Configuration method and device of optical storage system and nonvolatile storage medium

Abstract

The invention discloses a configuration method and device of an optical storage system and a nonvolatile storage medium. The method comprises the steps of determining an operation typical scene of the power distribution network based on historical data of the power distribution network; determining a plurality of initial optical storage system schemes based on a preset capacity threshold value; determining a plurality of investment cost indexes of the plurality of initial optical storage system schemes; based on the plurality of investment cost indexes, determining values of a plurality of first evaluation indexes, values of a plurality of second evaluation indexes and values of a plurality of third evaluation indexes of the plurality of initial optical storage system schemes through a plurality of preset constraint conditions and a particle swarm algorithm; and based on the values of the plurality of first evaluation indexes, the values of the plurality of second evaluation indexes and the values of the plurality of third evaluation indexes, determining a target optical storage system configuration scheme through an optimization algorithm. According to the invention, the technical problem that the multi-objective and whole-process optimization of economic benefit, carbon reduction effect and power grid safety is difficult to achieve at the same time during the configuration of the optical storage system in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of optical-storage collaborative configuration technology, and more specifically, to a configuration method, apparatus, and non-volatile storage medium for an optical-storage system. Background Technology

[0002] Against the backdrop of global energy transition, my country is actively responding to the goal of low-carbon and green energy development and vigorously promoting the application of distributed photovoltaic and other new energy sources in distribution networks. However, due to the significant randomness and intermittency of photovoltaic and other new energy power generation, disorderly integration into the distribution network will bring a series of challenges, including exacerbating the uncertainty of power flow distribution, deteriorating power quality, causing power waste, and increasing the difficulty of grid operation and dispatch. To address these challenges, configuring energy storage facilities has become an important strategy, aiming to utilize the bidirectional charging and discharging capabilities of energy storage to achieve time-shifted storage and release of electricity, thereby improving the flexibility and stability of grid operation.

[0003] Traditional planning methods for photovoltaic (PV) and energy storage (ESS) systems often focus on single-objective optimization, such as pursuing the lowest economic cost or prioritizing grid security, without comprehensively considering multi-dimensional needs such as investment, operational efficiency, carbon emission reduction, and grid security. Furthermore, current technologies lack accurate capture of typical scenarios and effective algorithms for solving multi-objective models during the optimization process, resulting in wasted computational resources. This limits the optimized application of PV-ESS systems in distribution networks, making it difficult to achieve optimal economic and social benefits.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and non-volatile storage medium for configuring a photovoltaic-storage system, which at least solves the technical problem that current technologies struggle to simultaneously achieve multi-objective, full-process optimization of economic benefits, carbon reduction effects, and grid security when configuring photovoltaic-storage systems.

[0006] According to one aspect of the present invention, a method for configuring a photovoltaic-storage system is provided, comprising: acquiring historical data of a power distribution network, wherein the historical data of the power distribution network includes historical load data and historical solar radiation data of the power distribution network within a preset historical time period; determining typical operating scenarios of the power distribution network based on the historical data of the power distribution network, wherein the typical operating scenarios are operating scenarios within a period in which the load data and solar radiation data exceed a preset threshold during the operation phase of the power distribution network; determining multiple initial photovoltaic-storage system schemes based on a preset capacity threshold, wherein the initial photovoltaic-storage system schemes include initial photovoltaic capacity and initial energy storage capacity, the initial photovoltaic capacity being the initial installed capacity of the photovoltaic power station, the initial energy storage capacity being the initial construction capacity of the energy storage power station, and the photovoltaic power station and the energy storage power station being located in the power distribution network; and determining multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes, wherein the multiple investment cost indicators include the investment cost of the energy storage power station. The evaluation criteria include: investment cost indicators for photovoltaic power plants; based on multiple investment cost indicators and typical operating scenarios, and through multiple preset constraints, the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes are determined. These constraints include energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The first evaluation indicator is the operating cost indicator, the second evaluation indicator is the coal saving indicator, and the third evaluation indicator is the safety risk indicator. Based on the values ​​of these indicators, an optimization algorithm is used to determine the target photovoltaic-storage system configuration scheme. The optimization algorithm is an improved multi-objective particle swarm optimization algorithm for optimal solution selection. The target photovoltaic-storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity.

[0007] Optionally, based on historical data of the distribution network, typical operating scenarios of the distribution network are determined, including: calculating the average load of the distribution network within a preset historical time period based on historical load data; calculating the maximum load standard value, minimum load standard value, and maximum illuminance standard value of the distribution network within a preset time period based on historical load data, historical illumination data, and the average load; calculating the load peak-valley deviation rate based on the maximum load standard value and the minimum load standard value; and determining typical operating scenarios based on the sum of the maximum load standard value, the maximum illuminance standard value, and the load peak-valley deviation rate.

[0008] Optionally, multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes are determined, including: obtaining parameter information of the distribution network, wherein the parameter information includes the construction cost information, equipment replacement information and battery life information of the distribution network; determining the energy storage construction power based on the initial energy storage capacity; determining the photovoltaic power station investment cost indicators based on the initial photovoltaic capacity and parameter information; and determining the energy storage power station investment cost indicators based on the initial energy storage capacity, energy storage construction power and parameter information.

[0009] Optionally, based on multiple investment cost indicators and typical operating scenarios, and through multiple preset constraints, the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes are determined. This includes: acquiring operating data of the distribution network under typical operating scenarios, wherein the operating data includes energy storage power, photovoltaic power, network loss power, voltage limit exceedance degree, and voltage standard value; constructing an operating benefit model based on multiple investment cost indicators, wherein the operating benefit model includes a set of first evaluation indicator functions, a set of second evaluation indicator functions, and a set of third evaluation indicator functions; and solving the operating benefit model based on the operating data, through multiple preset constraints and a particle swarm optimization algorithm, to determine the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes.

[0010] Optionally, an operational efficiency model is constructed based on multiple investment cost indicators, including: constructing a first set of evaluation indicator functions based on multiple investment cost indicators, wherein the first set of evaluation indicator functions includes a daily average investment cost function, a system energy purchase cost function, a system operation and maintenance cost function, and a curtailment penalty cost function; constructing a second set of evaluation indicator functions, wherein the second set of evaluation indicator functions includes a coal saving function for energy storage charging and discharging and a coal saving function for photovoltaic power generation; and constructing a third set of evaluation indicator functions, wherein the third set of evaluation indicator functions includes a function for the overall voltage limit exceedance in typical operating scenarios, a voltage fluctuation function, and a transformer overload rate function.

[0011] Optionally, based on the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators, an optimization algorithm is used to determine the target photovoltaic-storage system configuration scheme. This includes: standardizing the values ​​of the multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators to obtain standard values ​​for the multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators; calculating the ideal distance for each of the multiple initial photovoltaic-storage system schemes based on the standard values ​​of the multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators, wherein the ideal distance includes positive ideal distance and negative ideal distance; calculating the fitting degree for each of the multiple initial photovoltaic-storage system schemes based on the ideal distances of the multiple initial photovoltaic-storage system schemes, wherein the fitting degree is the degree of matching between the initial photovoltaic-storage system scheme and the target photovoltaic-storage system configuration scheme; comparing the fitting degrees of the multiple initial photovoltaic-storage system schemes to obtain the minimum fitting degree among the fitting degrees of the multiple initial photovoltaic-storage system schemes; and selecting the initial photovoltaic-storage system scheme corresponding to the minimum fitting degree as the target photovoltaic-storage system configuration scheme.

