JP2022125756A - Operation support method and operation support device for thermal power plant - Google Patents

Abstract

To provide an operation support method for a thermal power plant that selects an optimum carbon credit from a viewpoint of both a CO2 emission and a power generation cost.SOLUTION: An operation support method for a thermal power plant comprises: a calculation step of reading a fuel price and carbon credit information for a thermal power plant, and calculating an actual CO2 emission, a net CO2 emission obtained by subtracting a CO2 offset from a carbon credit from the actual CO2 emission, an actual power cost considering the fuel price, and a net power cost obtained by adding a price of the carbon credit to the actual power cost; and an optimization step of selecting an optimum condition consisting of a combination of a fuel, an operation condition and a carbon credit such that the net CO2 emission and the net power cost satisfy a predetermined condition.SELECTED DRAWING: Figure 2

Description

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

In a thermal power plant, various fuels are combusted in a combustor of a gas turbine or a furnace of a boiler, and the resulting heat source is used to generate power. In recent years, the use of carbon-offset fuels (biomass, etc.) and carbon-free fuels (hydrogen, ammonia, etc.) has been promoted in order to reduce the environmental impact of combustion exhaust gas.

In Patent Document 1, when adopting an operation plan for power generation equipment with the lowest power generation cost, environmental burden costs such as environmental taxes are generated based on the environmental load (including _CO2 emissions) emitted from each power generation equipment. It is disclosed that it is reflected in the cost.

Japanese Patent No. 3787761

In thermal power plants, _CO2 emissions can be reduced by devising operating methods in addition to changing fuels. It is also possible to evaluate the effects of CO ₂ absorption and carbon fixation through forest management and effective use of wood, and purchase CO ₂ offsets as carbon credits.

However, there are countless combinations of fuels, operating conditions, and carbon credits for thermal power plants, and it is difficult to select the optimum combination from the viewpoint of both CO ₂ 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) originally have different meanings. , and there is a need to separately evaluate the cost-effectiveness of _CO2 reduction.

The present invention has been made in view of the above circumstances, and it is possible to select the optimum carbon credits from the viewpoint of both _CO2 emissions and power generation costs, and to easily evaluate the cost-effectiveness of the selected carbon credits. It is an object of the present invention to provide an operation support method and an operation support device for a thermal power plant capable of

In order to achieve the above objects, the present invention has the configuration described in the claims. For example, the present invention includes an input step of reading the price of fuel for a thermal power plant, the price of carbon credits and carbon credit information including the amount of _CO2 offset, and the fuel and operating conditions of the thermal power plant. And by changing the combination of carbon credits, the actual _CO2 emissions in each combination, the net _CO2 emissions obtained by subtracting the _CO2 offset amount by the carbon credits from the actual CO2 emissions, and the price of the fuel a calculation step of calculating an actual power generation cost considered and a net power generation cost obtained by adding the price of the carbon _credit to the actual power generation cost; an optimization step of selecting an optimal condition consisting of a combination of fuel, said operating conditions and said carbon credits; said selected optimal condition and said actual _CO2 emissions at said optimal condition; said net _CO2 emissions; and an output step of outputting the actual power generation cost and the net power generation cost. Alternatively, it is an operation support device for a thermal power plant having the same function.

INDUSTRIAL APPLICABILITY According to the present invention, an operation support method for a thermal power plant can select the optimum carbon credits from the viewpoint of both _CO2 emissions and power generation costs, and easily evaluate the cost-effectiveness of the selected carbon credits. And a driving support device can be provided. Problems, configurations, and effects other than those described above will be clarified by the following description of the embodiments.

Schematic diagram of the boiler Schematic diagram of boiler operation support system 10 Diagram showing the hardware configuration of the driving support device Flowchart showing processing by the boiler operation support device 100 Flowchart showing details of processing from acquisition of driving data to construction of prediction model Diagram showing the contents of calculation in the calculation part A diagram showing an example of calculation contents in the optimization part and the optimum condition evaluation part Diagram showing an example of the output screen in the output section Diagram showing an example of operation support in a boiler A diagram showing an example of a business model using the driving support device 100

Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the present invention is not limited by this embodiment, and when there are a plurality of embodiments, a combination of each embodiment is also included. The same reference numerals are assigned to the same configurations and steps throughout the drawings, and redundant description is omitted.

In this embodiment, a boiler will be described as an example of a thermal power plant. FIG. 1 is a schematic configuration diagram of the boiler 1. As shown in FIG. The boiler 1 of the present embodiment is a boiler 1 that pulverizes solid fuel such as coal and biomass, and is capable of mono-combustion operation of coal and co-combustion operation of multiple types of fuel such as coal and biomass.

