US20140200717A1 - Node manager in an energy distribution system and related methods, devices, and systems - Google Patents

Abstract

Aspects of the disclosure are directed to, for example, methods for operating a node manager such as a node manager operably coupled to a solar tracker assembly. In one embodiment, the method includes providing control signals to motors of the solar tracker assembly to control an orientation of a photovoltaic array and receiving status information form one or more sensors configured to measure at least one of ambient, operating, and performance conditions local to tracker electronics of the tracker assembly. The method further includes modifying the control signals based, at least in part, on the status information and communicating, via the tracker electronics, at least a portion of the status information over a cellular communication via a cellular link.

Description

CROSS-REFERENCE TO RELATED APPLICATION
• The present application claims priority to U.S. Provisional Application No. 61/752,922, filed Jan. 15, 2013, which is incorporated herein by reference in its entirety.
TECHNICAL FIELD
• The following disclosure relates generally to energy distribution systems. Some embodiments, for example, are directed to methods for managing information and/or operation of power or communication nodes connected to end-users, distribution grids, and/or power generators.
BACKGROUND
• In a typical energy distribution system, a power plant produces energy with a power generator, such as a coal or gas fired generator, a hydro-powered generator, or a nuclear-powered generator. Power is then transmitted to an end user over a transmission grid. The transmission grid, in turn, supplies this power to a local distribution grid which supplies the power to end users via low-voltage transmission lines, substations, distribution circuits, etc. A utility company can meter the power at the end-user's premises to determine how much power has been consumed.
• One problem with traditional energy distributions is they employ antiquated transmission and distribution grids. This makes it difficult and cost prohibitive for utility companies to bring new and alternative power generators online, such as wind, solar, geothermal, etc. Another problem with these systems is that they centralize power distribution, which gives utility companies a market monopoly. Thus, many utility companies are reluctant to improve their power distribution infrastructure.
BRIEF DESCRIPTION OF THE DRAWINGS
• FIG. 1 is a block diagram of a system that includes a node manager configured in accordance with an embodiment of the present technology.
• FIG. 2A is an isometric view of a solar tracker system configured in accordance with an embodiment of the present technology.
• FIG. 2B is a side view of a tracker and a tracker assembly configured in accordance with embodiments of the present technology.
• FIG. 2C is a top plan view of a base configured in accordance with embodiments of the present technology.
• FIG. 2D is a partial backside view of a tracker assembly configured in accordance with an embodiment of the present technology.
• FIG. 2E illustrates a method of assembling the tracker assembly configured in accordance with an embodiment of the present technology.
• FIG. 3A is a block diagram illustrating tracker electronics configured in accordance with an embodiment of the present technology.
• FIG. 3B is a flow diagram illustrating a routine performed by a node manager in accordance with an embodiment of the present technology.
• FIG. 3C is a flow diagram illustrating a routine of a learning process employed by a node manager in accordance with an embodiment of the present technology.
• FIG. 4A is a diagram of an energy network configured in accordance with an embodiment of the present technology.
• FIG. 4B is a network diagram illustrating a cloud-based networked arrangement of node managers configured in accordance with an embodiment of the present technology.
• FIG. 4C is an diagram of a controller center arranged to operate various aspects of a microgrid in accordance with an embodiment of the present technology.
• FIG. 4D is a diagram of a microgrid licensing/investing/purchasing scheme configured in accordance with an embodiment of the present technology.
• FIGS. 5A-E illustrate various example user interfaces that can be used with a node manager in accordance with an embodiment of the present technology.
• FIG. 6 is a flow diagram illustrating an internodal bidding scheme in accordance with an embodiment of the present technology.
• FIG. 7 show various system logs than can be generated by a node manager during a single day of operation of a solar tracker in accordance with an embodiment of the present technology.
DETAILED DESCRIPTION
• As described in greater detail below, the technology disclosed herein relates to energy systems and, in particular. to a node manager configured to manage information and/or operation of various aspects of an energy distribution system.
• FIG. 1 is an overview block diagram showing the operational relationships of anode manager10 in accordance with an embodiment of the present technology. In general, thenode manager10 is configured to manage information and/or operation at one or more nodes in a power distribution system, such as power distribution nodes (e.g., a substation), end-user user nodes (e.g., a residential, commercial, governmental, or other suitable users), and/or communication nodes (e.g., a server, database, etc.).
• In one aspect, thenode manager10 communicates with or is integrated with one or more nodal equipment12 (e.g., nodal devices) at or in the vicinity of a node. As described in greater detail below, thenodal equipment12 can include, for example, energy equipment (e.g., a solar array), auxiliary equipment (e.g., a load balancer), and/or network equipment (e.g., a database).
• In another aspect, thenode manager10 operates to receivenodal information14. In one embodiment, thenodal information14 can include information relevant to a particular node or a number of nodes. For example, the resource information can relate to power demand, weather forecasts, and/or the ambient environment in the vicinity of the nodal devices.
• In yet another aspect, thenode manager10 operates to derivenodal analytics16 based on thenodal information14. In one embodiment, thenode manager10 can derive thenodal analytics16 based on various correlations in thenodal information14. For example, thenode manager10 can correlate power demand with the weather forecast and/or the ambient environment. In some embodiments, thenode manager10 generatesnodal analytics16 based on various rules. Thenode manager10 can use these rules to develop new or modified correlations, rules, and/or other analytics.
• In still yet another aspect, thenode manager10 operates to generatenodal knowledge18 based on thenodal information12 and thenodal analytics16. Thenodal knowledge18, for example, can allow a power utility to predict power demand based on a weather forecast. In some embodiments, thenodal knowledge18 can be bought, sold, or licensed according to a variety of transaction models. In other embodiments, thenodal knowledge18 can be used for auditing purposes.
• In general, the nodal equipment includes devices, apparatuses, systems, etc. that incorporate a node manager. Nodalequipment12 can be located, for example, at a power distribution node (e.g., a substation), an end-user user node (with e.g., a residential, commercial, governmental, etc.), and/or a communication node (e.g., a server, database, etc.). In some embodiments thenodal equipment12 operates independently of a node manager. In other embodiments, thenodal equipment12 allows a node manager to handle nodal information, analytics, and/or knowledge. As described in greater detail below, thenodal equipment12 can include energy equipment, auxiliary equipment, and/or network equipment. However,nodal equipment12 can include other types of equipment, devices, apparatus, etc. For example, thenodal equipment12 can include a mobile phone, a personal computer, and/or a remote controller device that incorporate a node manager.
• Nodal energy equipment generally includes equipment that is configured to supply and/or store energy (e.g., with a bank of batteries, fuel cells, compressed air, etc.). Nodal energy equipment can include, for example, conventional generators (e.g., coal, gas-fired, hydro-powered, nuclear, etc.) and/or renewable energy generators (solar, wind, geothermal, hydro-powered, etc.). In some embodiments, nodal energy equipment can both deliver and store energy. For example, in a smart grid, an electric or hybrid car can receive and store energy from the grid and provide energy back to the grid to meet peak demand, load balance, etc.
