KR20230010516A - Operation scheduling method and device for mobile charging station - Google Patents

Abstract

Disclosed are a method for scheduling an operation of a mobile charging station and a device therefor. The method for scheduling an operation of a mobile charging station comprises the steps of: (a) scheduling a routing path for moving a mobile charging station to an overloaded fixed charging station to reduce a number of electric vehicles waiting at the fixed charging station based on transportation network-related information; (b) performing charge-discharge scheduling of active and reactive power of the mobile charging station based on distribution network-related information; and (c) deriving an optimal path scheduling of the mobile charging station by using a routing path scheduling result and charge-discharge scheduling result. The present invention can minimize a number of electric vehicles waiting at fixed charging stations and maintain normal voltage levels along distribution feeders.

Description

Operation scheduling method and device for mobile charging station

The present invention is to provide a mobile charging station operation scheduling method and apparatus therefor.

Given that conventional gasoline vehicles generate significant greenhouse gas emissions and carbon pollution, electric vehicles (EVs) can gradually reduce environmental pollution. In addition, compared to gasoline vehicles, EVs can reduce noise pollution and maintenance costs, as well as reduce greenhouse gas emissions and carbon pollution in the transportation sector.

However, for EVs, insufficient charging infrastructure, long charging times, limited EV range, and high EV battery costs are slowing the deployment of EVs and related services in transportation and distribution networks.

In addition, a fixed charging station (FCS) is one of the most important tasks for the spread of EVs. FCS is a fixed facility with a limited number of charging poles, which uses power from public roads, commercial buildings, and residential distribution grids for EV charging. The lack of mobility and limited discharge capability of FCS greatly increases EV waiting time in FCS, causing dissatisfaction for EV drivers, as well as causing grid overload and instability when high charging demand occurs with a large number of EVs.

The present invention is to provide a method and apparatus for scheduling operation of a mobile charging station.

In addition, the present invention can simultaneously schedule charging and discharging of a mobile charging station (MCS) and road routing for supplying power to electric vehicles on standby at a fixed charging station (FCS) while maintaining normal operating conditions of the distribution system. It is to provide an operation scheduling method and device for a mobile charging station in the present invention.

In addition, the present invention is to provide a method and apparatus for scheduling operation of a mobile charging station capable of minimizing the number of electric vehicles on standby at a stationary charging station and maintaining a normal voltage level along a distribution feeder under actual operating conditions of a distribution system.

According to one aspect of the present invention, a method for scheduling operation of a mobile charging station may be provided.

According to an embodiment of the present invention, (a) scheduling a routing path for moving a mobile charging station to a stationary charging station in an overloaded state in order to reduce the number of electric vehicles waiting at the stationary charging station based on traffic network-related information; (b) performing charge-discharge scheduling of active and reactive power of the mobile charging station based on distribution network related information; and (c) deriving optimal path scheduling of the mobile charging station using the routing path scheduling result and the charge-discharge scheduling result.

The mobile charging station is a driving device equipped with an energy storage system (ESS).

In the step (b), charge-discharge scheduling may be performed in consideration of the number of electric vehicles waiting for charging at the stationary charging station and the total deviation of the voltage level for a specific bus connected to the distribution network at time t.

The step (b) further uses the state of charge (SOC) considering the state of charge, battery capacity, charge and discharge efficiency, movement efficiency, movement state, and charge and discharge power of the mobile charging station, so that the charge- Discharge scheduling can be performed.

In the step (a), the routing path may be scheduled so that the travel time to the stationary charging station in the overloaded state is minimized.

According to another aspect of the present invention, an apparatus for scheduling the operation of a mobile charging station is provided.

According to one embodiment of the present invention, a memory for storing at least one instruction; and a processor executing instructions stored in the memory, wherein the instruction executed by the processor comprises: (a) a stationary charging station in an overloaded state in order to reduce the number of electric vehicles waiting at the stationary charging station based on traffic network-related information; Scheduling a routing path for moving the mobile charging station to; and (b) performing charge-discharge scheduling of active and reactive power of the mobile charging station based on the information related to the distribution network. and (c) deriving optimal path scheduling for the mobile charging station using the routing path scheduling result and the charge-discharge scheduling result.