The boiler 1 has a furnace 11 , a combustion device 12 and a flue 13 . The furnace 11 has, for example, a hollow square shape and is installed along the vertical direction. The wall surface of the furnace 11 is composed of evaporating tubes (heat transfer tubes) and fins connecting the evaporating tubes. suppressing the rise. Specifically, on the side wall surface of the furnace 11, a plurality of evaporation tubes are arranged, for example, along the vertical direction and are arranged side by side in the horizontal direction. The fins block the space between the evaporation tubes. The furnace 11 is provided with an inclined surface 62 on the bottom, and the inclined surface 62 is provided with a furnace bottom evaporator 70 to form a 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 a plurality of combustion burners (eg 21, 22, 23, 24, 25) mounted on the furnace wall. For example, a plurality of combustion burners (burners) 21, 22, 23, 24, and 25 are arranged along the circumferential direction of the furnace 11 at equal intervals. However, the shape of the furnace, the arrangement of burners, the number of combustion burners in one stage, and the number of stages are not limited to this embodiment.

These combustion burners 21 , 22 , 23 , 24 , 25 are connected to pulverizers (coal pulverizers/mills: equivalent to auxiliary machines) 31 , 32 via pulverized coal supply pipes 26 , 27 , 28 , 29 , 30 . , 33, 34, 35. When coal is conveyed by a conveying system (not shown) and put into the pulverizers 31, 32, 33, 34, and 35, it is pulverized to a predetermined fine powder size, and is then pulverized together with air for transportation (primary air). Pulverized coal (pulverized coal) can be supplied to the combustion burners 21 , 22 , 23 , 24 and 25 from the pulverized coal supply pipes 26 , 27 , 28 , 29 and 30 .

Further, the furnace 11 is provided with a wind box 36 at the mounting position of each of the combustion burners 21, 22, 23, 24, 25. One end of the air duct 37b is connected to the wind box 36, and the other end of the air duct 37b is connected at a connection point 37d to an air duct 37a supplying air.

A flue 13 is connected vertically above the furnace 11, and a plurality of heat exchangers (41, 42, 43, 44, 45, 46, 47) for generating steam in the flue 13. are placed. Therefore, the combustion burners 21 , 22 , 23 , 24 , 25 inject a mixture of pulverized fuel and combustion air into the furnace 11 to form a flame and generate a combustion gas that flows into the flue 13 . . The combustion gas heats feed water and steam flowing through the furnace wall and the heat exchangers (41 to 47) to generate superheated steam. Power can be generated by rotating a generator (not shown) connected to the rotating shaft of the turbine. Further, the flue 13 is connected to an exhaust gas passage 48, and is connected to a denitrification device 50 for purifying combustion gas. An air heater 49 for exchanging heat with the air heater 49, a dust processing device 51, an induced draft fan 52, etc. are provided, and a chimney 53 is provided at the downstream end. Note that the denitrification device 50 may not be provided if the exhaust gas standard can be satisfied.

In the furnace 11 of the present embodiment, after excessive fuel combustion by air for pulverized coal carrier (primary air) and combustion air (secondary air) introduced into the furnace 11 from the wind box 36, new combustion air ( It is a so-called two-stage combustion type furnace in which after-air is introduced to perform fuel-lean combustion. Therefore, 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 for supplying air at a connection point 37d. Note that the after air port 39 may not be provided if the two-stage combustion method is not employed.

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

FIG. 2 is a schematic explanatory diagram of the operation support system 10 for the boiler 1. As shown in FIG. The operation assistance system 10 includes a boiler 1 , an operation assistance device 100 for the boiler 1 , and an operation control device 120 for the boiler 1 .

The operation support device 100 selects the optimum combination of fuel, operating conditions, and carbon credits from the viewpoint of both the CO ₂ emissions of the boiler 1 and the power generation cost, and easily evaluates the cost-effectiveness of the selected carbon credits. Aim to be able to

The driving support 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, and an optimum condition evaluation unit. 242 , an output section 250 and an input section 260 . The function of each part will be described later.

FIG. 3 is a diagram showing the hardware configuration of the driving support device 100. As shown in FIG. The driving support device 100 includes a processor 301, a RAM (Random Access Memory) 302, a ROM (Read Only Memory) 303, a HDD (Hard Disk Drive) 304, an input I/F 305, an output I/F 306, and a communication I/F 307. , which are connected to each other via a bus 308, using a computer. The processor 301 is a GPU (Graphics Processing Unit).
It) or a CPU (Central Processing Unit) may be used, and any type of device may be used as long as it executes an arithmetic function. Moreover, the hardware configuration of the driving assistance device 100 is not limited to the above, and may be configured by 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 implements each function of the driving assistance device 100, or by the control circuit performing calculations.

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

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

Each of the boiler 1 and the operation control apparatus 120 is connected to communication I/F307.

FIG. 4 is a flow chart showing the main flow of selecting the fuel, operating conditions, and optimum carbon credit conditions by the operation support system 10 of the boiler 1, evaluating the results, and outputting them.

