KR20130028507A - Method and system for computing optimal capacity of renewable energy - Google Patents
Method and system for computing optimal capacity of renewable energy Download PDFInfo
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- KR20130028507A KR20130028507A KR1020110092116A KR20110092116A KR20130028507A KR 20130028507 A KR20130028507 A KR 20130028507A KR 1020110092116 A KR1020110092116 A KR 1020110092116A KR 20110092116 A KR20110092116 A KR 20110092116A KR 20130028507 A KR20130028507 A KR 20130028507A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
A method and system for calculating an optimal capacity of renewable energy is provided. The method for calculating the optimal capacity of renewable energy according to an embodiment of the present invention is a method for calculating an optimal capacity of renewable energy for applying a plurality of renewable energy systems to a plurality of buildings, the building included in the target site Calculating energy demand for the military based on the size and use of the building; Determining whether the renewable energy system is applied to the building group based on the size of the building; Applying a genetic algorithm to energy demand for a plurality of buildings to which the renewable energy system is applied; And calculating an optimal capacity for each renewable energy system based on the result of applying the genetic algorithm.
Description
The present invention relates to a method and system for calculating an optimal capacity of renewable energy, and more particularly, to a method and system for calculating an optimal capacity for a plurality of renewable energy systems using a genetic algorithm.
Recently, in order to secure energy resources and overcome the global warming crisis, development of renewable energy has been actively conducted. Renewable energy usually refers to energy produced in the natural state, and the importance and the importance of renewable energy are increasing gradually due to the deepening of the climate change problem and the depletion of fossil fuel.
In particular, Korea relies on fossil fuels for most of its energy resources and imports most of its resources, and interest in renewable energy is increasing.
Currently, in the case of renewable energy system related technology, after the capacity of the system is determined by the designer, each performance of each renewable energy system is individually evaluated using a performance evaluation program.
However, when planning the application of a plurality of renewable energy systems to a plurality of buildings, it is difficult to perform a complex evaluation, there is a problem that the efficiency is reduced due to the individual evaluation.
In addition, after calculating the applied capacity of the renewable energy system by the user, the system performance evaluation for it, or when calculating the system capacity, the selection criteria of the user is limited to the economy, there is a problem that can not accommodate a variety of requirements.
In addition, since it is not considered whether the installation is possible according to the capacity of the applied system, the capacity calculation is different from the actual system applicability, and there is a problem that the installation of the system depends only on the discretion of the designer.
The present invention is to solve the above problems, to provide a method and system for calculating the optimal capacity of renewable energy to calculate the proper capacity of the system when applying a plurality of renewable energy system to a plurality of buildings.
The problem to be solved by the present invention is that, as it is expected to expand the plan of renewable energy in a complex building rather than an individual building, the capacity of the system is calculated by considering the renewable energy system in a complex manner and efficient renewable energy To provide a system.
Problems to be solved by the present invention are not limited to the above-mentioned problems, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.
In accordance with an aspect of the present invention, there is provided a method of calculating an optimal capacity of renewable energy, in order to calculate an optimal capacity of renewable energy in order to apply a plurality of renewable energy systems to a plurality of buildings. Calculating an energy demand for the group of buildings included in the site based on the size and use of the building; Determining whether the renewable energy system is applied to the building group based on the size of the building; Applying a genetic algorithm to energy demand for a plurality of buildings to which the renewable energy system is applied; And calculating an optimal capacity for each renewable energy system based on the result of applying the genetic algorithm.
The system for calculating the optimal capacity of renewable energy according to an embodiment of the present invention for achieving the above object, building the energy demand for the building group included in the target site to apply a plurality of renewable energy system to a plurality of buildings A calculation unit calculating based on the size and use of the; A determination unit that determines whether the renewable energy system is applied to the building group based on the size of the building; And a controller configured to calculate an optimal capacity for each renewable energy system by applying a genetic algorithm to energy demand for a plurality of buildings to which the renewable energy system is applied.
Other specific details of the invention are included in the detailed description and drawings.
According to the present invention, it is possible to calculate the capacity of the system in consideration of the renewable energy system that is expected to be expanded in a plurality of buildings in combination, it is possible to establish an efficient plan for the application of renewable energy system.
