Distributed photovoltaic on-site consumption rate evaluation method and system considering energy storage configuration

Abstract

The present application relates to a kind of distributed photovoltaic in-situ consumption rate evaluation method and system considering energy storage configuration, the method includes determining the annual net load characteristic curve according to the difference between the typical annual power consumption data of distribution network and the typical annual output data of distributed photovoltaic;According to the proportion of energy storage configuration and the duration of energy storage configuration, generate energy storage configuration scheme, set energy storage charging and discharging strategy, determine the energy storage charging and discharging power curve;Determine the net load power curve after superimposing energy storage according to the difference between the annual net load characteristic curve and the energy storage charging and discharging power curve;According to the net load power curve after superimposing energy storage, calculate the net load reverse power in statistical period, according to the typical annual output data of distributed photovoltaic, calculate the photovoltaic power generation in statistical period, according to the net load reverse power and photovoltaic power generation, calculate the distributed photovoltaic in-situ consumption rate.Application of the present application can determine the photovoltaic in-situ consumption level of the energy storage of certain proportion and duration, help to improve load characteristic.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a method and system for evaluating the local consumption rate of distributed photovoltaic power considering energy storage configuration. Background Technology

[0002] Large-scale distributed renewable energy integration will become an inevitable trend in the development of power distribution networks, profoundly impacting the source-load characteristics of the distribution side. With the large-scale development and integration of distributed photovoltaic (PV) power, the net load characteristics of the distribution system have undergone significant changes, even leading to situations where PV power generation is difficult to absorb locally, exhibiting a cascading backflow phenomenon. Configuring a certain proportion and duration of energy storage can effectively improve the local absorption rate of PV power, improve load characteristics, and promote the coordinated development of the power distribution system's source, grid, and load.

[0003] Calculating the local grid integration rate of distributed photovoltaic (PV) power, especially considering energy storage configuration, and analyzing the role of energy storage in the local grid integration of distributed PV power, is of great significance for guiding the integration of distributed PV power, clarifying energy storage configuration requirements, and evaluating the value of energy storage. Summary of the Invention

[0004] To address the aforementioned problems, the purpose of this invention is to provide a method and system for evaluating the local grid integration rate of distributed photovoltaic power generation considering energy storage configuration. After considering the energy storage configuration, the local grid integration rate of distributed photovoltaic power generation is evaluated and calculated.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This invention provides a method for evaluating the local grid integration rate of distributed photovoltaic power generation considering energy storage configuration, comprising the following steps:

[0007] The annual net load characteristic curve is determined based on the difference between the typical annual electricity consumption data of the distribution network and the typical annual power output data of distributed photovoltaic power.

[0008] Generate an energy storage configuration plan based on the energy storage configuration ratio and the energy storage configuration duration;

[0009] Set the energy storage charging and discharging strategy according to the energy storage configuration scheme, and determine the energy storage charging and discharging power curve according to the energy storage charging and discharging strategy.

[0010] The net load power curve after superimposed energy storage is determined based on the difference between the annual net load characteristic curve and the energy storage charge and discharge power curve.

[0011] The net load backfeed power E1 within the statistical period is calculated based on the net load power curve after the superimposed energy storage. The photovoltaic power generation E2 within the statistical period is calculated based on the typical annual output data of distributed photovoltaic. The local consumption rate β of distributed photovoltaic is calculated based on the net load backfeed power E1 and photovoltaic power generation E2 as follows: β=(1-E1 / E2)*100%.

[0012] Preferably, the energy storage configuration ratio is a percentage of the rated charge and discharge power of the energy storage to the installed capacity of the distributed photovoltaic system, and its value is 10%, 20%, 30%, or 40%.

[0013] The duration of the energy storage configuration is 1 hour, 2 hours, or 4 hours;

[0014] The energy storage configuration scheme is generated based on the energy storage configuration ratio and the energy storage configuration duration. The energy storage configuration ratio is selected from 10%, 20%, 30%, or 40%, and the energy storage configuration duration is selected from 1 hour, 2 hours, or 4 hours.