• As described herein, nodal energy equipment is described in the context of a solar tracker system or equipment200 (“tracker200”;FIG. 2A). In other embodiments, however, other types of nodal energy equipment are possible. Further, some aspects of thetracker200 can be configured to provide other advantages. For example, the mechanical, operational, as well as the other aspects of thetracker200 can provide certain advantages relevant to equipment for harvesting solar energy generally.
• FIG. 2A is an isometric view of atracker200 configured in accordance with an embodiment of the present technology. Thetracker200 includes atracker assembly203 and tracker electronics205 (shown schematically) operably coupled to thetracker assembly203. Thetracker assembly203 includes abase210, arotatable frame220, and apivoting frame230 suitable to carry a solar panel array206 (e.g., one or more solar panels of photovoltaic cells). Various aspects of thebase210, therotatable frame220, the pivotingframe230 and associated motor(s) and actuator(s) are described in greater detail with reference toFIGS. 2A-2E together.
• FIG. 2B is a side view of atracker200 andtracker assembly203 andFIG. 2C is a top plan view of the base210 configured in accordance with embodiments of the present technology. In reference toFIGS. 2B and 2C together, the base210 can include atrack member212 supported by a plurality offoot plates214. In one embodiment, thetrack member212 can be a frame (e.g., metal tubing frame) having a symmetrical (e.g., round, circular, etc.) shape with an outer circumference C₁. Thefoot plates214 can, for example, be metal footings configured to support thetrack member212 above the ground or other surface and to anchor thetracker200 to the ground or other surface. Thefoot plates214 can be secured to thetrack member212 around the circumference C₁of thetrack member212. In some embodiments, additional support members216 (e.g., angled support bars or straps) can couple thetrack member212 to thefoot plates214.
• Referring toFIG. 2C, the base210 can also include a motor240 (e.g., a slew motor; drawn in phantom) on which abase member217 can be fixed. Themotor240 can be configured with support structures (not shown) and configured to rotationally turn thebase member217 around a Z-axis (e.g., azimuth,FIG. 2A) with respect to thetrack member212. In one particular embodiment, themotor240 can include a slew motor from Kinematics Manufacturing, Inc., of 21410 N. 15^thLane, 104, Phoenix, Ariz. 85027. Thebase member217 includes radially extendingarms218 coupled thereto. Theradially extending arms218 can include wheels219 (drawn in phantom) for engaging and moving along thetrack member212. For example the individual wheels can be attached at adistal end215 of eacharm218 such that eachindividual arm218 has an arm length L₁that extends between thebase member217 and thetrack member212. Also, the wheels can ride on a round tube to minimize friction and minimize dirt/dust accumulation on the track. Minimal surface contact between the wheels and the track allows wheels to cut grass or other plants that grow over the track, negating the need to significantly alter the landscape surrounding the tracker. The wheels can be made from a pipe surrounding low friction bearings to minimize the torque on the centralized slewing drive, allowing the use of a smaller, less expensive slewing drive. The wheels can also be made from a pipe long enough to still contact the track if any settling or anomalies occur that cause the track to be not perfectly circular.
• Themotor240 can drive rotation (e.g., in either direction around the Z-axis) of the base member217 (e.g., using a drive shaft; not visible inFIG. 2C) such that thewheels219 fixed to theradially extending arms218 move along thetrack member212. In some embodiments, themotor240 and/or drive shaft can include bearings or other friction-reducing members or coatings like Teflon®.
• FIG. 2D is a partial backside view of atracker assembly203 configured in accordance with an embodiment of the present technology. Referring toFIGS. 2B and 2D together, thetracker assembly203 also includes arotatable frame220 havingtowers222aand222b(referred to together as222) coupled to the base210 (e.g., to the arms218). Eachtower222 includes a plurality offrame members224 interconnected by bars226 (e.g., such as metal tubing). Theframe members224 can be coupled, at one end (e.g., a first end), to thearms218 of thebase210. Theframe members224 can culminate (e.g., at a second end) at atower peak225aand225bwhere ahinge228 is provided for pivotally attaching thepivoting frame230. Therotatable frame220 can also include the actuator250 (as best seen inFIG. 2B), such as a linear actuator) for engaging and moving (e.g., pivoting) thepivoting frame230 with respect to therotatable frame220 and around the X-Y axis (FIG. 2A). Theactuator250 can be coupled to therotatable frame220 and move with respect to the rotatable frame viapins251,252 (only thepin251 is visible inFIG. 2D) around which theactuator250 can pivot (e.g., pivot about 90°). In one particular embodiment, theactuator250 can include a linear actuator from Linak, Inc., of 2200 Stanley Gault Parkway, Louisville, Ky. 40223.
• The pivotingframe230 can be configured to carry, orient, position, or otherwise hold a solar panel array206 (e.g., via a unistrut attachment253). Thesolar panel array206 can comprise a plurality of photovoltaic cells suitable for converting solar energy into electrical energy. In one particular embodiment, thesolar panel array206 can include one or more solar panels (e.g., 240 Watt) from SunPower Corporation, of 77 Rio Robles, San Jose, Calif. 95134. As described above, the pivotingframe230 can be pivotally coupled to therotatable frame220 via thehinges228.
• In one aspect, thetracker assembly203 is configured to utilize thesolar panel array206 to convert solar energy into electrical energy. During operation, thetracker assembly203 can receive control signals (FIG. 2A) to move, adjust and position thesolar panel array206 in a desired location and orientation for collecting solar energy. For example, therotatable frame220 is arranged with the base210 such that actuation of themotor240 turns or rotates thebase member217 andarms218 in a manner that translates rotation of therotatable frame220 around the Z-axis to position azimuth. Additionally, theactuator250 receives control signals to position the pivotingframe230 in a manner that the pivotingframe230 and therotatable frame220 together position zenith. For example, thetracker electronics205 can communicate via the control signals the desired azimuth and zenith angles for positioning (e.g., via themotor240 and the actuator250) thesolar panel array206. As such, thetracker200 is able to position the rotating, pivotingtracker assembly203 and to generate electrical energy corresponding to the relative position of thesolar panel array206.
• In some embodiments, thetracker assembly203 can transmit the converted electrical energy directly to thetracker electronics205. In other embodiments, thetracker assembly203 may transmit at least a portion the electrical energy to other components of thetracker200 or to remote components. For example, thetracker assembly203 may transfer a portion of the electrical energy directly to a bank of batteries (not shown) for reserve power.
• As described above, thetracker assembly203 is configured to receive control signals from thetracker electronics205. In response to some commands, thetracker assembly203 positions thesolar panel array206 by changing the orientation of therotatable frame220 and thepivoting frame230 via the combination of associated motor(s) (e.g., motor240) and actuator(s) (e.g., actuator250). In the embodiment illustrated inFIGS. 2A-2D, for example, thetracker assembly203 positions thesolar panel array206 by rotation about a Z-axis (e.g., azimuth) in combination with rotation about an X-Y axis (e.g., in the X-Y plane). In response to other commands, thetracker assembly203 sets operational parameters. For example, thetracker assembly203 can enable and/or disable the operation of certain cells in thesolar panel array206 in response to the control signals.
• In another aspect, thetracker assembly203 is configured to provide status information to thetracker electronics205. The status information can include, for example, present orientation of the rotatable frame, the pivotingframe230, and/or thesolar panel array206. The status information can also include information about the ambient (e.g., of thesolar panel array206, the output voltage (or current) of the solar panel array206 (or individual cells therein)), operational aspect of the motor and/or actuator (e.g., encoder position, overheat detection, speed sensors, etc.), and ambient information (e.g., humidity, sunlight, temperature, wind speed, etc.) in the vicinity of thetracker assembly203. Status information can also relate to the overall status of the tracker, such as whether maintenance is required (e.g., when a motor has malfunctioned, a circuit board needs to be replaced, etc.).
