JP2018025962A - Operation plan planning method, operation plan planning device, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an operation plan planning method capable of planning an operation plan that meticulously deals with daily demand fluctuation within an optimal framework of all through the year.SOLUTION: A controller 11 of an operation plan planning device 1 performs: input of apparatus data (step S31); input of charge data (step S32); input of environment restriction data (step S33); and input of other constraint conditions (step S34). Next, the controller 11 performs annual demand prediction processing (step S35) and next day demand prediction processing (step S36). Then, the controller 11 calculates annual and next day optimal operation plan (step S37) and outputs them (step S38).SELECTED DRAWING: Figure 9

Description

  The present invention relates to an operation plan planning method for creating an optimal operation plan for an energy plant such as a district heating and cooling plant.

  The present inventors have devised an energy system optimization method for optimizing an operation plan of an energy system including heat source equipment and cogeneration introduced in an existing energy plant (see Patent Documents 1 and 2). . In the energy system optimization methods described in Patent Documents 1 and 2, an optimum operation plan is drawn up by representing 36 patterns of weekdays, Saturdays, and holidays from January to December, which are optimized throughout the year.

Japanese Patent No. 5179423 JP-A-2015-99417

  By the way, the energy system optimization methods described in Patent Documents 1 and 2 are positioned as guidelines in the creation of daily operation plans, and do not deal with daily demand fluctuations in detail.

  The present invention has been made in view of the above-mentioned problems, and the purpose of the present invention is to make it possible to formulate an operation plan that more closely responds to daily demand fluctuations within an annually optimal framework. Is to provide a simple operation planning method.

  A first invention for achieving the above-mentioned object is an operation plan planning method in which an operation plan planning apparatus plans an operation plan of an energy plant, and a storage means of the operation plan planning apparatus is provided for the energy plant. Demand data including power demand, cooling demand, heating demand, hot water supply demand and steam demand, environmental regulation data which is annual environmental compliance conditions, power supply, cooling supply, heating supply, hot water supply and steam supply Charge data including information on annual contractual compliance conditions regarding electricity and gas charges purchased from the outside to operate the equipment, equipment data including information on the equipment, and information on associated equipment attached to the equipment; , Device detailed data including detailed information of the device and the accompanying device, a primary energy conversion value per unit amount of energy to be input, and CO2 emission A storage step for storing environmental data including a coefficient; and a calculation unit of the operation planning apparatus, using the data stored by the storage unit as an input value, and calculating a cost, a primary energy consumption amount or a CO2 emission amount as an objective function And a calculation step of calculating an operation plan of the equipment on the next day, and a year or year remaining, and an operation plan drafting method. According to the first invention, it is possible to devise an operation plan that more precisely responds to daily demand fluctuations within an annually optimal framework.

  In the first aspect of the present invention, in the case of the beginning of the fiscal year, the next day demand is predicted by the next day demand forecasting process for forecasting the power demand, the cooling demand, the heating demand, the hot water supply demand, and the steam demand on the next day every predetermined time. Make a prediction, and make an annual demand prediction by an annual demand prediction process for predicting the electric power demand, the cooling demand, the heating demand, the hot water supply demand and the steam demand every predetermined time every month and day of the week pattern, The optimization calculation may be performed using the results of the next day demand forecast and the annual demand forecast. As a result, it is possible to accurately predict demand fluctuations for the next day, and to predict annual demand fluctuations efficiently, and thus to perform optimization calculations efficiently and accurately.

  In the first aspect of the invention, in the middle of the fiscal year, in the middle of the fiscal year, the actual values up to the previous day of the environmental compliance conditions and the contractual compliance conditions are input, and the power demand, cooling demand, and heating of the next day are input. Demand, hot water supply demand, and steam demand are predicted every day by the next day demand prediction process, and the power demand, cooling demand, heating, etc. for each remaining month and day of the week after the next day. The remaining demand forecast for the year is performed by the forecast remaining demand forecast process for forecasting the demand, the hot water supply demand and the steam demand every predetermined time, and the optimization calculation is performed using the results of the next day demand forecast and the remaining demand forecast for the next year. You may do it. As a result, it is possible to accurately predict demand fluctuations for the next day, and to predict annual demand fluctuations efficiently, and thus to perform optimization calculations efficiently and accurately. Further, it is possible to improve the accuracy of planning an operation plan that is optimal throughout the year in the middle of the year.

