JP2017182324A - Power generation plan automatic creation program, power generation plan automatic creation method and power generation plan creation device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To prepare a power generation plan based on a margin.SOLUTION: A power generation plane creation device 200 is configured to: calculate a prediction value of an amount of power demand on the basis of acquired weather information; automatically create a power generation plan on the basis of an amount of power demand having a margin of a prescribed amount added to the calculated prediction value of the amount of power demand; when acquiring an actual value of the amount of power demand corresponding to the calculated prediction value of the amount of power demand, calculate a new margin in accordance with an amount of deviation between the calculated prediction value of the amount of power demand and the acquired actual value of the amount of power demand; and when further calculating a prediction value of an amount of future power demand on the basis of the acquired weather information, automatically create a power generation plan on the basis of an amount of power demand having the calculated new margin added.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a power generation plan automatic generation program and the like.

  In recent years, there is a PPS (Power Producer and Supplier) business called new power. PPS operators aim to increase profits by supplying power to high-voltage consumers such as factories and companies as well as low-voltage consumers such as ordinary households.

  In addition, PPS operators actively use renewable energy and own power generation facilities to maintain a balance between power supply and demand.

  For example, the center of renewable energy is solar power generation and wind power generation, and may be affected by the natural environment. For this reason, when the supply is insufficient with respect to the demand for electric power, the PPS company controls its own power generation facility to compensate for the insufficient electric power or procure electric power from an external organization. For example, the PPS operator calculates a predicted value of power demand, generates a power generation plan that can cover the predicted value of power demand, and controls its own power generation equipment based on the generated power generation plan.

JP 2000-274308 A JP 2009-48353 A JP 2001-318970 A International Publication No. 2011-162025

  However, the above-described conventional technology has a problem that a power generation plan cannot be appropriately created.

  In one aspect, an object of the present invention is to provide a power generation plan automatic generation program, a power generation plan automatic generation method, and a power generation plan generation device capable of creating a power generation plan based on a margin.

  In the first plan, the computer executes the following processing. The computer calculates a predicted value of the power demand based on the acquired weather information. The computer automatically generates the power generation plan based on the power demand amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power demand amount. When the computer acquires the actual value of the power demand corresponding to the predicted value of the calculated power demand, the amount of deviation between the calculated predicted value of the power demand and the actual value of the acquired power demand Depending on, a new margin is calculated. When the computer further calculates a predicted value of the future power demand based on the acquired weather information, the computer automatically generates a power generation plan based on the calculated power demand including the new margin.

  A power generation plan based on margins can be created.

FIG. 1 is a diagram illustrating a configuration of a system according to the present embodiment. FIG. 2 is a functional block diagram illustrating an example of the configuration of the power generation plan generation device. FIG. 3 is a diagram illustrating an example of demand forecast data. FIG. 4 is a diagram illustrating an example of the data structure of the power generation prediction data. FIG. 5 is a diagram illustrating an example of the data structure of the generator information. FIG. 6 is a diagram illustrating an example of the data structure of the supplier data. FIG. 7 is a diagram illustrating an example of the data structure of the condition data. FIG. 8 is a diagram illustrating an example of a data structure of the power generation plan table. FIG. 9 is a diagram illustrating an example of power generation plan data. FIG. 10 is a flowchart illustrating a processing procedure of the power generation plan generation device according to the present embodiment. FIG. 11 is a flowchart illustrating a processing procedure of the demand prediction correction process. FIG. 12 is a flowchart illustrating a processing procedure of power generation prediction correction processing. FIG. 13 is a diagram illustrating an example of a computer that executes a power generation plan automatic generation program.

  Hereinafter, embodiments of a power generation plan automatic generation program, a power generation plan automatic generation method, and a power generation plan generation device disclosed in the present application will be described in detail with reference to the drawings. Note that the present invention is not limited to the embodiments.

  FIG. 1 is a diagram illustrating a configuration of a system according to the present embodiment. As shown in FIG. 1, this system includes power generators 10a to 10c, solar power generators 11a to 11c, wind power generators 12a to 12c, geothermal power generators 13a to 13c, hydroelectric power generators 14a to 14c, and biomass power generator 15a. ~ 15c. The system includes power companies 20a to 20c, high-voltage customers 30, low-voltage customers 40, power suppliers 50a to 50c, and PPS (Power Producer and Supplier) 100. Each apparatus 10-15,20,30,40,50 is mutually connected by the electric wire 5 grade | etc.,. The PPS 100 is not connected to the electric wire 5 and directly controls the devices 10 to 15 remotely by communication, or performs indirect control by instructing by telephone contact with an administrator of the device.

  The generators 10a to 10c are devices that generate electric power using something other than renewable energy. For example, the generators 10a to 10c correspond to gas turbine generators and the like. The generators 10a to 10c are collectively referred to as a generator 10 as appropriate. The generator 10 generates electric power based on a power generation plan created by a power generation plan generation device 200 described later. Although omitted in FIG. 1, generators other than the generators 10a to 10c may be included.

  The solar power generators 11a to 11c are devices that convert sunlight into electric power using a solar panel or the like. The solar power generators 11a to 11c are collectively referred to as the solar power generator 11 as appropriate. Although description is abbreviate | omitted in FIG. 1, solar power generators other than the solar power generators 11a-11c may be included.

  The wind power generators 12a to 12c are devices that rotate a windmill with wind power and convert the rotational motion into electric power. The wind power generators 12a to 12c are collectively referred to as a wind power generator 12 as appropriate. Although description is abbreviate | omitted in FIG. 1, wind power generators other than the wind power generators 12a-12c may be included.

  The geothermal power generators 13a to 13c are devices that generate electric power using steam extracted from deep underground. The geothermal power generators 13a to 13c are collectively referred to as a geothermal power generator 13 as appropriate. Although description is abbreviate | omitted in FIG. 1, geothermal power generators other than the geothermal power generators 13a-13c may be included.