<S1: Model Building Step>
The model building unit 220 of the operation support device 100 includes operation terminal parameters (one of the operating conditions) to be set for the operation terminal of the boiler 1, fuel parameters including the property and price of the fuel of the boiler 1 (the fuel property is the operating condition). become one) is added to the input parameters (explanatory variables in the regression model) to model the process value, that is, to build a predictive model of the process value (S1). The constructed prediction model is stored in the model storage unit 222 . FIG. 5 is a flow chart showing the details of processing from acquisition of driving data to construction of a 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 the sensors 1, 2, . . . , M during actual operation, and Acquire the set control device parameters (control device set values) and fuel parameters (property parameters, co-firing ratio, and price of each fuel if multiple fuels are co-combusted), and associate process values, control device parameters, and fuel parameters. The operating data is generated and stored in the operating data storage unit 112 .

The process values include, for example, the CO ₂ emissions contained in the gas discharged from the thermal power plant (actual CO ₂ emissions) and the cost of power generation at the thermal power plant (actual power generation cost). If there is no sensor for measuring CO ₂ emissions, the actual CO ₂ emissions are calculated by combining various process values (coal consumption, etc.). The same applies to actual power generation costs.

The data acquisition unit 110 may add time information to each of the operating-end parameters and the actual process values to generate operation data composed of time-series data.

The driving data acquired in this embodiment serves as teaching data for constructing prediction models 1 and 2. FIG. The teaching data is not limited to the actual process values and the operating conditions (operating end parameters, co-firing rate, fuel properties) when they are obtained, but may be calculated values obtained by analyzing the boiler 1 .

Preprocessing is performed by the data extraction unit 114 (S102). The data extraction unit 114 reads out the operation data already described in the operation data storage unit 112, and compensates for missing data by performing linear interpolation. Also, the operating conditions including the set values of the operation terminal parameters are read from the operating data, and the settling data are extracted.

At this time, using the actual process values obtained by actually measuring the sensors 1, 2, . . . A value may be output to the data acquisition unit 110 .

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

The model building unit 220 creates a prediction model based on preset conditions such as explanatory variables/objective variables and target errors.

FIG. 6 is a diagram showing an example of a prediction model created by the model construction unit 220 and explaining the contents of calculation in the calculation unit 230 using the prediction model. The model construction unit 220 constructs prediction models 1 and 2 corresponding to the process values shown in FIG. Specifically, the model building unit 220 uses control values (manipulator parameters) set to the manipulators 1 to M and fuel properties (including fuel co-firing ratio) as input parameters. Each process value (measured value) obtained by actual operation set to Prediction models 1 and 2 are constructed by machine learning prediction models corresponding to process values. The input parameters may include operating parameters (such as operating load) and other parameters such as atmospheric conditions.

When constructing prediction models 1 and 2 using a regression model, the model construction unit 220 constructs a regression model using the above input parameters as explanatory variables and the actual CO ₂ emissions as objective variables in the case of prediction model 1. let them learn Similarly, the predictive model 2 uses explanatory variables similar to those of the predictive model 1 and machine-learns a regression model with the actual power generation cost as the objective variable. These constructed prediction models 1 and 2 are stored in the model storage unit 222 . Although two prediction models 1 and 2 are constructed in this example, a prediction model for another process value may be added and constructed. In addition, although it was assumed that the actual _CO2 emissions and actual power generation costs can be obtained directly from prediction models 1 and 2, the actual _CO2 emissions and actual power generation costs were calculated by combining process values predicted from multiple prediction models. You can ask for it.

<S2: Input step>
The input unit 260 reads the fuel parameters including the property and price of the fuel for the thermal power plant and the carbon credit information (price, _CO2 offset amount) input by the input device 311 . Also, a target value of CO ₂ emissions or power generation cost of a thermal power plant may be read. Furthermore, the input device 311 also inputs the final parameters to be input to the prediction models 1 and 2 .

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

<S3: Calculation step>
The calculation unit 230 reads from the condition storage unit 232 possible options for fuel parameters, operating conditions (operating end parameters), and carbon credits (CO ₂ offset amount, price) of the thermal power plant. In the present embodiment, it is assumed that the operating terminal parameters are stored in the condition storage unit 232 , but the operation unit 230 may read them from the operation data storage unit 223 instead of storing them in the condition storage unit 232 .

