RESOURCES

This application is being filed on 11 March 2013, as a PCT international patent application and claims priority to U.S. Patent Application Serial No. 61/614,537 filed on 23 March 2012, the disclosure of which is incorporated herein by reference in its entirety

BACKGROUND

[001] Over the past few years technological innovations, changing economic conditions, changing regulatory environments, shifting of environmental conditions, and social priorities have spurred interest in Distributed Generation (DG) systems. Distributed Generation is a new model for power systems that is based on the integration of small and medium-sized generators into a utility grid. Such generators may be associated with new and renewable energy technologies, such as solar, wind, and fuel cells, into the utility grid. The generators may be

interconnected through a fully interactive intelligent electricity network. Most DG resources are primarily used to supplement the traditional electric power systems. For example, DG resources can be combined to supply nearby loads in specific areas with continuous power during disturbances and interruptions of the main utility grid.

[002] Managing DG systems, especially renewable energy sources such as wind or solar power, is of high relevance in common power grids. Conventional distributed renewable energy sources are hard to manage and their energy production is hard to accurately evaluate because of their vast geographical spread and the dependency of their energy production on weather conditions. As an example, predicting the energy production from the renewable energy resources requires use of expensive instruments (i.e. meteorological equipment) to measure weather parameters, such as wind, temperature, solar irradiance, pressure, humidity and cloud coverage. Large human capital may be required to monitor and evaluate the energy production from the renewable energy sources. SUMMARY OF THE INVENTION

[003] Systems and methods for managing distributed energy resources may include a server configured to receive energy generation data from plurality of distributed energy resources and analyze the received data to evaluate performance of the plurality of distrusted energy resources. The evaluation may include filtering the data received from the distributed energy resources into one or more groups. The data may be filtered into one or more groups based on filtering criteria. The evaluation may further include computing a mean and a standard deviation for each of the one or more groups. The evaluation may further include creating one or more

performance bands based on the computed mean and standard deviation of the one or more groups. Each of the plurality of distributed energy resources may be categorized into at least one of the one or more performance bands. An administrator may define an acceptable performance level for at least one of the one or more groups. The server may be configured to determine distributed energy resources from the at least one or more groups having energy generation data less than the acceptable performance level. The server may further be configured to associate the performance level with at least one predefined maintenance issue.

BRIEF DESCRIPTION OF THE DRAWINGS

[004] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present invention. In the drawings:

[005] FIG. 1 is a diagram of a system for managing distributed energy resources;

[006] FIG. 2 is a computer system configured to manage distributed energy system; and

[007] FIG. 3 is a flow diagram of a method for managing distributed energy resources;

[008] FIG. 4 is diagram illustrating filtered groups filtered based on geophysical location; [009] FIG. 5 is a diagram illustrating filtered groups;

[0010] FIG. 6 is a diagram illustrating evaluation of filtered groups;

[0011] FIG. 7 is a bar chart showing performance summary of distributed energy resources; and

[0012] FIG. 8 is a flow diagram illustrating steps of a method of diagnosis for distributed energy resources.

DETAILED DESCRIPTION

[0013] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While embodiments of the invention may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the invention. Instead, the proper scope of the invention is defined by the appended claims.

[0014] Embodiments of the invention may provide systems and methods for managing distributed energy resources. In one embodiment, a server may be configured to receive energy generation data from a plurality of distributed energy resources and analyze the received data to evaluate performance of the plurality of distrusted energy resources. The evaluation may include filtering the received data into one or more groups based on filtering criteria, and computing a mean and a standard deviation of each of the one or more groups. The evaluation may further include creating one or more performance bands based on the computed mean and standard deviation of the one or more groups. Each of the plurality of distributed energy resources may be categorized into at least one of the one or more performance bands. Furthermore, an administrator may define an acceptable performance level for at least one of the one or more groups. The server may be configured to determine distributed energy resources, from at least one of the one or more groups, having energy generation data less than the acceptable performance level. The server may further be configured to associate performance levels with at least one predefined maintenance issue.

[0015] FIG. 1 is a schematic diagram of a system 100 for managing distributed energy resources. As shown in FIG. 1 , the system 100 may include one or more Energy Resources (ERs) 102a, 102b, 102c (collectively referred to as DER 102), one or more aggregator 104a, 104b, 104c

(collectively referred to as aggregator 104), and a Network Operation Center (NOC) 106.