[0015] Preferably, the step of setting the energy storage charging and discharging strategy according to the energy storage configuration scheme and determining the energy storage charging and discharging power curve according to the energy storage charging and discharging strategy specifically includes:

[0016] Set the initial value of the energy storage charging threshold Pc0 and the initial value of the discharging threshold Pd0 on day n.

[0017] Calculate the energy storage charging power threshold value Pc on day n based on the initial energy storage charging threshold value Pc0 on day n, and calculate the energy storage discharging power threshold value Pd on day n based on the initial energy storage discharging threshold value Pd0 on day n.

[0018] Based on the charging power threshold Pc and discharging power threshold Pd of the energy storage on day n, the charging and discharging power of the energy storage and the battery capacity are calculated to obtain the energy storage charging and discharging power curve.

[0019] Preferably, the initial value of the energy storage charging threshold Pc0 on the nth day is the maximum net load value Lmax in the net load curve on the nth day;

[0020] The initial value of the energy storage discharge threshold Pd0 on the nth day is the minimum net load value Lmin in the net load curve on the nth day.

[0021] Preferably, the step of calculating the charging power threshold value Pc for energy storage on day n based on the initial value Pc0 of the energy storage charging threshold on day n specifically includes:

[0022] Let the initial value of the energy storage charging power threshold Pc on day n be Pc0.

[0023] For time t, calculate the charging power P_charge(t) = Pc - P_LPV(t). If the charging power is greater than the rated charging power of the energy storage, then the charging power P_charge(t) is the rated charging power of the energy storage S_ESS, and the charging amount E_charge = E_charge - P_charge(t) * Δh, where Δh is the calculation time step per day, and P_LPV(t) is the annual net load characteristic curve.

[0024] When the charging duration has not reached and the battery is not fully charged, it is judged whether the charging power threshold value Pc is less than the average value Lmean of the maximum and minimum net loads. If it is less, the charging power threshold value Pc of the energy storage on the nth day is updated to Pc = Pc + ΔP; where ΔP is the charging and discharging power iterative update step, and the average value Lmean of the maximum and minimum net loads is Lmean = (Lmax + Lmin) / 2.

[0025] Preferably, calculating the discharging power threshold value Pd of the energy storage on the nth day according to the initial discharging threshold value Pd0 of the energy storage on the nth day specifically includes:

[0026] Let the discharging power threshold value Pd of the energy storage on the nth day be initially the initial discharging threshold value Pd0 of the energy storage on the nth day;

[0027] For time t, the discharging power P_discharge(t) = P_LPV(t) - Pd. If the discharging power is greater than the rated discharging power of the energy storage, the discharging power P_discharge(t) is the rated charging power S_ESS of the energy storage, and the discharging electricity quantity E_discharge = E_discharge + P_discharge(t) * Δh, where Δh is the calculation time step per day, and P_LPV(t) is the annual net load characteristic curve;

[0028] When the discharging duration has not reached and the battery is not fully discharged, it is judged whether the discharging power threshold value Pd is greater than the average value Lmean of the maximum and minimum net loads. If it is greater, the discharging power threshold value Pd of the energy storage on the nth day is updated to Pd = Pd - ΔP; where ΔP is the charging and discharging power iterative update step, and the average value Lmean of the maximum and minimum net loads is Lmean = (Lmax + Lmin) / 2.

[0029] Preferably, calculating the charging and discharging power of the energy storage and the battery power according to the charging power threshold value Pc and the discharging power threshold value Pd of the energy storage on the nth day to obtain the charging and discharging power curve of the energy storage specifically includes:

[0030] If the annual net load characteristic curve P_LPV(t) < Pc at time t, the charging power of the energy storage P_charge(t) = P_LPV(t) – Pc;

[0031] If the annual net load characteristic curve P_LPV(t) > Pd at time t, the discharging power of the energy storage P_discharge(t) = P_LPV(t) – Pd;

[0032] The charging and discharging power P_ESS(t) of the energy storage at time t is obtained as the sum of the charging power P_charge(t) of the energy storage and the discharging power P_discharge(t) of the energy storage.

