KR101888877B1 - Method for designing renewalbe ennergy hibrid power generation system - Google Patents

Abstract

A method for designing a renewable energy hybrid power generation system including two or more renewable energy generation sources and an energy storage device is disclosed. In the disclosed design method, a device is classified into several clusters, and only a few central devices representative of each cluster are combined to perform a linear programming method to select a capacity and an optimum center device. Then, By only performing the integer programming for the devices included in the cluster and selecting the final device, it is possible to substantially reduce the time required to obtain the design while providing the effect of reviewing substantially all the devices.

Description

METHOD FOR DESIGNING RENEWALBE ENERGY HIBRID POWER GENERATION SYSTEM [0001]

The present invention relates to a method of designing a renewable energy hybrid power generation system, and more particularly, to a method of designing a renewable energy hybrid power generation system including two or more renewable energy generation sources and an energy storage device.

In recent years, the area where grid parity is achieved has expanded, and thus, the self-sustaining power for the growth of the renewable energy industry is secured. In addition, despite the drop in oil prices, the renewable energy market is maintaining a high growth rate. It is analyzed that this is due to differences in demand market such as transportation and power generation, policy will and technological development.

The renewable energy hybrid system is an energy (power, heat and gas) supply system that combines two or more renewable energy generation sources with energy storage devices. It replaces the problems of uneven renewable energy production and unequal distribution of resources by region. And contributes to expanding the supply of renewable energy. A variety of demonstration of new and renewable energy hybrid systems is underway in Korea and abroad. As a system for supplying electric power only, a solar-wind power hybrid power generation system or a solar-wind power-diesel hybrid power generation system is widely studied. In order to efficiently operate such a hybrid power generation system, system design technology utilizing demand forecasting and weather data is becoming more important.

Korean Patent Laid-Open Publication No. 2013-0141288 discloses an apparatus and a method for estimating an appropriate capacity of a distributed power source of a stand-alone micro grid system. In this document, for a given capacity candidate group, a linear programming method is used to determine the capacity candidate group and the optimum capacity that satisfy the objective function, for example, the cost is minimized. However, when a large number of equipments are provided for each facility, a linear programming method is performed for all the equipments. Therefore, when the number of equipments is large, the number of combinations to be calculated is greatly increased. In addition, since the optimum capacity is given as a real number instead of an integer, it is difficult to determine the actual number of devices.

Japanese Patent Publication No. 5179423 discloses an energy system optimization method and apparatus. In this document, for a given device combination, mixed integer linear programming is used to determine the combination of devices and the optimal capacity that satisfy the objective function, e.g., the cost is minimum. However, since the mixed integer programming is performed for all given devices, the number of combinations that must be calculated if the number of devices is large increases considerably, and it takes a considerable amount of time to derive the design. This problem is also mentioned in the succeeding patents of the same applicant. In the Japanese Patent Publication No. 5559414, the technology described in Japanese Patent Publication No. 5179423 is intended for individual buildings, There is no calculation logic, and there is a problem that the optimal solution is not obtained in the case where the constraint condition is large.

Regarding the arrangement of devices, U.S. Patent Publication No. 8751209 discloses a method of calculating where a sensor device is to be placed so as to quickly confirm the diffusion of a toxic substance using genetic algorithms.

KR 2013-0141288 A JP 5179423 B JP 5559414 B US 8751209 B

SUMMARY OF THE INVENTION It is an object of the present invention to provide a method for designing a new and renewable energy hybrid power generation system which can substantially reduce time required for deriving a design while carrying out a review of substantially all devices.

According to an aspect of the present invention, there is provided a computer-implemented method for designing a renewable energy hybrid power generation system arranged in a designated target area, the method comprising the steps of: (a) Based on the data for optimal design including data on the facilities and data on how many devices corresponding to the respective facilities are to be grouped (hereinafter referred to as "cluster number per facility"), Determining a number of devices and corresponding optimal devices; And (b) determining a place for arranging the power generation system in the target area based on the information about the optimum equipment for each facility and the number of the optimum equipment. A computer implemented method is provided.