• Additionally thebase210 supports stable mounting of thesolar panel array206 in a fail safe manner. For example, thetracker200 can include instructions for detection of imbalances and/or other mechanical issues that would create tipping or other unstable scenarios in conventional trackers. These imbalances and other issues could be addressed rapidly by thetracker assembly203 by moving the solar panel array (e.g., pivoting frame230) into a stable position (e.g., horizontal position, vertical position, in-line with a wind direction, etc.) in real-time.
• In one embodiment, many of the sensors257a-257e(referred to together as257) can be located at asensor box255 that can be attached at or near the pivotingframe230 or, in other embodiments, near to the actuator250 (e.g., linear actuator). The sensors257 can be located at the interior and/or exterior of thesensor box255 and can include, for example, aheat sensor257a(e.g., for detecting a temperature reading at thesolar panel array206 or elsewhere), alight sensor257b, anaccelerometer257c, acompass sensor257d(e.g., for detecting tilt azimuth), and aposition sensor257e(e.g., for detecting pivot zenith). Other sensors257 are contemplated for including in thesensor box255. For example, vibration sensors, moisture sensors, clocks, timers, etc. can also be included. By positioning all of the sensors257 at a common location (e.g., within a common housing or box255), all of the sensors257 and/or other related data collection devices can be mounted at the same time. Also by positioning thesensor box255 at or near thesolar panel array206 or, alternatively, near theactuator250, the sensors257 that detect position, velocity, etc. are appropriately positioned to measure the rotating and pivoting motion of thetracker assembly203. Similar to thesensor box255, thetracker electronics205 can be located within a common housing or box258 (e.g., a secure (lockable), weather proof box) that can be mounted at or near thetracker assembly203.
• Other aspects of the present technology are directed to methods of assembling thetracker assembly203. In general, methods in accordance with an embodiment of the present technology are suited for quick assembly.FIG. 2E illustrates amethod260 of assembling thetracker assembly203 in accordance with an embodiment of the present technology. Themethod260 of assembling atracker assembly203 includes preparing a deployment site by digging footing holes in the ground, or in other embodiments (e.g., roof top assembly applications), preparing support structures to couplefoot plates214 thereto (block262). In some embodiments, the deployment site does not need to be level. Themethod260 can also include assembling and leveling the base210 at the deployment site (block264). Assembly of the base210 can include, for example, assembling thetrack member212. In one embodiment, thetrack member212 may be provided as 4 arcuate pieces that can be coupled to form acircular track member212. Assembly of the base210 can also include attaching a plurality offoot plates214 to thetrack member212 and, in some embodiments, further securing the foot plates to the track member withsupport members216. Assembly of the base210 may further include providing thebase member217 and attaching the plurality of radially extendingarms218 to the base member in a manner where the arms engage the track member withwheels219. Once assembled, the base210 can be secured to the deployment site via attachment of thefoot plates214 via poured concrete or mechanical coupling (e.g., bolts, screws, pins, etc.) and leveled. For example, thefoot plates214 can be set in the holes in the ground or attached via other retaining structures (bolts, screws, brackets, etc.) in a manner that levels the base210 regardless of condition of the site.
• Themethod260 can continue with assembling the rotatable frame220 (e.g., assembling thetowers222, mounting the towers on thebase210 via mechanical coupling (screws, bolts, etc.), attaching thehinges228 to the towers, etc.) and connecting the motor240 (e.g., slew motor) to thebase210 for rotating the rotatable frame220 (block266). The method can further include attaching theactuator250 to therotatable frame220 with thepins251,252 (block268) and mounting the pivotingframe230 to thehinges228 of the rotatable frame (block270). In operation, the actuator250 (e.g., a linear actuator) is positioned such that the actuator can engage a backside of the pivotingframe230 to move the pivoting frame around the X-Y axis created by hinge points.
• If not already completed, solar panels can be attached to thepivoting frame230 in an array. For example, solar panels can be attached and carried by the pivoting frame in a 6×6solar panel array206 as shown inFIG. 2A. Other solar panel array configurations are contemplated. Themethod260 can also include installing a lockable and/or weatherproof electronics box258 (e.g., for housing an electronic control module or tracker electronics205) (block272). Themethod260 can further provide atblock274 assembling one or more wiring harnesses (e.g., having quick connect/disconnect cables) to facilitate electrical connections with themotor240,actuator250,sensor box255, andsolar panel array206 as well as other components, such as an array of batteries (not shown). Further assembly and startup steps are also contemplated. For example, themethod260 could include completing assembly and installation of the tracker assembly203 (block276) and initializing a startup calibration protocol (block278).
• Accordingly, in contrast to conventional trackers that have to have a large center hole dug for deep post installation, installation of thetracker assembly203 can be done with minimal equipment (e.g., a wrench, stepladder, etc) and with minimal effort (e.g., an average sized person). In one embodiment the components of thetracker assembly203 can be sized to meet Occupational Safety and Health Act (OSHA) requirements such that a single person of average size can carry, manipulate and/or otherwise assemble the components of thetracker assembly203. For example, themethod260 can be performed by one or two persons of average size without the use of a crane or other special equipment to assemble and deploy thetracker assembly203. Many of the components of thetracker assembly203 can be off-the-shelf components (e.g., slew motor, linear actuator, fabricated metal components) and could be relatively light-weight to facilitate ease of assembly at a desired site.
• Thetracker200, thetracker assembly203 and/or portions thereof may be assembled and distributed as kits. The kits can include tracker and/or tracker assembly components and instructions for assembling, installing, and/or initiating use of thetracker assembly203. For example, the kit may include all metal or other fabricated components for building and assembling thebase210, therotatable frame220, and pivoting frame230 (e.g., frame structures, bolts and other coupling devices, motors, electronic components, wiring harness, sensor box, etc.). As described above, the components can be sized and of suitable materials to meet OSHA requirements so that an individual person of average size can use the kit to assemble the tracker assembly. Additionally, the kit can include assembly instructions (written instructions, video explanations, computer simulations, etc), such as, for example, instructions on how to perform themethod260 described above. The kit may also include other instructions, for example, instructions on operation, maintenance and/or repair of thetracker200 and/ortracker assembly203. In some instances, the kit may also include one or more solar panels to mount in thepivoting frame230.
• FIG. 3A is a block diagram of thetracker electronics205. In the illustrated embodiment thetracker electronics205 include a CPU302 (central processing unit) including at least one programmed processor configured with memory to operate as anode manager303. TheCPU302 can also include interfaces and other components for communication over a network and/or with other devices (e.g., via a Mod Bus). Thetracker electronics205 further include awireless communication component305,controllers307, andinverter circuits308. Thewireless communication component305 can be configured to provide a direct wireless link for the CPU in addition to or in lieu of standard networking capabilities. Thecontrollers307 are configured to communicate control signals to thetracker assembly203 to control themotor240 and theactuator250. TheInverters308 are configured to receive solar generated electricity and convert the energy into AC form. Theinverters308 can supply the DC voltage to first and/or second power line interfaces310a,310b. Theinverters308 can also convert stored energy at abattery bank312 into AC form. In some embodiments, the inverters can convert AC power from the grid or microgrid into DC form suitable for charging the batteries at thebattery bank312 and/or for powering at least a portion of thetracker electronics205. In some embodiments, thetracker electronics205 can be powered by the solar energy collected by thesolar panel array206. In still other embodiments thebattery bank312 can be configured to provide power to the grid or the microgrid.
