WO2013169903A1

WO2013169903A1 - Methods and systems for managing distributed energy resources

Info

Publication number: WO2013169903A1
Application number: PCT/US2013/040142
Authority: WO
Inventors: Salem EL-NIMRI; Ruba Akram AMARIN; Nasser Kutkut; Mohammad KURAN
Original assignee: Petra Solar, Inc.
Priority date: 2012-05-09
Filing date: 2013-05-08
Publication date: 2013-11-14

Abstract

Systems and methods for managing distributed energy resources are disclosed. Methods and systems disclosed herein provide an energy prediction, forecasting and weather tracking tool used for distributed and centralized energy resources. One or more energy generation sources may communicate with an aggregator to provide energy generation data to a network operation center. The collected data may be organized based on certain parameters, such as geographical location and energy generation source. The energy generation data may be compared with a historical energy performance data. An alarm may be raised when the current energy generation data is not within a range of the historical energy generation data value and a predetermined energy tolerance value.

Description

METHODS AND SYSTEMS FOR MANAGING DISTRIBUTED ENERGY

RESOURCES

This application is being filed on 08 May 2013, as a PCT International Patent application and claims priority to U.S. Patent Application Serial No.

61/644,493 filed on 09 May 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] Conventional distributed and centralized renewable energy sources are hard to predict and their energy performance is highly correlated with weather parameters and conditions such as amount of sunlight, sky clarity, cloud, rain, snow, etc. These weather parameters dynamically change depending on time of the day and the geographical location. The dynamic nature of the weather parameters raises a concern regarding the amount of energy expected to be generated from

l different renewable energy sources (i.e. Solar, Wind), and prevents utilities and customers from investing in this field, and harness free energy.

[003] Knowing an exact amount of energy generated from installed energy resources is crucial for the stability of the utility grid. Therefore it is necessary to predict the amount of renewable energy generation in the future (in minutes, hours or days) to be able to effectively manage the utility grids and meet the electricity needs of the customers. In order to predict the amount of energy generation, expensive instruments are required. As an example meteorological equipment and/or weather satellites may be required to measure weather parameters (such as wind, temperature, solar irradiance, pressure, humidity and cloud coverage) and assist in the prediction of the renewable energy system performance. Additional difficulties are associated with collecting and analyzing the energy generation information, which will be a vital source in predicting the system performance, and the expected amount of energy that will be harvested from the renewable energy sources.

SUMMARY OF THE INVENTION

[004] Systems and methods for managing distributed energy resources are disclosed. Methods and systems disclosed herein provide an energy prediction, forecasting and weather tracking tool used for distributed and centralized energy resources. One or more energy generation sources may communicate with an aggregator to provide energy generation data to a network operation center. The collected data may be organized based on certain parameters, such as geographical location and energy generation source. The energy generation data may be compared with a historical energy performance data. An alarm may be raised when the current energy generation data is not within a range of the historical energy generation data value and a predetermined energy tolerance value.

BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

[009] FIG. 4 is diagram illustrating groups of distributed energy resources organized based on geophysical location;

[0010] FIG. 5 is a diagram illustrating filtered groups, filtered based on a type of energy resource;

[0011] FIG. 6 is a flow diagram of a method for energy prediction and forecasting for renewable energy resources, and weather tracking;

[0012] FIG. 7 is a graph illustrating an energy profile indicating drop in amount of energy generation vs time; and

[0013] FIG. 8 is a graph illustrating another energy profile indicating increase in amount of energy generation vs time. DETAILED DESCRIPTION

[0014] 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.

