Area design proposal system and area design proposal method

Abstract

Provided is an area design proposal system which proposes a facility arrangement and configuration of a local energy system, the area design proposal system comprising: a long-term operation optimization unit in which an operation device receives an input of parameters including energy facility information, vehicle facility information, an energy facility introduction cost, a vehicle introduction cost, and an initial cost upper limit, and outputs, by using a long-term operation algorithm, an optimal solution for the facility arrangement and configuration of the local energy system; and a short-term operation optimization unit in which the calculation device receives an input of the output of the long-term operation optimization unit and short-term environmental variation factors, and calculates, by using a short-term operation algorithm, a short-term operation evaluation result that is an evaluation result of the facility arrangement and configuration of the local energy system, wherein the long-term optimization unit receives the short-term operation evaluation result and optimizes the facility arrangement and configuration of the local energy system.

Description

[Technical field]

[0001] The present invention relates to an area design proposal system. [Background technology]

[0002] In order to ensure a safe and secure living infrastructure in each region, it is necessary to build a stable, inexpensive, low-carbon, and carbon-free energy system. The supply of renewable energy such as solar power generation fluctuates greatly. In addition, in regions where demand areas and suitable renewable energy sites are dispersed, increasing the capacity of transmission lines requires a lot of cost and construction time, making it difficult to connect new renewable energy systems to the grid. For this reason, the construction of low-carbon, carbon-free, independent energy systems that utilize a wide variety of renewable energy sources in nearby and remote regions has been proposed. For example, an energy system has been proposed in which small grids that integrate supply and demand are interconnected using EVs, etc. to adjust the energy balance between grids.

[0003] The following prior art is included as background art in this technical field. Patent Document 1 (International Publication No. 2020 / 4454) describes an energy system optimization program that causes a computer to process predetermined steps, and the steps include an input step of specifying multiple types of energy equipment that constitute an energy system, a calculation step of determining at least one of an optimal system configuration and an optimal operation pattern that minimizes a predetermined index among at least one of the system configuration and operation pattern of the energy system that satisfies a predetermined demand, and an output step of outputting at least one of the optimal system configuration and the optimal operation pattern. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2020 / 4454 Summary of the Invention [Problem to be solved by the invention]

[0005] In a regional energy system, it is necessary to determine the facility layout, configuration plan, and operation policy of multiple small grids and EVs while considering uncertainties such as weather, electricity demand, and transportation demand. To lower the cost hurdle for introduction, revenue from services other than electricity is necessary, but it is difficult to obtain an optimal facility plan including services other than electricity with conventional simulators using the Monte Carlo method. In addition, even if the electricity supply and demand and revenue in daily services (daily short-term operation) such as transporting people and cargo using EVs are optimized, the long-term electricity supply and demand and revenue may not be optimized. Therefore, it is necessary to present the optimal facility layout and optimal operation policy of small grids that are optimized including services other than electricity in long-term operation while optimizing the revenue obtained in short-term operation.

[0006] Therefore, the present invention optimizes the profits obtained from short-term operation while optimizing long-term operation. Equipment layout The purpose is to provide an area design proposal system that presents configuration plans. Means for solving the problem [Means for solving the problem]

[0007] A representative example of the invention disclosed in the present application is as follows: That is, an area design proposal system for proposing the equipment layout and configuration of a regional energy system is configured by a computer having an arithmetic unit that executes predetermined processing and a storage device connected to the arithmetic unit, the regional energy system includes a nanogrid including at least one of a power generation device and a power storage device, and a vehicle equipped with a storage battery, the area design proposal system includes a long-term operation optimization unit in which the arithmetic unit receives as input parameters including energy equipment information, vehicle equipment information, energy equipment installation cost, vehicle installation cost, and an upper limit of initial costs, and outputs an optimal solution for the equipment layout and configuration of the regional energy system using a long-term operation algorithm, and a short-term operation optimization unit in which the arithmetic unit receives as input the output of the long-term operation optimization unit and short-term environmental fluctuation factors, and calculates a short-term operation evaluation result that is an evaluation result of the equipment layout and configuration of the regional energy system, using a short-term operation algorithm; The short-term operation algorithm calculates the short-term operation evaluation result using meteorological information derived from changes in the natural environment, and transportation demand information and power demand information derived from human activities as environmental change factors; The long-term operation optimization unit includes: EV information, transportation demand information, electricity demand information, evaluation item information, and The short-term operation evaluation result is input, Calculate an electricity benefit function that represents the relationship between the total benefit and the total amount of electricity supplied per unit period, input energy facility information, weather information, and short-term simulation results, calculate an electricity cost function that represents the total cost for the total amount of electricity supplied per unit period, and use the amount of electricity supplied that maximizes the value of the electricity profit and loss function obtained by subtracting the electricity cost function from the calculated electricity benefit function. Optimize the layout and configuration of the district energy system. Effect of the Invention

[0008] According to one aspect of the present invention, the profits obtained from short-term operation are optimized while the profits obtained from long-term operation are optimized. Equipment layout The present invention provides a method for producing a semiconductor device according to an embodiment of the present invention. [Brief description of the drawings]

