JP2024059039A - Environmental value assessment system, environmental value assessment method, and program - Google Patents

Abstract

The present invention provides a system that enables electricity consumers to quantify the environmental value created by their electricity consumption.
[Solution] The system includes a data supply means for supplying power source type emission coefficient data indicating the amount of _CO2 emissions per unit of generated power for each power source type, power source type power generation amount data indicating the amount of power generated per unit time for each power source type in a group of power sources that supplies power to the area where the power consumer is located, and consumer power consumption data indicating the amount of power consumed per unit time by the power consumer, a processing means for calculating a consumer emission coefficient indicating the amount of _CO2 emissions per unit of power consumption by the power consumer based on the power source type emission coefficient data, the power generation amount data by power source type, and the consumer power consumption data, and a storage means for storing the power consumer in association with the calculated consumer emission coefficient.
[Selected Figure] Figure 6

Description

The present invention relates to technology that enables electricity consumers, electricity suppliers, etc. to calculate and evaluate the value of carbon dioxide emission reductions (environmental value) created by electricity consumption and generation.

In recent years, interest in environmental issues such as global warming has increased more than ever before due to the proposal for the Sustainable Development Goals (SDGs).

In this situation, people who find it difficult to engage in direct environmental activities can contribute to measures against global warming through environmental value trading, using offset certificates such as J-Credits, Green Power Certificates, and Non-Fossil Certificates, which embody the value of carbon dioxide ( _CO2 ) emission reductions.

In addition to using environmental value certificates, various technologies have been proposed to support various types of environmental value trading.

For example, Patent Document 1 proposes an environmental value trading system that can promote the effective use of unused energy generated in factories without going through a commercial power grid, and also stimulate trading of the environmental value obtained.

The inventor has also proposed an electricity trading system, etc., that effectively utilizes the environmental value of unsold renewable energy when renewable energy is left unsold in a transaction of electricity that includes renewable energy, as shown in Patent Document 2.

JP 2019-067250 A JP 2021-043669 A

However, the method of using environmental value offset certificates created in the past to provide renewable energy electricity rate options to electricity consumers and evaluating them as equivalent to the _CO2 emission reduction value of electricity actually generated from renewable energy sources makes it difficult to create an incentive to reduce electricity consumption during times when the proportion of renewable energy sources is actually low, such as at night, and to encourage a shift in electricity demand to times when the proportion of renewable energy sources is high.
In addition, in the current offset certificate trading, the value of _CO2 emission reductions achieved by installing power generation equipment using renewable energy on the premises of electricity consumers and self-consuming it is mainly recorded as J-Credits, while the value of _CO2 emission reductions from renewable energy sources circulating in the transmission and distribution network (power system) is recorded as Non-Fossil Certificates. These are not mutually interchangeable and are treated separately, with no consistency guaranteed and the system is complex, making it difficult for the general public to understand.
On the other hand, for renewable energy generators, under methods in which the environmental value is treated equally regardless of the time of generation, there is a tendency for power generation to be concentrated during the daytime on sunny days, for example. As a result, there has been a situation in which there is not enough incentive to encourage them to provide electricity during times when supply is short, for example by using storage batteries or electric vehicles, or by adopting methods that allow power generation at night.

In response to this, the inventors believed that if it were possible to quantify, using a unified index, the environmental value that ordinary electricity consumers can create through their electricity consumption, environmental value trading, including between individuals and between individuals and businesses, would be invigorated, and this would provide an incentive for electricity consumers to shift peak hours, reduce consumption during times of tight supply and demand (demand response), and engage in energy-saving behavior.
In addition, it was thought that if it were possible to quantify the rarity of value by time of day for generators using renewable energy and low-carbon power sources using a unified indicator, it would provide an incentive to promote the equalization of the supply of renewable energy electricity and low-carbon power sources.
We believe that it is necessary to further promote the use of renewable energy in an integrated supply and demand manner by having demand follow the supply of renewable energy electricity as closely as possible, and for the supply of renewable energy electricity to follow demand as closely as possible.

The present invention was made in consideration of the above-mentioned problems, and aims to provide a system that can quantify the environmental value that electricity consumers create through electricity consumption.

In order to solve the above problem, the present invention is characterized by comprising a data supply means for supplying power source type emission coefficient data indicating the amount of _CO2 emission per unit of generated power for each power source type, power source type power generation amount data indicating the amount of power generated per unit time for each power source type in a group of power sources that supplies power to an area where an electricity consumer is located, and consumer power consumption data (including power generation amount data within the electricity consumer's premises; the same applies below) indicating the amount of power consumed per unit time by the electricity consumer, a processing means for calculating a consumer emission coefficient indicating the amount of _CO2 emission per unit of power consumption by the electricity consumer based on the power source type emission coefficient data, the power generation amount data by power source type, and the consumer power consumption data, and a storage means for storing the electricity consumer in association with the calculated consumer emission coefficient.

In a preferred embodiment of the present invention, the processing means calculates a power source group emission coefficient indicating the amount of emissions per unit of generated power of the power source group based on the power source type emission coefficient data and the power generation amount data.

In a preferred embodiment of the present invention, the data supply means further supplies emission credit unit price data, and the processing means calculates the amount of emission credit purchase required by the electricity consumer associated with the consumer emission coefficient based on the power source group emission coefficient, the consumer emission coefficient, the emission credit unit price, and the consumer power consumption data.

In a preferred embodiment of the present invention, the system further includes a determination means for determining whether the electricity consumer is a supply consumer that generates its own electricity based on consumer power consumption and power generation data. When the determination means determines that the electricity consumer is a supply consumer, the processing means calculates leverage based on the supply consumer self-consumption amount, which indicates the amount of electricity that the supply consumer self-generated and self-consumed per unit time, and the supply consumer transmission and distribution network (system) power consumption, which indicates the net of the supply consumer's power consumption (purchased power amount) from the transmission and distribution network per unit time or the amount of surplus electricity supplied (sold power amount) to the transmission and distribution network, and calculates the leverage-adjusted time-of-day consumer _CO2 emissions for the unit time based on the leverage and the power source group power source emission coefficient.

In a preferred embodiment of the present invention, the processing means calculates the leverage-adjusted time-zone-specific consumer CO2 _emission reduction value for the unit time, including self-consumption, regardless of whether the difference obtained by subtracting the amount of electricity generated by the supply demander from the amount of electricity consumed by the supply demander is positive or negative.

In a preferred embodiment of the present invention, the processing means calculates the value of the leverage-adjusted time-zone-specific consumer _CO2 emissions in the unit time based on a predetermined value when the difference obtained by subtracting the amount of power generated by the supply demander from the amount of power consumed by the supply demander is zero.

The present invention also causes a computer to execute a data supply step of supplying power source type emission coefficient data indicating the amount of _CO2 emission per unit of generated power for each power source type, power source type power generation amount data indicating the amount of power generated per unit time for each power source type in a group of power sources that supplies power to an area where an electricity consumer is located, and consumer power consumption data indicating the amount of power consumed per unit time by the electricity consumer, a processing step of calculating a consumer emission coefficient indicating the amount of _CO2 emission per unit of power consumption by the electricity consumer based on the power source type emission coefficient data, the power generation amount data by power source type, and the consumer consumption/power generation amount data, and a storage step of storing the electricity consumer in association with the calculated consumer emission coefficient.

The present invention also includes a data supply means for supplying power source type emission coefficient data indicating the amount of _CO2 emissions per unit of generated power for each power source type, power source type power generation amount data indicating the amount of generated power per unit time for each power source type in a group of power sources that supplies power to an area where a power supplier that generates power using renewable energy power generation equipment or low-carbon power sources (low-carbon power sources are included in renewable energy power generation equipment; the same applies below) is located, and supplier power generation amount data indicating the amount of generated power per unit time by the power supplier, a processing means for calculating a renewable energy power supplier rarity coefficient indicating a weighted average of the rarity value of the time-zone-specific environmental value of electricity derived from renewable energy or low-carbon power sources supplied by the power supplier based on the power source type emission coefficient data, the power source type power generation amount data, and the supplier power generation amount data, and a storage means for storing the power supplier and the calculated supplier rarity coefficient in association with each other.