Furthermore, as shown in FIG. 6, the calculation unit 230 changes the combination of fuel parameters, operating conditions (operating end parameters), and carbon credits (CO ₂ offset amount, price) of the thermal power plant, and the actual CO ₂ emissions, the net _CO2 emissions obtained by subtracting the amount of _CO2 offset by carbon credits from the actual _CO2 emissions, the actual power generation cost considering the price of fuel, and the price of carbon credits added to the actual power generation cost. Calculate with the added net power generation cost. Specifically, the calculation unit 230 inputs the fuel parameters and operating conditions to prediction models 1 and 2 while changing them, and calculates a plurality of actual CO ₂ emissions and actual power generation costs. Furthermore, the calculation unit 230 combines each of the actual CO ₂ emissions and the actual power generation costs with at least one piece of carbon credit information to calculate the net CO ₂ emissions and the net power generation costs using the following equations (1) and (2). , (3).

Net _CO2 emissions (g/kWh) = Actual _CO2 emissions (g/kWh) - Unit _CO2 offset (g/kWh) (1)
Unit carbon credit price (yen/kWh) = Unit _CO2 offset amount (g/kWh) x _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 CO ₂ emissions and power generation costs are expressed per unit power generation kWh, it is necessary to match the units of CO ₂ offset amount (g) and carbon Gillette price (yen) (per kWh). Therefore, as shown in Equation (1), the net CO ₂ emissions (g/kWh) are obtained using the unit CO ₂ offset amount (g/kWh), which is the CO ₂ offset amount per power generation amount. Then, multiply the unit _CO2 offset amount (g/kWh) by the _CO2 offset unit price (yen/g) to obtain the unit carbon credit price (yen/kWh), which is the carbon credit price required per power generation amount. (see formula (2)), and use that value to calculate the net power generation cost (yen/kWh) (see formula (3)).

If there are restrictions on the amount of carbon credits supplied (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 assumed period, and the _CO2 offset amount (carbon credit purchase amount) is this value. Make sure it's over.

_CO2 offset amount (carbon credit purchase amount) (g) ≥ unit _CO2 offset amount (g/kWh) x power generation amount (kWh) (4)

<S4: Optimization Step>
An optimization unit 240 selects the optimal conditions consisting of combinations of fuels, operating conditions and carbon credits for which the net _CO2 emissions and net power generation costs satisfy predetermined conditions.

The content of the above calculation will be described with reference to FIG. FIG. 7 is a schematic diagram showing the contents of calculations in the optimization section and the optimum condition evaluation section.

First, a plurality of calculation results in the calculation unit 230 are plotted on the two axes of net CO ₂ emissions and net power generation cost. The white circles in FIG. 7 correspond to the calculation results, and it can be seen that the plot as a whole descends to the right. Of these, if the net _CO2 emissions (or net power generation costs) are the same, the one with the lower net power generation costs (or net _CO2 emissions) is preferable. Therefore, the calculation results enclosed by the dashed-dotted lines in FIG. 7 are candidates for the optimum conditions. From among these candidates, an optimum condition that satisfies a predetermined selection condition is selected.

As a predetermined selection condition for selecting the optimum condition, for example, there is a method of setting a predetermined net _CO2 emission amount range or a predetermined net power generation cost range. In addition, optimization is performed to maximize the evaluation value that is the sum of the difference between the net _CO2 emissions and net operating cost and the benchmark described later (and other evaluation indicators), and the optimum condition is selected from among the search points. , there is also a method.

If target values for CO ₂ emissions or power generation costs are stored in the condition storage unit 232, the target values are read by the optimization unit 240, and optimum conditions that match the target values are selected. For example, when the target value C1 of the power generation cost is stored, the optimum condition R1 is selected from the calculation results in which the net power generation cost matches C1. Similarly, when the target value E1 of the _CO2 emission amount is stored, the optimum condition R2 is selected from the calculation results in which the net _CO2 emission amount matches E1.

In Fig. 7, an example of selecting the optimum conditions on the two axes of net CO2 emissions and net power generation cost was explained, but in addition to that, control operation parameters such as the opening of the spray valve that controls the steam temperature and the opening of the gas path damper A method of quantifying the tolerance for alarm values such as the tolerance of the heat transfer tube surface temperature (metal temperature) and selecting the optimal condition that satisfies the above predetermined selection conditions from among the optimal conditions that also take these into account. good too.

<S5: Optimal condition evaluation step>
If the condition storage unit 232 stores the benchmark values of the CO ₂ emissions and the power generation cost, the optimum condition evaluation unit 242 reads the benchmark values and calculates the benchmark values and the optimum conditions selected in the optimization step S4. Evaluate the superiority and inferiority of

Specifically, for the benchmark value B1 indicated by the black circle in FIG. is judged to be “excellent”. If the optimal condition is in the upper right quadrant of the figure with respect to the benchmark value B1, it is evaluated as "inferior". If it is in other quadrants, it is determined that "detailed confirmation is required."

<Output step S6>
The optimum conditions selected by the optimization unit 240, the actual CO2 emissions, the net CO2 emissions, the actual power generation costs, and the net power generation costs under the optimum conditions are output from the output unit 250 to the output device (display 312). Also, after confirmation by the operator of the thermal power plant, the optimum conditions are transmitted to the operation control device 120 .