• In operation, thetracker electronics205 are configured to control thetracker assembly203 and to receive status from the tracker assembly. In one embodiment, a user can directly control aspects of thetracker200. For example, the user can directly connect with the CPU302 (e.g., via a USB link, wireless link, and/or radio link) to, e.g., control themotor240 and theactuator250 and/or to receive status from the individual sensors at the sensor box.
• In other embodiments, thenode manager303 can be incorporated into theCPU302 to provide control signals and/or receive status signals. In one aspect of operation, thenode manager303 passively collects information but does not act on the information. That is, thenode manager303 does not operate (i.e., control) the nodal equipment. In another mode of operation, thenode manager303 can at least partially operate the nodal equipment.
• FIG. 3B is a flow diagram illustrating a routine320 performed by thenode manager303. In the illustrated embodiment, the routine320 is performed by anode manager303 at theCPU302 of thetracker electronics205. In other embodiments, however, thenode manager303 can be incorporated at other portions of thetracker electronics205, such as at a logic controller. Further, in other embodiments, the routine320 can be executed at other types of nodal equipment, including other types of energy equipment (auxiliary).
• At block321, the routine320 applies one or more rules that dictate, at least in part, the operation of thenode manager303. As described in greater detail below, these rules can be based on nodal knowledge. In some embodiments, the rules can give thenode manager303 certain control over thetracker electronics205. For example, thenode manager303 can be given at least partial control over the motor240 (to orient azimuth) and/or the actuator250 (to orient zenith). In one embodiment, the rule would dictate that thenode manager303 automatically decrease the span range of the pivoting frame in certain wind conditions.
• In another embodiment, a first rule may dictate that thenode manager303 disconnect from the main grid when power generation at the grid is unstable (e.g., spikes) beyond a certain threshold of power stability. As described in greater detail below, the first rule may be based on a series of test rules and nodal analytics that arrived at this particular threshold. In some embodiments, a second rule may work in combination with the first rule to dictate when thetracker200 should connect to the microgrid. For example, the second rule could dictate to thenode manager303 to connect thebattery bank312 to the microgrid when the battery is holding a sufficient amount of charge, or to keep the battery bank disconnected until the battery bank has the appropriate amount of charge. The rule could be based on nodal knowledge (i.e.,nodal knowledge18,FIG. 1)) related to a weather model in response to a rule related to a predicted weather plan.
• In some embodiments, the rules can be “birth certificate” rules that set the initial operating behavior of thenode manager303. In one embodiment, a birth certificate can be loaded when thetracker200 is being assembled. When thetracker200 is operational, the birth certificate can dictate that thenode manager303 self-calibrate thetracker assembly203. By contrast, conventional tracker-type devices can be difficult to set up in the field because they can require complicated and time-intensive calibration procedures. For example, installers need to manually align these devices for compass heading and manually level the devices with, e.g., the ground.
• However, the birth certificate can provide rules that instruct thetracker200 to find proper compass direction using a GPS sensor. In another embodiment, the birth certificate can provide a rule that the node manager uses to auto-level the tracker assembly. A tracker can be off level if the foot plates are improperly installed or if the ground shifts. The birth certificate rule can provide tuning adjustments that compensate for any out of level alignment of the foot plates. In particular, the birth certificate rule can instruct themotor240 or theactuator250 to operate in a way that compensates for out of level alignment. In another embodiment, the sensors257 from thesensor box255 can be used, for example, to detect the position of the sun, GPS location, etc., which could also be useful for initial calibration.
• At block322, the routine acquires nodal information14 (FIG. 1) by sensing, detecting, requesting, or otherwise acquiring nodal information. For example, the routine320 can sense ambient conditions, request information over a network, or be pushed information without a request. Thenodal information14 can relate to, for example, ambient conditions (e.g., barometric pressure, humidity, sunlight, wind speed, temperature, etc.), operating conditions (e.g., operational status of sensors, motors, grid signal quality, battery capacity etc.), and/or performance (e.g., array output power, conversion efficiency, etc.). The routine320 can acquirenodal information14 using, for example, thesensor box255, network communications, and/or the wireless or radio network305 (FIG. 3A).
• In many embodiments, the routine320 can time/date stamp and GPS stamp thenodal information14. In this way thetracker200 or a remote device can use thenodal information14 to derive spatial temporal analytics information. As described in greater detail below with reference toFIGS. 4A-4D, thenodal information14 can be utilized in a variety of network configurations, such as cloud networks, local networks, network islanding, etc.
• Atdecision block323, the routine320 determines whether to push (e.g., transmit) some or all of thenodal information14 to at least one other nodal device (e.g.,nodal equipment12,FIG. 1), such as a remote server, a local computer, a maintenance technicians' portable computing device, etc. In some embodiments, the other nodal devices can use thenodal information14 to develop analytics that are fed back to the routine320 (see adjacent flow routine A to B). In one embodiment, the other nodal devices can use thenodal information14 to derive nodal analytics/knowledge16,18. In another embodiment, thenodal analytics16 and/ornodal knowledge18 could be used by a tracker manufacturer to develop birth certificates (see above) for new tracker installations. In this way,newer trackers200 could be as “smart” as existing trackers200 (i.e., by passing on the same knowledge to new trackers). For example, a “new”tracker200 could learn from an “old” tracker that operating at certain motor speeds for a given amount of temperature, humidity, etc., could cause malfunction.
• Atdecision block325, and if the routine320 determines that data is to be pushed (block323), the routine320 further determines if it should apply at least a portion of thenodal analytics16. In some embodiments, the routine320 can delegate more computationally intensive tasks to another nodal device (e.g., to perform processor intensive correlations), but still perform less computationally intensive tasks (e.g., data comparison). In other embodiments, the routine320 can have all analytics delegated to anothernode manager303.
• Atblock327 the routine320 processesnodal information14 according to variousnodal analytics16. An example of such correlations is described below with reference toFIG. 3C.
• Atblock328 the routine320 developsnodal knowledge18 based at least in part on thenodal analytics16 applied atblock327. In particular, the routine320 can decide in a rule making stage whether a particular rule should be tested (or further tested) before it is adopted, whether the rule should be discarded, or whether the rule should be deemed true (e.g., an expert opinion rule that is deemed to be fact or that is based on manual intervention from a user). In one embodiment, certainnodal information14 can causenodal analytics16 to substantially change a rule.
• Atdecision block329 the routine320 determines whether it should grant nodal control to the nodal device. In some embodiments the nodal device is passive. For example, the nodal device can be suited to gathering data but not controlling the position of thetracker200. In another embodiment, the nodal device could be a consumer device used to gather data for analyzing a user power consumption behavior. For example, a transaction could include incentives for users to provide information relating to their consumption behavior. A discounted price for such a unit could be offered in exchange for this information. This information could be useful for creatingnodal knowledge18, such as consumer power consumption. Table 1 provides various other examples ofnodal information14.