  The hydroelectric generators 14a to 14c are devices that are installed in the vicinity of a dam or the like and generate electric power using energy when water falls. The hydroelectric generators 14a to 14c are collectively referred to as a hydroelectric generator 14 as appropriate. Although description is abbreviate | omitted in FIG. 1, hydraulic power generators other than the hydraulic power generators 14a-14c may be included.

  The biomass power generators 15a to 15c are devices that generate electric power by directly combusting or gasifying biological resources. The biomass generators 15a to 15c are collectively referred to as a biomass generator 15 as appropriate. Although description is abbreviate | omitted in FIG. 1, biomass generators other than biomass generator 15a-15c may be contained.

  The electric power companies 20a to 20c perform control so that the overall supply / demand balance of the electric wire 5 can be maintained. When the PPS 100 cannot maintain the supply / demand balance, the electric power companies 20a to 20c supplement the electric power and maintain the supply / demand balance of the entire electric wire 5. It is also possible to play a role as a power supplier by making a contract. In the following description, the electric power companies 20a to 20c are collectively referred to as the electric power company 20 as appropriate.

  The high-voltage consumer 30 is a facility that is a power supply destination, and corresponds to, for example, a factory or a company. The low-voltage consumer 40 is a facility that is a power supply destination, and corresponds to, for example, a general house. Although FIG. 1 shows the high-pressure consumer 30 and the low-voltage consumer 40 as an example, other customers may be included.

  The power suppliers 50a to 50c are a kind of power generation company that can supply power when the power is insufficient by making a contract with the PPS 100. Procurement is done through the Japan Wholesale Power Exchange, or directly contracted with a power generation company. Further, the power suppliers 50a to 50c supply power based on a power procurement plan created by the power generation plan generation device 200 described later. The explanation regarding the procurement plan will be described later. In the following description, the power suppliers 50a to 50c are collectively referred to as a power supplier 50 as appropriate.

  The PPS 100 supplies power generated by the power generator 10, the solar power generator 11, the wind power generator 12, the geothermal power generator 13, the hydroelectric power generator 14, and the biomass power generator 15 to the high-pressure consumer 30 and the low-pressure consumer 40. It is a business operator that makes a plan and controls it directly, or indirectly gives control instructions to the operation manager of the generator. In addition, when the supply of power is insufficient with respect to the demand for power, the PPS 100 reviews the power generation plan of the generator 10 to compensate for the insufficient power. The PPS 100 procures the insufficient power from the power supplier 50 when the deficient power cannot be compensated by using the generator 10. For example, the PPS 100 includes a power generation plan generation device 200 and uses the power generation plan generation device 200 to generate a power generation plan for the generator 10.

  FIG. 2 is a functional block diagram illustrating an example of the configuration of the power generation plan generation device. As illustrated in FIG. 2, the power generation plan generation device 200 includes a communication unit 210, an input unit 220, a display unit 230, a storage unit 240, and a control unit 250.

  The communication unit 210 performs data communication with the power generator 10, the solar power generator 11, the wind power generator 12, the geothermal power generator 13, the hydroelectric power generator 14, the biomass power generator 15, the power supplier 50, and the like via the network. Is a processing unit. For example, the communication unit 210 corresponds to a communication device.

  The input unit 220 is an input device for inputting various types of information to the power generation plan generation device 200. For example, the input unit 220 corresponds to a keyboard, a mouse, a touch panel, or the like.

  The display unit 230 is a display device that displays information output from the control unit 250. For example, the display unit 230 corresponds to a liquid crystal display, a touch panel, or the like.

  The storage unit 240 includes weather forecast data 241a, weather record data 241b, demand forecast data 242a, demand record data 242b, power generation forecast data 243a, and power generation record data 243b. The storage unit 240 has maintenance plan planning data 244, supplier data 245, condition data 246, and a power generation plan table 247. For example, the storage unit 240 corresponds to a semiconductor memory device such as a RAM (Random Access Memory), a ROM (Read Only Memory), and a flash memory (Flash Memory), and a storage device such as an HDD (Hard Disk Drive).

  The weather prediction data 241a indicates the weather that is predicted from the present time to a predetermined time ahead. For example, in the weather forecast data 241a, each time zone is associated with a forecast value related to weather such as weather, temperature, humidity, and wind speed. The weather forecast data 241a is periodically notified and updated from a predetermined device in the system.

  The weather record data 241b indicates past record values of weather. For example, in the weather record data 241b, each time zone is associated with record values related to weather such as weather, temperature, humidity, and wind speed. The weather performance data 241b is periodically notified and updated from a predetermined device in the system.

  The demand prediction data 242a is time-series data of demand power predicted by the high-voltage consumer 30 and the low-voltage consumer 40 in the system. For example, the demand prediction data 242a is data in which each time zone in a day is associated with a demand power value. The demand power value is calculated by the demand prediction unit 252 using, for example, statistical data of past power consumption values, weather forecast data 241a, weather performance data 241b, and the like. FIG. 3 is a diagram illustrating an example of demand forecast data. The horizontal axis of FIG. 3 indicates time [h], and the vertical axis indicates the demand power value [kW].

  The actual demand data 242b indicates actual power demand values in the high-voltage consumer 30 and the low-voltage consumer 40 in the system. For example, the demand record data 242b associates each time zone and the actual value of demand power up to the present time. The demand record data 242b is periodically notified and updated from a predetermined device in the system. Moreover, the actual value of each time slot | zone of the demand track record data 242b is matched with the weather track record of the same time slot | zone of the weather track record data 241b.

  The power generation prediction data 243a is time series data of predicted power generated by the solar power generator 11, the wind power generator 12, the geothermal power generator 13, the hydroelectric power generator 14, and the biomass power generator 15. FIG. 4 is a diagram illustrating an example of the data structure of the power generation prediction data. The horizontal axis in FIG. 4 indicates time [h], and the vertical axis indicates the predicted generated power value [kW].

