Power operation assistance system and power operation assistance method

Abstract

This power operation assistance system is constituted of a computer comprising: a computation device for executing predetermined computation processing; and a storage device connected to the computation device. The storage device retains: field information indicating locations of a plurality of fields; demand information indicating power demand for the fields; equipment information including information pertaining to charging / discharging of a portable battery and a location of a vehicle; and an evaluation function corresponding to peak values of power demand in the plurality of fields. The computation device: calculates a parameter for optimizing the value of the evaluation function on the basis of the field information, the demand information, and the equipment information; generates an equipment operation plan corresponding to the calculated parameter; and outputs the generated equipment operation plan.

Description

Power operation support system and power operation support method

[0001] The present invention relates to a power operation support system that reduces the cost of using power in a farm field.

[0002] When multiple fields are spread over a wide area, the electricity used in each field is supplied from the grid, which means that electricity costs become a significant burden on farmers. Because the required power generation capacity is determined by peak power consumption that occurs when equipment used in the field is started, there is a problem in that even if a large amount of power is not required all the time, farmers incur costs for power contracts based on peak values.

[0003] An example of a power operation support system is disclosed in Patent Literature 1 (International Publication No. 2023 / 073863). Patent Literature 1 describes an area design proposal system that proposes the equipment layout and configuration of a district energy system, in which the calculation device includes a long-term operation optimization unit that receives parameters including energy equipment information, vehicle equipment information, energy equipment installation costs, vehicle installation costs, and an upper limit on initial costs as input, and uses a long-term operation algorithm to output an optimal solution for the equipment layout and configuration of the district energy system, and a short-term operation optimization unit that receives the output of the long-term operation optimization unit and short-term environmental fluctuation factors as input, and uses a short-term operation algorithm to calculate a short-term operation evaluation result that is an evaluation result of the equipment layout and configuration of the district energy system, and the long-term operation optimization unit receives the short-term operation evaluation result as input, and optimizes the equipment layout and configuration of the district energy system.

[0004] International Publication No. 2023 / 073863

[0005] To reduce the cost of electricity in farm fields, it is possible to transport batteries charged by power generation facilities and supply power to the fields in accordance with peak demand. To efficiently supply power from batteries to the fields, it is necessary to calculate the equipment configuration that can stably reduce peak demand while taking costs into account, and to formulate an appropriate equipment operation plan that takes into account spatiotemporal factors such as the location of the field and peak times.

[0006] However, the aforementioned prior art optimizes profit and loss by focusing primarily on the total amount of power supply, and does not consider fluctuations in profit and loss due to power consumption. Furthermore, it is designed for EVs (electric vehicles), not for batteries transported in vehicles. Furthermore, there is no information explaining the output facility configuration. From the user's perspective, it is desirable to provide explanatory information when adopting or referring to a configuration plan.

[0007] A representative example of the invention disclosed in the present application is as follows: That is, a power operation support system is configured by a computer having an arithmetic unit that executes predetermined arithmetic processing and a storage device connected to the arithmetic unit, wherein the storage device stores field information indicating the locations of a plurality of fields, demand information indicating the power demand in the fields, equipment information including information on charging and discharging of portable batteries and vehicle locations, and an evaluation function according to peak values ​​of power demand in the plurality of fields, and the arithmetic unit calculates parameters that optimize the value of the evaluation function based on the field information, the demand information, and the equipment information, generates an equipment operation plan corresponding to the calculated parameters, and outputs the generated equipment operation plan.

[0008] According to one aspect of the present invention, it is possible to reduce the cost of electricity in a farm field. Problems, configurations, and effects other than those described above will become apparent from the following description of the preferred embodiment of the present invention.

[0009] 1 is a diagram illustrating a configuration of a power operations support system according to an embodiment of the present invention. FIG. 2 is a flowchart of processing executed by a processing unit of the power operations support system according to the embodiment. FIG. 3 is a flowchart of processing executed by a processing unit of the power operations support system according to the embodiment. FIG. 4 is a diagram illustrating generation of an operation cost function according to the embodiment. FIG. 5 is a diagram illustrating an example of an operation cost function according to the embodiment. FIG. 6 is a diagram illustrating an example of an operation cost function according to the embodiment. FIG. 7 is a diagram illustrating an example of conversion from solar radiation forecast to statistics according to the embodiment. FIG. 8 is a flowchart of simulation processing according to the embodiment. FIG. 9 is a flowchart of simulation processing according to the embodiment. FIG. 10 is a diagram illustrating an example of an equipment configuration modification proposal created in the embodiment. FIG. 11 is a diagram illustrating an overview of processing by the power operations support system according to the embodiment. FIG. 12 is a diagram illustrating an example of auxiliary explanatory information output by the power operations support system according to the embodiment. FIG. 13 is a diagram illustrating an example of a screen display visualized by animation in the power operations support system according to the embodiment. FIG. 14 is a diagram illustrating an example of the configuration of geographic information according to the embodiment. FIG. 15 is a diagram illustrating an example of a field list according to the embodiment. FIG. 16 is a diagram illustrating an example of the configuration of demand information according to the embodiment. FIG. 17 is a diagram illustrating an example of the configuration of demand information according to the embodiment. 1 is a diagram showing an example of the configuration of solar radiation prediction information in the present embodiment. FIG. 2 is a diagram showing an example of the configuration of equipment configuration information (battery list) in the present embodiment. FIG. 3 is a diagram showing an example of the configuration of equipment configuration information (vehicle list) in the present embodiment. FIG. 4 is a diagram showing an example of the configuration of equipment configuration information (power generation equipment list) in the present embodiment. FIG. 5 is a diagram showing an example of the configuration of peak threshold information in the present embodiment. FIG. 6 is a diagram showing an example of the configuration of peak threshold information in the present embodiment. FIG. 7 is a diagram showing an example of the configuration of cost information in the present embodiment. FIG. 8 is a diagram showing an example of the configuration of power plan information in the present embodiment. FIG. 9 is a diagram showing an example of the configuration of an operation plan created by the power operations support system in the present embodiment. FIG. 10 is a diagram showing an example of the configuration of a battery equipment change plan created by the power operations support system in the present embodiment. FIG. 11 is a diagram showing an example of the configuration of a vehicle equipment change plan created by the power operations support system in the present embodiment. FIG. 12 is a diagram showing an example of the configuration of a power generation equipment equipment change plan created by the power operations support system in the present embodiment.FIG. 1 is a diagram showing an example of a cost breakdown, which is a change in cost due to an operation plan and an equipment plan created by the power operations support system of the present embodiment. FIG. 2 is a diagram showing an example of the configuration of a peak cut parameter (peak threshold) created by the power operations support system of the present embodiment. FIG. 3 is a diagram showing an example of the configuration of an electricity bill reduction amount and a proposed power plan created by the power operations support system of the present embodiment. FIG. 4 is a diagram showing an example of a display screen output by the power operations support system of the present embodiment. FIG. 5 is a diagram showing an example of a display screen output by the power operations support system of the present embodiment.

[0010] First Embodiment FIG. 1 is a diagram showing the configuration of a power operation support system 10 according to a first embodiment of the present invention.

[0011] The power operation support system 10 of this embodiment creates an operation plan to supply power to a farm field by moving a portable battery from a power generation facility to the farm field using a vehicle, and to suppress the peak value of power consumed in the farm field.