According to another embodiment of the present invention, a physical layer unit including at least one entity—the entity includes a load, a mobile charging station, a stationary charging station, and an electric vehicle connected to a power distribution network; a transportation layer unit containing information about a road network; and performing charge-discharge scheduling considering active and reactive power flow and consumption, reactive power capacity and voltage of entities included in the physical layer, and routing scheduling of mobile charging stations based on traffic network information included in the transportation layer. However, a framework device for scheduling operation of a mobile charging station including a cyber layer unit for deriving an optimal path scheduling of the mobile charging station using the routing path scheduling result and the charge-discharge scheduling result may be provided. .

Road routing and mobile charging station for supplying power to electric vehicles on standby at a fixed charging station (FCS) while maintaining normal operating conditions of a power distribution system by providing an operation scheduling method and apparatus for a mobile charging station according to an embodiment of the present invention. (MCS: mobile charging station) has the advantage of being able to schedule charging and discharging at the same time.

In addition, the present invention has an advantage of minimizing the number of electric vehicles on standby at a stationary charging station and maintaining a normal voltage level along the distribution feeder under actual operating conditions of the distribution system.

1 is a diagram schematically showing a system configuration for controlling a mobile charging station according to an embodiment of the present invention.
Figure 2 is a diagram showing a framework for optimal operation scheduling of a mobile charging station according to an embodiment of the present invention.
3 is a flowchart illustrating a method for scheduling operation of a mobile charging station according to an embodiment of the present invention.
4 is a diagram illustrating a system model for a mobile charging station control framework according to an embodiment of the present invention.
5 to 7 are diagrams for explaining EV queuing dynamics at stationary charging stations according to an embodiment of the present invention;
8 is a block diagram schematically showing the internal configuration of a server according to an embodiment of the present invention.

Singular expressions used herein include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "consisting of" or "comprising" should not be construed as necessarily including all of the various components or steps described in the specification, and some of the components or some of the steps It should be construed that it may not be included, or may further include additional components or steps. In addition, terms such as "...unit" and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented as hardware or software or a combination of hardware and software. .

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

1 is a diagram schematically showing a system configuration for controlling a mobile charging station according to an embodiment of the present invention, and FIG. 2 is a diagram showing a framework for optimal operation scheduling of a mobile charging station according to an embodiment of the present invention. it is a drawing

Referring to FIG. 1 , a system 100 according to an embodiment of the present invention includes a plurality of stationary charging stations 110, a plurality of mobile charging stations 120, and a server 130.

The plurality of stationary charging stations 110 are charging stations installed on a road or in a specific building and are connected to the distribution system, and supply power to an electric vehicle (EV: electric vehicle, hereinafter referred to as EV) while maintaining the operating conditions of the distribution system. is a means to

As shown in FIG. 2 , the stationary charging station 110 is connected to the power distribution network, and it is natural that the stationary charging station 110 may have a different number of chargeable poles depending on the size of the stationary charging station 110 . Accordingly, the power supplied to each stationary charging station 110 may also be different.

The stationary charging station 110 does not necessarily supply power limitedly only to EVs, and may supply power to the mobile charging station 120, or may perform EV charging in conjunction with the mobile charging station 120.

The plurality of mobile charging stations 120 are mounted on vehicles (eg, trucks) and are means capable of supplying power to EVs while moving. The mobile charging station 120 may be a vehicle equipped with a utility-scale energy storage system (ESS).

The plurality of mobile charging stations 120 move to the overloaded fixed charging station 110 in order to reduce the number of EVs waiting at the stationary charging station 110 while maintaining normal operation of the power distribution system (power grid), and the stationary charging station 110 In conjunction with the EV can be charged.

The server 130 may control the movement of the mobile charging station 120 to reduce the load of the overloaded stationary charging station 110 and the number of standby EVs while maintaining normal operation of the power grid. That is, the server 130 performs optimal operation scheduling of the mobile charging station to reduce the number of standby EVs within the allowable voltage range of the power grid by considering road routing and charging and discharging according to the movement of the mobile charging station 120. can do.

To this end, the server 130 may perform operation scheduling of the mobile charging station 120 in consideration of the three layers as shown in FIG. 2 .

This will be briefly described with reference to FIG. 2 .

A framework for optimal operation scheduling of a mobile charging station may include a physical layer, a cyber layer, and a transportation layer.

The physical layer includes a plurality of entities. Here, the plurality of objects may include a load connected to a power distribution system (power grid), a mobile charging station, a stationary charging station, an EV, and the like.

The cyber layer calculates road routing and energy scheduling of the mobile charging station in consideration of the active/reactive power flow and consumption of entities included in the physical layer and the reactive power capacity and voltage of the mobile charging station.

