Optical storage system income measurement and calculation and optimal configuration method and system

Abstract

The invention relates to an optical storage system income calculation and optimal configuration method and system, and belongs to the technical field of renewable energy sources and energy storage systems. A comprehensive solution is provided for solving the problems that in the prior art, income prediction accuracy is not high, dependence on complete historical electricity consumption data is high, and economical optimal configuration is difficult to obtain. The method comprises the following steps of: firstly, preferentially using historical electricity consumption data of a user, reversely deducing the total electricity consumption of the user according to an electricity price policy of a project location and annual total electricity charge information by adopting an equivalent simulation algorithm under the condition that the user lacks the historical electricity consumption data, and constructing annual hourly load time sequence data; secondly, simulating and generating hourly power generation time sequence data of the photovoltaic system in combination with meteorological data and a physical model of an installation site; the method comprises the following steps: acquiring user electricity price time sequence data and photovoltaic grid-connected electricity price time sequence data; and finally, carrying out global combination optimization by taking the photovoltaic capacity and the energy storage capacity as decision variables, and automatically determining a capacity configuration scheme with the optimal economy by simulating hourly operation of a light storage system in the whole year and carrying out evaluation based on a full-life-cycle economic model. According to the method, load simulation, physical simulation, operation optimization and economic evaluation are combined to form a complete closed loop, and the scientificity of optical storage system planning, the accuracy of income prediction and the efficiency of investment decision making are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of renewable energy and energy storage system technology, and is applicable to the economic assessment and capacity scheme recommendation of user-side photovoltaic and energy storage systems during the project planning stage. Background Technology

[0002] As the global energy structure accelerates its transformation towards cleaner and lower-carbon energy, photovoltaic power generation has become one of the main renewable energy sources. The integrated photovoltaic + energy storage system has moved from technology demonstration to large-scale application.

[0003] However, the return on investment for photovoltaic (PV) and energy storage (ESD) systems depends on multiple factors, including system capacity configuration, operational strategies, local electricity pricing policies, solar resource conditions, and equipment costs. The calculation process is complex and requires a high level of expertise. In actual project implementation, the following challenges are encountered: First, front-end sales often need to respond to a large number of inquiries in a short period, but the calculations for individual projects are time-consuming. Second, solution design relies on information such as location, meteorological data, and electricity load time-series data, which are often incomplete or difficult to obtain in actual projects, severely impacting the accuracy of calculations and the relevance of the solution. Finally, during initial contact, sales personnel, limited by objective conditions, often provide configuration suggestions that deviate significantly from the actual needs of users, requiring multiple revisions to the initial proposed solutions.

[0004] Therefore, the industry needs a method that can quickly and accurately assess the economic viability of photovoltaic (PV) and energy storage (ESS) systems. This method should comprehensively consider technical parameters, market environment, and policy guidance, providing users and investors with scientific and reliable investment decisions before installing PV and ESS systems. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for calculating and optimizing the revenue of a photovoltaic energy storage system to solve or partially solve the above-mentioned technical problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for calculating and optimizing the revenue of a photovoltaic-energy storage system, comprising photovoltaic modules and energy storage devices, includes the following steps: S1. Data Input and Processing: 1) Site and environmental parameters Obtain the geographical information and typical meteorological year data of the proposed installation site, which includes hourly total irradiance, diffuse irradiance, ambient temperature and wind speed datasets; obtain the number of photovoltaic modules to be installed and the maximum capacity of the energy storage equipment; set the installation tilt angle and azimuth angle of the photovoltaic modules; 2) Minimum configurable unit parameters of the equipment Obtain the model number of the photovoltaic module and read the electrical parameters of the minimum configurable module of the photovoltaic module; input or select the specifications of the energy storage unit and read the capacity and charge / discharge cycle efficiency of the minimum configurable module of the energy storage unit. S2. Construction of hourly load time series data: 1) Upload historical hourly electricity load data for at least one calendar year; if not, proceed to the equivalent simulation algorithm to fit and generate the user's hourly load time series data; 2) The equivalent simulation algorithm includes the following steps: a) Load the electricity price policy of the proposed installation location and enter the user's total electricity bill for the previous year or the estimated amount for this year; b) Calculate the user's total annual electricity consumption based on the input total electricity bill and electricity pricing policy; c) Based on the user's selected electricity consumption mode, the total annual electricity consumption is allocated to each day and each hour according to the preset proportion of each hour in the selected mode, generating the user's hourly load time sequence data; S3. Calculation of hourly irradiation time series data: Based on the latitude, longitude, and time zone of the proposed installation site, the solar altitude and azimuth angles are calculated hourly using astronomical algorithms. According to the geometric relationship between the sun and the tilted surface of the photovoltaic module, the projections of the direct, scattered, and reflected components onto the tilted surface of the photovoltaic module are calculated using the Perez irradiance model. The calculation results of the three components on the tilted surface of the photovoltaic module are synthesized hourly to obtain the hourly irradiance time series data of the total solar radiation received by the tilted surface of the photovoltaic module. S4. Photovoltaic power generation time series simulation: Based on the typical meteorological year data and the electrical parameters of the minimum configurable module of the photovoltaic module, generate hourly power time series data of the AC side of the proposed installation site under a typical meteorological year; S5. Generation of electricity price time-series data: 1) Read the hourly load time series data from step S2, combine it with the electricity price policy of the proposed installation location, determine the hourly electricity price, and generate the electricity unit price time series data corresponding to the hourly load time series data; 2) Generate hourly photovoltaic grid-connected electricity price time series data based on the photovoltaic grid-connected subsidy or electricity price policy of the project location; S6. Simulation of the cooperative scheduling model based on deterministic rules: 1) Using the minimum configurable module of photovoltaic modules and energy storage units obtained in step S2 as the basic step size, construct a two-dimensional discretized search space consisting of all feasible combinations of the number of photovoltaic modules and energy storage units under the constraints of the number of photovoltaic modules to be installed and the maximum capacity of the energy storage equipment. 2) For each combination in the two-dimensional discretized search space, the cooperative scheduling model performs a time-by-time simulation; 3) After traversing each combination in the two-dimensional discretized search space, compare the economic indicators of each combination and output the combination of photovoltaic modules and energy storage units with the best economic benefits.