• 	TABLE 1
  	Local Nodal Information	Generic Nodal Information
  	Wind speed	SCADA (e.g., data from grid)
  	Wind direction	Grid Voltage
  	Ambient temperature	Grid power factor
  	Barometric pressure	Grid configuration
  	Ambient/Planar irradiance (sun	Battery charge levels
  	ambient)	Available discharge power
  	Solar cell temperatures	Signal conditioning
  	Motor currents	User demand
  	Motor temperatures	Demand response
  	Tilt/Azimuth	User consumption
  	Real time (time stamp)	Consumer pricing
  	GPS location

• A fundamental problem with incorporating solar energy into existing electrical grids is that the grids must have enough capacity to meet peak demands when aggregate demands are highest. A conventional solution is “pure peaker” power plants. Conventional solar generation is typically not suitable as a pure peaker because even though its availability is predicable, it cannot be guaranteed. Solar generation has low inertia; generation can be online/offline almost instantaneously. Conversely traditional power plants (coal, nuclear) require days to cycle. Load following gas plants require 30 to 90 minutes. The demand for ancillary services, which is typically over multiple intervals on any given day, occurs when power must be added to or taken off grid in a matter of seconds to regulate voltage or power factor corrections.
• For these and other reasons the inclusion of conventional solar systems at significant levels introduces problems that will increase with additional solar deployment (PV penetration). Due to both the intermittent and low inertia of conventional solar generation, significant disruption to the generation hierarchy of base load and “pure peaker” plants results in significant price volatility. Additionally, utilities must keep electricity in reserve to provide for supply disruptions or demand spikes, and they must regulate voltage and keep it steady. Both imperatives are difficult when solar resources come online/offline abruptly.
• Referring toFIG. 4A, embodiments of energy distribution systems, or autonomous energy networks (AENs) configured in accordance with embodiments of the present technology can provide a complete solution from deployment to management of a fully autonomous distributed power plant. The successful realization of an efficient and reliable solar energy source can include:
• • Targeting “nonproductive,” sun-advantaged land close to power lines for the location of equipment. The ideal property is suburban/urban brown lots that are on the local substation feed to where the power will be used. Although, with the use of virtual net metering, these geographic limitations are bypassed allowing the generation site and energy consumption to spread across the utility service area. A low-profile, small footprint installation facilitates use of marginal nonproductive land and flat roofs.
  • Solar trackers can enable any solar installation to harvest high concentrations of solar flux anywhere on the globe. This can realize, for example, up to a 50% increase over fixed angle installations for the same solar panels. This increased production for the same number of solar panels saves on land, labor, and components costs. Matching production with local consumption and grid conditions is possible with the addition of multiple solar trackers. For any solar panel, maximum energy harvest is guaranteed throughout the year.
  • Thetracker electronics205 can employ a Smart Controller and Analysis Node (SCAN) that includes an Expert Systems Inference Engine (ESIE) embedded into each installation to provide dynamic, rules-based decision response to real-time events on the grid and at the local installation. Decisions whether to store, consume power onsite, or net meter are events that need to take in a myriad of changing conditions.
  • These rules can be generated in the AEN data center to take advantage of an aggregation of many installations and events that are happening on the grid (DNR, supply interruptions, real time pricing, etc.) and then transferred to the local installation.
• To take advantage of high efficiency solar trackers and provide reliable generation sources, there can be guaranteed minimum down times and the ability to proactively operate and maintain (O & M) a large number of installations remotely with a minimum of service truck rolls. Each local SCAN installation combined with the AEN can provide installation flexibly with the added sophistication of remote analytics and management. The AEN platform can provide an active intelligent “Expert System” presence at the edge of the grid through, e.g., an integrated cellular communications network.
• Each installation can include a computer, such as a Linux based computer, with SCADA (Supervisory Control And Data Acquisition) capabilities and an Expert Systems Inference Engine (ESIE). This decision expertise manages the local installation and provides remote access through, e.g., a cellular network connection. The AEN's SCADA API (Application Program Interface) allows QES, partners, and utilities access to local grid operational data and a platform for advanced smart grid applications. The ESIE enhanced supervisory capabilities manage the advanced power harvesting (Learned Energy Ray Normalization, LERN) algorithms for sun positioning for up to 20 solar trackers. Data is gathered, analyzed, and reported to the AEN network for near real-time and historical tracking. The ESIE management proactively monitors trending and exception events that are processed at the local site and reported to the AEN. Modified ESIE rules are downloaded when new “insight” is generated from the AEN master ESIE processing aggregate data sets of a large number of installations plus third party data bases (real time market pricing, DRM requests, national weather services, solar forecasts, etc.). Various related functions can include:
• • Solar PV panels and micro-inverters provide power and data (inverters; continuous power output and grid voltages). This data is cached, analyzed, and compressed for upload to the AEN storage for analysis providing predictive maintenance, warranty, and billing functions.
  • Voltage control at the point of power consumption. This function can monitor the voltage and power factor of the grid voltage. Problems can be rectified, for example, in one or more of the following ways:
    • Low voltage
      • Add capacitance to correct the power phase of the grid voltage. This action corrects under voltage conditions where there is distortion of grid power factor from large reactive loads (motors, NC units, etc.).
      • Discharge storage device.
    • High voltage
      • Slightly turning gantry away from sun to reduce generation.
      • Diverting generation into charging of storage devices.
  • Sensor inputs from the physical actions of the solar tracker and the local environment can include:
    • Weather station (wind speed, wind direction, temperature, humidity, solar irradiance, precipitation, barometer, reference solar irradiance).
    • Spatial orientation sensors of the real world position of the gantry as opposed to where it is calculated to be. This sensor can also be used to detect unauthorized gantry manipulation or attempted theft.
    • Absolute reference sensing of true south compass direction.
    • Additional I/O for controlling local hardware, measuring local variables, and discrete inputs.
      SCADA functions can include:
  • Control of each axis motor and correct operation throughout the controlled sun tracking moves.
  • Measurement of grid voltage.
  • Measurement of grid power factor.
  • Monitor of local controller supply voltage (voltages delivered to each motor).
  • Status of local network (Modbus, Ethernet, USB, Cellular radio).
  • Monitor and control override switches for local maintenance operations.
  • iOS (iPhone, iPad, etc.) control surface for local control of installation.
  • Calculation of the position of the sun.
  • Validate correct movement of the gantry, monitor long term mechanical drifts of motors and gantry movements, and apply correction.
  • Logging of power generation and performance metrics, compress, analyze, and transmit to AEN remote secure server.
  • Update software modules through secure network connection.
  • Provide real-time secure access for troubleshooting.
  • Provide local sun intensity information for cloud prediction services
  • Interface to existing utility or installed generation and control equipment.
  • Provide micro climate information, such as current solar insolation value, temperature, barometric pressure, wind speed, wind direction, and/or precipitation.
    ESIE functions can include:
  • Update local ESIE current rule sets.
  • Calculate long term drifts on power generation and apply corrections (LERN).
  • Provide voltage control action decisions.
  • DMR action decisions.
  • Manage intelligent power storage and dispatch with various storage and generation options, such as batteries, fuel cells, and/or compressed Air.
  • Provide intelligent islanding logic, such as automated islanding reconnection from grid when unstable grid conditions exist, demand response requests from utility to take site offline on low voltage (brown out) conditions, intermittent, or blackout conditions, and/or provide power to on-site customers during local blackouts.
  • Real time load measurement and management of customer electricity consumption for energy use profile optimization. This function can be augmented by use of a local appliance control network such as ZigBee for granular onsite energy management.
  • Secure site with motion (gantry) and video surveillance.
• In various embodiments, the AEN can include cloud based data and control network connects to each SCAN installation allowing the aggregation of information and services to be near real time, historical, proactive, and predictive. Each SCAN computer has the intelligence to perform the tasks demanded for complete autonomous operation if it never connects to the AEN cloud. This can allows for intermittent operations as standalone entities to optimize various network configurations and real world limitations of network performance. Referring toFIG. 4B, in the cloud, there can include a few replication nodes, a number of directory nodes, and a larger number of data nodes. In one implementation, one might run all of these nodes on virtual machines in, e.g., Amazon data centers. Directory nodes can manage information and topology about all other nodes and help nodes find one another. Data nodes store data, and replication nodes manage pulling data from less-accessible or slower data nodes (e.g. tracker nodes) and push data to bigger machines with more reliable network connectivity.
• In normal operation, a solar node collects data and then locates the local data node by consulting the directory node. The directory node also knows about other directory nodes in the cloud, and can consult them to find data nodes running in the cloud to push data to as well. Most of the intelligence about where data should go is handled by the replication nodes in the cloud; the solar node mostly pushes data to whichever is the closest data node according to the directory node.