  A second invention is an operation plan planning device for planning an operation plan of an energy plant, wherein demand data including power demand, cooling demand, heating demand, hot water demand and steam demand for the energy plant, and environmental annual Environmental regulation data, which is a compliance condition, and information on annual compliance conditions in the contract regarding electricity and gas charges purchased from outside to operate each device that supplies power, cooling, heating, hot water, and steam Per unit amount of energy to be input, device data including information on the device and information on associated devices associated with the device, device detailed data including detailed information on the device and the associated devices, and Storage means for storing the primary energy conversion value and environmental data including the CO2 emission coefficient, and the data stored by the storage means. And calculating means for calculating the operation plan of the equipment on the next day or the year or year, and performing the optimization calculation with cost, primary energy consumption or CO2 emission as an objective function Is an operation planning device characterized by According to the second invention, it is possible to formulate an operation plan that more precisely responds to daily demand fluctuations within an annually optimal framework.

  The third invention is a program for causing a computer to function as the operation plan planning device of the second invention. By installing the program of the third invention in a general-purpose computer, the operation planning device of the second invention can be obtained.

  According to the present invention, it is possible to provide an operation plan formulation method and the like that can formulate an operation plan that more precisely responds to daily demand fluctuations within an annually optimal framework.

Hardware configuration diagram of a computer that realizes an operation planning device Diagram showing data flow in operation planning method Diagram explaining the demand data used The figure which shows an example of the input screen of demand forecast of the next day The figure which shows an example of the input screen of annual demand forecast Diagram showing examples of annual compliance conditions and past performance values Flow chart showing the flow of next day demand forecast processing Flow chart showing the flow of annual demand forecast processing Flow chart showing the flow of optimal calculation processing at the beginning of the fiscal year Flow chart showing the flow of demand forecast processing for the remaining year Flow chart showing the flow of optimal calculation processing during the year

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. The operation plan planning apparatus 1 according to the embodiment of the present invention formulates an optimal operation plan for an energy plant such as a district cooling and heating plant.

  FIG. 1 is a hardware configuration diagram of a computer that realizes the operation planning apparatus 1. Note that the hardware configuration in FIG. 1 is an example, and various configurations can be adopted depending on applications and purposes.

  A computer that implements the operation planning device 1 includes a control unit 11, a storage unit 12, a media input / output unit 13, a communication control unit 14, an input unit 15, a display unit 16, a peripheral device I / F unit 17, etc. Connected through.

The control unit 11 is a CPU (Central
A processing unit (ROM), a read only memory (ROM), a random access memory (RAM), and the like. The CPU calls a program stored in the storage unit 12, ROM, recording medium, etc. to a work memory area on the RAM and executes it, drives and controls each device connected via the bus 18, and will be described later. Realize processing. The ROM is a non-volatile memory and permanently holds a computer boot program, a program such as BIOS, data, and the like. The RAM is a volatile memory, and temporarily stores a program, data, and the like loaded from the storage unit 12, ROM, recording medium, and the like, and includes a work area used by the control unit 11 to perform various processes.

  The storage unit 12 is, for example, an HDD (Hard Disk Drive), and stores a program executed by the control unit 11, data necessary for program execution, an OS (Operating System), and the like. As for the program, a control program corresponding to the OS and an application program for causing a computer to execute processing to be described later are stored. Each of these program codes is read by the control unit 11 as necessary, transferred to the RAM, read by the CPU, and executed as various means.

  The media input / output unit 13 (drive device) inputs / outputs data, for example, media such as a CD drive (-ROM, -R, -RW, etc.), DVD drive (-ROM, -R, -RW, etc.) Has input / output devices. The communication control unit 14 includes a communication control device, a communication port, and the like, and is a communication interface that mediates communication between a computer and a network, and performs communication control between other computers via the network. The network may be wired or wireless.

  The input unit 15 inputs data and includes, for example, a keyboard, a pointing device such as a mouse, and an input device such as a numeric keypad. An operation instruction, an operation instruction, data input, and the like can be performed on the computer via the input unit 15. The display unit 16 includes a liquid crystal panel, a display device such as an organic EL, and a logic circuit or the like (video adapter or the like) for realizing a video function of a computer in cooperation with the display device. The input unit 15 and the display unit 16 may be integrated like a touch panel display.

