rate of the aforementioned distribution network is: in, Let i be the maximum power output of the photovoltaic access node i in the aforementioned distribution network at time t. M represents the actual power of the photovoltaic access node i at time t. pv The number of grid-connected points for distributed photovoltaic power generation is denoted as , and T is the total operating time of the aforementioned distributed photovoltaic power generation system. The fourth sub-acquisition module is used to obtain the annual investment and operating cost of the aforementioned photovoltaic-storage power generation system. in, The annual installation and operation and maintenance cost of the photovoltaic grid at the aforementioned photovoltaic access node i is... c represents the annual installation and operation cost of the energy storage system for the aforementioned distribution network at the aforementioned photovoltaic access node i. t The unit power purchase price of the distribution network at time t is given above. M represents the required power consumption of the photovoltaic access node i at time t. ES M represents the number of grid-connected nodes in the aforementioned energy storage system. pv Here, T represents the total operating time of the distributed photovoltaic system, and M represents the number of nodes in the distribution network. The third sub-module is used to construct the lower-level configuration model based on network loss and power quality assessment values, where the network loss is: Where M is the number of nodes in the aforementioned distribution network; G ij P represents the electrical conductance between photovoltaic access node i and photovoltaic access node j in the aforementioned distribution network. ij,t and Q ij,t Let U be the active power and reactive power flowing between the aforementioned photovoltaic access node i and the aforementioned photovoltaic access node j at time t; ij,t Let M be the voltage between photovoltaic access node i and photovoltaic access node j at time t. iThe set of branches connected to the aforementioned photovoltaic access node i, where T is the total operating time of the aforementioned distributed photovoltaic system, and M is the number of nodes in the aforementioned distribution network; the fifth sub-acquisition module is used to acquire the aforementioned power quality assessment value: Among them, F i,t The above power quality assessment value of the above distributed photovoltaic access node i at time t.

[0140] In some examples, the configuration module includes a first determining module, an initialization module, a second determining module, a third determining module, a fourth determining module, a fifth determining module, a judgment module, and a sixth determining module. The first determining module performs a first determining step, determining the initial particle population of the upper-level configuration model based on the curtailment of solar power in the distribution network and the annual investment and operating costs of the solar-storage power generation system in the distribution network. The initialization module performs an initialization step, initializing the position, velocity, population extremum, and individual extremum of the initial particle population based on the constraints of the upper-level configuration model. The second determining module is used for... The second determination step involves using tent mapping to perform chaotic processing on the globally optimal particles with population extrema, resulting in a new particle population, and updating the chaotic population extrema and individual extrema. The third determination module executes the third determination step, obtaining the optimized position and velocity of the particle population based on the chaotic population extrema and individual extrema, as well as the fourth and fifth formulas. Based on the optimized position and velocity, the fitness of the particle population is obtained, and the updated population extrema and individual extrema are determined based on the fitness values. The fourth determination module executes the... The fourth determination step involves updating the particle population using a genetic algorithm and determining its fitness based on the updated particle population's position and velocity. The fifth determination module executes this step by comparing the fitness values ​​of the child particles in the updated particle population with the fitness values ​​of the parent particles in the unupdated particle population. Child particles with higher fitness values ​​replace the parent particles, generating a new particle population, and recalculating the fitness of this new population. The final judgment module executes this step to determine whether the maximum number of iterations in the fifth determination step has been reached. If the number of iterations calculated does not meet the maximum number of iterations, the third determination step is re-executed. The sixth determination module is used to execute the sixth determination step. If the current number of iterations calculated meets the maximum number of iterations, it determines whether the configuration model meets the lower-level configuration model. If the configuration model does not meet the lower-level configuration model, the current result is recorded as the upper-level configuration model. The network loss and power quality assessment values ​​are then input into the first determination step. If the configuration model meets the lower-level configuration model, the optimal solution of the configuration model is output.

[0141] In some instances, the fourth determining module further includes a first sub-determining module and a second sub-determining module, wherein the first sub-determining module is used to obtain the position of the particle population based on the aforementioned fourth formula, the aforementioned population extreme value, and the aforementioned individual extreme value, wherein the aforementioned fourth formula includes: x i (t+1)=x i(t)+v i (t+1); The second sub-determining module is used to obtain the velocity of the particle population based on the fifth formula, the population extreme value, and the individual extreme value, wherein the fifth formula includes: Where 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 For individual extreme values ​​and group extreme values, v i (t), x i (t) represents the velocity and position of the current iteration.

