Power station income prediction method, system and device, storage medium and program product

Abstract

The invention discloses a power station income prediction method, system and device, a storage medium and a program product, and the method comprises the steps: monitoring the power generation data of a target photovoltaic power station, and analyzing and determining the power generation amount of the target photovoltaic power station; analyzing the power consumption data of the user to determine the power consumption of the user, and calculating the predicted power consumption cost of the user; obtaining residual generating capacity according to the electricity consumption of the user and the generating capacity of the target photovoltaic power station, and performing electricity transaction on the residual generating capacity to obtain electricity selling income; and obtaining the predicted total income of the target photovoltaic power station according to the predicted power consumption cost and the electricity selling income. The prediction accuracy of the total cost of the photovoltaic power station can be improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy power station management technology, and in particular to a power station revenue forecasting method, system, equipment, storage medium and program product. Background Art

[0002] In recent years, the asset trading market for new energy power stations has become increasingly active, and the scale of transactions has continued to expand. The significant difference between the estimated cost and the actual cost of power stations has become one of the key factors restricting the healthy and efficient development of the industry. Therefore, how to narrow or even eliminate this cost difference has become an important issue that needs to be solved in the current management and investment decision-making of new energy power station projects. Summary of the invention

[0003] The present invention aims to provide a power station revenue prediction method, system, device, storage medium and program product to improve the prediction accuracy of the total cost of a photovoltaic power station.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present invention provides a power station revenue prediction method, comprising:

[0006] Monitor the power generation data of the target photovoltaic power station and analyze and determine the power generation of the target photovoltaic power station;

[0007] Analyze the user's electricity consumption data to determine the user's own electricity consumption and calculate the user's predicted electricity cost;

[0008] Obtain the remaining power generation based on the user's own power consumption and the target photovoltaic power station's power generation, and use the remaining power generation for power trading to obtain power sales income;

[0009] The predicted total revenue of the target photovoltaic power station is obtained based on the predicted electricity cost and electricity sales revenue.

[0010] In a second aspect, an embodiment of the present invention provides a power station cost prediction system, including:

[0011] A determination unit, used to monitor the power generation data of the target photovoltaic power station and analyze and determine the power generation of the target photovoltaic power station;

[0012] A calculation unit, used to analyze the user's electricity consumption data to determine the user's own electricity consumption and calculate the user's predicted electricity cost;

[0013] The trading unit is used to obtain the remaining power generation according to the user's own power consumption and the target photovoltaic power station's power generation, and to conduct power trading with the remaining power generation to obtain power sales income;

[0014] The obtaining unit is used to obtain the predicted total income of the target photovoltaic power station according to the predicted electricity cost and electricity sales income.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program or instructions to implement the foregoing power station revenue prediction method.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, in which a computer program or instructions are stored. When the computer program or instructions are executed by a processor, the foregoing power station revenue prediction method is implemented.

[0017] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program or instructions. When the computer program or instructions are executed by a processor, the foregoing power station revenue prediction method is implemented.

[0018] Technical effects and advantages of the present invention: The evaluation result of the available hours of the target photovoltaic power station calculated by the present invention is closer to the actual operation of the power station; the present invention obtains accurate user power consumption time and user power consumption by analyzing user power consumption behavior, and accurately calculates the predicted power consumption cost of the user according to the electricity price in different time periods; and accurately updates the subsidy policy and grid-connected electricity price through the electricity price configuration platform to obtain more accurate electricity sales revenue; the combination of the three makes the final calculated cost financial result of the target photovoltaic power station more accurate; and the accurate estimation of the revenue cost of the target photovoltaic power station facilitates the subsequent transaction of the target photovoltaic power station.