• In disconnected mode, the solar nodes function as before. However, the directory nodes cannot find the authoritative directory nodes, so they coordinate among themselves and elect a gantry to start a local replication node. This node manages local storage space for the island by prioritizing data so that the remaining storage is balanced between data nodes, and, in the event one needs to discard data, the lowest-priority data is discarded first. Once the network is functioning again, the replicator node coordinates reconnection to the rest of the network, and the queued-up data drains out to the data nodes in the cloud.
• Note that there may be situations where sometrackers200 in a group may be in a state of permanent disconnection while others in the same group have normal network access. In this case, the local replication node hangs around to replicate data from the partially-disconnected data nodes out to more available data nodes.
• The AEN can also provide other features, such as:
• • Aggregation and storage of information from all subscribing installations.
  • Provide API interface to data set for both in-house use and subscribers:
    • Micro environment weather data over a large geographic area, providing near real-time and historical information.
    • Near real-time and historical local grid conditions.
      • Voltage
      • Power factor
      • Outage information
    • Near real-time power and historical power generation from any subscribing SCAN installation.
    • Near real-time and historical power usage of local site consumption and energy use profiling.
    • Individual SCAN and aggregate solar generation forecasting.
    • DRM service requests.
    • Voltage regulation services.
    • Power factor correction services.
  • Operations & Management of SCAN installations.
    • SCAN ESIE rule sets generation and updating.
    • SCAN program updating.
    • Analyze long term drifts on power output of each PV panel.
      • Expert systems scheduling of service and maintenance.
      • Warranty monitoring for performance guarantees.
    • Power generation metrics for billing of PPA installations.
    • Large account corporate management of multiple sites
    • Utility planning and provisioning
• In some embodiments, the knowledge base on the network cloud handles logistics, the installation process, the sales process, and even the permitting process to ensure that there is maximum efficiency, reliability, and minimum cost from sales lead generation to maintenance schedules. This is all exposed to employees and dealers through a graphic interface (see below) that runs on a computer, laptop, tablet or smartphone. Each employee has a certain level of security clearance that allows them to see or not to see certain information on their particular account.
• The SCAN installation can be a complete turnkey solution that provides the site customer an efficient and reliable power source that is remotely managed and is maintained as an evolving intelligent smart grid power generation asset. The customer need not have any expertise in power production equipment, networking, or operations and maintenance. All functions are managed by onsite intelligence supported by integrated, offsite management.
• Smart grid capability built into both the onsite SCAN and remote AEN offer the utility the ability to see what is happening at the customer end of the grid network. These features alleviate the problems of blind solar deployments (residential rooftops) that offer no visibility to the grid operator. With the addition of a SCADA API, integration to the utility management software platforms is enabled. This offers real value to the utility by providing and integrated and reliable power source where power is consumed. By solving the PV issues articulated above, the PV penetration problem is minimized allowing solar energy to be an asset rather than a problem to the grid.
• Referring toFIG. 4C, an AEN controller center can be arranged to operate various aspects of a microgrid. Referring toFIG. 4D, a microgrid licensing/investing/purchasing scheme can include technology licensees, exchange investors, independent generators, independent dealers/franchisers, power purchase agreement providers (PPA), and a microGrid Energy Independence Exchange. As shown in this scheme, various network fees, service residuals, licensing fees, investments, and dividends can be generated.
• Referring toFIG. 3C, the following disclosure describes certain aspects of the learning process employed by node managers (e.g., via node nodal analytics and knowledge). In one embodiment, node managers can gather sensor data from onsite sensors with time stamps and serial numbers. Node managers can further gather third-party data based on the locations and time stamps of the sensor data. Some examples of possible third-party data may include, but are not limited to, one or more of:
• • a. Utility SCADA requests
  • b. Weather data
  • c. Real time energy pricing
  • d. Grid Battery storage data
• The node manager can also determine minimum sample size for 99% confidence using:
• i.
• $n={\left[\frac{Z_{a/2}\sigma }{E}\right]}^2$
• ii. n=sample size
• iii. Z_a/2=critical value=value of standard distribution at 97.5 percentile confidence level
• iv. E=margin of Error
  • 1. Simple approximation of margin of error at 99% confidence is:
• $E\approx \frac{1.29}{\sqrt{n}}=\frac{Z_{a/2}\sigma }{\sqrt{n}}$
• v. σ=standard deviation of x number of time stamped events
• $\sigma =\frac{1}{n}\sum _{i=1}^n{\left(x_i-\mu \right)}^2,\mathrm{where}\mu =\frac{1}{n}\sum _{i=1}^n\left(x_i\right)$
• In one embodiment, if minimal sample size is met for 2 or more data points, there can be a test for correlation:
• • a. If the absolute value of the correlation coefficient (determined by the dot product of two value vectors or by the Pearson product-moment correlation coefficient) is greater than 0.95 then a rule may be made automatically and sent to manual processing for quality assurance (since correlation does not always mean causation).
  • i. A theoretical example of this process is as follows:
    • 1. Sufficient sample size is met for correlation of 3 data points.
    • 2. Correlation coefficient of 0.96 between over voltage and irradiance levels over 980 W/m²when ambient temperature is below 84 degrees Fahrenheit.
    • 3. A New Rule is made for over voltage correction based on irradiance levels when temp is below 84 degrees.
    • 4. The software model would then test to find weather conditions that cause 84 degree temperatures and irradiance levels in excess of 980 W/m²to determine if over voltage may be forecasted.
  • ii. A theoretical counter example (local rule testing) is as follows:
    • 1. Sufficient sample size is met for correlation of 3 data points.
    • 2. Correlation coefficient of 0.74 between under voltage and battery bank discharge request for 500 kW+/−50 kW on Saturday between 1 and 3 pm PST in La Jolla, Calif.
    • 3. A New Rule is not made.
    • 4. Rule is sent for manual processing.
    • 5. Manual processing makes a new TEST rule that states:
      • a. Between 1 pm and 3 pm all units within a 20 mile radius of La Jolla, Calif. log voltage values and test against temperature range.
  • iii. A second theoretical counter example (the case of statistical insignificance) is as follows:
    • 1. Sufficient sample size is met for correlation of 2 data points.
    • 2. Correlation coefficient of 0.04 is determined between demand response requests from utility SCADA on Monday at 5:50 pm local time.
    • 3. Correlation is thrown out and determined statistically insignificant.
• The node manager can also employ manual intervention for low correlation coefficient (greater than 0.05, less than 0.95 correlation coefficient based on sufficient sample size):
• • a. Statistically significant correlations can be manually examined to further test the correlation.
  • b. Modifications in the rule set for both the network cloud and the distributed expert systems are used to reach a sufficiently high correlation coefficient.
    • i. For example: If the correlation coefficient is 0.05, a manual TEST rule can be implemented to broaden the scope of correlation in order raise or lower the coefficient.
      • 1. If the coefficient stays the same or goes below 0.05, the correlation is discarded.
      • 2. If the coefficient goes up but is still below 0.95 a new TEST rule can be implemented to broaden the scope of correlation.
• TEST rules are then used by the expert systems to find positive correlations, negative correlations, or statistical insignificance. The utility grid is affected by a statistically infinite number of stimuli from macro level weather patterns that cause droughts or floods to micro level human activities like sporting events. This makes manual processing important in order to zero in on new rules by expanding the scope of testing when necessary instead of trying to correlate the statistically infinite number of data points caused by grid stimuli. For example, if the grid is experiencing under voltage every weeknight between 5:00 pm and 6:00 pm with a correlation coefficient of 0.80, a TEST rule could be implemented to test this correlation vs. different temperature ranges. This is because under voltage may only occur at high or low temperatures due to air conditioning or heater use. The 65 to 80 degrees Fahrenheit temperature range may not experience under voltage, which may be causing the correlation coefficient to be 0.80. After the new TEST rule is implemented to check specific temperature ranges, the correlation coefficient of under voltage to time of day (5:00-6:00 pm) becomes 0.96. A new rule is made to charge batteries prior to 5:00 pm only when temperature ranges are lower than 65 or higher than 80 degrees Fahrenheit.
• TEST rules, in some embodiments, can be predictions, observations, metrics, or desired outcomes that can be tested against events that occur. Correlations can be made between TEST rules and mined data from sensors and third-party databases. Correlation coefficients of less than 0.05 are determined statistically insignificant and can be ignored.
• Rules can be made for:
• • a. How to make rules,
  • b. How to establish a value hierarchy of a set of rules,
  • c. When to log data,
  • d. What data to log,
  • e. How to log, analyze, and correlate data,
  • f. Taking action to solve problems on the grid, the network cloud management system, or the local sites,
  • g. When to buy electricity from the grid and when to sell power back to the grid,
  • h. When to charge batteries and when to discharge batteries,
  • i. When to pull off the grid when things become unstable (islanding),
  • j. How to respond to automated utility requests (i.e. demand response, microgrid, islanding, etc.),
  • k. When to respond to anomalies (based on standard deviation), and
  • l. Which predictions, observations, metrics, or desired outcomes should be cross correlated with mined data.
• FIGS. 5A-5E provide various example user interfaces that can be used with a node manager. Table 2, below, shows various examples of application interfaces for access with a node manager.