[0012] The power operation support system 10 of this embodiment is configured by a computer having a processor (CPU) 11, a memory 12, an auxiliary storage device 13, and a communication interface 16. The power operation support system 10 may also have an input interface 14 and an output interface 15.

[0013] The processor 11 is a computing device that executes programs stored in the memory 12. The processor 11 executes various programs to realize various functional units (e.g., input unit 21, processing unit 22, output unit 23, etc.) of the power operation support system 10. Note that some of the processing performed by the processor 11 when executing the programs may be executed by another computing device (e.g., hardware such as a GPU, ASIC, or FPGA). The input unit 21 provides an input interface for the power operation support system 10 and accepts input data from a user. The processing unit 22 executes the processing described below and generates output data from the input data. The output unit 23 provides an output interface for the power operation support system 10 and provides the results of the processing performed by the processing unit 22 to the user.

[0014] The memory 12 includes a ROM, which is a non-volatile storage element, and a RAM, which is a volatile storage element. The ROM stores unchanging programs (e.g., BIOS), etc. The RAM is a high-speed, volatile storage element such as a DRAM (Dynamic Random Access Memory), and temporarily stores programs executed by the processor 11 and data used when the programs are executed.

[0015] The auxiliary storage device 13 is a large-capacity, non-volatile storage device such as a magnetic storage device (HDD) or a flash memory (SSD). The auxiliary storage device 13 also stores data used by the processor 11 when executing a program and the program executed by the processor 11. That is, the program is read from the auxiliary storage device 13, loaded into the memory 12, and executed by the processor 11 to realize each function of the power operation support system 10.

[0016] The communication interface 16 is a network interface device that controls communication with other devices in accordance with a predetermined protocol.

[0017] The input interface 14 is an interface to which input devices such as a keyboard and a mouse are connected and which receives input from an operator. The output interface 15 is an interface to which output devices such as a display device and a printer are connected and which outputs the results of program execution in a format that can be viewed by the user. Note that a terminal connected to the power operation support system 10 via a network may provide the input device and the output device. In this case, the power operation support system 10 may have a web server function, and the terminal may access the power operation support system 10 using a predetermined protocol (e.g., http).

[0018] The programs executed by the processor 11 are provided to the power operation support system 10 from removable media (such as a CD-ROM or flash memory) or via a network, and are stored in a non-volatile auxiliary storage device 13, which is a non-transitory storage medium. For this reason, the power operation support system 10 preferably has an interface for reading data from removable media.

[0019] The power operation support system 10 is a computer system configured on a single physical computer or on multiple logically or physically configured computers, and may operate on a virtual computer constructed on multiple physical computer resources. For example, each functional unit may operate on a separate physical or logical computer, or multiple functional units may be combined to operate on a single physical or logical computer.

[0020] 2 and 3 are flowcharts of the processing executed by the processing unit 22 of the power operation support system 10 of this embodiment.

[0021] In the illustrated process, an operation is performed using as input equipment configuration information that can be changed as parameters (battery list 105 (see FIG. 23), vehicle list 106 (see FIG. 24), power generation equipment list 107 (see FIG. 25)), peak cut parameters (peak threshold information 108 (see FIG. 26)), the current plan ID of the contracted power of each field recorded in the field list 102, various data that cannot be changed (geographical information 101 (see FIG. 17), field list 102 (see FIG. 18), demand information 103 (see FIG. 19), solar radiation forecast information 104 (see FIG. 21), cost information 109 (see FIG. 28), etc.), the initial value of the operation plan 111 (initial operation plan), and power plan information 110, and the operation plan 111, equipment plan 112, cost breakdown 113, peak cut parameters 114, electricity bill reduction amount / proposed power plan 115, and auxiliary explanation information. The initial operation plan is optional input information. The current plan ID of the contracted power of each field and the power plan information 110 are input when the contract changes depending on the amount of power.

[0022] In the illustrated process, the processing unit 22 first uses input data to determine an operating cost function that includes a peak power demand value as a variable (201). In step 201, as shown in FIG. 4 , the operating cost function is generated using the input facility configuration information (battery list 105, vehicle list 106, power generation facility list 107; see FIGS. 23, 24, and 25) and power plan information 110 (see FIG. 29). That is, function parameters are determined from the input data, and the function is determined. The processing unit 22 may generate the operating cost function using a higher-order function that receives the facility configuration information 105, 106, and 107 and the power plan information 110 as inputs and outputs a function that calculates an operating cost corresponding to the peak power demand value from inputs including the peak power demand value derived from the demand information 103. Note that the parameters of the operating cost function are coefficients that determine the contribution of arguments to the output value. The function determined in this embodiment is a function that calculates the operating cost, and the specific form of the function varies depending on the implementation.

[0023] For example, the generated operational cost function may calculate the sum of power values.

[0024] The operation cost function shown in Fig. 5 receives a list of peak demand power values ​​as an explanatory variable and outputs the sum of the peak demand power values ​​as a response variable. First, the total peak power value and counter i, which are parameters used within the operation cost function, are set to 0 (2001). Then, the i-th peak demand power value [i] in the list is added to the total peak power value (2002). Then, it is determined whether counter i indicates the last peak demand power value in the list (2003). If counter i does not indicate the last peak demand power value in the list, there are unprocessed peak demand power values, so i is incremented by 1, and the process returns to step 2002 to process the next peak demand power value (2004). If counter i indicates the last peak demand power value in the list, addition of all peak demand power values ​​has been completed, so the total peak power value is output (2005).

[0025] Next, we will explain an example of determining an operation cost function from equipment configuration information, peak cut parameters, various data (including field power demand data), and power plan information. Since the operation cost function depends on the peak value of power demand, in the simplest case it is a monotonically increasing function with the peak power demand value as an argument, and is specifically expressed by the following formula. Note that if there are multiple fields, the peak power demand value of each field is expressed as a vector with the number of fields as a dimension: f (peak power value) = sum of peak power values

[0026] Auxiliary variables may be used when determining the function f. Also, f may take additional arguments. In that case, the function f takes the form of the following equation: f (peak value of each field, ...; equipment configuration information, peak cut parameters, various data, power plan information, ...) where x1, x2, ... in f (x1, x2, ...; α1, α2) represent arguments, and α1, α2, ... represent auxiliary variables.

[0027] For example, the travel distance of each vehicle may be added as an argument to determine the function f using the following formula: f = travel cost (equivalent to fuel costs, etc.) + cost dependent on equipment configuration (initial investment) + sum of electricity plan charges corresponding to the peak value of each field

[0028] When using an operation cost function to evaluate an operation plan without uncertainty, it is advisable to use a function p that converts the demand data contained in the operation plan and various data into the peak power demand value for each field, and use the following function g as the evaluation function for the operation plan. Note that the uncertainty typically refers to changes in the amount of power generation possible due to fluctuations in the amount of solar radiation. g (operation plan, ...; equipment configuration information, peak shaving parameters, various data, power plan information, ...) = f (p (operation plan, demand data), ...; equipment configuration information, peak shaving parameters, various data, power plan information, ...)

[0029] When calculating the peak value reduced by battery operation using the function p (operation plan; demand data), if there is no uncertainty, it is advisable to compare the demand data with the power supply included in the operation plan and calculate the reduced peak value.

[0030] On the other hand, when evaluating an operation plan taking uncertainty into consideration, it is advisable to assign to f the peak power demand value of each field obtained as a result of a simulation using the operation plan to be evaluated.