[0009] [Figure 1] 1 is a diagram showing a district energy system to which the present invention is applied; [Diagram 2] FIG. 1 is a diagram showing an overview of optimization of regional energy system equipment layout using the area design proposal system of this embodiment. [Diagram 3] FIG. 2 is a block diagram showing the logical configuration of the area design proposal system according to the present embodiment. [Figure 4]FIG. 1 is a diagram illustrating a physical configuration of an area design proposal system according to an embodiment of the present invention. [Diagram 5] 11 is a flowchart of an area design optimization process executed by the area design proposal system of the present embodiment. [Figure 6] FIG. 13 is a diagram illustrating the processing of a short-term calculation condition generating unit. [Figure 7] FIG. 1 is a diagram showing the flow of power and information in a district energy system. [Figure 8] FIG. 2 is a diagram illustrating the logical configuration of a short-term operation optimization unit. [Figure 9] FIG. 2 is a diagram illustrating a configuration of a batch-type optimization calculation unit. [Figure 10] 13 is a flowchart of a process executed by a batch type optimization calculation unit. [Figure 11] 13 is a flowchart of a process executed by an online optimization calculation unit. [Figure 12] 13 is a diagram illustrating the effect of a short-term operation optimization unit. FIG. [Figure 13] FIG. 13 is a diagram illustrating the processing of a long-term operation optimization unit. [Figure 14] 1A to 1C are diagrams illustrating effects achieved by the area design proposing system 100 of the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] FIG. 1 shows the present invention Applied and controlled 1 is a diagram showing a local energy system, in which solid lines indicate the travel route of an electric vehicle 30 and dotted lines indicate communication lines for transmitting information.

[0011] A control system 50, which is a power management system, is communicatively connected to a plurality of nanogrids 10.

[0012] The nanogrid 10 has a power source 11 that generates electricity, a load 12 that consumes electricity, and a power control device (not shown) that controls the amount of electricity generated and the electricity consumption. The nanogrid 10 may have a storage battery that stores electricity to fill the gap between electricity supply and demand. The power source 11 is a device that generates electricity mainly from natural energy such as solar power generation, wind power generation, and geothermal power generation. The load 12 is machinery and equipment that consumes electricity within the nanogrid 10, public institutions such as hospitals, agricultural machinery, and the like. In addition, electric vehicles 30 that run within the area also consume electricity.

[0013] The control system 50 controls a plurality of nanogrids 10 and a load. 12 The nanogrid 10 and the load 12 The power control device sends its own power supply and demand status and requests for transporting cargo or people to the control system 50. The control system 50 sends instructions to secure surplus power for charging the electric vehicle 30, and sends a transportation completion report obtained from the electric vehicle 30 to the nanogrid 10. The control system 50 also outputs the calculation results to the administrator, and receives input such as the selection of an EV operation plan from the administrator.

[0014] In an area including a nanogrid 10, at least one electric vehicle 30, which is a vehicle that runs on electricity, travels between the nanogrids 10 using charged electricity. The electric vehicle 30 is a delivery truck, an on-demand bus, a taxi, or the like that transports cargo and people within the area. The electric vehicle 30 is equipped with a rechargeable battery for running. In addition, the electric vehicle 30 is equipped with a rechargeable battery for power transport, and adjusts the power supply and demand between the nanogrids 10 by charging the battery in a nanogrid 10 where there is a surplus in power supply and demand and discharging the battery in a nanogrid 10 where there is a tight supply and demand. The battery for power transport and the battery for running may be the same battery or different batteries. The electric vehicle 30 is preferably equipped with a battery that can be quickly charged, but may also be equipped with a battery that can only be normally charged or a battery that can be easily replaced in a short time.

[0015] The electric vehicle 30 may be an electric vehicle that runs on charged electricity, but may also be a vehicle that runs on energy from an internal combustion engine or a fuel cell. In this case, the nanogrid 10 does not provide the electric vehicle 30 with power for running, but provides power to be transported between the nanogrids 10. The electric vehicle 30 does not need to be equipped with a battery for power transport. In this case, the nanogrid 10 does not provide the electric vehicle 30 with power to be transported between the nanogrids 10, but adjusts power supply and demand depending on the location where the power for running is provided.

[0016] FIG. 2 is a diagram showing an overview of the optimization of the facility layout of a local energy system by the area design proposal system 100 of this embodiment.

[0017] The area design proposal system 100 proposes a local energy system facility layout and configuration based on energy facility information 301, EV information 302, weather information 303, upper limit of initial cost 304, transportation demand information 305, power demand information 306, and evaluation item information 307. 108 Calculate.

[0018] FIG. 3 is a block diagram showing the logical configuration of the area design proposing system 100 of this embodiment.

[0019] The area design proposal system 100 has a long-term operation optimization unit 201 , a short-term calculation condition generation unit 202 , a short-term operation optimization unit 203 , and a short-term simulation result summary unit 204 .

[0020] The long-term operation optimization unit 201 calculates an optimal solution (regional energy system equipment layout and configuration 308) for the equipment layout and configuration of the regional energy system using a long-term operation algorithm based on energy equipment information 301, EV information 302, weather information 303, initial cost upper limit 304, transportation demand information 305, power demand information 306, evaluation item information 307 and short-term simulation results, and outputs the calculated optimal solution. Details of the long-term operation optimization unit 201 will be described later with reference to FIG. 13.

[0021] The short-term calculation condition generating unit 202 generates short-term calculation conditions to be used by the short-term operation optimizing unit 203, based on the weather information 303, the transportation demand information 305, and the power demand information 306. The short-term calculation condition generating unit 202 will be described in detail later with reference to FIG.

[0022] The short-term operation optimization unit 203 schedules events included in the short-term calculation conditions calculated by the short-term calculation condition generation unit 202, performs online scheduling based on the facility layout plan and individual evaluation conditions output from the long-term operation optimization unit 201, and further performs simulation using a short-term operation algorithm to output a short-term simulation result. In particular, the short-term operation optimization unit 203 calculates an operation schedule for the electric vehicle 30 and an operation for optimizing the amount of power generated by the generator of the nanogrid 10, and further simulates the operation of the generator and the operation schedule of the electric vehicle 30 so as to satisfy the power demand for human activities and agricultural work in the nanogrid 10. The short-term simulation result includes an evaluation result of a configuration plan related to the facility layout and configuration of the local energy system. The short-term operation optimization unit 203 can perform a simulation that takes into account the difference between the prediction and the actual measurement regarding the power supply and demand, and environmental fluctuations. The details of the short-term operation optimization unit 203 will be described later with reference to Figs. 7 to 12. In addition, the short-term operation optimization unit 203 can formulate the operation of the electric vehicles 30 between the nanogrids 10 as a delivery route optimization problem (Vehicle routing problem, hereinafter referred to as VRP), and may use a VRP (Vehicle routing problem) solver for the optimization process.

