JP7630296B2 - Operation support method and operation support device for thermal power plant - Google Patents

Description

The present invention relates to an operation support method and an operation support device for a thermal power plant, and more particularly to an operation support method and an operation support device that optimizes CO ₂ emissions and power generation costs by combining carbon credits.

At thermal power plants, various fuels are burned in the combustor of a gas turbine or in the furnace of a boiler, and electricity is generated using the resulting heat source. In recent years, in order to reduce the environmental impact of combustion exhaust gases, there has been an increase in the use of fuels that can offset carbon emissions (biomass, etc.) and carbon-free fuels (hydrogen, ammonia, etc.).

Patent Literature 1 discloses that when adopting an operation plan for a power generation facility with the lowest power generation cost, environmental burden costs such as an environmental tax are reflected in the power generation cost based on the environmental load (including _CO2 emissions) emitted from each power generation facility.

Patent No. 3787761

In addition to changing fuels, thermal power plants can reduce _CO2 emissions by improving operating methods. In addition, the effects of _CO2 absorption and carbon fixation through forest management and the effective use of wood can be evaluated, and the amount of _CO2 offset can be purchased as carbon credits.

However, there are countless combinations of fuel, operating conditions, and carbon credits for thermal power plants, and it is difficult to select the optimal combination from the perspective of both _CO2 emissions and power generation costs. In addition, _CO2 reduction (reduction of _CO2 emissions) by changing the fuel and operating conditions of thermal power plants and _CO2 reduction by carbon credits (offset by _CO2 absorption and fixation) are fundamentally different in meaning, and there is a need to evaluate the cost-effectiveness of _CO2 reduction separately.

The present invention has been made in consideration of the above-mentioned circumstances, and aims to provide an operation support method and an operation support device for a thermal power plant that can select optimal carbon credits from the standpoint of both _CO2 emissions and power generation costs, and easily evaluate the cost-effectiveness of the selected carbon credits.

In order to achieve the above object, the present invention has the configuration described in the claims. As an example, the present invention is an operation support method for a thermal power plant, including an input step of reading in carbon credit information including the price of fuel for a thermal power plant, the price of carbon credits, and a _CO2 offset amount, a calculation step of changing a combination of fuel, operating conditions, and carbon credits for the thermal power plant, and calculating an actual _CO2 emission amount, a net _CO2 emission amount obtained by subtracting the _CO2 offset amount by the carbon credits from the actual _CO2 emission amount, an actual power generation cost taking into consideration the price of the fuel, and a net power generation cost obtained by adding the price of the carbon credits to the actual power generation cost, an optimization step of selecting an optimal condition consisting of a combination of the fuel, the operating conditions, and the carbon credits for which the net _CO2 emission amount and the net power generation cost satisfy a predetermined selection condition, and an output step of outputting the selected optimal condition, and the actual CO2 emission amount, the net _CO2 emission amount, the actual power generation cost, and the net power generation cost under the optimal condition. Or, it is an operation support device for a thermal power plant having the same function.

According to the present invention, it is possible to provide an operation support method and an operation support device for a thermal power plant, which are capable of selecting an optimal carbon credit from the viewpoints of both _CO2 emissions and power generation costs, and easily evaluating the cost-effectiveness of the selected carbon credit. Problems, configurations, and effects other than those described above will become apparent from the description of the following embodiments.

Schematic diagram showing the boiler 1 is a schematic diagram of a boiler operation support system 10. FIG. 2 is a diagram showing a hardware configuration of a driving assistance device. 1 is a flowchart showing processing by the boiler operation support device 100. A flowchart showing the details of the process from acquiring driving data to building a predictive model. Diagram showing the calculation contents in the calculation unit A diagram showing an example of the calculation contents in the optimization unit and the optimal condition evaluation unit. FIG. 13 is a diagram showing an example of an output screen in the output unit. Diagram showing an example of boiler operation support FIG. 1 is a diagram showing an example of a business model using the driving assistance device 100.

Below, a preferred embodiment of the present invention will be described in detail with reference to the attached drawings. Note that the present invention is not limited to this embodiment, and when there are multiple embodiments, the present invention may be configured by combining the respective embodiments. The same components and steps are given the same reference numerals throughout the drawings, and duplicate explanations will be omitted.

In this embodiment, a boiler will be used as an example of a thermal power plant. FIG. 1 is a schematic diagram of a boiler 1. The boiler 1 of this embodiment pulverizes solid fuels such as coal and biomass, and is capable of single-fuel combustion of coal and mixed combustion of multiple types of fuels such as coal and biomass.

The boiler 1 has a furnace 11, a combustion device 12, and a flue 13. The furnace 11 is, for example, a hollow rectangular cylinder and is installed along the vertical direction. The wall surface of the furnace 11 is composed of evaporation tubes (heat transfer tubes) and fins connecting the evaporation tubes, and the temperature rise of the furnace wall is suppressed by heat exchange between the feed water and steam flowing in the evaporation tubes and the combustion gas in the furnace 11. Specifically, on the side wall surface of the furnace 11, multiple evaporation tubes are arranged, for example, along the vertical direction and arranged side by side in the horizontal direction. The fins block the space between the evaporation tubes. The furnace 11 has an inclined surface 62 at the bottom of the furnace, and the bottom evaporation tube 70 is provided on the inclined surface 62 to form the bottom surface.