[0019] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures pointed out in the specification, claims, and drawings. Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a flowchart of the power station revenue prediction method according to an embodiment of the present invention;

[0022] Figure 2 It is a schematic diagram of the financial evaluation system for power station cost prediction in an embodiment of the present invention;

[0023] Figure 3 It is a schematic diagram of the time-of-use electricity price corresponding to residential electricity consumption in an embodiment of the present invention;

[0024] Figure 4 It is a schematic structural diagram of the power station cost prediction system according to an embodiment of the present invention;

[0025] Figure 5 It is a schematic structural diagram of an electronic device according to an embodiment of the present invention. Specific embodiments

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] To solve the deficiencies of the prior art, an embodiment of the present invention discloses a method for predicting power station income, as Figure 1 shown, including the following steps:

[0028] Step S1: Monitor the power generation data of the target photovoltaic power station, and analyze and determine the power generation amount of the target photovoltaic power station;

[0029] Step S2: Analyze the user's electricity consumption data to determine the user's own electricity consumption, and calculate the predicted electricity consumption cost of the user;

[0030] Step S3: Obtain the remaining power generation amount based on the user's own electricity consumption and the power generation amount of the target photovoltaic power station, and conduct power trading with the remaining power generation amount to obtain electricity sales income;

[0031] Step S4: Obtain the predicted total income of the target photovoltaic power station based on the predicted electricity consumption cost and the electricity sales income.

[0032] In some specific embodiments, Step S1: Monitor the power generation data of the target photovoltaic power station, and analyze and determine the power generation amount of the target photovoltaic power station, including the following contents:

[0033] (1) For the target photovoltaic power station in the operation period, monitor the power generation data of the target photovoltaic power station, and directly calculate and determine the power generation amount of the target photovoltaic power station according to the power generation data of the target photovoltaic power station, that is, obtain the actual operation time of the target photovoltaic power station in a year.

[0034] Among them, the power generation data of the photovoltaic power station includes the real-time power generation amount and real-time power generation data of the photovoltaic power station.

[0035] (2) For the target PV power station during the construction period, monitor the power generation data of the PV power stations near the target PV power station to analyze and obtain the effective utilization hours of the area where the target PV power station is located; according to the effective utilization hours, calculate and determine the power generation of the target PV power station, that is, obtain the predicted operation time of the target PV power station in a year.

[0036] Among them, to monitor the power generation data of the nearby PV power stations to analyze and obtain the effective utilization hours of the area where the target PV power station is located, the specific operations are as follows:

[0037] Real-time monitor the power generation data of the nearby PV power stations, and collect and analyze the meteorological conditions and terrain conditions of the area where the target PV power station is located, as well as the power station scale and power generation records of the target PV power station, etc.;

[0038] Based on the power generation data, meteorological conditions and terrain conditions of the area where the target PV power station is located, comprehensively analyze and obtain the available hours of the area where the target PV power station is located;

[0039] Based on the available hours of the area where the target PV power station is located, combined with the installed capacity of the target PV power station, obtain the effective utilization hours of the area where the target PV power station is located;

[0040] Among them, the effective utilization hours = the installed capacity of the target PV power station * the available hours.

[0041] Exemplarily, since the effective utilization hours of the area where the target PV power station is located is the predicted operation time of the target PV power station in a year, which can directly affect the power generation, grid-connected power and electricity sales revenue of the target PV power station, the effective utilization hours is one of the key indicators for measuring the operation efficiency and economic benefits of the target PV power station during the total revenue evaluation process of the target PV power station.

[0042] Since the installed capacity is a fixed value after each PV power station project is approved or completed, there is generally no significant difference; however, affected by the weather conditions and terrain conditions in different regions, the lighting duration and equipment status of each PV power station will be different, affecting the available hours of the PV power station, and thus affecting the actual power generation capacity of the PV power station.

