A method and system for optimal matching of distributed photovoltaic and energy storage in low-voltage transformer area

Abstract

The application discloses a kind of optimal matching method and system of low-voltage area inside distributed photovoltaic and energy storage, the method includes: based on the energy consumption data in area and the operating mode of distributed photovoltaic and energy storage, simulate the power generation system state and distribution system state in area;Distributed photovoltaic access capacity and energy storage configuration capacity are gradually increased in simulation process, and the corresponding reliability index is calculated;Based on distributed photovoltaic access capacity and energy storage configuration capacity, draw reliability index curve, find the saturation point of the reliability index on the reliability index curve, and the distributed photovoltaic access capacity and energy storage configuration capacity corresponding to the saturation point are used as the optimal matching capacity of distributed photovoltaic and energy storage.The technical scheme provided by the application maximizes the utilization rate of substation in area.

Description

Technical Field

[0001] This invention relates to the field of distributed power supply, specifically to an optimal ratio method and system for distributed photovoltaic and energy storage in a low-voltage distribution area. Background Technology

[0002] Currently, many distribution areas are experiencing rapid growth in electricity load, especially air conditioning load, leading to short-term overcapacity and generally low utilization rates in the power distribution system. During peak summer periods, air conditioning load exceeds 30%, and the load rate of some transformers approaches 90%, necessitating capacity expansion. However, the average annual load rate is mostly around 30%, resulting in low utilization and high no-load losses.

[0003] With the large-scale and high-proportion integration of distributed photovoltaic power into low-voltage distribution area power grids, distributed photovoltaic power will gradually become an important alternative energy source, enabling energy to develop in a clean, low-carbon, safe and efficient direction. Distributed photovoltaic power, electric vehicles and energy storage have entered thousands of households. However, due to the more obvious diurnal and seasonality of distributed photovoltaic power output, the utilization rate of substations in the distribution area has decreased. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention provides an optimal ratio method and system for distributed photovoltaic (PV) and energy storage within a low-voltage distribution area. To improve transformer utilization and reduce peak-valley differences, this invention configures user energy storage devices with matching capacity within the distributed PV system. This can, to a certain extent, compensate for insufficient PV output and maximize the distributed PV power generation capacity.

[0005] This invention provides an optimal ratio method for distributed photovoltaic and energy storage within a low-voltage distribution area, comprising:

[0006] Based on energy consumption data within the distribution area and the operation modes of distributed photovoltaic and energy storage, the power generation system status and distribution system status within the distribution area are simulated.

[0007] During the simulation, the capacity of distributed photovoltaic access and energy storage configuration are gradually increased, and the corresponding reliability indicators are calculated.

[0008] A reliability index curve is plotted based on the distributed photovoltaic access capacity and energy storage configuration capacity. The saturation point of the reliability index is found on the reliability index curve, and the distributed photovoltaic access capacity and energy storage configuration capacity corresponding to the saturation point are taken as the optimal ratio of distributed photovoltaic and energy storage capacity.

[0009] Preferably, the simulation of the power generation system status and distribution system status within the transformer substation based on energy consumption data and the operation modes of distributed photovoltaic and energy storage includes:

[0010] Load curves are generated based on energy consumption data within the transformer area;

[0011] Based on the pre-built photovoltaic output curve, energy storage charging and discharging model and the load curve, the operation mode of distributed photovoltaic and energy storage is used to simulate the power generation system status and distribution system status within the transformer area.

[0012] Preferably, the operation mode of the distributed photovoltaic and energy storage includes:

[0013] When the distributed photovoltaic power output is excessive, the excess power is used to charge the battery; otherwise, the relationship between the sum of the maximum output power of the distributed photovoltaic and the battery and all loads is determined.

[0014] When the combined maximum output power of the distributed photovoltaic system and the battery cannot meet all the loads, the power generation system first supplies the loads and then charges the batteries to the specified state of charge.

[0015] The energy storage is a battery.

[0016] Preferably, the step of gradually increasing the distributed photovoltaic access capacity and energy storage configuration capacity during the simulation process, and calculating the corresponding reliability indicators, includes:

[0017] Gradually increase the photovoltaic grid connection capacity and energy storage configuration capacity during the simulation process;

[0018] The power outage time and power supply shortage of the transformer area are obtained based on the simulated power generation system status;

[0019] The number of power outages, outage time, and power shortage at load points within the transformer substation are obtained based on the simulated power distribution system status.

[0020] Calculate the reliability index of the power generation system based on the power outage time and power outage amount;

[0021] The reliability index of the power distribution system is calculated based on the number of power outages, the duration of power outages, and the amount of power shortage at the load points.

[0022] Preferably, obtaining the power shortage time and power shortage amount of the transformer area based on the simulated power generation system status includes:

[0023] Obtain the total output of all distributed photovoltaic systems and the state of charge of each battery pack in the current simulation.

