A distributed photovoltaic peak shaving auxiliary service income calculation method and system

Abstract

This invention discloses a method and system for calculating the revenue of distributed photovoltaic (PV) systems participating in peak-shaving ancillary services. The system includes a data acquisition module, an information storage module, and a revenue calculation module. By acquiring historical and future meteorological information, it predicts the user's power generation and consumption data. Simultaneously, it acquires information such as the time-of-use electricity price in the province where the distributed PV user is located, the PV feed-in tariff, demand information in the peak-shaving ancillary service market, clearing volume and price information, and user contract information. It calculates the user's grid-connected electricity and self-consumption rate, and determines the feasible time periods for the user to participate in peak-shaving ancillary services. Finally, it calculates the user's subsidy revenue, curtailment losses, and investor losses for participating in peak-shaving ancillary services, thereby obtaining the net revenue of the distributed PV user. This invention provides a scientific revenue assessment scheme for distributed PV users participating in peak-shaving ancillary services, assisting them in simultaneously achieving the dual goals of maximizing power generation revenue and optimizing energy costs.

Description

Technical Field

[0001] This invention relates to a distributed photovoltaic technology, and more particularly to a method and system for calculating the revenue of distributed photovoltaic power generation participating in peak shaving ancillary services. Background Technology

[0002] The installed capacity of distributed photovoltaic (PV) power is growing rapidly, but the inherent intermittency and volatility of PV power output pose significant challenges to the safe, stable, and economical operation of the power system. With the large-scale integration of distributed PV into the grid, the peak-to-valley difference in grid net load is widening, forming a typical "duck curve." Against this backdrop, incentivizing distributed PV to participate in system regulation through a market mechanism for power peak-shaving ancillary services, and promoting its transformation from the traditional "passive absorption" model to "active support," is of great significance for improving grid flexibility and ensuring power supply security.

[0003] Currently, the methods for calculating the revenue of distributed photovoltaic (PV) power participating in peak shaving ancillary services still have significant shortcomings. Existing technologies mostly focus on the economic assessment of traditional models such as self-consumption and surplus power fed into the grid, primarily calculating revenue based on the amount of electricity consumed and fed into the grid. A systematic calculation method applicable to distributed PV power participating in peak shaving ancillary services has not yet been established. In particular, existing methods lack a systematic quantitative consideration of the opportunity costs lost due to responding to peak shaving orders, leading to significant deviations in revenue assessment results and making it difficult to support reliable market investment and operational decisions. Furthermore, there is a lack of integrated, automated tools capable of integrating multi-source data, dynamic model calculations, and multi-scenario simulation analysis, which restricts the actual efficiency and accuracy of revenue calculation. Therefore, there is an urgent need to propose a calculation method and system that can accurately quantify peak shaving revenue, comprehensively incorporate various cost and risk factors, and support practical business applications, to fill the gaps in the current technological system and effectively promote the large-scale development of the distributed PV power participating in peak shaving ancillary services market. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a method for calculating the revenue of distributed photovoltaic power generation participating in peak shaving ancillary services. Another purpose of this invention is to provide a system for implementing this method.

[0005] Technical solution: The present invention provides a method for calculating the revenue of distributed photovoltaic power generation participating in peak shaving ancillary services, comprising:

[0006] (1) Obtain historical and future meteorological information, and combine it with the historical power generation and consumption data of distributed photovoltaic users to predict the power generation and consumption data of users at multiple time periods on the operating day;

[0007] (2) Obtain information on the installed capacity of distributed photovoltaic users, the time-of-use tariff in the province, the current photovoltaic grid connection policy in the province, the market demand for peak-shaving auxiliary services, and the clearing volume and price information;

[0008] (3) Based on the predicted power generation and consumption data in step (1), calculate the user's on-grid power and self-consumption rate, and determine the time period information when the user can participate in peak shaving auxiliary services;

[0009] (4) Based on the information obtained in step (2) and the time period information determined in step (3), calculate the user's subsidy income for participating in peak shaving auxiliary services, the curtailment loss and the investor's loss, and obtain the net income of distributed photovoltaic users.

