KR102550808B1 - Method for desinging v2g electric vehicle charging station and apparaturs thereof - Google Patents

Abstract

The present invention relates to a method for designing a demand management type V2G electric vehicle charging station, comprising the steps of selecting a resource capacity candidate group of distributed resources constituting the V2G electric vehicle charging station; performing long-term economic feasibility evaluation through an optimal management strategy for candidate capacity combinations included in the resource capacity candidate group; determining a target resource capacity combination from among the resource capacity candidates based on the long-term economic feasibility evaluation; and deriving an optimal resource capacity combination of the distributed resources constituting the charging station by evaluating sensitivity for each distributed resource based on the target resource capacity combination.

Description

Design method and device of V2G electric vehicle charging station {METHOD FOR DESINGING V2G ELECTRIC VEHICLE CHARGING STATION AND APPARATURS THEREOF}

The present invention relates to a design method and device for a demand management type V2G electric vehicle charging station, and more particularly, to a method for optimally calculating the capacity of a plurality of distributed resources constituting a demand management type V2G electric vehicle charging station and the same. It's about the device.

Current power consumption is increasing, but there is a limit to increasing the amount of power generation due to environmental problems and the like. In order to solve these problems, the development of efficient power use and demand response has emerged as an important issue.

Demand management means reducing the electricity bill of the consumer by using a device capable of storing electricity. Electricity rates consist of a basic rate and an electricity rate. The basic rate is based on the contract power, but the customer who installed the peak power demand meter is the highest among the peak power demand for December, January, February, July, August, and September among the immediately preceding 12 months including the month of meter reading. The base rate is calculated by using the largest peak demand power as the rate applied power. On the other hand, the energy rate is calculated by multiplying the amount of electricity used in a specific time slot by the rate for each time slot. Therefore, in order to reduce the electricity cost of consumers, it is necessary to reduce the basic rate by reducing the maximum demand for electricity, and to reduce the amount of electricity by transferring the amount of electricity in the time period with high rates to the time period with low rates.

On the other hand, due to the continuous increase in electric vehicles (EV) and the development of battery technology, if the battery installed in an electric vehicle is used as an energy storage system (ESS) for demand management, it is more efficient than before. Effective demand management can be achieved.

Furthermore, research to increase the mileage of electric vehicles continues to increase the battery capacity of electric vehicles, and the battery capacity of electric vehicles is not small. enough to apply. Therefore, it is necessary to design a demand management type V2G electric vehicle charging station capable of charging/discharging in both directions and connect it to the power system.

By the way, many techniques for calculating the optimal capacity of distributed resources constituting the microgrid system have been developed, but the development of a technique for calculating the optimal capacity of distributed resources constituting a V2G electric vehicle charging station capable of bi-directional charging / discharging has been developed. situation is insignificant.

The present invention aims to solve the foregoing and other problems. Another object is to provide a charging station design method and device for optimally calculating the capacity of a plurality of distributed resources constituting a demand-managed V2G electric vehicle charging station.

According to one aspect of the present invention in order to achieve the above or other object, selecting a resource capacity candidate group of distributed resources constituting a V2G electric vehicle charging station; performing long-term economic feasibility evaluation through an optimal management strategy for candidate capacity combinations included in the resource capacity candidate group; determining a target resource capacity combination from among the resource capacity candidates based on the long-term economic feasibility evaluation; and deriving an optimal resource-capacity combination of the distributed resources constituting the charging station by evaluating the sensitivity of each distributed resource based on the target resource-capacity combination.

According to another aspect of the present invention, a capacity candidate selection unit for selecting a resource capacity candidate group of distributed resources constituting a V2G electric vehicle charging station; an economic evaluation unit that performs long-term economic evaluation through an optimal operation strategy for candidate capacity combinations included in the resource capacity candidate group; And based on the long-term economic feasibility evaluation, a target resource capacity combination is determined from among the resource capacity candidates, and a sensitivity for each distributed resource is evaluated based on the target resource capacity combination to determine an optimal resource capacity combination of the distributed resources constituting the charging station. Provides a design device for a V2G electric vehicle charging station including an optimal capacity detector for detecting.

According to another aspect of the present invention, the process of selecting a candidate group of resource capacity of the distributed resources constituting the V2G electric vehicle charging station; A process of performing long-term economic feasibility evaluation through an optimal operation strategy for candidate capacity combinations included in the resource capacity candidate group; determining a target resource capacity combination from the resource capacity candidate group based on the long-term economic feasibility evaluation; and a computer program stored in a computer readable recording medium so that a process of deriving an optimal resource capacity combination of the distributed resources constituting the charging station by evaluating the sensitivity of each distributed resource based on the target resource capacity combination is performed on a computer. .

