Operation support method and operation support device for thermal power plant

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]

[0001] 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 CO2 emissions and power generation costs by combining carbon credits. [Background technology]

[0002] In thermal power plants, various fuels are burned in the combustor of a gas turbine or in the furnace of a boiler to generate electricity. In recent years, in order to reduce the environmental impact of combustion exhaust gas, the use of fuels that can offset carbon emissions (biomass, etc.) and carbon-free fuels (hydrogen, ammonia, etc.) has been promoted.

[0003] Patent Document 1 discloses that when adopting an operation plan for the power generation facility with the lowest power generation cost, environmental burden costs such as environmental taxes are reflected in the power generation cost based on the environmental load (including CO2 emissions) emitted by each power generation facility. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 3787761 Summary of the Invention [Problem to be solved by the invention]

[0005] In addition to changing fuels, thermal power plants can also reduce CO2 emissions by improving their 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 CO2 offset amount can be purchased as carbon credits.

[0006] 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 in terms of both CO2 emissions and power generation costs.In addition, CO2 reductions (reducing CO2 emissions) through changes in fuel and operating conditions for thermal power plants and CO2 reductions through carbon credits (offsetting through CO2 absorption and fixation) are fundamentally different in meaning, and there is a need to evaluate the cost-effectiveness of CO2 reductions separately.

[0007] The present invention has been made in consideration of the above-mentioned situation, and aims to provide an operation support method and 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. [Means for solving the problem]

[0008] 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 the amount of CO2 offset, 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 amount of CO2 offset 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 in 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, 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, the present invention is an operation support device for a thermal power plant having the same function. Effect of the Invention

[0009] According to the present invention, it is possible to provide an operation support method and an operation support device for a thermal power plant that can select an optimal carbon credit from the viewpoints of both CO2 emissions and power generation costs and easily evaluate the cost-effectiveness of the selected carbon credit. Problems, configurations, and effects other than those described above will become clear from the description of the following embodiments. [Brief description of the drawings]

[0010] [Figure 1] Schematic diagram showing the boiler [Diagram 2] Schematic diagram of boiler operation support system 10 [Diagram 3] FIG. 2 is a diagram showing a hardware configuration of a driving assistance device. [Figure 4] 1 is a flowchart showing processing by the boiler operation support device 100. [Diagram 5] A flowchart showing the details of the process from acquiring driving data to building a predictive model. [Figure 6] Diagram showing the calculation contents in the calculation unit [Figure 7] A diagram showing an example of the calculation contents in the optimization unit and the optimal condition evaluation unit. [Figure 8] FIG. 13 is a diagram showing an example of an output screen in the output unit. [Figure 9] Diagram showing an example of operational support for a boiler [Figure 10] FIG. 1 shows an example of a business model using the driving support device 100. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] A preferred embodiment of the present invention will be described in detail below with reference to the accompanying drawings. Note that the present invention is not limited to this embodiment, and when there are multiple embodiments, the present invention also includes a configuration in which each embodiment is combined. The same components and steps are given the same reference numerals throughout the drawings, and duplicated explanations will be omitted.

[0012] In this embodiment, a boiler will be taken as an example of a thermal power plant for explanation. Fig. 1 is a schematic configuration 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 operation with coal and mixed-fuel combustion operation with multiple types of fuels such as coal and biomass.

[0013] 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, a plurality of 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 furnace bottom, and the furnace bottom evaporation tube 70 is provided on the inclined surface 62 to form the bottom surface.

[0014] 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 (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.

[0015] Each of these combustion burners 21, 22, 23, 24, 25 is connected to pulverizers (coal pulverizers / mills: corresponding to auxiliary equipment) 31, 32, 33, 34, 35 via pulverized coal supply pipes 26, 27, 28, 29, 30. When coal is transported by a transport system (not shown) and input into the pulverizers 31, 32, 33, 34, 35, it is pulverized into 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 transport air (primary air).

[0016] In addition, the furnace 11 is provided with a wind box 36 at the mounting positions of the combustion burners 21, 22, 23, 24, and 25. One end of an air duct 37b is connected to the wind box 36, and the other end is connected to an air duct 37a that supplies air at a connection point 37d.

[0017] Moreover, a flue 13 is connected to the furnace 11 above in the vertical direction, and a plurality of 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. Then, the combustion gas 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 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 is provided with a chimney 53 at the downstream end. Note that the denitration device 50 does not have to be provided if the exhaust gas standards can be satisfied.

[0018] 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 from a wind box 36 to the furnace 11, 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. If the two-stage combustion system is not adopted, the after-air port 39 does not need to be provided.

