KR102599639B1 - Method and apparatus for controlling micorgrid system - Google Patents

Abstract

This technology discloses a control method and device for a microgrid system. According to a specific example of the present invention, an objective function is designed by reflecting the error between the target tracking value and the actual measured value of the charging power of the ESS, and then the optimal solution of the designed objective function is derived to determine load power and renewable energy power fluctuations within the microgrid, It is possible to derive an optimal solution of the objective function that is robust against fluctuations in distributed power located within the microgrid, such as ESS charging power, thereby improving income from the operator's perspective, and using the receding horizon technique and QP (where the size is reduced). By simplifying the objective function into a single state variable based on the Quadratic Programming system, the computational complexity for deriving the optimal solution can be reduced, and a reduction in computational resources and computational time can be expected.

Description

Control method and device for microgrid system {METHOD AND APPARATUS FOR CONTROLLING MICORGRID SYSTEM}

The present invention relates to a control method and device for a microgrid system. More specifically, the objective function is set to minimize the grid power, which is the output power of the microgrid, by reflecting the error between the estimated and actual measured charging power of the energy storage device ESS. It is about a technology that maximizes economic benefits while maintaining grid power and ESS charging power in an optimal state by deriving the optimal solution of the set objective function.

Microgrid is a small-scale smart grid system that can be self-sufficient in electricity in small areas.

In other words, microgrid is a small-scale independent power grid and is a next-generation power system that combines renewable energy sources such as solar and wind power and energy storage systems (ESS) such as batteries. It is attracting attention as a means of operating a distribution network due to its high efficiency due to short transmission and distribution distances, independent power supply to major loads in preparation for disasters, and efficient distribution of distributed power sources.

This microgrid system converts the output power of the analog grid supplied from the outside into digital form and controls renewable energy sources and battery energy storage devices by optimally solving the objective function that schedules the grid power delivered to the system.

Here, the grid power is treated as a disturbance by applying the equality constraint condition to the load power within the microgrid and the renewable energy power included in the grid power, which is the consumption power of the distributed power source.

Therefore, the optimal solution of the objective function is determined by the grid power P _GRID and the charging power of the ESS P _ESS .

The microgrid system derives the optimal solution of the objective function based on QP under equality constraint conditions, and the grid power P _GRID is managed in an optimized state.

However, in deriving the optimal solution of the objective function reflecting the equality limit condition for grid power and ESS charging power, in the case of a microgrid system with large fluctuations in load power and renewable energy power, the goals for grid power and ESS charging power The success rate has decreased, and the operator's income has reached a limit.

Korean Patent Publication No. 10-1569144

The present invention designs an objective function by reflecting the error between the target tracking value and the actual measured value of the charging power of the ESS and then derives the optimal solution of the designed objective function, thereby reducing load power and renewable energy power fluctuations within the microgrid and ESS charging power fluctuations. The purpose is to provide a control method and device for a microgrid system that can derive an optimal solution of a robust objective function and thereby improve income from the operator's perspective.

In addition, the present invention simplifies the objective function into a single state variable based on the receding horizon technique, thereby reducing the computational complexity for deriving the optimal solution of the objective function by the QP (Quadratic Programming) system. The purpose is to provide a control method and device for a microgrid system that can be expected to reduce computational resources and computing time.

The object of the present invention is not limited to the object mentioned above, and other objects and advantages of the present invention that are not mentioned can be understood through the following description and will be more clearly understood through the examples of the present invention. In addition, it will be readily apparent that the objects and advantages of the present invention can be realized by means and combinations thereof as indicated in the claims.

A control device for a microgrid system according to an embodiment of the present invention,

In the microgrid system control device that optimally operates grid power and ESS charging power provided within the microgrid,

The control device is,

It is equipped to derive the optimal solution of the determined objective function to minimize grid power and energy storage device ESS charging power,

The objective function is,

One feature is that it is provided as the sum of a first-order term including grid power and a second-order term including the error between the target tracking value and the actual measured value of ESS charging power.

A control method of a microgrid system according to another embodiment of the present invention,

In the control method of a microgrid system that optimally operates grid power and ESS charging power provided into the microgrid based on a control device,

It is equipped to derive the optimal solution of the determined objective function to minimize grid power and energy storage device ESS charging power,

The objective function is,

One feature is that it is provided as the sum of a first-order term including grid power and a second-order term including the error between the target tracking value and the actual measured value of ESS charging power.