The V2G electric vehicle charging station design method and effects of the device according to embodiments of the present invention will be described as follows.

According to at least one of the embodiments of the present invention, by calculating the optimal capacity of the distributed resources constituting the V2G electric vehicle charging station, EV charging service providers can maximize long-term profits of the V2G electric vehicle charging station. .

However, the effects that can be achieved by the V2G electric vehicle charging station design method and device according to the embodiments of the present invention are not limited to those mentioned above, and other effects not mentioned are the present invention from the description below. It will be clearly understood by those skilled in the art.

1 is a block diagram of a demand management type V2G electric vehicle charging station according to an embodiment of the present invention;
2 is a flowchart illustrating a design method of a demand management type V2G electric vehicle charging station according to an embodiment of the present invention;
3 is a diagram referenced to explain a method of selecting a resource capacity candidate group;
4 is a diagram referenced to describe V2G EV information necessary for deriving an optimal operating strategy;
5 is a diagram referenced to explain a method for evaluating long-term economic feasibility through an optimal operation strategy;
6 is a flowchart illustrating a method of generating V2G EV information necessary for deriving an optimal operating strategy;
7 is a flowchart illustrating a method of deriving an optimal resource capacity combination of distributed resources;
8 is a block diagram of a V2G EV charging station design device according to an embodiment of the present invention.

Hereinafter, the embodiments disclosed in this specification will be described in detail with reference to the accompanying drawings, but the same or similar elements are given the same reference numerals regardless of reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "unit" for components used in the following description are given or used together in consideration of ease of writing the specification, and do not have meanings or roles that are distinct from each other by themselves. That is, the term 'unit' used in the present invention means a hardware component such as software, FPGA or ASIC, and 'unit' performs certain roles. However, 'part' is not limited to software or hardware. A 'unit' may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, 'unit' refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. The functionality provided within the components and 'parts' may be combined into a smaller number of elements and 'parts' or further separated into additional elements and 'parts'.

In addition, in describing the embodiments disclosed in this specification, if it is determined that a detailed description of a related known technology may obscure the gist of the embodiment disclosed in this specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in this specification, the technical idea disclosed in this specification is not limited by the accompanying drawings, and all changes included in the spirit and technical scope of the present invention , it should be understood to include equivalents or substitutes.

The present invention proposes a charging station design method and apparatus for optimally calculating the capacity of a plurality of distributed resources constituting a demand-managed V2G electric vehicle charging station. The present invention will basically be described assuming that it is applied to a power system system equipped with an EV charging/discharging device capable of bi-directional charging/discharging. The configuration of the simplest charging station is a V2G EV charging and discharging device, and a load, a power storage system (ESS), and a renewable energy source can be additionally connected to this. Hereinafter, in this specification, the power storage device (ESS) refers to any device that stores power and makes it available when needed. In addition, the present invention can be applied to power system systems composed of AC and/or DC power sources .

Hereinafter, various embodiments of the present invention will be described in detail with reference to the drawings.

1 is a block diagram of a demand management type V2G electric vehicle charging station according to an embodiment of the present invention.

Referring to FIG. 1, a V2G electric vehicle charging station 100 according to an embodiment of the present invention includes a V2G electric vehicle 110, an EV charging and discharging device 120, a power storage device 130, a load 140, and A renewable energy source 150 may be included. Here, the power storage device 130, the load 140, and the renewable energy source 150 are not essential components and may be configured to be omitted according to embodiments.

A V2G electric vehicle (110, V2G EV) includes one or more batteries having a predetermined energy capacity. A lithium ion battery, lead-acid battery, etc. may be used as the battery, but is not necessarily limited thereto.

The V2G electric vehicle 110 is linked to a power system and supports a vehicle to grid (V2G) function. Here, the V2G function refers to a function enabling bidirectional charging/discharging between the electric vehicle 110 and the power system.

The EV charging and discharging device (or EV charger, 120) functions to charge the battery of the electric vehicle 110 by receiving power from the power system, and transfers the power stored in the battery of the electric vehicle 110 to the power system to charge the battery. can perform the function of discharging. Accordingly, the V2G electric vehicle 110 can be charged/discharged in both directions through the EV charging/discharging device 120.

The power storage device (or energy storage device, 130) refers to any device that stores power and makes it available when needed. The power storage device 130 may perform a function of stabilizing irregular power produced through renewable energy such as wind power or sunlight. In addition, the power storage device 130 may perform a function of minimizing expensive peak power demand by storing power in a time zone other than the peak power usage time zone and using the stored power during the peak power usage time zone. .