The transportation layer includes information about road networks.

The server 130 according to an embodiment of the present invention relates to an optimization framework of a cyber layer that calculates road routing scheduling in a transport layer and energy charge/discharge scheduling of a mobile charging station in a physical layer.

In other words, the server 130 can reduce the number of waiting EVs and maintain the normal voltage level of the power distribution system by quickly moving the mobile charging station to the stationary charging station in an overloaded state in consideration of road routing and energy scheduling of the mobile charging station. there is. This will be more clearly understood by the following description.

3 is a flowchart illustrating a mobile charging station operation scheduling method according to an embodiment of the present invention, and FIG. 4 is a diagram illustrating a system model for a mobile charging station control framework according to an embodiment of the present invention. 7 is a diagram illustrating EV queue dynamics at a stationary charging station according to an embodiment of the present invention.

In step 310, the server 130 receives basic information for scheduling the operation of the mobile charging station. Here, the basic information may be power distribution network related information and transportation network related information.

This will be described in more detail.

Hereinafter, we will consider a situation in which at least one mobile charging station is moved to a stationary charging station in an overloaded state through a road network for fast charging of an EV waiting for charging at a stationary charging station connected to a distribution network (power grid). Also, as already mentioned above, a mobile charging station may be a truck equipped with a large-scale ESS.

Also, as shown in FIG. 4, it is assumed that there are two types of nodes, such as a node set (I) on a road network and a bus set (B) in a distribution network (power network).

Each node and bus may represent a starting point and an ending point in the road network and distribution network, respectively.

Depending on whether stationary charging stations are connected to nodes and buses in road networks and grids, sets I and B can be decomposed into the following sub-sets:

집합과

집합은 동일할 수 있다. If the stationary charging station is located at the intersection of the road network and the distribution network,

set and

Sets can be identical.

와 같이 나타낼 수 있다. S and T represent a set of mobile charging stations and a mobile charging station scheduling period, respectively. The number of elements in set A is

can be expressed as

In addition, hereinafter, it is assumed that a distribution system operator (DSO) owns a storage capable of parking and charging a mobile charging station having the same battery capacity before dispatching.

The DSO has a server 130 that performs optimal operation scheduling for each mobile charging station, and can reduce the number of standby EVs while maintaining normal distribution system operation. In addition, it is assumed that input data required for scheduling are pre-input into the server 130 prior to optimal scheduling of the mobile charging station.

Input data can be classified into fixed parameters and predictive parameters.

내 충전 폴의 수,

내 충전 폴의 수, 버스

에서

와

의 충전율, 도로 네트워크와 배전망의 토폴로지 정보, 배전망 파라미터(예를 들어, 버스 h와 버스 b 사이의 분배 라인 저항

, 리액턴스

)를 포함할 수 있다. Fixed parameters are

the number of my charging poles,

Number of my charging poles, bus

at

Wow

, topological information of the road network and distribution network, distribution network parameters (e.g. distribution line resistance between bus h and bus b)

, reactance

) may be included.

, 이동형 충전소의 디스패치 전에 고정 충전소 i에 도착하는 예측된 EV의 수

, 및 버스 b 및 시간 t에서 비 EV 부하를 위한 예측된 유효 및 무효 전력 소비

를 포함할 수 있다. The prediction parameter is the predicted travel time of the mobile charging station between node i and node j

, the predicted number of EVs arriving at stationary charging station i before dispatch to the mobile charging station.

, and predicted active and reactive power consumption for non-EV loads at bus b and time t

can include

In an embodiment of the present invention, it is assumed that the power distribution system and the transportation system are equipped with smart meters for monitoring active and reactive power consumption and traffic sensors for monitoring road traffic conditions, respectively.

Smart meters can monitor consumers' real-time energy usage through advanced metering infrastructure and provide energy usage data to consumers and DSOs to achieve energy savings and efficient power distribution system operation.

It is assumed that the smart traffic sensor is used to collect road traffic information, and the collected road traffic information is provided to the DSO for routing reservation of a mobile charging station (MSC).

It is assumed that all of the input data as described above are input to the server 130, and a transportation scheduling method for a mobile charging station will be described below.

In step 315, the server 130 schedules a road routing path of a mobile charging station based on traffic network related information.

In step 320, the server 130 performs charge-discharge scheduling of active and reactive power of the mobile charging station based on the distribution network related information.