[0043] Therefore, as Figure 2 shown, build a PV operation monitoring platform based on a large number of power stations, use the PV operation monitoring platform in the energy management system to monitor the distributed PV power stations in most regions of the country in real time, and use the Internet of Things devices to transmit the power generation data of each PV power station back to the distributed PV intelligent analysis system in real time for storage;

[0044] During the monitoring period, meteorological conditions and topographical conditions in different regions, as well as power station information of different photovoltaic power stations (including power station type, power station scale, power generation records), etc. are collected and analyzed simultaneously, and stored in the distributed photovoltaic intelligent analysis system to provide data support for the analysis of the distributed photovoltaic intelligent analysis system.

[0045] Based on the power station information of different photovoltaic power stations and the meteorological conditions and topographical conditions in the region where each photovoltaic power station is located, the distributed photovoltaic intelligent analysis system analyzes and obtains the available hours in the area where each photovoltaic power station project is located;

[0046] Based on the power station information of different photovoltaic power stations, the distributed photovoltaic intelligent analysis system analyzes and obtains the installed capacity of different photovoltaic power stations;

[0047] Based on the available hours in the area where each photovoltaic power station project is located and the installed capacity of each photovoltaic power station project, the distributed photovoltaic intelligent analysis system calculates and obtains the effective utilization hours in the area where each photovoltaic power station project is located.

[0048] When relevant personnel want to query the effective utilization hours in the area where the target photovoltaic power station is located, they only need to input the address, longitude and latitude of the target photovoltaic power station project to be queried in the financial evaluation system. The financial evaluation system searches and displays the available hours in the area where the photovoltaic power station near the input address coordinates is located and the effective utilization hours in the area according to the input data.

[0049] The reference values of the effective utilization hours provided in the current market are generally limited to the county level. However, the photovoltaic operation monitoring platform adopted in this embodiment can narrow the reference range of the evaluated power station at the village level, making the evaluation result of the available hours it provides closer to the actual operation of the power station, and further making the final calculated result of the predicted total income of the photovoltaic power station more accurate.

[0050] Through the photovoltaic operation monitoring platform in the embodiment of the present application, it is possible to collect the power generation data of the target photovoltaic power station and the data of the meteorological conditions and topographical conditions where the target photovoltaic power station is located, and to analyze and calculate the available hours and effective utilization hours in the area where the target photovoltaic power station is located.

[0051] Step S12: Calculate and determine the power generation of the target photovoltaic power station according to the effective utilization hours of the target photovoltaic power station;

[0052] The power generation of the target photovoltaic power station = the effective utilization hours * the installed capacity of the target photovoltaic power station.

[0053] In some specific implementations, step S2: Analyze the user's electricity consumption data to determine the user's own electricity consumption and calculate the user's predicted electricity cost, specifically including:

[0054] The target photovoltaic power station in the present invention is a distributed photovoltaic power generation station, and the operation models of the distributed photovoltaic power generation station include full grid connection, self-consumption with surplus power grid connection, and full self-consumption;

[0055] Among them, the self-consumption with surplus power grid connection model has attracted much attention due to its high flexibility and significant economic benefits, and self-consumption with surplus power grid connection is adopted in the embodiments of the present invention;

[0056] Self-consumption with surplus power grid connection means that the power generation user preferentially uses the power generated by the target photovoltaic power station for its own use, and the power generation exceeding its own demand (i.e., the surplus power generation) is then sold to the power grid.

[0057] Analyze the user's electricity consumption behavior data to obtain the user's electricity consumption time and the corresponding electricity consumption; based on the user's electricity consumption time and the corresponding electricity consumption, combined with the residential electricity prices in different time periods, calculate the predicted electricity cost of the user within a predetermined time;

[0058] Among them, the predicted electricity cost = the user's own electricity consumption * the residential electricity price in the area where the user is located during the corresponding period.

[0059] Exemplarily, in the embodiments of the present invention, an electricity consumption analysis platform is used to analyze the user's electricity consumption behavior data, obtain the user's electricity consumption time and the corresponding electricity consumption and store them in the distributed photovoltaic intelligent analysis system; or by the user uploading their detailed electricity bill payment situation in recent years, or connecting to the electricity consumption analysis platform and collecting from the IoT layer through an installed gateway, so as to obtain accurate user electricity consumption time and electricity consumption.

