JP2007220665A - Operation planning device and operation planning method for cogeneration system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an operation planning device for a cogeneration system in which the optimum operation pattern of high energy saving characteristics can be searched at a small calculation cost. <P>SOLUTION: This is provided with a load predicting part 101 to predict an energy load at customers in an operation planning period, a division part 102 to divide the operation planning period into a plurality of periods, and an optimum operation pattern decision part 103 to determine the optimum operation pattern in the operation planning period by sequentially repeating until the last period of the plurality of periods a treatment of searching the operation pattern suitable to supply energy coping with the predicted energy load for a first period of the plurality of divided periods, and of searching the operation pattern suitable to supply energy coping with the predicted energy load as the operation pattern succeeding the operation pattern searched in the immediately prior period. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an operation planning apparatus that determines an optimal operation pattern of a cogeneration system during an operation planning period.

  Cogeneration systems such as fuel cell cogeneration systems and gas engine cogeneration systems, which are equipped with a combined heat and power generator that generates electricity and heat, are installed in consumers, supplying the generated power to consumers and reducing the power load. In addition to providing heat, it collects waste heat from power generation and stores it to cover the hot water supply load of consumers. This system has a very high energy efficiency as compared with conventional power generation systems such as thermal power generation, and is expected to spread as an energy saving device.

  However, in order to maximize the energy-saving performance of the cogeneration system, it is necessary to perform an operation suitable for the power / heat load pattern of the consumer. For example, when heat is generated by a cogeneration system according to the power load even though heat is not used by consumers, the amount of stored heat continues to increase, eventually exceeding the capacity that can store heat. In this case, power generation is continued while throwing away heat, or power generation is stopped so as not to generate unnecessary heat any more. Regardless of which measure is taken, energy efficiency is reduced.

  Therefore, a cogeneration system is required to predict a future load of an automatically installed customer and to plan an operation that exhibits energy savings based on the prediction.

  An example of such a system is shown in Patent Document 1. In the technique of Patent Document 1, power demand and heat demand in a period for which a predetermined operation is planned are predicted in advance, and within the period in which the predetermined operation is planned with respect to the predicted demand, the operation is stopped or stopped at predetermined time intervals. An operation plan in which one of the states is set is set as an operation pattern, and a primary energy usage amount when the cogeneration system is operated according to the operation pattern is calculated by a predetermined calculation formula. The primary energy usage of all possible operation patterns is obtained, and the cogeneration system is operated according to the operation pattern with the smallest primary energy usage among all the operation patterns.

  By this process, it becomes possible to select the operation pattern with the highest energy saving performance from all the operation patterns that can be taken in the operation plan period, and to operate the cogeneration system.

  In addition to Patent Document 1, Patent Document 2 discloses a system that predicts a future load of a consumer and plans an operation that exhibits energy saving performance based on the prediction. In the technique of Patent Document 2, the start time is provisionally determined, the power generation output, the amount of exhaust heat recovery, and the like are obtained by simple calculation with a low calculation cost, and the stop time satisfying a predetermined condition is obtained. Shift the start time to obtain the stop time at various start times in the same way, and calculate the amount of energy consumption by calculating detailed operation with high calculation cost considering the heat dissipation loss for the temporarily determined start / stop time. Then, the operation pattern that minimizes the energy consumption is selected from the temporarily determined start / stop times. And the stop time which satisfy | fills the conditions that the waste heat recovery amount at the time of performing a follow-up operation | movement with electric power from provisionally determined start time covers the heat amount required for the day or most of it is calculated | required.

With this process, it is possible to select the operation pattern having the highest energy saving performance among the operation patterns satisfying the predetermined condition at a low calculation cost and to operate the cogeneration system.
JP 2002-213303 A JP-A-2005-30211

  However, according to the technique of Patent Document 1, there is a problem that the calculation cost becomes enormous.

  For example, in the technique of Patent Document 1, the total number of possible operation patterns is 2 to the 48th power, that is, about 28 trillion. This is the total number of patterns that can be taken when selecting either the rated operating state or the stopped state for 48 periods. It is not realistic to calculate the energy saving performance for all such a large number of driving patterns and search for the driving pattern having the highest energy saving because the calculation cost is enormous.

  Further, according to the technology of Patent Document 2, although the calculation cost is small, since the operation pattern that maximizes the energy saving performance is searched only among the operation patterns that satisfy the predetermined condition, not all the operation patterns are required. However, there is a problem that an operation pattern with the highest energy saving performance cannot be obtained.

  For example, in the technique of Patent Document 2, in order to reduce the calculation cost, a condition that the exhaust heat recovery amount when the follow-up operation with electric power is performed from the temporarily determined start time covers the heat amount required for the day or most of it is provided. For customers with high power and heat loads in the early morning and evening, but low power and heat loads in the daytime, operate the cogeneration system early in the morning and stop the operation in the daytime. However, driving that covers the amount of heat necessary for one day with the driving pattern of starting twice, such as driving again in the evening, has the highest energy saving performance. However, in the technique of Patent Document 2, as described above, since there is a restriction condition of starting and stopping once a day, an operation pattern with the highest energy saving performance is not searched.

  Therefore, the present invention has been made in view of the above problems, and provides an operation planning apparatus and an operation planning method for a cogeneration system that can search for an optimal operation pattern with high energy saving performance at a low calculation cost. For the purpose.

  In order to achieve the above object, an operation planning apparatus for a cogeneration system according to the present invention is an operation planning apparatus that determines an optimal operation pattern of a cogeneration system in an operation planning period, and the cogeneration system saves energy. A load predicting means for predicting an energy load in the operation plan period in a consumer who is a supply destination; a dividing means for dividing the operation plan period into a plurality of periods; and a first period of the divided periods For the driving pattern suitable for supplying energy corresponding to the energy load predicted by the load predicting means, and for the subsequent period, the driving pattern following the driving pattern searched for in the immediately preceding period is used as the load pattern. Energy corresponding to the energy load predicted by the prediction means And an optimum operation pattern determining means for determining an optimum operation pattern in the operation plan period by sequentially repeating a process of searching for an operation pattern suitable for supplying the energy to the last period of the plurality of periods. .

  As a result, in each divided period, in the search for the optimum driving pattern, only some driving patterns are narrowed down as candidates for the optimum driving pattern, and only the driving patterns that follow those part driving patterns are searched for in the next dividing period. Therefore, an optimal operation pattern is searched for at a low calculation cost compared to the conventional technique in which all operation patterns that can be taken in the entire operation plan period are evaluated brute force.