Although step 320 is described as being performed after step 315 in this specification, it is natural that step 315 and step 320 may be performed simultaneously in parallel.

In step 325, the server 130 derives an optimal route scheduling for a mobile charging station using the road routing path scheduling result and the charge-discharge scheduling result.

This will be more clearly understood by the following description.

As shown in FIG. 4 , the server 130 simultaneously executes road routing scheduling of the mobile charging station in the transportation network and active and reactive power charging/discharging scheduling of the mobile charging station in the power distribution network for optimal transportation scheduling of the mobile charging station.

The server 130 may calculate an optimal routing scheduling for moving a mobile charging station to an overloaded stationary charging station in order to reduce the number of EVs waiting at the stationary charging station. In addition, the server 130 may perform charging of the mobile charging station in the power grid and scheduling of the mobile charging station discharged to the stationary charging station and the standby EV in the power grid while ensuring a normal voltage profile.

을 가지는 각 노드

와 버스

에 대해 이동형 충전소의 최적 라우팅 스케줄링과 충전과 방전 스케줄링 문제는 수학식 1과 같은 각기 다른 의사 결정 변수

를 갖는 두개의 항으로 구성될 수 있다. scheduling period

Each node with

and bus

For the mobile charging station, the optimal routing scheduling and charging and discharging scheduling problems are different decision-making variables such as Equation 1

It can be composed of two terms with

는 전체 스케줄링 기간 T 동안 모든 고정 충전소에서 대기중인 EV의 전체 개수를 나타낸다. here,

represents the total number of EVs waiting at all stationary charging stations during the entire scheduling period T.

는 전압 크기 기준

에서 시간 t에서 버스 b에 대한 전압 크기

의 총 편차를 나타낸다. also,

is based on voltage magnitude

voltage magnitude across bus b at time t at

represents the total deviation of

는 배전 시스템의 전압 품질을 나타내며, 전압 품질이란 허용 전압 범위

내에서 전압 크기 기준

주변의 노드 전압 크기를 유지하는 것을 의미한다. also,

represents the voltage quality of the distribution system, and the voltage quality is the allowable voltage range

voltage magnitude standard within

It means maintaining the magnitude of the node voltage around it.

의 최소화는 전압 품질 관점에서 안정적인 배전 시스템 작동을 보장할 수 있다. 공식화된 최적화 문제는 배전 시스템의 전압 품질

을 유지하면서 운송 시스템의 고정 충전소에서 대기중인 EV의 수

를 최소화하는 것을 목표로 한다.

Minimization of can ensure stable power distribution system operation in terms of voltage quality. The formulated optimization problem is the voltage quality of the power distribution system.

The number of EVs waiting at stationary charging stations in the transport system while maintaining

aims to minimize

와

는 각각 대기중인 EV의 감소 및 노드 전압 크기의 평탄화에 대한 페널티 가중치를 나타내며,

이다. 가중치는 전압 편차에 상대적인 고정 충전소에서 대기중인 EV 수에 대한 상대적 중요성을 결정할 수 있다. Also, the parameter

Wow

denotes the penalty weight for the reduction of waiting EV and the flattening of the node voltage magnitude, respectively,

to be. The weighting can determine the relative importance of the number of EVs waiting at stationary charging stations relative to the voltage variation.

은 더 작은

로 인해 전압 크기의 평탄화를 줄이는 대신 대기중인 EV의 수를 추가로 감소시킬 수 있다.

과

의 단위는 서로 다르다. 따라서, 가중치를 정규화시킬 수 있다. For example, larger

is smaller

, it is possible to further reduce the number of standby EVs instead of reducing the flattening of the voltage magnitude.

class

units are different. Thus, the weights can be normalized.

과

는

과

로 각각 정규화될 수 있다. 여기서,

이고,

로 정규화될 수 있다.

class

Is

class

can be normalized, respectively. here,

ego,

can be normalized to

The constraints on the scheduling problem of mobile charging stations will be classified into three sections and explained.

Node visitation, traveling, and charging/discharging states of the mobile charging station will be described first.

는 노드 i 및 스케줄링 시간 t에서의 이동형 충전소 방문에 대한 상태를 결정하는 변수이다. binary decision variable

is a variable that determines the state of visiting a mobile charging station at node i and scheduling time t.

The visit state may include two operations, such as arrival at a node of a mobile charging station and stay according to the presence or absence of charging/discharging.

