A method for calculating the capacity of a battery pack in a photovoltaic building energy storage system

Abstract

A method for calculating the capacity of a battery pack in an energy storage system in a photovoltaic building. The method includes the following steps: S1, obtaining historical data or forecast data of the building load, obtaining historical data or forecast data of the building photovoltaic power generation, S2, calculating the unit cost of the battery pack, S3, obtaining the electricity purchase method and unit price of the building and the power supply bureau, S4, determining the best economic control sequence, S5, calculating according to the peak and valley electricity price or the maximum demand method, S6, comparing and determining the calculated battery capacity, and S7, determining the calculated battery capacity. The present invention aims to solve the problem that the cost of batteries varies greatly due to different regions, climate conditions, location and orientation of photovoltaic power generation units, differences in power generation curves, building usage functions, number of people, usage areas, differences in load power curves, types of batteries, procurement methods and operation and maintenance methods. The method uses economic sorting to determine the priority of energy flow control, and then compares the final system benefits, thereby determining the best economic capacity of the battery in the building energy storage system.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic building energy management systems, and in particular to a method for calculating the economic efficiency of a battery pack capacity of a photovoltaic building energy storage system. Background Art

[0002] New energy power generation equipment such as photovoltaics and wind power are intermittent and have large fluctuations in power generation. Therefore, some energy storage equipment is often added to ultra-low energy consumption buildings to smooth out the fluctuations of new energy systems. Energy storage equipment is of high value, and determining the optimal capacity of energy storage equipment is an important part of controlling investment in building energy systems. Insufficient energy storage capacity will affect the utilization rate and economic benefits of photovoltaic energy, while excessive energy storage capacity will increase investment and reduce the rate of return on investment. This method can adapt to the load and photovoltaic power generation conditions of various buildings, and more accurately calculate the optimal economic capacity of the battery pack. Summary of the invention

[0003] The purpose of the present invention is to provide a method for calculating the capacity of a battery pack of a photovoltaic building energy storage system based on economy, which has the effect of calculating the best economic battery capacity based on historical photovoltaic and load hourly data for one to three years or predicted photovoltaic and load hourly data for one to three years under peak and valley electricity prices or two-part electricity prices, with high accuracy.

[0004] The overall process of battery capacity calculation method in photovoltaic building energy storage system is as follows: Figure 1 As shown, it is characterized in that it includes the following steps:

[0005] S1-1 collects historical energy consumption data of the total building load for one to three years. If it is a newly built building, general software can be used according to the design drawings and energy-consuming equipment types to simulate and predict the energy consumption data for 8,760 hours throughout the year.

[0006] S1-2 collects historical data on the building's photovoltaic power generation for one to three years. If it is a newly built building, it can use general photovoltaic power station design software to simulate and predict the power generation data for 8,760 hours throughout the year based on the longitude and latitude of the location, the angle of the photovoltaic cell laying, the type of photovoltaic cell, local climatic conditions and other information.

[0007] S2 calculates the unit discharge cost of the battery pack as:

[0008] C batt =(C buy+ C upkeep ) / B cap *(D per *N tol ) Formula (1)

[0009] Where: C batt——Battery discharge unit cost unit: Yuan / kWh;

[0010] C buy ──Battery pack purchase cost;

[0011] C upkeep ──Battery group operation and maintenance costs;

[0012] B cap ——Rated capacity of battery pack;

[0013] D per ──Discharge depth. Value range (0.7~0.5) Default recommended value 0.6

[0014] N tol ──Number of full life charge and discharge cycles (provided by the battery manufacturer or calculated by the life formula); value range (3000-8000) default recommended value 6000

[0015] S3 determines the power purchase method, voltage level, transformer capacity and other information used in the power purchase contract between the construction unit and the power supply bureau, or uses the current power sales method and unit electricity price announced by the local power supply bureau. For example, when using peak and valley electricity prices, record the peak and valley time periods and unit electricity prices respectively; when using a two-part electricity price, record the transformer capacity and maximum demand electricity price in the basic electricity price.