[0010] In step (1), the specific steps for predicting the user's power generation and consumption data include:

[0011] (11) Obtain historical and future meteorological information through the public Internet, including region, date, temperature, daily irradiance, daily wind speed variation curve, and maximum and minimum temperature information;

[0012] (12) Combine the acquired meteorological information, clean the meteorological information, remove outliers, and generate a standard format file including fields such as region, date, temperature, irradiance, and wind speed;

[0013] (13) Obtain historical power generation and consumption data of distributed photovoltaic power generation, and distinguish the date type as working day, rest day and statutory holiday;

[0014] (14) Analyze the information from steps (12) and (13) in a unified manner. If the deviation between the highest and lowest daily irradiance of the same type of date is less than the preset threshold, it is defined as a similar date; otherwise, the recent similar date is considered as a similar date.

[0015] (15) Based on the generated meteorological standard format parsing file, analyze the internal and external laws of the periodic changes in power generation and consumption of distributed photovoltaic users in years, months, weeks and days. Combine the time series method and the statistical model method to predict the power generation and power consumption of distributed photovoltaic users on the operating day.

[0016] In step (3), the specific steps for calculating the user's internet usage and self-consumption rate, and determining the time period information for which the user can participate in peak shaving auxiliary services include:

[0017] (31) Obtain the power generation and consumption forecast data of distributed photovoltaic users, divide the day into m time periods, and obtain the predicted power generation for each time period as follows: ,…, And the predicted electricity consumption for each time period is ,…, ;

[0018] (32) Calculate the predicted on-grid power generation of photovoltaic power for each time period based on the predicted power generation and consumption. ,…, ;

[0019] (33) Calculate the self-consumption rate of photovoltaic power for each time period based on the predicted power generation and consumption. ,…, ;

[0020] (34) Determine whether distributed photovoltaic users can participate in the peak shaving ancillary service market during the corresponding time period based on the calculation results of step (33).

[0021] In step (32), if ,but =0, if ,but = , where i represents the specific time period.

[0022] In step (33), if If ≠0, then:

[0023]

[0024] otherwise, =0, where i is the specific time period.

[0025] In step (34), if If ≠0, then distributed photovoltaic users can participate in the peak-shaving ancillary service market during the corresponding i-th time period.

[0026] In step (4), the specific steps for obtaining the net income of distributed photovoltaic users are as follows:

[0027] (41) Based on the above-obtained predicted power generation is ,…, And the self-consumption rate of photovoltaic power. ,…, Calculate the predicted adjustable power generation for distributed photovoltaic users. ,in Let be the predicted adjustable power for the i-th time period;

[0028] (42) Obtain market information on peak-shaving ancillary services, assuming the demand period is …, ,in For the first period of demand, For the last period of demand, the clearing electricity price is …, ;

[0029] (43) Based on the obtained information on the time periods during which distributed photovoltaic users can participate in peak shaving ancillary services, the market demand periods, and the clearing price, calculate the corresponding market subsidy for distributed photovoltaic users participating in peak shaving ancillary services. * ,in for Predictable and adjustable power consumption for different time periods. for The clearing price for a given time period corresponds to the curtailment loss for that time period. * ,in for Predicted internet usage for different time periods for The on-grid electricity price for a given time period;

[0030] (44) Obtain the contracted electricity price from distributed photovoltaic users and investors. …, If participating in peak shaving ancillary services, then Investor losses during the period were * ,in, for Forecasted electricity consumption for a given period of time for Contracted electricity price for a given time period;

[0031] (45) Based on the obtained time-of-use electricity price information for peak and off-peak periods and the demand periods for peak-shaving ancillary services, calculate the additional electricity charges for distributed photovoltaic users. *( ),in, for Time-of-use electricity pricing for different time periods;

[0032] (46) Calculate the net income of distributed photovoltaic power generation participating in peak shaving ancillary services based on market subsidies, curtailment losses, investor losses, and additional electricity charges incurred by distributed photovoltaic users, and then calculate the total net income.