와

는 각각 노드 i 및 스케줄링 시간 t에서 이동형 충전소의 충전 및 방전 상태를 각각 결정한다. 이진 결정 변수

는 스케줄링 t에서 이동형 충전소의 이동 상태를 결정한다. Also, the binary decision variable

Wow

determine the charging and discharging states of the mobile charging station at node i and scheduling time t, respectively. binary decision variable

determines the movement state of the mobile charging station at scheduling t.

Equation 2 allows the mobile charging station to visit at most one node at time t.

내의 폴을 통해 충전되고 디스패치될 수 있는 추가적인 이동형 충전소가 없는 상태를 나타낸다. Equation 3 shows that when the sum of the number of EVs being charged and the number of mobile charging stations being charged is greater than the number of poles in the stationary charging station, at scheduling time t

It represents the absence of additional mobile charging stations that can be charged and dispatched via poles within.

Equation 3 can ensure that the mobile charging station does not interfere with the charging of the EV being charged at the stationary charging station.

The mobile charging station may be allowed to charge and discharge when not moving through the road network at scheduling time t, which can be expressed as Equations 4 and 5.

At the scheduling time t, charging and discharging of the mobile charging station are mutually exclusive, and when the mobile charging station does not visit the stationary charging station, charging and discharging do not occur can be expressed as Equation 6.

Hereinafter, routing scheduling, charging and discharging scheduling, and operation of a power distribution system of a mobile charging station will be described respectively.

(1) Routing scheduling for mobile charging stations

는 이동형 충전소가 스케줄링 시간 t에서 노드 i를 출발하여 노드 j에 도착할 때 이동형 충전소에 대한 경로 i-j의 연결 상태를 결정한다. binary decision variable

determines the connection state of the path ij to the mobile charging station when the mobile charging station departs from node i at scheduling time t and arrives at node j.

The maximum single path of the mobile charging station in the entire scheduling time can be expressed as Equation 7.

와 이동형 충전소에 대한 경로 i-j의 연결 상태

사이의 관계는 수학식 8과 같이 나타낼 수 있다. Stationary charging station visit status

and the connection state of route ij to the mobile charging station

The relationship between them can be expressed as in Equation 8.

는 노드 i에 인접한 노드를 포함한다.

는 집합

에 기초한 인디케이터 함수를 나타낸다.

이면

이고, 그렇지 않은 경우

이다. Here, set

contains nodes adjacent to node i.

is the set

represents an indicator function based on

the other side

and, if not

to be.

가 항상 1인 것을 보장한다(

). Equation 8 shows that when a mobile charging station passes and visits a non-fixed charging station node (non-FCS node),

is always 1 (

).

는 두가지 케이스에 따라 다른 값을 가질 수 있다. However, when a mobile charging station visits a stationary charging station node,

may have different values depending on the two cases.

Case 1

If a mobile charging station passes through a stationary charging station node,

이다.

to be.

Case 2

이다. When the mobile charging station is in charge/discharge mode or idle mode, the mobile charging station stays at the stationary charging station node,

to be.

동안은 어떠한 노드에도 머무르지 않음은 수학식 9와 같이 나타낼 수 있다. When a path connects between node i and node j, the mobile charging station is the travel time

Not staying at any node for a while can be expressed as Equation 9.

는 정수값으로 정규화된 이동 시간을 나타내며,

와 같이 나타낼 수 있다. 또한,

는 스케줄링 단위 시간

에 대한 예측된 이동 시간

의 비율을 올림하는 함수를 나타낸다. here,

represents the normalized travel time as an integer value,

can be expressed as also,

is the scheduling unit time

Predicted travel time for

Represents a function that rounds the ratio of .

시간 이후에 노드 j에 방문하며, 이를 수학식으로 나타내면 수학식 10과 같이 나타낼 수 있다. On the connected route ij, the mobile charging station is

After time, node j is visited, and this can be expressed as Equation 10.

In addition, while the mobile charging station is staying at the stationary charging station, the connected path i-j is not allowed, which can be expressed as Equation 11.

), 이동형 충전소가 노드 i에 방문한 경우(

), 이동형 충전소는 이동하지 않는 것을 의미한다(

). 이를 수학식으로 나타내면 수학식 12와 같다. The associated path does not exist (

), when a mobile charging station visits node i (

), a mobile charging station means that it does not move (

). If this is expressed as an equation, it is equal to Equation 12.

