WO2022145552A1

WO2022145552A1 - Apparatus and method for scheduling movement path of energy storage device to minimize energy loss

Info

Publication number: WO2022145552A1
Application number: PCT/KR2020/095159
Authority: WO
Inventors: 김영진; 권순영; 박재영; 함승준
Original assignee: 포항공과대학교 산학협력단
Priority date: 2020-12-29
Filing date: 2020-12-29
Publication date: 2022-07-07

Abstract

The present invention relates to an apparatus and a method for scheduling a movement path of an energy storage device to minimize energy loss, wherein an optimal movement path in which a movable energy storage device can move is configured by minimizing energy loss according to a time-based load demand and traffic density. According to an embodiment of the present invention, a movement path scheduling apparatus for configuring a movement path of an energy storage device (mobile energy storage device; MESD) comprises a collection unit which collects grid information, traffic information, and MESD information of a day before, a model generation unit which generates a first model and a second model for generating an optimal movement path on the basis of the pieces of information collected by the collection unit, and a path generation unit which schedules a movement path of the energy storage device by using the first model and the second model.

Description

Apparatus and method for scheduling a movement path of an energy storage device for minimizing energy loss

The present invention relates to an apparatus and method for scheduling a movement route of an energy storage device for minimizing energy loss, and more particularly, to an optimal method for moving a portable energy storage device by minimizing energy loss according to load demand and traffic congestion for each time period. A moving path scheduling apparatus and method for an energy storage device for minimizing energy loss for setting a moving path.

Energy Storage Device (ESD) is installed in the distribution system and provides system auxiliary services such as peak load shaving and reactive power compensation to reduce energy loss and increase the acceptance rate of new and renewable energy to operate the system can be efficient.

For this, it is very important to determine the location where the energy storage device is installed. In general, the energy storage device is installed in a position where the energy storage device can be most effectively utilized in consideration of the power load for each node in the distribution system, the amount of renewable power generation, and the current of line power.

However, selecting the location of the energy storage device determines the optimal installation location for the system condition at a specific point in time. However, there is a problem in that it is difficult to guarantee the optimality of the installation location of the energy storage device because the load density in the distribution system or the amount of new and renewable generation is continuously changed over time.

The present invention is to solve the problems described above, and to minimize energy loss according to the load demand amount and traffic congestion by time period to set an optimal movement path for the portable energy storage device to move. Energy storage for minimizing energy loss An object of the present invention is to provide an apparatus and method for scheduling a moving path of an apparatus.

In addition to the technical problems of the present invention mentioned above, other features and advantages of the present invention will be described below, or will be clearly understood by those of ordinary skill in the art from such description and description.

A movement route scheduling device for setting a movement route of a Mobile Energy Storage Device (MESD) according to an embodiment of the present invention for achieving the above-described object is to collect system information, traffic information and MESD information of the day before. A collection unit, a model generation unit generating first and second models for generating an optimal movement path based on the information collected by the collection unit, and the energy storage device using the first model and the second model It may include a route generator for scheduling a movement route.

On the other hand, the moving route scheduling method for setting the moving path of the energy storage device (Mobile Energy Storage Device, MESD) according to an embodiment of the present invention for achieving the above-described object is system information, traffic information and MESD information of the day before The steps of collecting, generating first and second models for generating an optimal movement route based on the collected information, and scheduling the movement route of the energy storage device using the first model and the second model may include the step of

An apparatus and method for scheduling a movement path of an energy storage device for minimizing energy loss according to an embodiment of the present invention minimizes energy loss according to load demand and traffic congestion for each time period, thereby providing an optimal movement path for a portable energy storage device to move By setting , power and energy loss can be minimized.

In addition, energy costs can be reduced by minimizing power and energy losses.

In addition, other features and advantages of the present invention may be newly recognized through embodiments of the present invention.

1 is a diagram showing the configuration of a system for providing charging/discharging power of an energy storage device according to an embodiment of the present invention.