[0012] According to another aspect of the present invention, a configuration device for a photovoltaic-storage system is also provided, comprising: an acquisition module, configured to acquire historical data of a power distribution network, wherein the historical data of the power distribution network includes historical load data and historical solar radiation data of the power distribution network within a preset historical time period; a selection module, configured to determine a typical operating scenario of the power distribution network based on the historical data of the power distribution network, wherein the typical operating scenario is an operating scenario within a period in which the load data and solar radiation data exceed a preset threshold during the operation phase of the power distribution network; an enumeration module, configured to determine multiple initial photovoltaic-storage system schemes based on a preset capacity threshold, wherein the initial photovoltaic-storage system scheme includes an initial photovoltaic capacity and an initial energy storage capacity, the initial photovoltaic capacity being the initial installed capacity of the photovoltaic power station, the initial energy storage capacity being the initial construction capacity of the energy storage power station, and the photovoltaic power station and the energy storage power station being located in the power distribution network; and a first determination module, configured to determine multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes, wherein the multiple investment cost indicators include energy storage... The system comprises three modules: a first module for determining the investment cost of power plants and a second module for determining the investment cost of photovoltaic power plants; a third module for determining the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for multiple initial photovoltaic-storage system schemes based on multiple investment cost indicators and typical operating scenarios, and through multiple preset constraints. The constraints include energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The first evaluation indicator is the operating cost indicator, the second evaluation indicator is the coal saving indicator, and the third evaluation indicator is the safety risk indicator. An optimization calculation module is used to determine the target photovoltaic-storage system configuration scheme based on the values ​​of the first, second, and third evaluation indicators through an optimization algorithm. The optimization algorithm is an improved multi-objective particle swarm optimization algorithm for optimal solution selection. The target photovoltaic-storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity.

[0013] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, it controls the device where the non-volatile storage medium is located to execute any of the above-described optical storage system configuration methods.

[0014] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program executes any of the above-described configuration methods for an optical storage system.

[0015] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the configuration method of any of the above-described optical storage systems.

[0016] In this embodiment of the invention, a configuration method for a photovoltaic-storage system is adopted. This involves acquiring historical data from the distribution network, including historical load data and historical solar irradiance data within a preset historical time period. Based on this historical data, typical operating scenarios for the distribution network are determined. These typical operating scenarios are those occurring during periods when load and solar irradiance data exceed preset thresholds. Based on preset capacity thresholds, multiple initial photovoltaic-storage system schemes are determined. Each initial scheme includes initial photovoltaic capacity and initial energy storage capacity. The initial photovoltaic capacity is the initial installed capacity of the photovoltaic power station, and the initial energy storage capacity is the initial construction capacity of the energy storage power station. Both the photovoltaic power station and the energy storage power station are located within the distribution network. Multiple investment cost indicators are determined for each of the initial photovoltaic-storage system schemes. These indicators include investment cost indicators for both the energy storage power station and the photovoltaic power station. Based on these investment cost indicators and the typical operating scenarios, multiple first evaluation indicators for each of the initial photovoltaic-storage system schemes are determined through preset constraints. The evaluation criteria include the values ​​of multiple second and third evaluation indicators, among which multiple constraints include energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The first evaluation indicator is the operating cost indicator, the second evaluation indicator is the coal saving indicator, and the third evaluation indicator is the safety risk indicator. Based on the values ​​of multiple first, second, and third evaluation indicators, an optimization algorithm is used to determine the target photovoltaic-storage system configuration scheme. The optimization algorithm is an improved multi-objective particle swarm optimization algorithm for optimal solution selection. The target photovoltaic-storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity, achieving the goal of balancing the investment cost and operating efficiency of the photovoltaic-storage system and optimizing resource allocation. This achieves the technical effects of improving the economic efficiency of the distribution network, enhancing the carbon reduction effect of the distribution network, and ensuring the safe operation of the distribution network. In turn, it solves the technical problem that current technologies are unable to simultaneously achieve multi-objective, full-process optimization of economic efficiency, carbon reduction effect, and grid security when configuring photovoltaic-storage systems. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a computer terminal for implementing a configuration method of an optical storage system is shown.

[0019] Figure 2 This is a flowchart illustrating the configuration method of a photovoltaic storage system provided according to an embodiment of the present invention;

[0020] Figure 3 This is a flowchart illustrating the configuration method design for a photovoltaic energy storage system according to an optional embodiment of the present invention.

[0021] Figure 4 This is a structural block diagram of a configuration device for a photovoltaic energy storage system provided according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a 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.

[0024] According to an embodiment of the present invention, a configuration method for an optical storage system is provided. 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. Furthermore, 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.

[0025] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a configuration method for an optical storage system is shown. Figure 1As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. 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 aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0026] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0027] The memory 104 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the configuration method of the optical storage system in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the optical storage system configuration method of the aforementioned application. 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, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0028] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0029] Figure 2 This is a flowchart illustrating the configuration method of a photovoltaic storage system provided according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0030] Step S201: Obtain historical data of the distribution network, wherein the historical data of the distribution network includes historical load data and historical illumination data of the distribution network within a preset historical time period.

[0031] In this step, the historical data of the distribution network includes historical load data and historical solar radiation data. Historical load data reflects the electricity consumption of users over a past period, including daily, weekly, monthly, and even annual load curves, covering peak, valley, and average load values. Historical solar radiation data measures the intensity of solar radiation received by photovoltaic panels, including hourly solar radiation intensity values ​​of the distribution network, reflecting the periodic and seasonal variations in solar radiation conditions. In this optional embodiment, to ensure the representativeness and reliability of the selected typical scenario, 8760 hours of historical load and solar radiation data from the distribution network over a year can be selected as the research object.

[0032] Step S202: Based on historical data of the distribution network, determine the typical operating scenarios of the distribution network. The typical operating scenarios are the operating scenarios during the period when the load data and solar illumination data exceed the preset threshold during the operation of the distribution network.

[0033] In this step, because there are many factors to consider when configuring a photovoltaic-storage system during the operation phase, using an annual research period would result in an excessively long time frame and a large computational load. Therefore, it is necessary to select a representative daily typical scenario as the research object to optimize the research period. The typical operation scenario refers to the operation scenario within a period when load data and solar illumination data exceed preset thresholds during the distribution network operation phase. In essence, it is a high-level summary of the portion of the dataset where the distribution network load and solar illumination conditions reach or exceed preset index levels within a specific period. It captures the extreme states and normal performance of load demand and solar intensity fluctuations during distribution network operation.

[0034] Specifically, firstly, through in-depth analysis of historical data from the distribution network, characteristic values ​​for selection are established, including load values, illuminance values, and load peak-valley deviation rates. Calculating these characteristic values ​​quantifies the differences in distribution network operation across different days. Then, using extreme case thinking, the focus is on days in the distribution network operation phase where load and illuminance data exceed preset thresholds. Specifically, the day with the highest sum of the maximum load standard value, maximum illuminance standard value, and load peak-valley deviation rate can be selected and defined as the typical operating scenario.