In a preferred embodiment of the present invention, the processing means calculates a power source group emission coefficient indicating the amount of _CO2 emissions per unit of generated power of the power source group, based on the power source type emission coefficient data and the power source type generated power amount data.

In a preferred embodiment of the present invention, the data supply means further supplies bonus unit price data, and the processing means calculates a bonus amount for the electricity supplier associated with the supplier rarity coefficient based on the power source group emission coefficient, the supplier rarity coefficient, and the bonus unit price data.

The present invention also causes a computer to execute a data supply step of supplying power source type emission coefficient data indicating the amount of _CO2 emissions per unit of generated power for each power source type, power source type power generation amount data indicating the amount of generated power per unit time for each power source type in a group of power sources that supplies power to a location area of a power supplier that generates power using renewable energy power generation equipment, and supplier power generation amount data indicating the amount of power generated per unit time by the power supplier, a processing step of calculating a supplier rarity coefficient indicating the rarity of the _CO2 emission reduction value created in the group of power sources per unit of generated power by the power supplier based on the power source type emission coefficient data, the power source type power generation amount data, and the supplier power generation amount data, and a storage step of associating and storing the power supplier with the calculated supplier rarity coefficient.

The present invention also causes one or more computers to function as any of the above environmental value assessment systems.

The present invention provides a system that allows electricity consumers to quantify the environmental value they create through electricity consumption and generation.

1 is a diagram showing an example of a network configuration to which an environmental value assessment system according to an embodiment of the present invention is connected; 1 is a diagram showing a configuration of an environmental value assessment system according to an embodiment of the present invention. FIG. 4 is a diagram showing an example of power generation amount data by power source type according to the embodiment of the present invention. FIG. 2 is a diagram showing an example of consumer consumption and power generation amount data according to the embodiment of the present invention. 5 is a diagram showing an example of data generated in the process of a consumer emission coefficient processing and a power source group emission coefficient calculation processing according to the embodiment of the present invention. FIG. 4 is a flowchart illustrating an example of a demander emission coefficient calculation process according to the embodiment of the present invention. 10 is a flowchart illustrating an example of a power source group emission coefficient calculation process according to the embodiment of the present invention. 4 is a flowchart showing an example of a process for calculating a required amount of emission credits to be purchased according to the embodiment of the present invention. FIG. 11 is a diagram showing an example of data that may be generated in the course of supply demander processing according to an embodiment of the present invention. 11 is a flowchart illustrating an example of a supply demander process according to an embodiment of the present invention. FIG. 4 is a diagram showing an example of supplier power generation amount data according to the embodiment of the present invention. A figure showing an example of data generated during the supplier rarity coefficient calculation process in an embodiment of the present invention. 13 is a flowchart illustrating an example of a supplier rarity coefficient calculation process according to an embodiment of the present invention. 13 is a flowchart showing an example of a reward amount calculation process according to the embodiment of the present invention.

An environmental value assessment system according to an embodiment of the present invention will be described below with reference to Figures 1 to 14. Note that the embodiment shown below is an example of the present invention, and the present invention is not limited to the following embodiment, and various configurations can be adopted.
In the following explanation, renewable energy will be referred to as "renewable energy." Note that "renewable energy" includes low-carbon energy sources.

For example, in this embodiment, the configuration, operation, etc. of a video sharing system will be described, but a method, device, computer program, etc. with a similar configuration can also achieve the same effects. The program may also be stored on a recording medium. By using this recording medium, the program can be installed on a computer, for example. Here, the recording medium on which the program is stored may be a non-transitory recording medium, such as a CD-ROM.

1 is a diagram showing an example of the configuration of a network to which an environmental value assessment system X is connected. Note that the dashed dotted lines connecting each component indicate the flow of electricity transmission and distribution, and the dotted lines connecting each component indicate the flow of signals and data.
Here, the signal and data flows indicated by the dotted lines may be wired or wireless.
In addition, for some components, the symbol "..." in the drawing indicates that there may be a plurality of similar components other than those shown in the drawing.

The power transmission and distribution networks PNW1 and PNW2 are networks that connect power plants and power consumers in respective predetermined regions.
For example, in Japan, electricity transmission and distribution networks in areas where different electricity transmission and distribution companies are in charge, such as Hokkaido, Tohoku, Tokyo, Chubu, Hokuriku, Kansai, Chugoku, Shikoku, Kyushu, and Okinawa, can be divided into PNW1, PNW2, etc.
In some cases, the power transmission and distribution network may be redefined in categories different from those described above.
In addition, there may be power transmission and distribution networks other than PNW1 and PNW2.

The power transmission and distribution network PNW1 is connected to a power source group PSG having various power sources such as a thermal power plant PS1, a pumped storage power plant PS2, a nuclear power plant PS3, and a renewable energy power plant PS4, and transmits and distributes the electricity generated by each power source.
The power supply group PSG may have power supplies other than the above-mentioned PS1 to PS4.

The renewable energy power plant PS4 refers to a power source capable of generating electricity without (or with little) _CO2 emissions, and specifically refers to a power plant that generates electricity using energy generated by solar, wind, biomass, hydroelectric, etc. Note that a function of storing and releasing renewable energy power generation in a storage battery or an electric vehicle, etc., or a pumped-storage power plant that uses hydroelectric energy pumped up by electricity from a renewable energy power plant may also be included in the renewable energy power plant in whole or in part.

The power transmission and distribution network PNW1 is connected to other power transmission and distribution networks PNW2, etc., via interconnection lines, and can share electricity with each other.

As described above, electricity is distributed to each electricity consumer including electricity consumers C1 to C3 through the electricity transmission and distribution network PNW1.
It should be noted that there may be many power consumers other than C1 to C3.

The power consumers consume the power distributed from the power transmission and distribution network PNW1 through their respective activities. For example, general households such as the power consumers C1 and C2 may consume power in connection with their daily lives, and businesses such as the power consumer C3 may consume power in connection with their business activities.
In addition, the power consumer can return the excess power generated by the solar power generation facility SP installed on the premises of the consumer (on-site) to the power transmission and distribution network PNW1 (sell the power by reverse power flow) even after the consumer consumes the excess power. (For example, the power consumer C2)

The status of the above-mentioned power consumption, private power generation, etc. is transmitted to the communication network DNW at regular intervals (e.g., every 30 minutes, every 60 minutes, etc.) by smart meters SM1 to SM3, HEMS (Home Energy Management System) devices H installed on the premises of the power consumer, measuring devices using power conditioners, etc., and is stored in the database DB1.
Database DB1 is, for example, a database managed by a retail electricity supplier, and stores data including the amount of power consumed and the amount of self-generated power per unit time for each consumer, in association with the ID, etc., of each electricity consumer.

In addition, in the various power sources PS1 to PS4 in the power source group PSG mentioned above, power generation records, charging/discharging records, and power linkage records are transmitted to the communication network DNW at regular intervals (for example, every 30 minutes, every 60 minutes, etc.) and stored in the database DB2.
The database DB2 is, for example, a database managed by an electricity transmission and distribution company or the like, and stores data including the amount of power generated per unit time for each type of power source.

The communications network DNW can provide access to databases of various related organizations in addition to the above-mentioned databases DB1 and DB2.
For example, it may be possible to optionally access the database of the Organization for Cross-regional Coordination of Transmission Operators of Japan (OCCTO) or the database of the Japan Electric Power Exchange (JEPX).