[0033] Another aspect of the present invention provides a distributed photovoltaic local consumption rate assessment system considering energy storage configuration, comprising:

[0034] The annual net load characteristic curve module determines the annual net load characteristic curve based on the difference between the typical annual electricity consumption data of the distribution network and the typical annual power output data of distributed photovoltaic power.

[0035] The configuration scheme generation module generates energy storage configuration schemes according to the energy storage configuration ratio and energy storage configuration duration.

[0036] The charge / discharge power curve module sets the energy storage charge / discharge strategy according to the energy storage configuration scheme and determines the energy storage charge / discharge power curve according to the energy storage charge / discharge strategy.

[0037] The net load power curve module determines the net load power curve after superimposed energy storage based on the difference between the annual net load characteristic curve and the energy storage charge and discharge power curve.

[0038] The local grid integration module calculates the net load backflow power E1 within the statistical period based on the net load power curve after adding energy storage, and calculates the photovoltaic power generation E2 within the statistical period based on typical annual output data of distributed photovoltaic power. Based on the net load backflow power E1 and photovoltaic power generation E2, the local grid integration rate β of distributed photovoltaic power is calculated as follows:

[0039] β = (1 - E1 / E2) * 100%.

[0040] The present invention also provides a processing device, the processing device comprising at least a processor and a memory, the memory storing a computer program, wherein when the processor runs the computer program, it performs steps to implement the distributed photovoltaic local consumption rate assessment method considering energy storage configuration described above.

[0041] The present invention also provides a computer storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor to implement the steps of the distributed photovoltaic local consumption rate assessment method considering energy storage configuration according to any of the preceding claims.

[0042] The present invention has the following advantages due to the adoption of the above technical solutions:

[0043] This invention fully considers various energy storage configuration schemes and determines the charging and discharging strategy based on the energy storage characteristics, enabling a scientific and accurate assessment and calculation of the local photovoltaic (PV) absorption capacity. It provides a clear and concise understanding of the local PV absorption capacity when energy storage is configured at a certain proportion and duration, which helps improve load characteristics and promotes the coordinated development of power generation, grid, and load in the distribution system. Attached Figure Description

[0044] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0045] Figure 1 This is a flowchart of a method for evaluating the local consumption rate of distributed photovoltaic power generation considering energy storage configuration;

[0046] Figure 2 This is a typical load characteristic curve for a residential area;

[0047] Figure 3 This is a typical photovoltaic power output characteristic curve;

[0048] Figure 4 It shows the net load curves before and after the superimposed energy storage of the R3-10-1 scheme.

[0049] Figure 5 It shows the net load curves before and after the superimposed energy storage of the R3-40-4 scheme. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0051] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0052] One embodiment of the present invention provides a method for evaluating the local absorption rate of distributed photovoltaic (PV) power generation considering energy storage configuration. The method involves determining the annual net load characteristic curve based on the difference between typical annual electricity consumption data of the distribution network and typical annual power output data of distributed PV; generating an energy storage configuration scheme according to the energy storage configuration ratio and configuration duration; setting an energy storage charging and discharging strategy according to the energy storage configuration scheme; determining the energy storage charging and discharging power curve according to the energy storage charging and discharging strategy; determining the net load power curve after adding energy storage based on the difference between the annual net load characteristic curve and the energy storage charging and discharging power curve; calculating the net load feedback power E1 within the statistical period based on the net load power curve after adding energy storage; calculating the PV power generation E2 within the statistical period based on the typical annual power output data of distributed PV; and calculating the local absorption rate β of distributed PV power generation β as: β = (1 - E1 / E2) * 100%.

[0053] Accordingly, another aspect of the present invention provides a distributed photovoltaic local consumption rate assessment system that takes into account energy storage configuration.

[0054] Example 1

[0055] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the local grid integration rate of distributed photovoltaic power generation considering energy storage configuration, comprising the following steps:

[0056] The annual net load characteristic curve is determined based on the difference between the typical annual electricity consumption data of the distribution network and the typical annual power output data of distributed photovoltaic power.