[0023] The short-term simulation result summarizing unit 204 generates data that scores the short-term operation evaluation results calculated by the short-term operation optimization unit 203. This data may use the output suppression amount of photovoltaic power generation or wind power generation, the storage battery consumption rate estimated from the number of times the storage battery is charged and discharged, and the transportation amount supplied by the operation of an electric vehicle.

[0024] When the short-term operation optimization unit 203 performs simulations under multiple conditions, the simulations may be calculated in parallel. In this case, the short-term calculation condition generation unit 202 can generate multiple calculation conditions at once, and The short-term operation optimization unit 203 is A probabilistic evaluation can be made by statistically processing multiple short-term performance evaluation results.

[0025] As described above, the short-term operation optimization unit 203 simulates the operation of the nanogrid 10 and the operation of the electric vehicle 30 through optimization calculations, and the long-term operation optimization unit 201 has the function of improving the equipment layout and configuration plan of the local energy system using a long-term operation algorithm based on the simulation results by the short-term operation optimization unit 203 and the summary results by the short-term simulation result summary unit 204.

[0026] The energy equipment information 301 is information on equipment installed in the local energy system (e.g., the nanogrid 10), and includes the type and specifications of the power generation equipment. The EV information 302 is information on the electric vehicles 30, and includes vehicle specifications, the number of vehicles in operation, and operation policies. The weather information 303 is the weather (amount of sunshine, temperature) of the area. The initial cost upper limit 304 is the upper limit of the initial cost for expanding the energy equipment. The transportation demand information 305 is the actual results and predictions of transportation orders generated in the area, and for example, when the occurrence probability of transportation is given for each time and each location, information on transportation that is expected to occur is generated. The power demand information 306 is the power demand consumed in the area, the amount of power generated, and the amount of power purchased from the system. The evaluation item information 307 is the conditions for determining the optimal solution for the equipment layout and configuration of the local energy system.

[0027] FIG. 4 is a diagram showing the physical configuration of the area design proposing system 100 of this embodiment.

[0028] The area design proposing system 100 of this embodiment is configured by a computer having a processor (CPU) 1, a memory 2, an auxiliary storage device 3, and a communication interface 4.

[0029] The processor 1 executes a program stored in the memory 2. Note that a part of the processing performed by the processor 1 executing the program may be executed by another arithmetic device (for example, a hardware arithmetic device such as an FPGA or an ASIC).

[0030] The memory 2 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) and the like. 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 1 and data used when the programs are executed.

[0031] The auxiliary storage device 3 is a large-capacity non-volatile storage device such as a magnetic storage device (HDD) or a flash memory (SSD), and stores the programs executed by the processor 1 and data used when the programs are executed. That is, the programs are read from the auxiliary storage device 3, loaded into the memory 2, and executed by the processor 1.

[0032] The communication interface 4 is a network interface device that controls communication with other devices (such as the power control devices in the nanogrid 10) according to a predetermined protocol.

[0033] The area design proposal system 100 may have an input interface 5 and an output interface 8. The input interface 5 is an interface to which a keyboard 6, a mouse 7, etc. are connected and which receives input from an operator. The output interface 8 is an interface to which a display device 9, a printer, etc. are connected and which outputs the results of program execution in a format that can be viewed by the operator. Note that the input interface 5 and the output interface 8 may be provided by a terminal connected to the area design proposal system 100 via a network.

[0034] The programs executed by the processor 1 are provided to the area design proposal system 100 from removable media (CD-ROM, flash memory, etc.) or via a network, and are stored in a non-volatile auxiliary storage device 3, which is a non-transitory storage medium. For this reason, it is preferable that the area design proposal system 100 has an interface for reading data from removable media.

[0035] The area design proposal system 100 is a computer system configured on one physical computer, or on multiple logically or physically configured computers, and may operate on one or multiple threads on the same computer, or may operate on a virtual computer constructed on multiple physical computer resources. Each part of the area design proposal system 100 may operate on a different computer.

[0036] FIG. 5 is a flowchart of the area design optimization process executed by the area design proposing system 100 of this embodiment.

[0037] First, the area design proposal system 100 accepts input of simulation conditions (S301). The input simulation conditions include energy facility information 301, EV information 302, weather information 303, upper limit of initial cost 304, transportation demand information 305, power demand information 306, and evaluation item information 307.

[0038] Next, the short-term calculation condition generating unit 202 generates short-term calculation conditions from the input simulation conditions, such as meteorological information 303, transportation demand information 305, and power demand information 306. The area design proposing system 100 of this embodiment: Figure 3 3, the long-term operation optimization unit 201 and the short-term operation optimization unit 203 perform repeated simulations, and the short-term calculation conditions generated by the short-term calculation condition generation unit 202 are used as the initial placement plan for the repeated simulations (S302).

[0039] Next, the short-term calculation condition generation unit 202 calculates conditions for performing an individual short-term operation evaluation based on the created initial layout plan or the equipment layout and configuration of the regional energy system created by the long-term operation optimization unit 201 (S303).

[0040] Then, the process of steps S304 to S306 is executed for each short-term operation evaluation condition. In the loop, first, the short-term operation optimization unit 203 calculates short-term calculation conditions that take fluctuations into account (S304). Next, the short-term operation optimization unit 203 performs a simulation using a short-term operation algorithm based on the short-term calculation conditions generated by the short-term calculation condition generation unit 202, generates short-term simulation results (S305), and calculates a short-term operation evaluation value from the generated short-term simulation results (S306).