  The peripheral device I / F (interface) unit 17 is a port for connecting a peripheral device to the computer, and the computer transmits and receives data to and from the peripheral device via the peripheral device I / F unit 17. The peripheral device I / F unit 17 is configured by a USB or the like, and usually includes a plurality of peripheral devices I / F. The connection form with the peripheral device may be wired or wireless. The bus 18 is a path that mediates transmission / reception of control signals, data signals, and the like between the devices.

  FIG. 2 is a diagram showing a data flow in the operation planning method. As shown in FIG. 2, in the operation plan planning method according to the embodiment of the present invention, the operation plan planning apparatus 1 uses demand data 21, environmental regulation data 22, fee data 23, equipment data 24, equipment detailed data 25, and environmental data. 26 and the like are input, optimization calculation processing is performed, and a calculation result 27 is output. Each data is not limited to the example shown in FIG.

  The operation planning apparatus 1 includes input means (a media input / output unit 13, a communication control unit 14, an input unit for inputting demand data 21, environmental regulation data 22, fee data 23, device data 24, device detailed data 25, environment data 26, and the like. Input unit 15, peripheral device I / F unit 17 and the like) and storage means (storage unit 12, external storage device, etc.) for storing them.

  In addition, the operation planning device 1 uses the data stored by the storage means as an input value in a linear programming problem or a nonlinear programming problem in which various energy demands are supplied by a device group, and the cost, primary energy consumption or CO2 emission amount. And calculating means (control unit 11) for calculating an operation plan of the device in the calculation target period.

  The demand data 21 includes information on a plurality of energy demands at the calculation target facility. The demand data 21 is data relating to energy demand such as electric power, cooling, heating, hot water supply, steam, etc., for example, by month, by weekday / Saturday / holiday (hereinafter also referred to as “day of the week pattern”), It is data by time. The demand data 21 may be past history data, or may be prediction data in consideration of the future usage status of the calculation target facility.

  The environmental regulation data 22 is an annual compliance condition on the environment, and is used as a constraint condition in the optimization calculation process. For example, the environmental regulation data 22 includes an upper limit of annual primary energy consumption, an upper limit of annual CO2 emissions, and the like. As a result, if the calculation target facility is an existing facility, a reduction plan for primary energy consumption and CO2 emission can be established. Moreover, it becomes possible to calculate the limit value of the reduction amount in the existing equipment and the timing of device update, and to consistently select the optimum device at the time of device update.

  The charge data 23 includes information on contractual annual compliance conditions regarding electricity and gas charges purchased from the outside in order to operate each device that performs power supply, cooling supply, heating supply, hot water supply supply, and steam supply. The fee data 23 may include detailed contract conditions in addition to the basic fee and the metered fee. For example, in the case of a gas charge, the gas flow rate multiplication factor (= annual gas usage / maximum hourly gas usage), load factor (= monthly average usage / maximum demand period monthly average usage × 100), and the like.

  The equipment included in the equipment data 24 is, for example, a chiller (water cooling, air cooling), a heat pump water heater, a hot water boiler, a steam boiler, a gas engine heat pump, a gas cold water heater, a hot water absorption cold water heater, a steam heat exchanger (hot water supply) , Heating), hot water heat exchanger (hot water supply, heating), cogeneration system (hereinafter also abbreviated as “CGS”), exhaust gas charging type absorption chiller / heater system, GENELINK (registered trademark), and the like. In addition, the equipment included in the equipment data 24 includes chillers, gas chiller / hot water machines, hot water absorption chiller / heaters, exhaust gas input type absorption chiller / heater systems, Genelink®, etc. Cooling water pump, cooling tower fan, etc.).

  The device data 24 includes information on the device and information on accompanying devices attached to the device, and specifically includes capacity data, rated efficiency, CGS maintenance cost, and the like of each device. The capacity data includes the amount of power consumed by the associated devices (cold / hot water pump, cooling water pump, cooling tower fan, etc.). For example, a cooling tower is a heat exchanger that cools a heat medium such as water by direct or indirect contact with the atmosphere, and consumes a large amount of power. When the device data 24 includes the power consumption of the associated device, the operation planning device 1 can perform the optimization calculation process in consideration of the associated device, and the calculation accuracy is improved. The rated efficiency indicates the energy consumption efficiency at the rated capacity of each device. For example, cold water COP (Coefficient of Performance: coefficient of performance) represents the cold water capacity per unit power consumption, and the larger the number, the better the efficiency.