According to another aspect of the present invention, there is provided an apparatus comprising: at least one processor; And at least one memory for storing computer-executable instructions, wherein the computer-executable instructions stored in the at least one memory comprise instructions for causing the at least one processor to perform the steps of: A method for designing a power generation system, comprising the steps of: (a) determining at least one facility constituting a power generation system and a plurality of devices corresponding to each of the facilities, Determining a number of the optimum apparatus and the number of the optimum apparatus for each apparatus based on the data for the optimum design including the number of the optimum apparatuses; And (b) determining a place for disposing the power generation system in the target area based on the information about the optimum apparatus for each facility and the number of the optimum apparatuses, An apparatus for designing a system is provided.

According to another aspect of the present invention there is provided a method for designing a renewable energy hybrid power generation system that is stored in a non-volatile storage medium and is executed by a processor and is located in a designated target area, the method comprising: (a) Based on data for optimum design including at least one facility and data on how many devices corresponding to each of the facilities are to be divided into several communities (hereinafter referred to as "number of clusters per facility"), Determining an optimal device and a number of the optimum device; And (b) determining a location for arranging the power generation system in the target area based on the information about the optimum equipment for each facility and the number of the optimum equipment. A program product is provided.

According to the present invention, there is provided a method of designing a new and renewable energy hybrid power generation system capable of substantially reducing the time required for deriving a design while carrying out a review of substantially all of the devices.

1 schematically illustrates an example of a renewable energy hybrid power generation system that can be designed by the method according to the present invention;
2 is a flowchart showing a method of designing a renewable energy hybrid power generation system according to an embodiment of the present invention;
Figure 3 is a flow diagram illustrating a portion of a method according to one embodiment of the present invention shown in Figure 2;
FIG. 4 is a block diagram illustrating an example of a computing device that may be used to implement the methods shown in FIGS. 2 and 3; FIG.
5 is a sequence diagram illustrating an embodiment of a method according to the present invention that may be performed in the computing device shown in the block diagram shown in Fig.
FIG. 6 is a sequence diagram illustrating another embodiment of a method according to the present invention that may be performed in the computing device shown in the block diagram shown in FIG. 4; FIG.
FIG. 7 shows an example of predicting the amount of new and renewable energy production over time for one year in the embodiment of the present invention, wherein (a) represents solar energy, and (b) represents wind energy.
FIG. 8 shows an example of predicting the energy demand amount over time for one year in the embodiment of the present invention, and shows that the energy demand amount is predicted differently depending on the type of the load.
9 is a diagram showing a result of selecting a device and the number of the devices for each device in the embodiment according to the present invention.
10 is a diagram showing a process of arranging devices of a power generation system using a grid map in an embodiment according to the present invention.
11 is a view for explaining a method of arranging facilities of the same kind adjacent to each other in the embodiment according to the present invention.
12 is a diagram showing an example of a result of arrangement of facilities of a power generation system depending on a constraint condition in an embodiment according to the present invention;

Hereinafter, embodiments of a method for designing a renewable energy hybrid power generation system according to the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 shows an example of a renewable energy hybrid power generation system that can be designed by a design method according to the present invention. The illustrated power generation system includes a solar power generator, a wind power generator, an energy storage system (ESS), and a diesel generator. Other devices, except for diesel generators, are connected to the grid by DC / AC inverters. Also, the energy loads are connected to the same grid. The grid may be connected to other external grids, or may be connected only between devices shown independently. Depending on the amount of new and renewable energy produced, the charge and discharge operation of the energy storage device and / or the operation of the diesel generator can be controlled by a control section (not shown).

The renewable energy hybrid power generation system shown in FIG. 1 is not limited to such a configuration as an example, and may include other types of power generation apparatuses. For example, the renewable energy generation device may include all kinds of renewable energy generation devices such as solar thermal generators, geothermal generators, and the like, and various types of generators using fuel as conventional power generators may be included.

In this specification, the term " facility " is used to refer collectively to generators belonging to one type, and the generators of individual models corresponding to each facility are expressed by the term " apparatus ".

Hereinafter, a method of designing a renewable energy hybrid power generation system according to an embodiment of the present invention will be described with reference to FIGS. 2 and 3. FIG.

First, it is given whether the power generation system is made up of certain facilities. For example, the power generation system can be composed of a solar generator, a wind power generator, an energy storage device, and a diesel generator, as shown in Fig.

Next, in step S100, data (hereinafter referred to as "data for optimum design ") including data on how to divide a plurality of devices corresponding to each facility constituting the power generation system into a plurality of clusters The optimum capacity for each facility, the optimum operation schedule for each facility, and the number of the optimum apparatus and the number of the optimum apparatus for each facility are determined. The data for the optimum design includes climate information of the target area, energy load related information of the target area, and optimization objective function.