(2) Charging and discharging of mobile charging station

The dynamics of the state of charge (SOC) for the mobile charging station at the current scheduling time t can be expressed as Equation 13.

, 충전과 방전 효율

, 이동 효율

, 이동 상태

, 충전과 방전 전력

을 기반으로 현재 스케줄링 시간 t에서의 이동형 충전소에 대한 충전 상태를 도출할 수 있다. Equation 13 is the state of charge (SOC), battery capacity at the previous scheduling time (t-1)

, charge and discharge efficiency

, transfer efficiency

, moving state

, charging and discharging power

Based on , the charging state of the mobile charging station at the current scheduling time t can be derived.

The SOC capacity constraint for the mobile charging station may be defined as in Equation 14.

After completing all charging services at scheduling time t, the SOC minimum constraint on which the mobile charging station can return to the depot may be defined as in Equation 15.

과 방전

전력 제약은 수학식 16 및 수학식 17과 같이 나타낼 수 있다. Charging at mobile charging stations

over discharge

Power constraints can be expressed as Equations 16 and 17.

In order to reduce the frequency of charging and discharging of the mobile charging station during charging of the mobile charging station, the charging duration of the mobile charging station may be longer than three consecutive scheduling time slots, which can be expressed as in Equation 18.

The sum of charging and discharging power at all stationary charging stations can be expressed as Equation 19.

3) Queuing dynamics for EVs at stationary charging stations

에서 감소된 대기중인 EV의 수

를 나타낸다. Depending on the operation of the mobile charging station, at time t

The number of waiting EVs reduced in

indicates

)에서 이동형 충전소의 동작에 따라 감소된 대기중인 EV의 수는 수학식 20에서 보여지는 바와 같이, 이동형 충전소 없이 시간 (t-1)에서 예측된 대기중인 EV의 수

와 시간 (t-1)에서 대기중인 EV의 수

의 차이로 계산될 수 있다. stationary charging station (

As shown in Equation 20, the number of waiting EVs reduced according to the operation of the mobile charging station in ) is the number of waiting EVs predicted at time (t-1) without a mobile charging station.

and the number of waiting EVs at time (t-1)

can be calculated as the difference between

의 값은 시간 (t-1)에서 이동형 충전소의 직접적인 충전과 시간 (t-1) 이전의 이동형 충전소에서의 충전으로 인한 EV의 간접적인 감소에 의해 영향을 받는다. in Equation 20

The value of is affected by the direct charging of the mobile charging station at time (t-1) and the indirect reduction of EV due to charging at the mobile charging station before time (t-1).

)에서 시간 t에 고정 충전소의 EV의 수는 수학식 21과 같이 나타낼 수 있다. It is charged at a mobile charging station until time (t-1), and is charged at a stationary charging station (

), the number of EVs at the stationary charging station at time t can be expressed as in Equation 21.

The number of EVs being charged may be determined by the minimum value of the number of EVs located at the stationary charging station and the number of poles of the stationary charging station, as shown in Equation 22.

)로부터 전력을 충전한 후 대기중인 EV의 수는 수학식 23과 같이 나타낼 수 있다. Stationary charging station at scheduling time t (

), the number of standby EVs after charging power can be expressed as in Equation 23.

The number of standby EVs after charging from both the mobile charging station and the stationary charging station at time t can be expressed as Equation 24.

It can be guaranteed as shown in Equation 25 that the number of EVs charged from the mobile charging station is less than or equal to the number of standby EVs.

The number of EVs charged at the mobile charging station is limited by the discharge state of the mobile charging station and the number of poles, which can be expressed as Equation 26.

5 is a diagram illustrating an EV queue at a stationary charging station.

Assume that there are no EVs leaving the queue without charging. In FIG. 6, the table shows parameter and variable values for five scheduling times. Parameters and variable values can be used to constrain EV queue dynamics.

)를 계산하는 것이다. The results of the second and fourth rows in the table of FIG. 6 represent parameter values for the number of EVs, charging EVs, and standby EVs, respectively. The results of the fifth to tenth rows can be obtained using the constraints of Equations 20 to 26. Also, the last row of the table shows the total number of EVs in stationary chargers, which is the sum of EVs charged and on standby. The EV queuing dynamics described above is based on the reduced number of waiting EVs (

) to calculate.