[0041] After the evaluation values ​​for each short-term operation evaluation condition are calculated, the long-term operation optimization unit 201 calculates the equipment layout and configuration of the regional energy system using a long-term operation algorithm, and calculates a long-term operation evaluation value that indicates the evaluation of the calculated equipment layout and configuration of the regional energy system (S307).

[0042] After that, the long-term operation optimization unit 201 judges whether the calculation end condition is satisfied (S308). If the calculation end condition is not satisfied, the process returns to step S303, and further searches for the equipment layout and configuration of the district energy system with the highest long-term operation evaluation value. On the other hand, if the calculation end condition is satisfied, the equipment layout and configuration of the district energy system with the highest long-term operation evaluation value is output (S309).

[0043] Next, the processing of the short-term operation optimization unit 203 in the area design proposal system 100 will be described.

[0044] FIG. 6 is a diagram showing the process of the short-term calculation condition generation unit 202 that provides simulation conditions to the short-term operation optimization unit 203.

[0045] For example, when performing a short-term simulation using discrete time steps, the short-term calculation condition generating unit 202 generates simulation input parameters such as power supply and demand information at each step in a facility layout plan (initial layout plan or equipment layout and configuration of a regional energy system) from meteorological information 303 and power demand information 306 given with occurrence probability. More specifically, the short-term calculation condition generating unit 202 generates pairs of predicted and actual values ​​of power supply and demand for each step and for each nanogrid from given information.

[0046] In addition, the short-term calculation condition generating unit 202 determines at what step a transportation order will occur and from where to where, based on the transportation demand information 305. For example, when the occurrence probability of transportation is given for each time and each location, information on transportation (time and departure and arrival points) that is expected to occur is generated.

[0047] FIG. 7 is a diagram showing the flow of electricity and information in a local energy system, in which solid lines indicate electricity transmission and distribution lines, dotted lines indicate the flow of information, and dashed lines indicate the transportation of electricity, cargo, and people by electric vehicles 30.

[0048] The nanogrid 10 is, in principle, self-sufficient in electricity by balancing the supply and demand of electricity within the nanogrid 10, but is connected to a power grid 40 so that it can receive electricity from the power grid 40 of a power transmission and distribution company. A switch 15 is provided between the power grid 40 and the nanogrid 10, and when the switch 15 is closed, electricity can be supplied from the power grid 40 to the nanogrid 10. The nanogrid 10 includes a DC nanogrid that supplies DC power and an AC nanogrid that supplies AC power. In the DC nanogrid, an inverter 16 that converts AC power supplied from the power grid 40 to DC power is provided between the power grid 40 and the nanogrid.

[0049] As described above, the electric vehicle 30 travels between the nanogrids 10 to transport cargo and people, and transports power from a nanogrid 10 with a surplus in power supply and demand to a nanogrid 10 with a tight power supply and demand. The electric vehicle 30 is communicatively connected to the control system 50, receives instructions for movement, charging and discharging, etc., and sends reports on the start of transportation, completion of transportation, start of charging, start of discharging, amount of stored power, etc. Examples of instructions to the electric vehicle 30 include the following. ·stay: Remains in place without moving. The electric power stored in the electric vehicle 30 is not consumed, but during this time, the electric vehicle 30 can function as a storage battery for the nanogrid 10. pickup: The person or cargo to be transported is loaded onto the electric vehicle 30. The stored electricity of the electric vehicle 30 is not consumed. · move: move toward a destination (e.g., the nanogrid 10 or another destination). The amount of stored electricity in the electric vehicle 30 at the next time decreases by Δmove. · charge_from_grid: Charges the electric vehicle 30 by Δcharge in the nanogrid 10. A plurality of electric vehicles 30 can be charged simultaneously from the same nanogrid 10. The amount of stored electricity in the electric vehicle 30 after charging increases by Δcharge. · charge_to_grid: Discharges Δcharge from the electric vehicle 30 in the nanogrid 10. A plurality of electric vehicles 30 can simultaneously discharge to the same nanogrid 10. However, the amount of stored electricity in the electric vehicle 30 after discharging decreases by Δcharge.

[0050] load 12 is a facility that consumes power (for example, a factory or a general home) and does not have a power source 11. 12 A power control device similar to that of the nanogrid 10 may be provided in addition.

[0051] FIG. 8 is a diagram showing the logical configuration of the short-term operation optimization unit 203. As shown in FIG.

[0052] The short-term operation optimization unit 203 has a batch type optimization calculation unit 110 that performs multi-objective optimization over a relatively long period of time, an online type optimization calculation unit 120 that performs dynamic optimization, an input unit 130 to which data to be used for processing is input, and an output unit 140 that outputs the processing results.

[0053] The batch type optimization calculation unit 110 is started, for example, once a day, and creates a tentative EV operation plan for one day that optimizes value indexes (particularly power supply value and transportation service value) described below using a multi-objective optimization method such as Pareto optimization based on predicted values ​​and past data of power generation, power demand, transportation, and weather. The tentative EV operation plan created by the batch type optimization calculation unit 110 includes provisional instructions to the electric vehicles 30 for loading and moving cargo and people, and charging and discharging in the nanogrid 10. The calculation results by the batch type optimization calculation unit 110 are output to the administrator via the output unit 140, and are output to the online optimization calculation unit 120 together with the administrator's selection.

[0054] The online optimization calculation unit 120 uses a multi-objective optimization method such as Pareto optimization in a short time span (for example, every 10 minutes) to optimize value indexes (particularly power supply value and transportation service value) described later based on actual measured values ​​from the power control device of the nanogrid 10 and the electric vehicle 30 to correct the EV operation plan. That is, the online optimization calculation unit 120 creates a new operation plan every predetermined time (for example, every 10 minutes) when a deviation occurs between the provisional operation plan and the operation record, a deviation occurs between the provisional operation plan and the operation forecast based on the transportation request, a deviation occurs between the power supply and demand forecast and the power supply and demand record, or a deviation occurs between the power supply and demand forecast and the power supply and demand forecast value based on the power supply and demand record. By correcting the EV operation plan, the power consumption and power transportation amount of the electric vehicle 30 change, and the power supply and demand in each nanogrid 10 can be controlled.