In addition, by selecting a system selection criteria by a user having various system application viewpoints, various requirements of the user can be considered.
In addition, the present invention may be utilized as decision data for selecting a plurality of renewable energy systems.
1 is a flowchart of a method for calculating an optimal capacity of renewable energy according to an embodiment of the present invention.
2 is a flowchart of a method for calculating an optimal capacity of renewable energy according to another embodiment of the present invention.
3 is a flowchart of a method of calculating an optimal capacity of renewable energy to which a detailed genetic algorithm according to another embodiment of the present invention is applied.
4 is a block diagram of a system for calculating an optimal capacity of renewable energy according to an embodiment of the present invention.
5 is a diagram illustrating a detailed algorithm of an optimal capacity calculation system of renewable energy according to an embodiment of the present invention.
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention and the manner of achieving them will become apparent with reference to the embodiments described in detail below with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout.
Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used in a sense that can be commonly understood by those skilled in the art. Also, commonly used predefined terms are not ideally or excessively interpreted unless explicitly defined otherwise.
Hereinafter, the present invention will be described in more detail with reference to the accompanying drawings.
1 is a flowchart of a method for calculating an optimal capacity of renewable energy according to an embodiment of the present invention.
First, in the method of calculating the optimal capacity of renewable energy in order to apply a plurality of renewable energy systems to a plurality of buildings, the energy demand for the group of buildings included in the target site is calculated based on the size and use of the building. (S105). In addition, it is determined whether the renewable energy system is applied to the building group based on the scale of the building (S110), and the building to which the renewable energy system is not applied is excluded from whether the renewable energy system is applied (S112). Thereafter, for the building to which the renewable energy system is applied, the genetic algorithm is applied to the energy demand for the plurality of buildings to which the renewable energy system is applied (S115). Finally, an optimal capacity is calculated for each renewable energy system based on the result of applying the genetic algorithm (S120).
Here, the target site refers to a certain area encompassing a plurality of buildings. For example, cities such as Seoul, Busan, and Daejeon will correspond. The plurality of renewable energy systems are composed of a solar system, a solar system, a wind system, and a geothermal system. However, the present invention is not limited thereto, and may be used to convert existing fossil fuels or include all renewable energy, that is, sustainable energy including sunlight, water, geothermal energy, bioorganisms, and the like. In addition to the four systems, it may include biomass energy, marine energy, waste energy, fuel cells, hydrogen energy and the like.
Since the energy demand for the building depends on the size and use of the building, the energy demand included in the target site is calculated based on the size and use of the building (S105). The size of the building depends on the area and the number of floors. In addition, the use of the building can be divided into office, commercial use, residential use, public use. The larger the building, the higher the consumption of energy, and industrial buildings such as factories have higher energy demand than buildings of other uses.
In the present invention, the renewable energy system is largely composed of solar, solar, wind, geothermal system, the renewable energy systems are required to have a separate power generation to operate, renewable energy applicable to the size of the building The system is determined. In addition, solar heat and geothermal heat dissipate heat, which is advantageous for heating liquids, and solar and wind power are advantageous for power generation.
Therefore, solar systems and wind systems are used for power supply, while solar systems and geothermal systems are used for heat supply. In particular, solar systems can be used to heat water to supply hot water, and geothermal systems can be used for cooling and heating purposes. However, although it is advantageous for the renewable energy systems to be used for the applications described above, it is obvious to one skilled in the art that the renewable energy system is not limited to any particular application. For example, the solar system may be used for heating and cooling, and the geothermal system may be used for hot water supply. Of course, solar systems and geothermal systems can also be used for the same purpose.
And, it is determined whether the renewable energy system is applied to the building group based on the size of the building (S110), so that the roof top area of the building, the area of the facade of the building, and the available area outside the building are determined. On the basis, an operable renewable energy system is determined.
PV system (PV system, Photovoltaic system) is a power generation system that produces electricity on a large scale by using solar energy by unfolding a panel with a solar cell attached on a large scale. It generates electricity and can be installed on the roof of a large building or on an available land outside the building, etc. Also, in the case of a large building, it is possible to install a solar system on the front and side surfaces of the building. For the solar system to be installed, the roof of the building, the elevation of the building, and the available land outside the building will be required.