A terminal group DG including terminals D1 to D4, etc., is connected to the communications network DNW, and the terminals D1 to D4, etc. included therein can constitute an environmental value assessment system X either individually or in cooperation with each other.
That is, the terminals D1 to D4, etc. can constitute the elements of a data supplying means 1, a processing means 2, a determining means 3, and a storage means 4, which will be described later, either alone or in cooperation with one another.

The terminals D1 to D4, etc. included in the terminal group DG may be any computer device, such as a personal computer, a server computer, a smartphone, a tablet, etc. Furthermore, the physical locations of the terminals D1 to D4, etc. included in the terminal group DG are not limited, and they may be distributed across multiple locations or centrally located in one location.

As shown in FIG. 2, the environmental value assessment system X includes a data supply means 1, a processing means 2, and a determination means 3. It may further include a storage means 4.

For each of the data supply means 1, the processing means 2, the determination means 3, and the storage means 4, any computer device (personal computer, server computer, smartphone, tablet, etc.) or its components can be appropriately used, including a calculation device such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), a main memory device such as a RAM (Random Access Memory), an auxiliary memory device such as a HDD (Hard Disk Drive), an SSD (Solid State Drive), or a flash memory, and various input/output devices including a means for connecting to a network.

The data supply means 1 utilizes the communication network DNW to supply various data to the environmental value assessment system X, including power source type _CO2 emission coefficient data indicating the amount of _CO2 emission per unit of generated power for each power source type, power source type generated power amount data indicating the amount of power generated per unit time for each power source type in a group of power sources that supplies power to the area where the power consumer is located, and consumer power consumption/generated power amount data indicating the amount of power consumed/generated power consumed per unit time for the power consumer.

The "data supply" process performed by the data supply means 1 includes collection of desired data on the network, prediction, estimation, and calculation of desired data, reception of input by the user, referencing stored predetermined values, etc.

The processing means 2 calculates a consumer CO2 emission coefficient indicating the amount of _CO2 emissions per unit of power consumption of the electricity consumer based on the power source type _CO2 emission coefficient data, the power generation amount data by power source type, and the consumer power consumption and _power generation amount data, and also performs various other arithmetic processing.

The determination means 3 determines whether the electricity consumer is a supply consumer that generates and consumes electricity in-house using renewable energy power generation facilities. This determination can be made based on a label indicating the supply consumer associated with the ID of each electricity consumer, the electricity generation and sales records of each electricity consumer, the power consumption data of each electricity consumer (e.g., whether there are time periods when the power consumption is negative), etc.

The storage means 4 can store data supplied by the data supply means 1, processing results by the processing means 2, etc.

FIG. 3 is a diagram illustrating an example of power generation amount data by power source type.
As shown in FIG. 3, the data on the amount of power generated by power source type is stored as data on the amount of power generated by power source type in each time period on each day as shown in columns CL3 to CL10, in association with at least the date shown in column CL1 and the time period shown in column CL2.
For example, in the example of Figure 3, it can be seen from the data that as of 11:00 on April 1, 2022, 23.92 million kWh of thermal power generation, 1.28 million kWh of hydroelectric power generation, 360,000 kWh of biomass power generation, 8.06 million kWh of solar power generation, and 180,000 kWh of wind power generation were generated.

Returning to the example in FIG. 1, the power generation performance of each type of power source at regular intervals for each of the various power sources PS1 to PS4 in the power source group PSG is transmitted to the communication network DNW and stored as needed in the database DB2 of the power transmission and distribution company in charge of the power source group PSG. Therefore, the data supply means 1 can collect such power generation data by type of power source by any method, such as API linkage via the communication network DNW or CSV file transfer.

FIG. 4 is a diagram illustrating an example of consumer power consumption data.
As shown in Figure 4 (A), the consumer power consumption data stores data on the power consumption of each consumer during a given time period on a given day as shown in column CL13, in association with at least a consumer ID shown in row RW1, a date shown in column CL11, and a time period shown in column CL12.
For example, in the example of Figure 4 (A), it can be read from the data that the electricity consumer with consumer ID 000000001 consumed 10 kWh of electricity in the 5:00 am hours, 20 kWh in the 6:00 am hours, 15 kWh in the 7:00 am hours, 10 kWh in the 17:00 pm hours, and 25 kWh in the 18:00 pm hours on April 1, 2022 (column CL13).

Returning to the example of Fig. 1, the power consumption status of the power consumer C1 at regular intervals is transmitted by the smart meter SM1 to the communication network DNW at regular intervals and stored in the database DB1 as needed. This is also true for the power consumers C2, C3, etc.
Therefore, the data supply means 1 can collect consumer power consumption and power generation data associated with each power consumer by any method such as API linkage or CSV file transfer via the communication network DNW.

Furthermore, as shown in FIG. 4(B), in the case where the electricity consumer is a supply consumer who generates electricity in-house using renewable energy power generation equipment, the consumer power consumption/power generation data may include a label L, a record of the amount of electricity generated (column CL16), a record of the amount of grid power consumption which is the difference between the amount of electricity consumed and the amount of electricity generated (column CL18), and a record of the amount of electricity purchased/sold (columns CL19, CL20).
From the label L, the amount of generated electricity, the amount of electricity consumed in the transmission and distribution network (system), and records of the amount of electricity purchased and sold, it can be shown that the electricity consumer associated with this is a supply consumer who generates electricity at home.

Specific processes performed in the environmental value assessment system X will be described below with reference to FIGS.
The order of each process flow in the flowchart shown below can be changed, integrated, or divided as appropriate without impairing the function or results.
Furthermore, the values calculated during the process can be appropriately stored in the storage means 4 and used in subsequent processes.

First, the demander emission coefficient calculation process shown in FIG. 6 will be described.
6, first, the processing means 2 requests and refers to necessary data from the data supply means 1 (step S11). At least the power generation amount data by power source type (illustrated in FIG. 3), the consumer power consumption amount data (illustrated in FIG. 4), and the emission coefficient data by power source type are requested and referred to.

The emission coefficient data by power source type indicates the amount of _CO2 emissions per unit of power generation for each power source type, and is data that does not usually fluctuate greatly. Therefore, in this embodiment, the specified values of 0.8 (kg- _CO2 /kWh, units omitted below) for thermal power, 0 for hydroelectric power, 0 for biomass power, 0 for solar power, and 0 for wind power are used as shown in Figure 5.
The emission factor data by power source type is not limited to the above-mentioned predetermined values, and can naturally be estimated values, measured values, calculated values, etc., including the amount of charging and discharging using the power storage function, data on the entire life cycle including not only power generation but also manufacture and disposal. In addition, estimated values, measured values, calculated values, etc. can naturally be used for the emission factors of pumped storage power generation and the emission factors of interchanged power from other networks.
Furthermore, even for the same power source type, the emission coefficients differ for each power plant, depending on the power generation method, fuel, etc., and the power source type may be subdivided and a coefficient may be set for each type.

Next, the processing means 2 calculates the total amount of generated power by time period for the power source groups based on the power source type generated power amount data (step S12).
At this time, the amount of generated power from a power generation system such as a solar power generation system installed on the premises of the power consumer may be included.
Specifically, using Figure 5 as an example, at midnight on April 1, 2022, thermal power generation was 20.65 million kWh, hydroelectric power generation was 1.33 million kWh, biomass power generation was 370,000 kWh, and wind power generation was 170,000 kWh.
Then, the total power generation amount by power source group time period during the 0:00 hours on April 1, 2022 is calculated using the following formula.

The processing means 2 similarly calculates the total amount of generated power by power source group time period for each day and each time period for a certain period (one day, one month, one year, etc.) (FIG. 5, column CL21).
In this embodiment, the calculation period is specified as one day.