[0015] Embodiments of the invention may provide systems and methods for managing distributed energy resources. Methods and systems disclosed herein may provide an energy prediction, forecasting and weather tracking tool, which may be used for distributed and centralized energy resources. Distributed energy resources may communicate with an aggregator (decentralized decision point) to provide energy generation data. The collected energy generation data may be organized based on certain parameters, such as geographical location and energy generation source. The energy generation from the distributed energy resources may be compared with historical energy performance data. Based on the comparison results, the aggregator may generate an alarm to a network operation center. The network operation center, based on the alarm, may monitor the distributed energy resources to track an effect of weather conditions, and pattern of the effect. The network operation center, based on the monitoring results, predict a pattern in the energy generation and predict a change in the energy generation from the distributed energy resources. The method and systems will now be described in detail with reference to the figures.

[0016] 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.

[0017] 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.

[00 8] In an embodiment, each ER in DER 102 may be configured to communicate, either directly or indirectly (hoping through another ER), with aggregator 104. The ER may be able to communicate with 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 at the ER with an associated time stamp. [0019] Consistent with embodiments of the disclosure, each ER or the electricity metering device of the ER may include a network device, for example a communicator. The communicator may be configured to transmit the energy generation data of the ER over the first communication system to aggregator 104. The communicator may also be configured to receive a request for the energy generation data from aggregator 104, and in response to the received request, send the energy generation data and other telemetry data over the first communication channel. The communicator may include a receiver and a transmitter. The receiver may further be configured to receive requests from a remote monitoring station, such as NOC 106. Similarity, the transmitter may be configured to send data packets to NOC 106. The energy generation data and other telemetry data may be sent by the communicator in periodic manner. Aggregator 104 may be configured to receive energy generation data reported by ERs and forward the collected data to NOC 106.

[0020] Although system 100 of FIG.1 , is shown to include only three energy resources, 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 energy resources present in a geographical area may depend on the power requirements at of the geographical area where a decentralized distributed energy resources is installed.

[0021] Aggregator 04 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 energy generation and other telemetry data received 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 to NOC 106, and forward the requests received from NOC 106 to DER 102.

Aggregator 104 may further include a processor to process the received data, and generate notification to the NOC 106 based on the processing.

[0022] 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 a 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 delivery of the energy generation data and other telemetry data from DER 102 to NOC 106. 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.

[0023] 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.

[0024] 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)).

[0025] 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 2 0 (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.

[0026] 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 δ 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.

[0027] 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 06 may be configured to execute a method for forecasting energy generation data from one or more energy resources. An example, FIG. 3 illustrates steps of an algorithm to organize data received at the NOC 106.

[0028] Consistent with embodiments of the invention, NOC 106 may be configured to provide a platform to monitor the energy generation data provided by DER 102. As an example, NOC 106 may provide a platform for an administrator for energy prediction and forecasting for renewable energy resources, and weather tracking. As an another example, NOC 106 may include algorithms for energy prediction, energy forecasting and weather tracking, which may be used for managing distributed and centralized energy resources. The algorithms to manage DER 102 are described in following part of the disclosure. As an example, FIG. 3 illustrates a flow diagram of a method for managing distributed energy resources.

[0029] At block 302 of FIG.3, energy generation data from DER 102 may be collected at a memory at the aggregator 104. The energy generation data may further be collected at storage device 212 at NOC 106. As an example, NOC 106 may be configured to collect the energy generation data received from aggregator 104 and store the energy generation data in a database 218. The energy generation data may include an amount of energy produced or generated by an energy resource in a predetermined time period. The energy generation data received at the aggregator 104 and the NOC 106 may include a time stamp along with the amount of energy generated.

[0030] At block 304, the collected energy generation data may be organized based on one or more predetermined categories. The collected energy generation data may be organized at the memory in at aggregator 104. The collected energy generation data may be organized at database 218 at NOC 106. 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, location of the energy resources, time stamp associated with the energy generation data, and type of energy generation devices.

[0031] 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 or aggregator 104 may be configured to filter the energy generation data 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 monitoring. As an example, at block 306a, the energy generation data may be filtered based on type of energy resources. The energy resources 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.

[0032] 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 pre-determined 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.

[0033] 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 may 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.

