Apparatus for diagnosing photovoltaic power generation through analysis of power generation trend

The present disclosure relates to an apparatus for diagnosing a state of a photovoltaic device, a building Integrated Photovoltaics (BIPV) device, etc., and more particularly to an apparatus for diagnosing photovoltaic power generation, which diagnoses a state of the specific photovoltaic device by comparing the difference in power generation between grouped photovoltaic devices through analysis of power generation for the same period in the past through machine learning, etc., wherein the apparatus processes power generation information, which is collected from the photovoltaic devices, based on failure history and maintenance and repair information of each photovoltaic device and performs precise grouping by minimizing error information regarding a power generation trend based on information such as regional weather information and environment information for a region where each photovoltaic device is located, so that a power generation trend can be analyzed with improved accuracy of analysis of state.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. section 371, of PCT International Application No.: PCT/KR2019/017300, filed on Dec. 9, 2019, which claims foreign priority to Korean Patent Application No.: KR10-2019-0017759, filed on Feb. 15, 2019, in the Korean Intellectual Property Office, both of which are hereby incorporated by reference in their entireties.

TECHNICAL FIELD

The present disclosure relates to an apparatus for diagnosing a state of a photovoltaic device, a building Integrated Photovoltaics (BIPV) device, etc., and more particularly to an apparatus for diagnosing photovoltaic power generation, the apparatus that does not perform diagnosis based on comparison between predicted power generation of a specific photovoltaic device and actual power generation, and which diagnoses a state of the specific photovoltaic device by comparing the difference in power generation between grouped photovoltaic devices through analysis of power generation for the same period in the past through machine learning, etc., wherein the apparatus processes power generation information, which is collected from the photovoltaic devices, based on failure history and maintenance and repair information of each photovoltaic device and performs precise grouping by minimizing error information regarding a power generation trend based on information such as regional weather information and environment information for a region where each photovoltaic device is located, so that a power generation trend can be analyzed with improved accuracy of analysis of state.

BACKGROUND ART

Solar photovoltaic power generation, which is a field of new and renewable energy, has recently been rapidly increasing in demand due to its many advantages, and technologies to increase power generation efficiency have been developed. When these photovoltaic devices fail to produce normal power generation output from photovoltaic modules due to various reasons such as shading, failure, aging, etc. in operation processes, the importance of maintenance to quickly diagnose and respond to these reasons is increasing.

These existing technologies for diagnosing a failure of solar photovoltaic power generation suggests a concept, which calculates predicted power generation using various prediction techniques in consideration of various environmental factors in a corresponding photovoltaic equipment (or module) and, in response to actual power generation out of a predetermined range from the predicted power generation, determines an abnormality and cause precise diagnosis and maintenance to be performed. Since various factors for solar photovoltaic power generation cannot be precisely reflected, the predicted power generation is not accurate enough. Therefore, the technique of diagnosing a failure based on predicted power generation with a large error range has a limitation that a rate of false diagnosis rate increases.

<Patent Literature> Korean Patent No. 10-1728692 “SYSTEM AND METHOD OF PREDICTING AND MONITORING ANOMALITY OF PHOTOVOLTAIC MODULE”

The related art disclosed in the <Patent Literature> also calculates real-time predicted power generation of a photovoltaic module based on data such as an existing change trend and solar radiation information of the photovoltaic module and then determine a failure of the photovoltaic module based on a degree of correspondence between the predicted power generation and actual power generation, that is, a difference therebetween, and therefore, the related art still has the same problem that a diagnosis error is large.

Accordingly, there is increasing need for devices and technologies capable of accurately diagnosing and responding to a state of solar power generation for efficiency of maintenance of solar power generation.

DISCLOSURE

Technical Problem

The present disclosure was devised to solve the above problems.

An aspect of the present disclosure is to provide an apparatus for diagnosing a photovoltaic device, the apparatus which does not perform diagnosis based on comparison between predicted power generation of a specific photovoltaic device and actual power generation, and which diagnoses a state of the specific photovoltaic device by comparing the difference in power generation between grouped photovoltaic devices through analysis of power generation for the same period in the past through machine learning, etc., so that accuracy of diagnosis of state is improved.