[0057] Generate an energy storage configuration plan based on the energy storage configuration ratio and the energy storage configuration duration;

[0058] Set the energy storage charging and discharging strategy according to the energy storage configuration scheme, and determine the energy storage charging and discharging power curve according to the energy storage charging and discharging strategy.

[0059] The net load power curve after superimposed energy storage is determined based on the difference between the annual net load characteristic curve and the energy storage charge and discharge power curve.

[0060] The net load backfeed power E1 within the statistical period is calculated based on the net load power curve after the superimposed energy storage. The photovoltaic power generation E2 within the statistical period is calculated based on the typical annual output data of distributed photovoltaic. The local consumption rate β of distributed photovoltaic is calculated based on the net load backfeed power E1 and photovoltaic power generation E2 as follows: β=(1-E1 / E2)*100%.

[0061] The energy storage configuration ratio is the percentage of the rated charging and discharging power of the energy storage to the installed capacity of the distributed photovoltaic system, and its value is 10%, 20%, 30%, or 40%; the energy storage configuration duration is 1 hour, 2 hours, or 4 hours.

[0062] The energy storage configuration scheme is generated based on the energy storage configuration ratio and the energy storage configuration duration. The energy storage configuration ratio is selected from 10%, 20%, 30%, or 40%, and the energy storage configuration duration is selected from 1 hour, 2 hours, or 4 hours.

[0063] like Figure 2 As shown, the load characteristic curves of a typical residential area are presented, revealing that the load in residential areas exhibits strong seasonality, large daily peak-to-valley differences, and a distinct evening peak. During the daytime, the load level is relatively low; at night, the load gradually increases, reaching its peak between 20:00 and 22:00.

[0064] like Figure 3 As shown, typical photovoltaic (PV) output characteristic curves are presented. It can be seen that distributed PV has the characteristics of indistinct seasonality, significant weather influence, large daily peak-to-valley difference, and power generation only during the day. On sunny days, distributed PV output exhibits the characteristic of "high during the day and none at night," reaching its peak at noon; on cloudy or rainy days, with limited solar resources, the daytime output level of distributed PV decreases to almost zero.

[0065] The residential area load is set at 5MW, the photovoltaic access capacity is 5MVA, and the photovoltaic access penetration rate is 100%.

[0066] First, collect the maximum load of the distribution network (S_load), typical annual electricity consumption data (P_load), distributed photovoltaic installed capacity (S_PV), and typical annual power output data (P_PV); then, obtain the annual net load characteristic curve P_LPV = P_load - P_PV by superimposing the source-load data curves of typical years.

[0067] The configuration ratio refers to the percentage of the rated charging and discharging power of energy storage to the installed capacity of distributed photovoltaic power. The energy storage configuration capacity is calculated as (S_ESS=S_PV*δ%, E_ESS=S_ESS*h_ESS). Considering four configuration ratios δ of 10%, 20%, 30%, and 40%, and three configuration durations h_ESS of 1 hour, 2 hours, and 4 hours respectively, a total of 12 configuration schemes are generated, as shown in Table 1.

[0068] Table 1 Energy Storage Configuration Scheme Settings

[0069]

[0070] Based on the configuration scheme, set the energy storage charging and discharging strategy and determine the energy storage charging and discharging power P_ESS(t) at time t.

[0071] Next, calculate the net load power curve P_LPVE = P_LPV - P_ESS after adding energy storage; calculate the distributed photovoltaic local consumption rate index β, as follows:

[0072] Calculate the net load reverse power E1 within a certain period (N days) = -∑(P_LPVE(t)|P_LPVE(t)<0), (t=0,Δh,2Δh,…,24N);

[0073] Calculate the photovoltaic power generation E2 within a certain period (N days) = ∑(P_PV(t)), (t = 0, Δh, 2Δh, ..., 24N);

[0074] Calculate the local grid integration rate of distributed photovoltaic power β = (1 - E1 / E2) * 100%.