  Note that the present invention can be realized not only as such an operation planning apparatus but also as an operation planning method, or as a program for causing a computer to execute steps included in the operation planning method. And it goes without saying that such a program can be stored and distributed in a recording medium such as a CD-ROM or distributed via a transmission medium such as the Internet.

  With the operation planning device for a cogeneration system according to the present invention, an optimum operation pattern with high energy saving performance is searched for at a lower calculation cost.

  Therefore, the practical value of the present invention in which fuel cell cogeneration systems and gas engine cogeneration systems have become widespread is extremely high.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
FIG. 1 is a functional block diagram showing the configuration of a cogeneration system operation planning apparatus (hereinafter simply referred to as “operation planning apparatus”) 100 of the present invention. In this figure, a fuel cell cogeneration system 110 is also shown as an example of a cogeneration system to be operated.

  The operation planning apparatus 100 is an apparatus that determines an optimum operation pattern of a cogeneration system in a given future operation plan period at a low calculation cost. Functionally, the operation planning apparatus 100 includes a load prediction unit 101, a division unit 102, and an optimum operation pattern. An operation pattern determination unit 103 is provided. In the present embodiment, the load predicting unit 101, the dividing unit 102, and the optimum operation pattern determining unit 103 are realized in software by a CPU, a ROM storing a program, a RAM, an input / output device, and the like.

  The load prediction unit 101 measures an energy load (“electric power load” and “thermal load” in the present embodiment) at a consumer to which the fuel cell cogeneration system 110 supplies energy, and measures the measured energy load. It is a processing part which predicts the energy load in the consumer in an operation plan period based on.

  The division unit 102 is a processing unit that divides a given operation plan period into a plurality of periods (hereinafter, this period is referred to as “division period”).

  The optimum operation pattern determination unit 103 is a processing unit that determines an optimum operation pattern in the operation plan period, and is predicted by the load prediction unit 101 for the first division period of the plurality of division periods obtained by the division unit 102. An energy load predicted by the load prediction unit 101 is searched for an operation pattern suitable for supplying energy corresponding to the energy load, and the operation pattern following the operation pattern searched for in the immediately preceding division period for the subsequent division period. The process of searching for an operation pattern suitable for supplying energy corresponding to is sequentially repeated until the last divided period of the plurality of divided periods. At this time, the optimum operation pattern determination unit 103 uses the fuel among the operation patterns that can supply energy corresponding to the energy load predicted by the load prediction unit 101 for each of the plurality of divided periods obtained by the division unit 102. When the battery cogeneration system 110 is operated, the evaluation value, which is information related to the cumulative energy consumption at the customer at the end of the division period, and the fuel cell cogeneration system 110 at the end of the division period Based on the heat storage amount, an operation pattern suitable for supplying energy is searched.

  The optimum driving pattern determination unit 103 includes a driving pattern generation unit 103a, a simulation unit 103b, and a driving candidate determination unit 103c.

  The driving pattern generation unit 103a is a processing unit that generates a plurality of driving patterns to be searched for each of the plurality of divided periods obtained by the dividing unit 102. In the present embodiment, the operation pattern generation unit 103a has, as an operation pattern, an operation pattern characterized by the start time and stop time of the fuel cell cogeneration system 110, for example, in a certain time interval for each divided period. All the operation patterns that can be taken when the fuel cell cogeneration system 110 is selected to start or stop in each section are generated.

  The simulation unit 103b performs the fuel cell cogeneration system 110 when the fuel cell cogeneration system 110 is operated with each of the plurality of operation patterns generated by the operation pattern generation unit 103a for each of the plurality of divided periods obtained by the division unit 102. By simulating the consumption and supply of energy by the generation system 110 and the consumption of energy at the customer, (1) an evaluation value that is information related to the cumulative energy consumption at the customer at the end of the division period (In the present embodiment, cumulative primary energy consumption that is cumulative energy consumption in the consumer including energy required for operation of the fuel cell cogeneration system 110), (2) fuel cell cogeneration at the end of the divided period The amount of heat stored by the system 110, and (3) an operating state indicating whether the cogeneration system is activated or stopped at the end of the division period (in this embodiment, if it is activated, Power generation amount is included) for each of the plurality of operation patterns generated by the operation pattern generation unit 103a. And the simulation part 103b is the information regarding the driving | running pattern simulated now (specifically, the total driving pattern which connected the last state or driving | running pattern, and the driving | running pattern simulated now), and the obtained simulation result (cumulative primary). Energy use amount, heat storage amount, operation state) are output to the operation candidate determination unit 103c.

  For the first divided period, the simulation unit 103b receives “initial information” indicating the state immediately before the fuel cell cogeneration system 110 from the fuel cell cogeneration system 110, and as an operation pattern continuous with the initial information. Execute a driving simulation.

  The driving candidate determination unit 103c has, for each of the plurality of divided periods obtained by the dividing unit 102, an evaluation value for each of a plurality of driving patterns calculated by the simulation unit 103b (accumulated primary energy use amount in the present embodiment). Based on the heat storage amount and the operating state, it is determined which of the plurality of predetermined heat storage amount ranges, and only the operation pattern having the optimum evaluation value for each determined heat storage amount range. By leaving, zero or one operation pattern is specified for each range of the plurality of heat storage amounts. Information relating to the operation pattern specified here (cumulative operation pattern, evaluation value, heat storage amount, operation state) is sent to the simulation unit 103b as “operation candidate information” and used for simulation in the next divided period. That is, the simulation unit 103b continues using the operation candidate information (cumulative operation pattern, evaluation value, heat storage amount, operation state) obtained by the operation candidate determination unit 103c for the immediately preceding divided period among the plurality of divided periods. An operation simulation in the divided period is executed, and an evaluation value, a heat storage amount, and an operation state are calculated.

  And after this operation candidate determination part 103c specifies an operation pattern about the last division | segmentation period of several division periods, among the operation patterns specified for every range of several thermal storage amount, an optimal evaluation value is obtained. The operation pattern (in this case, the cumulative operation pattern) is finally determined as the optimum operation pattern in the operation plan period.

  Hereinafter, a fuel cell cogeneration system having a heat storage tank with a heat storage capacity of 5000 wh is installed in the consumer, and the operation plan period of the fuel cell cogeneration system is set to 24 hours from 0 o'clock the next day. As an example of determining the optimal driving pattern from a total of 2 48 power driving patterns that can be taken when either the starting state or the stopping state is selected in steps of 30 minutes. The function of each component of the operation planning device 100 in the embodiment will be specifically described.

  The load prediction unit 101 measures a customer's power load and heat load, stores changes with respect to time, and predicts a power load and heat load for 24 hours from 0:00 of the next day, which is an operation plan period, based on this storage. And output to the simulation unit 103b.