Another aspect of the present disclosure is to provide an apparatus for diagnosing a photovoltaic device, the apparatus which processes power generation information, which is collected from photovoltaic devices, based on failure history and maintenance and repair information of each photovoltaic device and performs precise grouping by minimizing error information regarding a power generation trend based on information such as regional weather information and environment information for a region where each photovoltaic device is located, so that precise grouping is enabled and hence accuracy of diagnosis of state is improved.

Yet another aspect of the present disclosure is to provide an apparatus for diagnosing photovoltaic power generation, the apparatus which receives a final result of an abnormality as to a diagnosed photovoltaic device and reflects the final result to modify information from a power generation data processing module or a power generation data cleansing module and perform regrouping, so that accuracy of diagnosis of state is improved.

Yet another aspect of the present disclosure is to provide an apparatus for diagnosing photovoltaic power generation, the apparatus which performs precise grouping of photovoltaic devices by a grouping criteria that is selected from among a trend of daily cumulative power generation for a predetermined period, a trend of average power generation for the predetermined period, and a trend of maximum daily power generation compared to an installed capacity for the predetermined period.

Yet another aspect of the present disclosure is to provide an apparatus for diagnosing photovoltaic power generation, the apparatus which provides a power balancing device to allow a photovoltaic device, which is diagnosed as abnormal, to generate optimal photovoltaic power generation before maintenance such as cleaning and replacement, wherein the power balancing device is not limited to a method of compensating with low power for a difference in power generated between series circuits (strings) constituting a solar panel (array) and the power balancing device balances generated power between the strings in a manner of minimizing or eliminating a power deviation between the strings, thereby enhancing efficiency of photovoltaic power generation.

Technical Solution

In order to achieve the above goals, the present disclosure is realized by embodiments having the following configuration.

According to an aspect of the present disclosure, there is provided an apparatus for diagnosing photovoltaic power generation through analysis of a power generation trend, the apparatus including: a photovoltaic device configured to generate power using sunlight; and a diagnostic server configured to diagnose a state of the photovoltaic device based on power generation data transmitted from the photovoltaic device. The diagnostic server may be further configured to group photovoltaic devices similar in power generation trends for a same period in the past, and diagnose a specific photovoltaic device by comparing a difference in power generation of a corresponding group.

According to another aspect of the present disclosure, the diagnostic server may include: a grouping unit configured to group photovoltaic devices similar in power generation trends for the same period in the past among the entire photovoltaic devices; and an abnormality diagnosis unit configured to select a specific photovoltaic device with power generation out of an error range among the grouped photovoltaic devices.

According to yet another aspect of the present disclosure, wherein the grouping unit may include: a power generation data collection module configured to collect information such as previous daily power generation and a daily power generation deviation from the photovoltaic devices; a power generation data processing module configured to process information collected by the power generation data collection module based on information such as failure history and maintenance history of each of the photovoltaic devices; a power generation data cleansing module configured to minimize error information regarding a power generation trend based on information such as regional weather information and environmental information for a region in which each of the photovoltaic devices is located; and a grouping module configured to group the photovoltaic devices by applying a grouping algorithm to data calculated by the power generation data cleansing module. The abnormality diagnosis unit may include: an abnormality diagnosis module configured to diagnose an abnormality by calculating an error range for a photovoltaic device in a corresponding group based on a grouping criteria applied by the grouping module; and a diagnosis accuracy review module configured to provide a final result of the abnormality in the photovoltaic device by the abnormality diagnostic module.

According to yet another aspect of the present disclosure, the grouping unit may further include a grouping modification module configured to modify information from the power generation data processing module or the power generation data cleansing module by reflecting the final result provided by the diagnosis accuracy review module and regroup the photovoltaic devices.