Next, the processing unit 2 calculates the CO ₂ emission amounts by time period for each power source group based on the power source type emission coefficient data and the power generation amount data by power source type (step S13).
The CO ₂ emission amounts by time period for each power source group are calculated by multiplying the emission coefficient of each power source type by the amount of power generated by each power source type, and then summing them up.
Specifically, using Figure 5 as an example, at midnight on April 1, 2022, thermal power generation was 20.65 million kWh, hydroelectric power generation was 1.33 million kWh, biomass power generation was 370,000 kWh, and wind power generation was 170,000 kWh.
Using the emission coefficients for each power source type, which are thermal power 0.8 (kg- _CO2 /kWh, units omitted below), hydroelectric power 0, biomass 0, solar power 0, and wind power 0, the _CO2 emissions by power source group time period during the midnight hours on April 1, 2022 can be calculated using the following formula.

The processing means 2 similarly calculates the amount of _CO2 emissions by power source group time period for each day and each time period for a certain period (one day, one month, one year, etc.) (FIG. 5, column CL22).
In this embodiment, the calculation period is one day.

Next, the processing means 2 calculates the emission coefficient by time period for the power source groups based on the total generated electric power amount by time period for the power source groups and the _CO2 emission amount by time period for the power source groups (step S14).
The emission coefficient for each time period of the power source group is calculated by dividing the _CO2 emission amount for each time period of the power source group by the total amount of power generated for each time period of the power source group.
Specifically, using Figure 5 as an example, at midnight on April 1, 2022, the _CO2 emissions by power source group time period were calculated to be 16.52 million kg, and the total power generation by power source group time period was calculated to be 22.52 million kWh.
Then, the emission coefficient by time period for each power source group at midnight on April 1, 2022 is calculated using the following formula.

The processing means 2 similarly calculates the emission coefficient by time period for each power source group for each day and time period for a certain period (one day, one month, one year, etc.) (FIG. 5, column CL23).
In this embodiment, the calculation period is one day.

Next, the determination means 3 determines, based on the consumer power consumption data, whether or not the power consumer is a supply consumer that generates private power using renewable energy power generation equipment (step S15).
If it is determined that the power consumer is a supply consumer (Y in S15), the process proceeds to supply consumer processing (step S40). The details of the supply consumer processing will be described later.
If it is determined here that the power consumer is not a supply consumer (N in S15), the process proceeds to step S16).

Next, the processing means 2 calculates the consumer's CO ₂ emission amounts by time period based on the consumer power consumption data and the power source group time period emission coefficients (step S16).
Specifically, in the example of FIG. 5, consumer A consumes 10 kWh of electricity in the 5:00 hour on April 1, 2022 (column CL24).
In this case, the amount of _CO2 emissions that is considered to have been indirectly emitted by consumer A through electricity consumption during the 5:00 a.m. hour on April 1, 2022 can be expressed as the product of the power source group time-specific emission coefficient for the 5:00 a.m. hour on April 1, 2022 and the amount of electricity consumed by consumer A, and is calculated using the following formula.

Similarly, the processing means 2 calculates the amount of _CO2 emission by time period of the consumer A in each time period of each day for a certain period (one day, one month, one year, etc.) (FIG. 5, column CL25).
In this embodiment, the calculation period is one day.

Next, the processing means 2 calculates the periodic energy consumption of the consumer based on the consumer energy consumption data (step S17).
The periodic power consumption of a consumer is the total amount of power consumed by the consumer within a certain period of time.
Specifically, using Figure 5 as an example, on April 1, 2022, consumer A consumes 10 kWh of electricity in the 5:00 am hour, 20 kWh in the 6:00 am hour, 15 kWh in the 7:00 am hour, 10 kWh in the 17:00 pm hour, and 25 kWh in the 18:00 pm hour (column CL24).
Then, on April 1, 2022, consumer A's periodic power consumption (per day) is calculated using the following formula.

Next, the processing means 2 calculates the periodic CO ₂ emission amount of the consumer based on the consumer power consumption data (step S18).
The periodic CO ₂ emission amount of a consumer is the total amount of CO ₂ emission that is considered to have been indirectly emitted by the consumer through electricity consumption within a certain period of time.
Specifically, using Figure 5 as an example, it is calculated that on April 1, 2022, consumer A will indirectly emit 7.4 kg of _CO2 in the 5:00 am hour, 14.8 kg in the 6:00 am hour, 10.95 kg in the 7:00 am hour, 7.3 kg in the 5:00 pm hour, and 18.75 kg in the 6:00 pm hour through electricity consumption (column CL25).
Then, on April 1, 2022, consumer A's periodic _CO2 emissions (daily) are calculated using the following formula.

Finally, the processing means 2 calculates the consumer emission coefficient for the period based on the consumer's period power consumption amount and the consumer's period CO ₂ emission amount (step S19).
The consumer emission coefficient is calculated by dividing the consumer's periodic _CO2 emission amount by the periodic power consumption amount.
Specifically, using Figure 5 as an example, on April 1, 2022, consumer A's periodic _CO2 emissions (daily) are calculated to be 59.2 kg/day, and periodic power consumption (daily) is calculated to be 80 kWh/day.
Then, the consumer emission coefficient for consumer A on April 1, 2022 is calculated using the following formula.

The consumer emission coefficient calculated in this manner represents the weighted average amount of _CO2 emitted from a group of power sources, such as power plants, to generate 1 kWh of electricity used by the consumer, and can be stored in the memory means 4 in association with the consumer.
Similarly, a consumer emission coefficient can be calculated for each consumer and stored in the storage means 4 in association with each consumer.

The smaller the consumer emission coefficient, the more electricity the consumer consumed during times when the proportion of electricity generated by renewable energy sources in the power source group in the consumer's location was higher (times when _CO2 emissions from power generation were relatively low), and therefore the consumer can be said to have made a greater contribution to the environment.
In this way, the low cost of _CO2 emissions per unit of power consumption of each consumer and its relative advantage over other consumers, that is, the environmental value created by the consumer, can be quantified and compared for evaluation.
For example, the consumer emission coefficient for consumer B is calculated in a similar manner to be 0.51 (kg/kWh), so it can be said that consumer B's electricity consumption pattern is more environmentally friendly than that of consumer A, who has a consumer emission coefficient of 0.74.

Next, the power source group emission coefficient calculation process shown in FIG. 7 will be described.
7, first, the processing means 2 requests and refers to necessary data from the data supplying means 1 (step S21). At least the power generation amount data by power source type (illustrated in FIG. 3) and the emission coefficient data by power source type are requested and referred to.

Next, the processing unit 2 calculates the total amount of generated power by time period for the power source groups based on the power source type generated power amount data (step S22, Equation 1).
The processing means 2 similarly calculates the total amount of generated power by power source group time period for each day and each time period for a certain period (one day, one month, one year, etc.) (FIG. 5, column CL21).
In this embodiment, the calculation period is one day.

Next, the processing means 2 calculates the periodic power generation amount of the power sources based on the time-zone-specific total power generation amount of the power sources in each time zone on each day (step S23).
The periodic power generation amount of a group of power sources is the sum of the amounts of power generated by the group of power sources within a certain period of time.
Specifically, in the example of Fig. 5, on April 1, 2022, the amount of power generated by the power source group is calculated as 2252 kWh at 0:00, 2148 kWh at 1:00, 2170 kWh at 2:00, ... and 2790 kWh at 23:00 (column CL21).
Then, on April 1, 2022, the periodic power generation amount (per day) of the power source group is calculated using the following formula.

Next, the processing unit 2 calculates the time-slot-specific CO ₂ emission amounts for each power source group based on the power generation amount data by power source type and the emission coefficient data by power source type (step S24, Equation 2).
The processing means 2 similarly calculates the amount of _CO2 emissions by power source group time period for each day and each time period for a certain period (one day, one month, one year, etc.) (FIG. 5, column CL22).
In this embodiment, the calculation period is one day.