2 is a diagram showing the configuration of a moving path scheduling apparatus according to an embodiment of the present invention.

3 to 5 are exemplary views illustrating a process of generating a second model according to an embodiment of the present invention.

6 is a diagram illustrating a movement path of an energy storage device by scheduling according to an embodiment of the present invention.

7A to 7C are diagrams illustrating positions and power of the energy storage device according to a movement path of FIG. 6 .

8 is a diagram illustrating an optimal path scheduling method according to an embodiment of the present invention.

9 is a diagram illustrating a method for generating a second model according to an embodiment of the present invention.

Hereinafter, with reference to the accompanying drawings, embodiments of the present invention will be described in detail so that those of ordinary skill in the art can easily implement them. However, the present invention may be implemented in several different forms and is not limited to the embodiments described herein.

Referring to FIG. 1 , a system 10 for providing charging/discharging power of an energy storage device according to an embodiment of the present invention includes a distribution system 100 , an energy storage device 200 , and a movement path scheduling device 300 . can

The energy storage device 200 may be installed in the distribution system 100 to provide services for assisting the distribution system 100 such as peak load shaving of the distribution system and compensation for reactive power. For this reason, the energy storage device 200 can improve the efficiency of the operation of the distribution system 100 by reducing energy loss and increasing the acceptance rate of new and renewable energy.

In addition, the energy storage device 200 may maintain a balance of power through instantaneous charging/discharging at a frequency that changes in real time, and may respond to intermittent wind power and solar power generation power output correction and power feeding instructions. In addition, the energy storage device 200 may be used as an emergency power source, which is a stable power supply means through blackout prevention.

That is, the energy storage device 200 may assist the distribution system 100 by charging/discharging power to the distribution system 100 .

The energy storage device 200 can provide power by moving to a place requiring power. According to an embodiment of the present invention, the movement path of the energy storage device 200 is determined by the movement path scheduling device 300 . can

Specifically, the existing energy storage device 200 is installed at a specific location to supply power to the distribution system 100 . The energy storage device 200 may be installed at a specific location where charging/discharging can be optimally performed for a system condition at a specific point in time. However, since the energy storage device 200 is installed at an optimal location that is selected by reflecting only a specific point in time, optimal charging/discharging from the installation location of the energy storage device 200 to the distribution system 100 may vary. have.

Accordingly, the energy storage device 200 according to the embodiment of the present invention is a mobile type, and may be moved to a place requiring additional power to supply power to the distribution system 100 . Here, energy may be lost as the energy storage device 200 moves, and power may be lost due to power supply of the energy storage device 200 . Accordingly, the moving path scheduling apparatus 300 may schedule the moving path of the energy storage device 200 to minimize loss of energy and power. That is, the moving path scheduling apparatus 300 may derive a schedule for optimizing the charge/discharge power and the moving path of the energy storage device 200 . In addition, the moving route scheduling apparatus 300 according to an embodiment of the present invention may be mounted on or linked to a terminal of a distribution system operator (DSO).

Referring to FIG. 2 , the moving path scheduling apparatus 300 according to an embodiment of the present invention may include a collecting unit 310 , a model generating unit 320 , and a path generating unit 330 .

The collection unit 310 may collect system information, traffic information, and MESD information through a network. The system information may include a load prediction amount, a line impedance, and a charging station capacity, and the traffic information may include geographic information, a traffic congestion level, and a charging station location. In addition, the MESD information may include the number of available mobile energy storage devices (MESDs), movement start and end locations, battery information, and the like. Here, the collection unit 310 may collect system information, traffic information, and MESD information prior to a predetermined time, for example, before a predetermined time may be one day before.

In generating the optimal route, the moving route scheduling apparatus 300 according to an embodiment of the present invention uses the previous day's system information, traffic information, and MESD information to load demand within the distribution system 100 for the day. , the amount of renewable energy generation and traffic congestion can be predicted.