The combustion device 12 is provided on the vertically lower side of the furnace wall that constitutes the furnace 11. In this embodiment, the combustion device 12 has multiple combustion burners (e.g., 21, 22, 23, 24, 25) attached to the furnace wall. For example, the combustion burners (burners) 21, 22, 23, 24, 25 are arranged at equal intervals along the circumferential direction of the furnace 11. However, the shape of the furnace, the arrangement of the burners, the number of combustion burners in one stage, and the number of stages are not limited to this embodiment.

Each of these combustion burners 21, 22, 23, 24, 25 is connected to pulverizers (coal pulverizers/mills: equivalent to auxiliary equipment) 31, 32, 33, 34, 35 via pulverized coal supply pipes 26, 27, 28, 29, 30. When coal is transported by a conveying system (not shown) and fed into these pulverizers 31, 32, 33, 34, 35, it is pulverized to a predetermined fine powder size, and the pulverized coal (pulverized coal) can be supplied to the combustion burners 21, 22, 23, 24, 25 from the pulverized coal supply pipes 26, 27, 28, 29, 30 together with conveying air (primary air).

Furnace 11 also has a wind box 36 at the mounting position of each combustion burner 21, 22, 23, 24, 25, and one end of air duct 37b is connected to this wind box 36, and the other end is connected to air duct 37a that supplies air at connection point 37d.

In addition, a flue 13 is connected vertically above the furnace 11, and multiple heat exchangers (41, 42, 43, 44, 45, 46, 47) for generating steam are arranged in the flue 13. Therefore, a flame is formed by the combustion burners 21, 22, 23, 24, 25 injecting a mixture of pulverized fuel and combustion air into the furnace 11, and combustion gas is generated and flows into the flue 13. The combustion gas then heats the feedwater and steam flowing through the furnace wall and the heat exchangers (41-47) to generate superheated steam, which is supplied to rotate a steam turbine (not shown), which then rotates and drives a generator (not shown) connected to the rotating shaft of the steam turbine to generate electricity. In addition, this flue 13 is connected to an exhaust gas passage 48, and is provided with a denitration device 50 for purifying the combustion gas, an air heater 49 for exchanging heat between the air sent from the blower 38 to the air duct 37a and the exhaust gas sent through the exhaust gas passage 48, a dust treatment device 51, an induced draft blower 52, and the like, and a chimney 53 is provided at the downstream end. Note that the denitration device 50 does not need to be provided if the exhaust gas standards can be met.

The furnace 11 of this embodiment is a so-called two-stage combustion furnace, in which after excess fuel combustion using pulverized coal transport air (primary air) and combustion air (secondary air) fed into the furnace 11 from the wind box 36, new combustion air (after air) is fed to perform lean fuel combustion. For this reason, the furnace 11 is provided with an after-air port 39, one end of an air duct 37c is connected to the after-air port 39, and the other end is connected to an air duct 37a that supplies air at a connection point 37d. Note that if the two-stage combustion method is not adopted, the after-air port 39 does not need to be provided.

The air sent from the blower 38 to the air duct 37a is heated by heat exchange with the combustion gas in the air heater 49, and at the connection point 37d, it branches into secondary air that is led to the air box 36 via the air duct 37b, and after-air that is led to the after-air port 39 via the air duct 37c.

Figure 2 is a schematic diagram of the operation support system 10 for the boiler 1. The operation support system 10 includes the boiler 1, an operation support device 100 for the boiler 1, and an operation control device 120 for the boiler 1.

The operation support device 100 aims to select the optimal combination of fuel, operating conditions, and carbon credits from the standpoint of both _CO2 emissions and power generation costs of the boiler 1, and to easily evaluate the cost-effectiveness of the selected carbon credits.

The driving assistance device 100 includes a data acquisition unit 110, a driving data storage unit 112, a data extraction unit 114, a model construction unit 220, a model storage unit 222, a calculation unit 230, a condition storage unit 232, an optimization unit 240, an optimal condition evaluation unit 242, an output unit 250, and an input unit 260. The functions of each unit are described below.

3 is a diagram showing a hardware configuration of the driving assistance device 100. The driving assistance device 100 includes a processor 301, a random access memory (RAM) 302, a read only memory (ROM) 303, a hard disk drive (HDD) 304, an input I/F 305, an output I/F 306, and a communication I/F 307, and is configured using a computer in which these are connected to each other via a bus 308. The processor 301 is a graphics processing unit (GPU),
The hardware configuration of the driving assistance device 100 is not limited to the above, and may be a combination of a control circuit and a storage device. The driving assistance device 100 is configured by the processor 301 executing a driving assistance program that realizes each function of the driving assistance device 100, or by the control circuit performing calculations.

An input device 311 such as a mouse, keyboard, or touch panel is connected to the input I/F 305.

A display 312 such as an LCD is connected to the output I/F 306.

The communication I/F 307 is connected to the boiler 1 and the operation control device 120.