[0142] In some instances, the configuration module includes constraints on the configuration model of the aforementioned photovoltaic and energy 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 aforementioned configuration device for the photovoltaic and energy storage capacity of the distribution network includes a processor and a memory. The first acquisition module and other modules are stored as program units in the memory, and the processor executes these program units to achieve the corresponding functions. All of the aforementioned modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0144] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and their parameters can be adjusted to address the limitations of existing power quality optimization models and the low power quality level of distribution networks.

[0145] The memory may include non-permanent memory in computer-readable media, such as 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] This invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is executed, it controls the device containing the computer-readable storage medium to perform the configuration method for the photovoltaic and energy storage capacity of the power distribution network.

[0147] This invention provides a processor for running a program, wherein the program executes the method for configuring the photovoltaic and energy storage capacity of the power distribution network.

[0148] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0149] Step S201: Obtain the power quality parameters of the above-mentioned distribution network and construct the power quality assessment system of the above-mentioned 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 above-mentioned distribution network.

[0150] Step S202: Based at least on the above power quality, construct a configuration model for photovoltaic and energy storage capacity;

[0151] Step S203: Solve the above configuration model according to the optimization algorithm, and configure the photovoltaic and energy storage capacity of the above distribution network according to the solution of the above configuration model.

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

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

[0154] Step S201: Obtain the power quality parameters of the above-mentioned distribution network and construct the power quality assessment system of the above-mentioned 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 above-mentioned distribution network.

[0155] Step S202: Based at least on the above power quality, construct a configuration model for photovoltaic and energy storage capacity;

[0156] Step S203: Solve the above configuration model according to the optimization algorithm, and configure the photovoltaic and energy storage capacity of the above distribution network according to the solution of the above configuration model.

[0157] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0158] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0160] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function 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] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0164] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0165] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0166] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0167] 1) The method for configuring photovoltaic (PV) and energy storage (ESS) capacity in a distribution network according to this application first obtains the power quality parameters of the distribution network and constructs a power quality assessment system for the distribution network. The power quality parameters include at least one of the following: voltage deviation, voltage fluctuation, and harmonic voltage at the nodes of the distribution network. Then, based on at least the power quality, a configuration model for PV and ESS capacity is constructed. Finally, the configuration model is solved using an optimization algorithm, and the PV and ESS capacity of the distribution network is configured based on the solution of the configuration model. This method can effectively improve the power quality of the distribution network, providing a theoretical basis for the planning and construction of new distribution network systems. It is also economical and solves the problem of limitations in existing power quality optimization models and low power quality levels in distribution networks.

[0168] 2) The distribution network photovoltaic-storage capacity configuration device of this application includes: a first acquisition module, a first construction module, and a configuration module. The first acquisition module is used to acquire power quality parameters of the distribution network and construct a power quality assessment 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 in the distribution network. The first construction module is used to construct a configuration model for photovoltaic-storage capacity based at least on the power quality. The 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. This method can effectively improve the power quality of the distribution network, provide a certain theoretical basis for the planning and construction of new distribution network systems, and is also economical, solving the problem of limitations in the power quality optimization model and low power quality level of the distribution network in the existing technology.