  The division unit 102 divides the operation plan period into a plurality of predetermined periods, for example, three division periods, and outputs the divided period to the operation pattern generation unit 103a.

  The driving pattern generation unit 103a receives the division period from the division unit 102, and sequentially outputs all the operation patterns that can be taken within the division period to the simulation unit 103b for each division period received, After completing the output of the driving pattern, a division period completion signal indicating that is output to the driving candidate determination unit 103c.

  The simulation unit 103b receives the predicted power load and thermal load from the load prediction unit 101, sequentially receives the operation pattern from the operation pattern generation unit 103a, and receives the first operation plan period from the fuel cell cogeneration system 110. Initial information of the fuel cell cogeneration system 110 at 0:00 the next day, which is the time (here, the initial operation state indicating the amount of power generation or stoppage, and the heat storage amount of the heat storage tank of the fuel cell cogeneration system 110 at 0:00 the next day) For the second and subsequent divided periods, from the operation candidate determination unit 103c, the operation candidate information in the immediately preceding divided period, that is, the optimum specified for each range of the plurality of heat storage amounts. Information on operation patterns (cumulative operation pattern, cumulative primary energy consumption, heat storage And acquiring the operation state).

  The simulation unit 103b is assumed to have operated the fuel cell cogeneration system 110 so as to supply energy corresponding to the predicted load received from the load prediction unit 101 for each operation pattern received from the operation pattern generation unit 103a. The driving simulation is executed. That is, for the first divided period, the operation when the fuel cell cogeneration system 110 is operated with the cumulative operation pattern in which the initial state of the fuel cell cogeneration system 110 and the operation pattern received from the operation pattern generation unit 103a are continued. On the other hand, for the second and subsequent divided periods, the cumulative operation pattern in which the cumulative operation pattern in the immediately preceding divided period acquired from the driving candidate determination unit 103c and the driving pattern received from the driving pattern generation unit 103a are continued. An operation simulation is performed when the fuel cell cogeneration system 110 is operated in an operation pattern, and as a result, the accumulated primary energy use required for the operation of the fuel cell cogeneration system 110 at the end of the accumulated operation pattern is executed. , Heat storage amount, and the operating state (distinction between start / stop state, when the running is information indicating the amount of power generation) is calculated.

  The simulation unit 103b compares the cumulative operation pattern that is the target of the operation simulation with the heat storage completion operation pattern that is stored in advance immediately before the operation simulation, so that the operation pattern that is the target of the operation simulation has completed the heat storage. It is determined whether or not the driving pattern is included, and the driving simulation is executed only when the driving pattern is not included. Here, the heat storage completion operation pattern is an operation pattern in which the heat storage amount obtained by the operation simulation exceeds the capacity (here, 5000 wh) that can be stored in the heat storage tank provided in the fuel cell cogeneration system 110. In addition, the simulation unit 103b compares the heat storage amount at each time of the cumulative operation pattern with the capacity (5000wh) immediately after the operation simulation, so that the cumulative operation pattern that has just finished the operation simulation is the heat storage completed operation. If it corresponds to the heat storage completion operation pattern, it is stored as a new heat storage completion operation pattern, and the result of the operation simulation is obtained only when it is not the heat storage completion operation pattern. At the same time, it is output to the driving candidate determination unit 103c.

  The driving candidate determination unit 103c receives a simulation result for each cumulative driving pattern from the simulation unit 103b, and receives a division period completion signal from the driving pattern generation unit 103a. This operation candidate determination unit 103c previously divides the entire range of heat quantity that can be stored into a plurality of predetermined ranges (in this embodiment, equal to 10), and uses the simulation results received from the simulation unit 103b. The simulation unit 103b sequentially sends a process of determining which category the stored heat storage amount belongs to, and storing only the operation pattern that is the minimum cumulative primary energy consumption for each category. When the division period completion signal is received from the operation pattern generation unit 103a repeatedly with respect to the simulation result coming, information related to all the accumulated operation patterns stored, that is, the optimum accumulated operation pattern for each division of the heat storage amount in the division period Information (cumulative operation pattern, cumulative primary energy consumption, heat storage and operation And outputs to the simulation unit 103b to state) as the operation candidate information.

  In addition, when the operation candidate determination unit 103c receives the division period completion signal for the last division period from the operation pattern generation unit 103a, the operation candidate determination unit 103c is the smallest of all accumulated operation patterns stored for each heat storage amount category. The accumulated operation pattern that is the accumulated primary energy consumption is output to the fuel cell cogeneration system 110 as the optimum operation pattern in the operation plan period.

  The fuel cell cogeneration system 110 receives the optimum cumulative operation pattern sent from the operation candidate determination unit 103c, operates according to the cumulative operation pattern, generates electric power and heat, and supplies it to the consumer. The fuel cell cogeneration system 110 outputs the initial operation state and the initial heat storage amount at 0:00 the next day, which is the first time of the operation plan period, to the simulation unit 103b prior to the operation simulation in the simulation unit 103b. .

  Next, an example of operation | movement of the driving | operation planning apparatus 100 in this Embodiment comprised as mentioned above is demonstrated using the flowchart of FIG.

  First, the operation planning apparatus 100 issues an instruction to start operation planning processing at 0:00 every day (S201).

  Next, the load prediction unit 101 predicts a power / hot water supply load (S202). Specifically, the load predicting unit 101 acquires the customer's power load and thermal load every minute, integrates them in units of 30 minutes, stores the load up to 7 days in advance in association with the time when the load was measured. The average value of the load for 7 days at each time is output to the simulation unit 103b as a predicted load up to 24 hours ahead. For example, the predicted power load from 0:00 to 0:30 is the average value of the measured power load from 0:00 to 0:30 in the past seven days.

  Next, the dividing unit 102 divides the operation plan period of 24 hours into three periods (S203). Specifically, the dividing unit 102 performs a 24-hour period from 0:00 to a first period from 0:00 to 8:00, a second period from 8:00 to 16:00, and a third period from 16:00 to 24:00. It divides | segments into three division | segmentation periods and outputs it to the driving | running pattern generation part 103a.

  Next, the optimum operation pattern determination unit 103 determines an optimum operation pattern in the operation plan period (S204), and outputs the calculated optimum operation pattern to the fuel cell cogeneration system 110 (S205). As a result, the fuel cell cogeneration system 110 operates according to the optimal operation pattern, generates electric power and heat, and supplies them to consumers.

This completes the operation plan process (S206).
FIG. 3 is a flowchart showing a detailed procedure of step S204 (determination of the optimum operation pattern) in FIG.