The power storage device 130 may be charged by receiving power from the power system according to a control command of an energy management system (EMS, not shown), and provide the stored power therein to the power system to discharge. can do.

The load 140 may receive power through a power system, the power storage device 130, or the V2G electric vehicle 110, and may include a specific building or facility. Hereinafter, in the present embodiment, the load 140 is a load of the charging station and means pure load power corresponding to power of buildings or facilities of the charging station.

Renewable energy source 150 may generate power using solar power, wind power, water power, and the like. Power generated through the renewable energy source 150 may be stored in the power storage device 130 (ESS) and/or the V2G electric vehicle 110 or supplied to the load 140 through the power system.

An EV charging service provider can design such a V2G electric vehicle charging station 100 and link it to a consumer's power system. At this time, in order to maximize the return on investment for long-term operation, the EV charging service provider uses distributed resources constituting the V2G electric vehicle charging station 100 (e.g., ESS, renewable energy sources, EV charging and discharging devices, etc.) It is necessary to calculate the optimal dose. Here, "optimal" means that the profits of EV charging companies that generate profits by providing charging services for electric vehicles are maximized through additional demand management, thereby maximizing the overall net profit from long-term operation. Hereinafter, a method of calculating the optimal capacity of distributed resources constituting the demand management type V2G electric vehicle charging station 100 will be described in detail.

2 is a flowchart illustrating a design method of a demand management type V2G electric vehicle charging station according to an embodiment of the present invention.

Referring to FIG. 2 , the V2G EV charging station design device according to an embodiment of the present invention can calculate the optimal capacity of distributed resources constituting a demand-managed V2G electric vehicle charging station that can be linked to a power system.

The V2G EV charging station design device may receive site information (or location information) where the V2G electric vehicle charging station is to be installed in connection with the power system from an EV charging company or system operator (S205).

When the installation location of the V2G electric vehicle charging station is determined, the V2G EV charging station design device may configure one or more databases for storing data related to distributed resources constituting the V2G electric vehicle charging station (S210).

The V2G EV charging station design device is a renewable energy source database for storing data related to new and renewable energy sources, a load database for storing data related to the load of the charging station, an ESS database for storing ESS-related data, and a V2G An EV database for storing data related to electric vehicles and EV charging/discharging devices, and an additional information database for storing additional information related to design of V2G electric vehicle charging stations may be configured.

The new and renewable energy source database may store weather data corresponding to the installation location of the V2G EV charging station, and data on the output pattern and generation amount of the new and renewable energy sources. In this case, the meteorological data may be received from an external meteorological server. Data on the output pattern and generation amount of the renewable energy source may be calculated through a V2G EV charging station design device. That is, the V2G EV charging station design device can calculate the daily output pattern and annual power generation amount of the photovoltaic power generation system using the amount of solar radiation, cloudiness, and outdoor temperature of the site. In addition, the V2G EV charging station design device can calculate the daily output pattern and annual power generation amount of the wind power generation system using past wind speed data of the corresponding site.

The load database may store data related to the net load of the charging station. The load database may store data on daily load patterns and load power consumption consumed by the V2G EV charging station.

The ESS database may store data related to battery capacity, power conditioning system (PCS) capacity, battery efficiency, PCS efficiency, charge/discharge constraints, and the like. The EV database includes EV PCS capacity, EV charge/discharge unit efficiency, charge/discharge constraints, V2G EV model, battery capacity by model, battery efficiency by model, SOC (State Of Data on charge constraints and regional penetration rate by model can be stored. The additional information database may store data on grid connection conditions, electricity rates, EV charging rates, V2G incentives, power supply point constraints, and the like.

The V2G EV charging station design device may select a resource capacity candidate group of distributed resources constituting the charging station in consideration of cost and installation environment that can be input to the design of the V2G EV charging station (S215). Here, the distributed resources to be selected for the resource capacity candidates may include at least one of a renewable energy source, an ESS, and an EV charging and discharging device.

For example, as shown in FIG. 3, the V2G EV charging station design device may classify renewable energy sources into predetermined capacity units (eg, 10 kW). In addition, the V2G EV charging station design device may classify the PCS of the ESS into a predetermined capacity unit (eg, 50 kW). In addition, the V2G EV charging station design device may classify the battery of the ESS into a predetermined capacity unit (eg, 250 kWh). In addition, the V2G EV charging station design device may classify EV charging and discharging devices into units of a predetermined number (eg, 1EA). In a state where such distributed resources are classified into a certain capacity unit, the V2G EV charging station design device may select a resource capacity candidate group by mapping the capacity of each distributed resource using a full combination method. For example, if the capacity of new and renewable energy sources, the capacity of ESS PCS, the capacity of ESS batteries, and the number of V2G EV charging and discharging devices are each classified by 10, a total of 10000 (= 10 * 10 * 10 * 10) combinations having the number of cases A resource capacity candidate group can be selected. For each of the resource capacity candidates selected in this way, a long-term economic feasibility evaluation is performed through an optimal operation strategy to derive a target resource capacity candidate (or target resource capacity combination) corresponding to the optimal capacity combination.