[0075] Considering the typical diurnal fluctuation characteristics of distributed photovoltaic (PV) power, with its output curve exhibiting a "hill" shape under normal circumstances, energy storage is used to charge during peak PV power generation in the daytime and discharge during peak load periods in the evening. The energy storage charging and discharging strategy is based on a daily basis, employing a "one charge, one discharge" mode. Simultaneously, the energy storage charging and discharging power can be flexibly adjusted. The energy storage charging and discharging power can be flexibly adjusted according to the net load power to minimize the peak-to-valley difference in net load. Firstly, the energy storage charging power can flexibly track the net load power, prioritizing charging during periods of higher reverse power transmission to minimize reverse power transmission. Secondly, the energy storage discharging power can flexibly track the peak net load power to minimize load peaks and reduce the peak-to-valley difference in load. However, the energy storage charging and discharging power cannot exceed the maximum allowable charging and discharging power (i.e., the rated power of the energy storage).

[0076] The specific implementation of the energy storage charging and discharging strategy includes the following steps:

[0077] S1, Generate energy storage charging and discharging strategies daily, with the total number of days as N, the current number of days as n, the initial value as 0, and set the daily calculation time step Δh, and the charging and discharging power iteration update step step as ΔP.

[0078] S2, let n = n + 1;

[0079] S3, set the initial values ​​Pc0 and Pd0 for the energy storage charge and discharge thresholds on day n, as follows:

[0080] (3a) Calculate the maximum and minimum net load values ​​in the net load curve on day n, denoted as Lmax and Lmin; denote the charging threshold Pc0 = Lmax and the discharging threshold Pd0 = Lmin; (3b) Calculate the average of the maximum and minimum net load values, denoted as Lmean = (Lmax + Lmin) / 2;

[0081] S4, calculate the energy storage charge / discharge threshold values ​​Pc and Pd, as follows:

[0082] (4a) Let Pc = Pc0 and Pd = Pd0;

[0083] (4b) Calculate the charging power threshold value Pc for energy storage on day n;

[0084] (4c) Calculate the discharge power threshold value Pd of the energy storage;

[0085] (4d) Calculate the charging and discharging power and battery capacity of the energy storage under the current Pc and Pd threshold values;

[0086] S5. If n = N, then end; otherwise, return to step S2.

[0087] In step S4, calculating the charging power threshold value Pc for energy storage on day n according to (4b) specifically includes:

[0088] b1. Let time t = 0, E_charge = 0;

[0089] b2. For time t, calculate the charging power P_charge(t)=Pc-P_LPV(t) and check it. If the charging power is greater than the rated charging power of the energy storage, then the charging power P_charge(t)=S_ESS.

[0090] b3. For time t, the charging amount E_charge = E_charge - P_charge(t) * Δh;

[0091] b4. Let t = t + Δh. If t < 24, then return to step b2; otherwise, proceed to step b5.

[0092] b5. If the battery is fully charged, i.e., E_charge>=E_ESS, then end; otherwise, proceed to step b6.

[0093] b6. If Pc >= Lmean, then end; otherwise, proceed to step b7.

[0094] b7. Update the charging threshold, Pc = Pc + ΔP, and return to step b1.

[0095] In step S4, calculating the energy storage discharge power threshold value Pd according to (4c) specifically includes:

[0096] c1. Let time t = 0, E_discharge = 0;

[0097] c2. For time t, the discharge power P_discharge(t) = P_LPV(t) - Pd, and check it. If the discharge power is greater than the rated discharge power of the energy storage, then the discharge power P_discharge(t) = S_ESS.

[0098] c3. For time t, the discharge charge E_discharge=E_discharge+P_discharge(t)*Δh;

[0099] c4. Let \(t = t+\Delta h\). If \(t < 24\), then go back to step c2; otherwise, execute step c5.

[0100] c5. If the battery is fully discharged, i.e., \(E_{discharge}\geq E_{ESS}\), then end; otherwise, execute step c6.

[0101] c6. If \(P_d\leq L_{mean}\), then end; otherwise, execute step c7.

[0102] c7. Update the discharge threshold, \(P_d = P_d-\Delta P\), and go back to step c1.