According to yet another aspect of the present disclosure, the grouping module may be further configured to group the photovoltaic devices by a criteria that is selected from among a trend of daily cumulative power generation for a predetermined period, a trend of average power generation for the predetermined period, and a trend of maximum daily power generation compared to an installed capacity for the predetermined period.

According to yet another aspect of the present disclosure, the apparatus may further include a power balancing unit connected to each of a plurality of strings in an array of each photovoltaic device and configured to minimize a power deviation between the strings due to a shading or a failure in a specific module when an abnormality occurs in a corresponding photovoltaic device.

According to yet another aspect of the present disclosure, the power balancing unit may include: a measurement unit configured to measure a current or voltage for each of the plurality of strings; an Energy Storage System (ESS) unit configured to perform power compensation or power absorption with respect to each of the plurality of strings; and a controller configured to store the ESS unit based on data from the measurement unit. The controller may include: an ESS state determination module configured to determine an ESS charge capacity of the ESS unit; an ESS control module configured to determine whether to discharge or charge the ESS unit according to the ESS charge capacity of the ESS unit, which is determined by the ESS state determination module; and a string-ESS connection module configured to determine a string subject to power compensation or power absorption according to the determination made by the ESS control module as to whether to charge or discharge the ESS unit and connect the determined string to the ESS unit.

According to yet another aspect of the present disclosure, the ESS control module may include: an ESS discharge control module configured to, when the ESS charge capacity of the ESS unit is sufficient, discharge the ESS unit to perform power compensation with respect to a string with output power reduced; and an ESS charge control module configured to, when the ESS charge capacity of the ESS unit is insufficient, charge the ESS unit to perform power absorption with respect to a string with high output power, so that a power deviation between the strings is minimized.

According to yet another aspect of the present disclosure, the string-ESS connection module may include: a power compensation connection module configured to, when the ESS discharge control module discharges the ESS unit, specify a string with output power reduced among the plurality of strings and connect the specified string to the ESS; and a power absorption connection module configured to, when the ESS charge control module charges the ESS unit, connect a string with high output power among the plurality of strings to the ESS.

Advantageous Effects

The present disclosure can achieve the following effects according to the above embodiments, configuration, combination, and use relationship described below.

The present disclosure has an effect of improving accuracy of diagnosis of state not by performing diagnosis based on comparison between predicted power generation of a specific photovoltaic device and actual power generation, but by diagnosing a state of the specific photovoltaic device by comparing the difference in power generation between grouped photovoltaic devices through analysis of power generation for the same period in the past through machine learning, etc.

The present disclosure has an effect of enabling precise grouping and improving accuracy of diagnosis of state by processing power generation information, which is collected from photovoltaic devices, based on failure history and maintenance and repair information of each photovoltaic device and by performing precise grouping by minimizing error information regarding a power generation trend based on information such as regional weather information and environment information for a region where each photovoltaic device is located.

The present disclosure has an effect of improving accuracy of diagnosis of state by receiving a final result of an abnormality as to a diagnosed photovoltaic device and reflects the final result to modify information from a power generation data processing module or a power generation data cleansing module and performing regrouping.

The present disclosure has an effect of performing precise grouping of photovoltaic devices by a grouping criteria that is selected from among a trend of daily cumulative power generation for a predetermined period, a trend of average power generation for the predetermined period, and a trend of maximum daily power generation compared to an installed capacity for the predetermined period.

The present disclosure has an effect of providing a power balancing device to allow a photovoltaic device, which is diagnosed as abnormal, to generate optimal photovoltaic power generation before maintenance such as cleaning and replacement, wherein the power balancing device is not limited to a method of compensating with low power for a difference in power generated between series circuits (strings) constituting a solar panel (array) and the power balancing device balances generated power between the strings in a manner of minimizing or eliminating a power deviation between the strings, thereby enhancing efficiency of photovoltaic power generation.