The V2G EV charging station design device may sequentially receive candidate capacity combinations of distributed resources existing in the resource capacity candidate group. The V2G EV charging station design device can calculate the daily charge/discharge schedule of ESS and V2G EV by deriving an optimal operation strategy based on the input candidate capacity combination.

Prior to calculating this daily charge/discharge schedule, the V2G EV charging station design device provides information on V2G electric vehicles expected to visit the V2G EV charging station to be installed in a specific area (hereinafter, 'V2G EV information' for convenience of description). referred to as) can be derived (S220). Here, the V2G EV information may include information about EV battery capacity, EV SOC constraints, expected EV charger connection, EV charger connection time, and the like.

The V2G EV can be charged by receiving power from the charging station, and can also be discharged to send power to the charging station. That is, from an energy point of view, the V2G EV during charging can be treated as the same as the net load, and the V2G EV during discharging can be treated as the same as the ESS.

Since V2G EVs are not located in a fixed location but can be linked to or separated from a charging station at any time, it is difficult to determine the charge/discharge pattern of V2G EVs unequivocally and is closely related to the optimal operation strategy described later. Therefore, based on statistical data or other data of other charging stations, V2G EV's specifications (battery capacity), V2G EV's SOC constraints (charging start SOC, charging end SOC, dischargeable SOC, etc.), V2G EV's charging station usage frequency It is necessary to reflect the uncertainty of V2G EV by stochastically generating

As an example, as shown in FIG. 4, the V2G EV charging station design device is based on information about battery capacity for each model of V2G electric vehicles, SOC constraints for each model, regional penetration rate for each model, and EV charger usage probability for each model. Information on V2G electric vehicles expected to visit the corresponding charging station (ie, V2G EV information) may be probabilistically derived. A detailed description of this will be described later with reference to FIG. 6 below.

The V2G EV charging station design device may calculate a daily charge/discharge schedule for the V2G EV and ESS with minimization of the daily electricity charge through load movement as an objective function (S225).

The V2G EV charging station design device calculates revenue for a certain period based on the daily charging/discharging schedule of V2G EV and ESS, and performs long-term economic evaluation in consideration of the calculated revenue and investment cost (S230). .

As an example, as shown in FIG. 5, the V2G EV charging station design device includes load patterns, renewable energy source output patterns, V2G EV information, ESS information, electricity rate information, EV charging rates, V2G incentive information, power supply point restrictions Using condition information, etc., an optimal operation strategy can be established so that the daily electricity charge can be minimized, and based on this, the daily charge/discharge schedule of the V2G EV and ESS can be calculated. Here, the ESS information may include information about ESS battery capacity, ESS battery efficiency, ESS PCS capacity, ESS PCS efficiency, charge/discharge constraints, and the like. The V2G EV information may include information about EV battery capacity, EV SOC constraints, EV charger expected to be connected to an electric vehicle, EV charger connection time, and the like.

The V2G EV charging station design device can calculate the amount of daily electricity cost savings based on the daily charge/discharge schedule of V2G EV and ESS. The design of the V2G EV charging station can calculate economic returns in units of one week, one month, one year, or several years, based on the amount of daily electricity bill savings. The V2G EV charging station design device can conduct a long-term economic evaluation by comprehensively considering economic returns and investment costs for a certain period of time. That is, the V2G EV charging station design device can derive Return On Investment (ROI) and Internal Return Rate (IRR) according to long-term operation.

The V2G EV charging station design device may perform economic evaluation according to long-term operation for all candidate capacity combinations existing in the resource capacity candidate group by repeatedly performing the operations of steps 220 to 230 described above (S235).

Based on this long-term economic evaluation, the V2G EV charging station design device may detect a candidate capacity combination with the highest economic efficiency among candidate capacity combinations of distributed resources existing in the resource capacity candidate group (S245). The V2G EV charging station design device may determine the detected candidate capacity combination as a target resource capacity candidate (or target resource capacity combination).