[0035] Step S203: Based on a preset capacity threshold, determine multiple initial photovoltaic-storage system schemes. The initial photovoltaic-storage system scheme includes initial photovoltaic capacity and initial energy storage capacity. The initial photovoltaic capacity is the initial installed capacity of the photovoltaic power station, and the initial energy storage capacity is the initial construction capacity of the energy storage power station. The photovoltaic power station and the energy storage power station are located in the distribution network.

[0036] In this step, the preset capacity thresholds are the actual configurable installed capacity of the photovoltaic power station and the construction capacity of the energy storage power station at the planned configuration node. This optional embodiment uses an enumeration and trial-and-error method to determine the initial photovoltaic-energy storage system scheme. Specifically, the photovoltaic capacity and energy storage capacity are incremented from zero at certain intervals until they reach the preset capacity thresholds, resulting in multiple photovoltaic capacity values ​​and multiple energy storage capacity values. The interval between these values ​​can be selected and set according to specific circumstances. Subsequently, each photovoltaic capacity value is combined with multiple energy storage capacity values ​​to obtain multiple initial photovoltaic-energy storage system schemes, each scheme containing one initial photovoltaic capacity and one initial energy storage capacity.

[0037] Step S204: Determine multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes, including investment cost indicators for energy storage power stations and investment cost indicators for photovoltaic power stations.

[0038] This step involves a detailed economic analysis of each initial photovoltaic-storage system scheme to quantify the financial costs of building such a system and fully consider the long-term economic benefits of investing in it. Specifically, multiple investment cost indicators cover a comprehensive cost assessment of both energy storage and photovoltaic equipment, including but not limited to the initial purchase cost of the equipment, the cost of auxiliary equipment and site, the cost-effectiveness of technological maturity, and potential subsidies. The investment cost for energy storage power stations not only considers the capacity and power characteristics of the batteries but also deeply analyzes the associated auxiliary equipment, site usage, and depreciation and replacement costs over the battery's lifespan, ensuring a comprehensive understanding of the long-term investment in energy storage systems. The investment cost for photovoltaic power stations focuses on the equipment costs and additional costs influenced by their technological characteristics and subsidy policies.

[0039] Step S205: Based on multiple investment cost indicators and typical operating scenarios, and through multiple preset constraints, determine the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes. Among them, the multiple constraints include energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The first evaluation indicator is the operating cost indicator, the second evaluation indicator is the coal saving indicator, and the third evaluation indicator is the safety risk indicator.

[0040] In this step, the comprehensive operational benefits of each initial photovoltaic-storage system scheme are quantitatively evaluated to ensure that it achieves optimization goals in terms of economic benefits, environmental benefits, and grid safety. The first evaluation indicator, the operating cost indicator, reflects the total economic burden during system operation, including electricity purchase costs, operation and maintenance costs, grid loss costs, and curtailment costs, comprehensively measuring the economic feasibility of the photovoltaic-storage system. The second evaluation indicator, the coal saving indicator, essentially quantifies the system's environmental performance, reflecting the carbon emission reduction effect of the photovoltaic-storage system replacing traditional coal-fired power generation, demonstrating its contribution to promoting green energy application and reducing greenhouse gas emissions. The third evaluation indicator, the safety risk indicator, comprehensively considers the degree of voltage exceeding limits, voltage fluctuation quality, and the risk of power backfeeding to transformer substations, ensuring the overall safety and operational stability of the grid after the photovoltaic-storage system is connected to the distribution network.

[0041] Specifically, this optional embodiment can employ a particle swarm optimization (PSO) algorithm to solve the problem under preset constraints. These constraints include the state of charge and charging / discharging power limits of the energy storage system, the upper limit of photovoltaic operating power, the acceptable range of the distribution network operating voltage, the safe load rate of the transformer substation, and the system power balance requirements, ensuring that the photovoltaic-energy storage system operates within practically feasible boundaries. Through iterative calculations using the PSO algorithm, the performance of each initial photovoltaic-energy storage system scheme on the above three key indicators is evaluated, thereby forming a set of first, second, and third evaluation indicators. These indicators are used to comprehensively evaluate the economic benefits, environmental benefits, and grid security of the initial photovoltaic-energy storage system scheme.

[0042] Step S206: Based on the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators, the target photovoltaic-storage system configuration scheme is determined through an optimization algorithm. The optimization algorithm is an improved multi-objective particle swarm optimization algorithm for optimal solution selection. The target photovoltaic-storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity.

[0043] In this step, an improved multi-objective particle swarm optimization algorithm is used to integrate the calculated values ​​of multiple first, second, and third evaluation indicators to obtain the target photovoltaic-storage system configuration scheme. The core of this scheme lies in determining the optimal combination of the target photovoltaic capacity and the target energy storage capacity, ensuring that the configured photovoltaic-storage system achieves a comprehensive optimal solution in terms of economic benefits, environmental contribution, and grid operation safety.

[0044] Specifically, the optimization algorithm in this optional embodiment is based on multi-objective optimization theory. Unlike traditional single-objective-oriented methods, it finds the optimal solution among multiple mutually constraining objectives. Specifically, the optimization algorithm first standardizes the data of each indicator, then uses the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to integrate the indicators, handling dimensional differences and conflicts between different indicators, and evaluating the comprehensive performance of the three types of indicators in each initial photovoltaic-storage system configuration scheme. This results in selecting the photovoltaic-storage system configuration scheme with the best overall performance, which is the target photovoltaic-storage system configuration scheme. The target photovoltaic-storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity. Using the target photovoltaic capacity and target energy storage capacity for photovoltaic-storage system configuration can effectively control operating costs, maximize coal savings, and ensure grid security.

[0045] Through the above steps, the goal of balancing the investment cost and operational efficiency of the photovoltaic-storage system and optimizing resource allocation is achieved. This results in improving the economic efficiency of the distribution network, enhancing its carbon reduction effect, and ensuring its safe operation. Furthermore, it solves the technical problem that current technologies struggle to simultaneously achieve multi-objective, full-process optimization of economic efficiency, carbon reduction effect, and grid security when configuring photovoltaic-storage systems.

[0046] As an optional implementation, based on historical data of the distribution network, typical operating scenarios of the distribution network are determined, including: calculating the average load of the distribution network within a preset historical time period based on historical load data; calculating the maximum load standard value, minimum load standard value, and maximum illuminance standard value of the distribution network within a preset time range based on historical load data, historical illumination data, and the average load; calculating the load peak-valley deviation rate based on the maximum load standard value and the minimum load standard value; and determining typical operating scenarios based on the sum of the maximum load standard value, the maximum illuminance standard value, and the load peak-valley deviation rate.

[0047] Optionally, firstly, calculate the average load of the distribution network over a preset historical period (annual). :

[0048]

[0049] in, The load power at node n in the distribution network at time t. The value is the annual average load of the distribution network, T is the total number of hours throughout the year (8760h in this optional embodiment), and N is the total number of nodes in the distribution network.

[0050] Secondly, calculate the maximum standard value of the distribution network load within the preset time range (daily). Minimum load standard value and the standard value of maximum light intensity :

[0051]

[0052]

[0053]

[0054] in, In the first t 0 days t The regional light intensity at time ' is given by the formulas max{} and min{}, which are functions for finding the maximum and minimum values ​​of the sequence, respectively.