[0016] S4-1 When using peak and valley electricity prices, determine the best economic control sequence. Calculate the unit price of direct use income during the photovoltaic peak period (yuan / kWh), that is, P 峰 Peak hour unit electricity price, ② photovoltaic normal direct use income unit price (yuan / kWh) 平 The unit price of electricity during normal times, ③ the unit price of income during peak hours after photovoltaic storage (yuan / kWh), ④ the unit price of income during normal times after photovoltaic storage (yuan / kWh), ⑤ the unit price of income from direct photovoltaic electricity sales to the grid (yuan / kWh), that is, P 售 The unit price of electricity sold by photovoltaic power to the grid, ⑥ the unit price of income during peak hours after storing the grid electricity in the battery during valley hours (yuan / kWh), ⑦ the unit price of income during normal hours after storing the grid electricity in the battery during valley hours (yuan / kWh), ⑧ the unit price of income during normal hours after storing the grid electricity in the battery during valley hours (yuan / kWh).

[0017] Among them: ③PV storage post-peak usage income unit price P3

[0018] P3=P 峰 -C batt Formula (2)

[0019] ④ Unit price of income from normal use after photovoltaic storage P4

[0020] P4=P 平 -Cbatt Formula (3)

[0021] ⑥ During off-peak hours, the city electricity is stored in the battery and then used during peak hours. The unit price of income is P6

[0022] P6=P 峰 -P 谷 -C batt Formula (4)

[0023] ⑦ During off-peak hours, the city electricity is stored in the battery and then used normally. The unit price of income is P7

[0024] P7=P 平 -P 谷 -C batt Formula (5)

[0025] ⑧ During off-peak hours, the city electricity is stored in the battery and then sold to the grid for a unit price of P8

[0026] P8=P 售 -P 谷 -C batt Formula (6)

[0027] Where: P 峰 ──Peak hour unit electricity price P 平 ──Usually the unit electricity price

[0028] P 谷 ──Usually the unit electricity price P 售 ──Unit price of electricity sold by photovoltaic power to the grid

[0029] Sort ①~⑧ from large to small and determine the priority of energy flow control.

[0030] When S4-2 adopts a two-part electricity price, there are several ways for energy to flow in the system: ① direct use of photovoltaics; ② direct sale of photovoltaics to the grid; ③ use after photovoltaic storage; ④ use the mains to store energy and then stabilize the maximum power. From ① to ④, the benefits go from large to small, from positive to negative. Although the benefits of stabilizing the maximum power after ④ using the mains to store energy is negative, which is equal to the unit price of electricity + the unit cost of battery discharge, it is possible to gain benefits on the basic electricity fee by reducing the maximum demand. Therefore, the priority control order of energy is ① direct use of photovoltaics ② excess photovoltaics are stored in the battery group ③ the battery group is fully charged and sold to the grid ④ using energy storage to stabilize the maximum power.

[0031] S4-2 When a two-part electricity price is used, but the electricity price is divided into peak and valley prices, the peak and valley prices should be used for calculation first. This is because the economic benefits of controlling the maximum demand by the battery group are far lower than the benefits obtained by the peak and valley prices.

[0032] When S5-1 adopts peak and valley electricity prices, calculate the battery capacity. Figure 2The process is to convert the battery capacity B cap The calculation is performed by rounding off the annual average daily energy consumption of the building to 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, or 3.0. The predicted battery capacity SOC th The data can be directly calculated based on the load power consumption and photovoltaic power generation of the day, as shown in the following formula (8), or it can be multiplied by a random coefficient of prediction uncertainty, as shown in the following formula (9).

[0033] The best overall economic situation of the system is that the amount of electricity that the system battery group should store = the electricity consumption during peak hours - the electricity used by photovoltaic power generation to offset the load during peak hours.

[0034]

[0035] Where: P pv ──Photovoltaic power generation unit: Kw;

[0036] P load ──Load power unit: Kw;

[0037] P di ──Photovoltaic offset power unit: Kw;

[0038]

[0039] Where: SOC th ——Daily optimal theoretical remaining capacity of battery: Kw.h;

[0040] peak - daily peak hours

[0041]

[0042] Where: Rnd is a random number, range + / -0.3

[0043] When S5-2 adopts a two-part electricity price, calculate the battery capacity according to the attached Figure 3 The process is to convert the battery capacity B cap The maximum demand is calculated by rounding off 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, or 3.0 times the annual average daily energy consumption of the building. need The initial value is 40% of the transformer capacity, and the reasonable battery capacity SOC is predicted. need The data can be directly calculated based on the load power consumption and photovoltaic power generation of the day, as shown in the following formula (11). If SOC need If it exceeds 100%, the maximum demand control value P can be increased step by step need , recalculate and predict the reasonable battery capacity SOC need , until SOCneed When it drops below 100%, record the SOC at that time. need and P need Used for subsequent control and calculations on the same day.