  The device detailed data 25 includes detailed information of the device and the accompanying devices, and specifically includes partial load efficiency data, auxiliary machine power, and the like. The partial load efficiency data is a partial load efficiency value when the load factor is 25%, 50%, 75%, or 100%, for example. Auxiliary power is the power of accompanying equipment (cold / hot water pump, cooling water pump, cooling tower fan, water pump, etc.) attached to each equipment. The data regarding the auxiliary machine power includes, for example, an auxiliary machine power coefficient which is a coefficient of the total auxiliary machine power with respect to the rated capacity of each device.

  The environmental data 26 includes a primary energy conversion value per unit amount of input energy, a CO2 emission coefficient, and the like. Primary energy is energy that can be obtained in the natural form such as coal, oil and natural gas. The primary energy conversion value per unit amount of energy to be input is used to convert the amount of energy used to cover the final energy consumption into the amount of primary energy input. The CO2 emission coefficient is a CO2 emission amount per unit usage amount of electric power, gas, etc. purchased from the outside.

  The calculation result 27 is an optimum operation plan of the device, an annual total cost, an annual CO2 emission amount, or an annual primary energy consumption amount. The operation planning device 1 determines the optimum solution for the operation plan of the device based on the demand data 21, the environmental regulation data 22, the charge data 23, the device data 24, the device detailed data 25, and the environment data 26, for example, for each month and each day of the week. And calculated by time.

  The optimization calculation process by the operation planning apparatus 1 uses, for example, a strict solution relaxation method. The exact solution relaxation method is a method that uses not only the exact solution obtained per unit time by the mixed integer linear programming method but also a set of neighboring solutions slightly away from the exact solution as a final solution candidate.

  Specifically, first, the control unit 11 of the operation planning apparatus 1 calculates the exact solution of the output value of the optimum device that satisfies the energy load for every unit period included in the calculation target period. Next, the control unit 11 calculates a candidate solution set whose elements are the exact solution and solutions around the exact solution for every unit period. Finally, the control unit 11 calculates the optimal solution for the calculation target period from the combinations of the elements of the candidate solution set.

  Further, the storage unit 12 may store a relaxation coefficient indicating an allowable range of the degree of separation from the exact solution, and the control unit 11 may calculate a solution around the exact solution based on the relaxation coefficient. When the calculation is performed with the relaxation coefficient set to a small value, a candidate solution set whose only elements are slightly separated from the exact solution is calculated. In addition, when the calculation is performed with a large relaxation coefficient, a candidate solution set including a solution that is somewhat distant from the exact solution is calculated.

  For example, the control unit 11 calculates an exact solution every hour, calculates a candidate solution set every hour, and satisfies all the constraints among the combinations of elements of the candidate solution set for one year. The optimal solution is calculated. Here, the combination of elements of the candidate solution set for one year is, for example, a set of output values set of each device for 12 months × 3 (weekdays, Saturdays, holidays) × 24 hours = 864 units. .

  In addition, the control part 11 may perform an optimization calculation process not only using a exact solution relaxation method but using another well-known method. Moreover, the control part 11 may perform the optimization calculation process not only for a linear problem but for a nonlinear problem.

  In the embodiment of the present invention, not only an annual optimum operation plan represented by 36 patterns of weekdays, Saturdays, and holidays from January to December, which are optimum for the whole year, but also an optimum operation plan for the next day is drawn up.

  The control unit 11 performs two demand forecasts: “next day demand forecast” and “annual demand forecast”. The “next day demand forecast” is a forecast of demand for power, cooling, heating, hot water supply, and steam for the next day for every day and every hour. However, the power demand may be every 30 minutes if necessary. The control unit 11 automatically inputs the predicted demand data to the optimal calculation model. “Annual demand forecast” is a forecast of demand for electricity, cooling, heating, hot water supply, and steam for a total of 36 hours on weekdays, Saturdays, and holidays representing January to December every hour. The control unit 11 automatically inputs the predicted demand data to the optimal calculation model.

  For example, the control unit 11 predicts future demand data by analyzing the correlation between past demand data and weather forecast data. As for the timing of the forecast, the next day forecast is carried out on the previous day, and the annual forecast is carried out on a regular basis, such as monthly or weekly for a long-term forecast, or an event in which the demand changes greatly from the past (for example, a large tenant Etc.) should be carried out at the time of occurrence.