[0055] Subsequently, the peak-valley deviation rate of the distribution network load within the preset time range was calculated. :

[0056]

[0057] Finally, considering the operating conditions of the distribution network, the configurable photovoltaic capacity is maximized when the standard load value and standard solar intensity value are at their maximum within the preset time range. Simultaneously, the larger the peak-valley deviation rate, the larger the configurable energy storage capacity. This optional embodiment selects the peak load and solar intensity values ​​within the whole year. , , The typical operating scenario is the one with the largest sum within a given period.

[0058] As an optional embodiment, multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes are determined, including: obtaining parameter information of the distribution network, wherein the parameter information includes the construction cost information, equipment replacement information and battery life information of the distribution network; determining the energy storage construction power based on the initial energy storage capacity; determining the photovoltaic power station investment cost indicators based on the initial photovoltaic capacity and parameter information; and determining the energy storage power station investment cost indicators based on the initial energy storage capacity, energy storage construction power and parameter information.

[0059] Optionally, in this optional embodiment, the distribution network topology and parameter information are known, and the access nodes of photovoltaic and energy storage to the distribution network are known according to the application conditions of the user. However, the photovoltaic capacity to be built and the energy storage capacity are unknown. Therefore, the above unknown conditions can be used as optimization variables to establish the first-stage investment cost model.

[0060] Specifically, firstly, the investment and construction costs of a photovoltaic power station mainly consist of equipment costs and other additional expenses. The investment cost indicators for a photovoltaic power station are determined as follows:

[0061]

[0062] in, The unit is the investment cost indicator for photovoltaic power plants, expressed in ten thousand yuan. For the unit photovoltaic construction cost, this optional embodiment is taken as 0.5 million yuan / kW; Photovoltaic installed capacity, in kW; , The proportions are the photovoltaic surcharge per unit capacity and the site cost, which are taken as 0.15 million yuan / kW and 0.05 million yuan / kW, respectively. , These are the photovoltaic subsidy coefficient and the technology maturity coefficient, respectively, which are 0.2 and 0.1.

[0063] Secondly, the investment in an energy storage power station can be divided into two parts: capacity investment and power investment cost. Power cost involves the cost of power conversion devices, battery management systems, etc., while capacity cost is related to the energy storage battery itself. During the investment and construction of an energy storage power station, auxiliary equipment, environmental and site costs, and lifespan depreciation costs are also involved. All of these factors are accumulated in the initial stage of project investment and construction, determining the following investment cost indicators for an energy storage power station:

[0064]

[0065] in, The unit is the investment cost indicator for energy storage power stations, expressed in ten thousand yuan. This represents the initial energy storage capacity. The energy storage capacity is the installed capacity in kW, and its specific value is 20% of the initial energy storage capacity. The unit capacity for energy storage is kWh, and the value is 0.4 million yuan / kWh. The construction cost per unit power is set at 0.3 million yuan / kW; N C , n c These represent the total number of energy storage battery replacements and the current number of energy storage battery replacements, respectively. N C The value is 1; Z represents the discount rate for funds, with a value of 0.03; Z represents the battery lifespan, with a value of 10 years. The cost of auxiliary equipment per unit capacity of the energy storage power station is taken as 0.2 million yuan / kW; The site cost per unit of energy storage capacity is 0.13 million yuan / kW.

[0066] As an optional implementation, based on multiple investment cost indicators and typical operating scenarios, and through multiple preset constraints, the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes are determined. This includes: acquiring operating data of the distribution network under typical operating scenarios, wherein the operating data includes energy storage power, photovoltaic power, network loss power, voltage limit exceedance degree, and voltage standard value; constructing an operating benefit model based on multiple investment cost indicators, wherein the operating benefit model includes a set of first evaluation indicator functions, a set of second evaluation indicator functions, and a set of third evaluation indicator functions; and solving the operating benefit model based on the operating data, through multiple preset constraints and a particle swarm optimization algorithm, to determine the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes.

[0067] Optionally, firstly, obtain the operating data of the distribution network under typical operating scenarios, including key parameters such as distribution network energy storage power, photovoltaic power, network loss power, voltage limit exceedance degree and voltage standard value, which can directly reflect the dynamic characteristics of the photovoltaic-storage system during operation and its impact on the performance of the distribution network.

[0068] Subsequently, based on multiple investment cost indicators, a first set of evaluation indicator functions is constructed to calculate economic benefit-related assessment indicators; a second set of evaluation indicator functions is constructed to calculate carbon reduction effectiveness-related assessment indicators; and a third set of evaluation indicator functions is constructed to calculate safety risk-related assessment indicators. These three sets of indicator functions together constitute the second-stage operational benefit model.

[0069] Finally, based on operational data and considering the coordinated operation of photovoltaic and energy storage, the hourly power of photovoltaic and energy storage are used as variables. Through multiple preset constraints and particle swarm optimization, the operational efficiency model is solved to determine the specific function values ​​of the three evaluation indicators under each initial photovoltaic-energy storage system scheme.

[0070] Specifically, the constraints include energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The energy storage charge and power constraints are described as follows:

[0071]

[0072] in, The rated maximum charge capacity of the energy storage; Let be the energy charge stored at time t; This refers to the rated power for charging and discharging energy storage, where positive numbers represent charging and negative numbers represent discharging. Let be the real-time power of the energy storage at time t.

[0073] The power constraints for photovoltaic operation are stated as follows:

[0074]

[0075] in, Let be the real-time power of the photovoltaic system at time t.

[0076] The constraints on the operating voltage of the distribution network and the load rate of the transformer substations are described as follows:

[0077]

[0078] in, , These are the upper and lower limits of the distribution network voltage, with values ​​of 1.05 and 0.95 respectively. The load factor is the area of ​​the transformer station, and areas exceeding 80% are considered heavily loaded. U n For nodes n The voltage.

[0079] The power balance constraints of the distribution network are described as follows:

[0080]

[0081] in, P G_n and Q G_n For the active and reactive power of the generator; P Load_n and Q Load_n The active and reactive power of the load; M This also represents the number of nodes in the distribution network; G nm , B nm , θ nm For nodes n and m The electrical conductance, susceptance, voltage phase angle difference between them.

[0082] As an optional implementation, an operational efficiency model is constructed based on multiple investment cost indicators, including: constructing a first set of evaluation indicator functions based on multiple investment cost indicators, wherein the first set of evaluation indicator functions includes a daily average investment cost function, a system energy purchase cost function, a system operation and maintenance cost function, and a curtailment penalty cost function; constructing a second set of evaluation indicator functions, wherein the second set of evaluation indicator functions includes a coal saving function for energy storage charging and discharging and a coal saving function for photovoltaic power generation; and constructing a third set of evaluation indicator functions, wherein the third set of evaluation indicator functions includes a function for the overall voltage limit exceedance in typical operating scenarios, a voltage fluctuation function, and a transformer overload rate function.

[0083] Optionally, firstly, a first set of evaluation index functions is constructed, and the first evaluation index is calculated, which consists of the following four parts:

[0084]

[0085] in, The primary evaluation indicator represents the overall operating cost of the distribution network; This represents the average daily cost of investment. For system energy purchase costs; For system operation and maintenance costs; The cost of penalties for abandoning light.