[0034] After filtering the energy generation data, at block 308, the filtered data may be stored as filtered groups. The filtered groups may be stored at aggregator 104. The filtered groups may also be stored in database 218 of NOC 106. As an example, the filtered groups may be stored on a separate database located on memory 202 or mass storage device 212 of NOC 106.

[0035] At block 310, a historical energy generation value for each ER in DER 102 may be updated based on received energy generation data. The historical energy generation value may be created and maintained either at aggregator 104 or NOC 106 or both at aggregator 104 and NOC 06. The historical energy generation value may be updated continuously and adaptively. In one embodiment, aggregator 104 or NOC 106 or both at aggregator 104 and NOC 106, may use the historical energy generation value to predict and maintain a predicted/expected energy generation value for a time period for each ER. The prediction of the

predicted/expected energy generation value may be performed using a prediction algorithm.

[0036] At block 312, the energy generation data received from DER 102 may be monitored. The energy generation data may be monitored either by aggregator 104 or NOC 106 or by both aggregator 104 and NOC 106. The algorithm for monitoring the energy generation data from DER 102 is described in reference for FIG. 6 of this disclosure.

[0037] FIG. 6 is a flow diagram of a method 600 for energy prediction and forecasting for renewable energy resources, and weather tracking. The method 600 may provide an energy prediction, forecasting and weather tracking algorithms which may be used for distributed and centralized energy resources. Distributed energy resources may communicate with an aggregator to provide energy generation data. The collected data may be organized based on certain parameters, such as geographical location and energy generation source. The energy generation data may be monitored. The monitoring may include comparing the energy generation data with a historical energy performance for a particular energy resource at a particular time and location with the current generation.

[0038] At block 602 of FIG. 6, energy generation data from DER 102 may be collected at a memory at the aggregator 104. The energy generation data may further be forwarded by aggregator 104 to NOC 106, and collected at storage device 212 at NOC 106. As an example, NOC 106 may be configured to collect the energy generation data received from aggregator 104 and store the energy generation data in a database 218. The energy generation data received from DER 102 may include an amount of energy produced by each energy resource and an associated time stamp. The energy generation data may further include identifiers to enable aggregator 104 to identify a source and a location of the source.

[0039] At block 604, the collected energy generation data may be organized based on one or more predetermined categories. The collected energy generation data may be organized at the memory at aggregator 104. The collected energy generation data may be organized at database 218 at NOC 106. The energy generation data may be organized based on predetermined organization

parameters. As an example, the energy generation data may be organized based on type of energy resource and geographical location of the energy resources.

[0040] At block 606, the organized energy generation data may be processed. The processing may be performed either at aggregator 104 or NOC 106 or at both aggregator 104 and NOC 106. The processing may include comparing the energy generation data with a historical or a predicted energy generation value. The comparison may be done for amount of energy generated by an energy resource (i) with a historical/predicted amount for the energy resource (i). The comparison may be performed using the following equation:

{Eh/p i - ki) ≤ Ecurrent i ≤ (^ft/p i + ^i) (1) where:

- £: Energy,

- /^' : Specific energy resource

- k: Amount of energy tolerance that will not result in an alarm

- h : History

- : Predicted

[0041] The equation (1) above may be used to compare the amount of energy generated by the energy resource (i) with the historical value or predicted value for the energy generation for the time stamp associated with the amount. The equation (1) above may utilize intelligent techniques where both e_{h i} (historical energy) and k\ values are continuously adapting to the performance of the specific energy resource (/) with time. As an example, the e_h i (historical energy) and k\ may continuously adapt to a current working condition of the energy resource, and update the historical recorded performance accordingly. The equation (1) above may enable the method 600 to be able to detect slight performance degradation due to system aging for the energy resource (/). The processing of the energy generation data using equation (1) is described in detail in following part of the disclosure.