[0007] Preferably, in step S6, for each combination of the two-dimensional discretized search space, the following rules are followed when performing hourly simulation: at each moment, the photovoltaic modules generate electricity to meet the current load; if there is surplus power generated by the photovoltaic modules, the energy storage device is charged first, and the surplus power is fed into the grid after the energy storage device is fully charged; if the photovoltaic modules generate insufficient power, the energy storage device is discharged first to supplement the power, and if it is still insufficient, electricity is purchased from the grid.

[0008] Preferably, step S3 includes the following steps: 1) Based on the typical meteorological year data, calculate the hourly total solar irradiance received by the photovoltaic modules under horizontal conditions: (1) In the formula, D T The total solar irradiance is measured hourly under direct sunlight. D b This represents the hourly direct irradiance under direct sunlight conditions. D d The hourly scattered irradiance under direct sunlight. D r Hourly reflected irradiance under direct sunlight, all in W / m² 2 ; 2). Based on the installation tilt angle and azimuth angle of the photovoltaic module, the total solar irradiance under horizontal conditions is decomposed into the direct sunlight component under oblique sunlight, the sky scattering component under oblique sunlight, and the ground reflection component under oblique sunlight. The hourly total solar irradiance received by the photovoltaic module under oblique sunlight conditions is then calculated: (2) In the formula, D T,T This represents the hourly total solar irradiance under oblique illumination. D b,T The hourly direct irradiance under oblique illumination is given. D d,T The hourly scattered irradiance under oblique illumination. D r,T The reflected irradiance is under oblique illumination, and the unit is W / m². 2 ; in, (3) (4) (5) Substituting the solutions, we get: (6) In the formula, θ is the angle of incidence of sunlight; θ z ρ is the solar zenith angle; F1 is the solar orbit coefficient; F2 is the sky brightness coefficient; X1 and X2 are corrections for the effects of the sun being in a special position in the solar model; ρ is the ground reflectivity; β is the tilt angle of the photovoltaic module. in, (7) (8) (9) (10) (11) In the formula, Δ represents brightness; f 11 f 12 f 21 f 22 f 23 All values ​​are Perez brightness empirical coefficients; δ is the solar declination angle; φ is the latitude of the proposed installation site; ω is the solar hour angle; and γ is the solar azimuth angle.

[0009] Preferably, when the collaborative scheduling model performs operation simulation, it also performs efficiency calculation on the charging and discharging process of the energy storage device and records its charging and discharging energy loss.

[0010] Preferably, when the collaborative scheduling model performs operational simulation, it records the annual electricity purchase cost, self-consumption electricity savings, grid-connected electricity sales revenue, and energy storage cycle losses for each simulation to form an annual cash flow. Based on this, combined with the initial investment cost, operation and maintenance cost, and equipment residual value, a cash flow model for the entire life cycle of the system is constructed to calculate the net present value and internal rate of return.

[0011] Preferably, step S4 includes the following steps: 1) Based on the physical model of the single diode equivalent circuit, the IV curve of the minimum configurable module of the photovoltaic module under standard conditions is solved according to the electrical parameters of the minimum configurable module of the photovoltaic module, and then the PV curve is solved; then, the actual irradiance and temperature in the typical meteorological year data are corrected to solve the PV curve under non-standard conditions, and then the maximum power value is obtained, which is the maximum output power of the minimum configurable module of the photovoltaic module. 2) The maximum output power of the minimum configurable module of the photovoltaic module is converted to the AC side of the grid connection point by multiplying it by the loss factor step by step, and finally outputs the hourly power time series data of the AC side of the minimum configurable module of the photovoltaic module.

[0012] The present invention also provides a system for calculating and optimizing the revenue of a photovoltaic energy storage system, including a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the method described above.