[0086] Specifically:

[0087]

[0088]

[0089]

[0090]

[0091] in, The power loss of the distribution network at time t is expressed in kW. For the design life of photovoltaic power plants and energy storage power plants, this optional embodiment is taken as 25 years; The time-of-use electricity price at time t is expressed in yuan / kWh. , , These represent the real-time power of energy storage and photovoltaic power at time t, respectively, and the power loss of the distribution network, in kW. Let be the maximum power output of photovoltaic power generation at time t. The difference is the amount of electricity generated by the photovoltaic system at time t. The curtailment penalty coefficient is set to 2.5 yuan / kWh in this optional embodiment; , The percentage represents the daily operation and maintenance cost of photovoltaic and energy storage, and in this optional embodiment, it is taken as 0.03 million yuan / day.

[0092] Secondly, a second set of evaluation index functions is constructed, and the second evaluation index is calculated, which consists of two parts:

[0093]

[0094] in, The second evaluation indicator represents the amount of coal saved, in kg. , These are the equivalent coal savings during energy storage charging and discharging, and photovoltaic power generation, respectively.

[0095] Specifically:

[0096]

[0097] in, The power generated by energy storage is expressed in kW. The charging and discharging efficiency of the energy storage system is set to 0.85 in this optional embodiment; s1 and s2 are the unit coal consumption of the energy storage system replacing the thermal power unit during power generation and charging operation, respectively, and both are set to 0.3025 kg / kWh.

[0098]

[0099] in, For the photovoltaic power generation efficiency, this optional embodiment is set to 0.8.

[0100] Finally, a third set of evaluation index functions is constructed, and the third evaluation index is calculated, which consists of three parts:

[0101]

[0102] in, The third evaluation indicator is the safety risk evaluation indicator for power grid operation. This refers to the overall degree of limit exceedance under typical operating scenarios of the power distribution network; This refers to the voltage fluctuation of the distribution network throughout the day. This serves as an assessment indicator for the heavy load of transformers throughout the entire Tiantai area.

[0103] Specifically:

[0104]

[0105]

[0106] in, The degree of voltage exceedance at node n at time t; The voltage standard value of node n at time t is calculated by the ratio of the voltage value of node n at time t to the reference voltage. In this optional embodiment, the reference voltage is 12.66V.

[0107]

[0108]

[0109]

[0110] in, This refers to the rated capacity of the transformer in the distribution area, expressed in kVA. The load factor is the area load rate, and areas with a load factor exceeding 80% are considered heavily loaded.

[0111] As an optional embodiment, based on the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators, an optimization algorithm is used to determine the target photovoltaic-storage system configuration scheme. This includes: standardizing the values ​​of the multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators to obtain standard values ​​for the multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators; calculating the ideal distance for each of the multiple initial photovoltaic-storage system schemes based on the standard values ​​of the multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators, where the ideal distance includes positive ideal distance and negative ideal distance; calculating the fitting degree for each of the multiple initial photovoltaic-storage system schemes based on their ideal distances, where the fitting degree is the degree of matching between the initial photovoltaic-storage system scheme and the target photovoltaic-storage system configuration scheme; comparing the fitting degrees of the multiple initial photovoltaic-storage system schemes to obtain the minimum fitting degree among the multiple initial photovoltaic-storage system schemes; and selecting the initial photovoltaic-storage system scheme corresponding to the minimum fitting degree as the target photovoltaic-storage system configuration scheme.

[0112] Optionally, after multiple iterations of the particle swarm optimization algorithm, the evaluation index value corresponding to each initial photovoltaic-storage system configuration scheme can be obtained. , , These values ​​correspond to the first, second, and third evaluation indicators, respectively, and are used as objective functions. A multi-objective optimal solution selection algorithm based on TOPSIS is employed to process these multiple sets of indicator values. First, standardization is required to eliminate dimensional differences, as shown in the following formula:

[0113]

[0114] in, The first set of evaluation index values ​​is standardized and belongs to the interval [0,1]. Similarly, the other two sets of indicators are standardized to obtain... and .

[0115] Next, the ideal distance for each initial photovoltaic-storage system scheme is calculated, including both positive and negative ideal distances. The calculation formula is as follows:

[0116]

[0117] in, , These represent the positive and negative ideal distances, respectively.

[0118] Finally, based on the positive and negative ideal distances, the fit of each initial photovoltaic-storage system scheme is calculated using the following formula:

[0119]

[0120] in, Let be the fit degree of the initial scheme j. The smaller this value, the closer the corresponding initial configuration scheme is to the target configuration scheme. After calculation by the particle swarm optimization algorithm, there are optimal operating cost indicators, coal saving indicators, and safety risk indicators in the three sets of evaluation indicators. The fit degree can be used to describe the degree of matching between each initial scheme evaluation indicator and the optimal solution of the three objective function values. The smaller the value, the better the corresponding initial photovoltaic-storage configuration scheme. The initial photovoltaic-storage configuration scheme with the smallest fit degree is selected as the target photovoltaic-storage system configuration scheme, and the photovoltaic capacity and energy storage capacity included in it are the target photovoltaic capacity and the target energy storage capacity.

[0121] As an optional embodiment, Figure 3 This is a flowchart illustrating the configuration method design for a photovoltaic storage system according to an optional embodiment of the present invention. Figure 3 As shown, this optional embodiment first acquires historical operating data of the distribution network, including historical records of load and solar intensity, and then extracts characteristic values ​​of load and solar intensity, such as extreme values, average values, and peak-valley deviation rates, in order to identify the most representative operating scenarios. Subsequently, based on the above characteristic values, using extreme thinking, typical operating scenarios throughout the year are selected as optimization cycles. The selection principle of this cycle is to maximize the comprehensive characteristics of load and solar intensity to simulate the most challenging operating conditions.

[0122] Secondly, an investment cost model is constructed, focusing on the capacity of the proposed photovoltaic and energy storage system. Through techniques such as enumeration and trial-and-error, the construction costs of various configuration combinations are comprehensively evaluated, covering the equipment costs, auxiliary costs, depreciation costs, and related operating expenses of both photovoltaic and energy storage systems. Subsequently, an operational efficiency model is constructed and solved. In this stage, the photovoltaic and energy storage power under typical operating scenarios becomes the optimization variables. The objective function is constructed around operating cost indicators, coal saving indicators, and safety risk indicators. Using a multi-objective particle swarm optimization algorithm as the core, the optimal photovoltaic-energy storage operation strategy within the constraints is solved.

[0123] Finally, based on the TOPSIS multi-objective optimal solution selection algorithm, a comprehensive comparison of the overall evaluation under different configuration schemes is conducted to identify the most ideal photovoltaic-storage system configuration. The effectiveness of the above method is verified by simulation using the IEEE 33 distribution network standard example.

[0124] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0125] Through the above description of the embodiments, those skilled in the art can clearly understand that the configuration method of the optical storage system according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0126] According to embodiments of the present invention, an apparatus for implementing the above-described configuration method for an optical storage system is also provided. Figure 4 This is a structural block diagram of a configuration device for a photovoltaic energy storage system provided according to an embodiment of the present invention, such as... Figure 4 As shown, the device includes: an acquisition module 41, a selection module 42, an enumeration module 43, a first determination module 44, a second determination module 45, and an optimization calculation module 46. The device will be described below.

[0127] The acquisition module 41 is used to acquire historical data of the distribution network, including historical load data and historical illumination data of the distribution network within a preset historical time period.