[0060] Based on the user's electricity consumption time and the corresponding electricity consumption, combined with the residential electricity prices in different time periods (such as peak-hour electricity price and off-peak-hour electricity price, etc.), use the distributed photovoltaic intelligent analysis system to calculate the predicted electricity cost of the user within a predetermined time (such as one month or one year); that is, obtain the electricity bill price required for the residential electricity consumption equivalent to the user's own electricity consumption, so as to know the electricity price saved by the user through building the target photovoltaic power station for self-consumption with surplus power grid connection.

[0061] Since the traditional electricity price calculation model is often based on the simple relationship between power generation and user consumption ratio, and does not fully consider the peak-valley electricity price fluctuations in the power market, resulting in deviations in the calculation results. However, the embodiments of the present invention adopt a distributed photovoltaic intelligent analysis system that can accurately calculate the true electricity price cost of the user in different time periods according to the user's electricity consumption behavior data transmitted back by the electricity consumption analysis platform, so as to obtain the predicted user cost of the user within a predetermined time, thus greatly improving the accuracy and scientificity of the electricity price calculation.

[0062] Compare the traditional calculation method with the calculation method of the present invention through the following formula:

[0063] Traditional calculation method: electricity cost = user electricity consumption * average electricity price of the user. (Note: user consumption ratio = self - consumption of photovoltaic power generation / photovoltaic power generation amount);

[0064] Calculation method of the present invention: predicted electricity cost = user's own electricity consumption * residential electricity price in the corresponding area and corresponding time period.

[0065] For example: As shown in Figure 3 of a certain area, the residential electricity price has peak, valley, and flat periods, and the residential electricity prices in different time periods are different. Suppose a user in this area uses 100 degrees of electricity during peak hours and 100 degrees of electricity during valley hours. Then, the electricity costs calculated by the traditional calculation method and the calculation method of the present invention are as follows:

[0066] Traditional calculation result: electricity cost = user electricity consumption * average electricity price of the user

[0067] = 200 * 0.5380 = 107.6 yuan;

[0068] Calculation result of the present invention: predicted electricity cost = user's own electricity consumption * electricity price in this area during the corresponding time period = 100 * 0.5680 + 100 * 0.2880 = 85.6 yuan;

[0069] That is, according to the calculation result of the present invention, it can be known that the electricity price saved by the user using self - consumption and selling the surplus electricity to the grid is 85.6 yuan.

[0070] In some specific embodiments, step S3: Obtain the surplus power generation according to the user's own electricity consumption and the power generation amount of the target photovoltaic power station, and conduct power trading on the surplus power generation to obtain electricity sales revenue; specifically including:

[0071] Subtract the user's own electricity consumption from the power generation amount of the target photovoltaic power station to obtain the surplus power generation of the target photovoltaic power station;

[0072] As known from the foregoing description, the power generation of the target photovoltaic power station is preferentially supplied for the user's own electricity consumption. The user uses the electricity price configuration platform to sell the surplus power generation of the target photovoltaic power station through power spot trading to obtain electricity sales revenue.

[0073] Among them, the electricity sales revenue of a distributed photovoltaic power generation project is divided into two parts: on - grid electricity price and subsidy electricity price. Therefore, electricity sales revenue = (on - grid electricity price + subsidy electricity price) * surplus power generation.