[0016] Consistent with embodiments of the invention, DER 102 may include either traditional energy resources or renewable energy sources, or both traditional and renewable energy resources. As an example, DER 102 may include, but not limited to fossil fuels, nuclear, hydro, wind, photovoltaic, batteries, and geo-thermal based energy resources. The energy generated from DER 102 may be consumed locally, i.e. within premises of DER 102, or may be supplied to a distribution/transmission system by connecting DER 102 to the distribution/transmission system. Each Energy Resource (ER) in DER 102 may be configured to report the amount of energy generated by it to the NOC 106.

[0017] In an embodiment, each ER in DER 102 may be configured to communicate either directly or indirectly (hoping through another ER) to aggregator 104 through a first communication system (not shown). The first communication system may include ZigBee, WiFi, power-line

communications, GSM, Fiber, or any other reliable communication protocol. Each ER may include a device, for example an electricity metering device, configured to measure an amount of energy being produced by the ER. The measured energy generation data may be temporarily stored on a local memory with an associated time stamp.

[0018] Moreover, each ER or the electricity metering device of the ER may include a network device, for example a communicator, configured to transmit the energy generation data of the ER over the first communication system. The communicator may be configured to receive a request for the energy generation data, and in response to the received request, send the energy generation and other telemetry data over the first communication channel. The communicator may include a receiver and a transmitter. The receiver may be configured to receive requests from a remote monitoring station, such as NOC 106. The transmitter may be configured to send data packets to NOC 106. The communicator may be configured to send the energy generation and other telemetry data in periodic manner over the first communication channel to aggregator 104. Aggregator 104 may be configured to receive energy generation data reported by ERs and forward the collected data to NOC 106.

[0019] Although system 100 of FIG.1 , is shown to include only three ERs, it will be apparent to those skilled in the art that the embodiments of the invention may include any number of distributed energy resources. The number of ERs present in a geographical area may depend on the power requirements at of the geographical area where a decentralized distributed energy resources is installed.

[0020] Aggregator 104 may forward the energy generation and other telemetry data received from the ERs to NOC 106 through a second communication system. The second communication system may include ZigBee, WiFi, power-line communications, GSM, Fiber, or any other reliable communication protocol. Aggregator 104 may include a memory device to temporarily store the received energy generation and other telemetry data from the ERs. Aggregator 104 may further include a receiver and a transmitter. The receiver at the aggregator 104 may be configured to receive data from ERs and/or requests from NOC 106. The transmitter at the aggregator 104 may be configured to send the received data from the ERs to NOC 06, and forward the requests received from NOC 106 to DER 102. NOC 106 may be a computer system having a memory and a processor. NOC 106 is described in more detail with respect to FIG. 2 of this disclosure.

[0021] Although the system 100 of FIG.1 , is shown to include only three aggregators 104, it will be apparent to those skilled in the art that the embodiments of the invention may include any number of aggregators. The number of aggregators installed may depend on number of ERs, types of ERs, geographical distribution of ERs, range of communicator on ERs, if communicator is a wireless device, range of aggregator 104 if aggregator 104 is a wireless device, etc. One aggregator may be able to collect energy generation and other telemetry data from a predetermined number of ERs. Aggregator 104 may be further configured to act as a relay point to facilitate deliver of energy generation and other telemetry data from DER 102 to NOC 106.

[0022] FIG. 2 shows an example of a NOC 106, which may be a computer system configured to manage DERs 102. NOC 106 may include at least one processor 204 coupled to a memory 202. Processor 204 may represent one or more processors (e.g., microprocessors), and memory 202 may represent random access memory (RAM) devices comprising a main storage of NOC 106, as well as any supplemental levels of memory e.g., cache memories, non-volatile or back-up memories (e.g. Programmable or flash memories), read-only memories, etc. In addition, memory 202 may be considered to include memory storage physically located elsewhere in NOC 106, e.g. any cache memory in processor 204 as well as any storage capacity used as a virtual memory, e.g., as stored on a mass storage device 212.

[0023] NOC 106 may be configured to receive a number of inputs and outputs for communicating information externally. For interface with a user or operator, NOC 106 may include one or more user input devices 206 (e.g., a keyboard, a mouse, imaging device, etc.), and one or more output devices 208 (e.g., a liquid crystal display (LCD) panel, a sound playback device (speaker, etc)).

[0024] For additional storage, NOC 106 may also include one or more mass storage devices 212, e.g., a floppy or other removable disk drive, a hard disk drive, a direct access storage device (DASD), an optical drive (e.g. a compact disk (CD) drive, a digital versatile disk (DVD) drive, etc.), and a tape drive, among others. Furthermore, NOC 106 may include an interface with one or more networks 210 (e.g., a local area network (LAN), a wide area network (WAN), a wireless network, and/or the internet among others) to permit the communication of information with other computers coupled to the networks. NOC 106 may include suitable analog and/or digital interfaces between processor 204 and each of the components 202, 206, 208, and 210.