The V2G EV charging station design apparatus may derive an optimal resource capacity combination of the distributed resources constituting the V2G EV charging station by evaluating sensitivity for each distributed resource with respect to the target resource capacity combination (S245). That is, the V2G EV charging station design device can derive the optimal capacity that has the greatest impact on economic feasibility while changing the capacity of each distributed resource of the target resource capacity combination by a predetermined minimum unit capacity. A detailed description of this will be described later with reference to FIG. 7 below.

When the optimal capacity combination of the distributed resources is derived through the above-described process, the V2G EV charging station design device may perform a system impact assessment based on the optimal capacity combination of the distributed resources (S250). This is because even if the optimal capacity combination of distributed resources is economical or meets a special purpose, an imbalance of the corresponding combination may have an unexpected effect on the power system in actual system operation.

As a result of performing the system impact assessment, the V2G EV charging station design device may check whether the capacity combination of the distributed resources violates the grid connection standard (S255).

As a result of checking in step 255, if the capacity combination of the corresponding distributed resources violates the grid connection standard, the V2G EV charging station design device selects the next optimal resource capacity combination and performs the grid impact assessment again based on this. It can be performed (S260).

Meanwhile, as a result of checking in step 255, if the capacity combination of the corresponding distributed resources satisfies the grid connection standard, the V2G EV charging station design device converts the capacity combination of the distributed resources to the optimal capacity of the distributed resources constituting the V2G EV charging station. It can be determined as (S265). Meanwhile, steps 250 to 265 described above are not necessarily necessary components in the charging station design method according to the present invention, and may be omitted according to an embodiment.

As described above, the V2G electric vehicle charging station design method according to the present invention calculates the optimal capacity of a plurality of distributed resources constituting the charging station, and constructs a V2G electric vehicle charging station based on the distributed resources having the optimal capacity. By designing, long-term profits of the V2G electric vehicle charging station can be maximized.

6 is a flowchart illustrating a method of generating V2G EV information necessary for deriving an optimal operating strategy.

Referring to FIG. 6, the V2G EV charging station design device may set a time unit (t) for generating information (ie, V2G EV information) about V2G electric vehicles expected to visit the V2G EV charging station. (S605). For example, the time unit may be set to 15 minutes, but is not necessarily limited thereto.

The V2G EV charging station design device may arbitrarily set the EV charger (i) accessible by the V2G electric vehicle in the set time zone (S610). In addition, the V2G EV charging station design device may call information about the probability of using the charger by time of the set EV charger (i) (S615).

The V2G EV charging station design device may check whether the V2G electric vehicle is connected to the corresponding EV charger (i) by using the information on the charger usage probability per hour (S620).

As a result of checking in step 620, if the V2G electric vehicle is not connected to the corresponding EV charger (i), the V2G EV charging station design device may set another EV charger (i+1) accessible by the corresponding V2G electric vehicle. (S660). Thereafter, the V2G EV charging station design device may move to step 615 and repeatedly perform the above-described operations below step 615.

Meanwhile, as a result of checking in step 620, when the V2G electric vehicle is connected to the corresponding EV charger (i), the V2G EV charging station design device may call information on the regional penetration rate by model (S625).

The V2G EV charging station design device may determine the model of the V2G electric vehicle connected to the corresponding EV charger (i) by using the information on the regional penetration rate for each model (S630). In addition, the V2G EV charging station design device may probabilistically determine the SOC constraints of the V2G electric vehicle corresponding to the determined model (S635).

The V2G EV charging station design device may generate information about the V2G electric vehicle connected to the EV charger (i) at the corresponding time (t) (S645). For example, the V2G EV charging station design device can generate information about the battery capacity, SOC constraints, charger connection time, etc. of the V2G electric vehicle connected to the EV charger (i) at a corresponding time (t).

The V2G EV charging station design device may check whether V2G EV information on all EV chargers is generated (S645).

As a result of checking in step 645, if V2G EV information on all EV chargers is not generated, the V2G EV charging station design device may set another EV charger (i+1) accessible by the corresponding V2G electric vehicle (S660). ). Thereafter, the V2G EV charging station design device may move to step 615 and repeatedly perform the above-described operations below step 615.

Meanwhile, as a result of checking in step 645, if V2G EV information for all EV chargers is generated, the V2G EV charging station design device can check whether V2G EV information for all time units (or time zones) is generated ( S655).

As a result of checking in step 655, if V2G EV information for all time units is not generated, the V2G EV charging station design device may set the next time unit (t+1) for generating V2G EV information. Thereafter, the V2G EV charging station design device may move to step 610 and repeatedly perform the above-described operations below step 610.