[0042] At block 608, method 600 may compare the energy generation data with the historical/predicted value using equation (1) for the energy resource (/). More specifically at block 608, method 600 may determine whether the amount of energy generated by the energy source (i) is less than the historical/predicted value for the time period of comparison. The determination may be represented using the following equation:

E current i ^ {βΐι/ρ i ~ ^i) (2. )

The equation (2. a) may enable method 600 to determine if there is a drop in energy generation by the energy source (i). As illustrated in equation (2. a) the drop in energy generation value may be determined by comparing, for a time period, the historical/predicted value of energy generation for the energy source (i) with a current value of the energy generation. If there is a drop in the energy generation value, method may proceed to block 610, else to block 622.

[0043] At block 610, method 600 may raise an alarm, if there is a drop in the energy generation value from the energy resource (i). As an example, method 600 may raise an alarm to a central monitoring system or NOC 106. The alarm may be sent by aggregator 104 along with other information such as the current energy generation value and the historical/expected energy generation value for the energy resource (i).

[0044] At block 612, in response to the alarm from aggregator 104, NOC 106 may compare the received current energy generation value and the historical/expected energy generation value for the energy resource (i). Upon confirming the drop in energy generation, NOC 106 at block 612 may compare other energy resources in near proximity of the energy resource (i). As an example, NOC 106 may determine if the neighboring energy resources are behaving in a similar fashion as of the energy resources (i). If there are other energy resources, similar in type of the energy resource (i), NOC 106 may be configured to geographically size the area in effect. If there are no other energy resources in the in near proximity of the energy resource (i) with dropped energy generation value, NOC 106 may be configured to attribute the drop to a maintenance problem associated with the energy resource (i).

[0045] At block 614, NOC 106 may be configured to track the affected areas. The tracking may include determining one or more other geographical areas with drop in the energy generation value. At block 616, NOC 106 may calculate an amount of energy loss. The amount of energy loss may be calculated for the specific energy source (i) as well as the one or more geographical areas in effect,

surrounding the specific energy source (i).

[0046] At block 618, NOC 106 may be configured to determine a pattern in the amount of energy loss. The pattern may include a trend of increase or decrease in the amount of energy loss in the one or more geographical areas. Depending on the determined pattern, NOC 106 may be able to predict an estimated time of arrival of a predetermined drop in the amount of energy loss in the one or more geographical areas. As an example, NOC 106 may determine a direction of the propagation of the energy loss and an estimated time of arrival (ETA) for neighboring energy resources.

[0047] Consistent with the embodiments of the disclosure, NOC 106 may be configured to correlate a weather pattern based on the patter in the amount of energy losses. Since the energy resource (i) is a renewable energy resource, the amount of energy generated in a time period may be dependent on a weather condition in the time period. As an example, an amount of energy generated by a wind mill will be dependent on an average speed to winds in the time period. Hence, the drop in the amount of energy generated by the wind mill may be attributed to a change in the average wind speed from a historical/expected value. Hence by tracking the pattern of change in amount of energy generated by the wind mills in different geographical area, NOC may be able to predict a change in wind pattern in those different geographical areas. Moreover, NOC 106 may be able to predict arrival of a certain wind speeds in a geographical area based on tracking of the pattern of the amount of the energy generated by the wind mills in a certain direction. As an example, by tracking the change in the amount of energy generated in certain direction, NOC may be configured to predict an estimated change in average wind speed at a particular geographical location.

[0048] At block 620, NOC 106 may be configured to notify customers of expected energy losses. The notification may be based on the amount of energy losses, and the pattern of the energy loses. The notification may be provided as a graph indication an amount of energy loss vs time as shown in FIG. 7. NOC 106 may be configured to send these notifications to grid management applications such as supervisory control and data acquisition (SCADA). In addition, NOC 106 may be configured to notify the customers of a changing weather condition.

[0049] At block 622, method 600 may determine whether the amount of energy produced by the energy source (/) is more than the historical/predicted value. The determination may be represented by the following equation:

^current i > (βη/ν i ⁺ ^i) (²- The equation (2.b) may enable method 600 to determine if there is an increase in energy generation from the energy source (/). As illustrated in equation (2.b) the increase in the energy generation may be determined by comparing a current energy generation value with a historical/predicted value for the energy source (/). If there is an increase in the amount of energy generated by energy resource (i), method may proceed to block 628, else will proceed to block 624.