  When a start instruction is issued for step S204 in FIG. 2 (S301), the driving pattern generation unit 103a changes the period for generating the driving pattern (S302). That is, the driving pattern generation unit 103a changes the divided period for generating the driving pattern from the three divided periods received from the dividing unit 102 in the order of the first period, the second period, and the third period. For example, when the process of step S302 is performed for the first time (first time), the period for generating the driving pattern is the first period, and when the process of step S302 is performed again, the period is changed to the second period. Is done.

  Subsequently, the driving pattern generation unit 103a generates one new driving pattern in the divided period (S303). Then, the simulation unit 103b executes an operation simulation when the fuel cell cogeneration system 110 is operated with a cumulative operation pattern including the operation pattern generated by the operation pattern generation unit 103a (S304).

  Next, the driving candidate determination unit 103c determines whether or not the cumulative driving pattern simulated by the simulation unit 103b is a candidate for the optimal driving pattern in the driving plan period (S305). And the driving candidate determination part 103c determines whether the candidate determination with respect to all the driving patterns in the said division | segmentation period was completed by determining whether the division | segmentation period completion signal was input (S306). As a result, if candidate determination for all driving patterns has not been completed (NO in S306), steps S303 to S306 are repeated.

  On the other hand, when the candidate determination for all the driving patterns is completed (YES in S306), the driving candidate determination unit 103c determines whether the candidate determination for all the divided periods, that is, the entire driving plan period is completed. (S307). Specifically, when the division period is the third period, the operation candidate determination unit 103c determines that the candidate determination for all the operation plan periods has ended (YES in S307), and stores all the heat storage ranges. The cumulative operation pattern that is the smallest among the accumulated primary energy usage that has been output is output to the fuel cell cogeneration system 110 as the optimum operation pattern in the operation plan period, and the process is terminated (S308).

  On the other hand, when the divided period is not the third period, it is determined that the candidate determination for all the operation plan periods is not completed (NO in S307), and the operation candidate determination unit 103c stores the accumulated total heat storage ranges. The operation pattern, the accumulated primary energy use amount, the heat storage amount, and the operation state are output as operation candidate information to the simulation unit 103b, and thus the above steps S302 to S306 are repeated.

  FIG. 4 is a flowchart showing a detailed procedure of the process (generation of operation pattern) in step S303 in FIG.

  When a start instruction is issued for step S303 in FIG. 3 (S401), the driving pattern generation unit 103a generates a driving pattern and outputs it to the simulation unit 103b (S402). Specifically, the driving pattern generation unit 103a generates a driving pattern that is one of driving patterns that can be taken in the target divided period, and outputs the driving pattern to the simulation unit 103b.

  At this time, the operation pattern generation unit 103a generates an operation pattern characterized by the start time and stop time of the fuel cell cogeneration system 110. However, the fuel cell cogeneration system 110 is started in the target divided period. The operation time is output first from the one with the shortest total time (in the operation state) and the one with the earlier operation time in the operation pattern that is the total time of the same operation state.

  For example, the driving pattern generation unit 103a sequentially selects 2 <16> driving patterns obtained by selecting the driving state or the stopping state for each section divided by 30 minutes for each divided period of 8 hours. As an order at that time, first, an operation pattern having a total operation state time of 0 is generated, and then, for all operation patterns having a total operation state time of 30 minutes, the operation state time Are generated in order from the earliest arrival, and then for all driving patterns in which the total operating time is 1 hour, the driving state is generated in order from the earliest arrival time. All operation patterns in which the total time of the state is 1 hour 30 minutes to 8 hours are generated in order.

  Specifically, the first operation pattern to be output is that the total time of the operation state is 0 minutes in the first period indicating 0:00 to 8:00 “all stop states from 0:00 to 8:00:00” It is. Next, when the process of step S402 is performed again, the output operation pattern is “the operation state from 0:00 to 0:30, the remaining time is the total operation time of 30 minutes” Are all in the “stop state”, and the next is “the operation state from 0:30 to 10:00, and the remaining time is all in the stop state”.

  Next, the driving pattern generation unit 103a determines whether all driving patterns in the target divided period have been output to the simulation unit 103b (S403). For example, when the target divided period is the first period, when the driving pattern “all driving states from 0:00 to 8: 0” is generated and output to the simulation unit 103b, It is determined that the operation pattern is generated and output.

  As a result, when it is determined that all driving patterns have been output to the simulation unit 103b (YES in S403), the driving pattern generation unit 103a outputs a divided period completion signal to the driving candidate determination unit 103c (S404). The process is finished (S405). That is, every time the driving pattern generation unit 103a finishes outputting all possible driving patterns to the simulation unit 103b in the first period, the second period, and the third period, the driving period determination signal is output to the driving candidate determination unit 103c. Output to. On the other hand, when it is not determined that all the driving patterns have been output to the simulation unit 103b (NO in S403), the driving pattern generation unit 103a ends the process without outputting the division period completion signal (S405).

  FIG. 5 is a flowchart showing a detailed procedure of step S304 (operation simulation) in FIG.

  When an instruction to start step S304 in FIG. 3 is issued (S501), the simulation unit 103b receives the necessary information from each processing unit, and then stores the accumulated operation pattern that is the target of the operation simulation in advance. It is determined whether or not a completed operation pattern is included (S502). Specifically, first, if the operation pattern received from the operation pattern generation unit 103a is one of the operation patterns in the first period, the simulation unit 103b receives an initial operation state and an initial heat storage amount from the fuel cell cogeneration system 110. If it is one of the operation patterns of the second period or the third period, the operation pattern, the primary energy use amount, the heat storage amount, and the operation state of the operation plan period are acquired from the operation candidate determination unit 103c. . Furthermore, a predicted power load and a predicted heat load for 24 hours from 0:00 on the next day are received from the load prediction unit 101.

  When the driving pattern received from the driving pattern generation unit 103a is one of the driving patterns in the first period, the simulation unit 103b sets the driving pattern as a cumulative driving pattern that is a target of driving simulation, When the driving pattern received from the driving pattern generation unit 103a is one of the driving patterns in the second period or the third period, the cumulative driving pattern in the immediately preceding divided period acquired from the driving candidate determination unit 103c and An operation pattern in which the operation pattern received from the operation pattern generation unit 103a is made continuous is generated as a new cumulative operation pattern that is an object of the operation simulation. For example, the driving pattern of the first period acquired from the driving candidate determination unit 103c is “the driving state from 0:00 to 0:30, all the remaining times are in the stopped state” and sent from the driving pattern generation unit 103a. When the operation pattern of the two periods is “operating state from 8 o'clock to 8:30, all remaining times are in a stopped state”, the simulation unit 103b reads “from 0 o'clock to 0:30 and 8 o'clock to 8 o'clock. A cumulative operation pattern of “operating state until 30 minutes, all remaining times are in a stopped state” is generated as a new cumulative operating pattern to be a target of the driving simulation.