[0044]

[0045] Where: P dischange ──Battery discharge power unit: Kw;

[0046] P need ——Maximum demand control value unit: Kw;

[0047]

[0048] When S6-1 adopts peak and valley electricity prices, compare the increase and decrease of city electricity consumption and city electricity charges under different battery group capacities. Figure 4 The curve of battery capacity and utility electricity charges is shown in Figure 2. The battery capacity value at which the electricity charge decreases slowly is selected as the battery capacity value E. est .

[0049] When S6-2 adopts a two-part electricity price, compare the basic electricity charges under different battery pack capacities, and select the battery pack capacity value with a slow basic electricity charge decline rate as the calculated capacity value E est .

[0050] S7 is calculated from the battery capacity value E est , determine the calculated capacity of the battery pack according to the following formula (12).

[0051] E cal =λ redu ·λ fore ·E est Formula (12)

[0052] Where: E cal ──Battery pack capacity calculation unit: Kw.h;

[0053] λ redu ──Battery redundancy factor; (1.05~1.25)

[0054] λ fore ── Photovoltaic and load forecast accuracy coefficient; (1.1~1.3)

[0055] E est ──Battery pack capacity measurement unit: Kw.h;

[0056] After determining the calculated capacity of the battery pack, the actual purchase capacity of the battery pack can be determined by comprehensively considering factors such as the rated power, voltage level, specifications, model, and batch price of the battery pack supplier's products.

[0057] The beneficial effects of the present invention are as follows: different regions, different climate conditions, different photovoltaic power generation units have different positions and orientations, and the power generation curves are very different. Different buildings have different functions, different numbers of people, and different usage areas, and the load power curves are also very different. Different types of batteries, different procurement methods, and different operation and maintenance methods lead to very different battery group costs. By using the method of the present invention, the specific load, power generation, battery group cost, and city electricity pricing method of the above-mentioned buildings can be used to simulate and calculate the daily electricity bill, and the relationship between different battery group capacities and the annual total electricity bill can be compared, so as to determine the battery group capacity with the best economic benefits. It has strong adaptability to various types of buildings and their photovoltaic unit designs, and can also verify the algorithm of the building energy control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are used to further understand the present invention, which are part of the specification and together with the specific embodiments, explain the present invention in detail, but do not constitute a limitation of the present invention. In the accompanying drawings:

[0059] Attached Figure 1 This is the overall flow chart of the battery capacity calculation method for photovoltaic building energy storage system;

[0060] Attached Figure 2 The peak and valley electricity price calculation method and process;

[0061] Attached Figure 3 The maximum demand calculation method flow chart is shown below. For different load types, if the load does not change much and there are almost no peaks, it is very likely that after adding a battery pack, the impact on the basic electricity price will be small. At this time, from an economic point of view, it is not recommended to add a battery pack to the energy system. Otherwise, a battery pack should be installed according to the calculation results of the battery pack capacity.

[0062] Attached Figure 4 is the battery capacity and electricity cost curve; DETAILED DESCRIPTION

[0063] The present invention is described in detail below in conjunction with specific embodiments. Figure 1 .

[0064] Step S1-1 collects the historical energy consumption data of the total building load for one year. New buildings can use general software to simulate and predict the energy consumption data for 8760 hours throughout the year based on the design drawings and the types of energy-consuming equipment.

[0065] Step S1-2 collects the historical data of the building's photovoltaic power generation for one year. New buildings can use general photovoltaic power station design software to simulate and predict the power generation data for 8760 hours a year based on the longitude and latitude of the location, the angle of photovoltaic cell laying, the type of photovoltaic cells, local climatic conditions and other information.