[0050] When there is no increase in the energy generation value, method 600 may send a message to NOC 106 that the energy resource (i) is normal. The messages may be sent continuously at a predetermined time interval. NOC 106, after receiving the normal behavior notification, may be configured to notify customers. The notification may include a graph indicating an amount of energy generated vs time. NOC 106 may be configured to send these notifications to the grid management applications. In addition, NOC 106 may be further configured to notify the customers of a normal weather conditions.

[0051] When there is an increase in the energy generation value from the energy resource (i), at block 628, method 600 may raise an alarm. As an example, method 600 may raise an alarm to a central monitoring system or NOC 106. The alarm may be sent by aggregator 104 along with other information such as a current energy generation value and an historical/expected energy generation value for the energy resources.

[0052] At block 630, in response to the alarm from aggregator 104, NOC 106 may compare the received current energy generation value and the

historical/expected energy generation value for the specific energy source (/) for the time period associated with the stamp of the the current energy generation value. Upon confirming the increase in energy generation from energy resource (i), NOC 106 at block 630 may further monitor other energy resources in near proximity of the energy resource (i). As an example, NOC 106 may determine if neighboring energy resources are behaving in a similar fashion as of the energy resources (i). If there are other neighboring energy resources, similar in type of the energy resource (i), NOC 106 may be configured to geographically size the area in effect.

[0053] At block 632, NOC 106 may be configured to track the affected areas. The tracking may include determining one or more other geographical area with an increase in the energy generation value. As an example, NOC 106 may be configured to track one or more other geographical areas with the increase in the energy generation data. At block 634, NOC 106 may calculate an increase in an amount of energy generation. The increase in the amount of energy generation may be calculated for the specific energy source (/) as well as the one or more

geographical areas in effect, surrounding the specific energy source (/).

[0054] At block 636, NOC 106 may be configured to determine a pattern in the increase in amount of energy generation. The pattern may include a trend in the increase in the amount of energy generation in the one or more geographical areas. Depending on the determined pattern, NOC 106 may be able to predict an estimated increase in the amount of energy generation in the one or more geographical areas and estimated time of arrival of the estimated increase. As an example, NOC 106 may determine a direction of the propagation of the increment of the energy generation and an estimated time of arrival (ETA) for neighboring energy resources. NOC 106 may further be configured to attribute the increase in the energy generation to a change in weather conditions at the location of the energy resource (i). In addition, based on the tracking of the pattern of the increase in the energy generation, NOC 106 may be configured to track a pattern in the change in the weather conditions.

[0055] At block 638, NOC 106 may be configured to notify customers of expected additional energy generation. The notification may be based on the amount of energy increase in the energy generation, and the pattern of the increment. The notification may be provided as a graph indication an amount of energy generation vs time as shown in FIG. 8. NOC 106 may be configured to send these notifications to grid management applications. In addition, NOC 106 may further be configured to notify the customers of the change in the weather pattern along with estimated time of arrival of a certain weather conditions.

[0056] Consistent with the embodiments of the disclosure, an example is being provided to better understand the algorithms described with respect above FIGs 1-6. The example is provided to more specifically address the equation (2. a). In an area with distributed solar energy photovoltaic (PV) panels surrounding a photovoltaic solar farm, if a cloud passes by, a sudden decrease in the energy generation may be noticed for the distributed PV systems affected by the cloud coverage. Aggregator 04 may send an alarm to NOC 106. NOC 106 may evaluate a size (geographically) and value (in terms of energy) of a decrease in energy generation for the PV systems. NOC 106 may continuously monitor the clouds as it passes by directly monitoring the energy generation value of the affected panels. Database 218 at NOC 106 may capture every energy source location, type, historical and expected energy profiles. Using this information, NOC 106 may calculate an amount of energy loss due to the passing cloud, the location, size, direction and cloud speed. Customers (owners of distributed PV or solar farms), who are in the estimated path of the cloud may be notified with the expected time of arrival (referring to the cloud), the expected amount of energy loss, and the duration of this loss. The customers may be able to look at the energy profile, which may indicate the amount of energy vs. time as shown in FIG. 7. NOC 106 may continue to monitor the cloud movement and its effect until the cloud leaves the area of deployment.