  And the simulation part 103b determines whether the driving | running period in the accumulation driving | operation pattern used as driving | running simulation object contains all the driving | running periods of the heat storage completion driving | operation pattern memorize | stored beforehand. For example, the operation pattern received from the operation pattern generation unit 103a is the operation pattern of the first period, and one of the heat storage completion operation patterns stored in advance is “operating state from 1: 0 to 6: 0, remaining If all the times are in a stopped state, the cumulative operation pattern is “operating state from 1: 0 to 6:30, all remaining times are in a stopped state” or “10:00 to 6: 0. When the operation state is from 0 hour to 8: 0 hours, and the remaining time is all in the stopped state, the simulation unit 103b determines that the cumulative operation pattern to be subjected to the operation simulation includes the heat storage completion operation pattern. .

  As a result, if the simulation unit 103b determines that the cumulative operation pattern that is the target of the operation simulation includes the heat storage completion operation pattern (YES in S502), the simulation unit 103b does not execute the operation simulation for the operation pattern. The process is terminated (S506). If not (NO in S502), an operation simulation with the operation pattern is executed (S503). For example, when the driving pattern that is the target of the driving simulation is “driving from 1: 0 to 5:30”, the driving simulation is executed.

  At this time, when the operation pattern received from the operation pattern generation unit 103a is one of the operation patterns in the first period, the simulation unit 103b starts the operation simulation using the initial heat storage amount acquired from the fuel cell cogeneration system 110. As the heat storage amount at the time, the initial operation state acquired from the fuel cell cogeneration system 110 is the operation state at the start of the operation simulation, the operation simulation is performed with the operation pattern, the power generation amount and the heat storage amount at each time are calculated, Further, the heat storage amount and the operation state at the end of the operation pattern are calculated. On the other hand, when the driving pattern received from the driving pattern generation unit 103a is one of the driving patterns in the second period or the third period, the simulation unit 103b uses the previous divided period acquired from the driving candidate determination unit 103c. For each cumulative operation pattern, the heat storage amount at the end of the cumulative operation pattern is the heat storage amount at the start of the operation simulation, and the operation state at the end of the cumulative operation pattern is the operation state at the start of the operation simulation. An operation simulation is performed with the operation pattern received from the unit 103a, the power generation amount and the heat storage amount at each time are calculated, and the heat storage amount and the operation state at the end of the operation pattern are calculated.

  The details of the driving simulation are as follows. That is, the simulation unit 103b indicates the power generation efficiency indicating the power generation amount with respect to the gas usage amount of the fuel cell cogeneration system 110 set in advance, the exhaust heat recovery efficiency indicating the heat storage amount, and the variable amount of power generation per minute. Hardware characteristics such as power generation followability are stored in advance, and an operation simulation is executed using the hardware characteristics.

  At this time, as shown in the upper diagram of FIG. 6, the simulation unit 103 b is given as shown in the lower diagram of FIG. 6 with respect to temporal changes in the predicted power load and the predicted heat load in the operation plan period. The time variation of the power generation amount and the heat storage amount when the fuel cell cogeneration system 110 is operated with the operation pattern is calculated, and further, the power amount and the heat amount that are insufficient in the power generation amount and the heat storage amount (that is, from the power company and the gas company) The sum of the primary energy (gas amount) required to operate the fuel cell cogeneration system 110 and the primary energy (power amount and gas amount) consumed by the consumer by calculating the amount of electricity and heat to be purchased) Calculate the amount of primary energy used.

  More specifically, the simulation unit 103b calculates the amount of gas used by the fuel cell cogeneration system 110 when the fuel cell cogeneration system 110 is operated in a given operation pattern, The heat storage amount is calculated, and the calculated heat generation amount and heat storage amount and the predicted power load and predicted heat load at each time received from the load prediction unit 101 are used to compensate for the heat shortage due to the heat storage of the fuel cell cogeneration system 110. Therefore, the amount of gas used purchased from a gas company is calculated, and the amount of gas purchased, which is the sum of the amount of gas used, is calculated. Similarly, the total amount of electric power purchased, which is the amount of electric power purchased from an electric power company, is calculated in order to make up for the electric power deficient in power generation by the fuel cell cogeneration system 110. Further, by using the primary energy usage per unit of gas / power set in advance, the primary energy corresponding to the amount of gas purchased and the primary energy corresponding to the amount of electricity purchased are totaled, so that Calculate the amount of primary energy used in operation.

  When the driving pattern received from the driving pattern generation unit 103a is one of the driving patterns in the first period, the simulation unit 103b sets the primary energy usage amount in the driving pattern as the cumulative primary energy usage amount, When the heat storage amount and the operation state at the end of the operation pattern are calculated as a simulation result, while the operation pattern received from the operation pattern generation unit 103a is one of the operation patterns in the second period or the third period Is the primary energy usage obtained by adding the primary energy usage in the driving pattern to the cumulative primary energy usage in the immediately preceding divided period as the new cumulative primary energy usage, at the end of the driving pattern. Simulation results along with heat storage and operating conditions To be calculated.

  For example, as an example of calculating the accumulated primary energy consumption, the accumulated operation pattern of the first period acquired from the operation candidate determination unit 103c is “operating state from 0:00 to 0:30, all remaining times are stopped” When the operation pattern of the second period sent from the operation pattern generation unit 103a is “operation state from 8 o'clock to 8:30, all remaining times are in a stop state”, the simulation unit 103b determines “0 Primary energy consumption in the operation pattern of “operating state from 0:30 to 0:30, all remaining times are in a stopped state” and operating pattern “operating state from 8 o'clock to 8:30, all remaining times are in a stopped state” The total primary energy usage is calculated by adding the primary energy usage at.

  Next, the simulation unit 103b determines whether or not the cumulative operation pattern that has just finished the operation simulation is a heat storage completion operation pattern (S504). Specifically, the simulation unit 103b determines whether or not the heat storage amount at each time calculated by the operation simulation exceeds 5000wh that is a heat storage capacity.

