KR20210017656A - Apparatus and method for moving path scheduling of an energy storage device for minimizing energy loss - Google Patents

Abstract

The present invention relates to a moving path scheduling device of an energy storage device for minimizing energy loss, capable of setting an optimal moving path for a mobile energy storage device to move by minimizing energy loss in accordance with the degree of traffic congestion and load demand amount by time slot, and a method thereof. According to an embodiment of the present invention, the moving path scheduling device for setting a moving path of a mobile energy storage device (MESD) includes: a collection part collecting system information, traffic information, and MESD information of the previous day; a model creation part creating first and second models for creating an optimal moving path based on the information collected by the collection part; and a path creation part scheduling a moving path of the energy storage device by using the first and second models.

Description

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

The present invention relates to an apparatus and method for scheduling a movement path of an energy storage device for minimizing energy loss, and more specifically, an optimal energy loss for a mobile energy storage device to be moved by minimizing energy loss according to load demand and traffic congestion for each time period. It relates to an apparatus and method for scheduling a movement path of an energy storage device for minimizing energy loss for setting a movement path.

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

To this end, it is very important to determine where the energy storage device is installed. In general, the energy storage device is installed in a location 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 power flow of the line.

However, selecting the location of the energy storage device determines the optimum installation location for the system condition at a certain point in time. However, there is a problem in that it is difficult to guarantee the optimality for the installation location of the energy storage device since the load-intensive area or the amount of renewable power in the distribution system is continuously changed over time.

The present invention is to solve the above-described problem, and energy storage to minimize energy loss for setting an optimal path through which a mobile energy storage device can move by minimizing energy loss according to load demand and traffic congestion by time An object of the present invention is to provide an apparatus and method for scheduling a movement 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 technology and description.

A movement route scheduling device for setting a movement route of an energy storage device (MESD) according to an embodiment of the present invention to achieve the above-described object collects system information, traffic information, and MESD information from one day ago. The collection unit, a model generation unit that generates a first model and a second model 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 path generator for scheduling a moving path.

On the other hand, the movement route scheduling method for setting a movement route of a mobile energy storage device (MESD) according to an embodiment of the present invention to achieve the above-described object is a system information, traffic information, and MESD information of a day ago. Collecting, generating a first model and a second model for generating an optimal movement path based on the collected information, and scheduling a movement path of the energy storage device using the first model and the second model It 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 is an optimal movement path through which the mobile energy storage device can travel by minimizing energy loss according to the load demand and traffic congestion for each time period. By setting, it is possible to minimize power and energy loss.

In addition, energy cost can be reduced by minimizing power and energy loss.

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

1 is a diagram illustrating a configuration of a system for providing charging/discharging power of an energy storage device according to an exemplary embodiment of the present invention.
2 is a diagram illustrating a configuration of a moving path scheduling apparatus according to an embodiment of the present invention.
3 to 5 are exemplary diagrams showing 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.
FIG. 7 is a diagram illustrating a location and power according to a movement path of the energy storage device according to FIG. 6.
8 is a diagram illustrating an optimal route scheduling method according to an embodiment of the present invention.
9 is a diagram illustrating a method of generating a second model according to an embodiment of the present invention.

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

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

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. I can.

The energy storage device 200 may be installed in the distribution system 100 to provide a service to assist the distribution system 100 such as peak load shaving of the distribution system and compensation for reactive power. Accordingly, the energy storage device 200 can efficiently operate 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 can maintain the balance of power by instant charging/discharging at a frequency that changes in real time, and can respond to intermittent wind power, output correction of a solar power source, and power supply. In addition, the energy storage device 200 may be utilized as an emergency power source, which is a stable power supply means through prevention of power failure.

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 may move to a place where power is needed to provide power. According to an embodiment of the present invention, the movement path of the energy storage device 200 may be determined by the movement path scheduling device 300. I 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 position selected by reflecting only a specific point in time, the optimal charging/discharging from the installation position of the energy storage device 200 to the distribution system 100 may vary. have.

Accordingly, the energy storage device 200 according to an exemplary embodiment of the present invention may be of a mobile type, and may move to a place where additional power is required 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 movement path scheduling apparatus 300 may schedule a movement path of the energy storage device 200 that minimizes loss of energy and power. That is, the movement path scheduling apparatus 300 may derive a schedule for optimizing the charging/discharging power and the movement path of the energy storage device 200. In addition, the movement route scheduling apparatus 300 according to an embodiment of the present invention may be mounted or interlocked in a terminal of a distribution system operator (DSO).

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

Referring to FIG. 2, a movement path scheduling apparatus 300 according to an embodiment of the present invention may include a collection unit 310, a model generation unit 320, and a path generation 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 predicted amount, line impedance, and charging station capacity, and the traffic information may include geographic information, traffic congestion, and charging station location. In addition, the MESD information may include the number of available energy storage devices (MESD), movement start and end positions, and battery information. Here, the collection unit 310 may collect system information, traffic information, and MESD information before a certain time, for example, the certain time may be one day before.