[0055] The input unit 130 receives input (selection of a tentative operation plan or a selection policy) from the administrator of the area design proposal system 100, and sends it to the batch type optimization calculation unit 110 or the online type optimization calculation unit 120. The output unit 140 outputs the calculation results by the batch type optimization calculation unit 110 and the online type optimization calculation unit 120.

[0056] The output of the short-term operation optimization unit 203 differs in that the batch type optimization calculation unit 110 outputs predicted values ​​and plans, while the online type optimization calculation unit 120 outputs actual values ​​and performance values, but it outputs the following items. (1) Provisional EV operation plan or electric vehicle operation record · Electric car 30 transportation Transportation by electric vehicles 30 (origin, destination, volume, type of transportation (people, cargo)) Charging and discharging of electric vehicles 30 (charging location, charging amount, discharging location, discharging amount) 30 electric vehicle shipments Power consumption of electric vehicles 30 - Multiple tentative EV operation plans that can be selected by the administrator may be output, or a recommended tentative EV operation plan may be output by ranking them according to a policy specified by the administrator. (2) Changes over time in surplus power allocated to transportation (3) Amount of charge from nanogrid 10 and amount of discharge to nanogrid 10 (4) Value index generated by electricity supply and transportation (4-1) Electricity supply value - Reduction in electricity costs through reduction in the amount of electricity purchased from the power grid 40 and an increase in the proportion of natural energy generation -Increased business profits due to reduced opportunity losses resulting from a stable supply of electricity - Stabilizing power supply by securing storage capacity of Nanogrid 10 (4-2) Value of transportation services - Reduction of transportation waiting time (time from transportation request to transportation start) by optimizing EV temporary operation plans and electric vehicle deployment - Reduction of transportation costs by using surplus electricity from Nanogrid 10 at low cost -Reduction of power consumption by optimizing driving routes (4-3) Other values -Reduction of environmental impact by reducing carbon dioxide emissions - As people move around more, the economy will be revitalized through increased store sales.

[0057] FIG. 9 is a diagram showing the configuration of the batch type optimization calculation unit 110, and FIG. 10 is a flowchart of the process executed by the batch type optimization calculation unit 110. As shown in FIG.

[0058] The batch type optimization calculation unit 110 includes a traffic schedule initialization unit 111 , a traffic schedule update unit 112 , an EV / transport / electricity simulation unit 113 , a score calculation unit 114 , and an annealing temperature scheduling unit 115 .

[0059] The operation schedule initialization unit 111 creates an initial value of the EV operation plan based on the input power generation plan, power demand forecast, and transportation plan. The operation schedule update unit 112 updates the EV operation plan using the simulated annealing method. The EV / transport / power simulation unit 113 executes a simulation based on the updated EV operation plan, and estimates the movement / charging / discharging log of the electric vehicle 30 and the power supply / demand log of the nanogrid 10. The score calculation unit 114 calculates various value indexes based on the results of the simulation. The annealing temperature scheduling unit 115 updates the annealing temperature T used in the simulated annealing method, controls the repeated calculation, and outputs the final electric vehicle operation instructions.

[0060] In the batch type optimization calculation unit 110, as shown in Fig. 10, the operation schedule initialization unit 111 receives input of a power generation plan, a power demand forecast, and a transportation plan (S101). The power generation plan and the power demand forecast are input from the power control device of the nanogrid 10. The transportation plan includes data such as the type of transportation (people, cargo), the occurrence location (start point, arrival point), and the occurrence probability, and can be obtained from a shipper who sends cargo, a delivery company who transports cargo by electric vehicle 30, or an operation company who transports people by electric vehicle 30 (EV bus, EV taxi). The operation schedule initialization unit 111 may receive input of a policy in the nanogrid 10, such as a target value for the balance between power and transportation, as necessary.

[0061] Thereafter, the operation schedule initialization unit 111 initializes the temperature T used in the simulated annealing method to a large temperature (S102), and creates an initial value for the EV operation plan (S103).

[0062] Next, the operation schedule update unit 112 updates the EV operation plan by transitioning the EV operation plan (initial value) created by the operation schedule initialization unit 111 to a neighborhood in the simulated annealing method (S104). The neighborhood refers to a close solution, for example, an operation plan in which the operation of the electric vehicle 30 is slightly changed.

[0063] Next, the EV / transport / power simulation unit 113 operates the electric vehicle 30 in the simulator based on the EV operation plan updated by the operation schedule update unit 112, and simulates the real world, such as the operation of the nanogrid 10 and transportation by the electric vehicle 30 (S105).

[0064] Next, the score calculation unit 114 calculates the electricity, transportation value, and cost from the transportation log, charge / discharge log, and electricity supply and demand log of the nanogrid 10 (such as changes in the amount of stored electricity) of the electric vehicle 30 that are the results of the simulation by the EV / transport / electricity simulation unit 113.

[0065] The annealing temperature scheduling unit 115 lowers the annealing temperature T (S107) and determines whether the annealing temperature T is smaller than the minimum temperature Tmin (S108). If the annealing temperature T is equal to or greater than the minimum temperature Tmin, the process returns to step S104 and continues processing at the updated annealing temperature T. On the other hand, if the annealing temperature T is smaller than the minimum temperature Tmin, the process ends the repeated calculation and outputs the result to the user (S109). The calculation result by the batch type optimization calculation unit 110 is input to the online type optimization calculation unit 120 together with the user's selection.

[0066] Although an example in which the batch type optimization calculation unit 110 uses simulated annealing has been described, other methods for finding an optimal solution may also be used.