Figure 4 is a flowchart showing the main steps taken by the boiler 1 operation support system 10 to select optimal conditions for fuel, operating conditions, and carbon credits, evaluate the results, and then output them.

<S1: Model construction step>
The model construction unit 220 of the operation support device 100 adds an operating end parameter (one of the operating conditions) set in the operating end of the boiler 1 and a fuel parameter including the fuel property and its price for the boiler 1 (the fuel property is one of the operating conditions) to input parameters (explanatory variables in a regression model) to model the process value, that is, to construct a prediction model of the process value (S1). The constructed prediction model is stored in the model storage unit 222. Fig. 5 is a flowchart showing the details of the process from obtaining the operating data to constructing the prediction model.

The data acquisition unit 110 acquires operation data from the boiler 1 and stores it in the operation data storage unit 112 (S101). The data acquisition unit 110 acquires actual process values measured by each sensor 1, 2, ..., M during actual operation, operation terminal parameters (operation terminal setting values) set by the operation control device 120 for each operation terminal 1, 2, ..., N, and fuel parameters (property parameters, mixed combustion ratio, and price of each fuel when multiple fuels are mixed), generates operation data that associates the process values, operation terminal parameters, and fuel parameters, and stores them in the operation data storage unit 112.

The process values include, for example, the amount of _CO2 emissions contained in the gas discharged from a thermal power plant (actual _CO2 emissions) and the cost of generating electricity at the thermal power plant (actual power generation cost). If there is no sensor to measure the amount of _CO2 emissions, the actual amount of _CO2 emissions is calculated by combining various process values (such as coal consumption). The same applies to the actual power generation cost.

The data acquisition unit 110 may generate operation data consisting of time series data by adding time information to each of the operation terminal parameters and the actual process values.

The operating data acquired in this embodiment becomes training data for constructing prediction models 1 and 2. The training data is not limited to actual process values and the operating conditions when they were obtained (operating end parameters, mixed combustion ratio, and fuel properties), but may also be calculated values obtained by analyzing boiler 1.

Preprocessing is performed by the data extraction unit 114 (S102). The data extraction unit 114 reads out the operation data already stored in the operation data storage unit 112, and compensates for missing data by linear interpolation. In addition, the data extraction unit 114 reads out the operation conditions, including the setting values of the operation terminal parameters, from the operation data, and extracts setting data.

At this time, the actual process values obtained by measuring with sensors 1, 2, ..., M may be used to calculate the values of sensors not implemented in boiler 1, and soft sensor values consisting of the measured values may be output to the data acquisition unit 110.

The model construction unit 220 acquires the setting data extracted by the data extraction unit 114, generates learning data (S103), and sets the learning conditions (S104).

The model construction unit 220 creates a prediction model based on pre-set conditions such as explanatory variables, objective variables, and target error.

Figure 6 shows an example of a prediction model created by the model construction unit 220, and further illustrates the calculation content in the calculation unit 230 using the prediction model. The model construction unit 220 constructs prediction models 1 and 2 corresponding to each of the process values shown in Figure 6. Specifically, the model construction unit 220 uses control values (operation end parameters) set in the operation ends 1 to M and fuel properties (including the fuel co-firing ratio) as input parameters, and constructs prediction models 1 and 2 by machine learning prediction models corresponding to each of the multiple process values using teacher data in which the input parameters are set in the boiler 1 and the actual operation is performed, or the process values (calculated values) obtained by calculation using the input parameters are output parameters. The input parameters may include operating parameters (such as operating load) and other parameters such as atmospheric conditions.

When the model construction unit 220 constructs the prediction models 1 and 2 using a regression model, in the case of the prediction model 1, the above input parameters are used as explanatory variables, and the actual _CO2 emission amount is used as a response variable to machine-learn the regression model. Similarly, for the prediction model 2, the same explanatory variables as the prediction model 1 are used, and the regression model is machine-learned to machine-learn the regression model with the actual power generation cost as a response variable. These constructed prediction models 1 and 2 are stored in the model storage unit 222. In this example, two prediction models 1 and 2 are constructed, but a prediction model for another process value may be additionally constructed. In addition, the actual _CO2 emission amount and the actual power generation cost are directly obtained from the prediction models 1 and 2, but the actual _CO2 emission amount and the actual power generation cost may be calculated by combining process values predicted from a plurality of prediction models.

<S2: Input step>
The input unit 260 reads fuel parameters including the fuel properties and price of the thermal power plant and carbon credit information (price, _CO2 offset amount) inputted by the input device 311. In addition, the target value of the _CO2 emission amount or power generation cost of the thermal power plant may be read. Furthermore, the operation terminal parameters to be inputted to the prediction models 1 and 2 are also inputted from the input device 311.

The read information is sent to the condition memory unit 232 and stored.

<S3: Calculation step>
The calculation unit 230 reads possible options for the fuel parameters, operating conditions (operating end parameters), and carbon credits ( _CO2 offset amount, price) of the thermal power plant from the condition storage unit 232. In this embodiment, the operating end parameters are described as being stored in the condition storage unit 232, but the calculation unit 230 may read them from the operating data storage unit 223 without storing them in the condition storage unit 232.