Similarly, the solar thermal system (ST system) heats the heat medium with solar heat and heat-exchanges it with water to supply hot water.Same as the solar system, the area of the roof of the building, the area of the facade of the building, and the available space outside the building Based on the area, it is determined whether the solar system is applied.
Wind power system (WIND system) converts the energy of the wind into electrical energy, rotates the blades of the generator by the blowing wind, produces electricity by the rotational force of the wing, It is not possible to install on the facade. Therefore, based on the rooftop area of the building and the available area outside the building, it is determined whether to apply the wind power system. However, installing a wind system on the roof of a building is not practical, so it will generally be installed on available land.
GT system (Ground system) is a system that utilizes the ground temperature of 15 degrees Celsius which is kept constant throughout the year in the underground (usually 150m or less) with a certain depth to form a heat storage tank with a heat pump and utilize it for cooling and heating and hot water supply. Since the underground underground temperature is used, it is installed on the land outside the building.
Thus, whether or not solar and solar systems are applied is determined based on the rooftop area of the building, the area of the facade and the available area outside the building, and based on the rooftop area of the building and the available area outside the building. Whether to apply the wind system is determined, and whether or not to apply the geothermal system based on the available area outside the building.
In the case of a building in which the renewable energy system cannot be applied, the building is excluded from the demand of renewable energy (S112), and based on the energy demand of the building to which the renewable energy system is applied, a genetic algorithm is applied (S115), and the oil field is Based on the result of applying the algorithm, the optimal capacity is finally calculated for each renewable energy system (S120).
Genetic Algorithm (GA) is a probabilistic optimal solution search method that mimics the evolution of natural ecosystems, that is, natural selection and genetic laws, which will be described in detail later.
2 is a flowchart of a method for calculating an optimal capacity of renewable energy according to another embodiment of the present invention.
Finally, in order to calculate the optimal capacity for each renewable energy system, the size of the building, the purpose of the building, and the target amount of renewable energy to be applied are required, and such information may be input by a user or may be databased in advance. There is a need.
Therefore, the relevant information necessary to calculate the optimal capacity of the renewable energy system, such as the location of the target site, the size and use of the building constituting the building group, the target amount of renewable energy application, the priority priority of the renewable energy system, etc. After storing in the database (S201), the energy demand for the building group included in the target site is calculated based on the size and use of the building (S205), and applying the renewable energy system to the building group based on the size of the building. It is determined whether or not (S210), the building does not apply to the renewable energy system is excluded from the application of the renewable energy system (S212), for the building to which the renewable energy system is applied, the renewable energy system is applied Genetic algorithms are applied to the energy demand for a plurality of buildings (S215), and based on the results of applying the genetic algorithms, It calculates the optimal dose for each renewable energy system (S220).
Here, the location of the target site refers to a city or a specific area to which the renewable energy system is applied, and information about the size and use of individual buildings constituting the building group includes the size of the building such as the total area of the building, commercial districts, offices, It refers to the use of buildings such as residential districts. In addition, the application target amount of renewable energy refers to the ratio of renewable energy to the total demand energy or the required demand amount of renewable energy. In addition, the priority of importance of the renewable energy system is to weight the individual systems constituting the renewable energy system according to importance. For example, where wind speeds are high throughout the year depending on the climate of the destination, wind is prioritized, where solar radiation is high, or weight is high, or where hot water load is high depending on the load of the site. It gives a high weight to the solar system. As an example of the weighting, in windy and cold regions, one may consider giving a large weight to the wind system and / or the geothermal system, or a small weight to the solar system and the solar system.
After storing the relevant information necessary to calculate the optimal capacity of the renewable energy system in the database (S201) is the same as in FIG. 1 described above, it will be omitted below.
3 is a flowchart of a method of calculating an optimal capacity of renewable energy to which a detailed genetic algorithm according to another embodiment of the present invention is applied.
The energy demand for the building group included in the target site is calculated based on the size and use of the building (S305), and based on the size of the building, it is determined whether the renewable energy system is applied to the building group (S310). The building to which the renewable energy system is not applied is excluded from whether the renewable energy system is applied (S312), and then, for the building to which the renewable energy system is applied, energy for a plurality of buildings to which the renewable energy system is applied. Applying the genetic algorithm to the demand (S315, S320, S325, S330), based on the result of applying the genetic algorithm to calculate the optimal capacity for each renewable energy system (S335) is the same as in Figure 1, detailed genetic algorithm Let's only look at the order.