[0031] Furthermore, for example, the operating cost function may calculate the electricity cost and facility configuration cost from the basic charge, the output of the power generation facility, the maximum battery storage capacity, charging speed, and discharging speed, and the fixed cost of one vehicle.

[0032] 6 and 7 receive a list of power peak values ​​and a list of power plan IDs as explanatory variables, and output the sum of the power cost and the equipment configuration cost as a response variable. Each power demand peak value corresponds to a power plan. That is, the cost corresponding to the i-th power demand peak value is calculated using the i-th power plan.

[0033] First, the total power cost and counter i, which are parameters used within the operating cost function, are set to 0 (2011). Then, the basic charge required to supply the peak power demand value [i] when the i-th power plan in the list is used, i.e., the basic charge for the minimum contracted power exceeding the peak power demand value [i], is added to the total power cost (2012). Then, it is determined whether counter i indicates the last peak power demand value in the list (2013). If counter i does not indicate the last peak power demand value in the list, there are unprocessed peak power demand values, so i is incremented by 1, and the process returns to step 2012 to process the next peak power demand value (2014). If counter i indicates the last peak power demand value in the list, cost addition according to all peak power values ​​has been completed, so the calculation of the total power cost is terminated and the calculation of the facility configuration cost (power generation facility) begins.

[0034] In calculating the facility configuration cost (power generation equipment), the total facility configuration cost and counter i, which are parameters used within the operating cost function, are set to 0 (2021). Then, the cost corresponding to the maximum output of the i-th power generation equipment in the list is added to the total facility configuration cost (2022). Then, it is determined whether counter i indicates the last power generation equipment in the list (2023). If counter i does not indicate the last power generation equipment in the list, there is unprocessed power generation equipment, so i is incremented by 1, and the process returns to step 2022 to process the next power generation equipment (2024). If counter i indicates the last power generation equipment in the list, the addition of the costs of all power generation equipment has been completed, so the calculation of the facility configuration cost (power generation equipment) is terminated and the calculation of the facility configuration cost (battery) is started.

[0035] In calculating the equipment configuration cost (battery), a counter i, which is a parameter used within the operating cost function, is set to 0 (2031). Then, the cost according to the maximum storage capacity of the i-th battery in the list, the cost according to the maximum charging rate, and the cost according to the maximum discharging rate are added to the total equipment configuration cost (2032, 2033, 2034). Then, it is determined whether counter i indicates the last battery in the list (2035). If counter i does not indicate the last battery in the list, there are unprocessed batteries, so i is incremented by 1, and the process returns to step 2032 to process the next battery (2014). If counter i indicates the last battery in the list, the addition of the costs of all batteries has been completed, so the calculation of the equipment configuration cost (battery) is terminated and the calculation of the equipment configuration cost (vehicle) begins.

[0036] In calculating the equipment configuration cost (vehicles), counter i, a parameter used within the operating cost function, is set to 0 (2041). Then, the fixed cost of the i-th vehicle in the list is added to the equipment configuration cost (2042). Then, it is determined whether counter i indicates the last vehicle in the list (2043). If counter i does not indicate the last vehicle in the list, there are unprocessed vehicles, so i is incremented by 1, and the process returns to step 2042 to process the next vehicle (2044). If counter i indicates the last vehicle in the list, the addition of the costs of all vehicles has been completed, and the calculation of the equipment configuration cost (power generation equipment) is terminated. That is, since the calculation of the equipment configuration cost for the power generation equipment, batteries, and vehicles has been completed, the loop of the power cost calculated in step 2012 and the equipment configuration cost calculated in step 2042 is output (2045).

[0037] Then, the processing unit 22 determines whether an initial operation plan has been input (202). If an initial operation plan has been input, the process proceeds to step 208 (203).

[0038] The processing unit 22 then converts the solar radiation forecast information 104 into a power generation forecast value for each power generation facility (203), and converts the power generation forecast value into a statistical value (e.g., an expected value) (204). Note that if no power generation facility is provided in the power system simulated by the power operation support system 10 of this embodiment, it is not necessary to convert the solar radiation forecast information 104 into a statistical value for the power generation forecast value.

[0039] FIG. 8 is a diagram illustrating an example of the operation cost function of this embodiment.

[0040] Furthermore, for example, the operation cost function may be one that calculates the number of reductions in peak power demand value from the peak power demand value.

[0041] The operational cost function shown in FIG. 8 receives a list of peak power values ​​as an explanatory variable and outputs the number of peak power value reductions as a response variable. First, the number of peak power reductions and counter i, which are parameters used within the operational cost function, are set to 0 (2051). Then, it is determined whether the i-th peak power demand value [i] in the list is smaller than the reference value defined in the peak threshold information 108 (2052). If the peak power demand value [i] is smaller than the reference value defined in the peak threshold information 108, 1 is added to the number of peak power reductions (2053). Then, it is determined whether counter i indicates the last peak power demand value in the list (2054). If counter i does not indicate the last peak power demand value in the list, there are unprocessed peak power demand values, so i is incremented by 1, and the process returns to step 2052 to process the next peak power demand value (2055). If counter i indicates the last peak power demand value in the list, processing of all peak power demand values ​​has been completed, and the number of peak power value reductions is output (2056).

[0042] FIG. 9 is a diagram showing an example of conversion from the solar radiation prediction to statistics in this embodiment.

[0043] The amount of solar radiation can be converted into power output using the following formula. Figure 9 shows the power output when the panel capacity is 11 kW, the PCS capacity is 9.9 kW, and the loss coefficient is 0.75. Power output = min (amount of solar radiation × panel capacity × loss coefficient ÷ solar radiation intensity under standard test conditions, PCS capacity).

[0044] Statistics are then calculated from the power generation output. In the example shown, the most frequent value is calculated as the statistics, but other statistical values, such as expected values, may also be calculated. It is preferable to calculate statistics from the solar radiation forecast for each field.

[0045] After step 204, the processing unit 22 converts the amount of power into data for CVRPTW, which considers the amount of power as cargo (205). CVRPTW is a capacitated vehicle routing problem with time windows. Note that if there is no power generation facility, the conversion in step 205 is not necessary.

[0046] The CVRP (Capacity Constrained Vehicle Planning Problem) is a problem of delivering packages by traveling between points on a map. The problem involves determining a route that minimizes a given evaluation function. The points are called nodes, and do not necessarily correspond to physical locations; virtual locations can be set to solve slightly modified problems. TW stands for time window, and a limit (time frame) is set for the time at which a node can be visited. In the power operation support system 10 of this embodiment, the amount of energy is treated as packages, and an operation plan is generated by solving the CVRPTW. Therefore, the following constraints can be applied, data can be converted, and an optimal solution or an approximate solution can be derived. Since the number of packages is an integer, the minimum unit of energy is set (e.g., 0.1 kWh), and the set minimum unit is one package. The maximum number of packages that can be loaded corresponds to the battery capacity. For example, a 14 kWh battery can carry up to 140 packages. Charging corresponds to the operation of loading packages, and discharging corresponds to the operation of unloading packages. - The time series data of power generation amount of each power generation facility that has been converted into statistics is converted into charging (loading) nodes with time frames.