[0024] Calculate the maximum power that each battery pack can provide to the outside and the maximum acceptable continuous charging power based on the state of charge of each battery pack.

[0025] When the sum of the total output of all distributed photovoltaic power and the maximum power of the upstream substation is greater than the sum of the total load and total loss of the current power distribution system, each battery pack is charged based on the maximum acceptable continuous charging power of each battery pack; otherwise, each battery pack is discharged based on its corresponding state of charge and the actual released power of all battery packs is calculated.

[0026] When the sum of the actual power output of all battery banks, the total output of all distributed photovoltaic systems, and the maximum power of the upstream substation is less than the sum of the total load and total losses of the current power distribution system, calculate the power shortage time and power supply shortage of the distribution area.

[0027] Preferably, each battery pack discharges based on its corresponding state of charge, including:

[0028] When the distributed photovoltaic power output is insufficient to supply all loads and the state of charge of each battery bank is sufficient to supply power to all loads without activating the upstream substation, each battery bank discharges; or when the distributed photovoltaic power output is insufficient to supply all loads and neither the upstream substation nor each battery bank can make up for the current power deficit, each battery bank needs to discharge.

[0029] Preferably, the calculation of the reliability index of the power generation system based on the power shortage time and power shortage includes:

[0030] Calculate the expected power outage time based on the aforementioned disadvantage time;

[0031] Calculate the expected power shortage based on the aforementioned power shortage;

[0032] The reliability indicators corresponding to the power generation system include the expected power outage time and the expected power shortage.

[0033] Preferably, the calculation of the reliability index of the power distribution system based on the number of power outages, power outage time, and power shortage at the load points includes:

[0034] Calculate the average power outage frequency based on the number of power outages at the load points;

[0035] Calculate the average power outage duration based on the power outage time at the load points;

[0036] Calculate the expected power shortage based on the power shortage at the load point;

[0037] The reliability indicators corresponding to the power distribution system include average outage frequency, average outage duration, and expected power shortage.

[0038] Preferably, the reliability indicators corresponding to the power distribution system further include:

[0039] The average power availability index is calculated based on the actual total power supply hours and the required total power supply hours in the power distribution system.

[0040] Preferably, the average power availability index is calculated using the following formula:

[0041]

[0042] Where: ASAI is the average power availability index; N i U represents the number of users at load point i; i Let be the average annual power outage time at load point i.

[0043] Based on the same inventive concept, this invention also provides an optimal ratio system for distributed photovoltaic and energy storage within a low-voltage distribution area, comprising:

[0044] The simulation module is used to simulate the power generation system status and distribution system status within the transformer substation based on energy consumption data and the operation modes of distributed photovoltaic and energy storage.

[0045] The calculation module is used to gradually increase the distributed photovoltaic access capacity and energy storage configuration capacity during the simulation process, and calculate the corresponding reliability indicators.

[0046] The results module is used to plot a reliability index curve based on the distributed photovoltaic access capacity and the energy storage configuration capacity, find the saturation point of the reliability index on the reliability index curve, and take the distributed photovoltaic access capacity and energy storage configuration capacity corresponding to the saturation point as the optimal ratio of distributed photovoltaic and energy storage capacity.

[0047] Preferably, the simulation module includes:

[0048] The generation unit is used to generate load curves based on energy consumption data within the transformer area.

[0049] The simulation unit, based on the pre-built photovoltaic output curve, energy storage charging and discharging model and the load curve, simulates the power generation system status and distribution system status within the transformer area using the distributed photovoltaic and energy storage operation mode.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] The technical solution provided by this invention simulates the power generation and distribution system states within a transformer substation based on energy consumption data and the operation modes of distributed photovoltaic (PV) and energy storage. During the simulation, the distributed PV access capacity and energy storage configuration capacity are gradually increased, and the corresponding reliability indicators are calculated. A reliability indicator curve is plotted based on the distributed PV access capacity and energy storage configuration capacity. The saturation point of the reliability indicator is then found on the curve, and the distributed PV access capacity and energy storage configuration capacity corresponding to the saturation point are taken as the optimal ratio of distributed PV to energy storage. This invention configures energy storage devices with matching capacity within distributed PV systems, which can compensate for insufficient PV output, maximizing the distributed PV power generation capacity and thus improving the utilization rate of substations within the transformer substation area. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the relationship between reliability index and load level in this invention;

[0053] Figure 2 This is a flowchart of an optimal ratio method for distributed photovoltaic and energy storage in a low-voltage distribution area according to the present invention.

[0054] Figure 3 This is a schematic diagram of a typical load curve of the present invention;

[0055] Figure 4 This is a flowchart illustrating the reliability simulation of the power generation system state in this invention. Detailed Implementation

[0056] To better understand this invention, the following description, in conjunction with the accompanying drawings and examples, will further illustrate the invention.