As mentioned above, genetic algorithms are based on the principles of natural selection and biogenetics of the natural world, and are parallel and global search algorithms that allow all living things to survive by adapting to a variety of environments. The theory of Survival of the fittest is the basic concept.
Genetic algorithms represent possible solutions to the problem to be solved in a form of data structure, and then transform them gradually to produce more and more suitable solutions.
In other words, after forming the initial population, individuals are selected from the population, crosses and mutations are formed to yield an increasingly highly suitable individual.
Corresponding to the general genetic algorithm, in the present invention, the application of the genetic algorithm is as follows (S315, S320, S325, S330).
Arbitrarily form an initial solution set of a renewable energy system (S315), select a solution of high suitability from the solution set, and combine the selected solution with other high-fit solutions to produce a higher suitability solution (S320). In operation S325, a range in which the renewable energy system is applied according to an application target amount of renewable energy is determined, and when applied, an optimization condition is determined according to the priority of the renewable energy system (S330). Here, the application target amount of renewable energy and the priority priority of the renewable energy system may be preset by the user or may be stored in a database in advance.
Here, by determining the range to which the renewable energy system is applied according to the target amount of renewable energy (S325), if not applied, again select a solution with high suitability from the solution set (S320). In addition, the optimization condition is determined according to the priority of importance of the renewable energy system (S330). If the optimization is not performed, the solution having a high suitability is selected again from the solution set (S320).
For example, suppose four renewable energy systems, solar system, solar system, wind power system, and geothermal system, form a set of solutions for the optimal capacity of the four renewable energy systems to form a set of solutions from the solution set. Selecting a random solution and repeating the steps of combining with other goodness of fit solutions yields a goodness of fit solution. At this time, the range of application of the renewable energy system according to the target amount of renewable energy is determined, and the optimal solution is derived according to the priority priority condition of the renewable energy system.
Except for the detailed process of the genetic algorithm is the same as Figure 1, it will be omitted below.
4 is a block diagram of a system for calculating an optimal capacity of renewable energy according to an embodiment of the present invention.
The optimal
The calculating unit 120 calculates energy demand for the building group included in the target site based on the size and use of the building in order to apply the plurality of renewable energy systems to the plurality of buildings.
In addition, the determination unit 130 serves to determine whether the renewable energy system is applied to each building constituting the building group based on the size of the building.
In addition, the controller 140 calculates an optimum capacity for each renewable energy system by applying a genetic algorithm to energy demand for a plurality of buildings to which the renewable energy system is applied.
Preferably, the optimal capacity calculation system of
As described above, the plurality of renewable energy systems are composed of a solar system, a solar system, a wind system, and a geothermal system, but are not limited thereto.
In addition, the solar system and the wind system are used for power supply, the solar system is used to supply water by heating water, geothermal system may be designed to be used for heating and cooling, but is not limited thereto.
In particular, the determination unit 130 determines whether to apply the solar and solar system based on the area of the roof of the building, the area of the facade of the building and the available area of the outside of the building, the area of the roof of the building and the outside of the building It is desirable to determine whether to apply the wind power system on the basis of the available area of, and to determine whether to apply the geothermal system on the basis of the available area on the outside of the building. Of course, it is not limited.
The controller 140 may include a solution set forming unit 141, a dielectric calculation generating unit 142, a capacity determining unit 143, and a fitness determining unit 144. This is because the controller 140 controls the application of the genetic algorithm, but the solution set forming unit 141, the genetic operation generating unit 142, the capacity determining unit 143, and the fitness determining unit 144 are not separately configured. In addition, it may be configured as a module.
The solution set forming unit 141 optionally forms an initial solution set of the renewable energy system. This corresponds to the initial population formation or population formation of the genetic algorithm.
In addition, the dielectric calculation generator 142 selects a solution having a high suitability from the solution set formed by the solution set forming unit 141 and combines with a solution having a high suitability to generate a higher suitability solution. That is, the genetic operation generating unit 142 performs a pivotal operation of a genetic algorithm such as individual selection, crosses, and formation of mutations.