Next, the processing means 2 calculates the periodic CO ₂ emission amount of the power source group based on the time-zone-specific CO ₂ emission amount of the power source group in each time zone on each day (step S25).
The periodic CO ₂ emission amount of a power source group is the sum of the CO ₂ emissions due to the electricity generated by the power source group within a certain period of time.
Specifically, in the example of Figure 5, on April 1, 2022, the _CO2 emissions associated with power generation from the power source group are calculated as follows: 16.52 million kg at 0:00, 15.67 million kg at 1:00, 15.92 million kg at 2:00, ... 20.98 million kg at 23:00 (column CL22).
Then, the periodic CO ₂ emissions (daily) of the power source group on April 1, 2022 is calculated using the following formula.

Finally, the processing means 2 calculates the power source group emission coefficient for that period based on the periodic power generation amount of the power source group and the periodic CO ₂ emission amount of the power source group (step S26).
The power source group emission coefficient is calculated by dividing the periodic _CO2 emission amount of the power source group by the periodic generated electric power amount.
Specifically, using Figure 5 as an example, on April 1, 2022, the periodic _CO2 emissions (daily) of the power source group are calculated to be 475.93 million kg/day, and the periodic power generation amount (daily) is calculated to be 701.62 million kWh/day.
The power source group emission coefficient for April 1, 2022 is calculated using the following formula.

The power source group emission coefficient thus determined represents the amount of _CO2 emitted by the power source group to generate 1 kWh of electricity, and can be stored in the storage means 4 in association with the power source group.

The above-mentioned consumer emission coefficient and the above-mentioned power source group emission coefficient can be compared. The smaller the consumer emission coefficient is compared to the power source group emission coefficient, the more likely it is that the consumer consumed electricity during a time period when the proportion of electricity generated by renewable energy sources in the power source group in the consumer's location was greater (time periods when _CO2 emissions from electricity generation were relatively low). Therefore, the consumer's environmental contribution, i.e., the environmental value created by the consumer, can be evaluated using the power source group emission coefficient as a baseline.

For example, the emission credit purchasing amount calculation process shown in FIG. 8 will be described.
8, first, the processing means 2 requests and refers to necessary data from the data supply means 1 (step S31). At least the consumer emission coefficient of each consumer, the power source group emission coefficient, the emission credit unit price data, and the consumer power consumption data are requested and referred to.
The emission rights unit price data may be data indicating the trading price per unit of _CO2 emission associated with a date and time period, or a value indicating the average trading price per unit of _CO2 emission over a specified period, and may be a specified value, a calculated value, a value collected over a network, etc.

Next, the processing means 2 calculates excess emission coefficient points based on the consumer emission coefficient and the power source group emission coefficient (step S32).
The excess emission coefficient points are calculated by multiplying the difference between the consumer emission coefficient and the power source group emission coefficient by a predetermined multiplier. For example, the excess emission coefficient points for consumer A are calculated using the following formula.

Next, the processing means 2 calculates the consumer-specific emission credit unit price based on the emission credit unit price data and the excess emission coefficient points (step S33).
Specifically, for example, when a value of 2 yen/point is given as the emission allowance unit price data, the consumer-specific emission allowance unit price for consumer A is calculated by the following formula.

Finally, the processing means 2 calculates the amount of emission credits required for each consumer for a certain period of time based on the consumer-specific emission credit unit price and the consumer power consumption data (step S34).
Specifically, using Figure 5 as an example, on April 1, 2022, consumer A consumes 10 kWh of electricity in the 5:00 am hour, 20 kWh in the 6:00 am hour, 15 kWh in the 7:00 am hour, 10 kWh in the 17:00 pm hour, and 25 kWh in the 18:00 pm hour (column CL24).
Then, the required amount of emission rights purchase for consumer A on April 1, 2022 (per day) is calculated using the following formula.

Similarly, the amount of emission credits required for consumer B is calculated to be -1,700 yen, and consumer B has created environmental value of 1,700 yen through environmentally friendly electricity consumption.
Then, consumer B can sell this environmental value to consumer A or to the emission rights market.

In this way, the consumer's environmental contribution, i.e., the environmental value created by the consumer, can be evaluated using the power source group emission coefficient as a baseline, and environmental value trading can be promoted. By visualizing the value of peak shifting and demand response (consumption adapted to renewable energy power generation) by electricity consumers, incentives can be created. By improving the ability to match electricity demand with renewable energy supply, the flexibility of renewable energy power generation can be increased, and as a result, it can contribute to the expansion of the introduction of renewable energy power generation.
In addition, by visualizing that the reduction of _CO2 emissions from electricity consumption is brought about by a combination of not only a reduction in the amount of electricity consumed but also an improvement in efficiency, i.e., the amount of _CO2 emissions reduced per unit of electricity consumed, this is expected to become an incentive to promote more effective energy conservation. In terms of automobile driving, this is similar to the visualization of the reduction in gasoline consumption brought about by both a reduction in mileage and an improvement in fuel efficiency per unit mileage, which has established eco-driving promotion in the public's consciousness.

In the same way, the amount required to purchase emission rights for each consumer can be calculated for each fixed period and stored in the storage means 4 in association with each consumer.

Next, the supply and demand processing shown in step S40 of FIG. 6 will be explained using FIG. 9 and FIG. 10.

As shown in FIG. 10, first, the processing means 2 requests and references the necessary data from the data supply means 1 (step S41). Here, at least the emission coefficients by time period for each power source group and the consumer consumption and power generation data of the supply consumer (power consumption data of the supply consumer) are requested and referenced.

Next, the processing means 2 calculates the supply demander's system power consumption (amount of power bought and sold) based on the amount of power generated by the supply demander and the amount of power consumed by the supply demander for each day and each time period (step S42).
Grid power consumption is the amount of power consumed by a supply consumer after receiving power transmitted and distributed from a power transmission and distribution network, and can be calculated as the difference between the amount of power consumed and the amount of power generated.
Specifically, taking FIG. 9 as an example, at around 10:00 on April 1, 2022, the amount of power generated by supply demander C is 10 kWh and the amount of power consumed is 8 kWh (columns CL32, CL33).
Then, the grid power consumption of supply demander C at 10:00 on April 1, 2022 is calculated using the following formula.

Next, the processing means 2 judges whether the grid power consumption in the time slot is zero (step S43). If the grid power consumption in the time slot is zero (Y in S43), the leverage may not be calculated, and the time slot _CO2 emission amount after the leverage adjustment may be calculated by multiplying the power generation amount in the time slot of the supply demander by the power source group emission coefficient in the time slot and -1 (step S47). If the grid power consumption in the time slot is not zero (N in S43), the leverage is calculated based on the grid power consumption and the power generation amount in the time slot (step S45).

For example, since the grid power consumption of the supply demander C is 0 kWh in the 13:00 hours on April 1, 2022, the processing means 2 may calculate the leverage-adjusted time-slot CO2 emissions by multiplying the _power generation amount of the supply demander in that time slot by the power source group emission coefficient for that time slot and -1 (step S47) without calculating the leverage. In this way, division by zero in step S45 described below is prevented.

For example, at around 10:00 on April 1, 2022, the grid power consumption of supply demander C is -2 kWh, which is not zero, so the processing means 2 calculates the leverage for that time period based on the supply demander's self-power consumption (defined as the power consumption if the amount of power generated exceeds the amount of power consumed, and as the amount of power generated otherwise) for each day and each time period and the supply demander's grid power consumption (step S45).
The leverage may represent the ratio of the sum of the grid power consumption and the value obtained by multiplying the self-power consumption of the supply demander in the relevant time period by −1 relative to the grid power consumption.
Specifically, in the example of FIG. 9, at around 10:00 on April 1, 2022, the power generation amount of supply demander C is 10 kWh and the grid power consumption amount is −2 kWh (columns CL32, CL34).
Then, at around 10:00 on April 1, 2022, the leverage of supply demander C is calculated using the following formula.