DESCRIPTION OF REFERENCE NUMERALS

BEST MODE

Hereinafter, preferred embodiments of an Energy Storage System (ESS) including a cooling function according to the present disclosure will be described in detail with reference to the accompanying drawings. In the following description of the embodiment of the present disclosure, a detailed description of known functions and configurations incorporated herein will be omitted as it may make the subject matter of the present disclosure unclear. In the specification, unless explicitly described to the contrary, the word “comprise” and variations such as “comprises” or “comprising”, will be understood to imply the inclusion of stated elements but not the exclusion of any other elements, and the terms “unit” and “module” described in the specification indicate a unit for processing at least one function or operation, which may be implemented by hardware, software or a combination thereof.

Referring toFIGS.1to3, an apparatus for diagnosing photovoltaic power generation based on power generation trend according to an embodiment of the present disclosure may include a photovoltaic device1which generates power using sunlight, and a diagnostic server3which diagnoses a state of the photovoltaic device1based on data on a generated power data, which is transmitted from the photovoltaic device1, and the diagnostic server3may group photovoltaic devices1similar in power generation trends for the same period in the past, and diagnose a state of a specific photovoltaic device1by comparing the power generation in the group.

The photovoltaic device1is a device that generates electrical energy using sunlight (light energy). A string12and an array13may be formed by gathering a minimum unit of photovoltaic modules11, and the arrays13, which is generally so called a solar panel, may be grouped to form the photovoltaic device1. In the present disclosure, the photovoltaic device1refers not only to a photovoltaic power plant installed on the ground, but also to various types of photovoltaic devices1installed on a building rooftop, on a water surface, a building outer wall, and the like, such as a building-integrated photovoltaic device (BIPV).

The diagnostic server3is configured to diagnose a state of the photovoltaic device1based on power generation data transmitted from the photovoltaic device1, and the present disclosure may provide a distinctive diagnostic function, different from a related art, using a configuration (function) of the diagnostic server3. That is, as mentioned above as a problem of the prior art, predicted power generation is conventionally calculated using various prediction techniques in consideration of various environmental factors, etc. in a photovoltaic equipment (or module), and, If actual power generation exceeded a certain range compared to the predicted power generation, it is determined as abnormal and precise diagnosis or maintenance are performed. Due to the limitation that various factors affecting photovoltaic power generation cannot be accurately reflected, the predicted power generation is not accurate enough. A failure diagnosis technology based on the predicted power generation with a large error range also has a limitation that a false diagnosis rate increases. In the present disclosure, a state of a specific photovoltaic device1is diagnosed using the diagnostic server3by comparing a difference in power generation of grouped photovoltaic devices1(seeFIG.2) through analysis of a power generation trend for the same period in the past, and therefore, accuracy of the analysis of the state may improve. To this end, the diagnostic server3may include a grouping unit31for grouping photovoltaic devices1similar in power generation trends for the same period in the past among the entire photovoltaic devices1, and an abnormality diagnosis unit32for selecting a specific photovoltaic device1with power generation out of an error range among the grouped photovoltaic devices1.

The grouping unit31is configured to group photovoltaic devices1similar in power generation trends for the same period in the past among the entire photovoltaic devices1(seeFIG.2). Preferably, the grouping unit31may group photovoltaic devices1similar in power generation trends based on data on power generation for the same period in the past by using machine learning by which artificial intelligence is implemented in software. More specifically, the grouping unit31may include a power generation data collection module311for collecting information such as previous daily power generation and a daily power generation deviation from the photovoltaic devices1, a power generation data processing module312for processing information collected by the power generation data collection module311based on information such as failure history and maintenance history of each photovoltaic device1, a power generation data cleansing module313for minimizing error information regarding a power generation trend based on information such as regional weather information and environmental information for a region in which each photovoltaic device1is located, a grouping module314for grouping the photovoltaic devices1by applying a grouping algorithm to data calculated by the power generation data cleansing module313.

The power generation data collection module311is configured to collect information such as a previous daily power generation and a daily power generation deviation from the photovoltaic devices1. The power generation data collection module311may collect and data basic information for the respective photovoltaic devices1in order to grouping the photovoltaic devices1. To this end, the power generation data collection module311may be connected with the photovoltaic devices1using wired or wireless communication.