[0013] The beneficial effects of this invention are as follows: By constructing a "data-driven - physical simulation - collaborative optimization - full life cycle assessment" method, it solves the core pain points of inaccurate calculations, reliance on data, and difficulty in decision-making in the planning of photovoltaic and energy storage systems. First, by coupling time-series data of meteorology, power generation, load, and electricity prices, hourly power generation simulation based on the Perez model and single-diode model is adopted to improve the scientific nature of the calculations. Second, by introducing an "equivalent simulation algorithm," only a small amount of information such as the user's location, total annual electricity cost, and typical electricity consumption patterns is needed to reconstruct the annual load time-series data, breaking through the bottleneck of missing historical data and greatly expanding the applicability of the method. Finally, by traversing the combination of the number of installed components and the energy storage capacity and performing full life cycle economic optimization, the optimal configuration scheme with the best economic benefits can be directly output, providing users with a precise and efficient quantitative decision-making tool, significantly reducing investment risks and technical barriers. Attached Figure Description

[0014] Figure 1 This is a flowchart of the present invention; Figure 2 It is a flowchart for reconstructing the hourly electricity load time-series curve based on the equivalent simulation algorithm; Figure 3 This is the equivalent circuit diagram of a single diode. Detailed Implementation

[0015] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0016] In the description of this invention, it should be noted that the terms "inner", "outer", "upper", "lower", "horizontal", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0017] like Figures 1 to 3 As shown, the present invention provides a method for calculating and optimizing the revenue of a photovoltaic-energy storage system, comprising photovoltaic modules and energy storage devices, and includes the following steps: S1. Data Input and Processing: 1) Site and environmental parameters Obtain the geographical information and typical meteorological year data of the proposed installation site. The typical meteorological year data includes hourly total irradiance, diffuse irradiance, ambient temperature and wind speed datasets; obtain the number of photovoltaic modules to be installed and the maximum capacity of the energy storage equipment; set the installation tilt angle and azimuth angle of the photovoltaic modules; 2) Minimum configurable unit parameters of the equipment Obtain the model number of the photovoltaic module and read the electrical parameters of the minimum configurable module of the photovoltaic module; input or select the specifications of the energy storage unit and read the capacity and charge / discharge cycle efficiency of the minimum configurable module of the energy storage unit. S2. Construction of hourly load time series data: 1) Upload historical hourly electricity load data for at least one calendar year; if not, proceed to the equivalent simulation algorithm to fit and generate the user's hourly load time series data; 2) The equivalent simulation algorithm includes the following steps: a) Load the electricity price policy of the proposed installation location and enter the user's total electricity bill for the previous year or the estimated amount for this year; b) Calculate the user's total annual electricity consumption based on the input total electricity bill and electricity pricing policy; c) Based on the user's selected electricity consumption mode, the total annual electricity consumption is allocated to each day and each hour according to the preset proportion of each hour in the selected mode, generating the user's hourly load time sequence data; S3. Calculation of hourly irradiation time series data: Based on the latitude, longitude, and time zone of the proposed installation site, the solar altitude and azimuth angles are calculated hourly using astronomical algorithms. According to the geometric relationship between the sun and the tilted surface of the photovoltaic module, the projections of the direct, scattered, and reflected components onto the tilted surface of the photovoltaic module are calculated using the Perez irradiance model. The calculation results of the three components on the tilted surface of the photovoltaic module are synthesized hourly to obtain the hourly irradiance time series data of the total solar radiation received by the tilted surface of the photovoltaic module. S4, Photovoltaic power generation time series simulation: 1) Based on the physical model of the single diode equivalent circuit, the IV curve of the minimum configurable module of the photovoltaic module under standard conditions is solved according to the electrical parameters of the minimum configurable module of the photovoltaic module, and then the PV curve is solved; then, the actual irradiance and temperature in the typical meteorological year data are corrected to solve the PV curve under non-standard conditions, and then the maximum power value is obtained, which is the maximum output power of the minimum configurable module of the photovoltaic module. 2) The maximum output power of the minimum configurable module of the photovoltaic module is converted to the AC side of the grid connection point by multiplying it by the loss factor step by step, and finally outputs the hourly power time series data of the AC side of the minimum configurable module of the photovoltaic module.

[0018] S5. Generation of electricity price time-series data: 1) Read the hourly load time series data from step S2, combine it with the electricity price policy of the proposed installation location, determine the hourly electricity price, and generate the electricity unit price time series data corresponding to the hourly load time series data; 2) Generate hourly photovoltaic grid-connected electricity price time series data based on the photovoltaic grid-connected subsidy or electricity price policy of the project location; S6. Simulation of Cooperative Scheduling Model Based on Deterministic Rules 1) Using the minimum configurable module of photovoltaic modules and energy storage units obtained in step S2 as the basic step size, construct a two-dimensional discretized search space consisting of all feasible combinations of the number of photovoltaic modules and energy storage units under the constraints of the number of photovoltaic modules to be installed and the maximum capacity of the energy storage equipment. 2) For each combination in the two-dimensional discretized search space, the cooperative scheduling model performs a time-by-time simulation; 3) After traversing each combination in the two-dimensional discretized search space, compare the economic indicators of each combination and output the combination of photovoltaic modules and energy storage units with the best economic benefits.

[0019] In step S6, for each combination in the two-dimensional discretized search space, the following rules are followed when performing hourly simulation: at each moment, the photovoltaic modules generate electricity to meet the current load; if there is surplus power generated by the photovoltaic modules, the energy storage device is charged first, and the surplus power is fed into the grid after the energy storage device is fully charged; if the photovoltaic modules generate insufficient power, the energy storage device is discharged first to supplement the power, and if it is still insufficient, electricity is purchased from the grid.