[0103] In step S4, calculate the charge-discharge power and battery power of the energy storage at the current \(P_c\) and \(P_d\) thresholds according to (4d), specifically including:

[0104] d1. Record the time \(t=\Delta h\), \(E_{ESS}(0) = 0\).

[0105] d2. If \(P_{LPV}(t)<P_c\) at time \(t\), then the energy storage charging power \(P_{charge}(t)=P_{LPV}(t)-P_c\). If the charging power exceeds the rated charging power of the energy storage, then the charging power \(P_{charge}(t)= - S_{ESS}\).

[0106] d3. If \(P_{LPV}(t)>P_d\) at time \(t\), then the energy storage discharge power \(P_{discharge}(t)=P_{LPV}(t)-P_d\). If the discharge power exceeds the rated discharge power of the energy storage, then the discharge power \(P_{discharge}(t)=S_{ESS}\).

[0107] d4. The charge-discharge power of the energy storage at time \(t\), \(P_{ESS}(t)=P_{discharge}(t)+P_{discharge}(t)\); (discharge is positive)

[0108] d5. The电量 stored in the energy storage at time \(t\), \(E_{ESS}(t)=E_{ESS}(t - 1)-P_{charge}(t)\times\Delta h - P_{discharge}(t)\times\Delta h\).

[0109] d6. Let \(t = t+\Delta h\). If \(t = 24\), then end; otherwise, go back to step d2.

[0110] As shown in Table 2, the calculation results of the PV in-situ consumption rate under different configuration schemes are given.

[0111] Table 2 Calculation results of PV in-situ consumption rate under different energy storage configuration schemes

[0112]

[0113] As Figure 4As shown in Table 1, the net load curves before and after the superimposed energy storage for the R3-10-1 scheme are presented. Figure 5 As shown in Table 1, the net load curves before and after the superimposed energy storage for the R3-40-4 scheme are presented. According to... Figure 4 , Figure 5 It provides a clear overview of the local photovoltaic grid integration level when a certain proportion and duration of energy storage are configured, which helps improve load characteristics and promotes the coordinated development of power generation, grid, and load in the power distribution system.

[0114] Example 2

[0115] Embodiment 1 above provides a method for evaluating the local grid integration rate of distributed photovoltaic power generation considering energy storage configuration. Correspondingly, this embodiment provides a system for evaluating the local grid integration rate of distributed photovoltaic power generation considering energy storage configuration. The system provided in this embodiment can implement the method for evaluating the local grid integration rate of distributed photovoltaic power generation considering energy storage configuration of Embodiment 1. The system can be implemented through software, hardware, or a combination of both. For example, the system may include integrated or separate functional modules or functional units to perform the corresponding steps in the methods of Embodiment 1. Since the system in this embodiment is basically similar to the method embodiment, the description process in this embodiment is relatively simple. For relevant details, please refer to the description of Embodiment 1. The system embodiment provided in this embodiment is merely illustrative.

[0116] This embodiment provides a distributed photovoltaic local grid integration rate assessment system considering energy storage configuration, including:

[0117] The annual net load characteristic curve module determines the annual net load characteristic curve based on the difference between the typical annual electricity consumption data of the distribution network and the typical annual power output data of distributed photovoltaic power.

[0118] The configuration scheme generation module generates energy storage configuration schemes according to the energy storage configuration ratio and energy storage configuration duration.

[0119] The charge / discharge power curve module sets the energy storage charge / discharge strategy according to the energy storage configuration scheme and determines the energy storage charge / discharge power curve according to the energy storage charge / discharge strategy.

[0120] The net load power curve module determines the net load power curve after superimposed energy storage based on the difference between the annual net load characteristic curve and the energy storage charge and discharge power curve.

[0121] The local grid integration module calculates the net load backflow power E1 within the statistical period based on the net load power curve after adding energy storage, and calculates the photovoltaic power generation E2 within the statistical period based on typical annual output data of distributed photovoltaic power. Based on the net load backflow power E1 and photovoltaic power generation E2, the local grid integration rate β of distributed photovoltaic power is calculated as follows:

[0122] β = (1 - E1 / E2) * 100%.