[0033] In step (46), the net revenue from distributed photovoltaic power generation participating in peak-shaving ancillary services is: * * * *( Total net income is:

[0034]

[0035] Where j represents the period during which peak shaving ancillary services are provided.

[0036] The present invention discloses a distributed photovoltaic (PV) peak-shaving ancillary service revenue calculation system, comprising:

[0037] Data acquisition module: Integrates key data from multiple sources, collects meteorological information through access to meteorological data interface, analyzes the impact of meteorological conditions on distributed photovoltaics, connects to the provincial side load system to obtain peak shaving auxiliary service demand information and clearing information, and connects to distributed photovoltaic data acquisition terminal and user electricity consumption data acquisition terminal to obtain distributed photovoltaic user power generation and consumption information;

[0038] Information storage module: It is responsible for the storage and management of massive amounts of data, classifying and storing meteorological data, power generation and consumption data, and transaction regulation data in a hierarchical manner. By building a clear index and association mechanism, it supports the rapid retrieval and access of data.

[0039] Revenue Calculation Module: Deeply integrates data from the data acquisition and information storage modules, combines power generation and consumption forecasts, peak shaving auxiliary service requirements and clearing results, integrates provincial time-of-use tariffs, on-grid tariffs and contract information, constructs a refined revenue calculation model, and calculates potential revenue and costs in real time.

[0040] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: (1) By using advanced multi-dimensional data fusion algorithms, meteorological data, electricity generation and consumption metering information are integrated to predict the power generation and electricity consumption of distributed photovoltaic users, calculate the self-consumption rate, and realize the precise management and economic operation of distributed photovoltaic users; (2) By combining peak shaving auxiliary service market information and provincial time-of-use electricity price policy, a dynamic revenue analysis model is constructed. This model can clearly determine whether users' participation in the peak shaving auxiliary service market is economical under a specific market mechanism by comparing the economic benefits of the traditional self-consumption and surplus grid connection mode with the active participation in the peak shaving auxiliary service mode; (3) The expected revenue space of distributed photovoltaic users participating in peak shaving auxiliary services can be clearly defined and the expected revenue space can be quantified, thereby assisting distributed photovoltaic users to simultaneously achieve the dual goals of maximizing power generation revenue and optimizing energy consumption costs. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0042] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0043] Example 1

[0044] This embodiment provides a method for calculating the revenue of distributed photovoltaic power generation participating in peak-shaving ancillary services. The revenue calculation method includes the following steps:

[0045] (1) Obtain historical meteorological data and future meteorological forecast information through the Internet public network, including information such as region, date, temperature, daily irradiance, daily wind speed change curve, maximum and minimum temperature, etc.; Combine the obtained meteorological information to clean the meteorological information such as the highest and lowest temperatures, irradiance, and wind speed of historical days and future days, remove outliers, and generate a standard format file including key fields such as region, date, temperature, irradiance, and wind speed; Obtain historical power generation and consumption data of distributed photovoltaic, and distinguish the date type into working days, rest days and statutory holidays; Analyze the above information in a unified manner. If the deviation of the highest and lowest daily irradiance of the same type of date is less than 10%, it is defined as a similar day. If the daily irradiance information cannot be met, the same type of day in the last 60 days is used as the similar day; For distributed photovoltaic users, combine the generated meteorological standard format parsing file to analyze the internal and external laws of the periodic power generation and consumption changes of distributed photovoltaic users in years, months, weeks and days. Combine the time series method and the statistical model method to predict the power generation and user power consumption of distributed photovoltaic on the operating day.

[0046] (2) Obtain information on the installed capacity of distributed photovoltaic users, including the rated power of the photovoltaic system, inverter configuration parameters, etc.; obtain information on the time-of-use electricity price of the province where the distributed photovoltaic user is located, including the division standards of peak and valley periods and the corresponding catalog price or market-based transaction price information publicly released by the power grid company; obtain information on the photovoltaic grid connection policy currently in effect in the province where the distributed photovoltaic is located, including the grid connection price under the full grid connection or self-consumption surplus electricity grid connection mode, the standards, settlement methods and implementation periods of various national and local subsidies; obtain information on the market demand for peak-shaving auxiliary services on the operating day, the adjustment amount and clearing price information in the market clearing results.