[0128] Select module 42, connected to acquisition module 41, is used to determine the typical operating scenarios of the distribution network based on historical data of the distribution network. The typical operating scenarios are the operating scenarios within the period when the load data and illumination data exceed the preset threshold during the operation of the distribution network.

[0129] Enumeration module 43, connected to selection module 42, is used to determine multiple initial photovoltaic-storage system schemes based on preset capacity thresholds. The initial photovoltaic-storage system schemes include initial photovoltaic capacity and initial energy storage capacity. The initial photovoltaic capacity is the initial installed capacity of the photovoltaic power station, and the initial energy storage capacity is the initial construction capacity of the energy storage power station. The photovoltaic power station and the energy storage power station are located in the distribution network.

[0130] The first determining module 44, connected to the enumeration module 43, is used to determine multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes. Among them, the multiple investment cost indicators include the investment cost indicators of energy storage power stations and the investment cost indicators of photovoltaic power stations.

[0131] The second determining module 45, connected to the first determining module 44, is used to determine the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of multiple initial photovoltaic-storage system schemes based on multiple investment cost indicators and typical operating scenarios, through multiple preset constraints. The multiple constraints include energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The first evaluation indicator is the operating cost indicator, the second evaluation indicator is the coal saving indicator, and the third evaluation indicator is the safety risk indicator.

[0132] The optimization calculation module 46 is connected to the second determination module 45 and is used to determine the target photovoltaic and energy storage system configuration scheme based on the values ​​of multiple first evaluation indicators, multiple second evaluation indicators and multiple third evaluation indicators through an optimization algorithm. The optimization algorithm is an improved multi-objective particle swarm optimization optimal solution selection algorithm, and the target photovoltaic and energy storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity.

[0133] It should be noted that the acquisition module 41, selection module 42, enumeration module 43, first determination module 44, second determination module 45, and optimization calculation module 46 mentioned above correspond to steps S201 to S206 in the embodiments. Multiple modules implement the same instances and application scenarios as their corresponding steps, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0134] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0135] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the configuration method and apparatus of the optical storage system in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the configuration method of the optical storage system described above. The memory 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 may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0136] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: acquiring historical data of the distribution network, including historical load data and historical solar irradiance data within a preset historical time period; determining typical operating scenarios for the distribution network based on the historical data, where typical operating scenarios are those operating during periods when load and solar irradiance data exceed preset thresholds; determining multiple initial photovoltaic-storage system schemes based on preset capacity thresholds, where each initial scheme includes initial photovoltaic capacity and initial energy storage capacity, the initial photovoltaic capacity being the initial installed capacity of the photovoltaic power station and the initial energy storage capacity being the initial construction capacity of the energy storage power station, both located within the distribution network; and determining multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes, including investment in the energy storage power station. Cost indicators and photovoltaic power plant investment cost indicators; based on multiple investment cost indicators and typical operating scenarios, through multiple preset constraints, the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of multiple initial photovoltaic-storage system schemes are determined. These constraints include energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The first evaluation indicator is the operating cost indicator, the second evaluation indicator is the coal saving indicator, and the third evaluation indicator is the safety risk indicator. Based on the values ​​of these indicators, an optimization algorithm is used to determine the target photovoltaic-storage system configuration scheme. The optimization algorithm is an improved multi-objective particle swarm optimization algorithm for optimal solution selection. The target photovoltaic-storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity.

[0137] Optionally, the processor may also execute program code for the following steps: determining typical operating scenarios of the distribution network based on historical data of the distribution network, including: calculating the average load of the distribution network within a preset historical time period based on historical load data; calculating the maximum load standard value, minimum load standard value, and maximum illuminance standard value of the distribution network within a preset time period based on historical load data, historical illumination data, and the average load; calculating the load peak-valley deviation rate based on the maximum load standard value and the minimum load standard value; and determining typical operating scenarios based on the sum of the maximum load standard value, the maximum illuminance standard value, and the load peak-valley deviation rate.

[0138] Optionally, the processor may also execute program code for the following steps: determining multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes, including: obtaining parameter information of the distribution network, wherein the parameter information includes construction cost information, equipment replacement information and battery life information of the distribution network; determining the energy storage construction power based on the initial energy storage capacity; determining the photovoltaic power station investment cost indicators based on the initial photovoltaic capacity and parameter information; and determining the energy storage power station investment cost indicators based on the initial energy storage capacity, energy storage construction power and parameter information.

[0139] Optionally, the processor may also execute program code for the following steps: based on multiple investment cost indicators and typical operating scenarios, and through multiple preset constraints, determine the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes, including: acquiring operating data of the distribution network under typical operating scenarios, wherein the operating data includes energy storage power, photovoltaic power, network loss power, voltage limit exceedance degree, and voltage standard value; constructing an operating benefit model based on multiple investment cost indicators, wherein the operating benefit model includes a set of first evaluation indicator functions, a set of second evaluation indicator functions, and a set of third evaluation indicator functions; and solving the operating benefit model based on the operating data, through multiple preset constraints and a particle swarm optimization algorithm, to determine the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes.

[0140] Optionally, the processor may also execute program code for the following steps: constructing an operational efficiency model based on multiple investment cost indicators, including: constructing a first evaluation indicator function set based on multiple investment cost indicators, wherein the first evaluation indicator function set includes an average daily investment cost function, a system energy purchase cost function, a system operation and maintenance cost function, and a curtailment penalty cost function; constructing a second evaluation indicator function set, wherein the second evaluation indicator function set includes a coal saving function for energy storage charging and discharging and a coal saving function for photovoltaic power generation; and constructing a third evaluation indicator function set, wherein the third evaluation indicator function set includes a function for the overall voltage limit exceedance in typical operating scenarios, a voltage fluctuation function, and a transformer overload rate function.

[0141] Optionally, the processor may also execute program code for the following steps: determining a target photovoltaic-storage system configuration scheme based on the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators through an optimization algorithm, including: standardizing the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators to obtain standard values ​​for multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators; calculating the ideal distance for each of the multiple initial photovoltaic-storage system schemes based on the standard values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators, wherein the ideal distance includes positive ideal distance and negative ideal distance; calculating the fitting degree for each of the multiple initial photovoltaic-storage system schemes based on the ideal distances of each of the multiple initial photovoltaic-storage system schemes, wherein the fitting degree is the degree of matching between the initial photovoltaic-storage system scheme and the target photovoltaic-storage system configuration scheme; comparing the fitting degrees of each of the multiple initial photovoltaic-storage system schemes to obtain the minimum fitting degree among the fitting degrees of each of the multiple initial photovoltaic-storage system schemes; and using the initial photovoltaic-storage system scheme corresponding to the minimum fitting degree as the target photovoltaic-storage system configuration scheme.