[0047] For example, if the amount of power generated at 11:00 is 5.775 kW, the maximum number of packages that can be loaded during the time frame of 11:00 to 11:30 converted into the number of packages is 28, and the actual node information is "A maximum of 28 packages can be loaded at the location of the power generation facility (ID=1) from 11:00 to 11:30." More precisely, it is "A maximum of 28 packages can be loaded at the location of the power generation facility (ID=1). However, the visitable time range for this node is from 11:00 to 11:00, and 30 minutes are added to the actual physical travel time when moving to the next node." This conversion is performed similarly for all power generation facilities in all time periods.

[0048] The demand data is similarly converted into a discharge (unloading) node. Normally, the power demand in the field for all sections on the time axis is converted into a discharge node, but if a peak threshold is given, only the power demand for the time section exceeding the peak threshold may be converted into a discharge node.

[0049] In other words, it is sufficient to specify upper limits for both the charge amount and the discharge amount.

[0050] Furthermore, when the battery and the vehicle move separately, it is advisable to treat both as moving bodies and impose constraints such as the battery moving simultaneously with the vehicle.

[0051] Then, the processing unit 22 solves the CVRPTW using simulated annealing (206). Note that the CVRPTW may be solved using tabu search or guided local search instead of simulated annealing.

[0052] When solving the CVRPTW, the field list 102, the demand data converted into cargo information, the objective function, the geographic information 101, the vehicle list 106, and the battery list 105 are used. If a power generation facility is present, it is advisable to also use the power generation data converted into cargo information. If no power generation facility is present, it is advisable to perform optimization using only the initially given battery charge amount.

[0053] The processing unit 22 then generates an initial operation plan (207). For example, solving the CVRPTW provides information for all vehicles and batteries, including a list of nodes to visit, the time each node is visited, and the amount of cargo to be unloaded or loaded at each node. Therefore, by performing an inverse conversion from the amount of cargo to the amount of power from the nodes to locations (fields, power generation facilities, vehicle initial and final positions, and battery initial and final positions), the actions of all vehicles and batteries (movement, charging, discharging, battery loading, and battery unloading) and the times at which these actions are performed, i.e., an initial operation plan for vehicles and batteries, can be obtained. Regarding battery loading and unloading, it is preferable to treat the vehicle located at the same position as the battery when it starts moving as loading the battery, and the vehicle located at the same position as the battery when it stops moving as unloading the battery. The final output operation plan includes movement information and power supply information. The movement information includes the movement start time and the destination, and the power supply information includes the power supply start time, power supply output, and power supply end time. If a power generation facility is present, the operation plan may also include charging information. The charging information includes the charging start time, charging speed, and charging end time. If the battery and the vehicle move separately, it is preferable to include the actions of loading and unloading the battery.

[0054] The processing unit 22 then executes a simulation (208). The simulation may be executed multiple times, including stochastic behavior. Details of the simulation executed in step 208 will be described later with reference to FIGS. 10, 11, and 12.

[0055] Steps 208 to 212, described below, constitute an operation plan optimization process using loop A, which optimizes the operation plan by fixing the equipment configuration and parameters. Loop A performs a simulation using the geographic information 101, the vehicle list 106, the battery list 105, the demand information 103, the power generation equipment list 107, and the operation plan recorded in step 210. In this simulation, the output of the power generation equipment is determined probabilistically based on the solar radiation forecast. Therefore, when the post-reduction peak value of each field obtained by the simulation is evaluated using the operation cost function, a probabilistic value is obtained. Therefore, the simulation is performed multiple times, the values ​​of the operation cost function are sampled, and the obtained multiple values ​​are converted into a single statistical quantity, which is used as the evaluation value of the operation plan or the operation cost statistics. For example, the values ​​of the operation cost function obtained 10,000 times are statistically processed to calculate the relative frequency for each class, and the statistics (e.g., expected value, average value) of the values ​​of the operation cost function are calculated. This statistical value becomes the operation cost statistics, which are the evaluation values. Using this evaluation value as the objective function of optimization, the operation plan is optimized (for example, by repeating a loop of improvement and evaluation) using a simulated annealing method or the like.

[0056] Specifically, the processing unit 22 statistically processes function values ​​calculated using the operation cost function from multiple simulations to calculate an operation cost statistic (209). The operation cost statistic can be a representative value (such as the mean value, the mode in a frequency distribution, the maximum value, or the median value) or a value representing the characteristics of multiple operation cost function values. If the initial or operation cost statistic value of loop A is smaller than the recorded operation cost statistic, the processing unit 22 determines that the operation cost statistic has improved and records the operation cost statistic and the operation plan in memory (RAM) 12 (210). The operation plan recorded here is the data input in step 208. The processing unit 22 then determines whether the termination condition of loop A is met (211). The termination condition may be, for example, a predetermined execution time has elapsed, the loop has been executed a predetermined number of times, or the evaluation value no longer improves. If the determination result indicates that the termination condition of loop A is not met, the processing unit 22 modifies the operation plan (212) and returns to step 208. On the other hand, if the termination condition of loop A is satisfied, the processing unit 22 outputs the minimum value of the operating cost statistics obtained in loop A, the operating plan corresponding to the minimum value, and the variables recorded during the loop. The correction in step 212 may be performed by generating a neighborhood using, for example, the 2-Opt method or the Or-Opt method.

[0057] If the initial or operational cost statistics of loop B are smaller than the recorded operational cost statistics, the processing unit 22 determines that the operational cost statistics have improved. The processing unit 22 records the cost breakdown and the output of loop A recorded in step 210, i.e., the operation plan, equipment configuration (equipment plan 112), and parameters (peak threshold information 108, power plan for each field) in memory (RAM) 12 (213), and determines whether the termination condition of loop B is met (214). The termination condition may be, for example, a predetermined execution time has elapsed, the loop has been executed a predetermined number of times, or the evaluation value no longer improves. The operational cost can be calculated by multiplying the operation plan and equipment configuration by the unit price recorded in the cost information 109 (see FIG. 28). For example, the number of equipment configurations can be calculated by dividing the number of units, capacity, and output of the equipment configuration by the unit recorded in the cost information 109. The number of equipment configurations for the initial and final values ​​can be calculated, and the cost breakdown (difference) can be calculated from the difference between the number of equipment configurations for the initial and final values. Since the number of units, capacity, and output are integer multiples of the units recorded in the cost information 109, when calculating the number of equipment configurations, the smallest integer equal to or greater than the value of the equipment configuration used in the simulation is used.

[0058] If the determination result shows that the termination condition for loop B is not satisfied, the processing unit 22 modifies the equipment configuration and parameters (215) and returns to step 201. The modification in step 215 may be performed by randomly selecting the vicinity of the equipment configuration and parameters. Alternatively, the frequency of modification may be varied for each equipment configuration and parameter that is selectively modified. Furthermore, it is preferable to use random numbers that are likely to select values ​​close to the current parameter values ​​more frequently and values ​​far from the current parameter values ​​less frequently.

[0059] On the other hand, if the termination condition of loop B is met, the processing unit 22 outputs the minimum value of the operating cost statistics obtained in loop B, the equipment plan 112, peak cut parameter 114, and operation plan 111 corresponding to the minimum value, and the parameters recorded during the loop (operation plan, equipment plan configuration, cost breakdown, peak cut parameter) (216).

[0060] The steps 201 to 215 described above constitute the equipment configuration optimization process by loop B, which optimizes the equipment configuration and parameters. In loop B, the operation cost statistics and operation plan obtained at the end of loop A are considered to be the best values ​​obtained when the equipment configuration, peak cutting parameters, and contracted power plan are fixed, and then the equipment configuration, peak cutting parameters, and power plan for each field are optimized.