)의 영향을 나타낸 것으로, 세가지 케이스로 분류될 수 있다. 세가지 케이스에 대해 세개의 EV(EV1, EV2, EV3)와 세개의 EV(EV4, EV5, EV6)가 각각 시간 (t-1)과 시간 t에서 대기하는 것을 가정하기로 한다. 또한, 이동형 충전소의 방전으로 인해 시간 (t-1)에 세개의 EV가 제거되는 상황을 고려하기로 한다. 7 shows the number of standby EVs reduced relative to the number of charging and standby EVs (

), which can be classified into three cases. For the three cases, it is assumed that three EVs (EV1, EV2, EV3) and three EVs (EV4, EV5, EV6) wait at times (t-1) and times t, respectively. In addition, a situation in which three EVs are removed at time (t-1) due to discharging of the mobile charging station will be considered.

In Case A, EV2 and EV3 have already been removed at time (t-1), and at time t, EV2 and EV3 from the queue may also be removed.

Cases B1 and B2 assume that the standby EV at time (t-1) recharges power at a stationary charging station at time t. In case B1, since EV1 has already been removed at time (t-1), EV1 may also be removed from the charge queue at time t.

For case B2, EV4 moves from queue to charging queue and starts charging at time t via charging pole assigned to EV1 removed from case B1. Consequently, EV2, EV3 and EV4 can be removed at time t in cases A, B1 and B2.

FIG. 5 shows charging and standby EV queue dynamics based on the results related to the three cases mentioned based on FIGS. 6 and 7 .

(3) Distribution system operation

If the active and reactive power flows are reorganized using Equation 24, they can be expressed as Equations 27 and 28.

과 무효 전력 흐름

의 합을 나타낸 것이다. The first term in Equations 27 and 28 is the effective power flow from bus b to k at time t, respectively.

and reactive power flow

represents the sum of

집합에 포함되는 것을 가정하기로 한다. Bus k is a bus with all buses connected downstream to bus b.

Assume that it is included in the set.

및 무효

부하 소비를 나타낸다. Also, the second term is active on bus b and time c.

and invalid

Indicates load consumption.

항과 이동형 충전소에 대한 유효 충전 전력

과 무효 충전 전력 또는 방전 전력

의 합의 항으로 다시 정리하면 수학식 29 및 수학식 30과 같이 나타낼 수 있다. Active and reactive power consumption for non-EV loads

Effective charging power for ports and mobile charging stations

Over-reactive charge power or discharge power

When reorganized into terms of the sum of , it can be expressed as Equations 29 and 30.

The active power consumption for the EV load can be expressed as Equation 31.

과 버스 b에서 충전중인 EV 수의 곱으로 나타낼 수 있다. That is, the active power consumption for an EV load is the charging rate per pole of a stationary charging station, as shown in Equation 31.

and the number of EVs being charged on bus b.

The reactive power consumption of the EV load can be expressed as Equation 32.

과 유효 전력 소비

를 이용하여 계산될 수 있다. That is, as shown in Equation 32, the reactive power consumption of the EV load is the power factor

and active power consumption

can be calculated using

과 충방전 전력(

)을 이용하여 나타낼 수 있다. The reactive power capacity of the mobile charging station is calculated as in Equation 33, which is the minimum power factor of the mobile charging station.

Overcharge and discharge power (

) can be expressed using

는 이동형 충전소에 의한 대기중인 EV의 부하 수요를 나타내며, 충전률

과

의 곱으로 계산되며, 수학식으로 정리하면 수학식 34와 같다.

represents the load demand of standby EVs by mobile charging stations, and the charging rate

class

It is calculated as a product of , and the equation 34 is the same as the equation.

In addition, the sum of the charging and discharging power for the mobile charging station on the bus b can be derived as Equations 35 and 36, respectively.

In addition, the voltage drop equation and voltage magnitude constraints can be expressed as Equations 37 and 38.

은 노드 1의 공칭 전압 크기

를 나타낸다. here,

is the magnitude of the nominal voltage at node 1

indicates

과

는

와

로 설정될 수 있다. here,

class

Is

Wow

can be set to

The nonlinear equation for the second objective function in Equation 1 and the two nonlinear constraints of the EV queue dynamics in Equations 21 and 22 can be replaced by linearization as shown in Equations 39 to 41.

과

는 보조 이진 변수를 나타내고, M은 큰 양의 상수를 나타낸다. In Equations 40 and 41

class

denotes an auxiliary binary variable, and M denotes a large positive constant.

