JP6538428B2 - Charging facility operation support device, charging facility operation support program, and charging system - Google Patents

Description

  Embodiments of the present invention relate to a charging facility operation support device, a charging facility operation support program, and a charging system.

  If the contract power negotiated between the electric power company and the customer fluctuates, the operation plan of the power equipment in the customer and the charge / discharge plan of the storage battery are formulated, and the technology to operate each equipment based on the formulated plan It has been known. According to this technology, the amount of power consumed in the customer is adjusted so as not to deviate from the contract power. However, in the prior art, the degree of freedom regarding the content of control to be obtained as the optimum solution is low, and it may not be possible to obtain a solution suitable for the purpose of the user.

JP 2011-83165 A

  The problem to be solved by the present invention is to provide a charging facility operation support device, a charging facility operation support program, and a charging system capable of obtaining an operation schedule suitable for the purpose of the user.

  The charging facility operation support device of the embodiment includes a generation unit, a change processing unit, an evaluation unit, and an extraction unit. The generation unit generates a plurality of codes in which information on an operation schedule of a charger included in the charging facility, information on a storage battery included in the charging facility, and information on contract power is encoded. The change processing unit performs probabilistic processing on the plurality of codes generated by the generation unit to change the codes stochastically. The evaluation unit derives an evaluation function with the cost of the charging facility as one of the evaluation targets for the code that has been processed by the change processing unit. The extraction unit extracts a code for which the evaluation function derived by the evaluation unit is a suitable value from among the codes subjected to processing by the change processing unit.

BRIEF DESCRIPTION OF THE DRAWINGS The figure which shows an example of a structure of the charging system 1 containing the charging equipment operation assistance apparatus 100 in 1st Embodiment. BRIEF DESCRIPTION OF THE DRAWINGS The figure which shows an example of a structure of the charging equipment operation assistance apparatus 100 in 1st Embodiment. 6 is a flowchart showing an example of the flow of processing of the charging facility operation support device 100 in the first embodiment. FIG. 7 is a view showing an example of a solution x _i generated by the generation unit 112 in the first embodiment. The figure for demonstrating an example of the production | generation method of charging / discharging plan SKD. The figure which shows the usage example of the charger 16 of 1st. The figure for demonstrating an example of the method of ranking to solution x _i using a Pareto ranking method. Diagram showing an example of a case where the amount received power exceeds the contract power P _C. The figure which shows an example in case charging of the secondary battery of the route bus B is not complete | finished by the departure scheduled time. Diagram showing an example of the concept of roulette for extracting the solution leaving the solution of the current generation G _i to a next-generation G _{i + 1.} It illustrates an example of the solution _{x i} of the current generation _{G i.} It illustrates an example of a Pareto optimal solution, which is extracted before generation G _i-1 from the initial generation G _1. Shows the solution x _i of the current generation G _i, an example of the solution group sum of the Pareto optimal solution which is extracted before generation G _i-1 from the initial generation G _1. Figure from solution group shown in FIG. 13 shows an example of a specified rank r _{ref (x i)} = ₁ result of extracting the following solutions. The figure which shows an example of the Pareto optimal solution extracted after reaching predetermined _| prescribed generation Gmax. The figure for demonstrating an example of the crossover process by the change process part 124. FIG. The figure for demonstrating an example of the mutation process by the change process part 124. FIG. 6 is a flowchart showing an example of the flow of crossover processing performed by the change processing unit 124 in the first embodiment; 6 is a flowchart showing an example of the flow of mutation processing performed by the change processing unit 124 in the first embodiment. The figure which shows an example at the time of setting the target investment recovery period with respect to the Pareto optimal solution when generation G _i reaches predetermined generation G _max . The figure showing the change over time transition of profit obtained when using two kinds of charge-and-discharge plans SKD. The figure which shows an example of a structure of the charging system 1 containing the charging equipment operation assistance apparatus 100 in 4th Embodiment.

  Hereinafter, a charging facility operation support device, a charging facility operation support program, and a charging system according to the embodiment will be described with reference to the drawings.

First Embodiment
FIG. 1 is a diagram showing an example of a configuration of a charging system 1 including a charging facility operation support device 100 in the first embodiment. The charging system 1 in the present embodiment charges, for example, a secondary battery mounted on an electric vehicle (EV; Electric Vehicle) such as an electric bus. Secondary batteries include, for example, lithium ion batteries, lead storage batteries, sodium sulfur batteries, redox flow batteries, nickel hydrogen batteries, flywheel batteries, capacitors and the like. Hereinafter, in the present embodiment, the electric automobile EV will be described as a route bus B which regularly travels a predetermined operation route. The electric vehicle EV may be a taxi, a car sharing vehicle or the like instead of the route bus B, and any vehicle equipped with a secondary battery. Further, the electric vehicle EV may be a vehicle such as a hybrid car that has both an internal combustion engine that burns gasoline and the like and a secondary battery.

  Charging system 1 includes power system 2, charging facility 10, charging facility control device 30, and charging facility operation support device 100. In the charging system 1 in the present embodiment, a charging facility operation supporting apparatus 100 is connected to a charging facility control apparatus 30 that controls the charging facility 10 via a network NW. The network NW includes a LAN (Local Area Network), a WAN (Wide Area Network), a serial communication line, and the like. The charging facility operation support device 100 may not be connected to the charging facility control device 30 via the network NW, but may be built in or attached to the charging facility control device 30. The power system 2 is supplied with, for example, commercial power of direct current or alternating current. Hereinafter, in the present embodiment, commercial power is described as alternating current.

  The charging facility 10 includes a power receiving point 12, charger side communication devices 14-1 to 14-n, chargers 16-1 to 16-n, a battery side communication device 18, an AC-DC converter 19, and a storage battery. And 20. The charging facility 10 is provided, for example, for each sales office of the route bus B. For example, in order to charge the secondary battery, the route bus B goes to a sales office provided with the charging facility 10 periodically (for example, twice a day and a night of the day). Note that chargers 16-1 to 16-n and storage battery 20 are connected in parallel to power reception point 12 described above via power line PL, and power reception point 12 and charger communication devices 14-1 to 14- The n- and battery-side communication devices 18 are connected to the charging facility control device 30 via the communication line CL.

  The power receiving point 12 is connected to the power system 2, receives commercial power from the power system 2, and supplies the received commercial power to the power line.

  The charger side communication devices 14-1 to 14-n communicate with the charging facility control device 30 via the communication line CL, and the information acquired by the communication is transmitted to the chargers 16-1 to 16-n, respectively. Output. The charger side communication devices 14-1 to 14-n may communicate with the charging facility control device 30 by wireless communication using radio waves or the like without passing through the communication line CL. Hereinafter, the charger side communication devices 14-1 to 14-n will be simply described as "the charger side communication device 14" unless special distinction is made.

  The chargers 16-1 to 16-n convert the power supplied to the power line PL into DC power based on the control information acquired from the charging facility control device 30 via the charger communication device 14, and convert the power. The DC power is charged to the secondary batteries of the route buses B-1 to B-n connected to itself. For example, when the control information is a command to start charging, the chargers 16-1 to 16-n start charging the secondary batteries of the route buses B-1 to B-n. In addition, the chargers 16-1 to 16-n end charging when the control information is an instruction to end charging. The chargers 16-1 to 16-n may be, for example, devices having a power cable or plug for connecting to a secondary battery in the route bus B, or devices that supply power wirelessly. It is also good.

  In the present embodiment, as the chargers 16-1 to 16-n, those different in rated output are mixed. For example, the chargers 16-1 to 16-n can output 5 kW to 250 kW of electric power per unit time as rated output (hereinafter referred to as "high-speed charger"), 5 kW to 25 kW It is comprised by two types of the charger which can output about electric power (henceforth a "low-speed charger"). Further, identification information (hereinafter, referred to as “charger type”) for identifying the type of the charger is allocated to the chargers 16-1 to 16-n. For example, a value of "1" is assigned to the high-speed charger, and a value of "2" is assigned to the low-speed charger. The chargers 16-1 to 16-n are not limited to the high speed charger and the low speed charger, and may be configured by other chargers. Hereinafter, the chargers 16-1 to 16-n and the route buses B-1 to B-n will be referred to as "charger 16" and "route bus B", respectively, unless otherwise specified.