Further, as shown in Fig. 6, the calculation unit 230 changes the combination of fuel parameters, operating conditions (operating end parameters), and carbon credits ( _CO2 offset amount, price) of the thermal power plant, and calculates the actual _CO2 emissions for each combination, the net _CO2 emissions obtained by subtracting the _CO2 offset amount by the carbon credit from the actual _CO2 emissions, the actual power generation cost taking into account the fuel price, and the net power generation cost obtained by adding the price of the carbon credit to the actual power generation cost. Specifically, the calculation unit 230 inputs the fuel parameters and operating conditions into each of the prediction models 1 and 2 while changing them, and calculates multiple actual _CO2 emissions and actual power generation costs. Furthermore, the calculation unit 230 combines each of the actual _CO2 emissions and the actual power generation cost with at least one or more carbon credit information to calculate the net _CO2 emissions and the net power generation cost using the following formulas (1), (2), and (3).

Net _CO2 emission (g/kWh) = actual _CO2 emission (g/kWh) - unit _CO2 offset (g/kWh) (1)
Unit carbon credit price (yen/kWh) = unit _CO2 offset amount (g/kWh) × _CO2 offset unit price (yen/g) (2)
Net power generation cost (yen/kWh) = actual power generation cost (yen/kWh) + unit carbon credit price (yen/kWh) ... (3)

Since _CO2 emissions and power generation costs are expressed per unit power generation kWh, it is necessary to match the units of the _CO2 offset amount (g) and the carbon credit price (yen) (per kWh). Therefore, as shown in formula (1), the net _{CO2 emissions (g/kWh) are calculated using the unit CO2 offset} amount (g/kWh), which is the _CO2 offset amount per power generation amount. Then, the unit _CO2 offset amount (g/kWh) is multiplied by the _CO2 offset unit price (yen/g) to calculate the unit carbon credit price (yen/kWh), which is the carbon credit price required per power generation amount (see formula (2)), and the net power generation cost (yen/kWh) is calculated using this value (see formula (3)).

If there is a limit to the supply of carbon credits (amount that can be purchased), it is necessary to set an upper limit on the amount of _CO2 offset (amount of carbon credits purchased). In that case, as shown in formula (4), the unit _CO2 offset amount (g/kWh) is multiplied by the amount of power generation (kWh) for the expected period so that the amount of _CO2 offset (amount of carbon credits purchased) is equal to or greater than this value.

_CO2 offset amount (carbon credit purchase amount) (g) ≧ unit _CO2 offset amount (g/kWh) × power generation amount (kWh) ... (4)

<S4: Optimization step>
The optimization unit 240 selects an optimal condition consisting of a combination of fuel, operating conditions, and carbon credits that satisfies predetermined conditions for net _CO2 emissions and net power generation costs.

The above calculations will be explained using Figure 7. Figure 7 is an overview of the calculations performed by the optimization unit and the optimal condition evaluation unit.

First, a plurality of calculation results from the calculation unit 230 are plotted on two axes, net _CO2 emissions and net power generation cost. The white circles in FIG. 7 correspond to the calculation results, and it can be seen that they are plotted in a downward sloping manner overall. Among these, if the net _CO2 emissions (or net power generation cost) are the same, the one with the lower net power generation cost (or net _CO2 emissions) is more preferable. Therefore, the calculation results surrounded by the dashed line in FIG. 7 are candidates for the optimum conditions. From these candidates, the optimum conditions that satisfy the predetermined selection conditions are selected.

As a predetermined selection condition for selecting the optimum condition, for example, there is a method of setting a predetermined range of net _CO2 emission or a predetermined range of net power generation cost.Furthermore, there is also a method of optimizing to maximize an evaluation value obtained by adding up the difference between the net _CO2 emission and the net operating cost and a benchmark (to be described later) (and other evaluation indexes), and selecting the optimum condition from the search points.

If the condition storage unit 232 stores a target value for _CO2 emissions or power generation cost, the optimization unit 240 reads the target value and selects optimal conditions that match the target value. For example, if a target value C1 for power generation cost is stored, optimal conditions R1 are selected from among the calculation results in which the net power generation cost matches C1. Similarly, if a target value E1 for _CO2 emissions is stored, optimal conditions R2 are selected from among the calculation results in which the net _CO2 emissions match E1.

In Figure 7, an example of selecting optimal conditions based on two axes, net CO2 emissions and net power generation costs, is explained. However, other methods may be used that quantify the tolerances of control operating terminals, such as the spray valve opening and gas path damper opening that control steam temperature, and the tolerances for alarm values, such as the surface temperature (metal temperature) of heat transfer tubes, and select optimal conditions that satisfy the above-mentioned specified selection conditions from among the optimal conditions that also take these into consideration.

<S5: Optimal condition evaluation step>
If benchmark values for _CO2 emissions and power generation costs are stored in the condition memory unit 232, the optimal condition evaluation unit 242 reads the benchmark values and evaluates the relative merits of the benchmark values and the optimal conditions selected in the optimization step S4.