Alternatively, when the grid power consumption is negative, that is, when power is being sold, the value of the power sold may be evaluated higher than the power consumed at home, and the formula for calculating the leverage may be the following formula instead of the above. Also, the formula for calculating the leverage may be other than the above.

Next, the processing means 2 calculates a leverage-adjusted time-zone-specific CO ₂ emission coefficient based on the leverage and the power source group time-zone-specific emission coefficient for each day and each time zone (step S46).
The leverage-adjusted time-zone specific CO ₂ emission coefficient is calculated by multiplying the leverage and the power source group time-zone specific CO ₂ emission coefficient for that time zone.
Specifically, in the example of FIG. 9, in the 10:00 hour on April 1, 2022, the leverage of supply demander C is 5, and the time-of-day CO ₂ emission coefficient for the power source group is 0.64 kg/kWh (columns CL35 and CL31).
Then, the leverage-adjusted time-specific _CO2 emission coefficient for the 10:00 a.m. hour on April 1, 2022 is calculated using the following formula.

Finally, the processing means 2 calculates the leverage-adjusted time-zone-specific CO ₂ emission amount based on the grid power consumption amount and the leverage-adjusted time-zone-specific CO ₂ emission coefficient for each day and each time zone (step S47).
The leverage-adjusted time-of-day CO ₂ emission amount is calculated by multiplying the grid power consumption amount in the time period by the leverage-adjusted time-of-day CO ₂ emission coefficient.
Specifically, in the example of Figure 9, during the 10:00 hour on April 1, 2022, the grid power consumption of supply demander C is -2 kWh, and the leverage-adjusted time-of-day _CO2 emission coefficient is 3.2 kg/kWh (columns CL35 and CL31).
Then, the leverage-adjusted time-specific _CO2 emissions of supply demander C in the 10:00 a.m. hour on April 1, 2022 are calculated using the following formula.

In this way, supply and demand parties that generate their own electricity using renewable energy will be able to easily create significant environmental value, since the portion of the electricity they generate that they consume will also be evaluated as leverage.
In addition, it will become possible to calculate, evaluate, and trade in an integrated manner the value of _CO2 emission reductions from self-consumption and the renewable energy value of grid electricity, which have previously been treated separately.
This increases the incentive for electricity consumers to become supply consumers that generate their own power, and provides an incentive for electricity consumers to introduce on-site renewable energy power generation equipment, such as solar panels, on their premises.

Furthermore, for example, even if the grid power consumption is a positive value during the 11:00, 12:00, and 14:00 hours on April 1, 2022, as long as that amount is less than the self-generated power consumption, the leverage-adjusted time-specific _CO2 emissions will be calculated as a negative value.
This is based on the positive/negative inversion when calculating the leverage (step S45) and the multiplication by the grid power consumption (step S47). Regardless of whether the grid power consumption is positive or negative, the time-specific _CO2 emissions after the leverage adjustment are calculated as negative values.

In other words, even if a consumer consumes more electricity than it can generate in-house, the system is designed to recognize environmental value (negative _CO2 emissions) in the fact that part of the electricity consumed is covered by in-house generation.
This will further increase the incentive for electricity consumers to become supply consumers that generate their own electricity, and will provide electricity consumers with an incentive to introduce on-site renewable energy power generation facilities such as solar power generation panels.

In the case of a supplying and demanding party, an adjusted demanding party emission coefficient is calculated in steps S17 to S19 of FIG. 6 based on the leverage-adjusted time-of-day CO ₂ emission amount calculated as described above.

Below, another embodiment of the environmental value assessment system X will be explained using Figures 11 to 14.

Even the same renewable energy power plants have different contributions to increasing the renewable energy ratio in the entire power system. For example, in the Kyushu region, during the daytime hours on sunny weekends in spring and autumn when electricity demand is relatively low, the large amount of solar power plants installed generate electricity that greatly exceeds demand, and because electricity is basically difficult to store, and because of issues such as the low load-following ability (flexibility) of renewable energy power plants, there are cases where power generation is stopped by output curtailment.
Alternatively, in extreme cases, all power sources that emit _CO2 , such as thermal power plants that use fossil fuels, could be shut down, and the only electricity flowing into the power transmission and distribution network and grid system would come from renewable energy sources, resulting in the _CO2 emission coefficient of the entire network becoming zero.
In such cases, even if additional solar power plants are constructed, the additional _CO2 emission reduction per kWh of new power generation in the power transmission and distribution network/grid system will be zero or extremely small. In other words, the contribution of a newly started renewable energy power plant to increasing the renewable energy ratio in the entire power system per kWh of power generation tends to decrease relatively as the _CO2 emission coefficient of the entire network decreases.

On the other hand, for example, at night when solar power generation is not being used, the ratio of fossil fuel power generation increases, and the emission factor also tends to increase accordingly. During these times, renewable energy sources become more scarce.
In such cases, the contribution of 1 kWh of electricity generated at a renewable energy power plant to increasing the renewable energy ratio in the entire power system tends to increase the higher the emission coefficient due to its rarity.
The same applies not only to renewable energy sources, whose emission coefficients can be zero, but also to low-carbon power generators, whose emission coefficients are lower than the standard, such as combined cycle LNG thermal power plants. If the emission coefficient of a certain power supplier is lower than the emission coefficient of a power source group during a certain time period, it can be interpreted that the power supplier is contributing to reducing _CO2 emissions from the entire power system.
However, the value of renewable energy electricity (including electricity from low-carbon sources; the same applies below) is generally evaluated as the same regardless of the time of day. As a result, the scarcity value of time-shifting, such as generating electricity from renewable energy during times when the renewable energy power source mix is low, or charging electricity generated from renewable energy into storage batteries and discharging it when supply and demand are tight, is buried, and there is not enough incentive to improve, level out, or raise the renewable energy electricity ratio regardless of the time of day.

In consideration of this situation, an environmental value assessment system X will be described with reference to Figures 11 to 14, which determines that when the emission coefficient for each time period is large, the demand for renewable energy is relatively high, and evaluates the value of the generated renewable energy highly, while determining that when the emission coefficient for each time period is small, the demand for renewable energy is relatively low, and evaluates renewable energy taking into account its scarcity by time period.
The scarcity of renewable energy by time period can be considered to be positively correlated with the power source group emission coefficient for that time period. For example, the above-mentioned _CO2 emission reduction value for self-consumption is calculated using the power source group emission coefficient in J-Credit and Green Power Certificate. Therefore, the power source group emission coefficient for that time period can be used as an index showing the scarcity of renewable energy electricity for that time period.
In addition, in the case of a power supplier of low-carbon power sources whose emission coefficient is not zero but is below the standard, the value obtained by subtracting the power supplier's own emission coefficient from the power source group emission coefficient for that time period may be used as an indicator of the scarcity of renewable energy power in that time period.
The total scarcity value of renewable energy produced by an electricity supplier (power plant/power source) that generates electricity using renewable energy power generation equipment may be defined as the total amount of CO2 emission reduction calculated by multiplying the amount of electricity generated per time period, assuming that all of the electricity generated by the supplier is consumed by the supplier, and the value obtained by subtracting the _electricity supplier's own emission coefficient from the power source group emission coefficient (renewable energy scarcity coefficient).

FIG. 11 is a diagram showing an example of supplier power generation amount data indicating the amount of power generated per unit time by a power supplier (power plant/power source) that generates power using renewable energy power generation equipment.
As shown in FIG. 11 (A), the supplier power generation data stores data on the amount of power generated by the supplier during a given time period on a given day as shown in column CL43, in association with at least a supplier ID shown in row RW2, a date shown in column CL41, and a time period shown in column CL42.
For example, in the example of Figure 11 (A), it can be read from the data that the electricity supplier with supplier ID 000000001 generated 1 kWh in the 6:00s, 2 kWh in the 7:00s, 5 kWh in the 8:00s, ..., and 12 kWh in the 16:00s on April 1, 2022 (column CL43).
Also, for example, in the example of Figure 11 (B), it can be read from the data that the electricity supplier with supplier ID 000000002 generated 5 kWh at midnight, 5 kWh at 1 a.m., 5 kWh at 2 a.m., ..., 5 kWh at 11 p.m. on April 1, 2022 (column CL46).