  The battery side communication device 18 communicates with the charging facility control device 30 via the communication line CL, and the information acquired from the charging facility control device 30 through communication is stored in the AC-DC converter 19 and the charge / discharge circuit of the storage battery 20 (not It outputs to (figure) etc.

  When commercial power is AC power, the AC-DC converter 19 converts AC power into DC power, and supplies the converted DC power to the storage battery 20 in the subsequent stage. When the commercial power is DC power, the AC-DC converter 19 may be omitted.

  The charge / discharge circuit of storage battery 20 charges storage battery 20 using the commercial power supplied from power reception point 12 based on the control information acquired from charging facility control device 30 via battery side communication device 18 or The power stored in the storage battery 20 is discharged to the power line PL on the charger 16 side. The storage battery 20 includes, for example, a lithium ion battery, a lead storage battery, a sodium sulfur battery, a redox flow battery, a nickel hydrogen battery, a flywheel battery, a capacitor and the like.

  The amount of power consumed by the above-described chargers 16-1 to 16-n is the amount of power received from power system 2 to charging facility 10 through power reception point 12 and the amount of power discharged from storage battery 20. It will be the sum of

  The charging facility control device 30 controls the charging facility 10 based on an operation schedule (a charge / discharge plan SKD described later) for controlling the charging facility 10 generated by the charging facility operation support device 100. For example, according to the operation schedule, charging facility control device 30 causes charging facility 10 to receive commercial power supplied from power system 2 and causes charging facility 10 to receive commercial power received on route bus B (not shown). Charge the next battery.

  Specifically, charging facility control device 30 compares the amount of received power at receiving point 12 with the target value of the received power according to charge / discharge plan SKD described later, and generates a control signal instructing charging / discharging of storage battery 20. . The charge facility control device 30 outputs the generated control information to the battery side communication device 18 via the communication line CL. The charge and discharge circuit of the storage battery 20 performs charging or discharging based on control information input from the charging facility control device 30 to the battery-side communication device 18.

  Here, when the discharged power of storage battery 20 increases, the received power from power system 2 decreases. On the other hand, when the charging power of storage battery 20 increases, the received power from power system 2 increases. Therefore, the charge / discharge circuit of the storage battery 20 is controlled by the charge / discharge plan SKD, for example, when the received power amount in the power system 2 exceeds (or is likely to exceed) a predetermined contracted power amount. It is designed to be output to (battery side communication device 18). The contracted amount of power is the amount of power previously agreed between the power provider who supplies predetermined power to the power system 2 and the management provider who manages the charging equipment 10. For this reason, the charge facility control device 30 adjusts the amount of received power of the charge facility 10 so as not to exceed the contracted power amount.

  In addition, the charging facility control device 30 controls the charging start (on) and the charging stop (off) of the chargers 16-1 to 16-n for each charger according to the charge and discharge plan SKD. For example, when the amount of electric power charged to the secondary battery in the route bus B reaches the target remaining amount of power formulated by the charge / discharge plan SKD, the charge facility control device 30 performs control to command charge stop (off) Generate information. The charging facility control device 30 outputs the generated control information to the charger communication device 14 via the communication line CL. The charger 16 performs charge start (on) or charge stop (off) based on control information input from the charging facility control device 30 to the charger-side communication device 14.

  The charging facility operation support device 100 acquires an operation diagram of the route bus B from a not-shown higher-level device, and formulates a charge / discharge plan SKD of the charging system 1 based on the acquired operation diagram.

  Hereinafter, the specific configuration of charging facility operation support device 100 will be described with reference to the drawings. FIG. 2 is a diagram showing an example of a configuration of the charging facility operation support device 100 in the first embodiment. The charging facility operation support device 100 in the present embodiment includes a communication unit 102, a control unit 110, and a storage unit 150. Control unit 110 includes generation unit 112, charge / discharge plan generation unit 114, evaluation unit 116, penalty provision unit 118, probability calculation unit 120, extraction unit 122, change processing unit 124, output unit 126, and the like. Equipped with The communication unit is an example of the “acquisition unit”.

  The evaluation unit 116, the extraction unit 122, and the change processing unit 124 in the present embodiment perform probabilistic processing. In the probabilistic process, for example, the probability of whether or not to cause an event is determined in advance, and an event is generated according to the probability, using difficult-to-predict information such as random numbers and time information as a determination factor of the probability, It refers to the process that you do not let. In addition, probabilistic processing can also be defined as processing in which the result is not fixed while performing repetitive processing.

  Some or all of the functional units of the control unit 110 described above are software functional units that function when a processor such as a central processing unit (CPU) executes a program stored in the storage unit 150. Further, some or all of the functional units of the control unit 110 may be hardware functional units such as a large scale integration (LSI) and an application specific integrated circuit (ASIC).

The storage unit 150 includes, for example, a non-volatile storage medium such as a read only memory (ROM), a flash memory, a hard disk drive (HDD), and a volatile storage medium such as a random access memory (RAM) or a register. . The information stored in the storage unit 150 includes, in addition to a program executed by the processor, information such as an operation diagram, solution x _i , charge / discharge plan SKD, constraint condition IF1, numerical formula, Pareto optimal solution, etc. described later.

  FIG. 3 is a flowchart showing an example of the process flow of the charging facility operation support device 100 in the first embodiment. The charging facility operation support device 100 in the present embodiment performs the processing of this flowchart, for example, at a predetermined cycle. Hereinafter, each functional unit of the control unit 110 will be described with reference to FIG.

  The communication unit 102 communicates with a higher-level device (not shown), acquires an operation diagram of the route bus B (step S100), and stores the acquired operation diagram in the storage unit 150. In the operation diagram in the present embodiment, the time when route bus B is scheduled to arrive at the sales office provided with charging facility 10 (hereinafter referred to as "scheduled arrival time") and the business office provided charging facility 10 And the time when the route bus B is scheduled to leave (hereinafter referred to as "descheduled time").

The generation unit 112 generates a solution x _i (corresponding to “code”) having a predetermined structure for solving a mixed integer programming problem described later. The argument i of the solution x is an identifier of the solution, for example, a serial number. In this embodiment, a genetic algorithm (GA; Genetic Algorithm) is applied as an example for solving the mixed integer programming problem. Therefore, the generation unit 112 generates a plurality of solutions x _{i in} advance in order to perform a process of searching for an optimal solution (or approximate solution) by applying a genetic algorithm. In the present embodiment, the mixed integer programming problem may be solved by applying a local search method (iterative method) such as a tabu search method or an annealing method instead of the genetic algorithm.

FIG. 4 is a diagram illustrating an example of the solution x _i generated by the generation unit 112 in the first embodiment. Solution x _i shown in FIG. 4, for example, a charger type charger 16 to be used for each route bus B, a parameter P1 that combines the charge starting time, and the parameter P2 indicating the capacity of the storage battery 20, the contract power It has a solution structure including a parameter P3 indicating a quantity. Although FIG. 4 shows the parameter P2 and the parameter P3 as a bit string represented by a binary number, the parameter P1 is also converted into a bit string by some method for processing of a genetic algorithm to be described later. I assume.

The generation unit 112 generates a predetermined number N (for example, 100) of solutions x _{i in} which the values of the parameters P1 to P3 are set at random (random) (step S102). Hereinafter, the process of setting the values of the parameters P1 to P3 at random (random) by the generation unit 112 will be described as “initialization of solution x _i ”. When initializing the solution x _i , the generation unit 112 obeys the following constraint condition IF1.