[0057] Consistent with the embodiments of the disclosure, although algorithms in method 600 are described with reference to a specific energy resource (i), the algorithms may be used on a filtered group of energy resources. As an example, the algorithms may be used on one or more filtered group of energy resources, filtered based on a geographical location or type of the energy resources, as illustrated with reference to FIG. 4 and FIG. 5. In addition, some steps of the method 600 are described to be performed by either aggregator 104, and some steps are being performed by NOC 106, but it will be apparent to one skill in the art that NOC 106 may be configured to perform all the steps of method 600. Similarity aggregator 104 may be configured to perform all steps of method 600.

[0058] 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 resources, 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 renewable energy resources.

[0059] Consistent with embodiments of the disclosure, 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.

[0060] Consistent with the embodiments of the present disclosure, the algorithms described herein may eliminate a need collection and analysis of weather data for forecasting of the energy generation from the distributed energy resources, in particular renewable energy resources such as solar and wind. As an example, the algorithms described herein may eliminate the dependence on weather equipment by providing highly accurate forecast of the energy generation and losses. Moreover the methods and systems may provide a more economical and faster method of determining the expected energy loss in a geographical area at a given time. As an example, the algorithms may provide early warning to any sudden loss in the produced energy for solar and wind farms.

[0061] The algorithms described in this disclosure may provide an economical and faster method of determining the expected weather condition in a certain area at a given time. The energy evaluation, forecasting and profiling algorithms may provide the life time performance of the renewable energy source. In addition, the algorithms described herein may save customers with large deployments plenty of money by providing early generation prediction for the renewable energy resources. The energy evaluation and profiling algorithms described herein may be self-learning algorithms that may evaluate the performance of the energy resource with time and may make proper adjustments. The algorithms disclosed herein may provide enhanced renewable energy generation forecasting, which may allow power system operators to integrate more renewable energy resources into the electricity grid, and ensure the economic and reliable delivery of renewable energy resources to families and businesses.

[0062] Consistent with the embodiments of the invention, the methods and systems described herein may be used as a weather tracking tool. The methods and systems described above may enable an accurate forecasting of weather conditions, that help utilities and grid operators boost the reliability of their systems and reduce the cost of integrating renewable power plants into the grid. The algorithms disclosed herein may also be used as an energy forecasting tool, which may help utilities and power system operators better predict the amount of generated energy when clouds and other weather-related factors reduces the intensity of incoming sunlight or wind at renewable energy facilities. As an example, the algorithms may allow utilities and operators to anticipate changes in renewable power production more accurately and take actions to ensure the stability of the national power grid. As another example, the algorithms described above may enable administrators to detect slight performance degradation in the renewable energy source, such as system aging.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

Claims

WHAT IS CLAIMED IS:

1. A method of managing distributed energy resources, the method comprising:

receiving energy generation data from a first energy resource;

defining an energy tolerance value for the energy generation data for the first energy resource;

continuously monitoring the energy generation data for the first energy resource, wherein continuously monitoring comprises comparing the energy generation data with a historical energy generation data value;

raising an alarm when a current energy generation data is not within a range of the historical energy generation data value and the energy tolerance value; and determining, in response to the alarm, if there is a second energy resource in a first predetermined geographical area with a current energy generation data not within a range of the historical energy generation data value and the energy tolerance value defined for the second energy resource.