  “Next-day demand forecast” is for planning the next-day equipment optimal operation plan, and “Annual demand forecast” is used to comply with the constraints of the entire year, and is optimal for the entire year. Use it to create an operation plan. The next day's optimal operation plan that ignores the constraints of the entire year may eventually cause a problem of non-compliance. In order to avoid this, it is necessary to formulate a facility operation plan for the next day after satisfying the constraints of the entire year.

  Furthermore, since the optimum operation plan for the next day is drawn up while the operation plan for the entire year is optimized, the problem of the conventional optimum operation plan for the next day (partial optimization) can be avoided. The conventional sub-optimal stacking method has a problem that, in the latter half of the fiscal year, it may be forced to operate uneconomically in order to comply with the constraints of the entire year. In the embodiment of the present invention, the operation plan planning apparatus 1 can formulate an optimal operation plan for the next day while formulating an operation plan that is optimal for the entire year.

  FIG. 3 is a diagram for explaining the demand data to be used. In the case of the beginning of the year, the demand data to be used are the next day demand forecast and the demand forecast after the next day (annual demand forecast). On the other hand, in the case of the middle of the fiscal year, the past actual value is used in addition to the next day demand forecast and the demand forecast after the next day (the year remaining demand forecast). The past performance value is a performance value (gas consumption, power consumption, 30-minute maximum power purchase amount) before the current time point. Gas consumption and power consumption are input to the optimum calculation model as actual values. In relation to this, the CO2 emission amount and the primary energy consumption amount are also automatically calculated and input to the optimum calculation model. In addition, when electric power is sold to the outside by a generator, the amount of CO2 reduction and the amount of primary energy reduction for the sold power are automatically calculated and input to the optimum calculation model. As a result, an optimum plan for the next day can be made which is an overall optimum for the whole year in consideration of the upper limit of the annual CO2 emission amount and the upper limit of the annual primary energy consumption.

  In the middle of the fiscal year, as time elapses from the beginning of the fiscal year, (1) the use of actual values for the year increases, and (2) the increase in the use of actual values for the year increases the demand forecast value for the year. For the two reasons that accuracy increases, the accuracy of planning an operation plan that is optimal for the whole year will improve.

  FIG. 4 is a diagram illustrating an example of an input screen for demand forecast for the next day. The next day demand forecast input screen 31 shown in FIG. 4 is a screen for inputting hourly power demand, cooling demand, heating demand, hot water supply demand, steam demand, and flow rate demand for the next day. The operation planning device 1 performs next-day demand prediction in units of time and inputs it to the optimum calculation model. The user can correct data on the input screen 31 shown in FIG. 4 as necessary. The operation planning device 1 may predict the unit price of each energy in addition to the demand for each energy.

  FIG. 5 is a diagram illustrating an example of an input screen for annual demand prediction. An input screen 32 for annual demand prediction shown in FIG. 5 is a screen for inputting power demand, cooling demand, heating demand, hot water supply demand, steam demand and flow rate demand on weekdays in April and May. The same applies after June. The operation planning device 1 makes an annual demand forecast for each month and each day of the week pattern (= weekdays, Saturdays, holidays) and inputs it to the optimum calculation model. The user can correct data on the input screen 32 shown in FIG. 5 as necessary. The operation planning device 1 may predict the unit price of each energy in addition to the demand for each energy.

  FIG. 6 is a diagram illustrating an example of annual compliance conditions and past performance values. The annual compliance conditions 33 shown in FIG. 6A are environmental regulations and contract conditions that must be observed annually. The values of primary energy consumption and CO2 emission are upper limits in environmental regulations. The gas load factor and the gas contract amount are lower limit values in the contract. The contract power is the maximum power that can be used in the contract. On the other hand, the past performance value 34 shown in FIG. 6B is a performance value from the beginning of the fiscal year to the previous day.

  Hereinafter, the details of the operation planning method according to the embodiment of the present invention will be described with reference to FIGS.

  FIG. 7 is a flowchart showing the flow of next day demand prediction processing. As shown in FIG. 7, in the next day demand prediction process, the control unit 11 of the operation planning device 1 has the same pattern (for example, the same month and the same day pattern) as the next day, power demand, cooling demand, heating demand, hot water demand. The past performance data relating to the steam demand and the flow demand is input (step S11). Next, the control unit 11 inputs the weather forecast data (= temperature forecast data, humidity forecast data, and other related data) for the next day (step S12). And the control part 11 performs the correlation analysis (for example, multiple regression analysis and a neural network) regarding past performance data, weather forecast data, etc. (step S13), and outputs next day demand prediction data (step S14). The correlation analysis in step S13 is not limited to multiple regression analysis or a neural network, and other known techniques can be used.