Referring to FIG. 3, step S100 will be described in more detail as follows:

In step S110, a plurality of devices corresponding to each facility is grouped into a cluster of the number of clusters per facility, and a center device of each cluster is selected. The k-medoids algorithm is recommended for clustering and selection of the central devices of each cluster. In this algorithm, given the number of clusters, a plurality of devices are clustered into a number of clusters of a given number of clusters, and at the same time, one of the devices in which the central devices of each cluster actually exist is selected.

In step S120, the energy production amount of the renewable energy generation system is predicted based on information on each selected center apparatus, for example, information on performance of the center apparatus, and climate information of the target area. For example, we estimate the energy output of solar and wind power generators based on solar radiation, wind speed, and so on. Energy production can be predicted by hourly data over a year.

In step S130, the energy demand amount of the target area is predicted based on the energy load related information of the target area. The energy load related information may include the type of energy load in the target area, for example, the type of load such as factory, farm, home, school, and the number of each load. Energy demand can also be forecasted over time for one year.

In step S140, a combination of all possible center apparatuses constituting the power generation system is selected from the center apparatuses corresponding to the number of clusters per facility, and the optimal design according to the linear programming method is performed for each combination of the center apparatuses. The predicted energy production amount and the predicted energy demand amount calculated in step S130 may be included in any one of the constraints when performing the linear programming method. The objective function can be given as a single objective function, such as minimizing the price, or given as a multiple objective function that takes both price and CO2 emissions into consideration. By comparing the objective function values corresponding to the combinations of the respective center devices, it is possible to select the center device combination corresponding to the highest objective function value. In addition, when the linear programming method is performed, the optimal capacity for each facility and the optimal operation schedule for each facility can be determined. In this case, the optimal capacity for each facility and the optimal operation schedule for each facility corresponding to the selected center device combination are determined together.

In step S150, an optimal design according to the integer programming method including the optimal capacity for each facility and the optimum operation schedule for each facility as the constraint condition for the cluster including each center device of the selected center device combination, The number of optimum devices and the number of optimum devices are determined.

Finally, in step S200, a place for arranging the power generation system in the target area is determined based on the information about the optimum equipment for each equipment and the number of the optimum equipment. At this time, it is preferable that devices of the same facility are determined to be arranged adjacent to each other. There are many kinds of algorithms used in the arrangement of devices, for example, a genetic algorithm can be used.

The design method according to the embodiments of the present invention described herein, including the design method according to the above-described embodiments, may be implemented by a computer program and performed by a computing device. Fig. 4 schematically shows a computing device 1 for carrying out a designing method according to embodiments of the present invention implemented by a computer program. The computer device 1 may be a general computer system.

4, the computing device 1 may include one or more processors 10, one or more memories 20, one or more input devices 30, one or more output devices 40, one or more communication devices 50 One or more storage devices 70, and a communication channel 60 for communication therebetween.

The processor 10 is configured to execute instructions within the computing device 1. The memory 20 is a computer-readable temporary storage medium that does not retain stored data unless power is supplied. The memory 20 is used for temporarily storing, for example, a command for the processor 10 to execute, data necessary for executing the command, data generated by execution of the command, and the like.

The input device 30 is used to receive various types of data from a user, and various types of data may be used depending on the type of data and the computing device 1. [ For example, a keyboard and a mouse may be used when the computing device 1 is a general computer system, and a keyboard displayed on a touch display may be used when the computing device 1 is a tablet computer. A camera may be used when the input data is a video, and a microphone may be used when the input data is audio.

The output device 40 is used to provide various types of data to the user, and various types of data may be used depending on the type of data and the computing device 1. For example, an output device such as a display or a speaker may be used.

The computing device 1 communicates with an external device or network using the communication device 50. The communication using the communication device 50 may be performed by various methods such as wire, wireless, and the like.

Storage device 70 is a storage medium, such as a hard disk, for long term storage that is readable by a computer. The computing device 1 stores an operating system 2 that may be executed by the processor 10. The operating system 2 controls the operation of the computing device 1 and the execution of a computer program. The storage device 70 may store computer programs and / or data that are executed by the computing device 1.