[0019] 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 is branched at the connection point 37d 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.

[0020] 2 is a schematic explanatory diagram of an 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.

[0021] The purpose of the operation assistance device 100 is to select the optimal combination of fuel, operating conditions, and carbon credits from the standpoint of both the CO2 emissions and power generation costs of the boiler 1, and to easily evaluate the cost-effectiveness of the selected carbon credits.

[0022] 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 will be described later.

[0023] 3 is a diagram showing a hardware configuration of the driving support device 100. 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, and is configured using a computer that is connected to each other via a bus 308. The processor 301 includes a GPU (Graphics Processing Unit). It may be a GPU (Graphics Processing Unit) or a CPU (Central Processing Unit), and any device that executes an arithmetic function may be used regardless of its type. Further, the hardware configuration of the driving support device 100 is not limited to the above, and it may be configured by a combination of a control circuit and a storage device. The driving support device 100 is configured by the processor 301 executing a driving support program that realizes each function of the driving support device 100, or by the control circuit performing arithmetic operations.

[0024] An input device 311 such as a mouse, a keyboard, or a touch panel is connected to the input I / F 305.

[0025] A display 312 made of an LCD or the like is connected to the output I / F 306.

[0026] The boiler 1 and the operation control device 120 are respectively connected to the communication I / F 307.

[0027] FIG. 4 is a flowchart showing the main flow from selecting the optimum conditions of fuel, operation conditions, and carbon credits by the operation support system 10 of the boiler 1, evaluating the results, and then outputting them.

[0028] <S1: Model construction step> The model construction unit 220 of the driving support device 100 adds an operation end parameter (one of the operation conditions) set at the operation end of the boiler 1 and a fuel parameter (the fuel property is one of the operation conditions) including the property and price of the fuel of the boiler 1 to the input parameters (explanatory variables in the regression model), and models the process value, that is, constructs a prediction model for 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 operation data to constructing a prediction model.

[0029] 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, and 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 in the case of mixed combustion of multiple fuels), generates operation data in which the process values, operation terminal parameters, and fuel parameters are associated with each other, and stores the data in the operation data storage unit 112.

[0030] 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.

[0031] The data acquiring section 110 may generate operation data consisting of time-series data by adding time information to each of the manipulated variable parameters and the actual process values.

[0032] 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 (operating end parameters, mixed combustion ratio, and fuel properties) when the actual process values ​​were obtained, and calculated values ​​obtained by analyzing the boiler 1 may be used.

[0033] Pre-processing 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 set values ​​of the operation terminal parameters from the operation data, and extracts setting data.

[0034] In this case, the actual process values ​​obtained by measuring using sensors 1, 2, ..., M may be used to calculate the values ​​of sensors that are not implemented in the boiler 1, and soft sensor values ​​consisting of the measured values ​​may be output to the data acquisition unit 110.

[0035] The model constructing unit 220 acquires the setting data extracted by the data extracting unit 114, generates learning data (S103), and sets learning conditions (S104).

[0036] The model construction unit 220 creates a prediction model based on preset conditions such as explanatory variables, objective variables, and target errors.

[0037] FIG. 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 the process values ​​shown in FIG. 6. Specifically, the model construction unit 220 uses control values ​​(operational 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 the multiple process values ​​using teacher data in which the input parameters are set in the boiler 1 and the process values ​​(actual measured values) obtained by actual operation, or process values ​​(calculated values) obtained by calculation using the input parameters, as output parameters. The input parameters may include operating parameters (such as operating load) and other parameters such as atmospheric conditions.

[0038] When the model construction unit 220 constructs the prediction models 1 and 2 using a regression model, for prediction model 1, the above input parameters are used as explanatory variables, and the actual CO2 emission amount is used as the target variable to machine-learn the regression model. Similarly, for prediction model 2, a regression model is machine-learned using the same explanatory variables as prediction model 1 and the actual power generation cost as the target 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 prediction models for other process values may be added and constructed. Also, although the actual CO2 emission amount and the actual power generation cost are directly obtained from prediction models 1 and 2, the actual CO2 emission amount and the actual power generation cost may be calculated and obtained by combining process values predicted from multiple prediction models.

[0039] <S2: Input Step> In the input unit 260, the input device 311 reads the fuel parameters including the properties and prices of the fuels of the thermal power plant and the carbon credit information (price, CO2 offset amount). Also, the target value of the CO2 emission amount or the power generation cost of the thermal power plant may be read. Further, the operation terminal parameters input to the prediction models 1 and 2 are also input from the input device 311.

[0040] The read information is sent to and stored in the condition storage unit 232.