[0020] Step S3 includes the following steps: 1) Based on typical meteorological year data, calculate the hourly total solar irradiance received by photovoltaic modules under horizontal conditions: (1) In the formula, D T The total solar irradiance is measured hourly under direct sunlight. D b This represents the hourly direct irradiance under direct sunlight conditions. D d The hourly scattered irradiance under direct sunlight. D r Hourly reflected irradiance under direct sunlight, all in W / m² 2 ; 2). Based on the installation tilt angle and azimuth angle of the photovoltaic module, the total solar irradiance under horizontal conditions is decomposed into the direct sunlight component under oblique sunlight, the sky scattering component under oblique sunlight, and the ground reflection component under oblique sunlight. The hourly total solar irradiance received by the photovoltaic module under oblique sunlight conditions is then calculated: (2) In the formula, D T,T This represents the hourly total solar irradiance under oblique illumination. D b,T The hourly direct irradiance under oblique illumination is given. D d,T The hourly scattered irradiance under oblique illumination. D r,T The reflected irradiance is under oblique illumination, and the unit is W / m². 2 ; in, (3) (4) (5) Substituting the solutions, we get: (6) In the formula, θ is the angle of incidence of sunlight; θ z ρ is the solar zenith angle; F1 is the solar orbit coefficient; F2 is the sky brightness coefficient; X1 and X2 are corrections for the effects of the sun being in a special position in the solar model; ρ is the ground reflectivity; β is the tilt angle of the photovoltaic module. in, (7) (8) (9) (10) (11) In the formula, Δ represents brightness; f 11 f 12 f 21 f 22 f 23 All values ​​are Perez brightness empirical coefficients; δ is the solar declination angle; φ is the latitude of the proposed installation site; ω is the solar hour angle; and γ is the solar azimuth angle.

[0021] Furthermore, when conducting operational simulations, the collaborative scheduling model also performs efficiency calculations on the charging and discharging processes of energy storage devices and records their charging and discharging energy losses.

[0022] Furthermore, during the operational simulation, the collaborative scheduling model records the annual electricity purchase cost, self-consumption electricity savings, grid-connected electricity sales revenue, and energy storage cycle losses for each simulation, forming an annual cash flow. Based on this, combined with the initial investment cost, operation and maintenance cost, and equipment residual value, a cash flow model for the entire life cycle of the system is constructed to calculate the net present value and internal rate of return.

[0023] Definitions: Photovoltaic-energy storage system: This refers to an energy system that integrates a photovoltaic power generation system with an electrochemical energy storage system through power electronic conversion equipment and an intelligent control system. Its core function is to achieve self-consumption of photovoltaic power generation, storage of surplus electricity, and to shift energy over time through the charging and discharging regulation of the energy storage, thereby improving the photovoltaic absorption rate, reducing electricity costs, and ensuring power supply reliability.

[0024] Time series data refers to data sequences that are continuously collected and arranged at fixed time intervals (such as 1 hour, 15 minutes, 1 minute) and used to accurately describe the dynamic changes of meteorological conditions, power generation, power load and electricity price signals over 8760 consecutive hours throughout the year.

[0025] The Perez model is a key model widely used in fields such as solar energy, building energy consumption, and agricultural meteorology to estimate solar irradiance on tilted surfaces.

[0026] Direct radiation: refers to the radiation component of solar radiation that directly reaches the Earth's surface.

[0027] Scattered radiation: refers to the radiation component of the sun that reaches the earth's surface from all directions in the sky after being scattered by molecules, aerosols or clouds in the atmosphere; it is also called sky-scattered radiation.

[0028] Reflected irradiance: refers to the irradiance component of solar radiation reflected from the earth's surface or surrounding objects that reaches the surface of a tilted photovoltaic module. In photovoltaic system calculations, it usually specifically refers to the contribution from ground reflection.

[0029] Total irradiance: the sum of direct irradiance, diffuse irradiance, and reflected irradiance.

[0030] Irradiance: refers to the radiant power received per unit area per unit time, measured in W / m². It characterizes the instantaneous intensity of irradiance.

[0031] Irradiance: refers to the radiant energy received per unit area within a specific time period (such as one hour or one day), measured in J / m² or W / m². It is obtained by integrating the irradiance over that period and is used to measure the cumulative radiant energy.

[0032] Discrete capacity combination space: The discrete capacity combination space is a set consisting of finite and exhaustive capacity configuration schemes. Since photovoltaic modules and energy storage units exist in the form of standard modules, their capacity is not continuously variable, but rather increases discretely in fixed steps.

[0033] The Perez irradiance model is a key model widely used in fields such as solar energy, building energy consumption, and agricultural meteorology to estimate solar irradiance on tilted surfaces.

[0034] Electricity consumption patterns: Typical daily electricity consumption pattern example: Taking the "daytime electricity consumption" pattern as an example, this pattern consists of the load ratio corresponding to 24 hours. The specific ratio can be obtained by selecting typical objects for statistics.