4, the storage device 70 includes a plurality of modules including a computer program executed by the computing device 1, namely, a clustering module 82, a simulation module 84, A device selection module 88, and an optimum placement module 92. [ In addition, the storage device 70 stores data necessary for executing a computer program stored in these plurality of modules. In the embodiment shown in Fig. 4, as an example of the stored data, a climate information database 81, a facility-specific equipment database 83 and a grid map 91 are shown.

FIG. 5 illustrates a method of designing a power generation system according to an embodiment of the present invention, which is performed by executing a computer program including a plurality of modules shown in FIG. Hereinafter, the embodiment shown in Figs. 4 and 5 will be described.

Determining the configuration of power generation system

First, we decide the equipment that constitutes the renewable energy hybrid power generation system. For example, a solar generator, a wind turbine, an energy storage device, and a diesel generator, as in the power generation system shown in FIG. The decision on the equipment making up the power generation system is mainly given by the user.

Clustering by clustering module and selection of center devices for each cluster

The equipment-specific equipment database 83 stores information on a plurality of equipment for each equipment. The clustering module 82 classifies a plurality of devices for each facility stored in the equipment-specific equipment database 83 into a predetermined number of clusters, and selects the center devices of the respective clusters. To do this, clustering is used.

The clustering technique is an analytical technique in which individuals or objects are grouped into several clusters so that individuals having similar characteristics by similarity or distance are grouped together. The difference from the classification technique used similar to the clustering technique is that there is no assumption about the number of clusters or the structure of the group and only clusters are formed by the similarities or distances between the entities, It is an advantage that it can be utilized even when it is not known. Clustering techniques are classified into, for example, connectivity-based clustering, centroid-based clustering, distribution-based clustering, and density-based clustering. . In the present embodiment, a center-based clustering technique is used. In this technique, when a given data (object) is grouped into k clusters, k objects that are actually present are selected as the center object, and the remaining objects are grouped based on the closest k objects among the k center objects . Since the actual object clustering technique is a real object, that is, an actual renewable energy device is an object representative of the cluster, the central object clustering technique can be applied to a property of a device actually present in application of a linear programming method for subsequent simulation and capacity design Can be used.

If the number of types of facilities included in the renewable energy hybrid power generation system is n and the respective facilities are grouped into k, the number of possible combinations of renewable energy equipment equals to n. In the linear programming for the subsequent capacity design, since the linear programming method must be calculated for all these combinations, the number k of the clusters is adjusted in consideration of the actual calculation amount. The number of clusters may be determined differently for each facility.

In order to perform central object clustering, an attribute of an object to be used for calculating the distance between two objects, that is, an attribute of an individual device belonging to the facility, must be specified. Common attributes that are irrelevant to the type of equipment may include, for example, price and footprint. In addition, each renewable energy facility may include attributes that reflect the characteristics of the facility. For example, PV equipment can include maximum output, maximum output voltage, maximum output current, open voltage, short-circuit current, efficiency, number of cells per module, and cell type. In the case of wind turbines, it may include diameter, generator height, rated rotation speed, wind speed range (starting, rating, stop), and daily power output. Euclidean distances may be used for distance calculations, but are not necessarily limited thereto.

A plurality of algorithms for performing central object clustering are known. Among them, the central object clustering process based on the Partitioning Around Medoids (PAM) algorithm is briefly described as follows:

(1) Determine the number k of clusters to classify the data set, that is, the set of equipment for each plant.

(2) Designate k central objects at each facility.

(3) Repeat below

(3-1) Allocate the remaining objects to the cluster to which the closest center object belongs.

(3-2) Select non-center objects randomly for each cluster.

(3-3) The cost function (the sum of the distance from the object in each cluster to the center object and the distance to the non-center object and summing them together for all the clusters) is calculated. If the cost function decreases The noncentral object is designated as the new center object for the cluster.

(4) If the cost function does not decrease, the iteration is stopped and the clustering result is provided to the current central object and its corresponding cluster.

When the clustering process is performed on the devices included in each equipment constituting the power generation system, the central object (hereinafter, referred to as " center device ") assigned to each renewable energy facility A cluster of devices is determined (S10). The k center devices selected as the center objects of each cluster are provided to a simulation module to be described later and used in the simulation process (S12).

Energy production and energy by simulation module Simulation on Demand

In this step, the amount of energy production is estimated based on the climate data of the target area (S22), the load of the target area is estimated, and the amount of energy demand is estimated (S24).