(Restriction condition IF1 of initialization)
・ Arrival scheduled time ≦ charging start time ≦ (scheduled departure time-charging required time)
· Required time for charging = charge energy / rated output of charger 16 · lower limit capacity 容量 capacity of storage battery 20 上限 upper limit capacity · lower limit power amount 契約 contract power amount 上限 upper limit power amount

  The generation unit 112 randomly assigns a charger type to each route bus B so as to satisfy the above-described constraint. The generation unit 112 calculates the required charging time indicating the time required for charging by dividing by the rated output value of the allocated charger type. The generation unit 112 refers to the estimated arrival time and the estimated departure time included in the operation diagram stored in the storage unit 150, in a period from the estimated arrival time to the time obtained by subtracting the required charging time from the estimated departure time. , Randomly set the charging start time. As described above, the generation unit 112 performs processing in accordance with the above-described constraint condition to randomly set the parameter P1.

  In addition, generation unit 112 randomly sets the capacity of storage battery 20 to fall within the range from the capacity set at the lower limit (for example, 100 [kWh]) to the capacity set at the upper limit (for example, 750 [kWh]). Set As described above, the generation unit 112 performs processing in accordance with the above-described constraint condition IF1, and sets the parameter P2 at random.

  Further, the generation unit 112 randomly sets the contracted power amount so as to fall within the range from the power amount set to the lower limit to the power amount set to the upper limit. That is, the generation unit 112 performs processing in accordance with the above-described constraint condition, and sets the parameter P3 at random. In order to suppress the overflow, the number of digits of the parameter P2 in binary notation described above is made larger than the number of digits when the upper limit value of the capacity of the storage battery 20 indicated in the constraint condition is in binary notation, It is preferable that the number of digits of the written parameter P3 be larger than the number of digits when the upper limit value of the contracted energy amount is written in binary.

Charge / discharge plan generation unit 114 refers to a plan to be referred to when charge facility control device 30 controls charger 16 and storage battery 20 based on solution x _i generated by generation unit 112 (hereinafter referred to as “charge / discharge plan SKD (Step S104).

Specifically, charge / discharge plan generation unit 114 calculates the charge / discharge power amount of storage battery 20 for each time zone based on the charge start time and the type of charger included in parameter P1 set by generation unit 112. . Charge / discharge plan generation unit 114 performs charge / discharge with reference to calculated charge / discharge power amount of storage battery 20, parameter P2 (capacity of storage battery 20) set by generation unit 112, and parameter P3 (contracted power amount) Generate plan SKD. Charge / discharge plan generating unit 114 generates charge / discharge plan SKD for all solutions x _i generated by generation unit 112. That is, charge / discharge plan generation unit 114 repeats the process of generating charge / discharge plan SKD for the predetermined number N of times.

  For example, the charge / discharge plan generation unit 114 aims to reduce the amount of contracted power by peak cut, or to reduce the amount of received power in a time slot in which the power charge is expensive by peak shift. A plan is generated as a charge / discharge plan SKD. The charge / discharge plan generation unit 114 generates a charge / discharge plan SKD that satisfies the conditions represented by the following Expressions (1) and (2).

P _B (t) shown in the equation represents the output of charge and discharge of the storage battery 20 described above. A positive value P _B (t) represents discharge, and a negative value P _B (t) represents charge. Also, i represents a vehicle ID for identifying each route bus B, and n represents the number of route buses B that can be charged simultaneously at time t. P _EVi (t) represents the amount of power supplied to the secondary battery of the route bus B of the vehicle ID, and P _C represents the amount of contracted power. Also, T _low represents a time zone in which the power rate is low, and T _high represents a time zone in which the power rate is high. Further, C (t) represents SOC (State Of Charge) indicating the capacity of storage battery 20. Further, Δt represents a sampling period (a period between time t and time t ′ after (future) from time t), and t _max represents, for example, the last operation time of the route bus B in one day . Further, P _over represents the excess power amount of the contract power P _C at time t ′ after the current time t.

As represented by the conditional expression (a) in the equation (1), the charge / discharge plan generation unit 114 _calculates the sum of the electric energy P _EVi (t) of all the route buses B simultaneously charged at time t. If the contract power P _C is exceeded, a charge / discharge plan SKD is generated to discharge the excess power amount from the storage battery 20.

If the sum of the electric energy P _EVi (t) does not exceed the contracted electric power P _C , that is, if it does not fall under the conditional expression (a) in the expression (1), the charge / discharge plan generation unit 114 as represented by conditional expression of the inner (b), the time t is equal to or a time zone T _low (low cost time zone), the time t is in the case of a time period T _low , it generates the charge planning SKD as to charge the difference between the sum of the contract power _{P C} and the local bus B power amount _{P EVi} (t) to the battery 20.

In the case where the total amount of electric energy P _EVi (t) does not exceed the contracted electric power P _C and the time t is not in the time zone of T _low , the charge / discharge plan generation unit 114 If it does not correspond to a) and (b), as represented by the conditional expression (c) in the equation (1), whether the time t is a time zone of T _high (a high price time zone) or not In the case where it is determined that the time t is a time zone of T _high , a charge / discharge plan SKD that causes the storage battery 20 to be discharged is generated. At this time, as represented by the conditional expression (c) and the mathematical expression (2), the charge / discharge plan generating unit 114 has a period from time t ′ at which one sampling period has elapsed from the current time t to time t _max. In order to prepare for the case where the sum of the electric energy P _EVi (t ′) exceeds the contracted electric power P _C , a restriction is placed on C (t) indicating the SCO of the storage battery 20 to determine the amount of electric power to be discharged to the storage battery 20 Do. In the case where that total amount of power P _{EVi (t')} does not exceed the contracted electric power P _C is assumed, charge planning generator 114 generates a charge planning SKD discharging without restriction battery 20 It is also good.

  If the charge / discharge plan generation unit 114 does not correspond to the conditional expressions (a), (b), (c) in the expression (1), the charge / discharge plan generation unit 114 holds C (t) without charging / discharging the storage battery 20. Generate charge / discharge plan SKD.

FIG. 5 is a diagram for describing an example of a method of generating charge / discharge plan SKD. LN1 shown in FIG. 5 represents the sum of the electric energy P _EVi (t) of the route bus B which changes with time. LN2 represents the amount of received power of the charging facility 10 when the amount of power is adjusted to or below the contracted power P _{C in} accordance with the conditions represented by the above-described Equations (1) and (2). For example in FIG. 5, in the region where the power rate indicated by P1 in T _low is low cost time zone, LN1 to exceed the contracted electric power P _C. In such a case, charge / discharge plan generation unit 114 charges storage battery 20 with an amount of power corresponding to the area of area P1 shown in the figure in advance, according to conditional expression (a) of expression (1) described above. A charge / discharge plan SKD is generated which causes the storage battery 20 to discharge the stored power amount in a time zone in which the power charge is high.

Further, in the example of FIG. 5, in the region where the power rate indicated by P2 in T _low is low cost time zone, LN1 does not exceed contracted power P _C. In such a case, the charge / discharge plan generation unit 114 causes the storage battery 20 to charge the amount of power corresponding to the area of the region P2 shown in the figure according to the conditional expression (b) of the above-mentioned expression (1). Generate SKD.

Further, in the case of the example of FIG. 5, in the region indicated by P3 in the high price time zone T _high of the power rate, although the LN 1 does not exceed the contracted power P _C , the received power can not be reduced to the lower limit. In such a case, the charge / discharge plan generation unit 114 causes the storage battery 20 to discharge the amount of power corresponding to the area of the region of P3 shown in the figure according to the conditional expression (c) of the above-mentioned expression (1). Generate SKD. It should be noted that charge / discharge plan generation unit 114 is equal to the sum of the amount of power discharged to storage battery 20 when contract power P _C is exceeded, the amount of power discharged to storage battery 20 in time zone T _high , and time zone T _low . Although it is preferable to make it correspond with the electric energy with which the storage battery 20 is charged, it is not necessary to necessarily make it correspond.