FIG. 8 is an example of an output screen. The following contents are output on the output screen.
(1) Selection conditions: If there is a target value or benchmark value, display that value.
(2) Fuel condition: Large classification (classification) and detailed classification (production area, etc.) of fuel types, and when using multiple fuels, their co-firing ratios are displayed.
(3) Operating conditions: displays the major and detailed classifications of the types of final parameters and the set values.
(4) Carbon credits: Displays carbon credit providers (purchasers), their types (type of business related to _CO2 offset, location, etc.), _CO2 offset amount, and price (purchase price or _CO2 offset unit price). do.
(5) Calculation result: Displays the actual power generation cost, net power generation cost, actual CO2 emissions, and net CO2 emissions calculated under the optimum conditions shown in (2) to (4).
(6) Evaluation result: If there is a benchmark value, the result of comparison with the benchmark is displayed. Also, as shown in the lower right figure, the calculation results and benchmark values under the optimum conditions are displayed in a bar graph.

<Examination example for boiler>
An example of consideration of operation support for the boiler 1 will be described with reference to FIG. 9 . Figure 9 summarizes the concept of economic value and environmental value of _CO2 negative (offset) business. It shows the _CO2 emissions at the power plant. Comparing coal-fired (far left in the figure), biomass-fired (right and left center), and gas-fired (rightmost in the figure), power generation costs increase in the order of coal < gas < biomass, and _CO2 emissions generally increases when biomass < gas < coal.

Here, the actual power generation cost of biomass-fired power generation minus the power generation cost of coal-fired power generation is carbon negative cost A1, and the actual _CO2 emissions of coal-fired power generation minus the actual _CO2 emissions of biomass-fired power generation is When the carbon offset amount is B1, a _CO2 negative business is a business that creates a carbon offset amount of B1 by means of _CO2 fixation such as forest management/cultivation, biomass carbonization, etc. with carbon negative cost A1 as the base. Point.

If coal-fired thermal power uses a _CO2 negative business, the net power generation cost (same value as the actual power generation cost of biomass-fired thermal power), which is the carbon negative cost A1 added to the actual power generation cost of coal _- fired thermal power, is calculated as follows: If the net _CO2 emissions obtained by subtracting the carbon offset amount B1 from the emissions are the same as the actual _CO2 emissions of biomass-fired thermal power, the economic value and environmental value of coal-fired thermal power + _CO2 negative business and biomass-fired thermal power are equal. Therefore, coal-fired power is an option as a power generation method from the viewpoint of _CO2 emissions.

Similarly, by subtracting the actual power generation cost of coal-fired thermal power from the actual power generation cost of gas-fired thermal power, the carbon negative cost _A2 is used as the basis for the _CO2 negative business to If the carbon offset amount B2 can be created by subtracting _CO2 emissions, the economic value and environmental value of coal-fired thermal power + _CO2 negative business and gas-fired thermal power will be equal.

_In this figure, for the sake of understanding, the carbon negative cost A1 and the carbon offset amount B1 are set. It goes without saying that this may occur. In addition to the above-mentioned forest cultivation and management and biomass carbonization, CO ₂ negative projects can include various forms such as wood materials and bio-ethylene. Various means can be selected depending on the situation.

<Overview of business model>
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 considers the carbon credit system for the driving support device 100 and proposes economical fuels to power generation companies.

First, the power generation company entrusts (signals) low-carbon operation to the operation support device 100 . The signal here corresponds to the CO ₂ reduction target amount and the power generation cost upper limit amount.

When the driving support device 100 receives the low-carbon driving signal, it takes in the fuel information (calorific value, price, carbon credit) of zero-emission coal. Here, zero-emission coal is set at a price in which carbon credits are added to the normal fuel price, and when purchased by a power generation business that has introduced the driving support device 100, the carbon credits are transferred to the driving support device 100. It is designed to be able to Therefore, zero-emission coal is a fuel that has received carbon credit certification from the driving assistance device 100 in advance.

At the same time, fuel information (calorific value, price, co-firing ratio) such as biomass and carbon-neutral fuel is input to the driving support device 100 . Based on the input information, the driving support device 100 satisfies the CO ₂ reduction target or the upper limit of CO ₂ emissions presented by the power generation company when commissioning low-carbon operation, and can reduce the power generation cost as much as possible. Present the amount of zero-emission coal purchased and the co-firing rate. In addition, if the amount is still insufficient, a solution is calculated to purchase the insufficient amount of carbon credits from the carbon credit system. In some cases, if the unit price of carbon credits is low, the solution may be to procure carbon credits under the carbon credit system instead of purchasing zero-emission coal.