[0142] This invention provides a configuration scheme for a photovoltaic-storage system. By acquiring historical data from the distribution network, including historical load data and historical solar irradiance data within a preset historical time period; based on this historical data, typical operating scenarios for the distribution network are determined, where typical operating scenarios refer to periods during which load and solar irradiance data exceed preset thresholds; based on preset capacity thresholds, multiple initial photovoltaic-storage system schemes are determined, each including initial photovoltaic capacity and initial energy storage capacity. The initial photovoltaic capacity is the initial installed capacity of the photovoltaic power station, and the initial energy storage capacity is the initial construction capacity of the energy storage power station, both located within the distribution network; multiple investment cost indicators are determined for each of the initial photovoltaic-storage system schemes, including investment cost indicators for both the energy storage power station and the photovoltaic power station; based on these investment cost indicators and the typical operating scenarios, and through multiple preset constraints, the specific investment cost indicators for each initial photovoltaic-storage system scheme are further determined. The system employs multiple first, second, and third evaluation indicators, along with constraints including energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The first evaluation indicator is the operating cost indicator, the second is the coal saving indicator, and the third is the safety risk indicator. Based on these values, an optimization algorithm is used to determine the target photovoltaic-storage system configuration scheme. This algorithm is an improved multi-objective particle swarm optimization algorithm for optimal solution selection. The target photovoltaic-storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity. This achieves a balance between investment costs and operational benefits of the photovoltaic-storage system, optimizes resource allocation, and solves the technical problem of current technologies failing to simultaneously achieve multi-objective, full-process optimization of economic benefits, carbon reduction effects, and grid security in photovoltaic-storage system configuration.

[0143] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0144] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the configuration method of the optical storage system provided in the above embodiments.

[0145] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0146] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring historical data of the distribution network, wherein the historical data of the distribution network includes historical load data and historical solar radiation data of the distribution network within a preset historical time period; determining typical operating scenarios of the distribution network based on the historical data of the distribution network, wherein the typical operating scenario is an operating scenario within a period in which the load data and solar radiation data exceed a preset threshold during the operation phase of the distribution network; determining multiple initial photovoltaic-storage system schemes based on a preset capacity threshold, wherein the initial photovoltaic-storage system scheme includes initial photovoltaic capacity and initial energy storage capacity, wherein the initial photovoltaic capacity is the initial installed capacity of the photovoltaic power station, and the initial energy storage capacity is the initial construction capacity of the energy storage power station, and the photovoltaic power station and the energy storage power station are located in the distribution network; determining multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes, wherein the multiple investment cost indicators include the energy storage power station Investment cost indicators and photovoltaic power plant investment cost indicators; based on multiple investment cost indicators and typical operating scenarios, through multiple preset constraints, the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of multiple initial photovoltaic-storage system schemes are determined. These constraints include energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The first evaluation indicator is the operating cost indicator, the second evaluation indicator is the coal saving indicator, and the third evaluation indicator is the safety risk indicator. Based on the values ​​of these indicators, an optimization algorithm is used to determine the target photovoltaic-storage system configuration scheme. The optimization algorithm is an improved multi-objective particle swarm optimization algorithm for optimal solution selection. The target photovoltaic-storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity.

[0147] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining typical operating scenarios of the distribution network based on historical data of the distribution network, including: calculating the average load of the distribution network within a preset historical time period based on historical load data; calculating the maximum load standard value, minimum load standard value, and maximum illuminance standard value of the distribution network within a preset time range based on historical load data, historical illumination data, and the average load; calculating the load peak-valley deviation rate based on the maximum load standard value and the minimum load standard value; and determining typical operating scenarios based on the sum of the maximum load standard value, the maximum illuminance standard value, and the load peak-valley deviation rate.

[0148] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes, including: obtaining parameter information of the distribution network, wherein the parameter information includes construction cost information, equipment replacement information and battery life information of the distribution network; determining the energy storage construction power based on the initial energy storage capacity; determining the photovoltaic power station investment cost indicator based on the initial photovoltaic capacity and parameter information; and determining the energy storage power station investment cost indicator based on the initial energy storage capacity, energy storage construction power and parameter information.

[0149] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: based on multiple investment cost indicators and typical operating scenarios, and through multiple preset constraints, determining the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes, including: acquiring operating data of the distribution network under typical operating scenarios, wherein the operating data includes energy storage power, photovoltaic power, network loss power, voltage limit exceedance degree, and voltage standard value; constructing an operating benefit model based on multiple investment cost indicators, wherein the operating benefit model includes a first evaluation indicator function set, a second evaluation indicator function set, and a third evaluation indicator function set; and solving the operating benefit model based on the operating data, through multiple preset constraints and particle swarm optimization algorithm, to determine the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes.

[0150] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: constructing an operational efficiency model based on multiple investment cost indicators, including: constructing a first evaluation indicator function set based on multiple investment cost indicators, wherein the first evaluation indicator function set includes an average daily investment cost function, a system energy purchase cost function, a system operation and maintenance cost function, and a curtailment penalty cost function; constructing a second evaluation indicator function set, wherein the second evaluation indicator function set includes a coal saving function for energy storage charging and discharging and a coal saving function for photovoltaic power generation; and constructing a third evaluation indicator function set, wherein the third evaluation indicator function set includes a function for the overall voltage limit exceedance in typical operating scenarios, a voltage fluctuation function, and a transformer overload rate function.

[0151] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining a target optical-storage system configuration scheme based on the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators through an optimization algorithm, including: standardizing the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators to obtain standard values ​​for multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators; calculating the ideal distance for each of multiple initial optical-storage system schemes based on the standard values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators, wherein the ideal distance includes positive ideal distance and negative ideal distance; calculating the fitting degree for each of the multiple initial optical-storage system schemes based on the ideal distance for each of the multiple initial optical-storage system schemes, wherein the fitting degree is the degree of matching between the initial optical-storage system scheme and the target optical-storage system configuration scheme; comparing the fitting degrees for each of the multiple initial optical-storage system schemes to obtain the minimum fitting degree among the fitting degrees for each of the multiple initial optical-storage system schemes; and using the initial optical-storage system scheme corresponding to the minimum fitting degree as the target optical-storage system configuration scheme.

[0152] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire historical data of a distribution network, wherein the historical data of the distribution network includes historical load data and historical solar radiation data of the distribution network within a preset historical time period; determine typical operating scenarios of the distribution network based on the historical data of the distribution network, wherein the typical operating scenarios are operating scenarios within a period in which load data and solar radiation data exceed preset thresholds during the operation phase of the distribution network; determine multiple initial photovoltaic-storage system schemes based on preset capacity thresholds, wherein the initial photovoltaic-storage system schemes include initial photovoltaic capacity and initial energy storage capacity, wherein the initial photovoltaic capacity is the initial installed capacity of the photovoltaic power station, and the initial energy storage capacity is the initial construction capacity of the energy storage power station, and the photovoltaic power station and the energy storage power station are located in the distribution network; determine multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes, wherein the multiple investment costs... This indicator includes investment cost indicators for energy storage power stations and investment cost indicators for photovoltaic power stations. Based on multiple investment cost indicators and typical operating scenarios, and through multiple preset constraints, the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-energy storage system schemes are determined. These constraints include energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The first evaluation indicator is the operating cost indicator, the second evaluation indicator is the coal saving indicator, and the third evaluation indicator is the safety risk indicator. Based on the values ​​of these indicators, an optimization algorithm is used to determine the target photovoltaic-energy storage system configuration scheme. The optimization algorithm is an improved multi-objective particle swarm optimization algorithm for optimal solution selection. The target photovoltaic-energy storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity.