The power generation data processing module312is configured to process information collected by the power generation data collection module311based on information such as failure history and maintenance history of each photovoltaic device1. That is, even in the case where information indicating that power generation of a specific photovoltaic device1was dramatically reduced in a specific period is collected (by the power generation data collection module311), if a part or most of the corresponding photovoltaic device1went through maintenance in the specific period for the reason of a regular inspection or a failure, the power generation data processing module312may modify and process power generation information of the specific photovoltaic device1by reflecting the maintenance and obtain accurate power generation trend information regarding the specific photovoltaic device1. To this end, the power generation data processing module312may collect and manage information such as failure history and maintenance history for each photovoltaic device1.

The power generation data cleansing module313is configured to minimize error information regarding a power generation trend based on information such as regional weather information and environmental information for a region in which each photovoltaic device1is located. That is, although the photovoltaic devices1exhibit similar power generation over a specific period, if there is a difference in environment where the corresponding photovoltaic devices1are located, that is, a difference in daily solar radiation, temperature, humidity, or the like and such photovoltaic devices1exhibiting the similar power generation are grouped, it may deteriorate reliability of a result of the grouping. Therefore, by reflecting information such as regional weather information and environment information for a region in which each photovoltaic device1is located, in addition to the information provided by the power generation data collection module311to the power generation data processing module312, the power generation data cleansing module313may minimize error information regarding a power generation trend of each photovoltaic device1, whereby the grouping module315which will be described later is allowed to perform reliable grouping.

The grouping module314is configured to group photovoltaic devices1similar in power generation trends by applying a grouping algorithm (a program for grouping by a grouping criteria) to data calculated by the power generation data cleansing module313.

As an example, the grouping module314may perform grouping photovoltaic devices1most similar in daily deviations based on daily cumulative power generation data for the respective photovoltaic devices1for a predetermined period of time. That is, photovoltaic devices1most similar in daily power generation patterns are grouped by reflecting environment information or maintenance history information of each photovoltaic device1, so if one of the photovoltaic devices1in the group has a daily power generation pattern out of an error range unlike other photovoltaic devices1, the abnormality diagnosis unit32which will be described later may diagnose whether there is an abnormality.

As another example, the grouping module314may group photovoltaic devices1most similar in average power generation rates based on average power generation rate trend (data) for photovoltaic devices1for a predetermined period. That is, photovoltaic devices1exhibiting the most similar power generation patterns (trends) for the predetermined period are grouped, and, if one of the grouped photovoltaic devices1exhibits a previous day's power generation out of an error range from the average power generation rates unlike other photovoltaic devices1, the abnormality diagnosis unit32which will be described later may diagnose whether there is an abnormality.

As another example, based on a daily maximum power generation trend (data) compared to an installed capacity for a predetermined period, the grouping module314may group photovoltaic devices1most similar in daily maximum power generation deviations in consideration of the installed capacity. That is, the grouping may be performed based on data for a shortest period compared to cumulative data or average data, and if a maximum power generation deviation of a specific photovoltaic device1falls out of an error range compared to other photovoltaic devices1in the same group, the abnormality diagnosis unit32which will be described later may diagnose an abnormality, and therefore, it is possible to perform diagnosis relatively quickly.

The abnormality diagnosis unit32is configured to select a specific photovoltaic device1with power generation out of an error range among the grouped photovoltaic devices1. More specifically, the abnormality diagnosis unit32may include an abnormality diagnosis module321which diagnoses an abnormality by calculating an error range for a photovoltaic device1in a corresponding group based on a grouping criteria applied by the grouping module314, and a diagnosis accuracy review module322which provides a final result of the abnormality in the photovoltaic device diagnosed by the abnormality diagnosis module321.