Meanwhile, as a result of checking in step 655, if V2G EV information for all time units is generated, the V2G EV charging station design device collects information about V2G electric vehicles expected to visit the V2G EV charging station and stores it in the database. can be saved The collected V2G EV information can be used to calculate a daily charge/discharge schedule through an optimal operation strategy.

7 is a flowchart illustrating a method of deriving an optimal resource capacity combination of distributed resources.

Referring to FIG. 7 , the V2G EV charging station design device may perform long-term economic evaluation through an optimal operation strategy for a pre-selected resource capacity candidate group. The V2G EV charging station design device may determine a target resource capacity combination from among resource capacity candidates based on long-term economic evaluation (S705). In this embodiment, it is assumed that the target resource capacity combination is {PV A kW, ESS PCS B kW, ESS BAT C kWh, V2G EV Charger D EA}.

The V2G EV charging station design device may change the distributed resource capacity of any one of the target resource capacity combinations in a predetermined minimum unit (S710). At this time, the predetermined minimum unit may be set differently for each distributed resource.

The V2G EV charging station design device may calculate the total revenue change due to the change in capacity of the distributed resource using the above-described economic evaluation method (S715).

The V2G EV charging station design device may change the capacity of other distributed resources of the target resource capacity combination in a predetermined minimum unit and calculate the total revenue change due to the capacity change of the corresponding distributed resource (S720).

When the calculation of the total revenue change of all distributed resources is completed, the V2G EV charging station design device may arrange the distributed resources according to the size of the total revenue change according to the capacity change based on the calculated result value. That is, the V2G EV charging station design device may arrange the distributed resources according to the priority corresponding to the size of the total revenue change.

The V2G EV charging station design device may detect a distributed resource having the highest priority (ie, a distributed resource having a maximum profit change) among distributed resources of a target resource capacity combination (S725). In this embodiment, the distributed resource that provides the largest change in revenue is PV, the next distributed resource that provides the change in revenue is the V2G EV charging and discharging device, the next distributed resource that provides the change in revenue is the ESS PCS, Distributed resources that provide small changes in returns are assumed to be ESS BAT.

The V2G EV charging station design device may change the capacity of the detected distributed resource in a predetermined minimum unit (S730). In addition, the V2G EV charging station design device may calculate a rate of change in revenue due to a change in capacity of the detected distributed resource (S735).

The V2G EV charging station design device may compare the rate of change in revenue according to the capacity of the current distributed resource with the rate of change in revenue according to the capacity of the immediately previous distributed resource (S740).

As a result of the comparison in step 740, if the rate of change in revenue according to the capacity of the current distributed resource is smaller than the rate of change in revenue according to the capacity of the immediately preceding distributed resource, the V2G EV charging station design device may determine the capacity of the previous distributed resource as the optimal resource capacity. Yes (S750).

On the other hand, as a result of the comparison in step 740, if the rate of change in revenue according to the capacity of the current distributed resources is greater than the rate of change in revenue according to the capacity of the immediately preceding distributed resources, the V2G EV charging station design device determines that the rate of change in revenue according to the capacity of the current distributed resources is It is possible to check whether it is equal to or greater than a predetermined threshold (S745).

As a result of checking in step 745, if the rate of change in revenue according to the current distributed resource capacity is equal to or greater than a predetermined threshold, the V2G EV charging station design device moves to step 730 again to change the current distributed resource capacity to a predetermined minimum unit. . The V2G EV charging station design device may calculate a revenue change rate based on the changed distributed resource capacity, and compare the calculated revenue change rate with a revenue change rate according to the previous distributed resource capacity.

That is, when the rate of change in revenue according to the capacity of the current distributed resource is greater than the rate of change in revenue according to the capacity of the previous distributed resource and is greater than or equal to a predetermined threshold, the V2G EV charging station design device continues to change the capacity of the distributed resource in a predetermined minimum unit. While calculating the rate of change in profit, the calculated rate of change in profit can be compared with the immediately preceding rate of change in profit. By repeatedly performing these processes, it is possible to find the optimal capacity of the most profitable distributed resource.

On the other hand, as a result of checking in step 745, if the rate of change in revenue according to the capacity of the current distributed resource is less than a predetermined threshold (ie, there is no significant change in the rate of return), the V2G EV charging station design device further increases the capacity of the distributed resource Without any further change, the current distributed resource capacity may be determined as the optimal resource capacity (S750).

When the optimal capacity of the distributed resource is determined, the V2G EV charging station design device moves to step 725 to derive the optimal capacity of the next priority distributed resource. The V2G EV charging station design device may repeatedly perform the above-described operations of steps 725 to 750 until the optimal capacity of all distributed resources is determined (S755).