The amount of energy produced by each center device can be predicted by using the attributes of the k center devices selected for each facility and the climate data of the target area through the clustering process S10 described above. To this end, the simulation module 84 inquires and grasps information on the center-specific equipment provided by the clustering module 82 in the equipment-specific equipment database 81 and obtains climate data of the target area from the climate information database 81 . For example, energy production can be predicted (S22) using hourly climate data over a year in the area of interest. An example of predicted energy production is shown in FIG.

Various methods can be used to predict the energy demand of the target area. For example, by constructing energy demand models over time according to load types (houses, office buildings, factories, warehouses, street lamps, etc.), and estimating energy demand (S24). FIG. 8 shows an example of predicting the amount of energy demand over time for one year depending on the type of the load.

Determine capacity and operation schedule by capacity determination module

Linear programming can be used to determine the optimum capacity of each facility included in the renewable energy hybrid power generation system and to establish the operation plan. Linear programming is a methodology for deriving optimal results (maximum profit or lowest cost) in a mathematical model composed of linear relationships. It is a linear objective function optimization technique that satisfies constraints with linear inequalities or linear equations.

In the present embodiment, the capacity calculation module 86 determines the optimal capacity and the operation schedule according to each facility corresponding to each center device combination by applying the linear programming method to all possible combinations of the plurality of center devices selected for each facility S30). For example, if the central unit of photovoltaic generation is kpv and the central unit of wind power generation is kwt, then the number of possible combinations is the product of these two (kpv X kwt).

As an objective function of the linear programming method, it is possible to use a single objective function to minimize the total cost while maintaining a certain level of reliability. Constraints that apply to linear programming may include the following:

(1) Demand matching

The inconsistent load between demand and supply at any point should be within the specified range.

(2) Battery capacity limit

The battery has an upper limit of charge state and the maximum depth of discharge (DOD) that can be reached within one cycle must satisfy a predetermined relationship.

(3) Maximum charging and discharging ability

The power required to charge and discharge the battery can not exceed the maximum capacity of the battery.

(4) Operation reserve ratio

For operational reliability, the generators must be able to respond immediately to voltage drops due to power supply disturbances due to failure of any generator. The battery bank should be linked to the grid as an operational reserve asset.

(5) Maximum allowable supply disruption energy

The supply disruption energy is defined as the unadjusted energy estimate that occurred during the monitoring period due to capacity shortage. This energy must not exceed a certain range of limits to maintain a level of service to be tolerated.

(6) Battery State of Charge (SoC)

Apply energy conservation constraints associated with the energy stored in the battery at any time during the charging and discharging process.

(7) Charging and discharging time

Charging and discharging to and from the battery should not occur at the same time. However, this constraint can not be implemented with linear equations or inequalities alone. Thus, for charging and discharging, for example, the following constraints can be applied: "Only discharge occurs before 7:00 am or after 7:00 pm. Charging is only possible during other times. "

Diesel generators are sized so that every renewable energy source can fail in the worst-case scenario to meet the demand for electricity per hour. That is, the capacity of the diesel generator sets the maximum power demand.

Applying a single objective function that minimizes the total cost, the optimal capacity corresponding to each combination of center devices is determined, and the total cost is derived. In this case, the total cost corresponding to each combination of the center devices can be sorted in the order of smallest, and the combination of the center devices with the smallest total cost is selected.

In recent years, other objective variables have been integrated, such as maximizing reliability, minimizing losses and emissions pollution. Some of these objective variables are inherently contradictory, and system designers or planners apply the Multi-Objective Optimization (MOO) technique. In this case, the result is usually to find the Pareto front, which represents the trajectory of the solution providing the best trade-off between the various objective variables. In this case, the final decision is made by the Pareto front according to the designer's preference or preference. An embodiment using a Pareto front is shown in Fig. For example, if an objective function minimizing the total cost and emission of pollutants is applied, a Pareto front corresponding to each combination of the central equipments is provided to the system designer (S34), and a combination of the central equipments suitable for the intention of the system designer is selected S36). When the selected center device combination is selected, the optimum capacity and operation schedule corresponding thereto are also determined.

Each center device is a central object of a corresponding cluster and represents a plurality of objects (a plurality of devices included in a specific cluster of renewable energy generation facilities). Therefore, determining the center device alone does not determine the optimum device. Further, since the optimum capacity of the center apparatus determined by the linear programming method is given in the form of a real number rather than an integer, it is impossible to accurately reflect the number of devices given as an integer in reality. Thus, a device selection process as described below is performed.