[0041] <S3: Calculation Step> The calculation unit 230 reads from the condition storage unit 232 the options available regarding the fuel parameters, operating conditions (operation terminal parameters), and carbon credits (CO2 offset amount, price) of the thermal power plant. In this embodiment, the operation terminal parameters are described as being stored in the condition storage unit 232, but the calculation unit 230 may read them from the operation data storage unit 223 instead of storing them in the condition storage unit 232.

[0042] 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).

[0043] 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) × 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)

[0044] Since CO2 emissions and power generation costs are expressed per unit of power generation (kWh), it is necessary to align 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 unit of power generation. 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 unit of power generation (see formula (2)), and this value is used to calculate the net power generation cost (yen / kWh) (see formula (3)).

[0045] In addition, when there are restrictions on the supply amount (purchasable amount) of carbon credits, it is necessary to set an upper limit on the CO2 offset amount (carbon credit purchase amount). In that case, as shown in Equation (4), multiply the unit CO2 offset amount (g / kWh) by the power generation amount (kWh) during the assumed period so that the CO2 offset amount (carbon credit purchase amount) is equal to or greater than this value.

[0046] CO2 offset amount (carbon credit purchase amount) (g) ≥ unit CO2 offset amount (g / kWh) × power generation amount (kWh) ··· (4)

[0047] <S4: Optimization Step> The optimization unit 240 selects the optimal conditions consisting of a combination of fuel, operating conditions, and carbon credits that satisfy predetermined conditions for the net CO2 emissions and the net power generation cost.

[0048] The above calculation content will be described with reference to FIG. 7. FIG. 7 is a schematic diagram showing the calculation content in the optimization unit and the optimal condition evaluation unit.

[0049] First, a plurality of calculation results from the calculation unit 230 are plotted on two axes of the net CO2 emissions and the net power generation cost. It can be seen that the white circles in FIG. 7 correspond to the calculation results and are plotted with a downward slope from the right as a whole. Among these, for the same net CO2 emissions (or net power generation cost), the one with a lower net power generation cost (or net CO2 emissions) is more suitable. Therefore, the calculation results surrounded by the dashed-dotted line in FIG. 7 become candidates for the optimal conditions. From among these candidates, the optimal conditions that satisfy predetermined selection conditions are selected.

[0050] As a predetermined selection condition for selecting the optimal conditions, for example, there is a method of setting a range of predetermined net CO2 emissions or a range of predetermined net power generation costs. Furthermore, there is also a method of performing optimization so that the evaluation value obtained by summing the differences (and other evaluation indicators) between the net CO2 emissions and the net operating cost and a benchmark described later is maximized, and selecting the optimal conditions from among the search points.

[0051] In addition, when the target value of CO2 emission or power generation cost is stored in the condition memory unit 232, the optimization unit 240 reads the target value and selects the optimal condition that matches the target value. For example, when the target value C1 of the power generation cost is stored, the optimal condition R1 is selected from the calculation results where the net power generation cost matches C1. Similarly, when the target value E1 of the CO2 emission is stored, the optimal condition R2 is selected from the calculation results where the net CO2 emission matches E1.

[0052] In FIG. 7, an example of selecting the optimal condition with two axes of net CO2 emission and net power generation cost has been described. However, in addition to this, the margin of control operation ends such as the spray valve opening degree and the gas path damper opening degree for controlling the steam temperature, and the margin with respect to the alarm value such as the surface temperature (metal temperature) of the heat transfer tube are quantified, and among the optimal conditions that also take these into account, a method of selecting the optimal condition that satisfies the above-mentioned predetermined selection conditions may also be used.

[0053] <S5: Optimal Condition Evaluation Step> When the benchmark values of CO2 emission and power generation cost are stored in the condition memory unit 232, the optimal condition evaluation unit 242 reads the benchmark values and evaluates the superiority or inferiority between the benchmark values and the optimal condition selected in the optimization step S4.

[0054] Specifically, for the benchmark value B1 shown by the black circle in FIG. 7, it is confirmed whether the optimal condition is in the lower left quadrant (the area below B1 surrounded by the line of C1 and E1) in the figure. If so, it is determined as "superior". If the optimal condition is in the upper right quadrant 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".

[0055] <Output Step S6> The optimal condition selected by the optimization unit 240, the actual CO2 emission, the net CO2 emission, the actual power generation cost, and the net power generation cost under the optimal condition are output from the output unit 250 to the output device (display 312). In addition, after confirmation by the operator of the thermal power plant, the optimal condition is transmitted to the operation control device 120.