[0123] Example 3

[0124] This embodiment provides a processing device corresponding to the distributed photovoltaic local consumption rate assessment method considering energy storage configuration provided in Embodiment 1. The processing device can be a processing device for a client, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of Embodiment 1.

[0125] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the distributed photovoltaic on-site consumption rate assessment method considering energy storage configuration provided in Embodiment 1.

[0126] In some embodiments, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0127] In other embodiments, the processor can be a general-purpose processor of various types, such as a central processing unit (CPU) or a digital signal processor (DSP), and is not limited thereto.

[0128] Example 4

[0129] The method for assessing the local grid integration rate of distributed photovoltaic power considering energy storage configuration in Embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions are loaded for executing the method for assessing the local grid integration rate of distributed photovoltaic power considering energy storage configuration as described in Embodiment 1.

[0130] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the local grid integration rate of distributed photovoltaic power considering energy storage configuration, characterized in that, It includes the following steps: Determine the annual net load characteristic curve based on the difference between the typical annual power consumption data of the distribution network and the typical annual output data of distributed photovoltaics; Generate an energy storage configuration plan according to the energy storage configuration ratio and the energy storage configuration duration; Set the energy storage charge-discharge strategy according to the energy storage configuration plan, and determine the energy storage charge-discharge power curve according to the energy storage charge-discharge strategy. Specifically, it includes: setting the initial energy storage charging threshold value Pc0 and the initial discharge threshold value Pd0 on the nth day; calculating the energy storage charging power threshold value Pc on the nth day according to the initial energy storage charging threshold value Pc0 on the nth day, and calculating the energy storage discharge power threshold value Pd on the nth day according to the initial energy storage discharge threshold value Pd0 on the nth day; calculating the energy storage charge-discharge power and the battery power according to the energy storage charging power threshold value Pc and the energy storage discharge power threshold value Pd on the nth day, and obtaining the energy storage charge-discharge power curve; Among them, calculating the energy storage charge-discharge power and the battery power according to the energy storage charging power threshold value Pc and the energy storage discharge power threshold value Pd on the nth day to obtain the energy storage charge-discharge power curve specifically includes: if the annual net load characteristic curve P_LPV(t) < Pc at time t, the energy storage charging power P_charge(t) = P_LPV(t) – Pc; if the annual net load characteristic curve P_LPV(t) > Pd at time t, the energy storage discharge power P_discharge(t) = P_LPV(t) – Pd; obtaining the energy storage charge-discharge power P_ESS(t) at time t as the sum of the energy storage charging power P_charge(t) and the energy storage discharge power P_discharge(t); Determine the net load power curve after adding energy storage according to the difference between the annual net load characteristic curve and the energy storage charge-discharge power curve; The net load feedback power E1 within the statistical period is calculated based on the net load power curve after energy storage is added. The photovoltaic power generation E2 within the statistical period is calculated based on the typical annual output data of distributed photovoltaic power. The local consumption rate of distributed photovoltaic power is calculated based on the net load feedback power E1 and the photovoltaic power generation E2. for: 。 2. The method for evaluating the in-situ consumption rate of distributed photovoltaics considering energy storage configuration according to claim 1, wherein The energy storage configuration ratio is the percentage of the rated charge-discharge power of the energy storage to the installed capacity of distributed photovoltaics, and its value is 10%, 20%, 30%, or 40%; The value of the energy storage configuration duration is 1 hour, 2 hours, or 4 hours; Generating an energy storage configuration plan according to the energy storage configuration ratio and the energy storage configuration duration is an energy storage configuration plan generated by selecting one from 10%, 20%, 30%, or 40% as the energy storage configuration ratio and selecting one from 1 hour, 2 hours, or 4 hours as the energy storage configuration duration.