[0035] For example: (0.028,0.024,0.023,0.022,0.022,0.025,0.029,0.036,0.042,0.045,0.046,0.048,0.049,0.049,0.049,0.050,0.055,0.061,0.063,0.059,0.054,0.048,0.040,0.033) The sum of the 24 values ​​is 1, and each value represents the proportion of that hour in the total daily electricity consumption. For example, if the total daily electricity consumption is 1000 kWh, then according to this model, the hourly electricity consumption can be calculated as follows: 28 kWh, 24 kWh, 23 kWh, 22 kWh, 22 kWh, 25 kWh, 29 kWh, 36 kWh, 42 kWh, 45 kWh, 46 kWh, 48 kWh, 49 kWh, 49 kWh, 50 kWh, 55 kWh, 61 kWh, 63 kWh, 59 kWh, 54 kWh, 48 kWh, 40 kWh, 33 kWh.

[0036] The statistics yielded 24 other values, which, if they meet the above requirements, constitute other electricity usage patterns.

[0037] Photovoltaic module performance model: Under constant illumination, the internal structure of a photovoltaic module can be equivalently represented by a circuit model consisting of a photocurrent source, a parallel diode, a series resistor, and a parallel resistor, i.e., a single-diode equivalent circuit model. The photocurrent source describes the current generated by the conversion of light into electricity, the parallel diode simulates the nonlinear characteristics of the solar cell, the series resistor reflects the losses in the conductors and connections, and the parallel resistor represents the leakage current effect.

[0038] Its equivalent circuit diagram is as follows Figure 3 As shown, the IV characteristic equation of the single diode model is as follows: (12) (13) In the formula: I is the output current, A; I L For photocurrent to generate dark current, A; I o R is the equivalent diode reverse saturation current, A; V is the output voltage, V; R s For series resistance, Ω; R shγ is the parallel resistance, Ω; q is the charge (1.6×10-19), C; Ns is the number of cells in series; γ is the diode ideality factor; k is the Boltzmann constant (1.38×10-23), J / k; T is the cell temperature, K; α is the module's corrected ideality factor.

[0039] We obtain the "five parameters", namely I L I o α, R s R sh The IV characteristic curve of the module can be obtained, and then the IP characteristic curve can be obtained, thus allowing the calculation of the module's output power characteristics. The "five parameters" can be obtained using the photovoltaic module nameplate parameters under standard test conditions (i.e., AM1.5 ground-level solar irradiance distribution, solar irradiance of 1000 W / m², and photovoltaic module temperature of 25℃). In practical applications, the cells may not generate electricity under standard conditions, therefore, the "five parameters" under STC conditions need to be corrected. The subscript r is used to represent the standard test conditions.

[0040] The correction formula is as follows: (14) (15) (16) (17) (18) Where: G is the surface irradiance of the module, W / m2; μ is the temperature coefficient of short-circuit current, A / ℃; T C E represents the component's operating temperature in °C. g This refers to the bandwidth of the no-bandwidth area.

[0041] [Example 1] For example, the present invention is implemented in the following manner: S1. Basic Data Input: 1) The site is Shanghai XXX Building (121.317441°E, 31.095635°N, altitude 5m), and the system automatically connects to the local meteorological database.

[0042] 2) A certain brand of 450W high-efficiency module (1762×1134mm) was selected, with electrical parameters of V. oc =34.91V, I sc =15.86A, V mp =29.98V, I mp =15.01A. Installation angle is 15° tilt, 0° azimuth (due south). The basic module capacity of the energy storage unit is 5kWh, with a charge / discharge efficiency of 97%.

[0043] S2. Construction of User Load Time Series Data: Input hourly load data for at least one typical year.

[0044] If not, then use the equivalent simulation algorithm.

[0045] (a) Based on the electricity pricing policies of each province, hourly load data for the entire year is constructed. This paper uses an equivalent simulation algorithm as an example for illustration. Assuming a typical daily electricity consumption pattern is selected, its 24-hour load distribution is as follows: Table 1 Examples of Typical Daily Electricity Consumption Patterns

[0046] Table 2 Shanghai Municipality's Policy on One Electricity Meter Per Household

[0047] Table 2 shows the electricity pricing policy for "one meter per household" for Shanghai residents. As can be seen from the typical electricity consumption pattern in Table 1, peak hours account for 80.30% and off-peak hours account for 19.70% (this typical electricity consumption pattern is for illustrative purposes only).

[0048] (b) Taking an annual total electricity bill of 4000 yuan as an example, the "tiered pricing followed by peak-valley pricing" model is selected. First, the weighted average electricity price for each tier is calculated based on the peak-valley ratio: the first tier is approximately 0.544 yuan / kWh, the second tier is approximately 0.610 yuan / kWh, and the third tier is approximately 0.868 yuan / kWh. Then, the electricity bill thresholds are calculated: the threshold for the first tier is approximately 1697.28 yuan (0.544 yuan / kWh × 3120 kWh); the threshold for the second tier is approximately 2722.08 yuan. Since the total electricity bill of 4000 yuan is higher than the second threshold of 2722.08 yuan, the electricity consumption is determined to enter the third tier, and the calculated annual total electricity consumption is approximately 6272.49 kWh. Table 3 shows the total electricity consumption results calculated using this algorithm under different randomly selected electricity consumption patterns and total electricity bills.