In Equation (1) described above, generally, it is necessary to simultaneously consider the capacity value (for example, the unit is [Wh]) and the rated output value (for example, the unit is [W]) regarding charging and discharging of the storage battery 20. When considering the rated output value at the time of charge and discharge, upper and lower limits may be set by the rated output value with respect to the charge and discharge output P _B (t) of the storage battery 20 of Equation (1) described above. In addition, when the rated output value is determined from the capacity value by the charge / discharge rate (for example, the unit is [W / Wh]) or the like, the output P _B (t) of the charge / discharge of the storage battery 20 of the equation (1) described above The rated output value corresponding to the capacitance value may be calculated, and the upper and lower limits may be set by the calculated rated output value.

Evaluation unit 116, the parameter P1 (charging start time and the charger species) set by the generating unit 112, the parameter P2 (capacity of the storage battery 20), and parameter P3 and the solution _{x i} consisting of (contracted power amount), the charge and discharge Based on the charge / discharge plan SKD generated by the plan generation unit 114, an evaluation function for evaluating the solution x _i is derived (step S106). The evaluation function in the present embodiment is a function having the power rate ECOST and the cost ICOST of the charging facility 10, which is the sum of the cost of the charger 16 and the cost of the storage battery 20, as variables.

  For example, consider two cases where the cost of the charging facility 10 is high and low. When a large amount of money can be spent on the charging facility 10, the capacity of the storage battery 20 can be increased. In addition, when a large amount of money can be spent on the charging facility 10, a high performance charger can be used as the charger 16. As a result, it is possible to flexibly perform the peak shift of the received power according to the high price zone and the low price time zone, and to cope with the peak cut of the received power. That is, as the investment cost of the charging equipment 10 is increased, the power rate can be lowered and flexible operation change can be performed.

  On the other hand, when it is not possible to spend a large amount of money on the charging facility 10, it becomes difficult to perform the above-described peak shift of received power or peak cut of received power, etc., so the power rate increases and flexible operation change becomes difficult. There is a tendency. That is, there is a trade-off relationship between the power rate ECOST and the cost ICOST of the charging facility 10. Therefore, the evaluation unit 116 applies a genetic algorithm to derive an evaluation function value that makes both the power rate ECOST in a trade-off relationship and the cost ICOST of the charging facility 10 more suitable.

  Hereinafter, a method of calculating the power rate ECOST, which is a variable value of the evaluation function, and the cost ICOST of the charging facility 10 will be described. The evaluation unit 116 calculates the power rate ECOST (for example, the unit is “yen”) using the following formulas (3) and (4). Power rate ECOST represents a cost (corresponding to a running cost) that occurs when operating charging facility 10.

Each variable or constant shown in Formulas (3) and (4) is defined as follows. BASE represents a charge (for example, a unit is "yen") of contract power set based on the maximum used power of the month, and RATE is a unit charge of a pay-as-you-go charge set based on the received power (for example, a unit Represents [yen / kWh]). Also, P _wh (h) represents the amount of received power (for example, the unit is [kWh]), P _C represents the contract power (for example, the unit is [kW]), h represents the time, and D is Represents the number of business days of a sales office yearly. Further, CRATE represents a unit price of contract power (for example, a unit is [yen / kW]), and PRATE is a penalty coefficient to be multiplied to CRATE when received power P _wh (h) exceeds the contract power. Represent. P _max represents the maximum power (for example, a unit is [kW]) in a target period (for example, one year).

Evaluation unit 116 calculates a charge BASE of contract power calculated using Equation (4), and an integrated value obtained by integrating a value obtained by multiplying received power amount P _wh (h) by a unit charge RATE of usage-based charge for 24 hours. Is calculated, and the value obtained by the addition is calculated as a power rate ECOST.

  On the other hand, evaluation unit 116 calculates cost ICOST (for example, a unit is “yen”) of charging facility 10 using the following formulas (5) and (6).

Each variable or constant shown in Formulas (5) and (6) is defined as follows. CHCOST represents the cost of the charger 16 (for example, a unit is "yen"), and BTCOST represents the cost of the storage battery 20 (for example, a unit is "yen"). The cost CHCOST of the charger 16 and the cost BTCOST of the storage battery 20 represent the cost of the initial investment (corresponding to the initial cost). Also, chnum represents the number of charger types of the charger 16 (two in this embodiment), and i is a number ("1" or less in this embodiment) assigned to the charger 16 according to the charger type. It represents "2". Also, M _i represents the maximum number of overlapping uses of the charger type i when the charger 16 is used simultaneously. Further, chcost _i represents the unit price of the charger type i (for example, the unit is [yen]).

Evaluation unit 116, a value obtained by multiplying the unit price Chcost _i of maximum overlap using number _{M i} and charger species i charger species i, integrates several chnum minute charger species of the charger 16, the integrated value It is calculated as the cost CHCOST of the charger 16.

FIG. 6 is a view showing an example of use of the charger 16 per day. In the case of the example of FIG. 6, eight route buses B are charged per day using two types of chargers (chnum = 2), and at most three chargers indicating i = 1 are used at the same time. Also, at most three chargers indicating i = 2 are used at the same time. That is, M ₁ = 3 and M ₂ = 3. In such a case, for example, assuming that chcost ₁ is 2 million yen and chcost ₂ is 1 million yen, the evaluation unit 116 calculates 3 × 2 million yen + 3 × 1 million yen = 9 million yen as the cost of the charger 16 Calculated as CHCOST. Evaluation unit 116 calculates a value obtained by adding the calculated cost CHCOST of charger 16 and the cost BTCOST of storage battery 20 as cost ICOST of charging facility 10.

Hereinafter, a method of deriving an evaluation function value that makes both of the power cost ECOST and the cost ICOST of the charging facility 10 in a trade-off relationship more preferable will be described. The evaluation unit 116 derives a Pareto optimal solution for each solution x _i generated by the generation unit 112. The Pareto optimal solution is a solution that is never inferior to any other solution overall, ie, it can not necessarily be superior to any other solution, but it is better It is a solution (set of solutions) where no other solution exists.

  The evaluation unit 116 more preferably uses both the power rate ECOST and the cost ICOST of the charging facility 10 by using a multipoint search type genetic algorithm capable of obtaining a plurality of Pareto solution sets in one derivation process. Derive the evaluation function value to be

The evaluation unit 116 ranks each of the solutions x _i generated by the generation unit 112 using the Pareto ranking method in order to apply the multipoint search type genetic algorithm (step S108). In the Pareto ranking, a rank r (x _i ) when the solution x _i is superior to n _i individuals is determined as Expression (7). Here, "preceding" indicates that the evaluation function of a particular solution x _i is superior to all of the evaluation functions of other solutions x _i .

FIG. 7 is a diagram for describing an example of a method of ranking solutions x _i using the Pareto ranking method. In the example of FIG. 7, a total of six solutions of x1 to x6 are mapped on a two-dimensional graph represented by orthogonal coordinates in accordance with both the cost ICOST of the charging facility 10 and the value of the power rate ECOST. ing. For example, each solution is x1 = (ICOST2, ECOST2), x2 = (ICOST4, ECOST3), x3 = (ICOST5, ECOST1), x4 = (ICOST1, ECOST5), x5 = (ICOST3, ECOST6), x6 = (ICOST6) , ECOST 4).

  In such a case, the evaluation unit 116 is superior to the other solutions with respect to the solution x1 because there is no solution in x2 to x6 having a parameter having a value smaller than both the values of ICOST2 and ECOST2 of the solution x1. Give a rank of 1 to indicate that Further, when focusing on solution x2, evaluation unit 116 has one solution for solution x2 because there is one solution having a parameter value smaller than both the values of ICOST4 and ECOST3 of solution x2. It gives rank 2 indicating that it is superior to the individual (solution x1) of. The evaluation unit 116 similarly performs the above-described processing on the remaining solutions, assigns rank 1 to the solution x3, assigns rank 1 to the solution x4, and ranks 3 on the solution x5. And rank 4 for solution x6.

Evaluation unit 116, the rank value assigned to each solution x _i, is derived as the evaluation function value. In the example of FIG. 7 described above, the evaluation unit 116 sets the evaluation function value of the solution x1 to “1”, sets the evaluation function value of the solution x2 to “2”, and sets the evaluation function value of the solution x3 to “1”. The evaluation function value of solution x4 is "1", the evaluation function value of solution x5 is "3", and the evaluation function value of solution x6 is "4".