  FIG. 8 is a flowchart showing the flow of the annual demand prediction process. As shown in FIG. 8, in the annual demand forecasting process, the control unit 11 of the operation planning device 1 performs the power demand, cooling demand, heating demand, hot water demand, steam demand and flow demand for each month and day pattern. The past performance data regarding is input (step S21). Next, the control unit 11 inputs weather forecast data (= temperature forecast data, humidity forecast data, and other related data) for each month and day pattern of the year (step S22). And the control part 11 performs the correlation analysis (for example, multiple regression analysis and a neural network) regarding past performance data, weather forecast data, etc. (step S23), and outputs annual demand prediction data (step S24). The correlation analysis in step S23 is not limited to multiple regression analysis or a neural network, and other known techniques can be used.

  FIG. 9 is a flowchart showing the flow of the optimum calculation process at the beginning of the fiscal year. In the optimum calculation process at the beginning of the fiscal year shown in FIG. 9, the next day demand forecast process is performed by the next day demand forecast process shown in FIG. 7, and the annual demand forecast process is performed by the annual demand forecast process shown in FIG. Perform optimization calculations using the prediction results.

  As shown in FIG. 9, the control unit 11 of the operation planning device 1 inputs device data (step S31), fee data (step S32), environmental regulation data (step S33), and other constraints. Input (step S34) is performed. Next, the control part 11 performs the annual demand forecast process (step S35) shown in FIG. 8, and the next day demand forecast process (step S36) shown in FIG. Then, the control unit 11 calculates the optimum operation plan for the year and the next day (step S37), and outputs the optimum operation plan for the year and the next day (step S38).

  In step S37, the control part 11 performs the calculation which becomes the whole year optimal using the demand forecast value and energy unit price of the next day, and the demand forecast value and energy unit price after the next day. The objective function of optimization is either annual cost minimum, annual CO2 emission minimum or annual primary energy consumption minimum.

  FIG. 10 is a flowchart showing the flow of the remaining year demand forecast process. As shown in FIG. 10, in the remaining year demand forecasting process, the control unit 11 of the operation planning device 1 performs the following electricity demand for each month and day of the week after the next day, cooling demand, heating demand, hot water demand, The past performance data relating to the steam demand and the flow demand is input (step S41). Next, the control unit 11 inputs weather forecast data (= temperature forecast data, humidity forecast data, and other related data) for each remaining month and day pattern after the next day (step S42). And the control part 11 performs the correlation analysis (for example, multiple regression analysis and a neural network) regarding past performance data, weather forecast data, etc. (step S43), and outputs the demand forecast data remaining in the year after the next day (step S44). ). The correlation analysis in step S43 is not limited to multiple regression analysis or a neural network, and other known techniques can be used.

  FIG. 11 is a flowchart showing the flow of optimal calculation processing during the year. In the optimal calculation process in the middle of the fiscal year shown in FIG. 11, the actual values up to the previous day of the environmental compliance conditions and the contract compliance conditions are input, and the next day demand forecast process is performed by the next day demand forecast process shown in FIG. The remaining year demand forecast is performed by the remaining year demand forecast process shown in FIG. 10, and the optimization calculation is performed using the results of the next day demand forecast and the remaining year demand forecast.

  As shown in FIG. 11, the control unit 11 of the operation planning device 1 inputs device data (step S51), fee data (step S52), environmental regulation data (step S53), and other constraints. Input (step S54), input of annual results (including actual values up to the previous day of environmental compliance conditions and contract compliance conditions) (step S55). Next, the control unit 11 executes the remaining year demand forecasting process (step S56) shown in FIG. 10 and the next day demand forecasting process (step S57) shown in FIG. Then, the control unit 11 calculates the optimal operation plan for the remainder of the year and the next day (step S58), and outputs the optimal operation plan for the remainder of the fiscal year and the next day (step S59).

  In step S58, the control unit 11 confirms the past actual values (gas consumption, power consumption, 30-minute maximum power purchase amount, gas charge, electricity charge, environmental compliance conditions, contract compliance conditions, etc.), the next day. Using the demand forecast value and the energy unit price, and the demand forecast value and energy unit price after the next day, the calculation for the overall optimization for the remainder of the year is executed. The objective function of optimization is either annual cost minimum, annual CO2 emission minimum or annual primary energy consumption minimum.