Specifically, for benchmark value B1, shown by a black circle in Figure 7, check to see if the optimal conditions are in the lower left quadrant of the figure (the area below and to the left of B1, bounded by the lines C1 and E1), and if so, judge it to be "excellent." If the optimal conditions for benchmark value B1 are in the upper right quadrant of the figure, evaluate it as "poor." If they are in any other quadrant, judge it to be "more detailed check required."

<Output Step S6>
The optimal conditions selected by the optimization unit 240, and the actual CO2 emissions, net CO2 emissions, actual power generation cost, and net power generation cost under the optimal conditions are output from the output unit 250 to an output device (display 312). In addition, the optimal conditions are transmitted to the operation control device 120 after being confirmed by an operator of the thermal power plant.

8 is an example of the output screen. The following contents are output on the output screen.
(1) Selection conditions: If there is a target value or benchmark value, that value is displayed.
(2) Fuel conditions: The major classification (type) and detailed classification (place of origin, etc.) of fuel type, and the mixed combustion ratio if multiple fuels are used, are indicated.
(3) Operating conditions: Displays the major classification and detailed classification of the types of operation terminal parameters and the setting values.
(4) Carbon credit: Displays the carbon credit provider (purchaser) and its type (type of business related to _CO2 offset, location, etc.), _CO2 offset amount, and price (purchase price or _CO2 offset unit price).
(5) Calculation results: The actual power generation cost, net power generation cost, actual CO2 emissions, and net CO2 emissions calculated under the optimal conditions shown in (2) to (4) are displayed.
(6) Evaluation results: If there is a benchmark value, the comparison result with the benchmark is displayed. Also, as shown in the lower right figure, the calculation result under optimal conditions and the benchmark value are displayed in a bar graph.

<Example of a boiler>
An example of a study of operational support for a boiler 1 will be described with reference to Fig. 9. Fig. 9 summarizes the concept of economic value and environmental value of _{a CO2} negative (offset) business, with the upper side showing the power generation cost at a thermal power plant using a certain fuel, and the lower side showing the _CO2 emissions at a thermal power plant using a certain fuel. When comparing coal-only combustion (far left in the figure), biomass-only combustion (center left and right in the figure), and gas-only combustion (far right in the figure), the power generation cost increases in the order of coal < gas < biomass, and the _CO2 emissions generally increase in the order of biomass < gas < coal.

Here, if we define the carbon negative cost A1 as the actual power generation cost of biomass-fired power generation minus the _power generation cost of coal-fired power generation, and the carbon offset amount B1 as the actual _CO2 emissions of coal-fired power generation minus the actual CO2 emissions of biomass-fired power generation, a _CO2 negative business refers to a business that uses the carbon negative cost A1 to create carbon offset amount B1 through _CO2 fixation methods such as forest management and cultivation and biomass carbonization fixation.

If a coal-fired power plant uses a _CO2 negative business, and the net generation cost (the same as the actual generation cost of biomass-fired power plant) obtained by adding the carbon negative cost A1 to the actual generation cost of the coal-fired power plant is equal to the net _CO2 emissions obtained by subtracting the carbon offset amount B1 from the actual _CO2 emissions of the coal-fired power plant, then the economic and environmental value of the coal-fired power plant + _{CO2 negative} business and the biomass-fired power plant are equal, and therefore coal-fired power plant is an option from the perspective of _CO2 emissions as a power generation method as well.

Similarly, if a carbon negative cost A2, calculated by subtracting the actual power generation cost of coal-fired power generation from the actual power generation cost of gas-fired power generation, can be used to create a carbon offset amount B2, calculated by subtracting the actual _CO2 emissions of gas-fired power generation from the _CO2 emissions of coal-fired power generation, through a _CO2 negative business, then the economic and environmental value of the coal-fired power generation + _CO2 negative business and the gas-fired power generation will be equal.

For the sake of understanding, this figure sets a carbon negative cost A1 and a carbon offset amount B1, but it goes without saying that if a _CO2 negative project is implemented with a carbon negative cost A1, there is a possibility that the carbon offset amount B1 will be insufficient or excessive. In addition, in addition to the aforementioned forest cultivation and management and biomass carbonization, various forms of _CO2 negative projects are possible, such as wood materials and bioethylene, and various means can be selected depending on the carbon offset amount expected by coal-fired power generation companies, etc.

<Business model overview>
A business model using the driving support device 100 will be described with reference to Fig. 10. Fig. 10 is an explanatory diagram of a business model of a service that takes into account the carbon credit system in the driving support device 100 and proposes economical fuels to power generation companies.

First, a power generation company issues a commission (signal) for low-carbon operation to the operation support device 100. The signal here corresponds to a _CO2 reduction target amount and an upper limit on power generation costs.

When the driving assistance device 100 receives a signal for low-carbon operation, it imports fuel information for zero-emission coal (calorific value, price, carbon credits). Here, the price of zero-emission coal is set at the normal fuel price plus carbon credits, and when a power generation company that has introduced the driving assistance device 100 purchases it, the carbon credits are deposited into the driving assistance device 100. Therefore, zero-emission coal is a fuel that has already been carbon credit certified by the driving assistance device 100.