When the calculation is completed, in addition to the CO ₂ emissions and power generation costs that are the optimization results of the operation support device 100, the purchased amount of zero-emission coal, the purchased amount of various fuels, and the purchased amount of carbon credits from the carbon credit system can be operated. It is disclosed from the support device 100 to the power generation company. If the power generator commits this, the purchase of zero-emission coal, the purchase of various fuels, and the procurement of carbon credits will be carried out as presented by the driving support device 100 . The driving support device 100 drives the procured fuel as optimized by the driving support device 100, accurately monitors daily _CO2 emissions, and purchases carbon credits on behalf of the driver.

With such a mechanism, power producers can visualize _CO2 emissions, achieve _CO2 reduction targets including carbon credits, and operate power generation costs without exceeding the upper limit.

The contents described in each of the above embodiments are understood as follows, for example.

(1) A thermal power plant operation support method according to one aspect includes:
an input step of reading the price of fuel for a thermal power plant and carbon credit information including the price of carbon credits and the amount of _CO2 offset;
By changing the combination of fuel, operating conditions, and carbon credits of the thermal power plant, the actual _CO2 emissions in each combination, and the net _CO2 emissions obtained by subtracting the _CO2 offset amount by the carbon credits from the actual CO2 emissions a calculation step of calculating an emission amount, an actual power generation cost considering the price of the fuel, and a net power generation cost obtained by adding the carbon credit price to the actual power generation cost;
an optimization step of selecting an optimum condition consisting of a combination of the fuel, the operating condition, and the carbon credits for which the net _CO2 emissions and the net power generation cost meet predetermined selection conditions;
an output step of outputting the selected optimal condition, the actual _CO2 emissions, the net _CO2 emissions, the actual power generation cost, and the net power generation cost under the optimal conditions;
It is an operation support method for a thermal power plant including

According to the aspect (1) above, it is possible to select the optimum carbon credits from the viewpoint of both CO2 emissions and power generation costs, and to easily evaluate the cost-effectiveness of the selected carbon credits.

(2) A thermal power plant operation support method according to one aspect includes:
At least one fuel parameter indicating the price of fuel for a thermal power plant and the properties of the fuel, at least one operating condition of the thermal power plant, the amount of CO ₂ offset, and the price of carbon credits. an input step of reading one or more carbon credit information;
The fuel parameter and the operating conditions are changed and input to a first prediction model for calculating the predicted value of the actual _CO2 emissions generated when the thermal power plant is operated, and the predicted values of the actual _CO2 emissions are calculated. A plurality of calculations are performed, and based on a combination of these multiple predicted values of actual CO ₂ emissions and at least one or more carbon credits, the amount of CO ₂ offset by the carbon credits from each of the predicted values of the actual CO ₂ emissions. The fuel parameter and the operating condition are changed and input to a second prediction model that calculates the net CO ₂ emissions minus the to calculate a plurality of predicted values of actual power generation costs, and based on a combination of the plurality of predicted values of actual power generation costs and at least one or more carbon credits, the carbon credits are added to each of the predicted values of actual power generation costs. A calculation step of calculating the net power generation cost by adding the price of
an optimization step of selecting an optimum condition consisting of a combination of the fuel, the operating condition and the carbon credits for which the net _CO2 emissions and the net power generation cost meet predetermined selection conditions;
an output step of outputting the selected optimal condition, the actual _CO2 emissions, the net _CO2 emissions, the actual power generation cost, and the net power generation cost under the optimal conditions;
It is an operation support method for a thermal power plant including

According to the above aspect (2), AI prediction using a prediction model selects the optimal carbon credits from the viewpoint of both CO2 emissions and power generation costs, and facilitates the cost effectiveness of the selected carbon credits. can be evaluated.

(3) A thermal power plant operation support method according to one aspect includes:
The input step further reads a target value of the _CO2 emissions or power generation cost of the thermal power plant;
In the optimization step, as the predetermined selection condition, if the target value is a target value for the net _CO2 emission amount, a condition for selecting the optimum condition under which the net _CO2 emission amount matches the target value. is used, and if the target value is the target value for the net power generation cost, the condition for selecting the optimum condition where the net power generation cost matches the target value is used. It is the operation support method of the described thermal power plant.

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

(4) A thermal power plant operation support method according to one aspect includes:
The input step further reads benchmark values of CO2 emissions and power generation costs of thermal power plants,
Further comprising an optimum condition evaluation step of evaluating the superiority or inferiority of the optimum condition selected in the optimization step using the benchmark value,
A method for supporting the operation of a thermal power plant according to any one of aspects (1) to (3) above.

According to the aspect (4) above, it becomes easy to determine whether to adopt the optimum condition by comparing with the benchmark value.

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

According to the aspect (5) above, the optimum conditions can be evaluated in comparison with the results of other fuels and power generation means.

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

According to the aspect (6) above, the optimum conditions can be evaluated in comparison with the results of other fuels and power generation means.