[0056] Figure 8 is an example of the output screen. The following contents are output on the output screen. (1) Selection criteria: 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 a benchmark value is available, the results are displayed in comparison with the benchmark. Also, as shown in the lower right figure, the calculation results under optimal conditions and the benchmark value are displayed in a bar graph.

[0057] <Example of a boiler> An example of a study on operational support for boiler 1 will be explained using Fig. 9. Fig. 9 summarizes the concepts of economic value and environmental value of the CO2 negative (offset) business, with the upper side showing the power generation costs 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. 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), it is generally the case that power generation costs increase in the order coal < gas < biomass, and CO2 emissions increase in the order biomass < gas < coal.

[0058] Here, if we define the carbon negative cost A1 as the actual generation cost of biomass-fired power generation minus the generation cost of coal-fired power generation, and the carbon offset amount B1 as the actual CO2 emissions of biomass-fired power generation minus the actual CO2 emissions of coal-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.

[0059] If a coal-fired power plant uses a CO2 negative project, 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 coal-fired power plant is equal to the actual CO2 emissions of biomass-fired power plant, then the economic and environmental value of the coal-fired power plant + CO2 negative project and the biomass-fired power plant will be equal, and coal-fired power plant will be an option from the perspective of CO2 emissions as a power generation method as well.

[0060] 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 as capital 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.

[0061] For ease of understanding, this figure sets the carbon negative cost A1 and carbon offset amount B1, but it goes without saying that if a CO2 negative project is implemented using the carbon negative cost A1, there is a possibility that the carbon offset amount B1 may be insufficient or excessive. In addition, CO2 negative projects can take a variety of forms, such as forest cultivation and management, biomass carbonization, and wood materials and bioethylene, as mentioned above, and various methods can be selected depending on the carbon offset amount expected by coal-fired power generation companies, etc.

[0062] <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.

[0063] 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.

[0064] When the driving assistance device 100 receives a signal for low-carbon operation, it retrieves fuel information (calorific value, price, carbon credit) for zero-emission coal. Here, the price of zero-emission coal is set at a normal fuel price plus a carbon credit, and when a power generation company that has introduced the driving assistance device 100 purchases it, the carbon credit is transferred to the driving assistance device 100. Therefore, zero-emission coal is a fuel that has been carbon credit-certified by the driving assistance device 100 in advance.

[0065] At the same time, fuel information (heat value, price, co-firing ratio) of biomass, carbon-neutral fuel, etc. is input to the operation assistance device 100. Based on the input information, the operation assistance 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 commissioned. If there is still a shortage, a solution is calculated to purchase the shortfall in carbon credits from the carbon credit system. Note that, in some cases, if the unit price of carbon credits is low, it may be possible to find a solution to procure carbon credits from the carbon credit system without purchasing zero-emission coal.

[0066] 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.

[0067] 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.

[0068] The contents described in each of the above embodiments can be understood, for example, as follows.

[0069] (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, for each combination of fuel, operating conditions, and carbon credits of the thermal power plant, actual CO2 emissions, net CO2 emissions obtained by subtracting the CO2 offset amount by the carbon credits from the actual CO2 emissions, actual power generation costs taking into account the price of the fuel, and net power generation costs obtained by adding the price of the carbon credits to the actual power generation costs; An optimization step of selecting an optimal 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 selected optimum condition; The present invention relates to an operation support method for a thermal power plant, comprising:

[0070] According to the above aspect (1), it is possible to select an optimal carbon credit from the viewpoints of both CO2 emissions and power generation costs, and to easily evaluate the cost-effectiveness of the selected carbon credit.

[0071] (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 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 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 actual CO2 emissions generated when the thermal power plant is operated, calculating a plurality of predicted values ​​of actual CO2 emissions, and calculating net CO2 emissions by subtracting a CO2 offset amount by the carbon credit from each of the predicted values ​​of actual CO2 emissions based on a combination of the plurality of predicted values ​​of actual CO2 emissions and at least one or more carbon credits, and calculating a plurality of predicted values ​​of actual power generation costs by varying and inputting the fuel parameters and the operating conditions into a second prediction model that calculates a predicted value of actual power generation costs generated when the thermal power plant is operated, and calculating a net power generation cost by adding a 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; 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 selected optimum condition; The present invention relates to an operation support method for a thermal power plant, comprising:

[0072] According to the above aspect (2), by using AI predictions using a predictive model, it is possible to select optimal carbon credits in terms of both CO2 emissions and power generation costs, and to easily evaluate the cost-effectiveness of the selected carbon credits.

[0073] (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, when 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 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 matches the target value is used, which is the method for supporting operation of a thermal power plant according to the above aspects (1) and (2).