[0057] Example 1

[0058] To study the capacity benefits of photovoltaic (PV) power generation, we must first examine the credible capacity, also known as effective capacity, which is an effective indicator for measuring its capacity value. There are approximately ten quantitative assessment indicators for the credible capacity of PV power generation systems developed worldwide. These indicators can be categorized into four types based on their nature: first, indicators assessing the credible equivalent capacity; second, indicators assessing the credible capacity coefficient over a specific time period; third, indicators assessing additional measures to the credible capacity; and fourth, indicators assessing the ELCC (Effective Load Carry Capability) within the credible capacity. Among these four types of indicators, the last one is considered the best. This is because the ELCC assessment indicator can directly describe the power generation efficiency, and the assessment results do not show significant discrepancies. It has a moderate computational load, reliable theoretical basis, and can effectively compensate for the shortcomings of the other three assessment indicators. Therefore, when assessing the credible capacity of PV power generation systems, the ELCC assessment indicator is usually the first choice.

[0059] There are two main ways to understand the reliability of distributed photovoltaic capacity: (1) from the load side: the amount of additional load that the newly added photovoltaic can bear while maintaining a given system reliability level, i.e., effective load carrying capacity (ELCC); (2) from the generation side: the equivalent conventional capacity (ECC) that the newly added photovoltaic can replace under the same power supply reliability level. When the distributed photovoltaic in the distribution area has a high penetration rate, the confidence capacity is assessed by the effective load carrying capacity. The system reliability level is a function of the total installed capacity and the load level. When the power supply capacity of the upper substation is constant, the reliability decreases monotonically as the load level increases. The total installed capacity of the equivalent conventional generator set of the distribution substation is G, the load level is L, the reliability curve is f0(G), and the original reliability is R0. The larger the load in the distribution area, the lower the reliability level. After adding distributed photovoltaic, the reliability curve is f1(G+G). D At the same load level L, the system reliability is improved, reaching reliability R1. However, if the load level is gradually increased, the system reliability gradually decreases. When the load level reaches L', the reliability level recovers to R0, as... Figure 1 As shown. Its mathematical expression is:

[0060] R0=f0(G,L=f1(G+G) D ,L')

[0061] Taking the inverse function of both sides of the above equation, we get: L'=f1 -1 (G+G DTherefore, the confidence capacity and equivalent load capacity of the newly added distributed photovoltaic system are: G C =ELCC=△L=L'-L=f1 -1 (G+G D ,R0)-L

[0062] At this point, the capacity confidence level is:

[0063] In the formula, G D It refers to the installed capacity of distributed photovoltaic power, G. C ELCC represents the confidence level of distributed photovoltaic (PV) capacity. The capacity confidence level is evaluated using ELCC.

[0064] For a given capacity of energy storage, if the photovoltaic installed capacity is too small, the energy storage function cannot be fully utilized; if the photovoltaic installed capacity is too large, the energy storage cannot adequately smooth its output. Therefore, there is an optimal installed capacity that maximizes the system capacity equivalent to a unit of photovoltaic installed capacity.

[0065] Energy storage devices can suppress the intermittency of distributed photovoltaic (PV) output. By controlling the charging and discharging of the energy storage system, it can store the electrical energy generated by distributed PV when the system net load is low and the sufficiency is high, and release the electrical energy when the system net load is high and the sufficiency is low. From the perspective of system sufficiency, the reduction effect of charging during off-peak hours on system sufficiency is not significant, while the improvement effect of discharging during peak hours on system sufficiency is very significant. Therefore, considering the combined operation of PV and energy storage devices, the capacity reliability will be significantly higher than that of distributed PV power generation operating alone, especially in islanded systems.

[0066] like Figure 2 As shown, the present invention provides a method for optimal allocation of distributed photovoltaic and energy storage in multiple scenarios in low-voltage distribution areas, comprising:

[0067] S1. Based on the energy consumption data within the distribution area and the operation mode of distributed photovoltaic and energy storage, simulate the power generation system status and distribution system status within the distribution area.

[0068] S2. Gradually increase the distributed photovoltaic access capacity and energy storage configuration capacity during the simulation process, and calculate the corresponding reliability indicators;

[0069] S3. Based on the distributed photovoltaic access capacity and energy storage configuration capacity, plot a reliability index curve, find the saturation point of the reliability index on the reliability index curve, and take the distributed photovoltaic access capacity and energy storage configuration capacity corresponding to the saturation point as the optimal ratio of distributed photovoltaic and energy storage capacity.