Preferably, the objective function J is,

It includes the greedy power, the target tracking value of the ESS charging power, and the actual measured value of the ESS charging power, and satisfies the following equation 1.

[Equation 1]

Here, c _GRID (t) is the electricity rate for time, and c _SoC is a weight that limits charge/discharge power.

Preferably, the control method of the microgrid system is:

Setting an objective function with greedy power, target tracking value of ESS charging power, and actual measured value of ESS charging power; and

It may include deriving an optimal solution for the set objective function through a QP system.

Preferably, after the step of setting the objective function,

A step of optimizing the state variables of the set objective function using a receding horizon technique of decreasing size may be further included.

According to these characteristics, the objective function is designed by reflecting the error between the target tracking value and the actual measured value of the charging power of the ESS, and then the optimal solution of the designed objective function is derived, thereby influencing load power within the microgrid, renewable energy power fluctuations, and ESS charging. It is possible to derive an optimal solution of the objective function that is robust to fluctuations in distributed power sources such as electric power, thereby improving income from the operator's perspective.

In addition, by simplifying the objective function into a single state variable based on the receding horizon technique and the QP system, the computational complexity for deriving the optimal solution can be reduced, resulting in computational resources and computational time. A decrease can be expected.

The following drawings attached to this specification illustrate preferred embodiments of the present invention, and together with the detailed description of the invention described later, serve to further understand the technical idea of the present invention. Therefore, the present invention includes the matters described in such drawings. It should not be interpreted as limited to only .
1 is a configuration diagram of a microgrid system to which an embodiment is applied.
Figure 2 is an overall flowchart showing the control process of the system of one embodiment.
Figure 3 is an example diagram showing the robustness of one embodiment.

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

The advantages and features of the present invention and methods for achieving them will become clear with reference to the embodiments described below along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms. The present embodiments are merely provided to ensure that the disclosure of the present invention is complete and to be understood by those skilled in the art in the technical field to which the present invention pertains. It is provided to fully inform those who have the scope of the invention, and the present invention is only defined by the scope of the claims.

The terms used in this specification will be briefly explained, and the present invention will be described in detail.

The terms used in the present invention are general terms that are currently widely used as much as possible while considering the function in the present invention, but this may vary depending on the intention or precedent of a person working in the art, the emergence of new technology, etc. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the relevant invention. Therefore, the terms used in the present invention should be defined based on the meaning of the term and the overall content of the present invention, rather than simply the name of the term.

When it is said that a part "includes" a certain element throughout the specification, this means that, unless specifically stated to the contrary, it does not exclude other elements but may further include other elements. Additionally, the term “unit” used in the specification refers to a hardware component such as software, FPGA, or ASIC, and the “unit” performs certain roles. However, “wealth” is not limited to software or hardware. The “copy” may be configured to reside on an addressable storage medium and may be configured to reproduce on one or more processors.

Thus, as an example, “part” refers to software components, such as object-oriented software components, class components, and task components, processes, functions, properties, procedures, Includes subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within the components and “parts” may be combined into smaller numbers of components and “parts” or may be further separated into additional components and “parts”.

In one embodiment, an objective function is designed by reflecting the error between the target tracking value and the actual measured value of the charging power of the ESS, and then the optimal solution of the designed objective function is derived to determine load power and renewable energy power fluctuations within the microgrid, ESS charging power, etc. An optimal solution of the objective function that is robust to distributed power fluctuations located within the microgrid can be derived.

In addition, in one embodiment, the computational complexity for deriving the optimal solution can be reduced by simplifying the objective function into a single state variable based on the receding horizon technique and the QP system, thereby reducing the computational resources and computational time. A decrease can be expected.

Below, with reference to the attached drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the present invention. In order to clearly explain the present invention in the drawings, parts unrelated to the description are omitted.

Hereinafter, a control method of a microgrid system according to an embodiment will be described with reference to the attached drawings.