  As a result, only when the heat storage amount does not exceed the heat storage capacity at any time (NO in S504), the simulation unit 103b sends the operation simulation result to the operation candidate determination unit 103c together with the cumulative operation pattern. After the output, the operation simulation is terminated (S506). On the other hand, if there is a time when the amount of heat storage exceeds the heat storage capacity (NO in S504), the accumulated operation pattern is newly stored as the heat storage completion operation pattern. After that (S505), the driving simulation is terminated without outputting the driving simulation result to the driving candidate determination unit 103c (S506). Here, the newly stored heat storage completion operation pattern is determined in step S502 described above (whether or not the cumulative operation pattern subject to operation simulation includes the heat storage completion operation pattern stored in advance. Used for judgment).

  FIG. 7 is a flowchart showing a detailed procedure of step S305 (optimum operation pattern candidate determination) in FIG.

  When a start instruction is issued for step S305 in FIG. 3 (S601), the operation candidate determination unit 103c determines to which heat storage range the heat storage amount received from the simulation unit 103b belongs (S602). Specifically, after receiving the cumulative operation pattern, the cumulative primary energy usage amount, the heat storage amount, and the operation state from the simulation unit 103b, the operation candidate determination unit 103c sets the capacity 5000wh that can store heat to 0wh. To the first heat storage range indicating the range of 500wh, the second heat storage range indicating 501wh to 1000wh, and similarly to the tenth heat storage range equally divided from 4501wh to 5000wh.

  Next, the operation candidate determination unit 103c determines whether or not the cumulative primary energy use amount received from the simulation unit 103b is smaller than the cumulative primary energy use amount that is already stored in association with the currently determined heat storage amount range. Is determined (S603).

  As a result, when the cumulative primary energy usage received from the simulation unit 103b is smaller than the cumulative primary energy usage already stored in association with the currently determined heat storage amount range (YES in S603), the operation is performed. The candidate determination unit 103c updates the accumulated primary energy usage received from the simulation unit 103b as the accumulated primary energy usage stored in association with the range of the heat storage amount (S604). At this time, the operation candidate determination unit 103c stores not only the accumulated primary energy consumption amount but also the accumulated operation pattern, the heat storage amount, and the operation state in association with the range of the heat storage amount to which the heat storage amount belongs. On the other hand, when the cumulative primary energy usage received from the simulation unit 103b is not smaller than the cumulative primary energy usage already stored in association with the currently determined heat storage amount range (NO in S603), the The candidate determination for this cumulative operation pattern is terminated without updating the contents already stored in association with the range of the heat storage amount (S605).

  As a specific example, the operation candidate determination unit 103c now has a cumulative primary energy consumption amount of 4000wh as a simulation result of the first cumulative operation pattern “all stopped from 0:00 to 8: 0” from the simulation unit 103b. When the amount 0wh and the operation state “stopped” are received, the operation candidate determination unit 103c determines that the received heat storage amount 0wh belongs to the first heat storage range indicating the range of 0 to 500wh. Specifically, the operation candidate determination unit 103c has a data table for storing operation candidate information as shown in FIG. 8, that is, each range obtained by equally dividing each heat storage capacity into 10 equal parts. A data table for storing a simulation result (here, the cumulative operation pattern, the cumulative primary energy usage amount, the heat storage amount, and the operation state) relating to the cumulative operation patterns of the types is held inside. This data table is blank in the initial state. If the cumulative primary energy usage stored in this data table is blank, the driving candidate determination unit 103c determines that the cumulative primary energy usage 4000wh received from the simulation unit 103b is small, and the data shown in FIG. In the blank area corresponding to the first heat storage range of the table, the cumulative operation pattern and simulation result (the cumulative operation pattern is “all stopped from 0: 0 to 8: 0”, the cumulative primary energy consumption is 4000wh, the heat storage amount 0wh as the operation state and “stopped” as the operation state. As a result, the data table has the contents shown in FIG.

  Subsequently, the driving candidate determination unit 103c uses the cumulative primary energy usage as a simulation result of the next cumulative driving pattern “operating state from 0: 0 to 0:30, all remaining times are stopped” from the simulation unit 103b. When the amount 3800wh, the heat storage amount 100wh, and the operation state “stopped” at 8:00:00 are received, it is determined that the received heat storage amount belongs to the first heat storage range. Then, the operation candidate determination unit 103c compares the received cumulative primary energy usage amount 3800wh with the cumulative primary energy usage amount 4000wh stored in the first heat storage range of the data table, and receives the cumulative primary energy usage amount just received. Since 3800wh is known to be small, in the location corresponding to the first heat storage range of the data table, the cumulative operation pattern just received “operating state from 0: 0 to 0:30, all remaining times are stopped” and The simulation result (cumulative primary energy consumption amount 3800wh, heat storage amount 100wh, and operation state "stopped") is overwritten and updated. At this point, the data table has the contents shown in FIG.

  As described above, it is assumed that the candidate determination for all cumulative operation patterns in the first period is completed by the operation candidate determination unit 103c, and the data table at that time has the contents shown in FIG. Here, as shown in FIG. 11, a cumulative operation pattern serving as an operation candidate and a simulation result thereof are stored in a part of the ten heat storage ranges (heat storage ranges 0 to 500 wh and 4001 to 4500 wh). .

  Then, also about the second period, all the operation candidates shown in FIG. 11 (locations corresponding to the heat storage ranges 0 to 500wh and 4001 to 4500wh) for all the operation patterns given from the operation pattern generation unit 103a by the simulation unit 103b. The operation simulation is executed with the operation pattern following each of the cumulative operation patterns stored in the table. For example, as an operation pattern following the accumulated operation pattern stored in association with the heat storage range 0 to 500wh in FIG. 11, an operation simulation is executed for all operation patterns received from the operation pattern generation unit 103a. As an operation pattern following the accumulated operation pattern stored in association with the 11 heat storage ranges 4001 to 4500wh, an operation simulation is executed for all operation patterns received from the operation pattern generation unit 103a. As a result, it is assumed that the candidate determination for all the cumulative operation patterns in the second period is completed, and the data table at that time has the contents shown in FIG. Here, as shown in FIG. 12, cumulative operation patterns and simulation results as operation candidates are displayed in some of the 10 heat storage ranges (heat storage ranges 501 to 1000 wh, 4001 to 4500 wh, 4501 to 5000 wh). Stored.

  Similarly, it is assumed that the operation candidate determination unit 103c finishes candidate determination for all cumulative operation patterns in the third period, and the data table at that time has the contents shown in FIG. Here, as shown in FIG. 13, cumulative operation patterns that are operation candidates and simulation results thereof are displayed in some of the ten heat storage ranges (heat storage ranges 0 to 500 wh, 501 to 1000 wh, 4001 to 4500 wh). Stored.