[0047] (3) Obtain the power generation and consumption forecast data of distributed photovoltaic users. Assuming there are m time periods in a day, the predicted power generation for each time period is: ,…, The predicted electricity consumption for each time period is ,…, Based on the predicted power generation and consumption, calculate the predicted grid-connected power of photovoltaic power for each time period. ,…, ;like ,but =0, if ,but = Where i represents a specific time period; based on the predicted power generation and consumption, the self-consumption rate of photovoltaic power is calculated for each time period. ,…, ,like If ≠0, then:

[0048]

[0049] otherwise, =0, where i is the specific time period; if If ≠0, then distributed photovoltaic users can participate in the peak-shaving ancillary service market during the corresponding i-th time period.

[0050] (4) Based on the above-obtained predicted power generation is ,…, And the self-consumption rate of photovoltaic power. ,…, Calculate the predicted adjustable power generation for distributed photovoltaic users. ,in The predicted adjustable power consumption for the i-th time period; obtain peak-shaving ancillary service market information, assuming the demand period is... …, ,in For the first period of demand, For the last period of demand, the clearing electricity price is …, Based on the obtained information on the periods during which distributed photovoltaic (PV) users can participate in peak-shaving ancillary services, as well as market demand periods and clearing prices, the market subsidy for the corresponding periods is calculated as follows: * ,in for Predictable and adjustable power consumption for different time periods. for The clearing price for a given time period corresponds to the curtailment loss for that time period. * ,in for Predicted internet usage for different time periods for The feed-in tariff for a given period; obtaining contracted tariffs from distributed photovoltaic users and investors. …, If participating in peak shaving ancillary services, then Investor losses during the period were * ,in, for Forecasted electricity consumption for a given period of time for The contracted electricity price for the specified time period; based on the obtained time-of-use electricity price information for peak and off-peak periods and the time periods of peak-shaving ancillary service demand, the additional electricity charges for distributed photovoltaic users are calculated as follows: *( ),in, for Time-of-use pricing for different time periods; based on market subsidies, curtailment losses, investor losses, and additional electricity costs for distributed photovoltaic users, a comprehensive calculation shows that the net revenue from distributed photovoltaic participation in peak-shaving ancillary services is... * * * *( If the total net income is:

[0051]

[0052] Where j represents the period during which peak shaving ancillary services are provided.

[0053] Example 2

[0054] This embodiment provides a revenue calculation system for distributed photovoltaic (PV) power generation participating in peak shaving ancillary services. The revenue calculation system includes:

[0055] Data Acquisition Module: Integrates key data from multiple sources, collects meteorological information such as temperature, humidity, wind speed, and wind direction through access to meteorological data interfaces, and analyzes the impact of meteorological conditions on distributed photovoltaics; connects to the provincial side load line system to obtain peak-shaving auxiliary service demand information and clearing information; at the same time, it connects to distributed photovoltaic data acquisition terminals and user electricity consumption data acquisition terminals to obtain distributed photovoltaic user power generation and consumption information, providing data support for subsequent prediction of distributed photovoltaic user power generation and consumption information.

[0056] Information storage module: Responsible for the storage and management of massive amounts of data, classifying and stratifying meteorological data, power generation and consumption data, and transaction regulation data. Through the construction of a clear index and association mechanism, it supports rapid data retrieval and access. It also provides data backup and recovery capabilities to ensure data security and system stability, providing a data foundation for the reliable operation of overall business.

[0057] The revenue calculation module deeply integrates the basic data from the data acquisition and information storage module, combines it with power generation and consumption forecasts, peak-shaving ancillary service demand, and clearing results, and integrates information such as provincial time-of-use tariffs, feed-in tariffs, and agreements between distributed photovoltaic users and investors to construct a refined revenue calculation model. This module can calculate potential revenues and costs in real time, providing a clear basis for distributed photovoltaic users to participate in peak-shaving ancillary service market transactions, ensuring that each operation is economically controllable, and ultimately achieving accurate grasp and continuous optimization of investment returns.