[0061] For example, the best operating cost statistics obtained in loop A is used as an evaluation function, and the operation plan is optimized (for example, by repeating a loop of improvement and evaluation) using a simulated annealing method or the like with the addition, deletion, or modification of the equipment configuration, the increase or decrease of peak shaving parameters, and the modification of the contracted power plan (only when a power plan is used) as a neighborhood search operation. In the equipment configuration / parameter modification in step 215, the equipment configuration and peak shaving parameters (for example, the contracted power plan) are changed by a neighborhood search operation.

[0062] When optimizing an operation plan without optimizing equipment, the termination condition of loop B is set to always be true, and loop B is terminated after the first iteration. In this way, loop B does not actually exist, and the results of optimizing the operation plan are output as is.

[0063] Then, the processing unit 22 determines whether the power plan information 110 has been input (217), and if the power plan information 110 has been input, the processing unit 22 calculates and outputs the electricity bill reduction amount and proposed power plan 115 by simulation using the information recorded in step 213 (218).

[0064] Then, the processing unit 22 executes the same simulation as in step 208 and outputs the auxiliary explanation information (219).

[0065] 10, 11, and 12 are flowcharts of the simulation process executed in step 208 by the processing unit 22 of the power operation support system 10 of this embodiment.

[0066] In the illustrated simulation, a simulation is run using as input the start time, end time, geographic information 101 (see Figure 17), demand information 103 (see Figure 19), solar radiation forecast information 104 (see Figure 21), equipment configuration information (battery list 105 (see Figure 23), vehicle list 106 (see Figure 24), power generation equipment list 107 (see Figure 25)), operation plan, and seed value, and a post-reduced peak value is output.

[0067] In the simulation, the processing unit 22 (simulator) interprets the operation plan and operates each vehicle (and battery). As a simple example, the following operations are associated with various actions. Movement: Continue moving to the specified destination via the shortest route, and do nothing after arriving at the destination. Power supply: Discharge at the specified output in the specified field, and end discharging at the end time. If the charge amount is insufficient and discharging is not possible, do nothing. Charging: Charge at the specified input from the specified power generation equipment, and end charging at the end time. Even if the possible charging speed (for example, the output of the power generation equipment) is lower than the specified input, charge at the possible charging speed of the power generation equipment. Battery loading / unloading: The vehicle loads and unloads the specified battery. If the vehicle has a battery loaded, the movement of the battery and the vehicle is synchronized.

[0068] Note that the behavior may be an undefined behavior, such as charging beyond the designated input when there is surplus output from the power generation facility, or starting to move to the next scheduled power generation facility when the stored power becomes insufficient during discharging.

[0069] The simulation outputs the positions of all vehicles, the positions of batteries, the amount of battery charge, the charging source and charging rate, the discharging destination and discharging rate at each time point, as well as the output of all power generation facilities and the power demand of all fields.

[0070] Next, a specific procedure for the simulation will be described. In the simulation, the processing unit 22 first initializes the vehicle internal state (231), the battery internal state (232), the supplied and generated power time-series data (233), and sets time t to the start time (234). The vehicle internal state is an internal variable assigned to each vehicle to represent the vehicle state in the simulation, and includes the vehicle position and the loaded battery ID. During the initialization in step 231, the vehicle position is set to the initial position listed in the vehicle list 106, and the loaded battery ID is set to -1, indicating that the battery is not loaded. The battery internal state is an internal variable assigned to each battery to represent the battery state in the simulation, and includes the battery position and the amount of stored power. During the initialization in step 232, the battery position and the amount of stored power are set to the initial position and the initial amount of stored power, respectively, listed in the battery list 105. The supply and generation power time series data is an internal variable that records the time series data of the power supplied from the battery for each field and the time series data of the power supplied to the battery for each power generation facility. During initialization in step 233, 0 is set for all times in all fields, and 0 is set for all times in all power generation facilities.

[0071] The processing unit 22 then uses pseudo-random numbers obtained from the pseudo-random number generator initialized using the input seed value to probabilistically calculate the amount of solar radiation with reference to the solar radiation prediction information 104 (235), and converts the calculated amount of solar radiation into the power output of each power generation facility (236). The conversion from the amount of solar radiation to the power output may be performed in the same manner as in steps 203 and 204 (see FIG. 2).

[0072] Then, the processing unit 22 sets a counter i indicating the vehicle ID to 0 (237), and obtains the behavior of the vehicle with ID=i from the operation plan 111 input to the simulation (238).

[0073] The processing unit 22 then determines whether the vehicle's behavior in the operation plan is movement (239). If the vehicle's behavior is movement, the processing unit 22 proceeds along the shortest route to the destination (240). At this time, the processing unit 22 records the position of the vehicle one step (the smallest unit of time in the simulation) along the shortest route as the vehicle position in the vehicle's internal state.

[0074] Then, the processing unit 22 determines whether the vehicle action in the operation plan is battery loading (241). If the vehicle action is battery loading, the processing unit 22 checks whether a battery can be loaded, loads a battery that can be loaded, and records the ID of the battery being loaded as the loaded battery ID in the internal state of the vehicle with ID=i (242).

[0075] The processing unit 22 then determines whether the vehicle action in the operation plan is to unload the battery (243). If the vehicle action is to unload the battery, the processing unit 22 removes the battery, deletes the ID of the unloaded battery from the loaded battery ID in the internal state of the vehicle with ID=i, and sets it to −1, which indicates that the battery is not loaded (244).

[0076] The processing unit 22 then determines whether counter i is the final ID, which is the maximum value of the vehicle IDs in the vehicle list 106 (see FIG. 24) (245). If counter i is not the final ID, the processing unit 22 increments counter i by 1, returns to step 238, and determines the behavior of the next vehicle. On the other hand, if counter i is not the final ID, the processing unit 22 increments counter i by 1 (246), returns to step 238, and determines the behavior of the next vehicle. On the other hand, if counter i is the final ID, the processing unit 22 sets parameter j, which indicates the battery ID, to 0 (247).

[0077] Then, the processing unit 22 acquires the behavior of the battery with ID=j (248).

[0078] The processing unit 22 then determines whether the battery's behavior in the operation plan is movement (249). If the battery's behavior is movement, the processing unit 22 proceeds along the shortest route to the destination (250). At this time, the processing unit 22 records the battery position in the internal state of the battery, which is one step (the smallest unit of time in the simulation) along the shortest route.

[0079] The processing unit 22 then determines whether the battery's behavior in the operation plan is charging (251). If the battery's behavior is charging, the processing unit 22 then determines whether the battery is located at the same location as the charging source (252). If the location is different from the charging source, the process proceeds to step 259. On the other hand, if the battery is located at the same location as the charging source, the processing unit 22 calculates the charge amount, adds the calculated charge amount to the supply and generation power time-series data of the charging source (power generation facility) at time t (253), and adds the charge amount to the amount of charge stored in the battery's internal state (254). The charge amount at this time is calculated using a function that gives a minimum value: min (maximum output of the power generation facility that is the charging source - output already requested at time t, maximum charging rate of the battery, charging rate specified by the operation plan). The output already requested at time t is the value obtained by dividing the charge amount obtained from the supply and generation power time-series data of the charging source (power generation facility) at time t by the time for one step.