[0169] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for configuring photovoltaic and energy storage capacity in a distribution network, characterized in that, The method includes: Obtain the power quality parameters of the distribution network and construct a power quality assessment 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. Based at least on the aforementioned power quality parameters, a configuration model for photovoltaic and energy storage capacity is constructed; The configuration model is solved using an optimization algorithm, and the photovoltaic and energy storage capacity of the distribution network is configured based on the solution. The voltage fluctuation is obtained at least according to the second formula and the voltage fluctuation of the distribution network, wherein the second formula is: ， in: This represents the voltage fluctuation caused by the distributed photovoltaic (PV) access at the i-th PV access node; k is the number of the i-th PV access node in the distribution network. This represents the resistance of the power grid line before the i-th photovoltaic access node; This indicates the maximum output value of the distributed photovoltaic system; This represents the ratio of the instantaneous change in the output power of the distributed photovoltaic system to the rated output power of the photovoltaic system. This is the reference voltage for the power distribution network. The step of constructing a configuration model for photovoltaic storage capacity based at least on the power quality includes: Based on the curtailment of solar power in the distribution network and the annual investment and operating costs of the solar-storage power generation system in the distribution network, an upper-level configuration model is constructed. Based on the network loss and power quality assessment values, a lower-level configuration model is constructed. Solving the configuration model according to the optimization algorithm includes: The first determination step is to determine the initial particle population of the upper-level configuration model based on the curtailment of solar power in the distribution network and the annual investment and operating costs of the solar-storage power generation system in the distribution network. Initialization steps: Based on the constraints of the initial particle population and the upper-level configuration model, initialize the position, velocity, population extreme value, and individual extreme value of the initial particle population; The second determination step is to use tent mapping to perform chaotic processing on the global optimal particles with population extrema to obtain a new particle population, and update the population extrema and individual extrema after chaotic processing. The third determination step: Based on the population extreme value and the individual extreme value after chaos processing, as well as the fourth formula and the fifth formula, the optimized position and velocity of the particle population are obtained, and the fitness of the particle population is obtained based on the optimized position and velocity. The updated population extreme value and the individual extreme value of the particle population are determined based on the fitness value. Fourth determination step: Update the particle population according to the genetic algorithm, and obtain the fitness of the particle population based on the position and velocity of the updated particle population; Fifth determination step: Compare the fitness values ​​of the offspring particles in the updated particle population with the fitness values ​​of the parent particles in the unupdated particle population, replace the parent particles with offspring particles whose fitness values ​​are greater than those of the parent particles, regenerate a new particle population, and recalculate the fitness of the new particle population. Judgment step: Determine whether the number of iterations in the fifth determination step has reached the maximum number of iterations. If the number of iterations does not meet the maximum number of iterations, re-execute the third determination step. The sixth determination step: If the current number of iterations meets the maximum number of iterations, determine whether the configuration model meets the lower-level configuration model. If 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. If the configuration model meets the lower-level configuration model, output the optimal solution of the configuration model.

2. The configuration method according to claim 1, characterized in that, The acquisition of power quality parameters of the distribution network includes: The voltage deviation is obtained based on the 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 distribution network. This represents the actual voltage of the photovoltaic access node i. This represents the nominal voltage of the photovoltaic access node i. This indicates the voltage deviation; The harmonic voltage is obtained at least according to the third formula and the fundamental voltage, wherein the third formula is: ， in, The total harmonic distortion (THD) of the voltage at the grid connection point of the distributed photovoltaic system. The h-th harmonic content of the voltage at the grid connection point. and These are the total voltage distortion rate and the fundamental voltage at the grid connection point, respectively.

3. The configuration method according to claim 2, characterized in that, The construction of the power quality assessment system for the power distribution network includes: Based on the power quality parameters and evaluation indicators, an evaluation function for the power quality is constructed, wherein: the evaluation function is: ， in, The weighting coefficients for the evaluation indicators are as follows: The power quality assessment value at the photovoltaic access node i includes the voltage deviation, voltage fluctuation, and total voltage distortion rate at the grid connection point.

4. The configuration method according to claim 1, characterized in that, The curtailment rate of the solar power distribution network is: ， in, Let be the maximum power output of the photovoltaic access node i in the distribution network at time t. This represents the actual power of the photovoltaic access node i at time t. This refers to the number of grid connection points for distributed photovoltaic power generation. This refers to the total operating time of the distributed photovoltaic system. The curtailment rate of the solar power distribution network; The annual investment and operating cost of the photovoltaic-storage power generation system is: ， in, The annual installation and maintenance cost of the photovoltaic system at the photovoltaic access node i is [value missing]. The annual installation and operation cost of the energy storage system of the distribution network at the photovoltaic access node i is given. Let be the unit power purchase price of the distribution network at time t. Let be the required power consumption of the photovoltaic access node i at time t. The number of grid-connected nodes in the energy storage system. The number of nodes in the distribution network. This refers to the annual investment and operating cost of the photovoltaic-storage power generation system. The network loss is: ， in, The electrical conductance between photovoltaic access node i and photovoltaic access node j in the distribution network; and Let t be the active power and reactive power flowing between the photovoltaic access node i and the photovoltaic access node j at time t; Let be the voltage between photovoltaic access node i and photovoltaic access node j at time t. The set of branches connected to the photovoltaic access node i. The network loss is mentioned above; The power quality assessment value is: ， in, The power quality assessment value of the distributed photovoltaic access node i at time t. This refers to the power quality assessment value.