[0066] Step S2 calculates the unit discharge cost of the battery pack as:

[0067] C batt =(C buy+ C upkeep ) / B cap *(D per *N tol ) Formula (1)

[0068] Where: C batt ——Battery discharge unit cost unit: Yuan / kWh;

[0069] C buy ──Battery pack purchase cost;

[0070] C upkeep ──Battery group operation and maintenance costs;

[0071] B cap ——Rated capacity of battery pack;

[0072] D per ──Discharge depth. Value range (0.7~0.5) Default recommended value 0.6

[0073] N tol ──Number of full life charge and discharge cycles (provided by the battery manufacturer or calculated by the life formula); value range (3000-8000) default recommended value 6000

[0074] In order to facilitate the subsequent calculation of C batt The value is estimated to be 50 cents / kWh

[0075] Step S3 determines the power purchase method, voltage level, transformer capacity and other information used in the power purchase contract between the construction unit and the power supply bureau, or uses the current power sales method and unit electricity price announced by the local power supply bureau. For example, when using peak and valley electricity prices, record the peak and valley time periods and unit electricity prices respectively; when using a two-part electricity price, record the transformer capacity and maximum demand electricity price in the basic electricity price.

[0076] Step S4-1: When using peak and valley electricity prices, determine the optimal economic control sequence. Calculate ① the unit price of direct use income during photovoltaic peak hours (yuan / kWh), that is, P 峰 Peak hour unit electricity price, ② photovoltaic normal direct use income unit price (yuan / kWh) 平The unit price of electricity during normal times, ③ the unit price of income during peak hours after photovoltaic storage (yuan / kWh), ④ the unit price of income during normal times after photovoltaic storage (yuan / kWh), ⑤ the unit price of income from direct photovoltaic electricity sales to the grid (yuan / kWh), that is, P 售 The unit price of electricity sold by photovoltaic power to the grid, ⑥ the unit price of income during peak hours after storing the grid electricity in the battery during valley hours (yuan / kWh), ⑦ the unit price of income during normal hours after storing the grid electricity in the battery during valley hours (yuan / kWh), ⑧ the unit price of income during normal hours after storing the grid electricity in the battery during valley hours (yuan / kWh).

[0077] Among them: ③PV storage post-peak usage income unit price P3

[0078] P3=P 峰 -C batt Formula (2)

[0079] ④ Unit price of income from normal use after photovoltaic storage P4

[0080] P4=P 平 -C batt Formula (3)

[0081] ⑥ During off-peak hours, the city electricity is stored in the battery and then used during peak hours. The unit price of income is P6

[0082] P6=P 峰 -P 谷 -C batt Formula (4)

[0083] ⑦ During off-peak hours, the city electricity is stored in the battery and then used normally. The unit price of income is P7

[0084] P7=P 平 -P 谷 -C batt Formula (5)

[0085] ⑧ During off-peak hours, the city electricity is stored in the battery and then sold to the grid for a unit price of P8

[0086] P8=P 售 -P 谷 -C batt Formula (6)

[0087] Where: P 峰 ──Peak hour unit electricity price P 平 ──Usually the unit electricity price

[0088] P 谷 ──Usually the unit electricity price P 售 ──Unit price of electricity sold by photovoltaic power to the grid

[0089] Sort ①~⑧ from large to small to determine the priority of energy flow control. Take the peak-valley electricity price in Zhuhai as an example: 118.9 cents at peak time, 72.06 cents at normal time, and 36.03 cents at valley time ① Direct use income of photovoltaic power: 118.9 cents at peak time ② Direct use income of photovoltaic power: 72.06 cents at normal time ③ Use income after photovoltaic storage: 118.9-50=68.9 cents at peak time ④ Use income after photovoltaic storage: 72.06-50=22.06 cents at normal time ⑤ Direct sale income of photovoltaic power to the grid: 45.3 cents ⑥ Use income after storing municipal power in batteries at valley time:

[0090] Peak time 118.9-36.03-50=32.87 minutes⑦Off-peak time, the city electricity is stored in the battery and the income is:

[0091] Normal time: 72.06-36.03-50=-13.97 points⑧ During off-peak time, the revenue from selling electricity to the power grid after storing the city electricity in the battery:

[0092] 45.3-36.03-50=-40.73 points

[0093] When step S4-2 adopts a two-part electricity price, there are several ways for energy to flow in the system: ① direct use of photovoltaics; ② direct sale of photovoltaics to the grid; ③ use after photovoltaic storage; ④ use the mains to store energy and then stabilize the maximum power. From ① to ④, the benefits go from large to small, from positive to negative. Although the benefits of stabilizing the maximum power after ④ using the mains to store energy is negative, which is equal to the unit price of electricity + the unit cost of battery discharge, it is possible to gain benefits on the basic electricity fee by reducing the maximum demand. Therefore, the priority control order of energy is ① direct use of photovoltaics ② excess photovoltaics are stored in batteries ③ the battery pack is fully charged and sold to the grid ④ using energy storage to stabilize the maximum power.