8 is a block diagram schematically illustrating the internal configuration of a server according to an embodiment of the present invention.

Referring to FIG. 8 , a server 130 according to an embodiment of the present invention includes a communication unit 810 , a memory 815 and a processor 820 .

The communication unit 810 is a means for transmitting and receiving data with other devices through a communication network.

The memory 815 is a means for storing at least one instruction for performing the operation scheduling method of a mobile charging station according to an embodiment of the present invention.

The processor 820 is a means for controlling internal components (eg, the communication unit 810, the memory 815, etc.) of the server 130 according to an embodiment of the present invention.

In addition, the processor 820 may control to execute commands stored in the memory 815, and the commands executed by the processor 820 may perform each step described with reference to FIGS. 3 to 7 . Since this is the same as described above, redundant description will be omitted.

Devices and methods according to embodiments of the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded in computer readable media. Computer readable media may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on a computer readable medium may be specially designed and configured for the present invention, or may be known and usable to those skilled in the art in the field of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - Includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media and ROM, RAM, flash memory, etc. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler.

The hardware device described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

So far, the present invention has been looked at mainly by its embodiments. Those skilled in the art to which the present invention pertains will be able to understand that the present invention can be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from a descriptive point of view rather than a limiting point of view. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the equivalent scope will be construed as being included in the present invention.

100: system
110 Stationary charging station
120: mobile charging station
130: server

Claims (10)

(a) scheduling a routing path for moving a mobile charging station to a stationary charging station in an overloaded state in order to reduce the number of electric vehicles waiting at the stationary charging station based on traffic network-related information; and
(b) performing charge-discharge scheduling of active and reactive power of the mobile charging station based on distribution network related information; and
(c) deriving optimal route scheduling of the mobile charging station using the routing path scheduling result and the charge-discharge scheduling result.
According to claim 1,
The method of scheduling operation of a mobile charging station, characterized in that the mobile charging station is a driving device equipped with an energy storage system (ESS).
According to claim 1,
In step (b),
Operation scheduling method of a mobile charging station, characterized in that performing charge-discharge scheduling in consideration of the number of electric vehicles waiting for charging at the stationary charging station and the total deviation of the voltage magnitude for a specific bus connected to the distribution network at time t .
According to claim 3,
In step (b),
The charge-discharge scheduling is performed by further using a state of charge (SOC) considering the state of charge, battery capacity, charge and discharge efficiency, movement efficiency, movement state, and charge and discharge power of the mobile charging station. A method for scheduling the operation of a mobile charging station.
According to claim 1,
In step (a),
The method of scheduling operation of a mobile charging station, characterized in that for scheduling the routing path so that the travel time to the stationary charging station in the overload state is minimized.
A computer-readable recording medium product recording program codes for performing the method according to any one of claims 1 to 5.
a memory storing at least one instruction; and
A processor for executing instructions stored in the memory;
Instructions executed by the processor are:
(a) scheduling a routing path for moving a mobile charging station to a stationary charging station in an overloaded state in order to reduce the number of electric vehicles waiting at the stationary charging station based on traffic network-related information; and
(b) performing charge-discharge scheduling of active and reactive power of the mobile charging station based on distribution network related information; and
(c) The server characterized in that it performs the step of deriving optimal path scheduling of the mobile charging station using the routing path scheduling result and the charge-discharge scheduling result.
According to claim 7,
In step (b),
The server, characterized in that performing charge-discharge scheduling in consideration of the number of electric vehicles waiting for charging at the stationary charging station and the total deviation of the voltage size for a specific bus connected to the distribution network at time t.
According to claim 8,
In step (b),
The charge-discharge scheduling is performed by further using a state of charge (SOC) considering the state of charge, battery capacity, charge and discharge efficiency, movement efficiency, movement state, and charge and discharge power of the mobile charging station. server with .
a physical layer unit including at least one entity, the entity including a load connected to a power distribution network, a mobile charging station, a stationary charging station, and an electric vehicle;
a transportation layer unit containing information about a road network; and
Charge-discharge scheduling is performed considering active and reactive power flow and consumption, reactive power capacity and voltage of entities included in the physical layer, and routing scheduling of mobile charging stations is performed based on traffic network information included in the transportation layer. However, the mobile charging station included in the server including the cyber layer unit for deriving the optimal route scheduling of the mobile charging station using the scheduling result of the routing path and the charge-discharge scheduling result Framework device for scheduling operation of the mobile charging station.

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