FIG. 1 is an exemplary diagram showing the configuration of a microgrid system to which an embodiment is applied, and FIG. 2 is a flowchart showing the operation process of the control device shown in FIG. 1. Referring to FIGS. 1 and 2, the microgrid system (S) to which one embodiment is applied includes an Active Front End Converter (AFE) 110 that converts power from the grid into direct current form and outputs grid power P _GRID . do.

In addition, the grid power P _GRID can be transmitted to the EV charger 130 through the DC bus 120 and is transmitted to a renewable energy system including at least one of the solar power generation system 210 and the wind power generation system 310. It can be delivered.

Accordingly, the power P _{PV and} P _WT generated in the renewable energy system 210 can be transmitted to the energy storage system (ESS) 220, such as a battery, through the DC bus 120, and the DC bus 120 ) can be transmitted to the load 320 through.

Meanwhile, the microgrid system (S) further includes a control device 500, and the control device 500 is configured to derive an optimal solution of the objective function J that minimizes the grid power P _GRID and the ESS charging power P _ESS .

The process of deriving the optimal solution of the objective function J of the microgrid system (S) using the control device 500 and performing the ESS dispatch algorithm will be described with reference to FIG. 2.

First, the grid power P _GRID (t) is expressed as a power balance state between the input and output of the DC bus 120, and satisfies the following equation 1.

[Equation 1]

Here, grid power P _GRID (t) is the input power of the DC bus 120, P _ESS (t) is the power amount of ESS (Energy Storage System: 220) such as a battery, and P _PV (t) is renewable energy. This is the amount of power of the system, that is, the amount of power generated by the solar power generation system 210.

In step S10, the control device 500 of the microgrid system of one embodiment controls the grid power P _GRID (t) and the ESS charging power P such that the grid power P _GRID (t) and the ESS charging power P _ESS (t) are minimized. Reset the objective function J for scheduling _ESS (t).

In other words, the objective function J is the first term including grid power P _GRID (t) and grid weight c _GRID and the target tracking value of ESS charging power P _ESS (t) and actual values It includes a quadratic term Q that includes the error of , and the objective function J can be expressed as the following equation 2.

[Equation 2]

Here, c _GRID (t) is the electricity rate for time, c _SoC is the weight of the ESS standard charging power to track the ESS charging power with a weight that limits the charge/discharge power, is the target tracking value of ESS, and is the target charging amount of ESS is derived from the charging power of the ESS (220).

In other words, the target tracking value of ESS within the cost function and actual values The optimal solution of the objective function J is derived in the direction of reducing the difference between the is managed to minimize grid power P _GRID .

And in step S20, the control device 500 of one embodiment determines the state vector of the objective function J through a receding horizon technique in which the given size of N is reduced. are minimized and expressed as a single state variable using equality limit conditions, boiling limit conditions, upper limit conditions, and lower limit conditions. Therefore, since the objective function J is expressed only as a single state variable, the computational time and computational complexity for deriving the optimal solution can be shortened and computational resources can be reduced.

That is, for the receding horizon technique where the given N size is reduced, the grid power P _GRID in Equation 1 can be expressed as a matrix as in Equation 3.

[Equation 3]

here, , , , and am.

And, the load power amount and renewable energy power amount is available at every time interval, and the load power and renewable energy power amount The equality limit condition, boiling limit condition, upper bound condition, and lower bound condition for can be reconstructed in the vector format of the following equations 5 to 11.

[Equation 5]

[Equation 6]

[Equation 7]

[Equation 8]

[Equation 9]

[Equation 10]

[Equation 11]

here, has one value It is a procession.

And, upper and lower limits The conditions can be simplified as shown in Equation 12.

[Equation 12]

And the inequality constraint condition can be expressed as the following equation 13.

[Equation 13]

here, ego, Is It is the same matrix.

And in step S30, the control device 500 of one embodiment derives the optimal solution of the objective function J with a single state variable optimized according to step S20, and minimizes the grid power P _GRID with the derived optimal solution, while minimizing the distributed power, Economic benefits can be improved by optimally managing ESS and renewable energy sources.

That is, the state vector containing the decision variables. is defined by the following equation 14.

[Equation 14]

here, and is based on Equation 5 and Equation 6, and It can be expressed as, where: Only this is the decision variable and the remaining variables are eliminated. Therefore, the state vector in Equation 14 and state vector The objective function J , which is the response to, can be expressed as Equation 15 and Equation 16 below, respectively.