Selection of equipment by module selection

The device selection module 88 determines the devices constituting each new renewable energy generation system using the integer programming method (S40). Integer programming is a mathematical programming method in which decision variables have only integer values. The solution is to add a condition that the variables must be integers in linear programming, and use a solution that guarantees that the optimal solution is an integer.

Due to the development of computer technology, the linear programming method is hardly limited by the size of the problem, but in the integer programming method, it is difficult to obtain the optimal solution when the number of variables and the number of constraints increase. In this embodiment, the integer programming method is applied only to the devices included in the cluster corresponding to the selected center device using the linear programming method in the capacity determination process. Therefore, it is possible to select an optimum device within a practical time.

FIG. 9 shows an example of the number of equipment and equipment for each facility selected by the integer programming method.

In the device selection process (S40), the allowable area for the renewable energy hybrid power generation system and the allowable area for each facility can be added as the constraint. Therefore, the selected device satisfies this constraint.

Location Selection by Optimal Layout Module

When the information on the equipment for each equipment constituting the renewable energy hybrid power generation system determined in the equipment selection process is displayed in the equipment database 81 for each equipment, the area required for installing the equipment can be known and the number of the equipment can be multiplied (See FIG. 9). Once the required area for each facility is identified, these facilities can be deployed to the target area using a variety of methods.

In this embodiment, the optimal placement module 91 uses a genetic algorithm to select the location of the power generation system. Genetic algorithms are widely used in the field of machine learning as one of the optimization algorithms to find the minimum or maximum value for a given input condition. Although it is not optimal for the problem of too much computation to derive the optimum value mainly by mathematical calculation, it is used for many application problems because it derives a similar value in the optimal solution in a short time. Genetic Algorithm In the Genetic Algorithm, we set the output value randomly at the beginning of the solution set in the solution and then improve the solution gradually by transferring the better results to the next generation by repeating the generations (see Fig. 10).

In the location selection process according to the present embodiment, as an input to the genetic algorithm, data obtained by latticeizing ecological environment and spatial analysis GIS information and superimposing the data is used. The method of generating genetic algorithm input data using GIS information for each item is as follows:

(1) Selection of evaluation items to be applied in geographical space, humanities and society, environmental impact assessment

(2) Generate a grid map based on the GIS information of the target area for each evaluation item

(3) Assigning the value (evaluation value) related to each evaluation item as the attribute of each grid

(4) Apply GIS overlay operation to all grids to generate integrated grid map.

The generated integrated grid map 91 may be stored in the storage device 70.

Specific evaluation items include, for example:

In this embodiment, the problem of location selection can be expressed in the form of: < RTI ID = 0.0 >

When the number of facilities is n, and facility i occupies im cells, it is a problem to find the location that minimizes the sum of the amount of ecological destruction of each facility multiplied by the weight wi. In this case, Ecoi is determined by the location of the facility and the location of the facility which minimizes F is obtained by the genetic algorithm.

In this embodiment, devices of the same facility are arranged adjacent to each other. Specifically, a grid, which is a central point for each facility, is arbitrarily selected at the initial setting, and the radius of the circle having the same area as the area required for each facility is converted, and then a plurality Devices of the facility are arranged in the grid (see Fig. 11). In this case, the genetic algorithm for site selection is performed to minimize the sum of the evaluation values of a plurality of grids included in the circle. In this way, it is ensured that the equipment for each facility is all placed on the adjacent grid. If the facility includes a sea within the radius of the circle, it excludes it from the next generation of the genetic algorithm, thereby preventing an inaccessible area from being selected.

By performing the optimization according to the genetic algorithm, the optimized location result is obtained without overlapping each device constituting the renewable energy generation system. In addition, existing facilities that already exist in the target area may be classified as positive facilities and negative facilities by property setting of the facility, and may provide a close arrangement for positive facilities and a remote arrangement for negative facilities 12).

Although the present invention has been described in detail based on the embodiments shown in the accompanying drawings, it is apparent that various modifications are possible without departing from the scope of the present invention. Nothing in this specification is intended to limit the scope of the present invention to the scope of the appended claims. The above-described embodiments are for illustrative purposes and are not intended to exclude having other embodiments.