[0094] Take Guangxi's two-part electricity price as an example:

[0095] ① Direct benefit from photovoltaic use: 62.61 points

[0096] ② Revenue from direct sales of photovoltaic power to the grid: 45.2 points

[0097] ③ Income from post-storage use of photovoltaic power: 62.61-50=12.61 points

[0098] ④Use the mains energy storage to smooth the maximum power -62.61-50=-112.61 points The basic electricity price can be determined according to the transformer capacity or the maximum demand. When charged according to the transformer capacity, the basic electricity price is 27.5 yuan / Kw.A. When charged according to the maximum demand, the basic electricity price is 34 yuan

[0099] / Kw.A, that is, when the maximum demand is higher than the transformer capacity by 27.5 / 34=80.88%, it is more economical to use transformer capacity billing than maximum demand billing. When the user load is lower than 80.88% of the transformer capacity, it is more economical to use monthly declaration of maximum demand. According to the document "Development and Reform Commission Improves the Implementation Method of Basic Electricity Price for Users of Two-Part Electricity Price", when the maximum demand approval value is lower than 40% of the total of transformer capacity and high-voltage motor capacity, the contract maximum demand shall be approved at 40% of the total capacity.

[0100] When a two-part electricity price is used, but the electricity price is differentiated into peak and valley prices, the peak and valley prices should be used for calculation first, because the economic benefits of controlling the maximum demand by the battery group are lower than the benefits obtained by the peak and valley prices.

[0101] Step S5-1: When the peak and valley electricity prices are adopted, the battery capacity is calculated. Figure 2 The process is to convert the battery capacity B cap The calculation is performed by rounding off the annual average daily energy consumption of the building to 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, or 3.0. The predicted battery capacity SOC th The data can be directly calculated based on the load power consumption and photovoltaic power generation of the day, as shown in the following formula (8), or it can be multiplied by a random coefficient of prediction uncertainty, as shown in the following formula (9).

[0102] The best overall economic situation of the system is that the amount of electricity that the system battery group should store = the electricity consumption during peak hours - the electricity used by photovoltaic power generation to offset the load during peak hours.

[0103]

[0104] Where: P pv ──Photovoltaic power generation unit: Kw;

[0105] P load ──Load power unit: Kw;

[0106] P di ──Photovoltaic offset power unit: Kw;

[0107]

[0108] Where: SOC th ——Daily optimal theoretical remaining capacity of battery: Kw.h;

[0109] peak - daily peak hours

[0110]

[0111] Where: Rnd is a random number, range + / -0.3

[0112] Step S5-2: When the two-part electricity price is adopted, the battery capacity is calculated and the Figure 3 The process is to convert the battery capacity B cap The maximum demand is calculated by rounding off 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, or 3.0 times the annual average daily energy consumption of the building. need The initial value is 40% of the transformer capacity, and the reasonable battery capacity SOC is predicted. need The data can be directly calculated based on the load power consumption and photovoltaic power generation of the day, as shown in the following formula (11). If SOC need If it exceeds 100%, the maximum demand control value P can be increased step by step need , recalculate and predict the reasonable battery capacity SOC need , until SOC need When it drops below 100%, record the SOC at that time. need and P need Used for subsequent control and calculations on the same day.

[0113]

[0114] Where: P dischange ──Battery discharge power unit: Kw;

[0115] P need ——Maximum demand control value unit: Kw;

[0116]

[0117] Step S6-1: When the peak and valley electricity prices are adopted, the increase and decrease of the city electricity consumption and the city electricity charges under different battery pack capacities are compared. Figure 4 The curve of battery capacity and utility electricity charges is shown in Figure 2. The battery capacity value at which the electricity charge decreases slowly is selected as the battery capacity value E. est .