[0067] The batch type optimization calculation unit 110 may rank the multiple tentative operation plans according to a policy input by the manager. Furthermore, the output unit 140 may output the tentative operation plans according to the rank.

[0068] Next, a description will be given of the online optimization calculation unit 120. The online optimization calculation unit 120 can use simulated annealing and can be configured in the same manner as the batch optimization calculation unit 110 using the simulated annealing described in Fig. 9, so a description of the configuration will be omitted.

[0069] FIG. 11 is a flowchart of the process executed by the online optimization calculation unit 120.

[0070] First, the online optimization calculation unit 120 receives input of the administrator's selection of the interim EV operation plan along with the calculation results by the batch optimization calculation unit 110 (the interim EV operation plan, the predicted time course of surplus power value, the predicted amount of discharge to the nanogrid 10, the predicted value of value, etc.) (S111).

[0071] Next, the online optimization calculation unit 120 initializes time t to 0 (S112) and receives input of data at time t (S113). The data input to the online optimization calculation unit 120 includes the transportation performance at time t, the actual measured values ​​of power generation and power consumption, the transportation plan after time t, and the revised values ​​of the predicted power generation and power consumption.

[0072] Next, the online optimization calculation unit 120 updates the EV operation plan (transportation, charging and discharging) based on the input data at time t (S114).

[0073] Next, the online optimization calculation unit 120 instructs the electric vehicle 30 of the updated EV operation plan (S115).

[0074] Next, the online optimization calculation unit 120 receives the transportation results (such as the departure point, arrival point, transportation volume, type of transportation (people or cargo), charging / discharging locations, and charging / discharging volume, contained in the transportation log) and the status log (position, amount of stored electricity, etc.) from the electric vehicle 30 (S116).

[0075] Next, the online optimization calculation unit 120 increments the time t by one step to the next time (S117), and determines whether the time t is equal to or greater than the final time tlast (S118). If the time t is smaller than the final time tlast, the process returns to step S113 and continues the calculation.

[0076] On the other hand, if the time t is equal to or greater than the final time tlast, the calculation result (log) is output (S119). The output log includes the transportation result (transport log including departure point, arrival point, transportation volume, transportation type (person or cargo), charging / discharging location, and charging / discharging volume) and the status log (position, charging volume). Note that not only the final result but also the log at time t may be output.

[0077] Next, the online optimization calculation unit 120 calculates various value indexes from the final results based on the transportation records and actual measurements, and outputs the calculation results of the value indexes (S120).

[0078] FIG. 12 is a diagram for explaining the effect of the short-term operation optimization unit 203. In FIG.

[0079] 12, in the EV operation plan created by the short-term operation optimization unit 203, when the transportation service value improves, the electricity supply value decreases, and when the electricity supply value improves, the transportation service value decreases. This is because, for example, when the transportation service value is improved by increasing the number of operations to reduce waiting time or by increasing the transportation speed, energy consumption increases.

[0080] As described in Fig. 10, the batch type optimization calculation unit 110 creates and outputs multiple EV operation plans in which the power supply value and the transportation service value are optimized. For example, a diagram showing the degree of value as shown in Fig. 12 may be displayed on the screen to prompt the manager to select from multiple EV operation plans. On this screen, when a display portion of the EV operation plan is selected, the details of the selected EV operation plan may be displayed.

[0081] Furthermore, if an unexpected event such as a sudden change in weather occurs and the amount of power generation decreases, the scheduled operation of the electric vehicle 30 and the operation of the equipment will be stopped, resulting in a decrease in the power supply value and the transportation service value. However, the online optimization calculation unit 120 can suppress the degree of decrease in the power supply value and the transportation service value by optimizing the operation plan according to the current situation.

[0082] The EV operation plan created by the control system 50 described above can be provided, for example, for the movement of farm crops. That is, when a farmer operates a regular service of electric vehicle trucks to ship crops or transport them to a processing plant, the amount of electricity consumed and generated by agricultural work changes depending on the weather. For this reason, the frequency of regular services is adjusted according to the cause of the change, and the number of electric vehicles 30 (EV trucks) allocated for power storage purposes in the nanogrid 10 is optimized. This makes it possible to increase the value of local communities through multi-objective optimization of energy and transportation services in areas with weak transportation networks.

[0083] Next, a description will be given of the processing performed by the long-term operation optimization unit 201. FIG.

[0084] The long-term operation optimization unit 201 calculates an electricity benefit function based on the EV information 302, transportation demand information 305, electricity demand information 306, evaluation item information 307, and the short-term simulation results. The electricity benefit function is a function that expresses the relationship between the total benefit and the total amount of electricity supplied per unit period. The benefit may be directly expressed in monetary terms, or a scale corrected in consideration of social and environmental utility may be used. The user can control the relationship between the amount of electricity supplied and the benefit in the electricity benefit function by the evaluation conditions set in the evaluation item information 307.

[0085] The power benefit function can be calculated, for example, in the following manner. 1) Calculate the total power demand per unit period from the power demand information 306. 2) Up to the calculated total electricity demand, the benefit of electricity supply is expressed as a linear function with the electricity price as the proportional coefficient (slope). The electricity price may refer to the price set by the wholesale electricity market or a specific retail electricity supplier, or may be set independently within the system. 3) The average transport distance per transport order is calculated from the transport demand information 305, and the average power consumption per transport order is calculated by multiplying the average power consumption rate from the EV information 302. In addition, the required number of electric vehicles 30 is calculated based on the average speed of a given electric vehicle 30, the number of transport orders, and the average transport distance. 4) Calculate the total electricity demand required for transportation per unit period by multiplying the number of transportation orders expected within the unit period by the electricity consumption calculated in section 3. 5) From the total electricity demand calculated in Item 1 to the total electricity demand required for transportation calculated in Item 4, the benefit of electricity supply is expressed as a linear function with the electricity price as the proportionality coefficient (slope). This proportionality coefficient may be the same as the coefficient determined in Item 2, or may be determined separately.