The capacity determining unit 143 determines an applicable capacity of the renewable energy system by determining a range to which the renewable energy system is applied according to an application target amount of renewable energy. Here, the target amount of application of renewable energy may be input in advance in the database 160 and stored, or may be input by a user.
In addition, the suitability determination unit 144 determines the optimal suitability of the renewable energy system according to the priority priority of the renewable energy system. Priority priority of the system is stored in advance in the database 160, or is input by the user. Among the goodness of fit determined by the goodness-of-fit determining unit 144, the best fit is the optimum capacity of renewable energy.
The optimal
5 is a diagram illustrating a detailed algorithm of an optimal capacity calculation system of renewable energy according to an embodiment of the present invention.
In Fig. 5, all steps of the detailed algorithm to which a plurality of renewable energy systems are applied are shown.
In the INPUT step, an area to which a plurality of renewable energy systems are applied is selected (area selection), and information about all buildings located in the selected area is modeled (building group modeling). In particular, in the building group modeling step, modeling is performed according to the size of the building and the purpose of the building. The target amounts of the plurality of renewable energy systems are input (target amount input), and the selection criteria of the plurality of renewable energy systems are input (selection criteria input).
In this case, since all the information input in the INPUT step is necessary information for performing the optimization algorithm, it is preferable to store the information in the database so that it can be used continuously.
Areas selected at the INPUT level can be classified according to climate. At this time, the classification criteria of the climate is stored in advance in the database (climate DB). For example, the climate database is classified and stored according to the yearly average temperature, average solar radiation, and average wind speed. The climate database selects the regions to which the renewable energy system applies. The climate DB may be configured as part of a database in which information input at the INPUT stage is stored, but is not limited thereto.
Buildings modeled by building group modeling determine the application of renewable energy systems based on the size of the building, in particular, the rooftop area (roof), the facade area (elevation), and the available area outside the building (outside the building).
Preferably, based on the area of the roof of the building, the area of the facade of the building and the available area of the exterior of the building to determine whether to apply the PV (PV) system and solar (ST) system, the area of the roof of the building and the building It is determined whether to apply a wind power (WIND) system based on the available area of the outside, and whether to apply a geothermal (GT) system based on the available area of the outside of the building, but is not limited thereto. As shown.
The energy required in the selected area is divided into electric power, hot water supply, heating, and cooling through energy DB. At this time, the PV system and the wind system are used for power supply, the solar system is used for heating water supply, and the geothermal system is used for cooling and heating. But it is not limited to this as described above.
Then, a solution set (initial group formation) is formed using the application target amount (target amount input) of the renewable energy system input at the INPUT stage, the solution group is selected from the solution set, and the selected solution is selected. In combination with a high-fit solution, a higher-fit solution (object selection, crossover, and mutation) is generated, and finally, the range of application of the renewable energy system is determined according to the target amount of renewable energy (limit of application). .
Then, the optimization condition is determined according to the priority priority of the renewable energy system (system selection criteria input) (optimization suitability), and the optimal capacity of the renewable energy is derived at the output stage (system capacity).
By analyzing the optimal capacity (system capacity) of the renewable energy derived from the OUTPUT stage, it is possible to predict the demand and required performance of the renewable energy system according to the climate of the selected region. By using this, it is possible to plan the facility of rational renewable energy system and use it as decision data for selecting multiple renewable energy systems.
4 and 5 may refer to software or hardware such as a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC). However, the components are not limited to software or hardware, and may be configured to be in an addressable storage medium and configured to execute one or more processors. The functions provided in the above components may be implemented by more detailed components, or may be implemented as one component that performs a specific function by combining a plurality of components.
While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, You will understand. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive.
100: optimal capacity calculation system 110: input unit
120: Operation unit 130:
140: control unit 150: database
160: Output section
Claims (13)
Calculating an energy demand for the building group included in the target site based on the size and use of the building;
Determining whether the renewable energy system is applied to the building group based on the size of the building;
Applying a genetic algorithm to energy demand for a plurality of buildings to which the renewable energy system is applied; And
Calculating an optimal capacity for each renewable energy system based on a result of applying the genetic algorithm.