[0049] Table 3. Example of electricity consumption calculation for Shanghai electricity prices

[0050] (c) The calculated total electricity consumption is evenly distributed to each day, and the hourly electricity consumption can be calculated according to the typical electricity consumption pattern. By analogy and replication, the hourly load time series data of 8760 hours throughout the year is finally generated.

[0051] S3, Calculation of Solar Irradiance Time Series on Inclined Surface Based on the project site's latitude and longitude and the hourly timestamp, astronomical algorithms are used to calculate the sun's altitude and azimuth angle hourly.

[0052] The solar altitude angle is the angle between the sun's rays and the projection of its rays onto the ground, used to represent the sun's altitude relative to the horizontal plane. The specific formula is: (19) The solar azimuth angle is the angle γ between the projection of sunlight onto the horizon and the direction of true south. It describes the angle of deviation of the horizontal projection of sunlight relative to the direction of true south. True south corresponds to an azimuth angle of 0°, westward deviation is positive, and eastward deviation is negative. The solar azimuth angle has the following relationships with declination, altitude, latitude, and time angle: (20) Formula for calculating the solar direct vector: (twenty one) By combining the installation tilt angle and azimuth angle of the photovoltaic array input by the user, the system decomposes the total solar irradiance on the horizontal plane into direct radiation component, sky scattering component, and ground reflection component.

[0053] Solar irradiance can generally be divided into three parts: direct irradiance, diffuse irradiance, and reflected irradiance. The formula for calculating the irradiance received by a photovoltaic power station under ideal conditions is as follows: (twenty two) In the formula, D T The total solar irradiance is measured hourly under direct sunlight. D b This represents the hourly direct irradiance under direct sunlight conditions. D d The hourly scattered irradiance under direct sunlight. D r Hourly reflected irradiance under direct sunlight, all in W / m² 2 All of the above can be directly measured by relevant instruments.

[0054] If photovoltaic modules are laid flat on the ground, the value is the horizontal irradiance value. However, actual photovoltaic power plants or photovoltaic modules are tilted, and they are rarely laid flat. Therefore, the actual irradiance of photovoltaic modules is the irradiance of the tilted surface.

[0055] Based on the installation tilt angle and azimuth angle of the photovoltaic module, the total solar irradiance under horizontal conditions is decomposed into the direct sunlight component under oblique sunlight, the sky scattering component under oblique sunlight, and the ground reflection component under oblique sunlight. The hourly total solar irradiance received by the photovoltaic module under oblique sunlight is then calculated: (twenty three) In the formula, D T,T This represents the hourly total solar irradiance under oblique illumination. D b,TThe hourly direct irradiance under oblique illumination is given. D d,T The hourly scattered irradiance under oblique illumination. D r,T The reflected irradiance is under oblique illumination, and the unit is W / m². 2 ; (twenty four) (25) (26) Substituting the solutions, we get: (27) In the formula, θ is the angle of incidence of sunlight; θ z ρ is the solar zenith angle; F1 is the solar orbit coefficient; F2 is the sky brightness coefficient; X1 and X2 are corrections for the effects of the sun being in a special position in the solar model; ρ is the ground reflectivity; β is the tilt angle of the photovoltaic module. The formula for calculating the angle θ of sunlight incidence is: (28) In the formula, δ is the solar declination angle; φ is the project latitude; β is the tilt angle of the building surface or photovoltaic module; γ is the azimuth angle of the building surface or photovoltaic module; and ω is the solar hour angle.

[0056] This paper adopts the Perez model, which can accurately calculate the scattered irradiance based on the solar altitude angle, weather and atmospheric scattering characteristics. It is applicable to more complex weather conditions to improve the accuracy of photovoltaic power plant design and power generation efficiency.

[0057] (29) (30) (31) (32) In the formula, Δ represents brightness; f 11 f 12 f 21 f 22 f 23 All values ​​are Perez brightness empirical coefficients; δ is the solar declination angle; φ is the latitude of the proposed installation site; ω is the solar hour angle; and γ is the solar azimuth angle.

[0058] In summary, based on the total solar radiation received by the tilted surface in the Perez model, the calculation formula is as follows: (33) S4, Photovoltaic power generation time series simulation: 1) DC power calculation (component level): Using the hourly total irradiance of the tilted surface and the ambient temperature obtained from S3 as input, the physical model of the single diode equivalent circuit is called.

[0059] The IV characteristic equation for the single diode model is as follows: (34) (35) In the formula: I is the output current, A; I L For photocurrent to generate dark current, A; I o R is the equivalent diode reverse saturation current, A; V is the output voltage, V; R s For series resistance, Ω; R sh q is the parallel resistance, Ω; q is the charge (1.6 × 10⁻¹⁹), C; N s γ is the number of cells connected in series; γ is the diode ideality factor; k is the Boltzmann constant (1.38 × 10⁻²³), J / k; T is the cell temperature, K; α is the module's corrected ideality factor.

[0060] We obtain the "five parameters", namely I L I o α, R s R sh The IV characteristic curve of the module can be obtained, and then the PV characteristic curve can be obtained, thus allowing the calculation of the module's maximum output power. The "five parameters" can be obtained using the photovoltaic module's nameplate parameters under standard test conditions (i.e., AM1.5 ground-level solar irradiance distribution, solar irradiance of 1000 W / m², and photovoltaic module temperature of 25°C). In practical applications, the cells may not generate electricity under standard conditions; therefore, the "five parameters" under STC conditions need to be corrected, using the subscript r to represent standard test conditions.