The abnormality diagnosis module321is configured to diagnose an abnormality by calculating an error range for a photovoltaic device1in a corresponding group based on a grouping criteria applied by the grouping module314. That is, if the grouping criteria for grouped photovoltaic devices1is a daily cumulative power generation trend, the abnormality diagnosis module321may diagnose that a photovoltaic device1having a previous day's power generation deviation out of an error range compared to other photovoltaic devices1in the same group is in an abnormal state. If the grouping criteria is an average power generation rate, the abnormality diagnosis module321may diagnose that a photovoltaic device1having a previous day's power generation rate deviation out of an error range with respect to other photovoltaic devices1in the same group is in an abnormal state. If the grouping criteria is a daily maximum power generation with respect to an installed capacity, the abnormality diagnosis module321may diagnose that a photovoltaic device1having a previous day maximum power generation deviation out of an error range with respect to other photovoltaic devices in the same group is in an abnormal state.

The diagnosis accuracy review module322is configured to provide a final result of an abnormality in a photovoltaic device1diagnosed by the abnormality diagnosis module321. For accuracy of diagnosis by the diagnostic device, the diagnosis accuracy review module322may compare a result of diagnosis by the abnormality diagnosis module321with an actual result. In order to modify and update a grouping criteria or a discriminate algorithm to perform grouping later or perform grouping in response to diagnosis of an abnormality, the diagnosis accuracy review module322may provide a final result of an abnormality in a photovoltaic device1.

Meanwhile, the grouping unit31may further include a grouping modifying module315, which modifies information from the power generation data processing module312or the power generation data cleansing module313by reflecting a feedback result provided from the diagnosis accuracy review module322and regroups photovoltaic devices1. That is, the grouping modification module315may reflect data on a case where a diagnosis result is different from an actual result to the power generation data processing module312or the power generation data cleansing module313to enhance accuracy of data processing for grouping or may modify or update the grouping criteria applied by the grouping module314to regroup the already grouped photovoltaic devices1to enhance accuracy of diagnosis.

In another embodiment of the present disclosure, a power balancing device may be further included to enable a photovoltaic device1diagnosed as an abnormal state to perform optimal photovoltaic power generation even before maintenance such as cleaning and replacement. In this case, the power balancing device to be provided in the present disclosure is not limited to a method of compensating with low power for a difference in power generated between series circuits (strings) constituting a solar panel (array) as in a related art, but the power balancing device balances generated power between the strings in a manner of minimizing or eliminating a power deviation between the strings. This will be described in more detail in the following.

Referring toFIGS.4to6, in another embodiment of the present disclosure, the photovoltaic diagnosis device may further include a power balancing unit16connected to each of a plurality of strings12in an array13of each photovoltaic device1to minimize a power deviation between the strings12due to a shading or a failure in a specific photovoltaic module11when an abnormality occurs in a corresponding photovoltaic device1. (For reference, a string12forming a serial circuit with photovoltaic modules11connected in series, an array13connected with a plurality of string12in parallel, an inverter14for converting DC power generated using sunlight into AC power and supplying the AC power to a receiver, and a connector15for facilitating connection between the array13and the inverter14and performing various protection functions are well-known configurations of a photovoltaic device and thus a detailed description thereof will be omitted.)

The power balancing unit16is connected to each of the plurality of strings12and configured to minimize a power deviation between the strings12due to a shading or a failure in a specific module. To this end, the power balancing unit16may include a measurement unit161for measuring an output current or voltage from each of the plurality of strings12, an Energy Storage System (ESS) unit162for performing power compensation or power absorption with respect to each of the plurality of strings12, and a controller163for controlling the ESS unit162based on data from the measurement unit161.

The measurement unit161is configured to measure an output current or voltage from each of the plurality of strings12. To this end, the measurement unit161may measure an output current and/or voltage from each of the plurality of strings12through sensors1611respectively installed in the plurality of strings12and transmit information on output power from each string12to the controller163. As in the example shown inFIG.5, the sensors1611each may be formed as a current and/or voltage sensor installed at an end of the plurality of strings12in order to measure a current and/or voltage output from each of the plurality of strings12, and the measurement unit161may transmit the information on output power from each of the plurality of strings12to the controller163based on information transmitted from the sensors1611. Through the information measured and transmitted by the measurement unit161, it is possible to identify an amount of output power from each string12, such as whether power in a normal state is generated and output from each string12or whether power in an amount reduced due to a failure or a shading in a specific photovoltaic module11is output from a specific string12.