[0118] Step S6-2: When the two-part electricity price is adopted, the basic electricity charges calculated according to the two-part system are compared under different battery pack capacities, and the battery pack capacity value with the slowest basic electricity charge decrease rate is selected as the calculated capacity value E. est .

[0119] Step S7: The capacity value E of the battery pack is calculated. est , determine the calculated capacity of the battery pack according to the following formula (12).

[0120] E cal =λ redu ·λ fore·E est Formula (12)

[0121] Where: E cal ──Battery pack capacity calculation unit: Kw.h;

[0122] λ redu ──Battery redundancy factor; (1.05~1.25)

[0123] λ fore ── Photovoltaic and load forecast accuracy coefficient; (1.1~1.3)

[0124] E est ──Battery pack capacity measurement unit: Kw.h;

[0125] After determining the calculated capacity of the battery pack, the actual purchase capacity of the battery pack can be determined by comprehensively considering factors such as the rated power, voltage level, specifications, model, and batch price of the battery pack supplier's products.

[0126] The above is only an embodiment of the present invention, but the present invention is not limited to the above specific implementation. For those skilled in the art, it is still possible to modify the scheme described in the above embodiment or replace some of the features therein with equivalents. Any modification, improvement, etc. made without conflicting with the principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for calculating the capacity of a battery pack of a photovoltaic building energy storage system, characterized in that: The steps include: Step S1: Obtain historical data or forecast data of building load, and historical data or forecast data of building photovoltaic power generation. Step S2: Calculate the unit discharge cost of the battery pack. Step S3: Obtain the electricity purchasing method and unit price of the building from the power supply bureau. Step S4: determine the optimal economic control sequence. Step S5: Calculate the battery capacity according to the peak and valley electricity price or the maximum demand method. Step S6, comparing and determining the battery pack capacity, Step S7, determining the calculated capacity of the battery pack; In step S2, the unit discharge cost of the battery pack is calculated as formula (1), which only calculates the cost of single-side discharge: C batt =(C buy +C upkeep ) / [B cap* (D per *N tol )] formula (1); Where: C batt ──Battery discharge unit cost, unit: yuan / kWh; C buy ──Battery pack purchase cost; C upkeep ──Battery group operation and maintenance costs; B cap ——Rated capacity of battery pack; D per ──Discharge depth, value range (0.7~0.5), default recommended value 0.6; N tol ──Number of full life charge and discharge cycles; value range (3000~8000), default recommended value is 6000; Step S4-1: When using peak-valley electricity prices, determine the optimal economic control sequence; calculate ① the unit price of direct use income during photovoltaic peak hours (yuan / kWh), that is, P 峰 Peak hour unit electricity price, ② photovoltaic normal direct use income unit price (yuan / kWh) 平 The unit price of electricity during normal times, ③ the unit price of income during peak hours after photovoltaic storage (yuan / kWh), ④ the unit price of income during normal times after photovoltaic storage (yuan / kWh), ⑤ the unit price of income from direct photovoltaic power sales to the grid (yuan / kWh), that is, P 售 The unit price of electricity sold by photovoltaic power to the grid, ⑥ The unit price of income for using the grid during peak hours after storing the grid electricity in the battery during off-peak hours (yuan / kWh), ⑦ The unit price of income for using the grid during normal hours after storing the grid electricity in the battery during off-peak hours (yuan / kWh), ⑧ The unit price of income for selling the grid after storing the grid electricity in the battery during off-peak hours (yuan / kWh); Among them: ③ The unit price of income for using the grid during peak hours after photovoltaic storage is P3, P3=P 峰 -C batt Formula (2); ④ The unit price of the income from normal use after photovoltaic storage is P4, P4=P 平 -C batt Formula (3); ⑥ During off-peak hours, the city electricity is stored in the battery and then used during peak hours, and the unit price is P6. P6=P 峰 -P 谷 -C batt Formula (4); ⑦ During off-peak hours, the city electricity is stored in the battery and then used normally, and the unit price of income is P7. P7=P 平 -P 谷 -C batt Formula (5); ⑧ During off-peak hours, the city electricity is stored in the battery and then sold to the grid at a unit price of P8. P8=P 售 -P 谷 -C batt Formula (6); Where: P 峰 ──Peak hour unit electricity price, P 平 ──Usually the unit electricity price, P 谷 ──Off-peak unit electricity price, P 售 ──The unit price of electricity sold by photovoltaic power to the grid, sort ①~⑧ from large to small, and determine the priority of energy flow control; Step S4-2: When the two-part electricity price is adopted, there are several ways for energy to flow