[0025] NOC 106 may operate under the control of an operating system 214, and execute various computer software applications, components, programs, objects, modules, etc. to implement the techniques described in this description. Moreover, various applications, components, programs, objects, etc., collectively indicated by reference 216, may also execute on one or more processors in another computer coupled to NOC 106 via a network 210, e.g. in a distributed computing environment, whereby the processing required to implement the functions of a computer program may be allocated to multiple computers over a network. Application software 216 may include a set of instructions which, when executed by the processor 204, may cause NOC 106 to manage DER 102 as described.

[0026] Although the NOC 106 is shown to include a single computer system, it will be apparent to those skilled in the art that NOC 106 may be a distributed computing system with multiple processors and memory devices or a cloud computing system. Consistent with

embodiments of the invention, NOC 106 may be configured to manage DER 102. Processor 204 of NOC 106 may be configured to execute a method for managing distributed energy resources. An example flow diagram of a method of managing distributed energy resources is illustrated in FIG. 3.

[0027] At block 302 of FIG.3, energy generation data from DER 102 may be collected at memory 202 at NOC 106. As an example, NOC 106 may be configured to collect the energy generation data received from aggregator 04 and store the energy generation data in a database 218. In another example, the energy generation data may be stored at mass storage device 212 which is accessible to NOC 106.

[0028] At block 304, the collected energy generation data may be organized in database 218. The energy generation data may be organized based on predetermined organization parameters. As an example, the energy generation data may be organized based on organization parameters defined by a database administrator or a DER administrator. As another example, the energy generation data may be organized based on type of energy resource.

[0029] At block 306, the energy generation data stored in database 218 may be filtered to receive a set of desired energy generation data. NOC 106 may be configured to filter the energy generation data in database 218 in one or more groups based on one or more predetermined filtering criteria. As an example, the data may be filtered to evaluate performance of a certain types of DERs 102. The method 300, by assigning different filters, may better short and segment the energy generation data for performance evaluation. As an example, at block 306a, the energy generation data may be filtered based on energy resource type. The energy resource type may include, for example, solar, wind, etc. As another example, at block 306b, the energy generation data may be filtered based on geographical circles (areas) with a predefined radius. As yet another example, at block 306n, the energy generation data may be filtered based on physical orientation, power rating, and other predefined categories.

[0030] An example of filtering of DER 102, based on physical location of the ERs, is shown in FIG. 4. As illustrated in FIG. 4, the ERs may be filtered into multiple groups by assigning geographical circles 402a, 402b, 402c, 402d (collectively referred to as geographical circles 402) with a predetermined radius for each renewable energy resource. Each circle within Geographical circles 402 may have different radii, which may be determined by the DER administrator and may be different based on the geophysical location for more flexibility. The radii determination may depend on a number of DER 102 inside geographical circles 402 and the geophysical area. As an example, for wide and flat geophysical area, the radii may be as far as 20 miles, while for densely populated areas with many obstacles (i.e. buildings, trees, mountains, etc.) the radii may be shortened to 5 miles. The radii may further be determined based on historical weather conditions for the geophysical area. As an example, if the weather conditions for a geophysical area is diverse and dynamic, the radius of the geographical circles 402 for such geophysical area may be shortened, and vice versa. The radii may be unified for multiple geophysical areas, and the final judgment may depend on the DER administrators who may perform these analyses.

[0031] The DER groups may further be classified into sub-groups using one or more filters. For example, in a geographical circle, all solar generation sources with a present power level (e.g. 200 peak watt panels) oriented in a certain direction (e.g. True south at 30 degrees tilt) may be classified into a sub-group. Similar sources within each circle can be filtered out using a similar process in the database 218. An example organization of DER 102 based on energy source type and a physical alignment of the DER 102 is illustrated in FIG. 5. As illustrated in FIG. 5, the energy generation data may be classified into plurality of groups i.e. type 1 to type N based on filtering criteria. Each of the plurality of groups may include the energy generation data of the type of energy resources filtered using the filtering criteria. For example, type 1 group may include solar energy resources that are facing south, with 200 watt panels and are tilted 30 degrees.

[0032] After filtering the energy generation data, at block 308, the filtered data may be stored in database 218 as filtered groups. The filtered groups may be stored on a separate database located on memory 202 or mass storage device 212.