2. The method of claim 1 , further comprising:

determining a plurality of energy resources in the first predetermined geographical area with the current energy generation data not within the range of the historical energy generation data value and the energy tolerance value defined for each of the plurality of energy resources; and

calculating a change in value of the energy generation from the first predetermined geographical area.

3. The method of claim 2, further comprising: notifying to at least one customer of expected changes in the energy supply.

4. The method of claim 2, further comprising:

managing a power grid based in the change in the value of the energy generation.

5. The method of claim 1 , further comprising:

determining a second predetermined geographical area in which there is a change in value of the energy generation;

tracking a pattern of the change in the energy production.

6. The method of claim 5, further comprising:

predicting a third geographical area based in the tracking of the pattern where there will be a change in the energy generation.

7. The method of claim 1 , wherein comparing the energy generation data with the historical energy generation data value comprises comparing the energy generation data with the historical energy generation data value, wherein the historical energy data is continuously updated to include historical energy generation data.

8. The method of claim 1 , further comprising:

raising a maintenance alarm for the first energy source, when there is no second energy resource in the first predetermined geographical area with the current energy generation data not within a range of the historical energy generation data value and the energy tolerance value defined for the second energy resource.

9. The method of claim 8, further comprising:

labeling the first energy resources as degradation due to aging of the first energy resource.

10. A system for managing distributed energy resources, the system comprising:

a memory; and

a processor coupled to the memory, the processor configured to:

receive energy generation data from a plurality of distributed renewable energy resources located in a predetermined geographical area;

filter at least one group of energy resources from the plurality of distributed renewable energy resources based on a type of the energy resource;

define an energy tolerance value for the at least one group of energy resources;

compare the energy generation data for the least one group of energy resources a historical energy generation data value to determine if the energy generation data is within the energy tolerance value; and

raise an alarm when the energy generation the energy generation data is not within the tolerance value.

11. The system of claim 10, wherein the processor is further configured to determine a change in energy generation value from the at least one group of energy resources, wherein determining the change in energy generation value comprises determining at least one of: a decrease in the energy generation value, and an increase in the energy generation value.

12. The system of claim 11 , wherein the processor is further configured to attribute the change in energy generation value from the at least one group of energy resources to a change in a weather condition, wherein the weather condition is determined based on the type of the at least one group of energy resources.

13. The system of claim 12, wherein the at least one group of energy resources are of solar energy resource type, and wherein attributing the change in energy generation data to change in sun light in the predetermined geographical area.

14. The system of claim 13, wherein the processor is further configured to determine a change in intensity of sun light in the predetermined area based on the change in the energy generation value.

15. The system of claim 12, wherein the at least one group of energy resources are of wind energy resource, and wherein attributing the change in energy generation data to change in wind speed in the predetermined geographical area.

16. The system of claim 15, wherein the processor is further configured to determine a change in speed of the wind in the predetermined geographical area based on the change in the energy generation value.

17. The system of claim 12, wherein the processor is further configured to predict a weather condition of the predetermined geographical area based on the predicted change in the energy generation value of the predetermined geographical area.

18. A non-transitory computer readable storage medium which stores a set of instructions which when executed performs a method of managing distributed energy resources, the method executed by the set of instructions comprising:

receiving energy generation data from a first energy resources;

determining, when a current energy generation data is not within a range of the historical energy generation data value and the energy tolerance value, a type of the first energy resources;

filtering a plurality of energy resources of the type of the first energy resource in a predetermined geographical area surrounding the first energy resources; and monitoring the energy generation data for the plurality of energy resources in the a predetermined geographical area.

19. The non-transitory computer readable storage medium of claim 18, wherein the method executed by the set of instructions further comprising:

determining if there is a second energy resources in the filtered plurality of the energy resources in the predetermined geographical area having energy generation data not within the range.

20. The non-transitory computer readable storage medium of claim 18, wherein the method executed by the set of instructions further comprising:

attributing a change in the energy generation data, when there is a second energy resource, to a change in weather condition in the predetermined

geographical area.