In this way, the V2G EV charging station design apparatus may derive an optimal resource capacity combination of distributed resources constituting the V2G EV charging station by evaluating sensitivity for each distributed resource with respect to the target resource capacity combination.

8 is a block diagram of a V2G EV charging station design device according to an embodiment of the present invention.

Referring to FIG. 8 , the V2G EV charging station design device 800 according to an embodiment of the present invention includes a database 810, a capacity candidate selection unit 820, a V2G EV information generation unit 830, and an economic evaluation unit ( 840), an optimal capacity detection unit 850, and a system effect evaluation unit 860. The components shown in FIG. 8 are not essential to implement a V2G EV charging station design device, so the V2G EV charging station design device described herein may have more or less components than those listed above. can

The database 810 may store data related to the design of the V2G EV charging station. The database 810 may include a renewable energy source database, a load database, an ESS database, an EV database, and an additional information database.

The new and renewable energy source database may store weather data corresponding to the installation location of the V2G EV charging station, and data on the output pattern and generation amount of the new and renewable energy sources. The load database may store data on daily load patterns and load power consumption consumed by the V2G EV charging station. The ESS database may store data on battery capacity, PCS capacity, battery efficiency, PCS efficiency, charge/discharge constraints, and the like.

The EV database includes EV PCS capacity, EV charge/discharge unit efficiency, charge/discharge constraints, V2G EV models, battery capacity by model, battery efficiency by model, and SOC constraints by model (defined by EV owners). , data on regional penetration rate by model can be stored. The additional information database may store data on grid connection conditions, electricity rates, EV charging rates, V2G incentives, power supply point constraints, and the like.

The capacity candidate group selection unit 820 may select a resource capacity candidate group of distributed resources constituting the corresponding charging station in consideration of cost and installation environment that can be input to the design of the V2G EV charging station.

The capacity candidate group selector 820 may select a resource capacity candidate group by classifying the capacities of the distributed resources constituting the charging station into predetermined capacity units and mapping the classified distributed resources in a full combination method.

The V2G EV information generation unit 830 may derive information (ie, V2G EV information) about V2G electric vehicles expected to visit a V2G EV charging station to be installed in a specific region. The V2G EV information may include information about EV battery capacity, EV SOC constraints, expected connection charger of electric vehicle, charger connection time, and the like.

The V2G EV information generation unit 830 is a V2G EV that is expected to visit the corresponding charging station based on information about battery capacity for each model, SOC constraints for each model, regional penetration rate for each model, charger usage probability for each model, and the like of V2G electric vehicles. Information about electric vehicles can be probabilistically derived.

The economic feasibility evaluation unit 840 may establish an optimal operation strategy with minimization of the daily electricity charge through load movement as an objective function, and calculate a daily charge/discharge schedule for the V2G EV and ESS based on this.

The economic evaluation unit 840 may calculate revenue for a certain period based on the daily charge/discharge schedule of V2G EV and ESS, and perform long-term economic evaluation in consideration of the calculated revenue and investment cost.

The optimal capacity detection unit 850 may detect a capacity combination of distributed resources having the highest economical efficiency among resource capacity candidates, and may determine the capacity combination of the detected distributed resources as a target resource capacity candidate (or target resource capacity combination). .

The optimal capacity detection unit 850 may derive an optimal resource capacity combination of distributed resources constituting the V2G EV charging station by evaluating sensitivity for each distributed resource with respect to the target resource capacity combination. That is, the optimal capacity detection unit 850 may derive the optimal capacity that has the greatest effect on economic feasibility while changing each distributed resource capacity of the target resource capacity combination by a predetermined minimum unit capacity.

The system impact evaluation unit 860 may perform a system impact evaluation based on an optimal resource capacity combination of distributed resources. The system effect evaluation unit 860 may check whether the capacity combination of the corresponding distributed resources violates the grid linkage criterion based on the result of performing the system impact assessment.

As a result of the check, if the capacity combination of the distributed resources satisfies the grid linkage criterion, the grid effect evaluation unit 860 may determine the capacity combination of the distributed resources as the optimal capacity of the distributed resources constituting the V2G EV charging station. . On the other hand, as a result of the confirmation, the system effect evaluation unit 860 selects the optimal resource capacity combination corresponding to the next rank when the capacity combination of the distributed resources violates the grid connection standard, and then evaluates the system effect again based on this. can be performed.

As described above, the V2G electric vehicle charging station design device according to the present invention calculates the optimal capacity of the distributed resources constituting the charging station and designs the V2G electric vehicle charging station based on the distributed resources having the optimal capacity. , It is possible to maximize long-term profits of the V2G electric vehicle charging station.