• TABLE 2
  Tracker control:
  automatic/manual mode toggle
  manual positioning
  device calibration
  device configuration - birth certificates, re-provisioning, latitude/longitude,
  tracking
  interval, various other things we discover to be useful, knobs
  maintenance techs
  device security management
  Access to current/historical tracker log data:
  panel orientation
  motor motion statistics (movement errors, exceptional conditions, etc.)
  motor amperage
  computed sun position
  (later) LERN information - how far the sun is from where we computed
  it to be, and
  other data collected to refine our solar positioning model
  power generation metrics and other power-related data
  Access to node/network event logs (for diagnostics):
  related/neighbor nodes
  local software configuration (which nodes are running where, node status)
  data pushed (which data, and to where)
  rules available for decision-making
  rules applied
  startup/shutdown/sleep events
  Real-time and historical smart grid data access:
  aggregate grid statistics: power generation, changes throughout the day,
  seasons
  local and aggregate grid performance statistics
  “post-mortem” data on how well our tracking and rules performed
  compared to the
  “ideal model”
  aggregate statistics by location, kind of installation, size of installation,
  or age of installation

• The following description describes certain aspects of Internodal Bidding. Each node provides one or more services and/or products that vary in price based on the supply and demand of those services or products. In order to set an accurate market price for transactions that can happen frequently (e.g., every millisecond), there must be an automated system.
• In some embodiments, node managers can be accessed through the nodal access interface where a minimum price can be set for the services or products offered by that particular node. Throughout each day, Internodal Bidding occurs autonomously for each service or product based on the minimum acceptable value (MAV) for that service or product. For example, this could occur when a third party requests a particular service or product or when the energy network requires a certain service or product to maintain reliability.
• In some instances, Internodal Bidding can be similar to the way Google allows users to set a Maximum Cost Per Click (CPC) on certain advertising. In the Google scenario, users can set parameters for many different outcomes (e.g. maximum exposure of advertisement, lower cost per click, a mixture of exposure and cost considerations, etc.) Those willing to pay the highest CPC have the best chance of winning many bids and ensuring the most exposure. Those trying to save money, run the risk of limited or no advertising exposure. Internodal Bidding can occur in a similar fashion, with the exception that nodes are set to provide a service or product for a MINIMUM acceptable value. Suppliers of nodes that are willing to accept a smaller value for their service or product will likely sell more product or service. For example, if a node has a limited supply, suppliers will likely make their money in times of peak demand. If a node has a large supply, suppliers may choose to bid very low in order to sell the most services or products.
• One of ordinary skill in the art will recognize that many parameters associated with cost and demand as well as micro- and macro-transactions can apply to the concept of Internodal Bidding. In particular examples, Internodal Bidding has several parameters that can be altered or adapted to allow suppliers associated with nodes to maximize their revenue. For example, certain MAV parameters may include setting different MAVs for different times of day, setting different MAVs for different times of year, establishing different MAVs for different services or products, establishing different MAVs for different levels of storage capacity, determining different MAVs for different weather patterns, and/or establishing different MAVs during certain predictable local events (e.g. sports events, festivals, etc.).
• In many embodiments, Internodal Bidding can allow for energy products and services to maintain a reliable service while keeping the cost of energy and reliability as low as possible (e.g., much like Google has done for the advertising industry). Furthermore, it also allows the largest energy plants as well as the smallest single energy producer to participate in the energy market.
Example
• Node150 contains 64 kW of solar panel capacity and 128 kWh of storage. The Node's MAV for stored kilowatt hours is 60 cents/kWh. This is a very high price per kWh considering that the local retail price of electricity is 18 cents/kWh.
• The local utility sends out a “spinning reserve” peak demand request for 60 kW of capacity for the next 2 hours. Their bid price is 65 cents/kWh because the utility's only other option is to cut service at that particular substation.
• In this example, Node150 rises to the top of the bidding with its 60 cents/kWh bid. Node150 wins the bid and supplies 120 kWh of capacity for 60 cents/kWh. Node150 would have received 8 cents per kWh if sold during off-peak times or $9.60. Node150 would have received 18 cents per kWh if sold at retail or $21.60
• Because of Automated Internodal Bidding, Node150 was able to receive $72.00 for just one battery charge cycle. Accordingly, Internodal Bidding allows renewable energy and storage devices to provide the same services supplied today by fossil fuel powered generators and power plants in just milliseconds rather than several minutes, hours, or even days. In particular instances of application, the larger the network of nodes becomes, the more reliable the products and services can become.
• FIG. 6 is flow diagram illustrating anInternodal Bidding scheme600 in accordance with an embodiment of the present technology. Thescheme600 can include setting a MAV through a Nodal Access Interface (block602). Thescheme600 can continue atdecision block604 where thescheme600 can determine if the request is manual. If the request is manual, thescheme600 can continue to block606 where it is determined if the request is within bidding parameters (e.g., established parameters, pre-set parameters, etc.). If the request is not manual, thescheme600 can determine if an automated service is necessary (decision block605). If the automated service is necessary, thescheme600 can then continue on to block606 to determine if the request is within bidding parameters. If an automated service is not needed, thescheme600 can return to block602 where a MAV is set through the Nodal Access Interface.
• If the request is within bidding parameters, thescheme600 can continue to decision block608 where it is determined if the bid is low enough. If the bid is low enough, thescheme600 can provide the product or service requested (block610). If the bid exceeds a threshold (e.g., is not low enough), thescheme600 can return to block602 to set a MAV through the Nodal Access Interface.
• FIG. 7 show various system logs than can be generated by a node manager during a single day of operation of a solar tracker in accordance with an embodiment of the present technology. Table 3, below, shows example setup parameters corresponding to the system logs ofFIG. 7.