  The power generation result data 243b is information on the actual values of the power generated by the solar power generator 11, the wind power generator 12, the geothermal power generator 13, the hydroelectric power generator 14, and the biomass power generator 15, respectively. For example, the power generation result data 243b associates each time zone and generated power up to the present time. The power generation result data 243b is periodically notified and updated from a predetermined device in the system. Moreover, the actual value of each time slot | zone of the power generation performance data 243b is matched with the weather actual result of the same time slot | zone of the weather performance data 241b.

The maintenance planning data 244 includes generator information, hourly system loss rate information, hourly in-house loss rate information, CO ₂ conversion coefficient, cost data, and the like.

  Among these, the generator information holds various information related to the generator 10. FIG. 5 is a diagram illustrating an example of the data structure of the generator information. As shown in FIG. 5, this generator information associates generator identification information, rated output, maximum output ratio, maximum output, minimum output ratio, minimum output, and operation time. The generator identification information is information that uniquely identifies the generator. The rated output, maximum output ratio, and minimum output ratio indicate the rated output, maximum output ratio, and minimum output ratio of the generator identified by the generator identification information, respectively.

  The operation time indicates the time during which the generator identified by the generator identification information is operated. When maintenance work or inspection is performed on the generator, the operation of the generator is stopped. Although description is omitted in the example of FIG. 5, as information corresponding to the operation time, information on a time zone in which the generator can be operated may be included for each time of each date.

  The hourly system loss rate information is information on the hourly power loss rate in the electric wire 5.

  The hourly in-house loss rate information is information on the hourly power loss rate of the facilities constituting the generator 10 when the generator 10 supplies power to the high-voltage consumer 30 or the low-voltage consumer 40.

The CO ₂ conversion coefficient and cost data are parameters used when generating a power generation plan and a procurement plan described later.

The supplier data 245 holds various types of information related to suppliers that supply electric power. The power source is, for example, the power company 20. FIG. 6 is a diagram illustrating an example of the data structure of the supplier data. As shown in FIG. 6, the supplier data 245 associates supplier identification information, type, supplyable time, supply amount / supply unit price, and CO ₂ emission coefficient.

The supplier identification information is information that uniquely identifies the supplier. The type is information indicating the type of procurement. The supplyable time is information indicating the supplyable time. The supply amount / supply unit price indicates the supply amount per unit price. The CO ₂ emission coefficient is information indicating the CO ₂ emission amount per 1 kWh.

  The condition data 246 is a condition used when creating a power generation plan to be described later. FIG. 7 is a diagram illustrating an example of the data structure of the condition data. As shown in FIG. 7, the condition data 246 includes a plan unit, a plan time, a plan stop time, and a convergence condition. The planning unit indicates the minimum unit for switching the start / stop of the generator. The planned time indicates the period of the power generation plan. For example, when the plan unit is “30 minutes” and the plan time is “84 hours”, a power generation plan of 168 frames is created.

  The planned termination time is the time to cancel the generation of the power generation plan. The convergence condition is a convergence condition used when generating a power generation plan using a solution called a metaheuristic such as a local search method. When the difference between a certain power generation plan and the next power generation plan falls within the convergence condition, the search for the power generation plan by the local search method or the like is terminated.

The power generation plan table 247 is a table that holds a plurality of types of power generation plans. FIG. 8 is a diagram illustrating an example of a data structure of the power generation plan table. As shown in FIG. 8, this power generation plan table associates a power generation plan identification number, a power generation plan, a power generation cost, a CO ₂ emission amount, a selection ratio, a start time, and an end time.

Among these, the power generation plan identification number is information for uniquely identifying each power generation plan. The power generation plan is information on a power generation plan corresponding to each power generation identification number. The power generation cost is the power generation cost of the generator 10 corresponding to the power generation plan. CO ₂ emissions are emissions of CO ₂ is discharged from the generator 10 corresponding to the power generation planning.

The selection ratio is information indicating a ratio between the power generation cost and the CO ₂ emission amount. For example, the numerical value on the left side of the selection ratio indicates the power generation cost ratio, and the numerical value on the right side corresponds to the numerical value of the CO ₂ emission amount. When the emphasis is on making the power generation cost smaller than the CO ₂ emission amount, the numerical value on the left side of the selection ratio becomes larger than the numerical value on the right side. When the emphasis is on reducing the CO ₂ emission amount rather than the power generation cost, the numerical value on the right side of the selection ratio becomes larger than the numerical value on the left side.

  The date corresponds to the date when the corresponding power generation plan is created. The start time indicates the time when the generation of the corresponding power generation plan is started. The end time indicates the time when the creation of the corresponding power generation plan is ended.

  Next, an example of power generation plan data included in the power generation plan table 247 in FIG. 8 will be described. FIG. 9 is a diagram illustrating an example of power generation plan data. As shown in FIG. 9, the power generation plan data associates generator identification information with parameters. The generator identification information is information that uniquely identifies the generator.

  The parameter indicates a power generation plan every 30 minutes for each generator. When the plan unit is “30 minutes” and the plan time is “84 hours”, a power generation plan of 168 frames is created. The first frame at the left end shows the state of the generator from 0 to 29 minutes, and the second frame shows the state of the generator from 30 to 59 minutes. The 168th frame at the right end shows the state of the generator from 83 hours 30 minutes to 83 hours 59 sentences. 0 or 1 is set in each frame. “0” indicates that the state of the generator 10 is stopped. “1” indicates that the state of the generator 10 is activated.

  Returning to the description of FIG. The control unit 250 includes an acquisition unit 251, a demand prediction unit 252, a power generation prediction unit 253, and a power generation plan generation unit 254. The control unit 250 corresponds to an integrated device such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The control unit 250 corresponds to an electronic circuit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit).