[0080] The processing unit 22 then determines whether the battery's behavior in the operation plan is discharging (255). If the battery's behavior is discharging, the processing unit 22 then determines whether the battery is in the same location as the discharge destination (256). If the location is different from the discharge destination, the process proceeds to step 259. On the other hand, if the battery is in the same location as the discharge destination, the processing unit 22 calculates the amount of discharge, adds the calculated amount of discharge to the supply and generation power time-series data for the discharge destination (field) at time t (257), and subtracts the amount of discharge from the amount of power stored in the battery's internal state (258).

[0081] The processing unit 22 then determines whether the parameter j is the final ID, which is the maximum value of the battery IDs in the battery list 105 (see FIG. 23) (259). If the parameter j is not the final ID, the processing unit 22 adds 1 to the parameter j (260) and returns to step 248 to determine the next battery action. On the other hand, if the parameter j is the final ID, the processing unit 22 determines whether the parameter t is the end time (261).

[0082] If the parameter t is not the end time, the processing unit 22 adds 1 to the parameter t (263), returns to step 234, and performs the simulation for the next time. On the other hand, if the parameter t is the end time, the processing unit 22 calculates the maximum value of the power demand for each field from the discharge record (262). The power demand for each field can be calculated by subtracting the original power consumption from the supplied power, and this maximum value becomes the peak value after the reduction. The peak value after the reduction is then output (264).

[0083] FIG. 12 is a diagram showing an example of an operation plan created by the power operation support system 10 of this embodiment.

[0084] An operation plan is created for each vehicle and includes the time, the battery, and the planned behavior of the vehicle. The initial operation plan input to the simulation is also in the format shown in FIG.

[0085] FIG. 13 is a diagram showing an example of an equipment configuration modification proposal created in this embodiment.

[0086] The revised proposal includes information on the equipment configuration and cost required for a predetermined amount of peak shaving, and if the existing equipment configuration can accommodate it, the proposal will be "no change." Note that a revised proposal may also be created for changing the amount of peak shaving from the predetermined amount.

[0087] FIG. 14 is a diagram showing an outline of the processing performed by the power operation support system 10 of this embodiment.

[0088] Typically, the amount of solar radiation causes variations in the amount of power generation possible. The power operations support system 10 of this embodiment generates an operation plan that takes uncertainty into account, an equipment configuration, and a probability distribution of successful peak shaving, through a simulation using the amount of power generation that fluctuates stochastically. The power operations support system 10 of this embodiment also outputs auxiliary explanatory information. The auxiliary explanatory information is auxiliary information for explaining the appropriateness of the equipment configuration.

[0089] FIG. 15 is a diagram illustrating an example of auxiliary explanatory information output by the power operation support system 10 of this embodiment, and shows an example of auxiliary explanatory information for the entire region when the number of batteries is a changeable parameter.

[0090] The auxiliary explanatory information shown in Figure 15 indicates that: 1) the power generation capacity has reached its limit, and four batteries are the maximum utilization limit; 2) considering the addition of PV panels, while up to four PV panels contribute to cost reduction, installing five or more PV panels will result in a decrease in the utilization rate of the PV panels; and 3) because there is excess PV panel capacity, there is the possibility of adding other uses for the electricity. The auxiliary explanatory information can be calculated by executing the same simulation as in step 208, as described above, and changing the output information. For example, the utilization rate can be calculated by referring to hourly activity and calculating the ratio of idle standby time to total time. The cost reduction rate can be calculated by referring to hourly power supply and dividing the reduced peak value by the originally planned power consumption. The peak cut achievement rate (15 kW) can be calculated by referring to hourly power supply and calculating the ratio of demand information that was cut by 15 kW or more to the value obtained by subtracting the peak value from the original peak value.

[0091] FIG. 16 is a diagram showing an example of a screen display visualized by animation in the power operation support system 10 of this embodiment.

[0092] The screen shown in FIG. 16 displays a vehicle, a power generation facility, and a farm field, and an animation of the vehicle moving in accordance with the progress of time displayed in the upper left corner of the screen.

[0093] FIG. 17 is a diagram showing an example of the configuration of the geographic information 101 in this embodiment.

[0094] The geographic information 101 is represented by a graph of nodes and edges, and includes the number of vertices, which is the number of nodes, identification information of the node that is the start point of the edge, identification information of the node that is the end point of the edge, and the length of the edge (e.g., in meters).

[0095] FIG. 18 is a diagram showing an example of the configuration of the farm field list 102 in this embodiment.

[0096] The field list 102 includes a field ID, which is unique identification information for the field, the location of the field, identification information for the power supply and demand plan available for the field, and identification information for the power supply and demand plan currently being used for the field. The location of the field may be represented by the start point, end point, and distance from the start point (for example, in meters) of an edge.

[0097] 19 and 20 are diagrams showing examples of the configuration of the demand information 103 in this embodiment.

[0098] The demand information 103 records information on demand time-series change patterns, and includes identification information for fields whose power demand changes according to the demand time-series change patterns, and the time-series power demand of the fields for each predetermined time. For example, as shown in FIG. 20 , a field (32) with field ID 32 consumes a lot of power between 5:30 and 6:00. Therefore, the power operation support system 10 of this embodiment uses the demand information to create an operation plan for reducing power demand peaks by moving a charged battery to the field (32) and operating the battery to supply power.

[0099] 21 and 22 are diagrams showing examples of the configuration of the solar radiation prediction information 104 in this embodiment.

[0100] The solar radiation forecast information 104 includes a numerical value that expresses the probability of solar radiation (from 0 kW / m2 to the maximum solar radiation at the Earth's surface) for each hour as a percentage. For example, the solar radiation forecast information 104 for a sunny day, as shown in Fig. 22, has a high probability region distributed along a curve that peaks around noon in accordance with changes in the solar altitude, and low probability regions distributed adjacent to the high probability region (above and below in the figure).

[0101] Figures 23, 24, and 25 are diagrams showing examples of the configuration of equipment configuration information in this embodiment, and the equipment configuration information includes a battery list 105 (Figure 23), a vehicle list 106 (Figure 24), and a power generation equipment list 107 (Figure 25).

[0102] 23, the battery list 105 includes a battery ID, which is unique identification information for the portable battery, the initial position of the battery at the start of the simulation, the initial amount of charge of the battery at the start of the simulation (e.g., in kWh), the maximum amount of charge of the battery (e.g., in kWh), the maximum possible charge rate (e.g., in kW) which is the amount of power that can be charged per unit time, and the maximum discharge rate (e.g., in kW) which is the amount of power that can be discharged per unit time. The initial position may be represented by the start point, end point, and distance from the start point (e.g., in meters) of the edge.

[0103] As shown in FIG. 24, the vehicle list 106 includes a vehicle ID, which is unique identification information of the vehicle transporting the battery, the initial position of the vehicle at the start of the simulation, the maximum speed of the vehicle (e.g., in km / h), and the travel cost per unit distance of the vehicle (e.g., in yen / km).

[0104] As shown in Figure 25, the power generation equipment list 107 includes a power generation equipment ID, which is unique identification information for the power generation equipment that charges the battery, the location of the power generation equipment, the PV panel capacity (e.g., in kW) which is the amount of power generated by the solar panel, the PCS output (e.g., in kW) which is the amount of power that can be processed by the power conversion device, and the maximum engine output (e.g., in kW) which is the amount of power generated by the internal combustion engine power generation device.

[0105] 26 and 27 are diagrams showing examples of the configuration of the peak threshold information 108 in this embodiment.