5. The configuration method according to claim 1, characterized in that, Based on the population extrema and the individual extrema after chaotic processing, the position and velocity of the particle population are obtained, including: The position of the particle population is obtained based on the fourth formula, the population extreme value, and the individual extreme value, wherein the fourth formula includes: ; The velocity of the particle population is obtained based on the fifth formula, the population extreme value, and the individual extreme value, wherein the fifth formula includes: ， in, For inertial weights, This represents the current iteration number. , For acceleration factor, , A random number in the range [0,1]. , For individual extreme values ​​and group extreme values, , The velocity and position are the current iteration number.

6. The configuration method according to claim 1, characterized in that, The constraints of the configuration model for photovoltaic and energy 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.

7. A device for configuring photovoltaic and energy storage capacity in a power distribution network, characterized in that, include: The first acquisition module is used to acquire the power quality parameters of the distribution network and construct the power quality assessment 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 for photovoltaic storage capacity based at least on the power quality parameters; The configuration module is used to solve the configuration model according to the optimization algorithm, and configure the photovoltaic and energy storage capacity of the distribution network according to the solution of the configuration model. The first acquisition module includes a second sub-acquisition module, which is used to acquire the voltage fluctuation based at least on a second formula and the voltage fluctuation of the distribution network, wherein the second formula is: ,in, This represents the voltage fluctuation caused by the distributed photovoltaic (PV) grid connection at the i-th PV access node, where k is the number of the i-th PV access node in the distribution network. This represents the resistance of the power grid line before the i-th photovoltaic access node. This indicates the maximum output of the distributed photovoltaic system. This represents the ratio of the instantaneous change in the output power of the distributed photovoltaic system to the rated output power of the photovoltaic system. This is the reference voltage for the power distribution network. The first construction module includes a second sub-construction module and a third sub-construction module. The second sub-construction module is used to construct an upper-level configuration model based on the curtailment of solar power in the distribution network and the annual investment and operating costs of the solar-storage power generation system in the distribution network. The third sub-construction module is used to construct a lower-level configuration model based on network losses and power quality assessment values. 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. The first determination module executes a first determination step, determining the initial particle population of the upper-level configuration model based on the curtailment of solar power in the distribution network and the annual investment and operating costs of the solar-storage power generation system in the distribution network. The initialization module executes an initialization step, initializing the position, velocity, population extreme value, and individual extreme value of the initial particle population based on the constraints of the initial particle population and the upper-level configuration model. The second determination module executes a second determination step. The process involves several steps: First, a tent mapping is used to chaotically process the globally optimal particles with population extrema to obtain a new particle population. The chaotic population extrema and individual extrema are then updated. A third determining module executes this third determining step. Based on the chaotic population extrema and individual extrema, along with the fourth and fifth formulas, the optimized position and velocity of the particle population are obtained. The fitness of the particle population is then determined based on the optimized position and velocity. The updated population extrema and individual extrema are determined based on the fitness values. A fourth determining module executes this fourth determining step. The steps are as follows: First, according to the genetic algorithm, update the particle population and obtain the fitness of the particle population based on its position and velocity. Second, the fifth determination module performs the fifth determination step by comparing the fitness values ​​of the offspring particles in the updated particle population with the fitness values ​​of the parent particles in the unupdated particle population. Offspring particles with fitness values ​​greater than their parent particles replace the parent particles, generating a new particle population, and recalculating the fitness of the new particle population. Third, the judgment module performs the judgment step by determining whether the number of iterations in the fifth determination step has reached the maximum number of iterations. If the number of iterations does not meet the maximum number of iterations, the third determination step is re-executed; the sixth determination module is used to execute the sixth determination step, and if the current number of iterations meets the maximum number of iterations, it determines whether the configuration model meets the lower-level configuration model. If the configuration model does not meet the lower-level configuration model, the current result is recorded as the upper-level configuration model, and the network loss and the power quality assessment value are brought into the first determination step. If the configuration model meets the lower-level configuration model, the optimal solution of the configuration model is output.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the configuration method for the distribution network photovoltaic storage capacity as described in any one of claims 1 to 6.

9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the method for configuring the photovoltaic and energy storage capacity of the distribution network as described in any one of claims 1 to 6.

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