  The acquisition unit 251 is a processing unit that acquires various types of information from devices in the system and updates information included in the storage unit 240 with the acquired information. The acquisition unit 251 updates the weather prediction data 241a and the weather result data 241b with the acquired information when the weather prediction data and the weather result data are acquired from the devices in the system.

  When the acquisition unit 251 acquires the demand record data from the devices in the system, the acquisition unit 251 updates the demand record data 242b with the acquired information. The acquisition unit 251 updates the power generation result data 243b when acquiring power generation result data from the devices in the system.

  The demand prediction unit 252 is a processing unit that generates demand prediction data 242a. Further, the demand prediction unit 252 executes a process of correcting the demand prediction data 242a after generating the demand prediction data 242a.

  An example of processing in which the demand prediction unit 252 generates the demand prediction data 242a will be described. The demand prediction unit 252 compares the weather prediction data 241a with a demand prediction conversion table (not shown) to generate demand prediction data 242a. Here, the demand prediction conversion table is a table that associates the predicted value of the weather with the demand power predicted from the predicted value of the weather. The demand prediction unit 252 generates the demand prediction data 242a by comparing the forecast value of the weather in each time zone with the demand forecast conversion table and specifying the forecast value of the demand power in each time zone. .

  Here, although the case where the demand prediction unit 252 generates the demand prediction data 242a using the demand prediction conversion table is shown, the demand prediction data 242a may be generated based on other well-known techniques. Further, the demand prediction unit 252 may acquire and use the demand prediction data 242a predicted by other devices in the system.

  Next, an example of processing in which the demand prediction unit 252 corrects demand prediction data will be described. The demand prediction unit 252 switches and executes the correction process depending on whether or not the demand prediction data 242a and the demand record data 242b created in the past exist.

  A correction process executed by the demand prediction unit 252 when the demand forecast data 242a and the demand record data 242b created in the past exist will be described. The demand prediction unit 252 compares the demand prediction data 242a created in the past with the demand record data 242b, and calculates a difference value for each same time zone. The demand prediction unit 252 constructs a prediction model based on multiple regression analysis for the difference value for each time zone, and calculates a multiple regression coefficient. For example, this prediction model is a model in which weather results are explanatory variables and difference values are objective variables.

  The demand prediction unit 252 uses the multiple regression coefficient related to the demand prediction to determine how much the difference value is generated between the predicted value and the actual value of the demand power under what kind of weather conditions. Can be judged. For example, when the weather prediction data is A1, it can be determined that a difference value B1 of demand power is generated between the predicted value and the actual value.

  The demand prediction unit 252 specifies a difference value for each predicted time zone based on the weather prediction data 241a and the multiple regression coefficient related to the demand prediction. Below, the difference value for every time slot | zone estimated is suitably described with reserve electric power. The reserve power corresponds to the margin. The demand prediction unit 252 corrects the demand prediction data 242a by adding the reserved power for each specified time zone to the demand power of each time zone of the demand prediction data 242a.

  In addition, when correcting the demand prediction data 242a for the first time, the demand prediction unit 252 does not use the above-described multiple regression coefficient, but adds a predetermined value to the demand power in each time zone, thereby predicting the demand. The data 242a may be corrected. Then, the demand prediction unit 252 may correct the demand prediction data 242a using multiple regression coefficients as described above for the second and subsequent corrections.

  Next, correction processing executed by the demand prediction unit 252 when there is no demand forecast data 242a created in the past and past demand record data 242b will be described. For example, when the acquisition unit 251 does not acquire the demand record data 242b from the devices in the system, the demand record data 242b does not exist. In this case, the demand prediction unit 252 corrects the demand prediction data 242a by multiplying the demand power in each time zone of the demand prediction data 242a by a certain value.

  The power generation prediction unit 253 is a processing unit that generates power generation prediction data 243a. Further, the power generation prediction unit 253 executes a process of correcting the power generation prediction data 243a after generating the power generation prediction data 243a.

  An example of a process in which the power generation prediction unit 253 generates the power generation prediction data 243a will be described. The power generation prediction unit 253 compares the weather prediction data 241a with a power generation prediction conversion table (not shown) to generate power generation prediction data 243a. Here, the power generation prediction conversion table is a table that associates the predicted value of the weather with the generated power predicted from the predicted value of the weather. The power generation prediction unit 253 generates the power generation prediction data 243a by comparing the predicted value of the weather in each time zone with the power generation prediction conversion table and specifying the predicted value of the generated power in each time zone. .

  Here, although the case where the power generation prediction unit 253 generates the power generation prediction data 243a using the power generation prediction conversion table has been shown, the power generation prediction data 243a may be generated based on other known techniques. The power generation prediction unit 253 may acquire and use the power generation prediction data 243a predicted by other devices in the system.

  Subsequently, an example of a process in which the power generation prediction unit 253 corrects the power generation prediction data will be described. The power generation prediction unit 253 switches and executes the correction processing depending on whether or not the power generation prediction data 243a and the power generation result data 243b created in the past exist.

  A correction process executed by the power generation prediction unit 253 when the power generation prediction data 243a and the power generation result data 243b created in the past exist will be described. The power generation prediction unit 253 compares the power generation prediction data 243a created in the past with the power generation result data 243b, and calculates a difference value for each same time zone. The power generation prediction unit 253 constructs a prediction model based on multiple regression analysis for the difference value for each time zone, and calculates a multiple regression coefficient. For example, this prediction model is a model in which weather results are explanatory variables and difference values are objective variables.

  The power generation prediction unit 253 uses the multiple regression coefficient related to the power generation prediction to determine how much the difference value is generated between the predicted value of the generated power and the actual value under what kind of weather conditions. Can be judged. For example, when the weather prediction data is A1, it can be determined that a difference value B2 of the generated power is generated between the predicted value and the actual value.