[Equation 15]

[Equation 16]

And, if the QP system is performed on the objective function J , the matrix Η of the quadratic term (Q) and the linear term vector Each can be expressed as Equation 17 and Equation 18 below.

[Equation 17]

[Equation 18]

here, and am.

Accordingly, according to Equations 4 to 18, the size of the equality limit condition, the non-equality limit condition, and the upbound/lowbound matrix are reduced, and the execution time of the QP (Quadratic Programming) system that derives the optimal solution of the objective function J is shortened.

That is, according to step S20, the QP (Quadratic Programming) system is performed on the objective function J optimized as a single state variable using the equality limit condition, the non-equality limit condition, and the upper/lower limit condition matrix to obtain the objective function J. The optimal solution is derived.

In one embodiment, the optimal solution is derived through the QP (Quadratic Programming) system for the objective function to which the error between the target tracking value and the actual measurement value of the ESS is applied, thereby changing load power and renewable energy power within the microgrid, ESS charging power, etc. An optimal solution of the objective function that is robust to fluctuations in distributed power located within the microgrid can be derived, and the error in the ESS charging power of the objective function can be reduced to increase the target value of the ESS charging power, thereby compensating for economic benefits. . In addition, one embodiment simplifies each variable of the objective function into a single state variable based on the receding horizon technique, thereby reducing the derivation time and computational complexity of the optimal solution of the objective function, thereby reducing computational resources. can do.

Figure 3 is a diagram showing the ESS dispatch result as success “P” or failure “F” derived from a QP system using a receding horizon technique in which the size of N is reduced in one embodiment. Referring to Figure 3, P _GRID , of P _{LOAD, EST} , P _LOAD , P _PV , P _ESS When an unexpected failure occurs, you can confirm that ESS Dispatch is a success “P”. , It can be seen that ESS dispatch is successful even when an unexpected failure occurs.

Accordingly, one embodiment can derive an optimal solution of the objective function that is robust to fluctuations in distributed power located within the microgrid, such as load power and renewable energy power fluctuations within the microgrid, and ESS charging power.

The present invention has been described with reference to the embodiments shown in the drawings, but these are merely exemplary, and those skilled in the art will recognize that various modifications and other equivalent embodiments are possible therefrom. You will understand. Therefore, the scope of technical protection of the present invention should be determined by the scope of the patent claims below.

The objective function is designed by reflecting the error between the target tracking value and the actual measured value of the charging power of the ESS, and then the optimal solution of the designed objective function is derived to determine the microgrid load power and renewable energy power fluctuations within the microgrid, ESS charging power, etc. It is possible to derive an optimal solution of an objective function that is robust to distributed power fluctuations within By simplifying the state variables into a single state variable, the computational complexity for deriving the optimal solution can be reduced, and the accuracy and reliability of operation for the control method of the microgrid system can be expected to reduce computational resources and computational time. It can bring about great progress in terms of performance and efficiency, and it is an invention that has industrial applicability because it not only has sufficient potential for commercialization or sales of a microgrid system, but also can be clearly implemented in reality.

Claims (5)

In the microgrid system control device that optimally operates grid power and ESS charging power provided within the microgrid,
The control device is,
It is equipped to derive the optimal solution of the determined objective function to minimize grid power and energy storage device ESS charging power,
The objective function is,
It is provided as the sum of a first-order term including grid power and a second-order term including the error between the target tracking value and actual measurement value of ESS charging power, and is optimized based on a decreasing horizon technique and QP (Quadratic Programming) system. Provided to derive,
The receding horizon technique of decreasing size is
A control device for a microgrid system, characterized in that it is provided to derive a single state variable by applying the equal limit condition, the boiling limit condition, the upper limit condition, and the lower limit condition to the state vector of the determined objective function. delete delete The method of claim 1, wherein the control device:
Set the objective function as grid power, target tracking value of ESS charging power, and actual measured value of ESS charging power,
The state variables of the above-set objective function are reconstructed into a vector format using a receding horizon technique,
A control device for a microgrid system, characterized in that it is provided to derive an optimal solution for the reconstructed objective function through a QP system.

delete