At the same time, fuel information (heat value, price, co-firing ratio) of biomass, carbon-neutral fuel, etc. is input to the driving support device 100. Based on the input information, the driving support device 100 presents the purchase amount and co-firing ratio of zero-emission coal that can reduce power generation costs as much as possible while satisfying the _CO2 reduction target or _CO2 emission upper limit presented by the power generation company when the low-carbon operation was entrusted. If there is still a shortage, a solution is calculated to purchase the shortfall of carbon credits from the carbon credit system. In some cases, if the carbon credit unit price is low, it may be possible to obtain a solution to procure carbon credits from the carbon credit system without purchasing zero-emission coal.

Once _the calculation is complete, the driving assistance device 100 discloses to the power generation company the amount of zero-emission coal purchased, the amount of various fuels purchased, and the amount of carbon credits purchased from the carbon credit system, in addition to the CO2 emissions and power generation costs that are the optimization results of the driving assistance device 100. If the power generation company commits to this, zero-emission coal will be purchased, various fuels will be purchased, and carbon credits will be procured as presented by the driving assistance device 100. The driving assistance device 100 will operate the procured fuel as optimized by the driving assistance device 100, accurately monitor daily _CO2 emissions, and purchase carbon credits on behalf of the power generation company.

This system allows power generation companies to visualize their _CO2 emissions, achieve their _CO2 reduction targets, including carbon credits, and keep power generation costs within the upper limit.

The contents described in each of the above embodiments can be understood, for example, as follows:

(1) A method for supporting operation of a thermal power plant according to one embodiment of the present invention includes:
An input step of reading the price of fuel for the thermal power plant and carbon credit information including the price of carbon credits and the amount of _CO2 offset;
a calculation step of calculating an actual _CO2 emission amount, a net _CO2 emission amount obtained by subtracting a CO2 offset amount by the carbon credit from the actual CO2 emission amount, an actual _power generation cost taking into account the price of the fuel, and a net power generation cost obtained by adding the price of the carbon credit to the actual power generation cost, by changing a combination of fuel, operating conditions, and carbon credits of the thermal power plant, for each combination;
An optimization step of selecting an optimal condition consisting of a combination of the fuel, the operating conditions, and the carbon credits that satisfies a predetermined selection condition for the net _CO2 emission amount and the net power generation cost;
an output step of outputting the selected optimum condition, and the actual _CO2 emission amount, the net _CO2 emission amount, the actual power generation cost, and the net power generation cost under the optimum condition;
The present invention relates to an operation support method for a thermal power plant, comprising:

According to the above aspect (1), it is possible to select the optimal carbon credit in terms of both CO2 emissions and power generation costs, and to easily evaluate the cost-effectiveness of the selected carbon credit.

(2) A method for supporting operation of a thermal power plant according to one embodiment of the present invention includes:
An input step of reading at least one or more fuel parameters indicating the price of fuel and the properties of the fuel of a thermal power plant, at least one or more operating conditions of the thermal power plant, and at least one or more carbon credit information including a _CO2 offset amount and a carbon credit price;
a calculation step of varying and inputting the fuel parameters and the operating conditions into a first prediction model that calculates a predicted value of an actual _CO2 emission amount generated when the thermal power plant is operated, calculating a net _CO2 emission amount by subtracting a _CO2 offset _amount by the carbon credit from each of the predicted values of the actual _CO2 emission amount based on a combination of the plurality of predicted values of the actual _CO2 emission amount and at least one or more carbon credits, and calculating a net power generation cost by adding a price of the carbon credit to each of the predicted values of the actual power generation cost based on a combination of the plurality of predicted values of the actual power generation cost and at least one or more carbon credits;
An optimization step of selecting an optimal condition consisting of a combination of the fuel, the operating conditions, and the carbon credits that satisfies a predetermined selection condition for the net _CO2 emissions and the net power generation cost;
an output step of outputting the selected optimum condition, and the actual _CO2 emission amount, the net _CO2 emission amount, the actual power generation cost, and the net power generation cost under the optimum condition;
The present invention relates to an operation support method for a thermal power plant, comprising:

According to the above aspect (2), the AI prediction using the predictive model makes it possible to select the optimal carbon credit in terms of both CO2 emissions and power generation costs, and to easily evaluate the cost-effectiveness of the selected carbon credit.

(3) A method for supporting operation of a thermal power plant according to one embodiment of the present invention includes:
In the input step, a target value of a _CO2 emission amount or a power generation cost of the thermal power plant is further read,
In the optimization step, as the predetermined selection condition, if the target value is a target value for the net _CO2 emissions, a condition for selecting the optimal condition in which the net _CO2 emissions match the target value is used, and if the target value is a target value for the net power generation cost, a condition for selecting the optimal condition in which the net power generation cost matches the target value is used. This is the method for supporting operation of a thermal power plant according to the above aspects (1) and (2).

According to the above aspect (3), it is possible to present optimal conditions that match the target values of CO ₂ emissions or power generation costs in accordance with the needs of operators of thermal power plants, etc.