In generating an optimal route, the moving route scheduling apparatus 300 according to an embodiment of the present invention uses system information, traffic information, and MESD information of the day before the load demand in the distribution system 100 of the day. , Renewable energy generation and traffic congestion can be predicted.

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

Specifically, the model generation unit 320 may generate a first model including constraints related to the operation of the distribution system 100. Here, the constraints 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 current amount of line power in the distribution network and voltage constraints 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 generation unit 320 may generate a second model including a time required for passage for each road, and the second model may be a linear model. The model generation unit 320 may generate a road connection model using geographic information among traffic information collected by the collection unit 310. The road connection model may represent road connection information and distance for each designated point, and this may be expressed in the form of a matrix. Here, each designated point may be arbitrarily determined by the user, or may be determined by the model generator 320 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.

Also, the model generator 320 may calculate an average required time required to pass through each road. For example, for a road connected to two points, the model generator 320 may calculate an average required time required to move from one point to another point connected to a corresponding road. 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 as calculating the average time required from a point to which a road is connected to another point, but according to an embodiment of the present invention, the present invention is not limited thereto, You can also calculate the average time spent.

In addition, the average moving speed can be detected through a speed sensor installed for each road. That is, the speed sensor installed for each road can sense the speeds of vehicles, and 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 receive the moving speeds from the speed sensor to calculate the average moving speed, and the average moving 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 time required for each road. The model generator 320 may calculate the shortest time path using a Dijkstra algorithm.

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

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

The path generation unit 330 may schedule a movement path of the energy storage device 200 by using the first model and the second model generated by the model generation unit 320. Specifically, the path generation unit 330 may schedule an optimal movement path of the energy storage device 200 through a mixed integer linear programming (MILP) method by integrating the first model and the second model.

The path generation unit 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 only some variables to be integers.

Specifically, the path generator 330 may list 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 solutions that do not violate the constraint expression.

In addition, the path generation unit 330 may first calculate an optimal solution, not an integer, using the simplex method, and may select an optimal solution by rounding a necessary decision variable to have 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 the solution can be derived by continuously examining values of the objective function of adjacent points. Specifically, it is possible to start a search from an arbitrary solution (point) and proceed with the search while moving to a solution (point) that is adjacent to the current solution (point) and is closer to the optimal solution (point). Here, when there is no more solution (point) to move, the corresponding solution may be an optimal solution.

In addition, when the path generator 330 derives a solution using the simplex method in the linear programming method, when the solution derived as required by the decision variable is an integer, the derived solution may be selected as an optimal solution. However, when the derived solution is not an integer, the path generation unit 330 may add a new constraint condition expression capable of excluding non-integer solutions to the linear programming method. The path generator 330 may add a new constraint expression to derive a solution again using the simplex method. The path generation unit 330 may repeat this process and select an optimal solution.

In addition, the path generation unit 330 divides the applied region into several sub-groups, determines the upper and lower limits for the optimal solution in each sub-group, and removes the sub-groups that no longer need to be considered, while obtaining the optimum value of the objective function. The optimal solution can be selected by repeating the process of division and search until the coming year is selected.

The path generation unit 330 may schedule a moving path of the energy storage device 200 based on an optimal solution selected by the above methods.

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

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

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

For example, the model generator 320 includes 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. You can create road connection models for roads. That is, the model generator 320 may express a connection relationship between the first road and the second road as shown in FIG. 3 in the form of a matrix. In addition, the model generation unit 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 generation unit 320 may generate a road connection model including a connection relationship between not only the first road and the second road, but also other roads, and each of the points where several roads intersect. Can be determined as 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 generation unit 320 is 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 can be detected through a speed sensor installed for each road. That is, the speed sensor installed for each road can sense the speeds of vehicles, and 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 by using the average time required for each road. The model generator 320 may calculate the shortest time path between each charging station or each point using a Dijkstra algorithm. In addition, the model generator 320 may calculate the total energy required to move by multiplying the shortest moving time between each charging station or points by the power consumption of an energy storage device (MESD).

Through this, the movement path scheduling apparatus 300 according to an embodiment of the present invention may set an optimum path for minimizing energy lost while the energy storage device 200 is moving. Furthermore, the movement path scheduling apparatus 300 according to an embodiment of the present invention also reflects the first model of the charging/discharging power of the energy storage device 300 as described in FIG. 1 to provide the energy storage device 200. By generating the optimal path of the energy storage device 200, it is possible to minimize the power loss due to power supply.

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

A dotted line indicates a moving path of the first energy storage device, and a solid line may indicate a moving path of the second energy storage device. In addition, the intersection of each road is defined as each point (P1 to P16), and in the case of a hatched (painted) point, it may be a charging station point equipped with a charging station capable of charging the power of the energy storage device. . In addition, among the roads between points, a road that is not colored (eg, a road connected to the first point P1 and the second point P2) may be a road that is not congested, and a colored road In the case of (eg, a road connected to the second point P2 and the third point P3), it may be a congested road.