[0106] The peak threshold information 108 includes a reference value (e.g., in kW) indicating the maximum peak value allowed for each field represented by a field ID. The peak threshold information 108 indicates a time period during which power demand exceeds the reference value, as shown in Fig. 27 . The power operation support system 10 of this embodiment creates an operation plan to keep power demand within the reference value during the exceeding time period by moving a charged battery to a power demand area and supplying power.

[0107] FIG. 28 is a diagram showing an example of the configuration of the cost information 109 in this embodiment.

[0108] The cost information 109 includes items, units, and prices (i.e., unit prices) corresponding to the units. The cost information 109 is referenced in step 213 of the process executed by the processing unit 22 of the power operation support system 10.

[0109] FIG. 29 is a diagram showing an example of the configuration of the power plan information 110 according to this embodiment.

[0110] The power plan information 110 includes a plan ID, which is unique identification information for each power contract plan, the contracted power (e.g., in kW), the basic fee (e.g., in yen), and the metered fee (e.g., yen / kWh) for each plan ID. Generally, the higher the contracted power, the higher the basic fee. Since peak power consumption cannot exceed the contracted power, reducing peak power demand and keeping the basic fee low with a low contracted power will reduce costs.

[0111] FIG. 30 is a diagram showing an example of the configuration of the operation plan 111 created by the power operation support system 10 of this embodiment.

[0112] The operation plan 111 is created for each vehicle and battery, and includes the time, the vehicle or battery's planned action, and the content of the action. Note that Fig. 30 shows only the vehicle operation plan, and the battery operation plan is omitted.

[0113] 31, 32, and 33 are diagrams showing examples of the configuration of the facility plan 112 created by the power operation support system 10 of this embodiment.

[0114] The facility plan 112 includes at least one of a battery facility modification plan 112B illustrated in FIG. 31, a vehicle facility modification plan 112V illustrated in FIG. 32, and a power generation facility modification plan 112G illustrated in FIG.

[0115] The battery equipment modification plan 112B illustrated in FIG. 31 includes three types: battery addition, battery deletion, and battery change. An equipment modification plan for adding a battery includes the initial position of the battery to be added at the start of the simulation, the initial stored power amount (e.g., in kWh) of the battery at the start of the simulation, the maximum stored power amount (e.g., in kWh) of the battery, the maximum possible charge rate (e.g., in kW) which is the amount of power that can be charged per unit time, and the maximum discharge rate (e.g., in kW) which is the amount of power that can be discharged per unit time. An equipment modification plan for deleting a battery includes a battery ID which is identification information of the battery to be deleted. An equipment modification plan for changing a battery includes a battery ID which is identification information of the battery to be changed, the initial stored power amount (e.g., in kWh) of the battery at the start of the simulation, the maximum stored power amount (e.g., in kWh) of the battery, the maximum possible charge rate (e.g., in kW) which is the amount of power that can be charged per unit time, and the maximum discharge rate (e.g., in kW) which is the amount of power that can be discharged per unit time.

[0116] The vehicle equipment modification plan 112V illustrated in FIG. 32 includes three types: vehicle addition, vehicle deletion, and vehicle change. An equipment modification plan for adding a vehicle includes the initial position of the vehicle to be added at the start of the simulation, the initial amount of charge stored in the battery at the start of the simulation (e.g., in kWh), and the maximum speed of the vehicle (e.g., in km / h). An equipment modification plan for adding a vehicle may include the travel cost per unit distance of the vehicle (e.g., in yen / km). An equipment modification plan for deleting a vehicle includes a vehicle ID, which is identification information of the vehicle to be deleted. An equipment modification plan for changing a vehicle includes a vehicle ID, which is identification information of the vehicle to be changed, the initial position of the vehicle at the start of the simulation, the maximum speed of the vehicle (e.g., in km / h), and the travel cost per unit distance of the vehicle (e.g., in yen / km).

[0117] The power generation facility modification plan 112G illustrated in FIG. 33 includes three types: power generation facility addition, power generation facility deletion, and power generation facility modification. The power generation facility modification plan 112G for adding a power generation facility includes the location of the power generation facility to be added, the PV panel capacity (e.g., in kW) representing the amount of power generated by the solar cell panels, the PCS output (e.g., in kW) representing the amount of power that can be processed by the power conversion device, and the maximum engine output (e.g., in kW) representing the amount of power generated by the internal combustion engine power generation device. The power generation facility modification plan for deleting a power generation facility includes the power generation facility ID, which is identification information for the power generation facility to be deleted. The power generation facility modification plan for modifying a power generation facility includes the power generation facility ID, which is identification information for the power generation facility to be modified, the initial battery charge (e.g., in kWh) at the start of the simulation, the location of the power generation facility to be modified, the PV panel capacity (e.g., in kW) representing the amount of power generated by the solar cell panels, the PCS output (e.g., in kW) representing the amount of power that can be processed by the power conversion device, and the maximum engine output (e.g., in kW) representing the amount of power generated by the internal combustion engine power generation device.

[0118] FIG. 34 is a diagram showing an example of a cost breakdown 113, which is a change in cost due to the operation plan 111 and the facility plan 112 created by the power operation support system 10 of this embodiment.

[0119] The cost breakdown 113 includes profits and losses (for example, in yen units) for each item over a predetermined period (for example, six months). The items are, for example, peak cut, running costs, battery addition, battery replacement, power generation equipment replacement, and power generation equipment replacement. The total profit is calculated by adding up the profits and losses for each item.

[0120] FIG. 35 is a diagram showing an example of the configuration of the peak cut parameter 114 created by the power operation support system 10 of this embodiment.

[0121] The peak threshold 114P includes a field ID that is identification information of the field, and a difference (for example, in kW) between a reference value (for example, in kW) that is a target for power peak suppression and an initial value of the power peak.

[0122] FIG. 36 is a diagram showing an example of the configuration of the electricity bill reduction amount / proposed power plan 115 created by the power operation support system 10 of this embodiment.

[0123] The electricity bill reduction amount / proposed power plan 115 includes a field ID which is identification information of the field, a plan ID which is identification information of the power contract plan, the contracted power (difference) (for example, in kW), and the reduction amount (for example, in yen).

[0124] 37 and 38 are diagrams showing examples of a display screen 900 output by the power operation support system 10 of this embodiment. In Fig. 37, only a partial area of ​​the upper part of the display screen 900 is shown (the lower part of the auxiliary explanation information display area 920 is not displayed), but Fig. 38 shows a state in which the lower part of the auxiliary explanation information display area 920 of the screen 900 (the upper part of the vehicle information display area 930) is displayed by operating the scroll bar on the right side of the screen 900.

[0125] The display screen 900 shown in Figures 37 and 38 displays the calculation results by the power operation support system 10, and includes a simulation visualization display area 910, an explanation auxiliary information display area 920, a vehicle information display area 930, and a field information display area 940.

[0126] The simulation visualization display area 910 displays the positions of the vehicle (battery), power generation equipment, and field at a specific time (5:30 in FIG. 38 ) based on the geographic information 101, the field list 102, the power generation equipment list 107, and the vehicle (battery) position for each hour output by the simulation. By operating the time slider, an animation of the vehicle moving according to the time can be displayed. The user can visually grasp the vehicle's movement, making it possible to communicate the plan in an easy-to-understand manner. This information is particularly useful for promoting discussions with people who have a sense of the field.