[0153] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0154] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0156] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0158] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0159] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for configuring a photovoltaic energy storage system, characterized in that, include: Acquire historical data of the distribution network, wherein the historical data of the distribution network includes historical load data and historical illumination data of the distribution network within a preset historical time period; Based on the historical data of the distribution network, the typical operating scenarios of the distribution network are determined, wherein the typical operating scenarios are the operating scenarios during the period when the load data and the illumination data exceed the preset threshold during the operation phase of the distribution network. Based on a preset capacity threshold, multiple initial photovoltaic-storage system schemes are determined. Each initial photovoltaic-storage system scheme includes an initial photovoltaic capacity and an initial energy storage capacity. The initial photovoltaic capacity is the initial installed capacity of the photovoltaic power station, and the initial energy storage capacity is the initial construction capacity of the energy storage power station. The photovoltaic power station and the energy storage power station are located in the distribution network. Determine multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes, wherein the multiple investment cost indicators include investment cost indicators for energy storage power stations and investment cost indicators for photovoltaic power stations; Based on the aforementioned multiple investment cost indicators and the aforementioned typical operating scenarios, and through multiple preset constraints, the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes are determined. The multiple constraints include energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The first evaluation indicator is the operating cost indicator, the second evaluation indicator is the coal saving indicator, and the third evaluation indicator is the safety risk indicator. Based on the values ​​of the multiple first evaluation indicators, the multiple second evaluation indicators, and the multiple third evaluation indicators, an optimization algorithm is used to determine the target photovoltaic-storage system configuration scheme. The optimization algorithm is an improved multi-objective particle swarm optimization algorithm for optimal solution selection. The target photovoltaic-storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity.

2. The method according to claim 1, characterized in that, The determination of typical operating scenarios for the distribution network based on historical data includes: Based on the historical load data, calculate the average load of the distribution network within the preset historical time period; Based on the historical load data, the historical illumination data, and the average load, calculate the standard value of the maximum load, the standard value of the minimum load, and the standard value of the maximum illumination intensity of the distribution network within a preset time range. Calculate the load peak-valley deviation rate based on the maximum load standard value and the minimum load standard value; The typical operating scenario is determined based on the sum of the maximum power grid load standard value, the maximum light intensity standard value, and the load peak-valley deviation rate.

3. The method according to claim 1, characterized in that, The determination of multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes includes: Obtain parameter information of the power distribution network, wherein the parameter information includes construction cost information, equipment replacement information and battery life information of the power distribution network; Based on the initial energy storage capacity, determine the energy storage construction power; Based on the initial photovoltaic capacity and the parameter information, the investment cost index of the photovoltaic power station is determined; Based on the initial energy storage capacity, the energy storage construction power, and the parameter information, the investment cost index of the energy storage power station is determined.

4. The method according to claim 1, characterized in that, Based on the multiple investment cost indicators and the typical operating scenarios, and through multiple preset constraints, the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes are determined, including: Obtain the operation data of the distribution network under the typical operating scenario, wherein the operation data includes energy storage power, photovoltaic power, network loss power, voltage limit exceedance degree and voltage standard value; Based on the aforementioned multiple investment cost indicators, an operational efficiency model is constructed, wherein the operational efficiency model includes a first evaluation indicator function group, a second evaluation indicator function group, and a third evaluation indicator function group; Based on the operational data, the operational efficiency model is solved using the preset multiple constraints and particle swarm optimization algorithm to determine the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes.

5. The method according to claim 4, characterized in that, The construction of the operational efficiency model based on the multiple investment cost indicators includes: Based on the aforementioned multiple investment cost indicators, a first evaluation indicator function set is constructed, wherein the first evaluation indicator function set includes an average daily investment cost function, a system energy purchase cost function, a system operation and maintenance cost function, and a curtailment penalty cost function; Construct the second evaluation index function set, wherein the second evaluation index function set includes the energy storage charging and discharging coal saving function and the photovoltaic power generation coal saving function; Construct the third evaluation index function set, which includes the overall voltage over-limit degree function, voltage fluctuation function, and transformer overload rate function in the typical operating scenario.

6. The method according to claim 1, characterized in that, The process of determining the target photovoltaic-storage system configuration scheme based on the values ​​of the plurality of first evaluation indicators, the plurality of second evaluation indicators, and the plurality of third evaluation indicators through an optimization algorithm includes: The values ​​of the plurality of first evaluation indicators, the plurality of second evaluation indicators, and the plurality of third evaluation indicators are standardized to obtain the standard values ​​of the plurality of first evaluation indicators, the standard values ​​of the plurality of second evaluation indicators, and the standard values ​​of the plurality of third evaluation indicators. Based on the standard values ​​of the multiple first evaluation indicators, the multiple second evaluation indicators, and the multiple third evaluation indicators, the ideal distances of the multiple initial photovoltaic-storage system schemes are calculated, wherein the ideal distances include positive ideal distances and negative ideal distances. Based on the ideal distances of the multiple initial photovoltaic-storage system schemes, the fitting degree of each initial photovoltaic-storage system scheme is calculated, wherein the fitting degree is the degree of matching between the initial photovoltaic-storage system scheme and the target photovoltaic-storage system configuration scheme; By comparing the fit of the multiple initial optical-storage system schemes, the minimum fit among the fit of the multiple initial optical-storage system schemes is obtained; The initial photovoltaic-storage system scheme corresponding to the minimum fit is taken as the target photovoltaic-storage system configuration scheme.

7. A configuration device for a photovoltaic energy storage system, characterized in that, include: The acquisition module is used to acquire historical data of the distribution network, wherein the historical data of the distribution network includes historical load data and historical illumination data of the distribution network within a preset historical time period; The selection module is used to determine the typical operating scenarios of the distribution network based on the historical data of the distribution network, wherein the typical operating scenarios are the operating scenarios during the period when the load data and the illumination data exceed the preset threshold during the operation phase of the distribution network. An enumeration module is used to determine multiple initial photovoltaic-storage system schemes based on a preset capacity threshold. The initial photovoltaic-storage system scheme includes an initial photovoltaic capacity and an initial energy storage capacity. The initial photovoltaic capacity is the initial installed capacity of the photovoltaic power station, and the initial energy storage capacity is the initial construction capacity of the energy storage power station. The photovoltaic power station and the energy storage power station are located in the distribution network. The first determining module is used to determine multiple investment cost indicators for each of the multiple initial photovoltaic-storage system schemes, wherein the multiple investment cost indicators include investment cost indicators for energy storage power stations and investment cost indicators for photovoltaic power stations; The second determining module is used to determine the values ​​of multiple first evaluation indicators, multiple second evaluation indicators, and multiple third evaluation indicators for each of the multiple initial photovoltaic-storage system schemes based on the multiple investment cost indicators and the typical operating scenarios, through multiple preset constraints. The multiple constraints include energy storage charge and power constraints, photovoltaic power constraints, distribution network operating voltage and transformer load rate constraints, and distribution network operating power balance constraints. The first evaluation indicator is the operating cost indicator, the second evaluation indicator is the coal saving indicator, and the third evaluation indicator is the safety risk indicator. The optimization calculation module is used to determine the target photovoltaic-storage system configuration scheme based on the values ​​of the plurality of first evaluation indicators, the plurality of second evaluation indicators, and the plurality of third evaluation indicators through an optimization algorithm. The optimization algorithm is an improved multi-objective particle swarm optimization algorithm, and the target photovoltaic-storage system configuration scheme includes the target photovoltaic capacity and the target energy storage capacity.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the configuration method of the optical storage system according to any one of claims 1 to 6.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the configuration method of the optical storage system according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the configuration method of the optical storage system according to any one of claims 1 to 6.

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