The ESS unit162is configured to perform power compensation or power absorption with respect to each of the plurality of strings12. To this end, an ESS capable of charging and discharging power may be connected to each of the plurality of strings12to perform power compensation or power absoprtion with respect to a specific string12, thereby minimizing or eliminating a power deviation between the parallel-connected strings12constituting an array13.

The controller163is configured to control the ESS unit162based on data from the measurement unit161. As mentioned above as a problem of a related art, in the case of the related art that supplies compensative power through a power compensating device only to a string with power generation reduced due to a shading or a failure in a specific module, it is necessary to pre-store power in a separate ESS in order to compensate for power of the corresponding string, and accordingly, in a facility which outputs a large amount of photovoltaic power, a large-capacity ESS for power compensation is required (for example, if compensation is performed with power generation of 10 kW for one hour, a battery capacity of 10 kWh needs to be charged, and, in order to prepare for the case where the amount and time of decrease in power generation relatively increases, an ESS with a battery capacity larger than a photovoltaic capacity needs to be provided for power compensation.) In this case, there are problems that costs and economic efficiency are significantly reduced and that a system connection or configuration (a compensation photovoltaic panel and the like) for charging the separate ESS is required. The present disclosure does not just address the problems by compensating for power for a string12of which output power is reduced, but also provide a solution in a manner of minimizing or eliminating a power deviation between the respective strings12in consideration of an array13as a whole. To this end, the controller163may include: an ESS state determination module1631for determining an ESS charge capacity of the ESS unit162, an ESS control module1632for determining whether to discharge or charge the ESS unit162according to the ESS charge capacity of the ESS unit162, which is determined by the ESS state determination module1631, and a string-ESS connection module1633for determining a string12subject to power compensation or power absorption according to the determination made by the ESS control module1632as to whether to charge or discharge the ESS unit and connecting the string12to an ESS. That is, in order to minimize a power deviation between the strings12, in the case where an ESS connected to an end of each string12has a sufficient charge capacity, compensation power may be supplied from the ESS to a string12with output power reduced (that is, the ESS is discharged) to increase power of the corresponding string12, thereby eliminating a power deviation between the entire strings12. Conversely, in the case where an ESS connected to an end of each string12has an insufficient charge capacity, power may be absorbed into the ESS from strings12with high output power (that is, the ESS is charged) to reduce power of the corresponding strings12, thereby eliminating a power deviation between the entire strings12. Accordingly, since the ESS is not in a structure in which compensation power is required to be continuously supplied, the ESS does not necessarily have a large capacity. In addition, since charging and discharging of the ESS is performed in a corresponding array13, a separate system or configuration only for charging the ESS is not required.

The ESS state determination module1631is configured to determine an ESS charge capacity of the ESS unit162. If information on whether a charge capacity of the ESS is sufficient to supply compensation power is provided through the ESS state determination module1631, the ESS control module1632which will be described later may determine whether to discharge or charge the ESS unit.

The ESS control module1632is configured to determine whether to discharge or charge the ESS unit according to an ESS charge capacity of the ESS unit162, which is determined by the ESS state determination module1631. To this end, the ESS control module1632may include an ESS discharge control module16321for, when the ESS charge capacity of the ESS unit162is sufficient, discharging the ESS unit to compensate for power of a string12with output power reduced, and an ESS charge control module16322for, when the ESS charge capacity of the ESS unit162is insufficient, charging the ESS unit to absorb power from a string12with high output power, thereby minimizing a power deviation between strings12.