[0127] The auxiliary information display area 920 displays auxiliary information including the battery operation rate, cost reduction rate, and peak shaving (15 kW) achievement rate for each number of batteries, and provides auxiliary information explaining the optimality of the equipment configuration, peak shaving parameters, and contracted power plan obtained by the simulation. The auxiliary information includes values ​​that contribute to explaining the optimality.

[0128] The cost reduction rate, battery operation rate, and peak cut achievement rate can be calculated using the following formulas. Cost is a monetary cost that includes the initial investment. Cost reduction rate = 100 x (1 - cost when power is supplied from the battery / cost when power is not supplied) Battery operation rate = 100 x (1 - total time when the battery is not in use ÷ (total time x number of batteries)) Peak cut (15 kW) achievement rate = number of fields that have successfully achieved a peak reduction of 15 kW or more ÷ total number of fields

[0129] For example, for specified parameters (e.g., equipment configuration, peak shaving parameters, elements constituting the contracted power plan), multiple values ​​close to the optimal solution are inputted into a simulation, and the battery operating rate, cost reduction rate, and peak shaving achievement rate are calculated and displayed in the auxiliary explanatory information display area 920. The figure shows an example of auxiliary explanatory information for the entire region when the number of batteries is a changeable parameter. For example, in the auxiliary explanatory information shown in the figure, since the simulation calculated that four batteries is optimal, multiple values ​​close to four (e.g., 1, 2, 3, 5) are inputted into the parameter number of batteries, and the specified indicators are displayed.

[0130] The illustrated auxiliary explanatory information indicates that: 1) the power generation capacity has reached its limit, and four batteries are the limit of utilization; 2) considering the addition of PV panels, while up to four PV panels contribute to cost reduction, installing five or more PV panels will result in a decrease in the PV panel utilization rate; and 3) because there is excess PV panel capacity, there is the possibility of adding other power utilization sources. The auxiliary explanatory information can be calculated by executing the same simulation as in step 208, as described above, and changing the output information. For example, the utilization rate can be calculated by referencing hourly activity and calculating the percentage of idle time compared to total time. The cost reduction rate can be calculated by referencing hourly power supply and dividing the reduced peak value by the originally planned power consumption. The peak cut achievement rate (15 kW) can be calculated by referencing hourly power supply and calculating the percentage of demand information that achieved a cut of 15 kW or more, calculated by subtracting the peak value from the original peak value. The display items and evaluation values ​​(horizontal axis of the graph) in the auxiliary explanatory information display area 920 can be selected by operating checkboxes.

[0131] The vehicle information display area 930 displays the behavior of a specified vehicle (battery) and the change in the amount of stored power over time based on the operation plan. The amount of stored power can be calculated from the charge and discharge amounts in the operation plan. By selecting a vehicle or operating a check box in the simulation visualization display area 910, a vehicle for which the amount of charge is to be displayed in the vehicle information display area 930 can be selected. In addition, the amount of charge for each hour included in the simulation output may be plotted to display the difference from the plan.

[0132] The field information display area 940 displays the power demand of the specified field and the time change in the power supply from the battery based on the demand information 103 and the operation plan. It is preferable to display the power peak threshold, the battery arrival time, and the reduction in the peak due to the power supply together with the power demand. By selecting a vehicle or operating a checkbox in the simulation visualization display area 910, the field for which the power demand is displayed in the field information display area 940 can be selected. The difference from the plan may also be displayed based on the hourly charge and discharge amounts included in the simulation output.

[0133] As described above, the power operations support system 10 of this embodiment can obtain a specific operation plan for reducing power peaks by taking into account the stochastic fluctuations in power generation, thereby reducing the power costs in the farm field. It can also obtain the configuration and cost of equipment required to reduce power peaks by a predetermined amount. It can also obtain an appropriate operation plan in accordance with changes in the amount of reduction in power peaks.

[0134] The present invention is not limited to the above-described embodiments, and includes various modifications and equivalent configurations within the spirit and scope of the appended claims. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to configurations including all of the described configurations. Furthermore, part of the configuration of one embodiment may be replaced with the configuration of another embodiment. Furthermore, the configuration of another embodiment may be added to the configuration of one embodiment. Furthermore, part of the configuration of each embodiment may be added, deleted, or replaced with other configurations.

[0135] Furthermore, the aforementioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole in hardware, for example by designing them as integrated circuits, or may be realized in software by a processor interpreting and executing a program that realizes each function.

[0136] Information such as programs, tables, and files that realize each function can be stored in a storage device such as a memory, hard disk, or SSD (Solid State Drive), or in a recording medium such as an IC card, SD card, or DVD.

[0137] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and do not necessarily show all the control lines and information lines that are necessary for implementation. In reality, it can be considered that almost all components are interconnected.

Claims

1. A power operation support system, which is configured by a computer having an arithmetic unit that executes predetermined arithmetic processing and a storage device connected to the arithmetic unit, wherein the storage device holds farm information indicating the locations of a plurality of farms, demand information indicating the power demands of the farms, information regarding the charging and discharging of portable batteries, and equipment information including the positions of vehicles, and an evaluation function corresponding to the peak values of the power demands in the plurality of farms, and the arithmetic unit calculates a parameter for optimizing the value of the evaluation function based on the farm information, the demand information, and the equipment information, generates an equipment operation plan corresponding to the calculated parameter, and outputs the generated equipment operation plan.

2. The power operation support system according to claim 1, wherein the arithmetic unit receives power plan information including a basic charge and a consumption-based charge according to the contract power, selects a power plan suitable for the peak value of the power demand corresponding to the calculated parameter, and outputs the selected power plan.

3. The power operation support system according to claim 1, wherein the arithmetic unit outputs an equipment configuration corresponding to the calculated parameter.

4. The power operation support system according to claim 1, wherein the arithmetic unit calculates a parameter for optimizing the value of the evaluation function in a plurality of equipment configurations near the equipment configuration corresponding to the calculated parameter, generates an equipment operation plan corresponding to the parameter calculated in relation to the nearby equipment configuration, and outputs the generated equipment operation plan.

5. The power operation support system according to claim 1, wherein the arithmetic unit receives, as an input, a reduction target value for the power demand peak in the farm, and calculates a parameter for optimizing the value of the evaluation function based on the equipment information, the farm information, and the demand information within a range where the received reduction target value is not exceeded by the power consumption.

6. The power operation support system according to claim 1, wherein the arithmetic unit outputs information regarding the cost of the facility configuration corresponding to the calculated parameter and the cost of the power demand.

7. The power operation support system according to claim 1, wherein the arithmetic unit receives power generation prediction data expressed probabilistically, and calculates a parameter for optimizing the value of the evaluation function based on the facility information, the demand information, and the farm information within the range of the received power generation prediction data.

8. A power operation support method executed by a power operation support system, wherein the power operation support system is configured by a computer having an arithmetic unit that executes predetermined arithmetic processing and a storage device connected to the arithmetic unit, the storage device holds farm information indicating the positions of a plurality of farms, demand information indicating the power demand of the farms, facility information including information regarding the charge and discharge of a portable battery and the position of a vehicle, and an evaluation function corresponding to the peak value of the power demand in the plurality of farms, the power operation support method includes the arithmetic unit calculating a parameter for optimizing the value of the evaluation function based on the farm information, the demand information, and the facility information, the arithmetic unit generating a facility operation plan corresponding to the calculated parameter, and the arithmetic unit outputting the generated facility operation plan.

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