With respect to the charge / discharge plan SKD generated for each solution x _i by the charge / discharge plan generation unit 114, the penalty giving unit 118 receives the amount of received power, which is the sum of the power amount P _EVi (t) of the route bus B, It is determined whether it is a plan which exceeds P _C (step S110). Then, the penalty giving unit 118 gives a penalty to the solution x _{i at} the generation source of the charge / discharge plan SKD when the plan is to exceed (step S114).

In the present embodiment, it is necessary to reduce the amount of received power to the contracted power P _C or less, but the parameters are randomly set in the charge / discharge plan SKD generated by the charge / discharge plan generation unit 114. there are cases where force will exceed the contracted electric power P _C. In such a case, the penalty giving unit 118 gives a penalty to the solution x _i of the generation source of the charge / discharge plan SKD in which the amount of received power exceeds the contracted power P _C.

Figure 8 is a diagram showing how the amount of the received power exceeds the contract power P _C. LN 3 illustrated in FIG. 8 represents the amount of received power of the charging facility 10. For example, the received power amount exceeds the contract power P _C for ₁ second from time t. In such a case, the penalty giving unit 118 adds a penalty value according to the excess time k ₁ of the contract power P _C to the evaluation function value (rank value) of the solution x _i of the generator of the charge / discharge plan SKD. . Equation (8) is an equation for adding a penalty value according to the excess time k ₁ to the evaluation function value (rank value) of the solution x _i .

P ₁ shown in Equation (8) is a user-optional coefficient. That is, when the received power amount exceeds the contract power P _C , the penalty giving unit 118 multiplies the excess time k ₁ by the coefficient p ₁ as the evaluation function value (rank value) of the solution x _i. Add to.

In addition, for the charge / discharge plan SKD generated for each solution x _i by the charge / discharge plan generation unit 114, the penalty giving unit 118 determines whether or not charging of the secondary battery of the route bus B is completed by the expected departure time. It is determined (step S112). Then, the penalty giving unit 118 gives a penalty to the solution x _i of the generation source of the charge / discharge plan SKD when it is a plan not to end the charge (step S114).

In the present embodiment, it is necessary to start charging the secondary battery of the route bus B between the estimated arrival time of the route bus B and the scheduled departure time, and to finish the charging before the scheduled departure time. However, if a parameter is randomly set in charge / discharge plan SKD generated by charge / discharge plan generation unit 114, a plan is generated such that charging does not end before the scheduled departure time of route bus B. There is. In such a case, the penalty giving unit 118 gives a penalty to the solution x _i of the generation source of the charge / discharge plan SKD that does not end charging by the scheduled departure time of the route bus B.

FIG. 9 is a view schematically showing a situation where charging of the secondary battery of the route bus B is not completed by the scheduled departure time. In the case of the example of FIG. 9, in a plan in which charging is performed using the charger 16 of the charger type “2”, charging is performed after the scheduled departure time of the route bus B of the route bus B. In this case, the penalty assigning unit 118, a penalty value corresponding to the number k ₂ of the charger 16 for charging past the scheduled departure time, the charge planning evaluation function value of the origin of the solution x _i of SKD Add to (rank value). Equation (9) is an equation for adding a penalty value according to the number k ₂ of the chargers 16 to the evaluation function value (rank value) of the solution x _i .

P ₂ shown in Expression (8) is a user-optional coefficient. That is, when the charging of the secondary battery of the route bus B is not completed by the scheduled departure time, the penalty giving unit 118 evaluates the solution x _i by multiplying the number k ₂ of the chargers 16 by the coefficient p _2. Add to the function value (rank value).

The probability calculation unit 120 and the extraction unit 122 calculate the fitness F _i according to the evaluation function of the solution x _i after the evaluation function of the solution x _i is calculated by the evaluation unit 116, and Determine the solution to be left to generations. That is, the probability calculation unit 120 and the extraction unit 122 perform the eyebrow process on the solution of the current generation, and determine the solution to be left for the next generation.

Hereinafter, each functional unit that performs the eyebrow process will be described. Probability calculation unit 120, based on the evaluation function value of the solution x _i (rank value), to calculate the fitness F _i indicating that the value is higher excellent solution x _i, the calculated fitness F _i Based on the selection probability P _i for extracting a solution to be left for the next generation is calculated.

In the present embodiment, a solution x _i is performed in which a series of processes of the ranking process by the evaluation unit 116, the calculation process of the selection probability P _i by the probability calculation unit 120 described later, and the Pareto optimal solution extraction process by the extraction unit 122 are performed. It was expressed as generation G _i, if the processing described above in the second and subsequent is repeated, charging equipment operation support apparatus 100 counts only the generations G _i number as many as the number of times to repeat the series of processes. For example, when a series of processes are to be performed, the target solution x _i is represented as an initial generation G _1, and when a series of processes is performed once, the target solution x _i is represented as a generation G ₂ Be done.

In order to calculate the selection probability P _i , the probability calculation unit 120 calculates the fitness F _i for all the solutions x _i generated by the generation unit 112 based on the following equation (10) (step S116) ). The fitness F _i is represented, for example, by the reciprocal of the rank value, as shown in equation (10). Further, the fitness F _i may be a predetermined value minus the rank value or the square of the rank value or the like.

Probability calculation unit 120 uses the fitness _{F i,} solving Equation (11), it calculates a selection probability _{P i} (step S118).

J shown in equation (11) represents the total number of remaining solutions x _i . That is, the probability calculation unit 120, according to Equation (11), the total fitness of the solution _{x i} generated by the generating unit 112 the sum _{(F 1 + F 2 + ...} + F i + ... + F j) for each solution _{x i} The ratio of the fitness F _i is calculated as the selection probability P _i .

The extraction unit 122 extracts a solution to be left for the next generation G _{i + 1} from the solution of the current generation G _i based on the selection probability P _i of the solution x _i calculated by the probability calculation unit 120 (step S120). In addition, the extraction unit 122 discards solutions other than the solution to be left for the next generation Gi _{+ 1} .

Specifically, the extraction unit 122 generates random numbers R (for example, random numbers from 0 to 1) for the total number j of solutions x _i generated by the generation unit 112, and the generated random numbers R are It is determined whether the condition shown in is satisfied.

When the random number R satisfies the condition shown in the equation (12), the extraction unit 122 solves the solution x _i which is the calculation source of Q _i shown in the equation (12) to all the solutions x _i existing in the current generation G _i Extract from the number j.

FIG. 10 is a diagram showing an example of a roulette concept for extracting a solution to be left to the next generation G _{i + 1} from the solution of the current generation G _i . In the case of the example of FIG. 10, since Q ₁ ≦ R <Q ₂ , the extraction unit 122 extracts the solution x ₂ as a solution of the next generation G _{i + 1} . As shown in FIG. 10, the area on the roulette is proportional to the magnitude of the selection probability P _i . That is, the area on the roulette increases as the individual (solution x _i ) with a superior evaluation function value (rank value). Then, the extraction unit 122 determines a winning angle θ in the roulette based on the random number R, for example, and leaves a solution corresponding to the winning angle θ. Therefore, the extraction unit 122 can more easily extract a solution x _{i having} a superior evaluation function value (rank value) as a solution of the next generation G _{i +1} . Incidentally, the extraction unit 122, the evaluation function value (rank value) rather than extracting only the best solution x _i, for extracting the random solution x _i by a random number R, the diversity of the solution x _i It can be improved.

The extraction unit 122, in order to further improve the diversity of the solution x _i, after randomly by the solution x _i a random number R to leave the next generation G _{i + 1,} all solutions x _i of all generations G _i Extract Pareto optimal solution from. Hereinafter, the method of extracting the Pareto optimal solution will be described. The Pareto optimal solution may be a set of a plurality of solutions x _i or a single solution x _i .