The above-described present invention can be implemented as computer readable code on a medium on which a program is recorded. The computer-readable medium includes all types of recording devices in which data that can be read by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. there is Accordingly, the above detailed description should not be construed as limiting in all respects and should be considered illustrative. The scope of the present invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

100: V2G electric vehicle charging station 110: V2G electric vehicle
120: EV charging and discharging device 130: power storage device
140: load 150: renewable energy source
800: V2G EV charging station design device 810: database
820: Capacity candidate selection unit 830: V2G EV charge/discharge pattern calculation unit
840: economic evaluation unit 850: optimal capacity detection unit
860: grid impact evaluation unit

Claims (15)

Selecting a resource capacity candidate group of distributed resources constituting a V2G (Vehicle to Grid) electric vehicle charging station;
generating information about V2G electric vehicles expected to visit the charging station;
performing a long-term economic evaluation through an optimal management strategy for candidate capacity combinations included in the resource capacity candidate group;
determining a target resource capacity combination from among the resource capacity candidates based on the long-term economic feasibility evaluation; and
Evaluating the sensitivity of each distributed resource based on the target resource capacity combination to derive an optimal resource capacity combination of the distributed resources constituting the charging station. According to claim 1,
V2G electric vehicle charging station design method further comprising the step of performing a system impact assessment based on the optimal resource capacity combination of the distributed resources. According to claim 1,
The V2G electric vehicle charging station design method further comprising configuring one or more databases for storing data related to distributed resources constituting the charging station. The method of claim 1, wherein the selection step,
A method for designing a V2G electric vehicle charging station, characterized in that each of the distributed resources is classified into a predetermined capacity unit, and a resource capacity candidate group is selected by mapping the capacity of the classified distributed resources with a full combination method. . delete The method of claim 1, wherein the generating step,
Based on at least one information of the battery capacity of each type of V2G electric vehicle, state of charge (SOC) constraint condition for each type, regional penetration rate for each type, and electric vehicle (EV) charger usage probability per hour, the corresponding charging station may be visited. A method for designing a V2G electric vehicle charging station characterized by probabilistically generating information about V2G electric vehicles expected to be. The method of claim 1, wherein the performing step,
Calculating a daily charge/discharge schedule for a V2G electric vehicle and a power storage device with minimization of a daily electricity charge through load movement as an objective function; and
Calculating revenue for a certain period based on the daily charging / discharging schedule of the V2G electric vehicle and power storage system (ESS), and performing a long-term economic evaluation in consideration of the revenue and investment cost for the certain period Characteristics of V2G electric vehicle charging station design method. The method of claim 7, wherein the calculating step,
Optimum operation to minimize daily electricity cost by using at least one of load pattern, renewable energy source output pattern, V2G EV information, ESS information, electricity rate information, EV charging rate, V2G incentive information, and power supply point constraint information A method for designing a V2G electric vehicle charging station, characterized by establishing a strategy and calculating a daily charging / discharging schedule of the V2G electric vehicle and the power storage device based on the optimal operation strategy. The method of claim 1, wherein the determining step,
A method for designing a V2G electric vehicle charging station, wherein a candidate capacity combination having the highest economy among the candidate capacity combinations is detected, and the detected candidate capacity combination is determined as the target resource capacity combination. The method of claim 1, wherein the derivation step,
V2G electric vehicle charging station design method, characterized in that for detecting the optimal capacity that has the greatest effect on economic feasibility while changing the capacity of each distributed resource of the target resource capacity combination in a predetermined minimum unit. A computer program stored in a computer readable recording medium so that the method according to any one of claims 1 to 4 and 6 to 10 is performed on a computer. a capacity candidate selection unit that selects a resource capacity candidate group of distributed resources constituting a V2G (Vehicle to Grid) electric vehicle charging station;
a V2G EV information generating unit generating information about V2G electric vehicles expected to visit the charging station;
an economic evaluation unit for performing a long-term economic evaluation through an optimal operation strategy for candidate capacity combinations included in the resource capacity candidate group; and
Based on the long-term economic feasibility evaluation, a target resource capacity combination is determined from among the resource capacity candidates, and a sensitivity for each distributed resource is evaluated based on the target resource capacity combination to detect an optimal resource capacity combination of the distributed resources constituting the charging station. V2G electric vehicle charging station design device including an optimal capacity detection unit that According to claim 12,
A V2G electric vehicle charging station design device further comprising a system impact evaluation unit that performs a system impact evaluation based on the optimal resource capacity combination of the distributed resources. According to claim 12,
V2G electric vehicle charging station design device further comprising a database for storing data related to distributed resources constituting the charging station. delete

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