[0033] At block 310, a mean value and a standard deviation value of a filtered group that has been selected for energy generation performance evaluation may be computed. The filtered group for energy performance evaluation may be selected by the DER administrator. Processor 204 may be configured to compute the mean value and the standard deviation value of the energy generation data for the filtered group. FIG. 6 illustrates an example table, showing filtered groups, and the computed mean value and standard deviation value for each filtered group. The mean value may be arithmetic mean (i.e. geometric mean or harmonic mean) or population mean. The standard deviation value may represent a measure of variation or dispersion from the mean value of the group. As an example, a low standard deviation value may indicate that the energy generation of the ERs in the filtered group is very close to the mean value, whereas high standard deviation value may indicate that the energy generation of the ERs is spread over a large range of values.

[0034] At block 312, after computing the mean value and the standard deviation value for the filtered group, NOC 106 may evaluate the energy performance of ERs in the filtered group. To evaluate the energy

performance of the ERs, NOC 106 may define an energy performance reference. In one example, the energy performance evaluation reference may be defined as:

μ + k x σ where: μ is the mean value of the filtered group, is the standard deviation value of the filtered group, and k is a multiplier. As an example, for k = 1 , all energy resources that have energy generation will be compared to a reference of μ + one standard deviation. The percentage ratio is computed with respect to the reference (in this example μ + one standard deviation), where ERs with energy generation equal to the reference will result in a performance evaluation percentage of 100%, and ERs with energy generation greater than the reference will result in a value > 100%, and the same applies to ERs with energy generation of less than the reference, which will result in <100%.The grouping of ERs in the energy performance evaluation bands, based on mean and standard deviation, may provide with an indication on how close the energy generation data from the different ERs is to the reference value. As an example, the energy performance evaluation bands may be defined as step sizes. The step sizes may be defined as 5%, 10%, 25%, ...etc., where as an example for a step size of 10%, ERs that have energy generation of say 95% of the reference value are grouped together in the 90-100 % energy performance band as illustrated in Fig. 7.

[0035] Consistent with embodiments of the invention, the results from the performance evaluation in block 312 may be displayed in any form of result chart (for example, pie chart, bar chart... etc) to the DER administrator. The results may also be displayed in a table format. A bar chart showing the 'performance summary' example is provided in FIG. 7, with ERs count on the y-axis and the performance percentage bands on the x-axis (0-10%, 10- 20%, 20-30%, 40-50 %, 100%).

[0036] Consistent with the embodiments of the invention, the results from the performance evaluation may be utilized by the DER administrators to diagnose performance of the ERs. The NOC 106 may include a diagnostic algorithm which may specify how the filtered groups may be used to diagnose underperforming ERs in DER 102. An example method to diagnose DER 102 is described with reference to FIG. 8 in this disclosure. Processor 204 of NOC 106 may be configured to execute instructions to implement the diagnostic algorithm.

[0037] At block 802 of FIG. 8, the DER administrator may define an acceptable performance level for a filtered group of DER 102. The DER administrator may be prompted by processor 204 to enter the acceptable performance level for the filtered group. The acceptable performance level for the group may be defined in terms of percentage of the mean and standard deviation value of the filtered group or based on the rating of the ERs in the filtered group. As an example, the acceptable performance level may be defined as 75% of a reference value of the energy generation data of the filtered group. As another example, if all the ERs in a filtered group are solar energy resources with 200W panels, the acceptable performance level may be defined as 75% of amount of energy the solar panel is expected to generate for a time period for which the performance is being evaluated.

[0038] At block 804, the energy generation data of each ER may be compared with the acceptable performance level of the filtered group. More particularly, at block 804, it may be determined, for each ER, whether the energy generation data of the ER is above the acceptable performance level.

[0039] At block 806, if the energy generation data of an ER is above the acceptable performance level, the ER may be classified as ER performing as expected. All the ERs, having energy generation data above the acceptable performance level, of the filtered group may be displayed to the DER administrator.

[0040] At block 808, one or more performance windows may be defined for the filtered group. The performance windows may be defined by the DER administrator. In an example, the performance windows may automatically be defined by processor 204. The performance windows may be defined relative to the acceptable performance level defined at block 802. In another example, the performance windows may be defined in terms of percentage of rating of the ERs in the group. As an example, if all the ERs in a filtered group are solar energy resources with 200W panels, the performance windows may be defined as 0-25%, 25-50%, and 50-75% of amount of energy the solar panel is expected to generate for a time period for which the performance is being evaluated. The performance windows are dynamic and may be adjusted dynamically by the DER administrator or processor 204 any time during or after the diagnosis.