  The power generation prediction unit 253 specifies a difference value for each predicted time zone based on the weather prediction data 241a and the multiple regression coefficient related to power generation prediction. Below, the difference value for every time slot | zone estimated is suitably described with reserve electric power. The reserve power corresponds to the margin. The power generation prediction unit 253 corrects the power generation prediction data 243a by adding the reserved power for each specified time zone to the power generation power of each time zone of the power generation prediction data 243a.

  In addition, when correcting the power generation prediction data 242a for the first time, the power generation prediction unit 253 adds the predetermined value to the generated power in each time zone without using the multiple regression coefficient described above, thereby generating the power generation prediction. The data 243a may be corrected. The power generation prediction unit 253 may correct the power generation prediction data 243a using the multiple regression coefficient as described above for the second and subsequent corrections.

  Next, correction processing executed by the power generation prediction unit 253 when the power generation prediction data 243a and the past power generation performance data 243b created in the past do not exist will be described. For example, when the acquisition unit 251 does not acquire the power generation result data 243b from the devices in the system, the power generation result data 243b does not exist. In this case, the power generation prediction unit 253 corrects the power generation prediction data 243a by multiplying the generated power in each time zone of the power generation prediction data 243a by a certain value.

  The power generation plan generation unit 254 is a processing unit that generates power generation plan data based on the demand prediction data 242a and the power generation prediction data 243a. For example, the power generation plan generation unit 254 includes a procurement optimization processing unit 254a and a power generation plan optimization processing unit 254b.

The procurement optimization processing unit 254a compares the demand prediction data 242a, the power generation prediction data 243a, and the power generation plan created by the power generation plan optimization processing unit 254b, and specifies an insufficient amount of power for each hour. The procurement optimization processing unit 254 a specifies a combination of suppliers for compensating for the shortage of electric power every hour based on the supplier data 245. Then, the procurement optimization processing unit 254a identifies a combination having the lowest power generation cost and a small amount of CO ₂ emission among the combinations of the suppliers by using dynamic programming or the like.

  Note that the procurement optimization processing unit 254a may set the initial power generation plan such that all of the operable generators 10 generate power with the maximum generated power. The procurement optimization processing unit 254a outputs the created procurement plan information to the power generation plan optimization processing unit 254b. Then, the procurement optimization processing unit 254a newly acquires the updated power generation plan from the power generation plan optimization processing unit 254b, creates a procurement plan again based on the acquired power generation plan, and creates the procurement The plan is output to the power generation plan optimization processing unit 254b. The procurement optimization processing unit 254a repeatedly executes such processing to create an optimal procurement plan.

  The dynamic programming method etc. is disclosed in the literature <"Generation Operation Plan for Power Supply and Demand Operation", The Operations Research Society of Japan, Organizational Paper, Operations Research, May 1997, 341-344 Kenichi Kawada et al. Yes. The procurement optimization processing unit 254a may create a procurement plan using the disclosed conventional dynamic programming, or may create a procurement plan using another dynamic programming. .

  The power generation plan optimization processing unit 254b is a processing unit that creates a power generation plan using a local search method or the like. For example, the power generation plan is information defining start “1” and stop “0” for each frame, as shown in the parameters of FIG.

  The power generation plan optimization processing unit 254b specifies the power generated by the generator 10 to satisfy the demand based on the demand prediction data 242a, the power generation prediction data 243a, and the procurement plan. The power generation plan optimization processing unit 254b creates an initial power generation plan based on the generator information and the like shown in FIG. 5 under the constraint that satisfies the amount of power generated by the generator 10.

The power generation plan optimization processing unit 254b calculates the power generation cost of the initial power generation plan and the CO ₂ emission amount due to power generation as follows, and registers them in the power generation plan table 247. The power generation plan optimization processing unit 254b calculates the power generation cost of the power generation plan using Expression (1).

In equation (1), _pit represents the output of the generator at time t of the generator i. u _it shows the start and stop state of the power generator at time t of the generator i. If the generator is running, the value "1" next to the u, if the generator is not running, the value of u _it becomes "0". SC _i indicates the startup cost of the generator i.

In Equation (1), FCOST _i (p _it ) is defined by Equation (2).

In formula (2), a _i , b _i , and c _i indicate fuel characteristics for each generator.

In addition, the power generation plan optimization processing unit 254b calculates the CO ₂ emission amount due to power generation using Expression (3).

In Formula (3), FCO2 _i (p _it ) indicates the CO ₂ emission amount and is defined by Formula (4).

In Equation (4), RateCO2 _i represents a CO ₂ emission conversion coefficient.

The power generation plan optimization processing unit 254b changes some parameters of the power generation plan under a constraint condition that satisfies the amount of power generated by the generator 10 in order to satisfy the demand and supply, and generates the power generation cost of the previous power generation plan. and CO lower than ₂ emissions, power generation planning as a power generation cost and CO ₂ emissions, searches using a local search method. The power generation plan optimization processing unit 254b registers the searched power generation plan information in the power generation plan table 247 and outputs the power generation plan information to the procurement optimization processing unit 254a.

  When generating the power generation plan, the power generation plan optimization processing unit 254b may associate the procurement plan acquired from the procurement optimization processing unit 251 and register it in the power generation plan table 247.

When the power generation plan optimization processing unit 254b acquires the procurement plan from the procurement optimization processing unit 254a again, the power generation plan optimization processing unit 254b repeats the process of creating the power generation plan by the above-described local search method based on the acquired procurement plan. Run. Power program optimization processing unit 254b includes a power generation cost and CO ₂ emissions of the previous power program, changes in the power generation cost and CO ₂ emissions of this power program, when the convergence condition is satisfied, the power generation planning Finish creation.

  For example, when the expressions (5) and (6) are satisfied, the power generation plan optimization processing unit 254b ends the generation of the power generation plan.