The model generating unit 320 may generate models for generating an optimal moving path based on the information collected by the collecting unit 310 .

Specifically, the model generator 320 may generate a first model including constraints related to the operation of the distribution system 100 . Here, the constraint related to the operation of the distribution system 100 may include a line capacity condition according to the active/reactive power input/output of the MESD, a bus voltage constraint condition, and the like.

The first model is a linear model, which can be set by reflecting the amount of power flow in the power distribution network and the voltage constraint conditions for each node, and the system information collected by the collection unit 310 and contract conditions related to the operation of the distribution system 100 . A linear correlation between the charge/discharge power of the energy storage device 200 may be modeled.

In addition, the model generating unit 320 may generate a second model including the required transit time for each road, and the second model may be a linear model. The model generating unit 320 may generate a road connection model by using geographic information among the traffic information collected by the collecting unit 310 . The road connection model may represent road connection information and distance for each designated point, which may be expressed in the form of a matrix. Here, each designated point may be arbitrarily determined by the user, or may be determined as a point at which one or more roads intersect (eg, an intersection) or a point at which a charging station is located based on the road connection relationship by the model generator 320 .

Also, the model generator 320 may calculate an average time required to pass through each road. For example, for a road connected to two points, the model generator 320 may calculate an average time required to move from one point to another connected point. In this case, the average required time may be calculated by dividing the length of each road by the average moving speed of the vehicle. Here, it has been described that the average required time from the point where the road is connected to another point is calculated, but according to the embodiment of the present invention, it is not limited thereto, and a specific point of the road is set separately to pass between the set specific points. You can also calculate the average time taken.

In addition, the average moving speed may be detected through a speed sensor installed on each road. That is, the speed sensor installed on each road may sense the speed of the vehicles, and may calculate the average moving speed by averaging the speeds of the vehicles sensed by the speed sensor. In this case, the terminal of the DSO may calculate the average movement speed by receiving the movement speeds from the speed sensor, and the average movement speed calculated by a separate control device may be received by the terminal of the DSO.

In addition, the model generator 320 may calculate the shortest time path between each charging station or points in the distribution system 100 by using the average required time for each road. The model generator 320 may calculate the shortest time path using a Dijkstra algorithm.

Here, Dijkstra's algorithm is to find the shortest time path from one point to all other points, and it is possible to update the shortest time distance while adding points connected based on the first point.

The model generator 320 may calculate the total energy required to move the MESD by multiplying the calculated shortest time path by the power consumption of the MESD. Here, the energy required for movement of the MESD between points may be expressed in the form of a matrix, and may be integrated into the mixed integer linear programming method and used to determine the optimal movement path of the MESD.

The path generator 330 may schedule a movement path of the energy storage device 200 using the first model and the second model generated by the model generator 320 . Specifically, the path generator 330 may schedule the optimal movement path of the energy storage device 200 through Mixed Integer Linear Programming (MILP) by integrating the first model and the second model.

The path generator 300 may generate an optimal movement path of the energy storage device 200 by deriving an optimal solution using a mixed integer linear programming method.

Here, the mixed integer linear programming method is for obtaining an optimal integer solution for a linear model, and may be a model requiring that only some variables be integers.

Specifically, the path generator 330 may enumerate all possible solutions to derive an optimal solution, and then exclude impossible solutions that violate the constraint expression. Thereafter, the path generator 330 may select an optimal solution by selecting a solution that optimizes the objective function value Z from among the solutions that do not violate the constraint expression.

In addition, the path generator 330 may first calculate an optimal solution that is not an integer by the simplex method, and may select an optimal solution by rounding it so that a necessary decision variable has an integer value.

Here, the simplex method is a method of deriving a solution of a linear programming method having two or more variables, and a solution can be derived by continuously examining the values of the objective function of adjacent points. Specifically, the search may be performed while starting the search from an arbitrary solution (point) and moving to a solution (point) that is closer to the current solution (point) and closer to the optimum. Here, when there is no more solution (point) to move to, the corresponding solution may be an optimal solution.