[0041] At blocks 810a to 81 On, each of the performance windows defined at block 808, may be associated with a performance issue related to the ER. As an example, the performance window representing 0-25%, may be associated with a hardware issue. As another example, the performance window representing 25%-50% may be associated with physical obstructions (e.g. shading for solar resources). As yet another example, the performance window representing 50-75% may be associated with a temporary issue (e.g. leafs on solar panels).

[0042] At block 814, each ER having energy generation data less than the acceptable performance level may be classified into a performance window. As an example, an ER may be classified into a performance window if the energy generation data for the ER falls within the performance window.

[0043] Consistent with embodiments of the invention, the performance issue associated with a performance window may be assigned based on maintenance records, by incorporating maintenance records into database 218. The maintenance records may be used to automatically associate a performance issue to a performance window. Moreover, method 800 may thus incorporate maintenance records into database 218 to assist in providing a most probable cause for performance issues for ERs that fall within a performance window.

[0044] Consistent with embodiments of the invention, method 800 may further be refined to provide higher degrees of diagnostics. As an example, one may initially notice that 70% of the issues for sources within the performance window of 0-25% are related to hardware and 30% are related to obstacles. Over time, these percentages may change where for example the percentage of hardware issues may increase to 90%. The increase in percentage of the hardware issues may therefore increase a certainty of diagnosing hardware issues for the ERs in the 0-25%

performance band. The constant diagnosis of the ERs and mapping of the diagnosis results with energy generation data may create a self-learning algorithm that may take system evaluation feedback to create a more reliable diagnostics process.

[0045] Consistent with embodiments of the invention, NOC 106 may be configured to provide more statistics, other than the mean and the standard deviation, for a filtered group. As an example, NOC 106 may be configured to associate the performance issues of an ER with time, maintenance records, and rate of occurrence of the performance issues. The above association may serve in providing a more accurate judgment as to whether the performance issues are chronic (require intervention / maintenance) or temporary (e.g. dust over a panel that will clear with the next rain event).

[0046] As another example, NOC 106 may be configured to provide a platform to compare filtered groups in one geophysical area with a filtered group in another geophysical area over a number of sample frames. The platform may be used to assess whether other geophysical area consistently yields a higher energy output (e.g. better wind, less overall shading, etc.) than a present geophysical area. The platform to compare performance the DERs based on geophysical area may direct future installation of DERs where the relative energy output is higher. The platform may be more useful for renewable energy sources, for example, solar panels and wind turbines. [0047] Consistent with embodiments of the invention, the methods and systems disclosed herein may eliminate a need for taking weather data into account when evaluating performances of renewable energy sources, whose performance may be dependent on weather variables. In addition, the methods and systems described herein may provide a faster and economical way to identify faulty, misplaced, or badly installed distributed energy resource. As an example, the methods may assist in avoiding misjudgments due to certain intermittent weather conditions that may affect performance of an energy resource. As another example, the methods described above may reduce a number of field visits to evaluate under- performing or not performing energy resources, and to diagnose issues associated with fall in the performance level.

[0048] Consistent with embodiments of the invention, the methods and systems disclosed herein may tally data for every energy resource, which may be tabulated in a document file, mapped out with location identifiers or graphically represented. As an example, a DER administrator may be able to compare the energy generation data from different sources and at different time windows. In addition, the above described methods and systems may make it easy to scan large deployment of distributed energy resources in less time and in a more efficient way. Moreover, the above described methods and systems may provide diagnostic tools to better identify hardware issues, and other related obstacles in order to maximize the energy generation of a renewable energy resource. The diagnostics algorithm described above, may be a self-learning algorithm that may evaluate a percentage of performance occurrence and provide a feedback on expected issues if any.

[0049] Consistent with embodiments of the invention, since communication in nature might not be as reliable at all times (due to weather conditions, signal strength or faulty equipment), there may be a high chance that certain ERs may not be able to report their energy generation or other telemetry data on a given date and at a given time. The systems and methods to manage distributed energy resource, may address this issue by filtering and comparing ERs that have reported in the same time period, including comparing the accumulated energy generation data over a preset time period with the accumulated energy generation data from other ERs that reported in the same time period.

[0050] Embodiments of the invention may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the invention may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the invention may be practiced within a general purpose computer or in any other circuits or systems.

[0051] Embodiments of the invention, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Accordingly, the present invention may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present invention may take the form of a computer program product on a computer- usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0052] The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0053] Embodiments of the present invention, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the invention. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionalities/acts involved.

[0054] While the specification includes examples, the invention's scope is indicated by the following claims. Furthermore, while the specification has been described in language specific to structural features and/or methodological acts, the claims are not limited to the features or acts described above. Rather, the specific features and acts described above are disclosed as example for embodiments of the invention.