  Absolute value ((Power generation cost of previous power generation plan-Power generation cost of current power generation plan) / Power generation cost of current power generation plan) <Convergence condition (5)

Absolute value ((CO ₂ emissions of the previous power program - CO ₂ emissions of the current power program) / CO ₂ emissions of the current generation plan) <convergence condition (6)

  The local search method is disclosed in the literature <"Metaheuristics Application Technology to Power Systems", The Institute of Electrical Engineers of Japan, Metaheuristics Application Research Committee for Electric Power Systems, 2003/06/10>. Moreover, it is disclosed by Unexamined-Japanese-Patent No. 2013-132195. The power generation plan optimization processing unit 254b may create a power generation plan using the disclosed local search method, or may create a power generation plan using another local search method.

  The power generation plan generation unit 254 causes the display unit 230 to display the power generation plan data when generating the power generation plan data. The administrator may refer to the power generation plan data displayed on the display unit 230 and appropriately operate the input unit 220 to correct the power generation plan data. The power generation plan generation unit 254 stores the power generation plan data that has received the correction in the power generation plan table 247.

  Next, an example of a processing procedure of the power generation plan generation device 200 according to the present embodiment will be described. FIG. 10 is a flowchart illustrating a processing procedure of the power generation plan generation device according to the present embodiment. As illustrated in FIG. 10, the acquisition unit 251 of the power generation plan generation device 200 acquires the weather result data 241b (Step S101) and acquires the weather prediction data 241a (Step S102).

  The demand prediction unit 252 of the power generation plan generation device 200 generates demand prediction data 242a (step S103). The power generation prediction unit 253 of the power generation plan generation device 200 generates power generation prediction data 243a (step S104).

  The demand prediction unit 252 determines whether the past demand record data 242b and the demand prediction data 242a exist (step S105). When the past demand actual data 242b and the demand prediction data 242a do not exist (No at Step S105), the demand prediction unit 252 multiplies the demand prediction data 242a by a certain value (Step S106), and then proceeds to Step S108. Transition.

  On the other hand, when the past demand record data 242b and the demand forecast data 242a exist (step S105, Yes), the demand forecast unit 252 executes the demand forecast correction process (step S107), and proceeds to step S108. To do.

  The power generation prediction unit 253 of the power generation plan generation device 200 determines whether or not past power generation result data 243b and power generation prediction data 243a exist (step S108). When the past power generation result data 243b and the power generation prediction data 243a do not exist (step S108, No), the power generation prediction unit 253 multiplies the power generation prediction data 243a by a certain value (step S109), and then proceeds to step S111. Transition.

  On the other hand, when the past power generation result data 243b and the power generation prediction data 243a exist (step S108, Yes), the power generation prediction unit 253 executes the power generation prediction correction process (step S110) and proceeds to step S111. To do.

  The power generation plan generation unit 254 generates power generation plan data based on the demand prediction data 242a and the power generation prediction data 243a (step S111). The power generation plan generation unit 254 receives correction of the power generation plan data (step S112).

  Subsequently, a processing procedure of the demand prediction correction process shown in step S107 of FIG. 10 will be described. FIG. 11 is a flowchart illustrating a processing procedure of the demand prediction correction process. As illustrated in FIG. 11, the demand prediction unit 252 of the power generation plan generation device 200 calculates a difference value for each time zone based on the demand prediction data 242a and the demand record data 242b (step S201).

  The demand prediction unit 252 calculates a multiple regression coefficient based on the difference value (step S202). The demand prediction unit 252 calculates standby power based on the multiple regression coefficient (step S203). The demand prediction unit 252 corrects the demand prediction data 242a by adding reserve power to the demand prediction data 242a (step S204).

  Next, the processing procedure of the power generation prediction correction process shown in step S110 of FIG. 10 will be described. FIG. 12 is a flowchart illustrating a processing procedure of power generation prediction correction processing. As illustrated in FIG. 12, the power generation prediction unit 253 of the power generation plan generation device 200 calculates a difference value for each time zone based on the power generation prediction data 243a and the power generation result data 243b (step S301).

  The power generation prediction unit 253 calculates a multiple regression coefficient based on the difference value (step S302). The power generation prediction unit 253 calculates standby power based on the multiple regression coefficient (step S303). The power generation prediction unit 253 corrects the demand prediction data 243a by adding standby power to the power generation prediction data 243a (step S304).

  Next, effects of the power generation plan generation device 200 according to the present embodiment will be described. The power generation plan generation apparatus 200 calculates standby power (margin) according to the amount of deviation between the demand forecast data 242a created in the past and the demand record data 242b. The power generation plan generation device 200 corrects the demand prediction data 242a by adding reserve power to the demand prediction data 242a newly calculated based on the weather prediction data 241a, and then generates power based on the demand prediction data 242a. Generate plan data. For this reason, the power generation plan data based on the margin can be generated.

  For example, according to the power generation plan generation device 200, more accurate demand prediction data 242a can be generated based on an error between the past demand record data 242b and the demand prediction data 242a created in the past. Since the power generation plan generation device 200 generates power generation plan data using the demand prediction data 242a, the power generation plan data can be made more appropriate information.

  The power generation plan generation device 200 calculates standby power (margin) according to the amount of deviation between the power generation prediction data 243a created in the past and the power generation result data 243b. The power generation plan generation device 200 corrects the power generation prediction data 243a by adding reserve power to the power generation prediction data 243a newly calculated based on the weather prediction data 241a, and then generates power based on the power generation prediction data 243a. Generate plan data. For this reason, the power generation plan data based on the margin can be generated.

  For example, the power generation plan generation device 200 can generate more accurate power generation prediction data 243a based on an error between the past power generation performance data 243b and the power generation prediction data 243a created in the past. Since the power generation plan generation device 200 generates power generation plan data using the power generation prediction data 243a, the power generation plan data can be made more appropriate information.

  Next, an example of a computer that executes an operation plan automatic creation program that realizes the same function as the power generation plan creation device 200 shown in the above embodiment will be described. FIG. 13 is a diagram illustrating an example of a computer that executes a power generation plan automatic generation program.