  In this way, when the candidate determination for all the cumulative driving patterns in the third period is completed, the driving candidate determination unit 103c has the smallest cumulative primary energy usage (of the cumulative primary energy usage stored in the data table) ( Here, the cumulative operation pattern corresponding to the heat storage range 0 to 500wh (12,500wh stored) (here, “operation from 15: 00 to 16:30, remaining stop”) is the optimum operation in the operation plan period. It outputs to the fuel cell cogeneration system 110 as a pattern.

  As described above, the operation planning apparatus 100 according to the present embodiment predicts the energy load (electric power load and heat load) at the consumer in the operation plan period and also sets the operation plan period as shown in FIG. Dividing into a plurality of divided periods and searching for an operation pattern (“A”, “Z”) suitable for supplying energy corresponding to the predicted energy load for the first divided period, As an operation pattern following only the operation pattern (“A”, “Z”) searched for in the period of “”, an operation pattern suitable for supplying energy corresponding to the predicted energy load (“Z” following “A”) ”,“ Z ”following“ Z ”) is repeated until the last period of a plurality of periods is sequentially repeated, so that an optimum driving pattern ( A "-" Z "- has been searching for the" A ").

  As a result, in each divided period, in the search for the optimal driving pattern, only some driving patterns are narrowed down as candidates for the optimal driving pattern, and only the driving patterns that follow the partial driving pattern are searched for in the next dividing period. Therefore, an optimal operation pattern is searched for at a low calculation cost compared to the conventional technique in which all operation patterns that can be taken in the entire operation plan period are evaluated brute force.

Hereinafter, the effect in this Embodiment is demonstrated more concretely.
The calculation cost in the present embodiment is mostly that of the simulation unit 103b. This is because the calculations other than the simulation unit 103b only determine and store numerical values, but the simulation unit 103b calculates the power generation efficiency and exhaustion of the fuel cell cogeneration system set in advance when calculating the cumulative primary energy usage. This is to perform a complicated process of simulating the operation of the fuel cell cogeneration system 110 from hardware characteristics such as heat recovery efficiency and power generation follow-up to electric power.

  In the present embodiment, the driving candidate determination unit 103c calculates only ten driving patterns as driving candidates in the divided driving plan period. Only the driving pattern continuous from the driving candidates is used to calculate the accumulated primary energy usage in the subsequent divided period. Therefore, the calculation cost in the present embodiment is small compared to the case where the cumulative primary energy usage is calculated for all the operation patterns that can be taken in the entire operation plan period.

  The calculation cost for calculating the cumulative primary energy usage amount according to the present embodiment and the technique of Patent Document 1 will be compared. According to the technique of Patent Document 1, evaluation is performed on 2 to the 48th power operation patterns. On the other hand, in the present embodiment, even if any of the operation patterns is not determined as the heat storage completion operation pattern, the first period is the 2 <16> th operation pattern, and the second period is the first For each of up to 10 driving candidates specified in one period, 2 16 power driving patterns, and for the third period, 2 for each of up to 10 driving candidates specified in the second period Since the 16th driving pattern is evaluated, it is sufficient to evaluate the sum of them, that is, (2 16 +2 16 + 10 + 2 16 × 10) driving patterns. Furthermore, since the operation plan period is divided into three periods in the present embodiment, the calculation cost per way is one-third that of Patent Document 1. When this is calculated, the present embodiment has a calculation cost of about 1 / 60,000,000 of Patent Document 1. Actually, there is an operation pattern in which a heat storage completion operation pattern is calculated and an operation simulation is not performed. Therefore, the calculation cost of the present embodiment is further reduced.

  Further, in the present embodiment, the operation candidate determination unit 103c stores, as operation candidates, those that have the smallest accumulated primary energy usage among accumulated operation patterns that are close to each other (within a certain range) at the end of the divided period. To do. This means that if there are multiple operation patterns with the same heat storage amount, the operation pattern other than the operation pattern that minimizes the accumulated primary energy consumption is the smallest of all possible operation patterns in the entire operation plan period. This is because there is no possibility that the operation pattern becomes the cumulative primary energy consumption. For example, in a divided operation plan period from 0 o'clock to 8 o'clock, the operation is “starting from 0: 0 to 0:30, all remaining times are stopped” and “starting from 0:30 to 10:00” If the amount of accumulated primary energy is less in the former operation when the heat storage amount is the same, the same operation is performed in the remaining divided periods. In the case of the pattern, the cumulative primary energy consumption is always smaller in the cumulative operation pattern that is continuous from the former operation.

  However, since there are generally few operation patterns with the same amount of heat storage, applying only when the amount of heat storage is completely consistent results in the minimum cumulative primary energy usage among all operation patterns. There are also few operation patterns determined that there is no possibility of becoming an operation pattern. Therefore, in the present embodiment, by providing a certain range for the heat storage amount, the operation patterns that are determined to have a low possibility of becoming the operation pattern with the minimum cumulative primary energy usage amount are increased. However, by not excessively increasing the range of the heat storage amount, in this embodiment, the operation pattern that is the minimum cumulative primary energy usage amount or the cumulative primary energy usage amount that is close to it among all possible operation patterns. Is selected.

  Thus, according to the present embodiment, an operation pattern having a minimum cumulative primary energy usage amount or a cumulative primary energy usage amount close to it among all the operation patterns is searched for at a low calculation cost.

  Although the operation planning apparatus according to the present invention has been described based on the embodiment, the present invention is not limited to this embodiment. The present invention includes those in which various modifications conceived by those skilled in the art can be made with respect to the present embodiment without departing from the gist of the present invention.

  For example, in the present embodiment, the operation plan period is from 0:00 to 24:00, but may be any period such as 0:00 to 12:00. Moreover, although the division | segmentation part 102 divided | segmented the driving | operation plan period into 3, you may divide | segment into arbitrary numbers, such as two and four. Furthermore, although the operation pattern generation unit 103a generates an operation pattern for starting or stopping the fuel cell cogeneration system 110 every 30 minutes, the time interval when generating the operation pattern is not limited to 30 minutes. , Any time increment such as every 15 minutes may be used.

  Further, in the present embodiment, the operation candidate determination unit 103c stores the operation candidates by equally dividing the range of the heat storage amount into 10 equal parts, but the heat storage amount classification is not equal but biased. Alternatively, it may be an arbitrary number of divisions such as 15 equal parts or 20 equal parts.

  Moreover, in this Embodiment, although the simulation part 103b calculated the primary energy usage-amount as an evaluation value, as an evaluation value, it is not restricted to a primary energy usage-amount, A utility bill etc. may be sufficient. Furthermore, the target of the operation plan by the operation planning apparatus 100 is not limited to the fuel cell cogeneration system, but may be a gas engine cogeneration system.