[0074] According to the above aspect (3), it is possible to present optimal conditions that match the target values ​​of CO2 emissions or power generation costs in response to the needs of operators of thermal power plants, etc.

[0075] (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).

[0076] According to the above aspect (4), it becomes easy to determine whether to adopt the optimum conditions by comparing with the benchmark value.

[0077] (5) A method for supporting operation of a thermal power plant according to one embodiment of the present invention 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.

[0078] According to the above aspect (5), the optimum conditions can be evaluated in comparison with the track records of other fuels and power generation means.

[0079] (6) A method for supporting operation of a thermal power plant according to one embodiment of the present invention 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.

[0080] According to the above aspect (6), the optimum conditions can be evaluated in comparison with the results of other fuels and power generation means.

[0081] (7) A method for supporting operation of a thermal power plant according to one embodiment of the present invention includes: The carbon credits include those that offset CO2 by managing and cultivating forests and increasing carbon fixation using harvested timber. The driving assistance method according to any one of the above aspects (1) to (6).

[0082] 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 revitalization of forestry.

[0083] (8) An operation support device for a thermal power plant according to one embodiment includes: An input unit for reading 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 items included in the price of the fuel or available for additional purchase, the carbon credit information items including the price of the carbon credits 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.

[0084] According to the above aspect (8), it is possible to select an optimal carbon credit from the viewpoints of both CO2 emissions and power generation costs, and to easily evaluate the cost-effectiveness of the selected carbon credit.

[0085] (9) An operation support device for a thermal power plant according to one embodiment includes: The system further includes 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 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.

[0086] According to the above aspect (9), by using AI prediction with a predictive model, 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.

[0087] 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 the 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. [Explanation of symbols]

[0088] 10: Driving assistance systems 11: Furnace 12: Combustion equipment 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 Airport 48: Exhaust gas passage 49: Air heater 50:Denitration equipment 51: Soot and dust treatment equipment 52: Induced draft fan 53: Chimney 62: Inclined surface 70: Bottom evaporator tube 100: Driving support device 110: Data acquisition section 112: Operation data storage unit 114: Data Extraction Section 120: Operation control device 220: Model Construction Department 222: Model memory section 230: Arithmetic section 232: Condition memory section 240: Optimization Department 242: Optimal condition evaluation section 250: Output section 260: Input section 301: Processor 305: Input I / F 306: Output I / F 307: Communication I / F 308: Bus 311: Input device 312: Display

Claims

1. 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, and ２ An input step of reading at least one or more pieces of carbon credit information including an offset amount and a price of the carbon credit; Actual CO generated when the thermal power plant is operated ２ The fuel parameters and the operating conditions are changed and input into a first prediction model for calculating a predicted value of the emission amount, and the actual CO ２ Calculate multiple predicted values ​​of emission amount and compare these multiple actual CO ２ Based on a combination of the predicted emissions and at least one carbon credit, the actual CO ２ From each of the predicted values ​​of emissions, ２ Net CO minus offset amount ２ a calculation step of calculating a plurality of predicted values ​​of the actual power generation cost by inputting the fuel parameters and the operating conditions into a second prediction model which calculates a predicted value of the actual power generation cost incurred when the thermal power plant is operated while calculating the emission amount, and 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 based on a combination of the plurality of predicted values ​​of the actual power generation cost and at least one or more carbon credits; The net CO ２ An optimization step of selecting an optimal combination of the fuel, the operating conditions, and the carbon credits that satisfies predetermined selection conditions for emissions and the net electricity generation cost; The selected optimum condition and the actual CO ２ Emissions, net CO ２ an output step of outputting the emission amount, the actual power generation cost, and the net power generation cost; The present invention relates to a thermal power plant operation support method.

2. In the input step, the CO ２ Read the target value for emissions or power generation cost, In the optimization step, the target value is set to be equal to or smaller than the net CO ２ In the case of a target value for emissions, the net CO ２ A condition for selecting the optimum condition in which the emission amount coincides with 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 optimum 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 .

3. In the input step, the net CO ２ Loading the benchmark values ​​of emissions and the net electricity generation cost; 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.

4. 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.

5. 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.

6. The carbon credit information is the amount of CO generated by managing and cultivating forests and increasing the amount of carbon fixed by using harvested timber. ２ Including the offset amount, The method for supporting operation of a thermal power plant according to any one of claims 1 to 5.

7. 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, ２ An input unit for reading carbon credit information including an offset amount; 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; The system further includes a model storage unit that stores a first prediction model that calculates a predicted value of an actual CO 2 emission amount that occurs when the thermal power plant is operated, and a second prediction model that calculates a predicted value of an actual power generation cost that occurs 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 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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