In addition, when deriving a solution using the simplex method in the linear programming method, when the derived solution is an integer as required by the decision variable, the path generator 330 may select the derived solution as an optimal solution. However, when the derived solution is not an integer, the path generator 330 may add a new constraint expression capable of excluding the non-integer solution to the linear programming method. The path generator 330 may derive a solution again using the simplex method by adding a new constraint expression. The path generator 330 may select an optimal solution by repeating this process.

In addition, the path generating unit 330 divides the attack region into several subgroups, determines upper and lower limits for the optimal solution in each subgroup, and removes subgroups that no longer need to be considered while having the optimal value of the objective function. The optimal solution can be selected by repeating the division and search procedures until the next year is selected.

The path generator 330 may schedule the movement path of the energy storage device 200 based on the optimal solution selected by the above methods.

Accordingly, the path generator 330 may schedule an optimal movement path of the energy storage device 200 that minimizes loss of energy and power.

Referring to FIG. 3 , the model generating unit 320 may generate a road connection model by using geographic information among the traffic information collected by the collecting unit 310 . The road connection model may be expressed in the form of a matrix. Here, the model generator 320 may determine a point at which one or more roads intersect (eg, an intersection) or a point at which a charging station is located as the point P based on the road connection relationship.

For example, the model generating unit 320 may include a first road connected in a straight line from the fifth point P5 to the eighth point P8 and a second road connected in a straight line from the second point P2 to the 14th point P14. A road connection model can be created for a road. That is, in the form of a matrix, the model generator 320 may express a connection relationship for the first road and the second road as shown in FIG. 3 . Also, the model generator 320 may determine a point where the first road and the second road intersect as a point. In addition, as shown in FIG. 3 , the model generating unit 320 may generate a road connection model including connection relationships between the first road and the second road as well as other roads, and each of the points at which several roads intersect. can be determined as a point (P).

Referring to FIG. 4 , the model generator 320 may calculate an average required time Ta required to pass through each road. For example, the model generator 320 may calculate the average time required to move from the sixth point P6 to the seventh point P7 for the first road connected to the sixth point P6 and the seventh point P7. (Ta) can be calculated.

In this case, the average required time Ta may be calculated by dividing the length D of each road by the average moving speed Va of the vehicle. In addition, the average moving speed may be detected through a speed sensor installed on each road. That is, the speed sensor installed on each road may sense the speed of the vehicles, and may calculate the average moving speed by averaging the speeds of the vehicles sensed by the speed sensor.

Referring to FIG. 5 , the model generator 320 may calculate the shortest time path between each charging station or each point in the distribution system 100 using the average required time for each road. The model generator 320 may calculate the shortest time path between each charging station or each point using a Dijkstra algorithm. Also, the model generator 320 may calculate the total energy required to move by multiplying the shortest travel time between each charging station or point by the power consumption of the MESD (energy storage device).

Through this, the moving path scheduling apparatus 300 according to an embodiment of the present invention may set an optimal path for minimizing energy lost while the energy storage device 200 moves. Furthermore, as described with reference to FIG. 1 , the movement path scheduling apparatus 300 according to an embodiment of the present invention also reflects the first model of the charge/discharge power of the energy storage device 300 to the energy storage device 200 . By creating an optimal path of , power lost by power supply of the energy storage device 200 may also be minimized.

A dotted line may indicate a movement path of the first energy storage device, and a solid line may indicate a movement path of the second energy storage device. In addition, the intersection of the roads is defined as each point (P1 to P16), and in the case of the hatched (colored) points, it may be a charging station point equipped with a charging station that can charge the power of the energy storage device. . In addition, in the case of an uncolored road (eg, a road connected to the first point P1 and the second point P2 ) among the roads between the points, it may be an uncongested road, and the colored road (For example, a road connected to the second point P2 and the third point P3) may be a congested road.