[0070] S1. Based on energy consumption data within the distribution area and the operation modes of distributed photovoltaic and energy storage, simulate the power generation system status and distribution system status within the distribution area, specifically including:

[0071] Step 1: Describe the probabilistic characteristics of solar radiation intensity using the beta distribution, and study the relationship between solar radiation intensity and distributed photovoltaic power output;

[0072] The output power of solar cells is closely related to solar irradiance, which refers to the radiant energy per unit area per unit time that solar radiation reaches the Earth's surface after passing through the atmosphere. Its unit is watts per square meter (W / ㎡). Two factors affect solar irradiance: 1. The attenuation of solar radiation as it passes through the atmosphere, including scattering, absorption, and reflection; therefore, it is constrained by climate and meteorological factors. 2. Solar irradiance is significantly controlled by the solar altitude angle, which affects the propagation path of solar radiation; therefore, it is related to regional latitude, seasonal changes, and the sunrise and sunset times within a day. Solar irradiance and atmospheric irradiance together constitute the total irradiance on the Earth. The proportions of these two vary not only from place to place but also from time to time and are related to climate and meteorological conditions. The solar altitude angle plays a crucial controlling role in their proportions, and the proportion of solar irradiance is positively correlated with the solar altitude angle. Statistically, solar irradiance over a certain period can be approximated as a beta distribution, with its density function as shown in the following formula:

[0073]

[0074] In the formula: r and r max (W / m 2 ( ) represent the actual light intensity and maximum light intensity during this time period, respectively. α and β are the shape parameters of the beta distribution. Where:

[0075]

[0076] In the formula: μ and σ are the average value and variance of light intensity over a certain period of time.

[0077] The determining factors for photovoltaic power output are the energy conversion efficiency of the photovoltaic panel and the solar irradiance. The output power of a photovoltaic power station is:

[0078]

[0079] In the formula: P PV Y represents the actual output power of the photovoltaic power station. PV f is the rated power of the photovoltaic power station; PV r is the loss coefficient; t r is the actual light radiation intensity at time t; STCThe light radiation intensity under standard test conditions is 1 kW / m². 2 ; ap is the power temperature coefficient of the solar panels in a photovoltaic power station; T C T represents the battery temperature in the power station. STC The battery temperature under standard test conditions is 25℃ (operating at 25℃). Solar panel efficiency decreases with increasing temperature because the open-circuit voltage drops more significantly than the short-circuit current, thus power is inversely proportional to temperature. For every degree Celsius increase, power decreases by 0.3%.

[0080] Once the cell to be calculated is determined, Y can be obtained. PV The loss factor is the simulated inherent parameter f of a photovoltaic system in a certain residential area. PV The actual light radiation intensity r at time t t And the battery temperature T in the power station C .

[0081] Photovoltaic power plants typically generate power during the day, with no power output at night. In North China, the shortest daylight hours in winter are 7:00 AM to 5:00 PM (10 hours), while the longest daylight hours in summer are 5:00 AM to 7:00 PM (14 hours). Under clear, cloudless weather conditions, with strong solar irradiance, the daily power output curve of a photovoltaic power plant is characterized by "small at both ends and high in the middle," similar to a normal distribution curve, with smooth output and minimal fluctuations. The peak photovoltaic output occurs between 12:00 PM and 3:00 PM, reaching near full power output. Photovoltaic power plants are highly susceptible to weather conditions, exhibiting volatility and randomness. The output range of photovoltaic power plants is mainly concentrated within 20% to 80% of their installed capacity.

[0082] Step 2: Using lead-acid batteries as an example, establish a battery model for reliability assessment. The battery's state can be characterized by its State of Charge (Soc), which is the ratio of the battery's remaining capacity to its rated capacity. A Soc of 1 represents a fully charged battery, while a Soc of 0 represents a net discharge reaching the rated capacity. Ideally, the product of the charging / discharging power and the simulation step size represents the amount of electricity absorbed or released by the battery. The model is as follows:

[0083]

[0084] In the formula, ΔW t B represents the external charging and discharging capacity of the battery during time period t (the product of charging / discharging power and time period t); t B represents the remaining capacity of the battery before charging and discharging. t =B norm ×Soc(t), where B normWhere Soc(t) is the rated capacity of the battery, and B is the state of charge before charging and discharging. t+1 B represents the remaining capacity of the battery after charging and discharging. min B max These refer to the large and small capacities of the battery, respectively.

[0085] Step 3: Analyze the typical load curves for industrial load, commercial load, and residential load within the region, such as... Figure 3 The load time series model is established as shown; the load characteristics are analyzed by first collecting the energy consumption patterns of a household, multiple households, or a transformer area for 365 days a year, including daily peak load times and peak-valley differences, and then standardizing the data.

[0086] Industrial and commercial load curves show relatively regular daily patterns, with high load rates from 8:00 AM to 6:00 PM, peak load occurring during the daytime, and very low load during off-peak hours, resulting in a large peak-to-valley difference. Residential load generally exhibits two peaks: a morning peak and an evening peak. The peak-to-valley difference is also significant, with the morning peak occurring around 12:00 PM and the peak load around 8:00 PM, largely consistent with people's daily life patterns. The highest load occurs in August, and the lowest load occurs in February, showing a clear seasonality. The main influencing factors are air conditioning load and the Spring Festival holiday.