And storing related information in a database, such as the location of the target site, the size and use of buildings constituting the building group, an application target amount of the renewable energy, and information on the priority of importance of the renewable energy system. How to calculate the optimal capacity of renewable energy.
The plurality of renewable energy systems,
Optimum capacity calculation method of renewable energy consisting of solar system, solar system, wind power system and geothermal system.
The solar system and the wind system is used for power supply, the solar system is used to supply water by heating water, the geothermal system is used for calculating the optimal capacity of renewable energy used for heating and cooling.
Determining whether or not the renewable energy system is applied,
Based on the area of the roof of the building, the area of the facade and the available area outside the building, the application of the solar and solar systems is determined. The method of calculating the optimal capacity of renewable energy is determined whether or not to apply, and whether or not to apply the geothermal system based on the available area outside the building.
Applying the genetic algorithm,
Optionally forming an initial solution set of the renewable energy system;
Selecting a solution with high fitness from the set of solutions, and combining the selected solution with another solution with high fitness to produce a solution with higher fitness;
Determining a range to which the renewable energy system is applied according to a target amount of renewable energy; And
And determining an optimization condition according to the priority priority of the renewable energy system.
A determination unit that determines whether the renewable energy system is applied to the building group based on the size of the building; And
And a controller configured to calculate an optimal capacity for each renewable energy system by applying a genetic algorithm to energy demand for a plurality of buildings to which the renewable energy system is applied.
An optimal capacity of renewable energy further comprising a database having information on the location of the target site, the size and use of buildings constituting the building group, the target amount of renewable energy applied, and the priority of importance of the renewable energy system; Output system.
The plurality of renewable energy systems,
Optimum capacity calculation system of renewable energy consisting of solar system, solar system, wind power system and geothermal system.
The solar system and the wind system is used for power supply, the solar system is used to supply water by heating the water, the geothermal system is the optimal capacity calculation system of renewable energy used for heating and cooling.
The determination unit,
Based on the area of the roof of the building, the area of the facade and the available area of the exterior of the building, the application of solar and solar systems is determined. A system for calculating the optimal capacity of renewable energy, which determines whether or not to apply, and whether or not the geothermal system is applied based on the available area outside the building.
The control unit,
A solution set forming unit that optionally forms an initial solution set of the renewable energy system;
A dielectric calculation generation unit which selects a solution having a high degree of fitness from the solution set formed by the solution set forming unit and combines with another solution having a high degree of fitness to generate a solution having a higher degree of fitness;
A capacity determination unit determining an applicable capacity of the renewable energy system by determining a range to which the renewable energy system is applied according to an application target amount of renewable energy; And
A suitability determination unit for determining the suitability of the renewable energy system in accordance with the priority priority of the renewable energy system further comprises the optimum capacity calculation system of renewable energy.
The system is an optimal capacity calculation system of renewable energy implemented online.
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| KR1020110092116A KR20130028507A (en) | 2011-09-09 | 2011-09-09 | Method and system for computing optimal capacity of renewable energy |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112072640A (en) * | 2020-08-13 | 2020-12-11 | 清华大学 | Capacity optimization method for virtual power plant polymerization resources |
| KR102259860B1 (en) * | 2020-01-28 | 2021-06-03 | 주식회사 한일엠이씨 | Method for Designing the Renewable Energy System Reflecting Energy Consumption Characteristics of Buildings |
| KR20230116390A (en) * | 2022-01-28 | 2023-08-04 | 한국전기연구원 | Cloud-based building energy consulting system and method |
-
2011
- 2011-09-09 KR KR1020110092116A patent/KR20130028507A/en not_active Application Discontinuation
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102259860B1 (en) * | 2020-01-28 | 2021-06-03 | 주식회사 한일엠이씨 | Method for Designing the Renewable Energy System Reflecting Energy Consumption Characteristics of Buildings |
| CN112072640A (en) * | 2020-08-13 | 2020-12-11 | 清华大学 | Capacity optimization method for virtual power plant polymerization resources |
| KR20230116390A (en) * | 2022-01-28 | 2023-08-04 | 한국전기연구원 | Cloud-based building energy consulting system and method |
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