1: computing device
2: Operating system
10: Processor
20: Memory
30: Input device
40: Output device
50: communication device
60: communication channel
70: Storage device
81: Climate information database
82: Clustering module
83: Equipment database by equipment
84: Simulation module
86: Capacity design module
88: Device selection module
91: Grid map
92: Optimal placement module

Claims (9)

A computer-implemented method for designing a renewable energy hybrid power generation system disposed in a designated target area,
(a) an optimum design including at least one equipment constituting the power generation system and data on how many groups of equipment corresponding to each equipment are to be divided into groups (hereinafter referred to as "cluster number per equipment & A plurality of devices corresponding to each of the facilities are grouped into a cluster of as many as the number of clusters per facility and then a center device of each cluster is selected based on the data for each of the center equipments, The optimum equipment and the optimum operation schedule for each equipment corresponding to the selected optimum center equipment combination are determined and the optimal equipment and corresponding equipment Determining a number of optimal devices; And
(b) determining a place for disposing the power generation system in the target area based on the information on the optimum equipment for each equipment and the number of the optimum equipment
And a computer-implemented method for designing a renewable energy hybrid power generation system.
The method according to claim 1,
Wherein the data for the optimum design includes climate information of the target area, energy load related information of the target area, and optimization objective function.
The method of claim 2, wherein the step (a)
(a1) clustering a plurality of devices corresponding to each facility into clusters corresponding to the number of clusters per facility and selecting the central devices of the clusters;
(a2) estimating the energy production amount of the power generation system based on the information about each selected center device and the climate information of the target area;
(a3) estimating an energy demand amount of the target area based on the energy load related information of the target area;
(a4) A combination of all possible central equipments constituting the power generation system with the selected central equipments as many as the number of clusters per facility is made, and the optimization objective function and the energy production amount and the energy demand amount The optimal design according to the linear programming method included as one of the constraints is performed and then the center device combination corresponding to the highest objective function value is selected by comparing the objective function values corresponding to the combination of the respective center devices, Determining an optimal optimal capacity and an optimal operation schedule for each facility; And
(a5) An apparatus for constructing a power generation system by performing an optimal design according to the integer programming method including the optimum capacity for each facility and the optimal operation schedule for each facility, Determining a number of optimal and optimal devices;
And a computer-implemented method for designing a renewable energy hybrid power generation system.
The method of claim 3,
Wherein the clustering of step (a1) and the selection of the center devices of each cluster are performed using a k-medoids algorithm.
The method of claim 1, wherein in step (b)
And the devices of the same facility are determined to be disposed adjacent to each other. ≪ RTI ID = 0.0 > 15. < / RTI >
The method of claim 1, wherein in step (b)
And a genetic algorithm is used to design a new renewable energy hybrid power generation system.
An apparatus for designing a renewable energy hybrid power generation system disposed in a designated target area,
At least one processor; And
At least one memory for storing computer executable instructions,
The computer-executable instructions stored in the at least one memory may be, by the at least one processor,
(a) an optimum design including at least one equipment constituting the power generation system and data on how many groups of equipment corresponding to each equipment are to be divided into groups (hereinafter referred to as "cluster number per equipment & A plurality of devices corresponding to each of the facilities are grouped into a cluster of as many as the number of clusters per facility and then a center device of each cluster is selected based on the data for each of the center equipments, The optimum equipment and the optimum operation schedule for each equipment corresponding to the selected optimum center equipment combination are determined and the optimal equipment and corresponding equipment Determining a number of optimal devices; And
(b) determining a place for disposing the power generation system in the target area based on the information on the optimum equipment for each equipment and the number of the optimum equipment
The hybrid power generation system comprising:
The method of claim 7,
Further comprising a storage device for storing climate information of the target area and information on energy load of the target area.
A computer program for designing a renewable energy hybrid power generation system disposed in a designated target area,
Stored in a non-volatile storage medium,
(a) an optimum design including at least one equipment constituting the power generation system and data on how many groups of equipment corresponding to each equipment are to be divided into groups (hereinafter referred to as "cluster number per equipment & A plurality of devices corresponding to each of the facilities are grouped into a cluster of as many as the number of clusters per facility and then a center device of each cluster is selected based on the data for each of the center equipments, The optimum equipment and the optimum operation schedule for each equipment corresponding to the selected optimum center equipment combination are determined and the optimal equipment and corresponding equipment Determining a number of optimal devices; And
(b) determining a place for disposing the power generation system in the target area based on the information on the optimum equipment for each equipment and the number of the optimum equipment
A computer program stored in a non-volatile storage medium for designing a renewable energy hybrid power generation system.

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