• TABLE 3
  A day in the life of a distributed solar power plant deployed into the
  Southwest United States.

  Assumptions:
  Installed SCAN installations	3000
  Gantries installed	10,800 (average 3.6 gantries per SCAN)
  Installed Capacity	87.48 MWatt
  Yearly average capacity factor	31.25% (7.5 sun hours, yearly average)
  Total battery storage capacity	45,589 kWh
  Island enabled SCAN	758
  installations
  Voltage regulation of installed	55%
  capacity	15 MW (load shedding)
  Demand/Response capability

  Utility Dispatch Services

  SCADA Utility API

  Island demand requests

  Voltage control requests

  Storage discharge requests

  Load shedding requests

  Utility Data Services:

  SCAN SCADA Analytic API

  Solar forecasting data

  Local grid conditions data (voltage, power factor, outage)

  Micro climate data sets

• Embodiments of the subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus.
• A computer storage medium can be, or can be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially-generated propagated signal. The computer storage medium also can be, or can be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices). The operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.
• The term “programmed processor” encompasses all kinds of apparatus, devices, and machines for processing data, including, by way of example, a computer, a system on a chip (or multiple ones or combinations of the foregoing). The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus also can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures.
• A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
• The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. For example, tracker electronics, servers, mobile devices, etc., can be implemented as a controller in an auxiliary device. The processes and logic flows can also be performed by, and the apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA or an ASIC. For example, the node manager can be implemented as a controller in an auxiliary device.
• Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. In some embodiments, the processors can be selected according to the type of device.
• Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. For example, a server can transfer nodal information, nodal analytics, and/or knowledge can be transferred to flash memory. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
• To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., an LCD (liquid crystal display), LED (light emitting diode), or OLED (organic light emitting diode) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. In some implementations, a touch screen can be used to display information and to receive input from a user. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.
• Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).
• From the foregoing, it will be appreciated that specific embodiments have been described herein for purposes of illustration, but that various modifications may be made without deviating from the disclosed technology. The methods disclosed herein include and encompass, in addition to methods of making and using the disclosed devices and systems, methods of instructing others to make and use the disclosed devices and systems. For example, the operating instructions can instruct the user how to provide any of the operational aspects of the Figures discussed herein. In some embodiments, methods of instructing such use and manufacture may take the form of computer-readable-medium-based executable programs or processes. Moreover, aspects described in the context of particular embodiments may be combined or eliminated in other embodiments. Further, although advantages associated with certain embodiments have been described in the context of those embodiments, other embodiments may also exhibit such advantages, and not all embodiments need necessarily exhibit such advantages to fall within the scope of the presently disclosed technology.

Claims (21)

1-22. (canceled)

23. A method for operating a solar tracker having a tracker assembly, one or more motors coupled to the assembly, and tracker electronics operably coupled to the motors, the method comprising:

providing control signals to the motors to control an orientation of a photovoltaic array;

receiving status information form one or more sensors configured to measure at least one of ambient, operating, and performance conditions local to the tracker electronics; and

modifying the control signals based, at least in part, on the status information; and

communicating, via the tracker electronics, at least a portion of the status information over a cellular communication via a cellular link.

24. The method ofclaim 23 wherein the tracker electronics are configured to modify the control signals without intervention from a remote computer device.

25. The method ofclaim 23 wherein the status information is a first status information, and wherein the method further comprises:

locally storing the first status information;

receiving second status information form the sensors; and

comparing the second status information to at least the first status information;

further modifying the control signals based, at least in part, on the comparison of the second status information to the first status information.

26. The method ofclaim 25 wherein the tracker electronics:

compare the second status information to the first status information without intervention from a remote computer device; and

modify the control signals without intervention from the remote computer device.

27. The method ofclaim 23, further comprising:

communicating the status information to a remote computer device via the cellular link; and

modifying the control signals in response to at least one instruction from the remote computer device, wherein the instruction from the remote computer device is based, at least in part, on the status information.

28. The method ofclaim 23, further comprising deriving global positioning information and time stamp information from the cellular communication.

29. A method for operating a node manager device, comprising:

applying one or more rules that dictate, at least in part, operation of a controller device operably coupled to a node in a power distribution network;

acquiring information associated with at least one of ambient, operating, and performance conditions at the node;

deriving analytics based on the acquired information; and

modifying at least one of the rules based on the derived analytics.

30. The method ofclaim 29 wherein the acquired information includes a geographical location of the solar tracker and a time stamp.

31. The method ofclaim 29 wherein:

the controller device is operably coupled to a solar tracker at the node;

the solar tracker is configured to dynamically orient one or more photovoltaic arrays based on the modified rules.

32. The method ofclaim 31 wherein:

the rules identify an initial orientation of the solar tracker;

the modified rules correspond to a modification in orientation of the solar tracker; and

the method further comprises applying the modifying rules to further control the operation of the controller device.

33. The method ofclaim 32 wherein:

the node manager device is coupled between a solar tracker at the node and at least one power line in a power distribution grid; and

the acquired information includes information corresponding to one or more signal conditions at the power line.

34. The method ofclaim 33 wherein the acquired information further includes a time stamp corresponding to the signal conditions at a discrete time.

35. The method ofclaim 33 wherein the analytics correspond to whether the solar tracker is to disconnect from the power line.

36. The method ofclaim 29 wherein:

the node manager device is coupled between a solar tracker at the node and at least one energy storage component;

the acquired information includes information corresponding to an output power of the solar tracker; and

the modified rules correspond to at least one of—

whether a portion of the output power is to be diverted to charge the energy storage component, and

whether the energy storage component is to provide at least a portion of the output power.

37. A node manager device, comprising a processor and a memory storing instructions that are executable by the processor, wherein the instructions direct the processor to:

provide status information wirelessly over a cellular link to a remote computing device, wherein the status information is produced by nodal equipment coupled to a node in a power distribution grid, wherein the nodal equipment is configured to generate power and/or store power; and

provide control information over the cellular link to the node manager device that directs, at least in part, the operation of the nodal equipment.

38. The node manager device ofclaim 37 wherein:

the nodal equipment includes one or more solar panels operably coupled to a tracker assembly; and

the instructions further direct the processor to control an orientation of the solar panel arrays based, at least in part, on the control information.

39. The node manager device ofclaim 37 wherein the nodal equipment includes energy storage component, and wherein the instructions further direct the processor to store power from the distribution grid at the energy storage component based, at least in part, on the control information.

40. The node manager device ofclaim 37 wherein the instructions further direct the processor to isolate the nodal equipment from the distribution grid based, at least in part, on the control information.

41. The node manager device ofclaim 37 wherein the instructions further direct the processor to balance an electrical load at the node via the nodal equipment based, at least in part, on the control information.

42. The node manager device ofclaim 37 wherein the instructions further direct the processor to operate the node manager autonomously from the remote computing device.

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