Referring to FIG. 6 , the path generator 330 integrates the first model and the second model to schedule the optimal movement path of the energy storage device 200 through Mixed Integer Linear Programming (MILP). can

The path generator 330 may schedule a movement path of the energy storage device 200 that minimizes loss of energy and power based on an optimal solution selected by the mixed integer linear programming method.

The energy storage device 200 may move along a route scheduled by the route generator 330 . That is, the energy storage device 200 may move along a route scheduled for each time period, and the movement of the energy storage device 200 may be moving from one point to another.

Also, the energy storage device 200 may move to the charging station points P3, P8, P9, P11, and P14 to charge energy required while moving. This may also be scheduled by the path generator 330 .

FIG. 7 is a diagram illustrating a position and power according to a movement path of the energy storage device according to FIG. 6 .

7A is an exemplary diagram illustrating positions of the first energy storage device and the second energy storage device according to FIG. 6 . For example, a dotted line may indicate a location of the first energy storage device by time, and a solid line may indicate a location of the second energy storage device by time.

7B is an exemplary diagram illustrating charging/discharging power of the first energy storage device according to FIG. 6 . For example, a dotted line may indicate charging power of the first energy storage device, and a solid line may indicate discharging power of the first energy storage device.

FIG. 7C is an exemplary diagram illustrating charging/discharging power of the second energy storage device according to FIG. 6 . For example, a dotted line may indicate charging power of the second energy storage device, and a solid line may indicate discharging power of the second energy storage device.

Referring to FIG. 8 , the collection unit 310 may collect system information, traffic information, and MESD information of the previous day through the network ( S100 ). The system information may include a load prediction amount, a line impedance, and a charging station capacity, and the traffic information may include geographic information, a traffic congestion level, and a charging station location. In addition, the MESD information may include the number of available mobile energy storage devices (MESDs), movement start and end locations, battery information, and the like.

The model generating unit 320 may generate a distribution system and MESD movement linearization model based on the information collected by the collecting unit 310 ( S200 ).

The model generator 320 may generate a first model including constraints related to the operation of the distribution system 100 , and the first model may be a linear model. In addition, the model generating unit 320 may generate a second model including the required transit time for each road, and the second model may be a linear model.

The path generator 330 may perform Mixed Integer Linear Programming (MILP) by integrating the first model and the second model ( S300 ). The path generator 330 may select an optimal solution through a mixed integer linear programming method.

The path generator 300 may schedule the movement path of the energy storage device 200 by deriving an optimal solution using the mixed integer linear programming method (S400). The path generator 300 may schedule an optimal movement path of the energy storage device 200 that minimizes loss of energy and power based on the selected optimal solution.

Referring to FIG. 9 , the model generating unit 320 may generate a road connection model by using geographic information among the traffic information collected by the collecting unit 310 ( S210 ). The road connection model may represent road connection information and distance for each designated point, which may be expressed in the form of a matrix.

The model generator 320 may calculate an average required time required to pass through each road (S220). For example, for a road connected to two points, the model generator 320 may calculate an average time required to move from one point to another connected point. In this case, the average required time may be calculated by dividing the length of each road by the average moving speed of the vehicle, and the average moving speed may be detected through a speed sensor installed on each road.

The model generator 320 may calculate the shortest time path between each charging station or points in the distribution system 100 by using the average required time for each road (S230). The model generator 320 may calculate the shortest time path using a Dijkstra algorithm.

The model generator 320 may calculate the total energy required to move the energy storage device 200 ( S240 ). The model generator 320 may calculate the total energy required to move the MESD by multiplying the calculated shortest time path by the power consumption of the MESD. Here, the energy required for movement of the MESD between points may be expressed in the form of a matrix, and may be integrated into the mixed integer linear programming method and used to determine the optimal movement path of the MESD.

As described above, according to an embodiment of the present invention, energy loss is minimized to set an optimal movement path through which the portable energy storage device can move by minimizing energy loss according to the load demand amount and traffic congestion level for each time period. An apparatus and method for scheduling a moving path of an apparatus may be realized.