(4) A method for supporting operation of a thermal power plant according to one embodiment of the present invention includes:
In the input step, benchmark values of CO2 emissions and power generation costs of the thermal power plant are further read in,
The method further comprises the steps of: evaluating the relative merits of the optimal conditions selected in the optimization step by using the benchmark value;
The method for supporting operation of a thermal power plant according to any one of the above aspects (1) to (3).

According to the above aspect (4), it becomes easier to decide whether to adopt the optimal conditions by comparing with the benchmark value.

(5) A method for supporting operation of a thermal power plant according to one embodiment includes:
The target value is a value set based on the results of other fuels and power generation means.
The method for supporting operation of a thermal power plant according to the aspect (3) above.

According to the above aspect (5), the optimal conditions can be evaluated in comparison with the performance of other fuels and power generation methods.

(6) A method for supporting operation of a thermal power plant according to one embodiment includes:
The benchmark value is a value set based on the performance of other fuels and power generation means.
The method for supporting operation of a thermal power plant according to the aspect (4) above.

According to the above aspect (6), the optimal conditions can be evaluated in comparison with the performance of other fuels and power generation methods.

(7) A method for supporting operation of a thermal power plant according to one embodiment includes:
The carbon credits include those that offset _CO2 by managing and cultivating forests and increasing the amount of carbon fixation using harvested timber.
The driving assistance method according to any one of the above aspects (1) to (6).

According to the above aspect (7), it is possible to purchase cost-effective carbon credits that can offset _CO2 emissions through forest management and cultivation, and also contribute to the regeneration of forestry.

(8) An operation support device for a thermal power plant according to one aspect includes:
An input unit that reads at least one or more fuel parameters indicating the price of fuel for a thermal power plant and the properties of the fuel, at least one or more operating conditions of the thermal power plant, and at least one or more carbon credit information included in the price of the fuel or available for additional purchase, the carbon credit information including the price of the carbon credit and the amount of _CO2 offset;
a calculation unit that changes a combination of the fuel parameters, the operating conditions, and the carbon credits, and calculates an actual CO2 emission amount for each combination, a net CO2 emission amount obtained by subtracting a CO2 offset amount by the carbon credit from the actual CO2 emission amount, an actual power generation cost taking into account the price of the fuel, and a net power generation cost obtained by adding the price of the carbon credit to the actual power generation cost;
An optimization unit that selects optimal conditions consisting of a combination of the fuel, the operating conditions, and the carbon credits that satisfy predetermined selection conditions for the net CO2 emissions and the net power generation cost;
an output unit that outputs the selected optimal condition, and the actual CO2 emission amount, the net CO2 emission amount, the actual power generation cost, and the net power generation cost under the optimal condition;
This is an operation support device for a thermal power plant equipped with the above.

According to the above aspect (8), it is possible to select the optimal carbon credit in terms of both CO2 emissions and power generation costs, and to easily evaluate the cost-effectiveness of the selected carbon credit.

(9) An operation support device for a thermal power plant according to one aspect includes:
Further comprising a model storage unit that stores a first prediction model that calculates a predicted value of an actual _CO2 emission amount generated when the thermal power plant is operated, and a second prediction model that calculates a predicted value of an actual power generation cost generated when the thermal power plant is operated,
The calculation unit is
The fuel parameters and the operating conditions are varied and input into the first prediction model to calculate a plurality of predicted values of actual _CO2 emissions, and based on a combination of the plurality of predicted values of actual _CO2 emissions and at least one or more carbon credits, a net _CO2 emission amount is calculated by subtracting a _CO2 offset amount by the carbon credit from each of the predicted values of actual _CO2 emissions, and
a plurality of predicted values of actual power generation costs are calculated by varying and inputting the fuel parameters and the operating conditions into the second prediction model, and a net power generation cost is calculated by adding the price of the carbon credit to each of the predicted values of actual power generation costs based on a combination of the plurality of predicted values of actual power generation costs and at least one or more carbon credits;
The operation support device for a thermal power plant according to the aspect (8) above.

According to the above aspect (9), the AI prediction using the predictive model makes it possible to select the optimal carbon credit in terms of both CO2 emissions and power generation costs, and to easily evaluate the cost-effectiveness of the selected carbon credit.

The above embodiment does not limit the present invention, and various modifications are possible within the scope of the present invention. For example, the optimal condition selected to satisfy the conditions from two perspectives, the amount of _CO2 generated and the power generation cost, does not necessarily maintain stable and safe operation of the entire plant. Therefore, it is necessary to select the optimal condition with the constraint of the range in which the margin of the control operation terminal such as the superheater spray valve opening and the process value of the monitored object such as the metal temperature do not exceed the management value. In the present invention, it is possible to derive the optimal condition evaluated from various perspectives including the above constraints.