[0087] Step 4: Based on the cyclic charging operation strategy, analyze the capacity ratio between distributed photovoltaic (PV) and energy storage; PV and energy storage joint operation mode: When distributed PV output is excessive, the excess power charges the battery; when distributed PV output is insufficient, if the sum of the maximum output power of distributed PV and the battery is still insufficient to meet all loads, the system first supplies the remaining load, and then charges the battery. To prevent the battery from remaining at a low state of charge (SOC) level, once the battery starts charging, it must be charged to a specified SOC level. set The reliability simulation process for the power generation system state is as follows: Figure 4 As shown, it includes the following steps:

[0088] Step 101: Obtain the duration T of the system in its current state. k And the current operating status of each distributed photovoltaic and battery pack in the system.

[0089] Step 102: Set the system's charging flag S, where S=1 indicates that the upstream substation was charging the batteries at the previous moment and that the state of charge of some battery banks was less than Soc. set Initialize the system power outage time LLD = 0 and the battery pack charging flag S = 0.

[0090] Step 103: Determine the relationship between analog clock t and T k The relationship. If t <T kIf the condition is not met, proceed to step 104; otherwise, proceed to step 113.

[0091] Step 104: Apply the KiBaM model and, in conjunction with the state of charge of each battery pack at time t, calculate the maximum power that each battery pack can provide to the outside world within the simulation step size Δt. max(t) and the maximum acceptable continuous charging power externally max(t). The KiBaM model in this embodiment is a two-cell model of lead-acid batteries (Kinetic Battery Model, KiBaM), which can comprehensively reflect the above-mentioned charging and discharging constraints and is a classic model in battery research.

[0092] Step 105: Determine the value of the system charging flag S. If S = 1, proceed to step 106; otherwise, proceed to step 111.

[0093] Step 106: Determine the total output P of all distributed photovoltaic systems. DG Is the sum of (t) and the maximum power Ps(t) that the upstream substation can provide less than the sum of the current system's total load and total losses P? L (t). If it is less than, it means that the system is facing a power shortage risk and the battery needs to be discharged. Proceed to step 110. Otherwise, it means that the distributed photovoltaic system and the upstream substation can continue to charge the battery. Proceed to step 107.

[0094] Step 107: Since the state of charge (SOC) of each battery pack in the system may differ at time t, the SOC of some battery packs may be close to or exceed the SOC. set However, it did not reach the upper limit of the state of charge (Soc). max In order to allow battery packs with lower SOC to absorb more power and quickly approach SOC... set It is necessary to adjust the maximum acceptable continuous charging power of each battery pack.

[0095]

[0096]

[0097] in, Let t be the state of charge of the i-th battery pack. The maximum acceptable continuous charging power for each battery pack to external circuits; Constraints on the battery charging characteristics described by the KiBaM model. To constrain the high charging rate of the battery. To constrain the maximum permissible charging current of the battery. For the large capacity constraint, i.e., the high state of charge of the battery, ηc For charging efficiency.

[0098] In the formula and Calculate using the following formula:

[0099]

[0100] P mcr =(1-e -k△t (Q) max -Q) / △t

[0101] P mcc =I max V nom / 1000

[0102] Step 108: The upstream substation will participate in the battery charging process, therefore the net exchange power within the system is P. ex (t)=P L (t)-P DG (t)-Ps(t). Calculate the net exchange power P corresponding to each battery pack. ex (t), and then calculate the actual absorbed power of each battery pack.

[0103] Step 109: Determine the state of charge (SOC) of all battery packs in the system. If any SOC exists... i <Soc set If the system charging flag S is set to 1, then set S = 0. Afterwards, proceed to step 112.

[0104] Step 110: The net switching power within the system remains P. ex (t)=P L (t)-P DG (t)-Ps(t), calculate the net exchange power corresponding to each battery pack. Then calculate the actual power output of each battery pack. if This indicates that the combined maximum power provided by the upstream substation, distributed photovoltaic power, and battery banks is insufficient to meet the current load. The system experiences a power shortage during this period, with the shortage time LLD = LLD + Δt, and the power shortage amount being... Then proceed to step 112.

[0105] Step 111: Determine whether the battery needs to be discharged or charged. The battery needs to be discharged if: 1) Although the distributed photovoltaic output cannot supply all loads, the battery has sufficient charge to power all loads without activating the upstream substation; or 2) The distributed photovoltaic output cannot supply all loads, and neither the upstream substation nor the battery can compensate for the current power deficit, requiring simultaneous power supply from both the upstream substation and the battery. If either of these conditions is met, the battery is discharged, and step 110 is executed; otherwise, the battery is charged, and step 108 is executed.