[0086] Instead of the power benefit function, a list of pairs of power amounts and benefits may be created, and the benefits may be allocated in descending order of the benefit per power amount.

[0087] Furthermore, the long-term operation optimization unit 201 calculates an electric power cost function based on the energy facility information 301, the weather information 303, and the short-term simulation results. The electric power cost function is a function that expresses the total cost relative to the total amount of electric power supplied per unit period. The benefit may be directly described in monetary terms, or a scale corrected in consideration of social and environmental utility may be used. The user can control the relationship between the amount of electric power supplied and the cost in the electric power cost function by the evaluation conditions set in the evaluation item information 307.

[0088] The power cost function can be calculated, for example, in the following manner. 1) The total amount of power generation per unit period is calculated from the amount of solar radiation and wind force in the meteorological information 303. 2) The information on each product in the energy equipment information 301 is referenced, and the cost for the amount of power that can be supplied is calculated. 3) The costs for the available amount of power supply calculated in section 2 are accumulated in ascending order of cost per unit of power to create a power cost function.

[0089] In addition, the long-term operation optimization unit 201 creates an electric power profit and loss function by subtracting the electric power cost function from the electric power benefit function, and sets the supply amount of electric power at which the value of the electric power profit and loss function is maximized as the reference supply amount of electric power. When creating an electric power cost function using the above-mentioned method, the contents of the energy facilities to be arranged in the system can be calculated by introducing the facilities in order of lowest cost per amount of electric power.

[0090] As mentioned above Standard power supply amount In order to calculate a configuration for arranging facilities for supplying the above-mentioned energy in the local energy system, a passing frequency prediction is created using the transportation demand information 305. Specifically, the occurrence frequency of each transportation route estimated from the predicted value of the transportation demand information 305 is added at the node on the shortest route to obtain a passing frequency prediction value.

[0091] In addition, the long-term operation optimization unit 201 calculates a facility layout plan from the power profit and loss function and the predicted value of the passing frequency. It is preferable to calculate the cost (expenses, area, etc.) required for new installation of the facilities together with the facility layout and configuration of the local energy system.

[0092] The facility layout plan can be calculated, for example, in the following way. 1) Facilities are arranged to cover the power demand in the power demand area described in the power demand information 306 within the range of the standard power supply amount and the upper limit of the initial cost 304. Any surplus or shortage of power demand is calculated based on the integrated value during the unit period. 2) If the standard supply of electric power cannot be met even if facilities are placed in the electric power demand area, facilities to make up the shortage of supply capacity are placed in nodes with high predicted passing frequency values ​​within the upper limit of initial cost 304. 3) Electric vehicles 30 are deployed within the upper limit of the initial cost 304 in an amount sufficient to cover the transportation demand estimated from the transportation demand information 305 .

[0093] Furthermore, the long-term operation optimization unit 201 judges whether the calculation end condition is satisfied. If the calculation end condition is satisfied, the calculated facility layout plan is output as the regional energy system equipment layout and configuration. On the other hand, if the calculation end condition is not satisfied, the calculated Facility layout plan is output to the short-term calculation condition generating unit 202, and a short-term simulation is performed.

[0094] For example, the calculation end condition may be set based on the calculation resources. For example, it may be determined that the calculation is to end when the CPU time used in the simulation or the number of simulations reaches an upper limit. In addition, the calculation end condition may be that the evaluation value of the facility layout plan has reached a preset target value, that the evaluation value has converged, or the like.

[0095] Then, the power benefit function, the power cost function, and the passing frequency prediction are updated based on the obtained short-term simulation results, and a facility layout plan is further created. For example, the power benefit function is updated using the transportation demand included in the short-term simulation results, the power cost function is updated using the facility utilization rate included in the short-term simulation results, and the passing frequency prediction value is updated using the number of passes in the vehicle movement log included in the short-term simulation results.

[0096] The power benefit function, the power cost function, and the transit frequency forecast may be updated by inheriting the previous forecast values, as follows: New forecast value = (Current short-term simulation result forecast value + Previous forecast value) ÷ 2

[0097] FIG. 14 is a diagram showing the effect of the area design proposing system 100 of this embodiment.

[0098] FIG. 14(A) shows the power supply and demand optimized by a short-term simulation and the operation rate of the electric vehicle 30 before the application of the area design proposal system 100. Since optimization by a short-term simulation does not take into account long-term optimization, in terms of power demand, a surplus of power is expected in July, and it will be necessary to sell the power in futures. Also, a shortage of power to be used in harvesting is expected around October, and it will be necessary to distribute and store the power. Also, in terms of the operation of the electric vehicle 30, a decrease in the operation rate of the electric vehicle 30 is expected in February, and it will be possible to send it elsewhere for support. Also, an increase in the operation rate of the electric vehicle 30 is expected in August, and support from elsewhere will be necessary.

[0099] FIG. 14(B) shows the power supply and demand and the operation rate of the electric vehicle 30 optimized by a long-term simulation after the application of the area design proposal system 100. By optimizing by a long-term simulation, the surplus power demand in July can be sold and profits can be increased. In addition, work stagnation due to a power shortage in October can be avoided. Furthermore, the operation rate of the electric vehicle 30 in February can be improved.

[0100] Fig. 14(C) shows the difference in profits when only a short-term simulation is performed and when a long-term simulation is applied. When only a short-term simulation is performed, the profits from the operation of the electric vehicles 30 and power generation facilities alone are small, and opportunities to increase profits are missed. However, when a long-term simulation is performed, the layout and configuration of the local energy system facilities can be optimized, and the waste of surplus electricity and the number of electric vehicles 30 with low operation can be reduced, improving profits.