The ESS discharge control module16321is configured to, when the ESS charge capacity of the ESS unit162determined by the ESS state determination module1631is sufficient, discharge the ESS unit to compensate for power to a string12with output power reduced. When the ESS discharge control module16321determines a control to discharge the ESS unit to compensate for power of the string12with output power reduced to minimize a power deviation between the entire strings12, the string-ESS connection module1633which will be described later may determine a string12subject to power compensation and connect the string12to the ESS, so that the control by the ESS discharge control module16321can be performed smoothly.

The ESS charge control module16322is configured to, when the ESS charge capacity of the ESS unit162determined by the ESS state determination module1631is insufficient, charge the ESS unit to absorb power from a string12with high output power. When the ESS charge control module16322determines a control to charge the ESS unit to absorb power from a string12with high output power to thereby minimize a power deviation between the entire strings12, the string-ESS connection module1633which will be described later may determine a string subject to power absorption and connect the string to the ESS, so that the control by the ESS charge control module16322can be performed smoothly.

The string-ESS connection module1633is configured to determine a string12subject to power compensation or power absorption according to a determination whether to charge or discharge by the ESS control module1632, and connect the determined string12to the ESS. To this end, the string-ESS connection module1633may include a power compensation connection module16331configured to, when the ESS discharge control module16321discharges the ESS unit162, specify a string12with output power reduced among the plurality of strings12and connect the string12to the ESS, and a power absorption connection module16332configured to, when the ESS charge control module16322charges the ESS unit162, connect a string12with high output power among the plurality of strings12to the ESS.

The power compensation connection module16331is configured to, when the ESS discharge control module16321discharges the ESS unit162, specify a string12with output power reduced among a plurality of strings12. For example, referring toFIG.5, suppose that output power from a first string12is reduced due to a shading or a failure in a specific photovoltaic module11and remaining second to fourth strings12generates normal output power, and that an ESS charge capacity of the ESS unit162, which is determined by the ESS state determination module1631, is sufficient. In this case, when the ESS discharge control module16321determines a control to discharge the ESS unit162to compensate for the first string12with output power reduced, the power compensation connection module16331may connect the first string12with output power reduced to the ESS, so that compensation power can be provided from the ESS to the first string12to thereby minimize a power deviation between the entire strings12.

The power absorption connection module16332is configured to, when the ESS charge control module16322charges the ESS unit162, connect a string12with high output power among the plurality of strings12to the ESS. For example, referring toFIG.5, suppose that output power from the first string12is reduced due to a shading or a failure in a specific photovoltaic module11and normal output power is output from other remaining second to fourth strings12, and that an ESS charge capacity of the ESS unit162, which is determined by the ESS state determination module1631is insufficient. In this case, when the ESS charge control module16322determines a control to charge the ESS unit to absorb power from the second to fourth strings12with high output power, the power absorption connection module16332may connect the second to fourth strings12with high output power to the ESS, so that the ESS is discharged by absorbing power from the second to fourth strings12to thereby minimize a power deviation between the entire strings.

As described above, when it comes to the power balancing unit16according to the present disclosure, in the case where the ESS connected to an end of each string12has a sufficient charge capacity, compensation power may be supplied from the ESS to a string12with output power reduced (that is, the ESS is discharged) to increase power of the corresponding string12, thereby eliminating a power deviation between the entire strings12. Conversely, in the case where the ESS connected to an end of each string12has an insufficient charge capacity, power may be absorbed into the ESS from strings with high output power (that is, the ESS is charged) to reduce power of the corresponding strings12, thereby eliminating a power deviation between the entire strings12. Accordingly, since the ESS is not in a structure in which compensation power is required to be continuously supplied, the ESS does not necessarily have a large capacity. In addition, since charging and discharging of the ESS is performed in a corresponding array13, a separate system or configuration only for charging the ESS is not required. Further, since charging and discharging is performed without power conversion, this may improve efficiency.

Although the Applicant(s) have described various embodiments, the embodiments are only an example to achieve the technical spirit of the present disclosure and thus, it would be appreciated by those skilled in the art that changes or modifications may be made to the embodiments without departing from the principles and spirit of the present disclosure, the scope of which is defined by the claims and their equivalents.