[0106] Step 112: Let t = t + Δt, and use the formula Soc end =(Q 1,end +Q 2,end ) / Q max

[0107]

[0108]

[0109] Update the state of charge of each battery pack at time t, and return to step 103.

[0110] Step 113: Statistical system in T k The power outage time LLD and power outage quantity ENS within the period are ∑Ens(t).

[0111] Case Study on the Confidence Capacity of Distributed Photovoltaic Power Generation and Energy Storage Equipment in a Transformer Area

[0112] S2. Gradually increase the distributed photovoltaic access capacity and energy storage configuration capacity during the simulation process, and calculate the corresponding reliability indicators, specifically including:

[0113] Step 5: Select a certain capacity value as the step size and gradually increase the photovoltaic (PV) access capacity and energy storage configuration capacity for reliability calculation. Perform a non-sequential Monte Carlo simulation to calculate the original system reliability index. Add the PV power station and calculate the new reliability index. As the distributed PV penetration rate continuously increases, the system reliability improves. However, when the penetration rate is sufficiently high, the improvement effect tends to saturate, meaning the reliability index no longer increases with the increase of distributed PV access capacity. Adjust the system load level according to the reliability index equality rule to obtain the system ELCC value ΔLy.

[0114] When the penetration rate of distributed generation (DG) in an active distribution network is high, the impact of DG shutdown needs to be considered. Therefore, both the generation system and the distribution system need to be simulated. The following system reliability indices are adopted:

[0115] Expected power outage time (LOLE) in hours / year:

[0116] Expected LOEE (MWh / year) due to insufficient power:

[0117] System average outage frequency (SAIFI) (times / household × year):

[0118] System average outage duration (SAIDI) (hours / household × year):

[0119] Expected power shortage EENS (MWh / year):

[0120] Average power availability index:

[0121] Where P represents the number of simulations for the power generation system, Q represents the number of simulations for the power distribution system, and LLD k and ENS k The simulated power outage time and power shortage for the k-th generation system; and These represent the number of power outages, outage time, and power shortage at simulated load point i in the k-th power distribution system, respectively; C i The number of users at each load point, where n is the total number of load points; N i U represents the number of users at load point i; i Let be the average annual power outage time at load point i.

[0122] S3. Based on the distributed photovoltaic (PV) grid connection capacity and energy storage configuration capacity, plot a reliability index curve. Find the saturation point of the reliability index on the curve, and take the distributed PV grid connection capacity and energy storage configuration capacity corresponding to the saturation point as the optimal ratio of distributed PV and energy storage capacity. Specifically, this includes:

[0123] Step 6: Select a certain capacity value as the step size in Step 5, gradually increase the photovoltaic access capacity and energy storage configuration capacity, and find the saturation point, which is the optimal capacity ratio.

[0124] Step 7: Use machine learning methods to create a sample library for different scenarios, such as the time of daily peak occurrence, the characteristics of different loads, and the size of the peak-valley difference, and continuously enrich the sample library.

[0125] The confidence capacity of this invention is also the highest under the optimal ratio of photovoltaic and energy storage capacity.

[0126] 1. This invention is based on the requirement of ensuring the reliability of distributed photovoltaic (PV) after it is connected to a single household, multiple households, or a single transformer area. It studies the alternative capacity of distributed PV, configures the matching energy storage capacity, and optimizes the solution to minimize curtailment and reduce investment.

[0127] 2. This invention is based on a cyclic charging operation strategy and a combined photovoltaic and energy storage operation mode: when the distributed photovoltaic output is excessive, the excess power charges the battery; when the distributed photovoltaic output is insufficient, if the sum of the maximum output power of the distributed photovoltaic and the battery is still insufficient to meet all loads, the system first supplies the remaining load, and then charges the battery. To prevent the battery from remaining at a low state of charge level, once the battery starts charging, it must be charged to a specified state of charge.

[0128] 3. This invention employs non-sequential Monte Carlo simulation to calculate system reliability indicators. By selecting a certain capacity value as a step size, the photovoltaic access capacity and energy storage configuration capacity are gradually increased to find the saturation point and obtain the optimal capacity ratio.

[0129] 4. Using lead-acid batteries as an example, establish a battery model for reliability assessment;

[0130] 5. This invention addresses the issue of distributed photovoltaic penetration rates in active power distribution networks, where the impact of distributed power source shutdown needs to be considered, and establishes reliability indicators for the power distribution network.

[0131] 6. Employ machine learning methods to create sample libraries for different scenarios, such as the time of daily peak occurrence, characteristics of different loads, and the size of peak-to-valley differences, and continuously enrich the sample library.

[0132] Example 2

[0133] Based on the same inventive concept, this invention also provides an optimal ratio system for distributed photovoltaic and energy storage within a low-voltage distribution area, comprising:

[0134] The simulation module is used to simulate the power generation system status and distribution system status within the transformer substation based on energy consumption data and the operation modes of distributed photovoltaic and energy storage.