Returning to the example of FIG. 1, the power generation status of each power source PS1 to PS4 etc. at regular intervals is transmitted to the communication network DNW at regular intervals and stored in the database DB2 as needed.
Therefore, the data supplying means 1 can collect supplier power generation amount data associated with each power supplier by any method such as API linkage or CSV file transfer via the communication network DNW.

Hereinafter, the supplier renewable energy scarcity coefficient calculation process performed in the environmental value assessment system X will be described with reference to Figures 12 and 13 .
13, first, the processing means 2 requests and refers to necessary data from the data supply means 1 (step S51). At least the power generation amount data by power source type (illustrated in FIG. 3), the power generation amount data by supplier (illustrated in FIG. 11), and the emission coefficient data by power source type are requested and referred to.

Next, the processing means 2 calculates the total amount of generated power by time period for the power source groups based on the power source type generated power amount data (step S52).
Next, the processing unit 2 calculates the CO ₂ emission amounts by time period for each power source group based on the power source type emission coefficient data and the power generation amount data by power source type (step S53).
Next, the processing means 2 calculates the power source group time-zone emission coefficient (effective _CO2 emission reduction coefficient or scarcity coefficient) based on the power source group total power generation amount by time zone and the power source group _CO2 emission amount by time zone (step S54).
The specific processing in steps S52 to S54 is similar to the processing in steps S12 to S14 shown in FIG. 6, and therefore a description thereof will be omitted.

Next, the processing means 2 calculates the supplier's effective CO2 reduction amount by time period based on the supplier's power generation data, the power source group time-period emission coefficient (effective _CO2 emission reduction coefficient or _scarcity coefficient), and the supplier's own emission coefficient (step S55).
Specifically, in the example of FIG. 12, in the 8:00 hour on Apr. 1, 2022, supplier D generates 5 kWh of electricity (column CL54).
Then, if we define the renewable energy scarcity value brought about by the renewable energy electricity generation supplied by supplier D in the 8:00 a.m. hour on April 1, 2022 as the amount of _CO2 emission reduction (effective _CO2 emission reduction) if the supplier itself were to consume it for its own use, then it can be expressed as the product of the time-specific emission coefficient for the power source group in the 8:00 a.m. hour on April 1, 2022 (in the case of low-carbon power sources, the value obtained by subtracting the supplier's own emission coefficient) and the amount of electricity generated by supplier D, and is calculated using the following formula.

Similarly, the processing means 2 calculates the amount of _CO2 reduction by time period of the supplier D for each day and each time period for a certain period (one day, one month, one year, etc.) (FIG. 5, column CL25).

Next, the processing means 2 calculates the periodic power generation amount of the supplier based on the supplier power generation amount data (step S57).
The periodic generated energy amount of a supplier is the total amount of energy generated by the supplier within a certain period of time.
Specifically, taking FIG. 12 as an example, on April 1, 2022, supplier D generates 1 kWh in the 6:00 hours, 2 kWh in the 7:00 hours, 5 kWh in the 8:00 hours, ..., 12 kWh in the 16:00 hours (column CL54).
Then, on April 1, 2022, supplier D's periodic power generation amount (per day) is calculated using the following formula.

Next, the processing means 2 calculates the period effective CO ₂ reduction amount of the supplier based on the supplier power generation amount data (step S57).
The demander's effective _CO2 reduction during a certain period is defined as the sum of the effective _CO2 emission reductions for each time period achieved by the supplier through renewable energy power generation within a certain period.
Specifically, using Figure 12 as an example, it is calculated that on April 1, 2022, supplier D effectively reduced _CO2 emissions through renewable energy power generation by 0.74 kg in the 6:00 a.m. hour, 1.46 kg in the 7:00 a.m. hour, 3.55 kg in the 8:00 a.m. hour, ..., and 8.04 kg in the 16:00 p.m. hour (column CL55).
Then, on April 1, 2022, supplier D's effective _CO2 reduction amount (per day) for the period is calculated using the following formula.

Finally, the processing means 2 calculates the supplier's effective _CO2 reduction coefficient (renewable energy scarcity coefficient) for that period based on the supplier's periodic power generation amount and the supplier's periodic effective _CO2 reduction amount (scarcity value creation amount) (step S58).
The supplier effective reduction coefficient is calculated by dividing the supplier's effective _CO2 reduction amount for the period by the amount of power generated for the period.
Specifically, in the example of Figure 12, on April 1, 2022, the periodic _CO2 reduction amount (daily) of supplier D is calculated to be 131.38 kg/day, and the periodic power generation amount (daily) is calculated to be 240 kWh/day.
Then, the supplier effective _CO2 reduction coefficient (renewable energy scarcity coefficient) for supplier D on April 1, 2022 is calculated using the following formula.

The supplier effective reduction coefficient (renewable energy scarcity coefficient) calculated in this manner represents the amount of CO2 reduction that could have been achieved by a group of power sources, such as power plants, to generate 1 kWh _of electricity generated by the supplier, and can be stored in the memory means 4 in association with the supplier.
In a similar manner, the supplier effective reduction coefficient (renewable energy scarcity coefficient) can be calculated for each supplier and stored in the storage means 4 in association with each supplier.

The larger the supplier's effective reduction coefficient, the more likely it is that the supplier's renewable energy generation was conducted during a time period when the proportion of electricity generated by renewable energy sources in the power source group in the supplier's location is smaller (a time period when _CO2 emissions from power generation are relatively high). Therefore, the supplier's environmental contribution can be said to be relatively high.
In this way, the contribution to the environment by each supplier through renewable energy power generation, i.e., the environmental value created by the supplier, can be quantified and compared, taking into account rarity.
For example, in a similar manner, the supplier effective reduction coefficient (renewable energy scarcity coefficient) of supplier E is calculated to be 0.74 (kg- _CO2 /kWh), so it can be said that supplier E's renewable energy power generation mode has a relatively higher contribution to the transmission and distribution network and the environment compared to supplier D, whose supplier effective reduction coefficient (renewable energy scarcity coefficient) is 0.55.

The above supplier effective reduction coefficient (renewable energy scarcity coefficient) can be compared with the power source group emission coefficient (standard renewable energy scarcity coefficient) calculated by the processing flow shown in Fig. 7. The larger the supplier effective reduction coefficient (renewable energy scarcity coefficient) is compared with the power source group emission coefficient (standard renewable energy scarcity coefficient), the more likely it is that the supplier's renewable energy power generation was carried out during a time period in which the proportion of power generation by renewable energy sources in the power source group in the location area was smaller (a time period in which _CO2 emissions from power generation were relatively high). Therefore, the supplier's environmental contribution, i.e., the relative advantage of the scarcity value created by the supplier, can be quantified using the power source group emission coefficient as a baseline.

For example, the period renewable energy value rarity reward amount calculation process shown in FIG. 14 will be described.
14, first, the processing means 2 requests and refers to the necessary data from the data supply means 1 (step S61). At least the supplier effective reduction coefficient, the power source group emission coefficient, the incentive unit price data, and the supplier power generation amount data of each supplier are requested and referred to.
The scarcity reward unit price data may be data indicating the trading price per unit of _CO2 emission associated with a date and time period, or a value indicating the average trading price per unit of _CO2 emission over a specified period of time, and may be a predetermined value, a calculated value, or a value collected over a network.