in the system: ① direct use of photovoltaics; ② direct sale of photovoltaics to the grid; ③ use after photovoltaic storage; ④ use the mains energy storage to stabilize the maximum power; from ① to ④, the benefits go from large to small, from positive to negative; although the benefits of stabilizing the maximum power after ④ using the mains energy storage are negative, it can be obtained on the basic electricity fee by reducing the maximum demand; therefore, the priority control order of energy is ① direct use of photovoltaics, ② excess photovoltaics are stored in batteries, ③ the battery pack is fully charged and sold to the grid, and ④ the maximum power is stabilized by energy storage; Step S5: When a two-part electricity price is adopted, but the electricity price is divided into peak and valley prices, the peak and valley prices are preferred for calculation; Step S5-1: Calculate the battery capacity. When the peak and valley prices are adopted, the battery capacity B cap The calculation is carried out after rounding off the annual average daily energy consumption of the building to 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, and 3.0 times; the predicted reasonable capacity SOC of the battery th The data is calculated based on the load power consumption and photovoltaic power generation of the day or multiplied by a random coefficient of prediction uncertainty; the best overall economic situation of the system is that the amount of power that the system battery group should store = peak period power consumption - photovoltaic power generation during peak period to offset the load part of the power; Where: P pv ──Photovoltaic power generation, unit: Kw; P load ──Load power, unit: Kw; P di ──Photovoltaic offset power, unit: Kw; Where: SOC th ——Predict the reasonable capacity of the battery, unit: Kw·h; peak – the daily peak period; Where: Rnd is a random number, ranging from [-0.3, +0.3]; Step S5-2: Calculate the battery capacity. When the two-part electricity price is adopted, the battery capacity B cap The maximum demand control value P is set to 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0 times the annual average daily energy consumption of the building, and then calculated; need The initial value is 40% of the transformer capacity, and the reasonable battery capacity SOC is predicted. th The data is calculated based on the load power consumption and photovoltaic power generation of the day. If SOC th If it exceeds 100%, the maximum demand control value P will be increased step by step. need , recalculate and predict the reasonable battery capacity SOC th , until SOC th When it drops below 100%, record the SOC at that time. th and P need Used for subsequent control and calculation on the same day; Where: P dischange ——Battery discharge power, unit: Kw; P need ——Maximum demand control value, unit: Kw; In step S7, the capacity value E of the battery pack is calculated. est , determine the calculated capacity of the battery pack according to the following formula (12); E cal = λ redu · λ fore · E est Formula (12); Where: E cal ——Calculated capacity of battery pack, unit: Kw·h; λ redu ——Battery redundancy factor, the value range is (1.05~1.25); λ fore ——PV and load forecast accuracy coefficient, the value range is (1.1~1.3); E est ——Calculated capacity of battery pack, in Kw·h; After determining the calculated capacity of the battery pack, comprehensively consider the rated power, voltage level, specifications and models, and batch price factors of the battery pack supplier's products to determine the final actual purchase capacity of the battery pack.

2. The method for calculating the capacity of a photovoltaic building energy storage system battery pack according to claim 1, characterized in that: Step S6-1: Compare the increase and decrease of the utility power consumption and the utility electricity charges under different battery pack capacities, and select the battery pack capacity value with a slow rate of decrease of the utility electricity charges according to the curve of the battery pack capacity and the utility electricity charges as the battery pack calculated capacity value E est .

3. The method for calculating the capacity of a photovoltaic building energy storage system battery pack according to claim 1, characterized in that: Step S6-2: Compare the basic electricity charges calculated under the two-part system under different battery pack capacities, and select the battery pack capacity value with the slowest basic electricity charge decrease rate as the calculated capacity value E est .

4. The method for calculating the capacity of a photovoltaic building energy storage system battery pack according to claim 1, characterized in that: For lithium iron phosphate battery pack manufacturers with BMS battery management system that can accurately measure the battery pack SOC, redu The lower limit is 1.05; when the energy storage system can accurately predict the building load and photovoltaic power generation based on climate conditions and working day and holiday conditions, λ fore The lower limit is 1.1.

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