The extraction unit 122 determines whether the generation G _i of the solution x _i is the initial generation G ₁ (step S122). Then, when the generation G _i of the solution x _i is the initial generation G ₁ , the extraction unit 122 determines that the rank r (x _i ) is less than or equal to the designated rank r _ref (x _i ) from the solution x _i of the initial generation G ₁ Extract the solution x _i of. On the other hand, the extraction unit 122, when the generation _{G i} of the solution _{x i} is not the initial generation _{G 1,} the generation _{G 2} after the initial generation _{G 1} subsequent generations, with respect to the solution _{x i} of the current generation _{G i,} initial by adding the Pareto optimal solution from the generation _{G 1} to the previous generation _{G i-1} (step S124), from the addition the solution set, the rank r _{(x i)} rank designated _r ref _{(x i),} a solution _{x i} Are extracted (step S126). The extraction unit 122 counts the number of generations every time the Pareto optimal solution is extracted (Step S128), labels the counted generation number as the Pareto optimal solution, and stores the labeled generation number in the storage unit 150 (Step S130).

The method for extracting the Pareto optimal solution will be described below with reference to FIGS. 11 to 15. FIG. 11 is a diagram showing an example of the solution x _i of the current generation G _i . 12 is a diagram showing an example of a Pareto optimal solution, which is extracted before generation G _i-1 from the initial generation G _1. The Pareto optimal solution shown in FIG. 12 is a solution group extracted when the designated rank r _ref (x _i ) = 1. FIG. 13 is a diagram showing an example of a solution group obtained by adding the solution x _i of the current generation G _i and the Pareto optimum solution extracted from the initial generation G ₁ to the previous generation G _{i -1} . FIG. 14 is a diagram showing an example of a result of extraction of a solution having a specified rank r _ref (x _i ) = 1 or less from the solution group shown in FIG. The points (dots) shown in FIG. 11 to FIG. 14 represent the solution x _i , and the numerical values attached to the points (dots) represent the value of the rank r (x _i ). As shown in FIG. 13, the extraction unit 122, a solution x _i of the current generation G _i, Pareto optimal in the initial generation G ₁ previous generation G _i-1 solution group obtained by adding the Pareto optimal solution extracted until The process of extracting the solution is repeated each time the generation is updated. Charging equipment operation support apparatus 100, generation _{G i} of Pareto optimal solutions is determined whether the host vehicle has reached the predetermined generation _{G max} (step S132), the processing described above until the number of generations _{G i} reaches a predetermined generation _{G max} Repeat to make the Pareto optimal solution approach the optimal value.

FIG. 15 is a diagram showing an example of a group of Pareto optimal solutions extracted after reaching a predetermined generation G _max . As shown in FIG. 15, among the plurality of randomly generated solutions x _i , the charging facility operation supporting device 100 sets the power rate ECOST in a trade-off relationship and the cost ICOST of the charging facility 10 to suitable values. It is possible to extract Parley optimal solutions (solution x _i ) that can be done. Note that the initial generation of _{G 1} in _the specified rank _r ref _{(x i),} and the second generation _{G 2} specified in the subsequent rank _r ref _{(x i),} may be a different value.

In this embodiment, since the Pareto optimal solution is extracted from the solution of the next generation G _{i + 1} using the Pareto optimal solution accumulated for each generation, the solution x _i extracted as the Pareto optimal solution is a local solution (a constant solution Tends to converge on the solution group). Therefore, the change processing unit 124, for Pareto optimal solutions are extracted by the extraction unit 122 is prevented from converging to a local solution, each time a Pareto optimal solution is extracted for each generation G _i, generator 112 Among the plurality of solutions x _i generated by the above, some solutions x _i are randomly extracted, and the parameters of the extracted solution x _i are adjusted (step S 134). In this embodiment, the change processing unit 124 performs the crossover and mutation process in the genetic algorithm, to adjust the parameters of the solution x _i extracted.

The specific processing of the change processing unit 124 will be described below with reference to FIGS. 16 and 17. FIG. 16 is a diagram for explaining an example of crossover processing by the change processing unit 124. As shown in FIG. In the example of FIG. 16, for example, the change processing unit 124 extracts the solution x ₂ and the solution x ₄ with a predetermined probability from among the plurality of solutions x _i generated by the generation unit 112 illustrated in FIG. And cross over processing. The change processing unit 124 randomly sets a position at which crossover is performed on the extracted solution x ₂ and solution x ₄ . In the example of FIG. 16, the change processing unit 124, a crossing position, CR1, CR2, and CR3, it is set for the solution _{x 2} a solution _{x 4.} Change processing unit 124, based on the crossover position set randomly switch the respective parameters of the solution x ₂ a solution x _4. At this time, the change processing unit 124 randomly selects whether to replace the parameters of the two solutions in front of the crossover position (left side in the drawing) or to replace the parameters of the two solutions behind the crossover position (right side in the drawing) decide. For example, the change processing unit 124 interchanges between the solution x ₂ and the solution x ₄ a parameter located on the left side in the drawing with respect to the crossover position CR 1 and a parameter sandwiched between the crossover position CR 2 and the crossover position CR 3. Thereby, the change processing unit 124 can change the parameters randomly set by the generation unit 112 in the _two solutions (the solution x ₂ and the solution x ₄ ). In addition, although the solution of the pair used for crossover processing was made into one in the example mentioned above, it is not restricted to this, For example, two or more may be sufficient. Also, the solution used for the crossover process may be a trio (one set of three solutions). Also, the number of crossover positions may be arbitrarily changed by the user.

FIG. 17 is a diagram for explaining an example of mutation processing by the change processing unit 124. As shown in FIG. In the example of FIG. 17, the change processing unit 124, for example, from a plurality of solutions x _i generated by the generation unit 112 shown in FIG. 4, to extract the solution x ₂ by a predetermined probability, a mutation process Is going. Change processing unit 124, to the extracted solution x _2, selecting parameters for performing mutations randomly. In the case of the example of FIG. 17, the change processing unit 124 sets a total of three places of MT1, MT2, and MT3 as the parameters of the solution x ₂ as the positions to be mutated. The change processing unit 124 changes the parameter of the randomly set position to be randomly set at random. For example, the change processing unit 124 changes the charger type, the charge start time, the capacity of the storage battery 20 (arrangement of binary numbers), and the contract power (arrangement of binary numbers) to values different from the original values. Thereby, the change processing unit 124 can change the parameter randomly set by the generation unit 112 in one solution x ₂ . In the above-mentioned example, although the number of positions for mutation is three, the number is not limited to three, and may be one or two, for example, or four or more.

Change processing unit 124, either one of the crossover process and mutation process in order to adjust the parameters, or for the solution x _i embodying both, in performing the initialization of the generator 112 disintegrated x _i A process is performed to determine whether the initialization condition IF1 to be referenced is satisfied. If the solution x _i subjected to parameter adjustment does not satisfy the initialization condition IF1, the change processing unit 124 performs either or both of crossover processing and mutation processing until the condition IF1 is satisfied. Continue adjusting parameters.

On the other hand, when the solution x _{i for} which the parameter adjustment has been performed satisfies the initialization constraint condition IF1, the change processing unit 124 ends the parameter adjustment. As a result, the charging facility operation support device 100 adjusts the parameter of the solution x _i so as to satisfy the constraint condition IF 1 of initialization, whereby the amount of received power becomes equal to or less than the contracted power P _C. A charge / discharge plan SKD for which charging is completed by the scheduled time can be generated from the solution x _i whose parameter has been adjusted. As a result, the charging facility operation support device 100 does not give a penalty to the solution x _i whose parameter has been adjusted, and can easily extract the solution x _i whose parameter has been adjusted as a Pareto optimal solution. Therefore, the charging facility operation support device 100 can suppress convergence of the Pareto optimal solution extracted each time the generation is repeated on the local solution.

  FIG. 18 is a flowchart illustrating an example of the flow of crossover processing performed by the change processing unit 124 in the first embodiment. The process of this flowchart corresponds to the process of step S134 shown in FIG.