6, the path generation unit 330 integrates the first model and the second model to schedule the optimal movement path of the energy storage device 200 through a mixed integer linear programming (MILP). I can.

The path generation unit 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.

The path generator 330 may schedule a moving path of the energy storage device 200 to minimize loss of energy and power based on an optimal solution selected by a 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 a movement from a point to another point.

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

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

(a) is an exemplary view showing 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.

(b) is an exemplary diagram showing charge/discharge 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 discharge power of the first energy storage device.

(c) is an exemplary diagram showing charge/discharge 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 discharge power of the second energy storage device.

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

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

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

The model generation unit 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 generation unit 320 may generate a second model including a time required for passage for each road, and the second model may be a linear model.

The path generation unit 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 generation unit 300 may schedule a moving path of the energy storage device 200 by deriving an optimal solution using a mixed integer linear programming method (S400). The path generation unit 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.

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

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

The model generator 320 may calculate an average 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 required time required to move from one point to another point connected to a corresponding road. 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 for each road.

The model generator 320 may calculate the shortest time route between each charging station or points in the distribution system 100 using the average time required 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 for the movement of the energy storage device 200 (S240). The model generator 320 may calculate the total energy required for the movement of the MESD by multiplying the calculated shortest time path by the power consumption of the MESD. Here, the energy required to move the MESD between points may be expressed in the form of a matrix, and may be integrated into a 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 storage for minimizing energy loss in accordance with load demand and traffic congestion for each time period to minimize energy loss for setting an optimal movement path for the mobile energy storage device to move It is possible to realize an apparatus and method for scheduling a movement path of the apparatus.

Those skilled in the art to which the present invention pertains, since the present invention may be implemented in other specific forms without changing the technical spirit or essential features thereof, the embodiments described above are illustrative in all respects and should be understood as non-limiting. Only. The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. .

100: distribution system
200: energy storage device
300: movement route scheduling device
310: collector
320: model generation unit
330: path generation unit

Claims (18)

In the movement path scheduling device for setting the movement path of an energy storage device (Mobile Energy Storage Device, MESD),
A collection unit that collects system information, traffic information, and MESD information from the day before;
A model generator for generating a first model and a second model for generating an optimal movement path based on the information collected by the collection unit;
A movement path scheduling device comprising a path generator configured to schedule a movement path of the energy storage device using the first model and the second model.
The method of claim 1,
The system information includes a load predicted amount, line impedance and charging station capacity, and 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 positions, Travel route scheduling device including battery information.
The method of 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 input/output of active/reactive power of MESD,
The first model is a linear model, and a movement path scheduling device that is set by reflecting a line power flow amount and a voltage constraint condition for each node in a distribution network.
The method of claim 1,
The model generation unit generates a second model including a time required for passage for each road, and the second model is a linear model.
The method of claim 4,
The model generation unit generates a road connection model using the traffic information, and the road connection model is a model representing road connection information and distance for each designated point.
The method of claim 5,
The model generation unit calculates an average 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.
The method of claim 6,
The model generator is a moving route scheduling device that calculates the shortest time route between points by using the average time required for each road.
The method of claim 7,
The model generation unit adds points connected based on an initial point, updates a shortest time distance, and calculates a shortest time path.
The method of claim 7,
The model generation unit 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 1,
The path generation unit integrates the first model and the second model and schedules the movement path of the energy storage device through a mixed integer linear programming (MILP) method.
In the movement path scheduling method for setting the movement path of an energy storage device (Mobile Energy Storage Device, MESD),
Collecting system information, traffic information, and MESD information from the day before;
Generating a first model and a second model for generating an optimal movement path based on the collected information;
And scheduling a movement path of the energy storage device by using the first model and the second model.
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 input/output of active/reactive power of MESD,
The first model is a linear model, and a moving route scheduling method that is set by reflecting the amount of electric power of a line in a distribution network and a voltage constraint condition for each node.
The method of claim 11,
The second model includes a travel time for each road and is a linear model.
The method of claim 13, wherein generating 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 distance for each designated point.
The method of claim 14, wherein generating the first model and the second model comprises:
A travel route scheduling method that calculates the average time required to pass each road, but the average time required is calculated by dividing the length of each road by the average travel speed of the vehicle.
The method of claim 15, wherein generating the first model and the second model comprises:
A travel route scheduling method that calculates the shortest time route between points by using the average time required for each road.
The method of claim 16, wherein generating the first model and the second model comprises:
The model generation unit 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 scheduling the movement path comprises:
A movement path scheduling method for scheduling a movement path of the energy storage device through a mixed integer linear programming (MILP) method by integrating the first model and the second model.

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