  As illustrated in FIG. 13, the computer 300 includes a CPU 301 that executes various arithmetic processes, an input device 302 that receives input of data from a user, and a display 303. The computer 300 also includes a reading device 304 that reads a program or the like from a storage medium, and an interface device 305 that exchanges data with other computers via a network. The computer 300 also includes a RAM 306 that temporarily stores various types of information and a hard disk device 307. The devices 301 to 307 are connected to the bus 308.

  The hard disk device 307 includes a demand prediction program 307a, a power generation prediction program 307b, and a power generation plan generation program 307c. The CPU 301 reads out each program 307 a to 307 c and develops it in the RAM 306. The demand prediction program 307a functions as a demand prediction process 306a. The power generation prediction program 307b functions as a power generation prediction process 306b. The power generation plan generation program 307c functions as a power generation plan generation process 306c.

  For example, the process of the demand prediction process 306 a corresponds to the process of the demand prediction unit 252. The process of the power generation prediction process 306b corresponds to the process of the power generation prediction unit 253. The process of the power generation plan generation process 306c corresponds to the process of the power generation plan generation unit 254.

  Note that the programs 307a to 307c are not necessarily stored in the hard disk device 307 from the beginning. For example, each program is stored in a “portable physical medium” such as a flexible disk (FD), a CD-ROM, a DVD disk, a magneto-optical disk, and an IC card inserted into the computer 300. Then, the computer 300 may read the programs 307a to 307c from these and execute them.

  The following supplementary notes are further disclosed with respect to the embodiments including the above examples.

(Supplementary note 1)
Based on the acquired weather information, calculate the predicted value of power demand,
A power generation plan is automatically generated based on a power demand amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power demand amount,
The deviation amount between the calculated predicted value of the power demand and the acquired actual value of the power demand when the actual value of the power demand corresponding to the calculated predicted value of the power demand is acquired To calculate a new margin,
Based on the acquired weather information, when the predicted value of the future power demand is further calculated, the power generation plan is automatically generated based on the calculated power demand including the new margin.
A program for automatically generating a power generation plan, characterized in that processing is executed.

(Additional remark 2) The process which calculates the said new margin correlates weather information and deviation | shift amount, and calculates the said new margin based on the deviation | shift amount according to future weather information, It is characterized by the above-mentioned. An automatic generation program for power generation plans described in 1.

(Appendix 3)
Based on the acquired weather information, calculate the predicted value of power generation,
Based on the power generation amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power generation amount, a power generation plan is automatically generated,
The deviation amount between the calculated predicted value of the power generation amount and the acquired actual value of the power generation amount when the actual value of the power generation amount corresponding to the calculated predicted value of the power generation amount is acquired. To calculate a new margin,
Based on the acquired weather information, when the predicted value of the future power generation amount is further calculated, the power generation plan is automatically generated based on the calculated power generation amount including the new margin,
A program for automatically generating a power generation plan, characterized in that processing is executed.

(Supplementary note 4) The process for automatically generating the power generation plan generates the power generation plan based on a remaining power demand amount obtained by subtracting the power generation amount from a power demand amount. Automatic generation program for power generation.

(Additional remark 5) The process which calculates the said new margin associates weather information and deviation | shift amount, and calculates the said new margin based on the deviation | shift amount according to future weather information, It is characterized by the above-mentioned. Or the automatic generation program of the electric power generation plan of 4.

(Appendix 6) A method for automatically generating a power generation plan executed by a computer,
Based on the acquired weather information, calculate the predicted value of power demand,
A power generation plan is automatically generated based on a power demand amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power demand amount,
The deviation amount between the calculated predicted value of the power demand and the acquired actual value of the power demand when the actual value of the power demand corresponding to the calculated predicted value of the power demand is acquired To calculate a new margin,
Based on the acquired weather information, when the predicted value of the future power demand is further calculated, the power generation plan is automatically generated based on the calculated power demand including the new margin.
A method for automatically generating a power generation plan, wherein the process is executed.

(Additional remark 7) The process which calculates the said new margin associates weather information and deviation | shift amount, and calculates the said new margin based on the deviation | shift amount according to future weather information, It is characterized by the above-mentioned. A method for automatically generating a power generation plan described in 1.

(Appendix 8) A method for automatically generating a power generation plan executed by a computer,
Based on the acquired weather information, calculate the predicted value of power generation,
Based on the power generation amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power generation amount, a power generation plan is automatically generated,
The deviation amount between the calculated predicted value of the power generation amount and the acquired actual value of the power generation amount when the actual value of the power generation amount corresponding to the calculated predicted value of the power generation amount is acquired. To calculate a new margin,
Based on the acquired weather information, when the predicted value of the future power generation amount is further calculated, the power generation plan is automatically generated based on the calculated power generation amount including the new margin,
A method for automatically generating a power generation plan, wherein the process is executed.

(Supplementary note 9) The process of automatically generating the power generation plan generates the power generation plan based on a remaining power demand amount obtained by subtracting the power generation amount from a power demand amount. Method for automatically generating power generation plans.

(Supplementary Note 10) The process of calculating the new margin includes associating weather information with a deviation amount, and calculating the new margin based on a deviation amount according to future weather information. Or an automatic generation method of a power generation plan according to 9.

(Supplementary Note 11) Based on the acquired weather information, a demand prediction unit that calculates a predicted value of power demand,
A power generation plan generation unit that automatically generates a power generation plan based on a power demand amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power demand amount;
When the demand prediction unit obtains the actual value of the power demand corresponding to the predicted value of the power demand, the amount of deviation between the predicted value of the power demand and the actual value of the power demand In response to the calculation of a new margin, and further calculating a predicted value of the future power demand, a power demand amount is generated by adding the new margin to the predicted value of the future power demand. ,
The power generation plan generation unit automatically generates a power generation plan based on a power demand amount to which the new margin is added.

(Supplementary note 12) The power generation unit according to supplementary note 11, wherein the demand prediction unit associates weather information with a deviation amount, and calculates the new margin based on a deviation amount according to future weather information. Plan generator.