3. The method for evaluating the in-situ consumption rate of distributed photovoltaics considering energy storage configuration according to claim 1, wherein The initial energy storage charging threshold value Pc0 on the nth day is the maximum net load Lmax in the net load curve on the nth day; 4. The method for evaluating the local grid integration rate of distributed photovoltaic power considering energy storage configuration according to claim 1, characterized in that, The initial energy storage discharge threshold value Pd0 on the nth day is the minimum net load Lmin in the net load curve on the nth day. The calculation of the energy storage charging power threshold value Pc on the nth day according to the initial energy storage charging threshold value Pc0 on the nth day specifically includes: Let the initial energy storage charging power threshold value Pc on the nth day be the initial energy storage charging threshold value Pc0; For time t, calculate the charging power P_charge(t) = Pc - P_LPV(t). If the charging power is greater than the rated charging power of the energy storage, then the charging power P_charge(t) is the rated charging power of the energy storage, and the charging amount E_charge = E_charge - P_charge(t) * Δh, where Δh is the calculation time step per day, and P_LPV(t) is the annual net load characteristic curve. If the charging time has not been reached and the battery is not fully charged, determine whether the charging power threshold value Pc is less than the average value Lmean of the maximum and minimum net load values. If it is less, update the charging power threshold value Pc of the energy storage on day n as Pc = Pc + ΔP; where ΔP is the iteration update step size of the charging and discharging power, and the average value Lmean of the maximum and minimum net load values ​​is Lmean = (Lmax + Lmin) / 2.

5. The method for evaluating the local grid integration rate of distributed photovoltaic power considering energy storage configuration according to claim 1, characterized in that, The calculation of the discharge power threshold value Pd for energy storage on day n based on the initial value Pd0 of the energy storage discharge threshold on day n specifically includes: Let the initial discharge power threshold value Pd of the energy storage on day n be Pd0, which is the initial discharge threshold value of the energy storage on day n. For time t, the discharge power P_discharge(t) = P_LPV(t) - Pd. If the discharge power is greater than the rated discharge power of the energy storage, then the discharge power P_discharge(t) is the rated discharge power of the energy storage, and the discharge amount E_discharge = E_discharge + P_discharge(t) * Δh, where Δh is the calculation time step per day, and P_LPV(t) is the annual net load characteristic curve. If the discharge time has not been reached and the battery is not fully discharged, determine whether the discharge power threshold value Pd is greater than the average value Lmean of the maximum and minimum net load values. If it is greater, update the discharge power threshold value Pd of the energy storage on day n as Pd = Pd - ΔP; where ΔP is the iteration update step size of the charge and discharge power, and the average value Lmean of the maximum and minimum net load values ​​is Lmean = (Lmax + Lmin) / 2.

6. A distributed photovoltaic (PV) local grid integration rate assessment system considering energy storage configuration, used to implement the distributed PV local grid integration rate assessment method considering energy storage configuration as described in any one of claims 1 to 5, characterized in that, include: The annual net load characteristic curve module determines the annual net load characteristic curve based on the difference between the typical annual electricity consumption data of the distribution network and the typical annual power output data of distributed photovoltaic power. The configuration scheme generation module generates energy storage configuration schemes according to the energy storage configuration ratio and energy storage configuration duration. The charge / discharge power curve module sets the energy storage charge / discharge strategy according to the energy storage configuration scheme and determines the energy storage charge / discharge power curve according to the energy storage charge / discharge strategy. The net load power curve module determines the net load power curve after superimposed energy storage based on the difference between the annual net load characteristic curve and the energy storage charge and discharge power curve. The local grid connection rate module calculates the net load backflow power E1 within the statistical period based on the net load power curve after adding energy storage, and calculates the photovoltaic power generation E2 within the statistical period based on typical annual output data of distributed photovoltaic power. Finally, it calculates the local grid connection rate of distributed photovoltaic power based on the net load backflow power E1 and the photovoltaic power generation E2. for: 。 7. A processing apparatus, the processing apparatus comprising at least a processor and a memory, the memory storing a computer program, characterized in that, When the processor runs the computer program, it performs the steps of the distributed photovoltaic local consumption rate assessment method considering energy storage configuration as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, It stores computer-readable instructions that can be executed by a processor to implement the steps of the distributed photovoltaic local consumption rate assessment method considering energy storage configuration according to any one of claims 1 to 5.

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