  As described above, according to the operation plan formulation method of the embodiment of the present invention, it is possible to formulate an operation plan that more closely responds to daily demand fluctuations within an annual optimum framework.

  For example, by linking the operation plan planning device 1 and the existing central monitoring panel, it becomes possible to realize automatic control of the central monitoring panel according to the data of the optimum operation plan on the next day calculated by the operation plan planning device 1. .

  The preferred embodiments of the operation planning method and the like according to the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to such examples. It will be apparent to those skilled in the art that various changes or modifications can be conceived within the scope of the technical idea disclosed in the present application, and these naturally belong to the technical scope of the present invention. Understood.

DESCRIPTION OF SYMBOLS 1 ......... Operation planning device 11 ......... Control part 12 ......... Storage part 13 ......... Media input / output part 14 ......... Communication control part 15 ......... Input part 16 ......... Display part 17 ... ... Peripheral equipment I / F 18 ......... Bus 21 ......... Demand data 22 ......... Environmental regulation data 23 ......... Fare data 24 ......... Device data 25 ......... Detailed equipment data 26 ......... Environmental data 27 ......... Calculation results 31 ......... Demand forecast input screen for the next day 32 ......... Annual demand forecast input screen 33 ......... Annual compliance conditions 34 ......... Past performance values

Claims (5)

The operation plan planning device is an operation plan planning method for planning an energy plant operation plan,
The storage means of the operation planning device includes demand data including power demand, cooling demand, heating demand, hot water demand and steam demand for the energy plant, environmental regulation data which is an annual compliance condition on the environment, power supply, Rate data including information on annual compliance conditions on contracts for electricity and gas purchased from the outside to operate each device that performs cooling supply, heating supply, hot water supply supply and steam supply, information on the device, and An environment including device data including information on an accompanying device accompanying the device, device detailed data including detailed information on the device and the accompanying device, a primary energy conversion value per unit amount of energy to be input, and a CO2 emission coefficient A storage step for storing data;
The calculation means of the operation planning device performs the optimization calculation with the data stored in the storage means as an input value, and the cost, primary energy consumption amount or CO2 emission amount as an objective function, the yearly or yearly remainder, and A calculation step of calculating an operation plan of the device on the next day;
An operation planning method characterized by comprising: In the case of the beginning of the fiscal year, the calculation step performs next day demand prediction by next day demand prediction processing for predicting the power demand, cooling demand, heating demand, hot water supply demand and steam demand every predetermined time for the next day. The annual demand prediction is performed by an annual demand prediction process for predicting the power demand, the cooling demand, the heating demand, the hot water supply demand, and the steam demand for each month and every day of the week, and the next day demand forecast and The operation planning method according to claim 1, wherein the optimization calculation is performed using a result of the annual demand prediction. In the middle of the fiscal year, the calculation step inputs actual values up to the previous day of the environmental compliance conditions and the contractual compliance conditions, and the next day the power demand, the cooling demand, the heating demand, the hot water demand. And the next day demand prediction process for predicting the steam demand every predetermined time, and the electric power demand, the cooling demand, the heating demand, and the hot water demand for the remaining monthly and day of week patterns after the next day. And the remaining demand forecast for the year is performed by the remaining demand forecast process for the fiscal year for forecasting the steam demand every predetermined time, and the optimization calculation is performed using the results of the next day demand forecast and the remaining demand forecast for the year. The operation planning method according to claim 1. An operation plan planning device for planning an operation plan of an energy plant,
Demand data including power demand, cooling demand, heating demand, hot water demand, and steam demand for the energy plant, environmental regulation data that is an annual compliance condition for the environment, power supply, cooling supply, heating supply, hot water supply, and steam Charge data including information on annual terms and conditions of contract for electricity and gas purchased from the outside to operate each equipment to be supplied, information on the equipment, and information on accompanying equipment attached to the equipment Storage means for storing device data, device detailed data including detailed information of the device and the accompanying devices, and environmental data including a primary energy conversion value per unit amount of energy to be input and a CO2 emission coefficient,
The data stored in the storage means is used as an input value, optimization calculation is performed using cost, primary energy consumption or CO2 emission as an objective function, and the operation plan of the equipment for the year or year remaining and the next day is calculated. A calculation means;
An operation planning device characterized by comprising:   A program for causing a computer to function as the operation planning apparatus according to claim 4.

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