Those skilled in the art to which the present invention pertains should understand that the present invention may be embodied in other specific forms without changing the technical spirit or essential characteristics thereof, so the embodiments described above are illustrative in all respects and not restrictive. only do The scope of the present invention is indicated by the following claims rather than the detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be construed as being included in the scope of the present invention. .

The present invention has industrial applicability in reducing energy costs by minimizing power and energy losses.

Claims

In the moving path scheduling device for setting the moving path of the energy storage device (Mobile Energy Storage Device, MESD),

a collection unit that collects system information, traffic information, and MESD information of the previous day;

a model generation unit generating first and second models for generating an optimal movement path based on the information collected by the collection unit;

and a path generator configured to schedule a moving path of the energy storage device by using the first model and the second model.
According to claim 1,

The system information includes a load prediction amount, line impedance and charging station capacity, etc., the traffic information includes geographic information, traffic congestion and charging station location, and the MESD information includes the number of available energy storage devices, movement start and end locations, A travel route scheduling device including battery information.
According to claim 1,

The model generation unit generates a first model including constraints related to the operation of the distribution system,

The constraints include line capacity conditions and bus voltage constraints according to active/reactive power input/output of MESD,

The first model is a linear model, and the movement path scheduling device is set by reflecting the current amount of line power in the distribution network and voltage constraint conditions for each node.
According to claim 1,

The model generating unit generates a second model including a transit time required for each road, and the second model is a linear model.
5. The method of claim 4,

The model generator generates a road connection model by using the traffic information, and the road connection model is a model representing road connection information and distances for each designated point.
6. The method of claim 5,

The model generator calculates an average required time required to pass through each road, and the average required time is calculated by dividing the length of each road by the average moving speed of the vehicle.
7. The method of claim 6,

The model generator calculates a shortest time path between points by using the average required time for each road.
8. The method of claim 7,

The model generating unit adds connected points based on the first point, and updates the shortest time distance to calculate the shortest time path.
8. The method of claim 7,

The model generator multiplies the calculated shortest time path by the power consumption of the MESD to calculate the total energy required for the movement of the MESD.
According to claim 1,

The path generator integrates the first model and the second model to schedule a moving path of the energy storage device through Mixed Integer Linear Programming (MILP).
In the moving path scheduling method for setting the moving path of an energy storage device (Mobile Energy Storage Device, MESD),

collecting system information, traffic information and MESD information of the day before;

generating a first model and a second model for generating an optimal moving path based on the collected information;

and scheduling a moving path of the energy storage device by using the first model and the second model.
12. The method of claim 11,

The first model includes constraints related to the operation of the distribution system, and the constraints include line capacity conditions and bus voltage constraints according to active/reactive power input/output of MESD,

The first model is a linear model, and the movement path scheduling method is set by reflecting the current amount of line power in the distribution network and voltage constraint conditions for each node.
12. The method of claim 11,

The second model includes a travel time required for each road, and is a linear model moving route scheduling method.
The method of claim 13, wherein the generating of the first model and the second model comprises:

A road connection model is generated using the traffic information, and the road connection model is a model representing road connection information and distances for each designated point.
15. The method of claim 14, wherein generating the first model and the second model comprises:

A travel route scheduling method in which the average time required to pass through each road is calculated, and the average required time is calculated by dividing the length of each road by the average moving speed of the vehicle.
The method of claim 15, wherein the generating of the first model and the second model comprises:

A moving route scheduling method that calculates the shortest route between points by using the average required time for each road.
The method of claim 16, wherein generating the first model and the second model comprises:

The model generator multiplies the calculated shortest time path by the power consumption of the MESD to calculate the total energy required for the movement of the MESD.
The method of claim 11, wherein the scheduling of the movement route comprises:

A moving path scheduling method for scheduling the moving path of the energy storage device through Mixed Integer Linear Programming (MILP) by integrating the first model and the second model.