First, the change processing unit 124 randomly extracts two solutions from the plurality of solutions x _i generated by the generation unit 112 (step S200). Next, the change processing unit 124 randomly sets crossing positions for two solutions extracted at random (step S202). Next, the change processing unit 124 replaces the two parameters at the set crossover position (step S204). Next, the change processing unit 124 determines whether the solution in which the parameters have been replaced deviates from the initialization constraint condition IF1 (step S206). The change processing unit 124 returns to the process of step S202 when the solution in which the parameters are replaced deviates from the initialization constraint condition IF1. The change processing unit 124 ends the processing of this flowchart when the solution in which the parameters are replaced does not deviate from the initialization constraint condition IF1.

  FIG. 19 is a flowchart showing an example of the flow of mutation processing performed by the change processing unit 124 in the first embodiment. The process of this flowchart corresponds to the process of step S134 shown in FIG.

First, the change processing unit 124 randomly extracts one solution from the plurality of solutions x _i generated by the generation unit 112 (step S300). Next, the change processing unit 124 randomly sets a mutation position for one randomly extracted solution (step S302). Next, the change processing unit 124 changes the parameter at random at the mutation position set randomly (step S304). Next, the change processing unit 124 determines whether the solution whose parameter has been changed deviates from the initialization constraint condition IF1 (step S306). If the solution for which the parameter has been changed deviates from the initialization constraint condition IF1, the change processing unit 124 returns to the process of step S302. If the solution whose parameter has been changed does not deviate from the initialization constraint condition IF1, the change processing unit 124 ends the processing of this flowchart.

The output unit 126, generation G _i of Pareto optimal solutions when it reaches the predetermined generations G _max, the charge planning SKD indicated Pareto optimal solution extracted by the extraction unit 122, and outputs the charging facility controller 30 (step S136). By this, the charging facility operation support device 100 ends the process of the flowchart shown in FIG. As a result, the charging facility control device 30 that has output the charging / discharging plan SKD from the output unit 126 can control the charging facility 10 in accordance with the charging / discharging plan SKD.

According to the charging facility operation support apparatus 100 of the first embodiment described above, the charging start time of the charger 16 included in the charging facility 10, the capacity of the storage battery 20 provided in the charging facility 10, and the contracted power amount are encoded. A plurality of generated solutions x _i are generated, and the generated plurality of solutions x _i are subjected to stochastic processing such as mutation or crossover, and the parameters of the solutions x _i are stochastically changed to change the parameters x _{For i} , an evaluation function with cost ICOST of the charging facility 10 as one of the evaluation targets is derived, and a Pareto optimum solution with the derived evaluation function having a suitable value is extracted from among the solutions x _i with varying parameters In this way, it is possible to request an operation schedule that suits the purpose of the user.

Second Embodiment
Hereinafter, the charging facility operation support device 100 in the second embodiment will be described. Here, as a difference from the first embodiment, an evaluation process of the cost ICOST of the charging facility 10 performed by the evaluation unit 116 will be described. Hereinafter, the description of the functions and the like common to the first embodiment described above will be omitted.

  The evaluation unit 116 in the second embodiment evaluates the cost ICOST of the charging facility 10 based on the capacity of the storage battery 20 and the contracted power. Specifically, the evaluation unit 116 evaluates a period for recovering the cost ICOST of the charging facility 10 (hereinafter, referred to as “charging facility cost recovery period”). The charge equipment cost recovery period is based on the cost of fuel per year when a local bus (hereinafter referred to as "diesel bus") equipped with a diesel engine is operated for the cost ICOST of the charge equipment 10, and the secondary battery is It can be obtained by dividing the value obtained by subtracting the power cost per year when operating a mounted route bus (hereinafter referred to as "electric bus"). The fuel cost of the diesel bus corresponds to the fuel cost generated when it is assumed that the vehicle travels the same distance as the travel distance of the electric bus. Therefore, the evaluation unit 116 charges the battery according to the profit (the difference between the fuel cost of the diesel bus and the power cost of the electric bus) obtained by replacing the route bus carrying the diesel engine with the route bus carrying the secondary battery. Evaluate the time period for recovering the cost ICOST of the facility 10.

Based on the charging facility cost recovery period calculated by the evaluation unit 116, the extraction unit 122 further determines the solution x _i within the target investment recovery period from the Pareto optimal solution when the generation G _i reaches the predetermined generation G _max. Extract. The target investment recovery period is a period set by the user, for example, 10 years.

FIG. 20 is a diagram illustrating an example in which a target investment recovery period is set for the Pareto optimal solution when the generation G _i reaches the predetermined generation G _max . A straight line LN4 shown in FIG. 20 represents a target return on investment. As described above, since the charging facility cost recovery period is a function that uses the cost ICOST of the charging facility 10 and the power cost of the electric bus as variables, when the target investment recovery period is set by the user, the extraction unit 122 Draws a straight line LN4 representing the target return on investment on the Pareto optimal solution map shown in FIG. Extractor 122, from the map of Pareto optimal solutions depicting linear LN4, extracts the solution _{x i} indicating the linear LN4 following values (period). The output unit 126 outputs, for example, to a display device (not shown), a solution x _i is extracted target payback period by the extracting unit 122.

According to the charging equipment operation support apparatus 100 of the second embodiment described above, by calculating the charging facility cost recovery period, the Pareto optimal solutions when generation G _i has reached a predetermined generation G _max, target investment The solution x _i below the recovery period can be extracted. Generally, the electric bus charging equipment 10 tends to be expensive relative to the diesel bus fueling equipment, and the user desires an operating style that reduces the initial investment cost recovery period . In such a case, the charging facility operation support device 100 presents to the user a period in which the profit obtained by replacing the diesel bus with the electric bus can be recovered, thereby recovering the initial investment cost. Select an operation schedule that can reduce the running cost (power rate) of the charging facility 10, or reduce the initial cost of the charging facility 10 (initial cost of the charger 16 and the storage battery 20). It is possible to select a possible operation schedule.

Third Embodiment
Hereinafter, the charging facility operation support device 100 in the third embodiment will be described. Here, as a difference from the first and second embodiments described above, the extraction process of the solution x _i will be described which is performed by the extraction unit 122. Hereinafter, the description of the functions and the like common to the first and second embodiments described above will be omitted.

The extraction unit 122 in the third embodiment is a solution in which the profit after the return of investment is maximized when a predetermined period has elapsed with respect to the Pareto optimal solution when the generation G _i reaches the predetermined generation G _max. Extract x _i . For example, a replacement period of the charging facility 10 (the useful life of the charger 16 or the storage battery 20) or the like is set in the predetermined period.

  FIG. 21 is a diagram showing a change in profit with respect to time transition obtained when using two types of charge and discharge plans SKD. The horizontal axis shown in FIG. 21 represents the time elapsed since the start of operation (for example, the unit is [year]), and the vertical axis represents the profit (for example, the unit is [yen]). LN5 shown in FIG. 21 represents a change in profit when the charging facility 10 is operated based on the charge / discharge plan SKD1 in which the initial investment cost is small and the power cost is large. That is, LN 5 represents a case where the initial investment of the charging facility 10 is small, the power charge is high, and the profit by replacing the diesel bus with the electric bus is small. LN6 represents a change in profit when the charging facility 10 is operated based on the charge / discharge plan SKD2 in which the initial investment cost is large and the power cost is small. That is, LN 6 represents a case where the initial investment of the charging facility 10 is large and the electric power charge is cheap and a large amount of profit can be obtained by replacing the diesel bus with the electric bus.

  For example, when the charging facility 10 is operated based on the charge / discharge plan SKD1, at time t1, the profit from the start of operation becomes equal to the initial investment. Further, when the charging facility 10 is operated based on the charge / discharge plan SKD2, at time t2, the profit from the start of operation becomes the same as the initial investment. That is, charging facility operation support device 100 calculates time t1 or time t2 as a charging facility cost recovery period. LN5 and LN6 cross at time t3. That is, even when the charging facility 10 is operated based on either the charge / discharge plan SKD1 or the charge / discharge plan SKD2, the profits become equal when time t3 elapses. When time t4 passes, profit is greater in the case where the charging facility 10 is operated based on the charge / discharge plan SKD2 than when the charge facility 10 is operated based on the charge / discharge plan SKD1. Therefore, for example, when the predetermined period is time t4, the charging facility operation support device 100 outputs the charge / discharge plan SKD2.