(7) A thermal power plant operation support method according to one aspect includes:
The carbon credits include those that offset CO ₂ by managing and cultivating forests and increasing the amount of carbon sequestration using wood after logging.
The driving support method according to any of the aspects (1) to (6) above.

According to the above aspect (7), it is possible to purchase carbon credits with high cost effectiveness that can offset CO ₂ through forest management and cultivation, and contribute to forestry regeneration.

(8) An operation support device for a thermal power plant according to one aspect,
At least one or more fuel parameters indicating the price of fuel for a thermal power plant and properties of the fuel, at least one or more operating conditions of the thermal power plant, and included in the price of the fuel or available for additional purchase an input unit that reads at least one or more pieces of carbon credit information, the carbon credit information including the price of the carbon credit and the amount of _CO2 offset;
By changing the combination of the fuel parameters, the operating conditions, and the carbon credits, the actual CO2 emissions in each combination, and the net CO2 emissions obtained by subtracting the CO2 offset amount by the carbon credits from the actual CO2 emissions. , a calculation unit that calculates an actual power generation cost considering the price of the fuel and a net power generation cost that is obtained by adding the price of the carbon credit to the actual power generation cost;
an optimizing unit that selects an optimum condition consisting of a combination of the fuel, the operating condition, and the carbon credit that satisfies a predetermined selection condition for the net CO2 emissions and the net power generation cost;
an output unit that outputs the selected optimal condition, the actual CO2 emissions, the net CO2 emissions, the actual power generation cost, and the net power generation cost under the optimal conditions;
It is an operation support device for a thermal power plant.

According to the aspect (8) above, it is possible to select the optimum carbon credits from the viewpoint of both CO2 emissions and power generation costs, and to easily evaluate the cost-effectiveness of the selected carbon credits.

(9) A thermal power plant operation support device according to one aspect,
A first prediction model that calculates a predicted value of the actual _CO2 emissions generated when the thermal power plant is operated, and a second prediction model that calculates a predicted value of the actual power generation cost that occurs when the thermal power plant is operated. Further comprising a model storage unit that stores the prediction model,
The calculation unit is
A plurality of predicted values of actual CO ₂ emissions are calculated by changing and inputting the fuel parameter and the operating condition to the first prediction model, and these plurality of predicted values of actual CO ₂ emissions and at least one or more Based on the combination with the carbon credit, calculating the net CO ₂ emissions by subtracting the CO ₂ offset amount due to the carbon credits from each predicted value of the actual CO ₂ emissions,
The fuel parameter and the operating condition are changed and input to the second prediction model to calculate a plurality of predicted values of the actual power generation cost, and the plurality of predicted values of the actual power generation cost and at least one or more carbon credits are calculated. Based on the combination, calculating a net power generation cost by adding the price of the carbon credit to each of the predicted values of the actual power generation cost;
It is an operation support device for a thermal power plant according to the aspect (8) above.

According to the above aspect (9), AI prediction using a prediction model selects the optimal carbon credits from the viewpoint of both CO2 emissions and power generation costs, and facilitates the cost effectiveness of the selected carbon credits. can be evaluated.

The above embodiment does not limit the present invention, and there are various modifications without departing from the scope of the present invention. For example, the optimum conditions selected to satisfy the two conditions of CO ₂ generation amount and power generation cost do not necessarily maintain stable and safe operation of the entire plant. Therefore, it is necessary to select an optimum condition that restricts the range in which the process values to be monitored, such as the tolerance of the control operation terminal such as the opening of the superheater spray valve and the metal temperature, do not exceed the control values. In the present invention, it is possible to derive optimum conditions evaluated from multiple viewpoints including the above constraints.

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: Air box 37a-37c: Air duct 37d: Connection point 38: Blower 39: After airport 48: Exhaust gas passage 49: Air heater 50: Denitrification device 51: Dust treatment device 52: Induced draft fan 53: Chimney 62: Inclined surface 70: Furnace bottom evaporator 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 (9)