[0061] The correction formula is as follows: (36) (37) (38) (39) (40) Where: G is the surface irradiance of the module, W / m2; μ is the temperature coefficient of short-circuit current, A / ℃; T C E represents the component's operating temperature in °C. g This refers to the bandwidth of the no-bandwidth area.

[0062] The system first solves the core parameters of the model under standard conditions based on the parameters on the component nameplate, and then corrects them based on the actual irradiance and temperature, thereby calculating the maximum output power (DC side) of a single photovoltaic module under the current actual operating conditions hourly.

[0063] 2) Input the DC power time-series data obtained from the aforementioned calculations into an efficiency conversion model to obtain the AC output power at the grid connection point. This model is implemented by multiplying the power by a series of efficiency coefficients characterizing system losses, specifically including: (41) In the formula: P ac (t) represents the hourly power time-series dataset of the grid-connected AC side, P dc (t) represents the hourly power time-series dataset on the DC side, η dc η is the DC-side loss coefficient. inv η is the inverter loss factor. ac η is the AC side loss coefficient, and η is the loss coefficient for the remaining overall efficiency.

[0064] S5, Electricity Price Time Series Data Generation 1) Read the hourly load time series data generated in step S2, combine it with the regional electricity price rules, determine the applicable electricity unit price for each hour based on the electricity consumption period and cumulative electricity consumption, and generate hourly electricity unit price time series data.

[0065] 2) Generate time-series data of photovoltaic grid-connected electricity price based on the photovoltaic grid-connected subsidy or electricity price policy of the project location.

[0066] S6. Simulation and Capacity Optimization of Photovoltaic-Storage Co-operation 1) Model for constructing discretized capacity search space The system's capacity configuration uses the minimum configurable module size of photovoltaic modules and energy storage units as the basic step size to generate a set of all feasible technical solutions. This set is defined as the discretized capacity search space: (42) The total system capacity of any specific configuration scheme is calculated by the following formula: (43) Table 4. Variable and Parameter Definitions

[0067] 2) Deterministic Cooperative Scheduling Operation Model (Hourly Simulation) At each time t in the hourly simulation, the system calculates the current photovoltaic power generation P. PV (t), load demand P Load Power allocation and state updates are performed according to deterministic rules based on the current energy storage capacity SOC(t) and the energy storage current energy storage capacity SOC(t).

[0068] Power balancing and charge / discharge logic: (44) Energy storage status update: (45) Constraint 0 ≤ E(t) ≤ E sys Table 5. Variable and Parameter Definitions

[0069] 3) Full life cycle economic assessment model For each capacity combination, the economic indicators for the entire life cycle are calculated based on the simulation results, which serve as the basis for optimization.

[0070] Annual net cash flow calculation: (46) Net Present Value (NPV): (47) Table 6. Variable and Parameter Definitions

[0071] After iterating through all capacity combinations, the economic indicators of each scheme are compared, and the scheme with the optimal number of components installed and energy storage capacity is output.

[0072] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for calculating and optimizing the revenue of a photovoltaic-energy storage system, comprising photovoltaic modules and energy storage equipment, characterized in that, It includes the following steps: S1. Data Input and Processing: 1) Site and environmental parameters Obtain the geographical information and typical meteorological year data of the proposed installation site, which includes hourly total irradiance, diffuse irradiance, ambient temperature and wind speed datasets; obtain the number of photovoltaic modules to be installed and the maximum capacity of the energy storage equipment; set the installation tilt angle and azimuth angle of the photovoltaic modules; 2) Minimum configurable unit parameters of the equipment Obtain the model number of the photovoltaic module and read the electrical parameters of the minimum configurable module of the photovoltaic module; input or select the specifications of the energy storage unit and read the capacity and charge / discharge cycle efficiency of the minimum configurable module of the energy storage unit. S2. Construction of hourly load time series data: 1) Upload historical hourly electricity load data for at least one calendar year; if not, proceed to the equivalent simulation algorithm to fit and generate the user's hourly load time series data; 2) The equivalent simulation algorithm includes the following steps: a) Load the electricity price policy of the proposed installation location and enter the user's total electricity bill for the previous year or the estimated amount for the current year; b) Calculate the user's total annual electricity consumption based on the input total electricity bill and electricity pricing policy; c) Based on the user's selected electricity consumption mode, the total annual electricity consumption is allocated to each day and each hour according to the preset proportion of each hour in the selected mode, generating the user's hourly load time sequence data; S3. Calculation of hourly irradiation time series data: Based on the latitude, longitude, and time zone of the proposed installation site, the solar altitude and azimuth angles are calculated hourly using astronomical algorithms. According to the geometric relationship between the sun and the tilted surface of the photovoltaic module, the projections of the direct, scattered, and reflected components onto the tilted surface of the photovoltaic module are calculated using the Perez irradiance model. The calculation results of the three components on the tilted surface of the photovoltaic module are synthesized hourly to obtain the hourly irradiance time series data of the total solar radiation received by the tilted surface of the photovoltaic module. S4. Photovoltaic power generation time series simulation: Based on the typical meteorological year data and the electrical parameters of the minimum configurable module of the photovoltaic module, generate hourly power time series data of the AC side of the proposed installation site under a typical meteorological year; S5. Generation of electricity price time-series data: 1) Read the hourly load time series data from step S2, combine it with the electricity price policy of the proposed installation location, determine the hourly electricity price, and generate the electricity unit price time series data corresponding to the hourly load time series data; 2) Generate hourly photovoltaic grid-connected electricity price time series data based on the photovoltaic grid-connected subsidy or electricity price policy of the project location; S6. Simulation of the cooperative scheduling model based on deterministic rules: 1) Using the minimum configurable module of photovoltaic modules and energy storage units obtained in step S2 as the basic step size, construct a two-dimensional discretized search space consisting of all feasible combinations of the number of photovoltaic modules and energy storage units under the constraints of the number of photovoltaic modules to be installed and the maximum capacity of the energy storage equipment. 2) For each combination in the two-dimensional discretized search space, the cooperative scheduling model performs a time-by-time simulation; 3) After traversing each combination in the two-dimensional discretized search space, compare the economic indicators of each combination and output the combination of photovoltaic modules and energy storage units with the best economic benefits.