[0135] The calculation module is used to gradually increase the distributed photovoltaic access capacity and energy storage configuration capacity during the simulation process, and calculate the corresponding reliability indicators.

[0136] The results module is used to plot a reliability index curve based on the distributed photovoltaic access capacity and the energy storage configuration capacity, find the saturation point of the reliability index on the reliability index curve, and take the distributed photovoltaic access capacity and energy storage configuration capacity corresponding to the saturation point as the optimal ratio of distributed photovoltaic and energy storage capacity.

[0137] In this embodiment, the simulation module includes:

[0138] The generation unit is used to generate load curves based on energy consumption data within the transformer area.

[0139] The simulation unit, based on the pre-built photovoltaic output curve, energy storage charging and discharging model and the load curve, simulates the power generation system status and distribution system status within the transformer area using the distributed photovoltaic and energy storage operation mode.

[0140] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0141] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0142] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0143] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0144] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. An optimal matching method of distributed photovoltaic and energy storage in low-voltage transformer area, characterized in that, The application relates to a method for determining optimal distributed photovoltaic and energy storage ratio capacity. The method comprises the following steps: Simulating power generation system state and power distribution system state in a transformer area based on energy consumption data in the transformer area and operation mode of distributed photovoltaic and energy storage; Increasing distributed photovoltaic access capacity and energy storage configuration capacity step by step in the simulation process, and calculating corresponding reliability indexes; Drawing a reliability index curve based on distributed photovoltaic access capacity and energy storage configuration capacity, finding a saturation point of the reliability index on the reliability index curve, and taking the distributed photovoltaic access capacity and the energy storage configuration capacity corresponding to the saturation point as optimal distributed photovoltaic and energy storage ratio capacity; The step of increasing distributed photovoltaic access capacity and energy storage configuration capacity step by step in the simulation process and calculating corresponding reliability indexes comprises the following steps: Increasing photovoltaic access capacity and energy storage configuration capacity step by step in the simulation process; Obtaining power failure time and power supply shortage of the transformer area based on the simulated power generation system state; Obtaining power failure frequency, power failure time and power supply shortage of a load point in the transformer area based on the simulated power distribution system state; Calculating corresponding reliability indexes of the power generation system based on the power failure time and the power supply shortage; Calculating corresponding reliability indexes of the power distribution system based on the power failure frequency, the power failure time and the power supply shortage of the load point; The step of calculating corresponding reliability indexes of the power generation system based on the power failure time and the power supply shortage comprises the following steps: Calculating power failure time expectation based on the power failure time; Calculating power shortage expectation based on the power supply shortage; The reliability indexes of the power generation system comprise the power failure time expectation and the power shortage expectation; The step of calculating corresponding reliability indexes of the power distribution system based on the power failure frequency, the power failure time and the power supply shortage of the load point comprises the following steps: Calculating average power failure frequency based on the power failure frequency of the load point; Calculating average power failure duration based on the power failure time of the load point; Calculating expected power supply shortage based on the power supply shortage of the load point; The loss of electric power time expectation LOLE (hours / year) is determined by the following formula: The low energy expectation (LOEE) (MWh / year) is determined as follows: The average outage frequency SAIFI (outages per customer x year) is determined as follows: The average interruption duration SAIDI (hours / house x year) is determined as follows: The expected energy not supplied EENS (MWh / year) is determined as follows: where P is the number of power system state simulation, Q is the number of distribution system state simulation, LLD k and ENS k is the power shortage time and power shortage amount of the kth power system simulation; and are respectively the number of power outage, power outage time and power shortage amount of the kth distribution system simulation load point i; C i is the number of users of each load point, and n is the total number of load points.

2. The method of claim 1, wherein, The reliability indexes of the power distribution system comprise the average power failure frequency, the average power failure duration and the expected power supply shortage; The step of simulating power generation system state and power distribution system state in a transformer area based on energy consumption data in the transformer area and operation mode of distributed photovoltaic and energy storage comprises the following steps: Generating a load curve based on the energy consumption data in the transformer area; 3. The method of claim 2, wherein, Simulating power generation system state and power distribution system state in the transformer area by adopting the operation mode of distributed photovoltaic and energy storage based on a pre-constructed photovoltaic output curve, an energy storage charging and discharging model and the load curve. The operation mode of distributed photovoltaic and energy storage comprises the following steps: When distributed photovoltaic output is excessive, the excessive power is used to charge the battery; otherwise, the relationship between the sum of maximum release power of the distributed photovoltaic and the battery and all loads is judged; When the sum of maximum release power of the distributed photovoltaic and the battery cannot meet all loads, the power generation system supplies the loads first, and then charges the battery to a specified state of charge; 4. The method of claim 1, wherein, The energy storage is a battery. The step of obtaining power failure time and power supply shortage of the transformer area based on the simulated power generation system state comprises the following steps: Obtaining total output of all distributed photovoltaic and state of charge of each battery group of the power generation system under the current simulation times; The maximum power provided by each battery pack to the outside and the maximum acceptable continuous charging power of each battery pack to the outside are calculated based on the state of charge of each battery pack; When the sum of the total output of all distributed photovoltaics and the maximum power of the upper transformer substation is greater than the sum of the total load and total loss of the current power distribution system, the battery packs are charged based on the maximum acceptable continuous charging power of each battery pack to the outside; otherwise, the battery packs are discharged based on the corresponding state of charge and the actual release power of all battery packs is calculated; When the sum of the actual release power of all battery packs, the total output of all distributed photovoltaics and the maximum power of the upper transformer substation is less than the sum of the total load and total loss of the current power distribution system, the power supply shortage time and the power supply shortage amount of the transformer substation are calculated.