[0058] Example 3

[0059] For ease of understanding, this embodiment provides a specific implementation example:

[0060] A distributed photovoltaic (PV) user is projected to generate 242.97 kWh of electricity and consume 134.73 kWh between 10:00 and 10:15 AM. Therefore, the estimated grid-connected electricity during this period is 108.24 kWh, resulting in a self-consumption rate of 55.45%. If peak-shaving ancillary services are needed during this period, with a peak-shaving price of 1 yuan / kWh and an estimated peak-shaving capacity of 134.73 kWh, and considering the contracted price between the distributed PV user and the investor is 0.6 yuan / kWh, calculations show that if the user participates in peak-shaving ancillary services, the market subsidy during this period will be 134.73 yuan, the curtailment loss 42.32 yuan, the PV investor's loss 80.84 yuan, and the distributed PV user will incur an additional 8.44 yuan in electricity costs, resulting in a net profit of 3.13 yuan.

[0061] In summary, participating in the market during this period resulted in an additional profit of 3.13 yuan compared to not participating.

Claims

1. A distributed photovoltaic peak shaving auxiliary service income estimation method, characterized in that, include: (1) Obtain historical and future meteorological information, and combine it with the historical power generation and consumption data of distributed photovoltaic users to predict the power generation and consumption data of users at multiple time periods on the operating day; (2) Obtain information on the installed capacity of distributed photovoltaic users, the time-of-use tariff in the province, the current photovoltaic grid connection policy in the province, the market demand for peak-shaving auxiliary services, and the clearing volume and price information; (3) Based on the predicted power generation and consumption data in step (1), calculate the user's on-grid power and self-consumption rate, and determine the time period information when the user can participate in peak shaving auxiliary services; (4) Based on the information obtained in step (2) and the time period information determined in step (3), calculate the user's subsidy income for participating in peak shaving auxiliary services, the curtailment loss and the investor's loss, and obtain the net income of distributed photovoltaic users.

2. The distributed photovoltaic peak participation auxiliary service benefit calculation method according to claim 1, characterized in that, In step (1), the specific steps for predicting the user's power generation and consumption data include: (11) Obtain historical and future meteorological information through the public Internet, including region, date, temperature, daily irradiance, daily wind speed variation curve, and maximum and minimum temperature information; (12) Combine the acquired meteorological information, clean the meteorological information, remove outliers, and generate a standard format file including fields such as region, date, temperature, irradiance, and wind speed; (13) Obtain historical power generation and consumption data of distributed photovoltaic power generation, and distinguish the date type as working day, rest day and statutory holiday; (14) Analyze the information from steps (12) and (13) in a unified manner. If the deviation between the highest and lowest daily irradiance of the same type of date is less than the preset threshold, it is defined as a similar date; otherwise, the recent similar date is considered as a similar date. (15) Based on the generated meteorological standard format parsing file, analyze the internal and external laws of the periodic changes in power generation and consumption of distributed photovoltaic users in years, months, weeks and days. Combine the time series method and the statistical model method to predict the power generation and power consumption of distributed photovoltaic users on the operating day.

3. The distributed photovoltaic peak participation auxiliary service income calculation method according to claim 1, characterized in that, In step (3), the specific steps for calculating the user's internet usage and self-consumption rate, and determining the time period information for which the user can participate in peak shaving auxiliary services include: (31) Obtain distributed photovoltaic user power generation and consumption prediction data, divide a day into m time periods, obtain the predicted power generation of each time period as ,…, And the predicted power consumption of each time period is ,…, ; (32) According to the predicted power consumption, calculate the predicted on-grid power of photovoltaic in each period , ; (33) Calculate the self-generation self-use rate of photovoltaic power for each time period according to the predicted power consumption ,…, ; (34) Determine whether distributed photovoltaic users can participate in the peak shaving ancillary service market during the corresponding time period based on the calculation results of step (33).