Next, the processing means 2 calculates renewable energy value rarity points based on the supplier effective reduction coefficient and the power source group emission coefficient (step S62).
The renewable energy value rarity points are calculated by multiplying the difference between the supplier effective reduction coefficient and the power source group emission coefficient by a predetermined multiplier. For example, the points for supplier D are calculated using the following formula.

Next, the processing means 2 calculates the bonus unit price for each supplier based on the bonus unit price data and the excess reduction coefficient points (step S63).
Specifically, for example, when a value of 2 yen/point is given as the bonus unit price data, the bonus unit price by supplier D is calculated by the following formula.

Finally, the processing means 2 calculates the periodic renewable energy value rarity incentive amount for each supplier for a certain period based on the supplier-specific renewable energy value rarity incentive unit price and the supplier power generation amount data (step S64).
Specifically, taking FIG. 12 as an example, on April 1, 2022, supplier D generates 1 kWh in the 6:00 hours, 2 kWh in the 7:00 hours, 5 kWh in the 8:00 hours, ..., 12 kWh in the 16:00 hours (column CL54).
Then, the periodic renewable energy value scarcity incentive amount (per day) for supplier D on April 1, 2022 can be calculated using the following formula.

Similarly, the period renewable energy value rarity reward amount for supplier E is calculated to be 780 yen.
In step S64, the periodic renewable energy value rarity incentive amount calculated in this manner may be added to the standard assessment amount of the standard environmental value (environmental value in the case of the standard renewable energy scarcity coefficient) which is calculated uniformly according to the amount of renewable energy generated without taking rarity into account, to arrive at the final periodic incentive amount. For example, if the standard assessment amount of renewable energy value is set at 50 yen/kWh, even supplier D, whose calculated amount is once negative, can receive a periodic incentive amount of 5,760 yen, and motivation for renewable energy generation will not decrease significantly.

In this way, the supplier's environmental contribution, i.e., the rarity of the environmental value created by the supplier, can be quantified using the emission coefficient of the power source group as a baseline, and a bonus combined with the standard environmental value assessment amount creates an incentive for power suppliers to shift to times when renewable energy generation is low, thereby raising and leveling out the renewable energy power source composition ratio across all times.

In a similar manner, the periodic bonus amount for each supplier can be calculated for each fixed period and stored in the storage means 4 in association with each supplier.

The configurations and functions shown in the above embodiments are merely examples and can be modified in various ways based on design requirements, etc.

X Environmental value assessment system 1 Data supply means 2 Processing means 3 Determination means PNW1, PNW2 Power transmission and distribution network PSG Power source group PS1, PS2, PS3, PS4 Power source C1, C2, C3 Power consumers SM1, SM2, SM3 Smart meter DNW Communication network DB1, DB2 Database DG Terminal group D1, D2, D3, D4 Terminal

Claims (13)

a data supply means for supplying power source type emission coefficient data indicating the amount of _CO2 emission per unit of generated power for each power source type, power source type power generation amount data indicating the amount of generated power per unit time for each power source type in a group of power sources supplying power to an area where an electric power consumer is located, and consumer power consumption data indicating the amount of power consumed per unit time by the electric power consumer;
A processing means for calculating a consumer emission coefficient indicating the amount of _CO2 emission per unit of power consumption of the power consumer based on the power source type emission coefficient data, the power generation amount data by power source type, and the consumer power consumption data;
and a storage means for storing the electricity consumer and the calculated consumer emission coefficient in association with each other. The environmental value assessment system according to claim 1, characterized in that the processing means calculates a power source group emission coefficient indicating the amount of emissions per unit of generated power of the power source group based on the power source type emission coefficient data and the power source type generated power amount data. The data supply means further supplies emission rights unit price data,
3. The environmental value assessment system according to claim 2, wherein the processing means calculates an amount required to purchase emission rights for the electricity consumer associated with the consumer emission coefficient based on the power source group emission coefficient, the consumer emission coefficient, the emission right unit price data, and the consumer power consumption data. The power consumer is further provided with a determination means for determining whether or not the power consumer is a supply consumer that generates its own power based on the consumer power consumption data,
When the determination means determines that the electric power consumer is the supply consumer,
The processing means calculates leverage based on a supply demander's power generation amount indicating the amount of power self-generated by the supply demander per unit time and a supply demander's power consumption amount indicating the amount of power consumed by the supply demander per unit time,
3. The environmental value assessment system according to claim 2, further comprising: calculating a leverage-adjusted time-of-day consumer _CO2 emission amount in the unit time based on the leverage and the power source group emission coefficient. The environmental value assessment system according to claim 4, characterized in that the processing means calculates the leverage-adjusted time-zone-specific consumer _CO2 emissions in the unit time to be a negative value, regardless of whether the difference between the supply demander's power consumption and the supply demander's power generation is positive or negative, as long as the grid power consumption does not exceed the private power generation consumption. The environmental value assessment system according to claim 4, characterized in that the processing means calculates the value of the leverage-adjusted time-zone-specific consumer _CO2 emissions in the unit time based on a predetermined value when the difference between the supply demander's power consumption amount and the supply demander's power generation amount is zero. a data supply step of supplying power source type emission coefficient data indicating the amount of _CO2 emission per unit of generated power for each power source type, power source type power generation amount data indicating the amount of generated power per unit time for each power source type in a power source group that supplies power to the location area of the power consumer, and consumer power consumption data indicating the amount of power consumed per unit time by the power consumer;
A processing step of calculating a consumer emission coefficient indicating the amount of _CO2 emission per unit of power consumption of the power consumer based on the power source type emission coefficient data, the power generation amount data by power source type, and the consumer power consumption data;
and a storage step of storing the electricity consumer and the calculated consumer emission coefficient in association with each other. An environmental value assessment program for causing one or more computers to function as an environmental value assessment system according to any one of claims 1 to 6. data supply means for supplying: power source type emission coefficient data indicating the amount of _CO2 emissions per unit of generated power by the power source type and the power supplier; power source type generated power amount data indicating the amount of generated power per unit time by power source type in a group of power sources that supplies power to the location of a power supplier that generates power using renewable energy power generation equipment or low-carbon power generation equipment; and supplier generated power amount data indicating the amount of generated power per unit time by the power supplier;
An environmental value assessment system comprising: a processing means for calculating a renewable energy scarcity coefficient indicating the rarity of the renewable energy value per unit of generated electricity by the electricity supplier based on the emission coefficient data by power source type, the power generation amount data by power source type, and the supplier power generation amount data; and a storage means for storing the electricity supplier in association with the calculated supplier renewable energy scarcity coefficient. 10. The environmental value assessment system according to claim 9, wherein the processing means calculates a power source group emission coefficient indicating an amount of _CO2 emission per unit of generated power of the power source group based on the power source type emission coefficient data and the power source type generated power amount data. The data supply means further supplies incentive unit price data evaluating the scarcity value of renewable energy electricity by time period,
The environmental value assessment system described in claim 10, characterized in that the processing means calculates a reward amount for the electricity supplier associated with the supplier renewable energy scarcity coefficient based on the power source group emission coefficient, the supplier renewable energy scarcity coefficient, and the reward unit price data. a data supply step of supplying: power source type emission coefficient data indicating the amount of _CO2 emissions per unit of generated power for each power source type; power source type generated power amount data indicating the amount of generated power per unit time for each power source type in a group of power sources that supplies power to an area where a power supplier that generates power using renewable energy power generation facilities is located; and supplier generated power amount data indicating the amount of generated power per unit time by the power supplier;
A processing step of calculating a supplier renewable energy scarcity coefficient indicating the scarcity of the renewable energy value per unit generated power amount of the power supplier based on the power source type emission coefficient data, the power source type power generation amount data, and the supplier power generation amount data;
An environmental value assessment method that causes a computer to execute a storage step of associating and storing the electricity supplier with the calculated renewable energy scarcity coefficient of the supplier. An environmental value assessment program for causing one or more computers to function as an environmental value assessment system according to any one of claims 9 to 11.