[0074] The data information of the on - grid electricity price and the subsidy electricity price is updated through the electricity price configuration platform. Specifically: Expert personnel log in to and access the official website of the National Energy Administration every day, view the subsidy policies and on - grid electricity prices of new energy power stations in various regions, and then update the data on the electricity price configuration platform and display it on the distributed photovoltaic intelligent analysis system;

[0075] Meanwhile, the electricity price configuration platform will also act as an agent for wind farms, photovoltaic power stations, energy storage power stations, new energy storage combined power stations, electricity sales companies, etc. to participate in the electricity spot market transactions for electricity sales activities. While conducting electricity sales transactions, the database stores the electricity spot market prices for each region and each time period. The final electricity sales revenue obtained is sent to the financial evaluation system through the distributed photovoltaic intelligent analysis system for display.

[0076] Among them, the electricity spot market transaction price is usually higher than the on-grid electricity price from 9:00 to 11:00 and from 14:00 to 16:00 every day. This can provide users with methods and channels to obtain higher electricity sales revenue, as well as participate in the electricity spot market transactions, providing more investment reference value.

[0077] Through the adoption of the electricity price configuration platform in the embodiments of the present invention, the latest and most accurate subsidy policies in each region can be obtained, avoiding the electricity price prediction errors caused by policy changes, providing more reliable electricity price parameters for the financial evaluation of the target photovoltaic power station, and further improving the accuracy of the evaluation results; through the adoption of the electricity price configuration platform, the latest and most accurate on-grid electricity prices in each region can be obtained.

[0078] In some specific embodiments, step S4: According to the predicted electricity consumption cost and electricity sales revenue, obtain the predicted total revenue of the target photovoltaic power station (i.e., the financial evaluation price of the target photovoltaic power station);

[0079] The predicted total revenue of the target photovoltaic power station within a predetermined time = the predicted electricity consumption cost within the predetermined time + the electricity sales revenue within the predetermined time;

[0080] That is, the present invention can obtain the predicted total revenue of the target photovoltaic power station within one year, that is, the total revenue cost of the target photovoltaic power station in one year, by calculating the electricity price cost saved by the user's self-use and the electricity sales revenue within one year after building the target photovoltaic power station;

[0081] At the same time, according to the calculated total revenue cost of the target photovoltaic power station in one year, the total revenue cost of the target photovoltaic power station in the next ten or twenty years can be predicted, facilitating the user's accurate estimation of the revenue cost of the target photovoltaic power station and facilitating subsequent transactions of the target photovoltaic power station.

[0082] The embodiments of the present invention also provide a power station cost prediction system, as Figure 4 shown, including:

[0083] A determination unit, configured to monitor the power generation data of the target photovoltaic power station and analyze and determine the power generation amount of the target photovoltaic power station;

[0084] A calculation unit, configured to analyze the user's electricity consumption data to determine the user's own electricity consumption and calculate the user's predicted electricity consumption cost;

[0085] A trading unit is configured to obtain the remaining power generation based on the user's own power consumption and the power generation of the target photovoltaic power station, and conduct power trading on the remaining power generation to obtain revenue from selling electricity.

[0086] An obtaining unit is configured to obtain the predicted total revenue of the target photovoltaic power station based on the predicted power consumption cost and the revenue from selling electricity.

[0087] Regarding the system in the above embodiments, the specific manners in which each unit module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0088] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, the structure of which is as Figure 5 shown, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program or instruction to implement the foregoing power station revenue prediction method.

[0089] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, in which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the steps of the foregoing power station revenue prediction method are implemented.

[0090] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product, including a computer program or instruction. When the computer program or instruction is executed by a processor, the steps of the foregoing power station revenue prediction method are implemented.

[0091] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A power station income forecasting method, characterized in that: include: Monitor the power generation data of the target photovoltaic power station and analyze and determine the power generation of the target photovoltaic power station; Analyze the user's electricity consumption data to determine the user's own electricity consumption and calculate the user's predicted electricity cost; Obtain the remaining power generation based on the user's own power consumption and the target photovoltaic power station's power generation, and use the remaining power generation for power trading to obtain power sales income; The predicted total revenue of the target photovoltaic power station is obtained based on the predicted electricity cost and electricity sales revenue.