[0101] As described above, the area design proposal system 100 according to the embodiment of the present invention can optimize the local energy system according to the evaluation of short-term operation according to the equipment layout and configuration, and can optimize the detailed system including environmental changes. Furthermore, users can evaluate the impacts that are difficult to predict in advance at the planning and change stage of the equipment layout and configuration, thereby reducing business risks.

[0102] The present invention is not limited to the above-described embodiments, and includes various modified examples and equivalent configurations within the spirit 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 those including all of the configurations described. Furthermore, a 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, a part of the configuration of each embodiment may be added, deleted, or replaced with another configuration.

[0103] Furthermore, each of the aforementioned configurations, functions, processing units, processing means, etc. may be realized in hardware, for example by designing some or all of them as an integrated circuit, or may be realized in software by a processor interpreting and executing a program that realizes each function.

[0104] Information such as programs, tables, and files that realize each function can be stored in a storage device such as a memory, a hard disk, or an SSD (Solid State Drive), or in a recording medium such as an IC card, an SD card, or a DVD.

[0105] In addition, the control lines and information lines shown are those considered necessary for the explanation, and do not necessarily show all the control lines and information lines necessary for implementation. In reality, it can be considered that almost all components are connected to each other.

Claims

1. An area design proposal system that proposes the layout and configuration of facilities for a regional energy system, The computer includes an arithmetic unit that executes a predetermined process and a storage device that is connected to the arithmetic unit, The district energy system includes a nanogrid including at least one of a power generation device and a power storage device, and a vehicle equipped with a storage battery; The area design proposal system includes: The calculation device receives parameters including energy equipment information, vehicle equipment information, energy equipment installation cost, vehicle installation cost, and an upper limit of initial costs, and uses a long-term operation algorithm to output an optimal solution for the equipment layout and configuration of the regional energy system; and The arithmetic device has a short-term operation optimization unit that inputs the output of the long-term operation optimization unit and short-term environmental fluctuation factors and calculates a short-term operation evaluation result, which is an evaluation result of the equipment layout and configuration of the regional energy system, using a short-term operation algorithm; The short-term operation algorithm calculates the short-term operation evaluation result using meteorological information derived from changes in the natural environment, and transportation demand information and power demand information derived from human activities as environmental change factors; The long-term operation optimization unit includes: Calculating an electricity benefit function that represents the relationship between the total benefit and the total amount of electricity supply per unit period using the EV information, transportation demand information, electricity demand information, evaluation item information, and the short-term operation evaluation result as input; Energy facility information, weather information, and short-term simulation results are input to calculate an electricity cost function that represents the total cost for the total amount of electricity supplied per unit period. An area design proposal system characterized by optimizing the equipment layout and configuration of the regional energy system using the amount of supplied electricity that maximizes the value of the electricity profit and loss function, which is obtained by subtracting the electricity cost function from the calculated electricity benefit function.

2. The area design proposal system according to claim 1, An area design proposal system, characterized in that each of the meteorological information, the transportation demand information, and the electricity demand information is expressed as a probability of an event.

3. The area design proposal system according to claim 1, The area design proposal system, characterized in that the calculation device has a short-term simulation result summary unit that thins out the short-term operation evaluation results over time and generates data of the granularity required by the long-term operation optimization unit.

4. The area design proposal system according to claim 1, The short-term operation optimization unit calculates to optimize vehicle operation plans and plans power generation and vehicle operation to meet electricity demand for agricultural work, characterized by this area design proposal system.

5. The area design proposal system according to claim 1, The area design proposal system is characterized in that the short-term operation optimization unit processes vehicle operations between the nanogrids as a delivery route optimization problem.

6. The area design proposal system according to claim 1, The energy equipment information includes the type and specifications of the power generation equipment, The area design proposal system is characterized in that the vehicle equipment information includes vehicle specifications.

7. The area design proposal system according to claim 1, The area design proposal system is characterized in that the long-term operation optimization unit outputs the necessary costs along with the equipment layout and configuration of the regional energy system.

8. The area design proposal system according to claim 1, The area design proposal system is characterized in that the short-term operation optimization unit performs calculation processing in parallel under multiple conditions.

9. An area design proposal method in which an area design proposal system proposes equipment layout and configuration of a regional energy system, the area design proposal system is configured by a computer having an arithmetic unit that executes a predetermined process and a storage device connected to the arithmetic unit; The district energy system includes a nanogrid including at least one of a power generation device and a power storage device, and a vehicle equipped with a storage battery; The area design proposal method includes: A long-term operation optimization procedure in which the calculation device inputs parameters including energy equipment information, vehicle equipment information, energy equipment installation cost, vehicle installation cost, and initial cost upper limit amount, and outputs an optimal solution for the equipment layout and configuration of the local energy system using a long-term operation algorithm; The calculation device includes a short-term operation optimization procedure in which the output of the long-term operation optimization procedure and short-term environmental fluctuation factors are input, and a short-term operation algorithm is used to calculate a short-term operation evaluation result, which is an evaluation result of the equipment layout and configuration of the regional energy system; The short-term operation algorithm calculates the short-term operation evaluation result using meteorological information derived from changes in the natural environment, and transportation demand information and power demand information derived from human activities as environmental change factors; This area design proposal method is characterized in that, in the long-term operation optimization procedure, the calculation device inputs EV information, transportation demand information, electricity demand information, evaluation item information, and the short-term operation evaluation results, calculates an electricity benefit function that represents the relationship between the total benefit and the total amount of electricity supplied per unit period, inputs energy equipment information, weather information, and the short-term simulation results, calculates an electricity cost function that represents the total cost for the total amount of electricity supplied per unit period, and optimizes the equipment layout and configuration of the regional energy system using the amount of electricity supply that maximizes the value of the electricity profit and loss function obtained by subtracting the electricity cost function from the calculated electricity benefit function.

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