4. The method for calculating the revenue from distributed photovoltaic power generation participating in peak-shaving ancillary services according to claim 3, characterized in that, In step (32), if then = 0, if then = 1 where i is the particular time period.

5. The method for calculating the revenue from distributed photovoltaic power generation participating in peak-shaving ancillary services according to claim 3, characterized in that, In step (33), if ≠ 0, then: Otherwise, = 0, where i is the specific time period.

6. The method for calculating the revenue from distributed photovoltaic power generation participating in peak-shaving ancillary services according to claim 3 or 5, characterized in that, In step (34), if ≠ 0, then the distributed photovoltaic user can participate in the peak shaving auxiliary service market in the corresponding i period.

7. The distributed photovoltaic peak participation auxiliary service income estimation method according to claim 1, characterized in that, In step (4), the specific steps for obtaining the net income of distributed photovoltaic users are as follows: (41) Based on the above-obtained predicted power generation is ,…, And the self-consumption rate of photovoltaic power. ,…, Calculate the predicted adjustable power generation for distributed photovoltaic users. ,in Let be the predicted adjustable power for the i-th time period; (42) Obtain market information on peak-shaving ancillary services, assuming the demand period is …, ,in For the first period of demand, For the last period of demand, the clearing electricity price is …, ; (43) Based on the obtained information on the time periods during which distributed photovoltaic users can participate in peak shaving ancillary services, the market demand periods, and the clearing price, calculate the corresponding market subsidy for distributed photovoltaic users participating in peak shaving ancillary services. * ,in for Predictable and adjustable power consumption for different time periods. for The clearing price for a given time period corresponds to the curtailment loss for that time period. * ,in for Predicted internet usage for different time periods for The on-grid electricity price for a given time period; (44) Obtain the contracted electricity price from distributed photovoltaic users and investors. …, If participating in peak shaving ancillary services, then Investor losses during the period were * ,in, for Forecasted electricity consumption for the period for Contracted electricity price for a given time period; (45) Based on the obtained time-of-use electricity price information for peak and off-peak periods and the demand periods for peak-shaving ancillary services, calculate the additional electricity charges for distributed photovoltaic users. *( ),in, for Time-of-use electricity pricing for different time periods; (46) Calculate the net income of distributed photovoltaic power generation participating in peak shaving ancillary services based on market subsidies, curtailment losses, investor losses, and additional electricity charges incurred by distributed photovoltaic users, and then calculate the total net income.

8. The method for calculating the revenue from distributed photovoltaic power generation participating in peak-shaving ancillary services according to claim 7, characterized in that, In step (46), the net revenue from distributed photovoltaic power generation participating in peak-shaving ancillary services is: * * * *( ), where j represents the period during which peak shaving auxiliary services are provided.

9. The method for calculating the revenue from distributed photovoltaic power generation participating in peak-shaving ancillary services according to claim 7 or 8, characterized in that, The total net income is: Where j represents the period during which peak shaving ancillary services are provided.

10. A system for calculating the revenue of distributed photovoltaic power generation participating in peak shaving ancillary services, characterized in that, include: Data acquisition module: Integrates key data from multiple sources, collects meteorological information through access to meteorological data interface, analyzes the impact of meteorological conditions on distributed photovoltaics, connects to the provincial side load system to obtain peak shaving auxiliary service demand information and clearing information, and connects to distributed photovoltaic data acquisition terminal and user electricity consumption data acquisition terminal to obtain distributed photovoltaic user power generation and consumption information; Information storage module: It is responsible for the storage and management of massive amounts of data, classifying and storing meteorological data, power generation and consumption data, and transaction regulation data in a hierarchical manner. By building a clear index and association mechanism, it supports the rapid retrieval and access of data. Revenue Calculation Module: Deeply integrates data from the data acquisition and information storage modules, combines power generation and consumption forecasts, peak shaving auxiliary service requirements and clearing results, integrates provincial time-of-use tariffs, on-grid tariffs and contract information, constructs a refined revenue calculation model, and calculates potential revenue and costs in real time.

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