  In the present embodiment, the operation planning apparatus 100 is realized by software, but may be realized by hardware by a dedicated electronic circuit or the like.

The present invention is, as an operation planning device for determining an optimum operation pattern of the cogeneration system in the operation plan period, in particular, not only can be utilized as an operation planning device for determining an optimum operation pattern with a small computational cost, CO ₂ heat pumps Ya It is also useful as an operation planning device for electric water heaters.

The functional block diagram which shows the structure of the operation plan apparatus for cogeneration systems in embodiment of this invention Flow chart showing operation of operation planning device The flowchart which shows the detailed procedure of step S204 (determination of an optimal driving | operation pattern) in FIG. The flowchart which shows the detailed procedure of the process (generation of an operation pattern) of step S303 in FIG. The flowchart which shows the detailed procedure of step S304 (driving simulation) in FIG. Diagram for explaining the detailed operation of the simulation unit The flowchart which shows the detailed procedure of step S305 (candidate determination of the optimal driving | operation pattern) in FIG. The figure which shows the data table for storing driving candidate information The figure which shows the state where the data table was updated first The figure which shows the state where the data table was updated first The figure which shows the state of the data table when the first period is completed The figure which shows the state of the data table when the 2nd period is completed The figure which shows the state of the data table when the third period is completed The figure which shows the outline of the search processing of the optimum driving pattern by the driving planning device

Explanation of symbols

DESCRIPTION OF SYMBOLS 100 Operation planning apparatus 101 Load prediction part 102 Dividing part 103 Optimal operation pattern determination part 103a Operation pattern production | generation part 103b Simulation part 103c Operation candidate determination part 110 Fuel cell cogeneration system

Claims (10)

An operation planning device for determining an optimal operation pattern of a cogeneration system in an operation planning period,
A load prediction means for predicting an energy load in the operation plan period in a consumer to whom the cogeneration system supplies energy;
Dividing means for dividing the operation plan period into a plurality of periods;
An operation pattern suitable for supplying energy corresponding to the energy load predicted by the load prediction unit is searched for the first period of the plurality of divided periods, and the subsequent period is searched for in the immediately preceding period. As a driving pattern following the driving pattern, the process of searching for a driving pattern suitable for supplying energy corresponding to the energy load predicted by the load prediction means is sequentially repeated until the last period of the plurality of periods. An operation planning device for a cogeneration system, comprising: an optimum operation pattern determining means for determining an optimum operation pattern during the operation planning period. The optimum operation pattern determination means is a period when the cogeneration system is operated for a plurality of operation patterns that can supply energy corresponding to the energy load predicted by the load prediction means for each of the plurality of periods. An evaluation value, which is information related to cumulative energy consumption at the consumer at the end of the period, and a heat storage amount by the cogeneration system at the end of the period are calculated, and the calculated heat storage amount is predetermined. The operation pattern suitable for supplying the energy is determined by leaving the operation pattern having the optimum evaluation value for each determined heat storage amount range. Search and have the best evaluation value in the last period of the plurality of periods. Cogeneration system for the driver planning device according to claim 1, wherein determining the pattern as the optimum operation pattern in the operation plan period. The optimum operation pattern determining means includes:
For each of the plurality of periods, an operation pattern generation unit that generates a plurality of operation patterns to be searched;
For each of the plurality of periods, the evaluation value and the heat storage amount when the cogeneration system is operated with each of the plurality of operation patterns generated by the operation pattern generation unit are calculated for each of the plurality of operation patterns. A simulation unit to
For each of the plurality of periods, the heat storage amount belongs to any of a plurality of predetermined heat storage amount ranges based on the evaluation value and the heat storage amount for each of the plurality of operation patterns calculated by the simulation unit. An operation candidate determination unit that specifies zero or one operation pattern for each of the plurality of heat storage amount ranges by leaving only the operation pattern having an optimum evaluation value for each determined heat storage amount range; Have
The simulation unit calculates the evaluation value and the heat storage amount in the subsequent period using the heat storage amount of the operation pattern specified by the operation candidate determination unit for the immediately preceding period among the plurality of periods.
The operation candidate determination unit, after specifying the operation pattern for the last period of the plurality of periods, among the operation patterns specified for each range of the plurality of heat storage amount, the operation having an optimum evaluation value The operation planning device for a cogeneration system according to claim 2, wherein a pattern is determined as an optimum operation pattern in the operation planning period. The operation planning device for a cogeneration system according to claim 3, wherein the simulation unit does not perform the calculation when the heat storage amount to be calculated exceeds a capacity that the cogeneration system can store heat. The load prediction means predicts an electric load and a heat load as the energy load,
The cogeneration system operation plan according to claim 3, wherein the simulation unit calculates an evaluation value and a heat storage amount when the cogeneration system is operated so as to supply energy corresponding to the power load and the heat load. apparatus. The said operation pattern production | generation part produces | generates the operation pattern characterized by the starting time and the stop time of the said cogeneration system as the said driving pattern, The driving | operation planning apparatus for cogeneration systems of Claim 3. The operation planning device for a cogeneration system according to claim 3, wherein the simulation unit calculates, as the evaluation value, a primary energy consumption amount or a utility cost consumed by the consumer. The simulation unit further calculates an operation state indicating whether the cogeneration system is activated or stopped at each end point of the plurality of periods together with the evaluation value and the heat storage amount for each operation pattern. In addition to the heat storage amount of the operation pattern specified by the operation candidate determination unit for the immediately preceding period, by using the operation state, the evaluation value, the heat storage amount, and the operation state in the subsequent period are obtained. The operation planning device for a cogeneration system according to claim 3. An operation planning method for determining an optimal operation pattern of a cogeneration system during an operation planning period,
A load prediction step of predicting an energy load in the operation plan period in a consumer to whom the cogeneration system supplies energy;
A division step of dividing the operation plan period into a plurality of periods;
An operation pattern suitable for supplying energy corresponding to the energy load predicted in the load prediction step is searched for the first period of the plurality of divided periods, and the subsequent period is searched for in the immediately preceding period. As a driving pattern following the driving pattern, a process of searching for a driving pattern suitable for supplying energy corresponding to the energy load predicted in the load prediction step is sequentially repeated until the last period of the plurality of periods. An operation planning method for a cogeneration system including an optimum operation pattern determination step for determining an optimum operation pattern in the operation planning period. A program for an operation planning device for determining an optimal operation pattern of a cogeneration system in an operation planning period,
A program for causing a computer to execute the steps included in the operation planning method for a cogeneration system according to claim 9.

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