2. The method according to claim 1, characterized in that, In step S6, for each combination of the two-dimensional discretized search space, the following rules are followed when performing hourly simulation: at each moment, the photovoltaic modules generate electricity to meet the current load; if there is surplus power generated by the photovoltaic modules, the energy storage device is charged first, and the surplus power is fed into the grid after the energy storage device is fully charged; if the photovoltaic modules generate insufficient power, the energy storage device is discharged first to supplement the power, and if it is still insufficient, electricity is purchased from the grid.

3. The method according to claim 1, characterized in that, Step S3 includes the following steps: 1) Based on the typical meteorological year data, calculate the hourly total solar irradiance received by the photovoltaic modules under horizontal conditions: （1）， In the formula, D T The total solar irradiance is measured hourly under direct sunlight. D b This represents the hourly direct irradiance under direct sunlight conditions. D d The hourly scattered irradiance under direct sunlight. D r Hourly reflected irradiance under direct sunlight, all in W / m² 2 ; 2). Based on the installation tilt angle and azimuth angle of the photovoltaic module, the total solar irradiance under horizontal conditions is decomposed into the direct sunlight component under oblique sunlight, the sky scattering component under oblique sunlight, and the ground reflection component under oblique sunlight. The hourly total solar irradiance received by the photovoltaic module under oblique sunlight conditions is then calculated: （2）， In the formula, D T,T This represents the hourly total solar irradiance under oblique illumination. D b,T The hourly direct irradiance under oblique illumination is given. D d,T The hourly scattered irradiance under oblique illumination. D r,T The reflected irradiance is under oblique illumination, and the unit is W / m². 2 ; in, （3）， （4）， （5）， Substituting the solutions, we get: （6）， In the formula, θ is the angle of incidence of sunlight; θ z ρ is the solar zenith angle; F1 is the solar orbit coefficient; F2 is the sky brightness coefficient; X1 and X2 are corrections for the effects of the sun being in a special position in the solar model; ρ is the ground reflectivity; β is the tilt angle of the photovoltaic module. in, （7）， （8）， （9）， （10）， （11）， In the formula, Δ represents brightness; f 11 f 12 f 21 f 22 f 23 All values ​​are Perez brightness empirical coefficients; δ is the solar declination angle; φ is the latitude of the proposed installation site; ω is the solar hour angle; and γ is the solar azimuth angle.

4. The method according to claim 1, characterized in that, When the collaborative scheduling model performs operation simulation, it also performs efficiency calculations on the charging and discharging process of the energy storage device and records its charging and discharging energy loss.

5. The method according to claim 1, characterized in that, When the collaborative scheduling model performs operational simulations, it records the annual electricity purchase cost, self-consumption electricity savings, grid-connected electricity sales revenue, and energy storage cycle losses for each simulation, forming an annual cash flow. Based on this, combined with the initial investment cost, operation and maintenance cost, and equipment residual value, a cash flow model for the entire life cycle of the system is constructed to calculate the net present value and internal rate of return.

6. The method according to claim 1, characterized in that, Step S4 includes the following steps: 1) Based on the physical model of the single diode equivalent circuit, the IV curve of the minimum configurable module of the photovoltaic module under standard conditions is solved according to the electrical parameters of the minimum configurable module of the photovoltaic module, and then the PV curve is solved; then, the actual irradiance and temperature in the typical meteorological year data are corrected to solve the PV curve under non-standard conditions, and then the maximum power value is obtained, which is the maximum output power of the minimum configurable module of the photovoltaic module. 2) The maximum output power of the minimum configurable module of the photovoltaic module is converted to the AC side of the grid connection point by multiplying it by the loss factor step by step, and finally outputs the hourly power time series data of the AC side of the minimum configurable module of the photovoltaic module.

7. A system for calculating and optimizing the revenue of a photovoltaic-storage system, characterized in that, It includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the method as described in any one of claims 1 to 6.

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