5. The method of claim 4, wherein, The battery packs are discharged based on the corresponding state of charge, including: When the distributed photovoltaic output cannot supply all the loads and the state of charge of each battery pack is sufficient, the battery packs can supply all the loads without enabling the upper transformer substation; or when the distributed photovoltaic output cannot supply all the loads and neither the upper transformer substation nor the battery packs can make up for the current power shortage, the battery packs need to be discharged.

6. The method of claim 1, wherein, The reliability index corresponding to the power distribution system further includes: An average power supply availability index is calculated based on the actual power supply total time and the required power supply total time in the power distribution system.

7. The method of claim 6, wherein, The average power supply availability index is calculated according to the following formula: wherein: ASAI is the average supply availability index; N i is the number of users at load point i; U i is the annual average interruption time at load point i.

8. An optimal matching system of distributed photovoltaic and energy storage in low-voltage transformer area, characterized in that, It includes: A simulation module is configured to simulate the power generation system state and the power distribution system state in the transformer substation based on the energy consumption data in the transformer substation and the operation mode of the distributed photovoltaics and the energy storage; A calculation module is configured to gradually increase the distributed photovoltaic access capacity and the energy storage configuration capacity in the simulation process and calculate the corresponding reliability index; A result module is configured to draw a reliability index curve based on the distributed photovoltaic access capacity and the energy storage configuration capacity, find a saturation point of the reliability index on the reliability index curve, and take the distributed photovoltaic access capacity and the energy storage configuration capacity corresponding to the saturation point as the optimal matching capacity of the distributed photovoltaics and the energy storage; The gradually increasing of the distributed photovoltaic access capacity and the energy storage configuration capacity in the simulation process and the calculation of the corresponding reliability index include: The photovoltaic access capacity and the energy storage configuration capacity are gradually increased in the simulation process; The power supply shortage time and the power supply shortage amount of the transformer substation are obtained based on the simulated power generation system state; The number of power outages, the power outage time and the power supply shortage amount of the load points in the transformer substation are obtained based on the simulated power distribution system state; The reliability index corresponding to the power generation system is calculated based on the power supply shortage time and the power supply shortage amount; The reliability index corresponding to the power distribution system is calculated based on the number of power outages, the power outage time and the power supply shortage amount of the load points; The calculation of the reliability index corresponding to the power generation system based on the power supply shortage time and the power supply shortage amount includes: The power supply shortage time expectation is calculated based on the power supply shortage time; The power shortage expectation is calculated based on the power supply shortage amount; The reliability index corresponding to the power generation system includes the power supply shortage time expectation and the power shortage expectation; The calculation of the reliability index corresponding to the power distribution system based on the number of power outages, the power outage time and the power supply shortage amount of the load points includes: The average power outage frequency is calculated based on the number of power outages of the load points; calculate an average outage duration based on outage times of the load points; calculate an expected energy not supplied based on energy not supplied of the load points; the reliability indicators corresponding to the power distribution system include an average outage frequency, an average outage duration, and an expected energy not supplied; Loss of load expectation LOLE (hours / year): Loss of electric power expectation LOEE (MWh / year): Average outage frequency SAIFI (times per customer per year): Average interruption duration SAIDI (hours per customer x year): Expected energy not supplied, EENS (MWh / year): where P is the number of power system state simulation, Q is the number of distribution system state simulation, LLD k and ENS k is the power shortage time and power shortage amount of the kth power system simulation; and are respectively the number of power outage, power outage time and power shortage amount of the kth distribution system simulation load point i; C i is the number of users of each load point, and n is the total number of load points.

9. The system of claim 8, wherein, the simulation module comprises: a generation unit configured to generate a load curve based on energy consumption data within the transformer area; a simulation unit configured to simulate a state of a power generation system and a state of a power distribution system within the transformer area by using an operation mode of distributed photovoltaic and energy storage based on a pre-constructed photovoltaic output curve, an energy storage charging and discharging model, and the load curve.

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