Reference Signs List 10: Operation Support System 11: Furnace 12: Combustion Device 13: Flue 21-25: Combustion Burner 26-30: Pulverized Coal Supply Pipe 31-35: Crusher 36: Wind Box 37a-37c: Air Duct 37d: Connection Point 38: Blower 39: After Air Port 48: Exhaust Gas Passage 49: Air Heater 50: Denitrification Device 51: Soot and Dust Treatment Device 52: Induced Draft Fan 53: Chimney 62: Inclined Surface 70: Hearth Evaporation Tube 100: Operation Support Device 110: Data Acquisition Unit 112: Operation Data Storage Unit 114: Data Extraction Unit 120: Operation Control Device 220: Model Construction Unit 222: Model Storage Unit 230: Calculation Unit 232: Condition Storage Unit 240: Optimization Unit 242: Optimum Condition Evaluation Unit 250 : Output unit 260 : Input unit 301 : Processor 305 : Input I/F
306: Output I/F
307: Communication I/F
308: Bus 311: Input device 312: Display

Claims (7)

An input step of reading at least one or more fuel parameters indicating the price of fuel and the properties of the fuel of a thermal power plant, at least one or more operating conditions of the thermal power plant, and at least one or more carbon credit information including a _CO2 offset amount and a carbon credit price;
a calculation step of varying and inputting the fuel parameters and the operating conditions into a first prediction model that calculates a predicted value of an actual _CO2 emission amount generated when the thermal power plant is operated, calculating a net _CO2 emission amount by subtracting a _CO2 offset _amount by the carbon credit from each of the predicted values of the actual _CO2 emission amount based on a combination of the plurality of predicted values of the actual _CO2 emission amount and at least one or more carbon credits, and calculating a net power generation cost by adding a price of the carbon credit to each of the predicted values of the actual power generation cost based on a combination of the plurality of predicted values of the actual power generation cost and at least one or more carbon credits;
An optimization step of selecting an optimal condition consisting of a combination of the fuel, the operating conditions, and the carbon credits that satisfies a predetermined selection condition for the net _CO2 emissions and the net power generation cost;
an output step of outputting the selected optimum condition, and the actual _CO2 emission amount, the net _CO2 emission amount, the actual power generation cost, and the net power generation cost under the optimum condition;
The present invention relates to a thermal power plant operation support method. In the input step, a target value of a _CO2 emission amount or a power generation cost of the thermal power plant is further read,
In the optimization step, when the target value is a target value for the net _CO2 emission, a condition for selecting the optimal condition in which the net _CO2 emission coincides with the target value is used as the predetermined selection condition, and when the target value is a target value for the net power generation cost, a condition for selecting the optimal condition in which the net power generation cost coincides with the target value is used.
The method for supporting operation of a thermal power plant according to claim 1 . In the input step, benchmark values of the net _CO2 emissions and the net power generation cost of the thermal power plant are further read in,
Further comprising an optimal condition evaluation step of evaluating the superiority or inferiority of the optimal condition selected in the optimization step using the benchmark value.
The method for supporting operation of a thermal power plant according to claim 1 or 2 . The target value is a value set based on the results of other fuels and power generation means.
The method for supporting operation of a thermal power plant according to claim 2 . The benchmark value is a value set based on the performance of other fuels and power generation means.
The method for supporting operation of a thermal power plant according to claim 3 . The carbon credit information includes the amount of _CO2 offset achieved by managing and cultivating forests and increasing the amount of carbon fixation using harvested timber.
The method for supporting operation of a thermal power plant according to any one of claims 1 to 5 . An input unit that reads at least one or more fuel parameters indicating the price of fuel for a thermal power plant and the properties of the fuel, at least one or more operating conditions of the thermal power plant, and at least one or more carbon credit information included in the price of the fuel or available for additional purchase, the carbon credit information including the price of the carbon credit and the amount of _CO2 offset;
a calculation unit that changes a combination of the fuel parameters, the operating conditions, and the carbon credits, and calculates an actual CO2 emission amount for each combination, a net CO2 emission amount obtained by subtracting a CO2 offset amount by the carbon credit from the actual CO2 emission amount, an actual power generation cost taking into account the price of the fuel, and a net power generation cost obtained by adding the price of the carbon credit to the actual power generation cost;
An optimization unit that selects optimal conditions consisting of a combination of the fuel, the operating conditions, and the carbon credits that satisfy predetermined selection conditions for the net CO2 emissions and the net power generation cost;
an output unit that outputs the selected optimal condition, and the actual CO2 emission amount, the net CO2 emission amount, the actual power generation cost, and the net power generation cost under the optimal condition;
Further comprising a model storage unit that stores a first prediction model that calculates a predicted value of an actual _CO2 emission amount generated when the thermal power plant is operated , and a second prediction model that calculates a predicted value of an actual power generation cost generated when the thermal power plant is operated,
The calculation unit is
The fuel parameters and the operating conditions are varied and input into the first prediction model to calculate a plurality of predicted values of actual CO2 emissions, and based on a combination of the plurality of predicted values of actual CO2 _emissions and at least one or more carbon credits, a net _{CO2 emission} amount is calculated by subtracting a CO2 offset amount _by the carbon credit from each of the predicted values of actual CO2 emissions _, and
a plurality of predicted values of actual power generation costs are calculated by varying and inputting the fuel parameters and the operating conditions into the second prediction model, and a net power generation cost is calculated by adding the price of the carbon credit to each of the predicted values of actual power generation costs based on a combination of the plurality of predicted values of actual power generation costs and at least one or more carbon credits;
Operation support device for thermal power plants.

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