(Supplementary Note 13) A power generation prediction unit that calculates a predicted value of the amount of power generation based on the acquired weather information;
A power generation plan generation unit that automatically generates a power generation plan based on a power generation amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power generation amount,
The power generation prediction unit obtains the actual value of the electric power generation amount corresponding to the predicted value of the electric power generation amount, and the deviation amount between the predicted value of the electric power generation amount and the actual value of the electric power generation amount In response to the calculation of a new margin, and further calculating the predicted value of the future power generation amount, the power generation amount is generated by adding the new margin to the predicted value of the future power generation amount. ,
The power generation plan generation unit automatically generates a power generation plan based on a power generation amount with the new margin added.

(Additional remark 14) The said electric power generation plan production | generation part produces | generates the said electric power generation plan based on the remaining electric power demand amount which subtracted the said electric power generation amount from the electric power demand amount, The electric power generation plan of Additional remark 13 characterized by the above-mentioned. Generator.

(Supplementary note 15) The supplementary note 13 or 14, wherein the power generation prediction unit associates weather information with a deviation amount, and calculates the new margin based on a deviation amount according to future weather information. Power generation plan generator.

10a, 10b, 10c Generator 11a, 11b, 11c Solar generator 12a, 12b, 12c Wind generator 13a, 13b, 13c Geothermal generator 14a, 14b, 14c Hydroelectric generator 15a, 15b, 15c Biomass generator 20a, 20b, 20c Electric power company 30 High-voltage customer 40 Low-voltage customer 50a, 50b, 50c Power supplier 100 PPS
200 Power generation plan generator

Claims (9)

On the computer,
Based on the acquired weather information, calculate the predicted value of power demand,
A power generation plan is automatically generated based on a power demand amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power demand amount,
The deviation amount between the calculated predicted value of the power demand and the acquired actual value of the power demand when the actual value of the power demand corresponding to the calculated predicted value of the power demand is acquired To calculate a new margin,
Based on the acquired weather information, when the predicted value of the future power demand is further calculated, the power generation plan is automatically generated based on the calculated power demand including the new margin.
A program for automatically generating a power generation plan, characterized in that processing is executed.   The process of calculating the new margin associates weather information with a deviation amount, and calculates the new margin based on a deviation amount according to future weather information. Automatic generation plan generation program. On the computer,
Based on the acquired weather information, calculate the predicted value of power generation,
Based on the power generation amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power generation amount, a power generation plan is automatically generated,
The deviation amount between the calculated predicted value of the power generation amount and the acquired actual value of the power generation amount when the actual value of the power generation amount corresponding to the calculated predicted value of the power generation amount is acquired. To calculate a new margin,
Based on the acquired weather information, when the predicted value of the future power generation amount is further calculated, the power generation plan is automatically generated based on the calculated power generation amount including the new margin,
A program for automatically generating a power generation plan, characterized in that processing is executed.   The power generation plan according to claim 3, wherein the process of automatically generating the power generation plan generates the power generation plan based on a remaining power demand amount obtained by subtracting the power generation amount from a power demand amount. Automatic plan generator.   5. The process of calculating the new margin includes associating weather information with a deviation amount, and calculating the new margin based on a deviation amount according to future weather information. Automatic generation program for the described power generation plan. A method for automatically generating a power generation plan executed by a computer,
Based on the acquired weather information, calculate the predicted value of power demand,
A power generation plan is automatically generated based on a power demand amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power demand amount,
The deviation amount between the calculated predicted value of the power demand and the acquired actual value of the power demand when the actual value of the power demand corresponding to the calculated predicted value of the power demand is acquired To calculate a new margin,
Based on the acquired weather information, when the predicted value of the future power demand is further calculated, the power generation plan is automatically generated based on the calculated power demand including the new margin.
A method for automatically generating a power generation plan, wherein the process is executed. A method for automatically generating a power generation plan executed by a computer,
Based on the acquired weather information, calculate the predicted value of power generation,
Based on the power generation amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power generation amount, a power generation plan is automatically generated,
The deviation amount between the calculated predicted value of the power generation amount and the acquired actual value of the power generation amount when the actual value of the power generation amount corresponding to the calculated predicted value of the power generation amount is acquired. To calculate a new margin,
Based on the acquired weather information, when the predicted value of the future power generation amount is further calculated, the power generation plan is automatically generated based on the calculated power generation amount including the new margin,
A method for automatically generating a power generation plan, wherein the process is executed. Based on the acquired weather information, a demand prediction unit that calculates a predicted value of power demand,
A power generation plan generation unit that automatically generates a power generation plan based on a power demand amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power demand amount;
When the demand prediction unit obtains the actual value of the power demand corresponding to the predicted value of the power demand, the amount of deviation between the predicted value of the power demand and the actual value of the power demand In response to the calculation of a new margin, and further calculating a predicted value of the future power demand, a power demand amount is generated by adding the new margin to the predicted value of the future power demand. ,
The power generation plan generation unit automatically generates a power generation plan based on the power demand amount with the new margin added.
A power generation plan generation device characterized by that. A power generation prediction unit that calculates a predicted value of the amount of power generation based on the acquired weather information;
A power generation plan generation unit that automatically generates a power generation plan based on a power generation amount obtained by adding a predetermined amount of margin to the calculated predicted value of the power generation amount,
The power generation prediction unit obtains the actual value of the electric power generation amount corresponding to the predicted value of the electric power generation amount, and the deviation amount between the predicted value of the electric power generation amount and the actual value of the electric power generation amount In response to the calculation of a new margin, and further calculating the predicted value of the future power generation amount, the power generation amount is generated by adding the new margin to the predicted value of the future power generation amount. ,
The power generation plan generation unit automatically generates a power generation plan based on the amount of power generated by adding the new margin.
A power generation plan generation device characterized by that.

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