San Mateo, CA, United States
San Mateo, CA, United States

SolarCity is an American provider of energy services, headquartered in San Mateo, California. Among its primary services, the company designs, finances and installs solar energy systems, performs energy efficiency audits and retrofits and builds charging stations for electric vehicles. The company had more than 2,500 employees as of December 2012.SolarCity has grown in recent years to meet the rapidly growing installation of solar photovoltaic systems in the United States. The overall U.S. market has grown from 440 MW of solar panels installed in 2009 to 3,300 megawatts in 2012, and it was expected to grow to 4,300 megawatts in 2013. Wikipedia.

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News Article | May 16, 2017
Site: www.greentechmedia.com

After a record-breaking year for the solar market, the total global installed base of operational PV systems surpassed 300 gigawatts. And the identity of the top asset owner may come as a surprise: Tesla. According to a new report from GTM Research and SoliChamba, the top 11 solar PV investors outside of China now each manage more than a gigawatt of operational net solar PV capacity as of the end of 2016, for a total of 15.8 gigawatts in aggregate. The report, Solar PV Asset Management 2017-2022, analyzes 198 investors globally, and finds that eight of the top 11 and 17 of the top 25 solar PV owners are U.S.-based firms. FIGURE: PV Investor Landscape (Global Top 35) Source: Solar PV Asset Management 2017-2022 The world’s top asset owner is SolarCity (now Tesla). The report describes this a “remarkable” feat because the company is focused on distributed generation. The majority of the list is made up of firms specializing in utility-scale PV. In fact, seven of the top 11 firms are utilities or independent power producers (IPPs). NextEra Energy Resources, an IPP, ranks second globally with more than 2.5 gigawatts of net PV capacity owned, more than half of which was added in 2016 alone. The company’s YieldCo, NextEra Energy Partners, owns another 200 megawatts of net PV capacity, which put NextEra slightly ahead of SolarCity in aggregate. In the third-party asset management space, only five providers manage gigawatt-scale portfolios. “Pure-play service provider Quintas Energy jumps to first place by growing its portfolio tenfold in 2016, almost entirely in the U.K.," the report finds. “Wise Energy and Vector Cuatro slide to second and third place, respectively, although both players experienced healthy growth in 2016.” The growing market of asset management software is also analyzed in the report, which identifies German firm 3megawatt as the clear market leader, with 8.4 gigawatts of operational solar PV assets under management. The report, available to GTM Research subscribers or for purchase, details: • 198 investors accounting for 55 gigawatts in net operational solar PV capacity • 46 third-party asset managers providing services to 17.6 gigawatts of operational PV plants • 10 software vendors supplying asset management automation solutions for 12.5 gigawatts of operational PV plants • 13 key markets around the world, across four continents, including market size, forecast, trends, prices and competitive landscape