According to the charging facility operation supporting device 100 of the third embodiment described above, the profit in the period (predetermined period) designated by the user is obtained from the Pareto optimal solution when the generation G _i reaches the predetermined generation G _max. It is possible to extract the solution x _i which is calculated and the calculated profit is maximized. As a result, the charging facility operation support device 100 of the third embodiment prioritizes the user whether to prioritize the operation schedule with a quick return on investment, or prioritize the operation schedule with the largest profit after the return of investment. It can be selected.

Fourth Embodiment
Hereinafter, the charging system 1 in the fourth embodiment will be described. Here, as a difference from the first to third embodiments described above, the configuration of the charging facility 10 will be described. Hereinafter, the description of the functions and the like common to the first to third embodiments described above will be omitted.

  FIG. 22 is a diagram showing an example of a configuration of the charging system 1 including the charging facility operation support device 100 in the fourth embodiment. The charging facility 10 in the fourth embodiment further includes a power generation device 52 such as a solar power generation device or a wind power generation device, and a power generation communication device 50.

  From the relationship between the amount of power consumed in the charging facility 10 and the amount of contracted power, the charging facility control device 30 allows the storage battery 20 to discharge an amount of power insufficient for the received power from the power system 2. The adjustment is made or the power generation device 52 is adjusted to generate power and supply it. More specifically, charging facility control device 30 discharges storage battery 20 when received power in power system 2 connected to charging facility 10 exceeds contract power, and the usable charge amount of storage battery 20 ( For example, when it is 30% or less, the control information is output to the generator communication device 50. The power generation device 52 generates power based on the control information output from the charging facility control device 30 to the power generation communication device 50, and supplies the generated power to the power line PL. By this, charging system 1 can charge route bus B without the received power of charging facility 10 exceeding the contract power.

(Other Embodiments (Modifications))
Hereinafter, other embodiments will be described.
In the first to fourth embodiments described above, although the power charge ECOST and the cost ICOST of the charging facility 10 have been described as variables of the evaluation function, other than these, for example, the total time of usage intervals of the same charger 16 The optimization may be performed with the (maximization problem), the total of all the charge required times (minimization problem), etc. as variables. For example, when one charger 16 is used, the total time of usage intervals of the same charger 16 is the time from the completion of charging of one route bus B to the start of charging of the next route bus B. It is the value which calculated also to all the chargers 16, and calculated time was totaled. The charging facility operation support device 100 treats, for example, the total time of use intervals of the charger 16 as a variable of the evaluation function, and solves the genetic algorithm so as to maximize the total time of use intervals of the charger 16 When charging for the route bus B is delayed, it is possible to suppress the ball-push type charging delay.

According to at least one embodiment described above, a plurality of solutions x _i are generated by encoding the charging start time of the charger 16 included in the charging facility 10, the capacity of the storage battery 20 provided in the charging facility 10, and the contracted power amount. and performs probabilistic processing mutation and crossover or the like to the generated multiple solutions x _i, stochastically changing the parameters of the solution x _i, the solution x _i with varying parameters, the charging facility 10 The purpose of the user is to derive an evaluation function whose cost ICOST is one of the evaluation targets, and extract a Pareto optimal solution with a suitable value from the derived evaluation function from among the solutions x _i in which the parameters are changed. You can ask for an operation schedule that suits you.

  While certain embodiments of the present invention have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the invention. These embodiments can be implemented in other various forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the invention described in the claims and the equivalents thereof as well as included in the scope and the gist of the invention.

DESCRIPTION OF SYMBOLS 1 ... Charging system, 10 ... Charging installation, 12 ... Receiving point, 14 ... Charger side communication machine, 16 ... Charger, 18 ... Battery side communication machine, 19 ... AC-DC converter, 20 ... Storage battery, 30 ... Charging installation Control device, 100: charging facility operation support device, 102: communication unit, 110: control unit, 112: generation unit, 114: charge and discharge plan generation unit, 116: evaluation unit, 118: penalty provision unit, 120: probability calculation unit , 122: extraction unit, 124: change processing unit, 126: output unit, 150: storage unit, B: route bus

Claims (10)

A generation unit that generates a plurality of codes in which information on an operation schedule of a charger included in a charging facility, information on a storage battery included in the charging facility, and information on contract power is encoded;
A change processing unit that performs probabilistic processing on the plurality of codes generated by the generation unit and changes the codes stochastically;
An evaluation unit that derives an evaluation function with the cost of the charging facility as one of the evaluation targets for the code that has been processed by the change processing unit;
An extraction unit that extracts, from among the codes subjected to processing by the change processing unit, a code for which the evaluation function derived by the evaluation unit is a suitable value;
Charging equipment operation support device provided with. It further comprises an output unit for outputting the code extracted by the extraction unit,
The change processing unit, the evaluation unit, and the extraction unit repeatedly perform processing until a predetermined purpose is achieved.
The charge facility operation support device according to claim 1. The extraction unit adds the code extracted in the past repetition processing to the current extraction target code in the process of performing the iterative processing, and the evaluation function derived by the evaluation unit from the added code is Extract the code that has a suitable value,
The charging facility operation support device according to claim 2. The evaluation unit evaluates the cost of the charging facility based on the capacity of the storage battery and the contracted power.
The charging facility operation support device according to any one of claims 1 to 3. It further comprises an acquisition unit for acquiring an operation schedule of the electric vehicle to be charged using the charger.
The generation unit is configured to, based on the operation schedule acquired by the acquisition unit, identification information for identifying the charger, a capacity of the storage battery, the contracted power, and charging of the electric vehicle with respect to the charger. Generate multiple codes that encode the charge start time that indicates the time to start the
The change processing unit performs the stochastic processing on any one or more of the identification information, the capacity of the storage battery, the contracted power, and the charge start time included in the code generated by the generation unit. And stochastically change the code,
The charge facility operation support device according to any one of claims 1 to 4. The extraction unit obtains evaluation coordinates having the evaluation target of the evaluation function as an axis, and the coordinates of the code processed by the change processing unit in the evaluation coordinates are on a predetermined side of a threshold curve which is approximated by a curve. To extract
The charging facility operation support device according to any one of claims 1 to 5. The apparatus further comprises a probability calculation unit for calculating, for each of the codes, the probability of being referred to in the process of extracting a code by the extraction unit based on the value of the evaluation function derived by the evaluation unit.
The extraction unit extracts, from among the codes subjected to processing by the change processing unit, a code for which the evaluation function derived by the evaluation unit has a suitable value based on the probability calculated by the probability calculation unit. ,
The charging facility operation support device according to any one of claims 1 to 6. The extraction unit may include the cost of the charging facility required to operate the electric vehicle.
The operation time and the same time of the electric vehicle, the value the subtracting the cost of charging equipment, by dividing the cost of the charging facilities subtracted value required when operating a vehicle having an engine powered, predetermined which is the range code further extracted from the extracted code,
A charging facility operation support device according to claim 5. On the computer
Generating a plurality of codes encoding information on an operation schedule of a charger included in a charging facility, information on a storage battery provided in the charging facility, and information on contract power;
Stochastic processing is performed on the plurality of generated codes to stochastically change the codes,
With respect to the code that has been changed in a stochastic manner, an evaluation function is derived, which is one of the evaluation targets of the cost of the charging facility,
Extracting a code having a suitable value from the derived evaluation function from among the stochastically changed codes;
Charging facility operation support program. A charging facility operation support device according to any one of claims 1 to 8 .
A charger included in the charging facility;
A control device that controls the charger based on output information of the charging facility operation support device;
A charging system comprising

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