2. A power station income prediction method according to claim 1, characterized in that: Monitor the power generation data of the target photovoltaic power station and analyze and determine the power generation of the target photovoltaic power station, including: For the target photovoltaic power station in operation, the power generation of the target photovoltaic power station is determined according to the power generation data of the monitored target photovoltaic power station; For a target photovoltaic power station during the construction period, the power generation data of photovoltaic power stations near the target photovoltaic power station are monitored to analyze and obtain the effective utilization hours of the area where the target photovoltaic power station is located; based on the effective utilization hours, the power generation of the target photovoltaic power station is calculated and determined; The power generation data of the photovoltaic power station includes the real-time power generation and real-time power generation data of the photovoltaic power station; The target photovoltaic power station power generation=the effective utilization hours*the installed capacity of the target photovoltaic power station.

3. A power station income prediction method according to claim 2, characterized in that: Monitor the power generation data of photovoltaic power stations near the target photovoltaic power station to analyze and obtain the effective utilization hours of the area where the target photovoltaic power station is located, including: Real-time monitoring of power generation data of nearby photovoltaic power plants, and collection and analysis of meteorological and topographical conditions in the area where the target photovoltaic power plant is located; Based on the power generation data of nearby photovoltaic power stations, the meteorological conditions and terrain conditions of the area, a comprehensive analysis is conducted to obtain the available hours of the area where the target photovoltaic power station is located; According to the available hours of the target photovoltaic power station area, combined with the installed capacity of the target photovoltaic power station, the effective utilization hours of the target photovoltaic power station area are obtained; The effective utilization hours = installed capacity of the target photovoltaic power station * available hours.

4. A power station income prediction method according to claim 2, characterized in that: Analyze the user's electricity consumption data to determine the user's own electricity consumption and calculate the user's predicted electricity cost, including: Analyze the user's electricity consumption behavior data to obtain the user's electricity consumption time and corresponding electricity consumption; Based on the user's electricity usage time and corresponding electricity consumption, combined with the residential electricity prices in different time periods, calculate the user's predicted electricity cost within the scheduled time; The predicted electricity cost = the user's own electricity consumption * the residential electricity price in the user's area during the corresponding period of time.

5. A power station income prediction method according to claim 1, characterized in that: The remaining power generation is obtained based on the user's own power consumption and the target photovoltaic power station power generation, and the remaining power generation is used for power trading to obtain power sales income, including: The target photovoltaic power station is a distributed photovoltaic power station, and the operation mode of the target photovoltaic power station is self-generation and self-use, and the surplus power is connected to the grid; The power generated by the target photovoltaic power station is first used by the user, and the remaining power generated by the target photovoltaic power station is sold through power spot market transactions to obtain power sales income; Among them, electricity sales revenue = (on-grid electricity price + subsidized electricity price) * remaining power generation.

6. A power station income prediction method according to claim 1, characterized in that: The predicted total income of the target photovoltaic power station = the predicted electricity cost of the user * the income from electricity sales.

7. A power plant cost prediction system, characterized in that: include: A determination unit, used to monitor the power generation data of the target photovoltaic power station and analyze and determine the power generation of the target photovoltaic power station; A calculation unit, used to analyze the user's electricity consumption data to determine the user's own electricity consumption and calculate the user's predicted electricity cost; The trading unit is used to obtain the remaining power generation according to the user's own power consumption and the target photovoltaic power station's power generation, and to conduct power trading with the remaining power generation to obtain power sales income; The obtaining unit is used to obtain the predicted total income of the target photovoltaic power station according to the predicted electricity cost and electricity sales income.

8. An electronic device, characterized in that: It comprises a memory, a processor and a computer program stored in the memory, and the processor executes the computer program or instructions to implement a power station revenue forecasting method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program or instruction, and when the computer program or instruction is executed by a processor, a power station revenue prediction method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, a power station revenue prediction method as described in any one of claims 1-6 is implemented.

Images