an input step of reading the price of fuel for a thermal power plant and carbon credit information including the price of carbon credits and the amount of _CO2 offset;
By changing the combination of fuel, operating conditions, and carbon credits of the thermal power plant, the actual _CO2 emissions in each combination, and the net _CO2 emissions obtained by subtracting the _CO2 offset amount by the carbon credits from the actual CO2 emissions a calculation step of calculating an emission amount, an actual power generation cost considering the price of the fuel, and a net power generation cost obtained by adding the carbon credit price to the actual power generation cost;
an optimization step of selecting an optimum condition consisting of a combination of the fuel, the operating condition, and the carbon credits for which the net _CO2 emissions and the net power generation cost meet predetermined selection conditions;
an output step of outputting the selected optimal condition, the actual _CO2 emissions, the net _CO2 emissions, the actual power generation cost, and the net power generation cost under the optimal conditions;
A method for supporting operation of a thermal power plant, comprising: At least one fuel parameter indicating the price of fuel for a thermal power plant and the properties of the fuel, at least one operating condition of the thermal power plant, the amount of CO ₂ offset, and the price of carbon credits. an input step of reading one or more carbon credit information;
The fuel parameter and the operating conditions are changed and input to a first prediction model for calculating the predicted value of the actual _CO2 emissions generated when the thermal power plant is operated, and the predicted values of the actual _CO2 emissions are calculated. A plurality of calculations are performed, and based on a combination of these multiple predicted values of actual CO ₂ emissions and at least one or more carbon credits, the amount of CO ₂ offset by the carbon credits from each of the predicted values of the actual CO ₂ emissions. The fuel parameter and the operating condition are changed and input to a second prediction model that calculates the net CO ₂ emissions minus the to calculate a plurality of predicted values of actual power generation costs, and based on a combination of the plurality of predicted values of actual power generation costs and at least one or more carbon credits, the carbon credits are added to each of the predicted values of actual power generation costs. A calculation step of calculating the net power generation cost by adding the price of
an optimization step of selecting an optimum condition consisting of a combination of the fuel, the operating condition and the carbon credits for which the net _CO2 emissions and the net power generation cost meet predetermined selection conditions;
an output step of outputting the selected optimal condition, the actual _CO2 emissions, the net _CO2 emissions, the actual power generation cost, and the net power generation cost under the optimal conditions;
A method for supporting operation of a thermal power plant, comprising: The input step further reads a target value of the _CO2 emissions or power generation cost of the thermal power plant;
In the optimization step, as the predetermined selection condition, if the target value is a target value for the net _CO2 emission amount, a condition for selecting the optimum condition under which the net _CO2 emission amount matches the target value. and if the target value is a target value for the net cost of power generation, using a condition that selects the optimum condition where the net cost of power generation matches the target value;
The operation support method for a thermal power plant according to claim 1 or 2. The input step further reads benchmark values of the net _CO2 emissions and the net power generation cost of a thermal power plant;
further comprising an optimum condition evaluation step of evaluating the superiority or inferiority of the optimum condition selected in the optimization step using the benchmark value;
The operation support method for a thermal power plant according to any one of claims 1 to 3. The target value is a value set based on the results of other fuels and power generation means,
The operation support method for a thermal power plant according to claim 3. The benchmark value is a value set based on the results of other fuels and power generation means,
The operation support method for a thermal power plant according to claim 4. The carbon credit information includes the amount of _CO2 offset by managing and cultivating forests and increasing the amount of carbon sequestration using wood after logging.
The operation support method for a thermal power plant according to any one of claims 1 to 6. At least one or more fuel parameters indicating the price of fuel for a thermal power plant and properties of the fuel, at least one or more operating conditions of the thermal power plant, and included in the price of the fuel or available for additional purchase an input unit that reads at least one or more pieces of carbon credit information, the carbon credit information including the price of the carbon credit and the amount of _CO2 offset;
By changing the combination of the fuel parameters, the operating conditions, and the carbon credits, the actual CO2 emissions in each combination, and the net CO2 emissions obtained by subtracting the CO2 offset amount by the carbon credits from the actual CO2 emissions. , a calculation unit that calculates an actual power generation cost considering the price of the fuel and a net power generation cost that is obtained by adding the price of the carbon credit to the actual power generation cost;
an optimizing unit that selects an optimum condition consisting of a combination of the fuel, the operating condition, and the carbon credit that satisfies a predetermined selection condition for the net CO2 emissions and the net power generation cost;
an output unit that outputs the selected optimal condition, the actual CO2 emissions, the net CO2 emissions, the actual power generation cost, and the net power generation cost under the optimal conditions;
An operation support device for a thermal power plant. A first prediction model that calculates a predicted value of the actual _CO2 emissions generated when the thermal power plant is operated, and a second prediction model that calculates a predicted value of the actual power generation cost that occurs when the thermal power plant is operated. Further comprising a model storage unit that stores the prediction model,
The calculation unit is
A plurality of predicted values of actual CO ₂ emissions are calculated by changing and inputting the fuel parameter and the operating condition to the first prediction model, and these plurality of predicted values of actual CO ₂ emissions and at least one or more Based on the combination with the carbon credit, calculating the net CO ₂ emissions by subtracting the CO ₂ offset amount due to the carbon credits from each predicted value of the actual CO ₂ emissions,
The fuel parameter and the operating condition are changed and input to the second prediction model to calculate a plurality of predicted values of the actual power generation cost, and the plurality of predicted values of the actual power generation cost and at least one or more carbon credits are calculated. Based on the combination, calculating a net power generation cost by adding the price of the carbon credit to each of the predicted values of the actual power generation cost;
The operation support device for a thermal power plant according to claim 8.

Images