News Article | January 24, 2016
Site: techcrunch.com

VCs have avoided solar deals ever since Solyndra became a four-letter word. But while their attention has strayed, the industry has been on a tear. In 2010, U.S. solar installers hit a milestone of 1 GW per year. Five years later, they’re installing more than 1 GW per month. This tremendous growth has fed a swelling herd of solar unicorns populated by the likes of SolarCity, SunEdison, SunPower and more. Recently, the industry has been buffeted by a variety of tailwinds that should drive even faster expansion. The landmark Paris climate accord promises stronger regulatory support across the world. Concurrently, a group of billionaires led by Bill Gates announced the Breakthrough Energy Coalition to fund this roll out. And the U.S. Congress has extended the solar Investment Tax Credit (ITC), which has raised installation forecasts through 2020 by more than 50 percent. Add to this mix innovation in large-scale battery manufacturing and the future of distributed power generation looks bright indeed. It’s also creating an opportunity to build the first SaaS unicorn focused on distributed generation. As this industry grows, so does the need for software to improve efficiency and lower costs. “Soft costs,” like permitting, financing and customer acquisition, now represent roughly two-thirds of installed costs of residential deployments. As I’ve written about before, the best way to address such soft costs is with software. As an example, the manner in which solar developers identify, track and quote potential customers today is decades behind other industries. The leading solar players today use a “spit and glue” combination of Salesforce, homegrown code and Excel. It’s not shocking, therefore, that SolarCity’s customer acquisition costs have actually increased year over year, while installation costs have plummeted. Other industries have solved this problem with software tailored to the specific needs of industry users. In the pharmaceutical space, Veeva Systems (an Emergence portfolio company) built a customer relationship management (CRM) solution focused exclusively on solving customer acquisition problems in pharma. This vertical-specific solution improved sales productivity by an average of 66 percent. A similar tool for solar could marry building data with customer demographic information to make developers substantially more effective at closing deals. Solar needs a Veeva-like tool to accelerate the path to grid parity (the point at which solar electricity costs the same as average grid electricity and growth skyrockets). The good news is that a lot of folks are working to build this type of industry cloud application. The Department of Energy ’s SunShot Catalyst program is funding a slew of early stage solar software companies. Powerhouse, a solar-focused accelerator, is incubating still more. And even the VCs have started to put money back into the sector, including Obvious Ventures’ recent $3.5 million funding of Sighten. This momentum has resulted in an exciting emerging ecosystem of SaaS providers focused on the distributed generation (DG) opportunity created by the rise of solar and energy storage. I’ve taken a stab at illustrating it below. I’m sure I’ve left folks off, and others will become miscategorized over time, so feel free to ping me with hate (or love?) email. This is a great start, but it’s still very early innings. Most of these companies are addressing important problems, but doing so as point solutions. To scale, they’ll need to expand beyond these entry points. Here’s my stab at a recipe for how to build the first solar/DG software unicorn. Focus on the top line: Start by solving a revenue problem (lead generation, CRM, proposal generation, etc.) versus a cost problem (error reduction, headcount reduction, etc.). It’s typically easier to get in the door with top-line-focused solutions, which is particularly important in industries that are relatively new to significant software spend. Sell by showing a clear return on investment (ROI) of increased sales relative to software spend. Build sticky, scalable software: Ensure you are used every day. Get to a point where an important employee group literally can’t do their job without your software. Further, it’s critically important to make sure this is true recurring software revenue and not professional services. I can’t emphasize this point enough. Do not become a software consulting firm doing custom builds. Write flexible software that customers can configure themselves. Layer the cake: Become a suite (expand products). Once you’re sticky in your core product and have become your customers’ most trusted technology provider, expand to solve other pain points. Veeva (the pharma-focused CRM I mentioned earlier) did this and built a nearly $4 billion company in the process. Expand to the incumbents: To capture the largest possible market size, it will be important to sell not just to today’s distributed generation players but to service the much larger utilities and independent power producers (IPPs) moving into the space — and which desperately need help selling to customers. Remember, these are the folks that have traditionally viewed their customers as “rate payers.” Expand modalities: Aim to be the software platform for each component of the distributed generation ecosystem (not just solar). Elon’s Gigafactory will only accelerate the coming of distributed storage, which will also require smart software to be sold and integrated effectively. To clear the mythical $1 billion valuation hurdle, today’s DG software players will need to expand their addressable market. The good news is that the overall solar industry is growing at a rapid clip, so the underlying trends are favorable. Getting a firm grasp on total potential market size is much more of an art than a science, but we can take the International Renewable Energy Agency’s (IRENA) global renewable energy employment forecasts as a basis for estimation. I’ve roughly assumed 15 percent of IRENA’s global employment forecast will be distributed generation professionals in roles like sales, finance and diligence. I’ve also assumed these professionals would buy SaaS software at $100/seat/month. This rough math gets us to an addressable market today of roughly $1 billion, scaling to $2.25 billion by 2020 and more than $3.5 billion by 2030. This would be a very exciting future for today’s incipient DG SaaS market. But I see a critical element missing from most of the current players, which will prevent this scale: enterprise SaaS talent. Most DG software executives today have tremendous experience in renewable energy, but they haven’t built and scaled large subscription-based software companies. Getting this talent in the door and pairing it with the solar pros is the only way I see companies scaling the mountain. Unfortunately, there’s not a lot of cross-pollination going on today. Solar folks tend to hang with solar folks and SaaS folks with SaaS folks, like they’re on different planets. We have to find a way to bridge these worlds to birth a unicorn. Thus, I’ll leave you with a challenge. If you’re on the solar side of things, open up LinkedIn and find your buddy or your buddy’s buddy who works at Salesforce, Box, etc. and offer to buy them a beer. If you’re on the SaaS side of things and interested in applying those skills to solving the biggest existential crisis of our time, drop me a note and I’ll be happy to connect you. Speeding the transition toward clean energy is our best bet at averting a catastrophic temperature increase. With a little interplanetary collaboration, we can build the software necessary to do it.


Embodiments may include a method of curtailing an output level of an EG system. The method may include receiving, at a processor, a first dynamic control signal. The first dynamic control signal may include an instruction to adjust an output level of an EG system to a first output level. The method may also include maintaining the output level of the EG system at the first output level for a predetermined period. The method may further include determining, by the processor, whether a second dynamic control signal is received during the predetermined period. If a second dynamic control signal is not received during the predetermined period, the method may include ramping down the output level at a predetermined rate after the predetermined period until a predetermined failsafe output level is achieved. The predetermined failsafe output level may be maintained until a third dynamic control signal is received by the processor.

Claims which contain your search:

1. A method comprising: receiving, at a processor, a first dynamic control signal comprising an instruction to adjust an energy generation output level of an energy generation (EG) system to a first energy generation output level; maintaining the energy generation output level of the EG system at the first energy generation output level for a predetermined period; determining, by the processor, whether a second dynamic control signal is received during the predetermined period; and if a second dynamic control signal is not received during the predetermined period: (i) ramping down the energy generation output level at a predetermined rate after the predetermined period until a predetermined failsafe output level is achieved, and (ii) maintaining the predetermined failsafe output level until a third dynamic control signal is received by the processor.

4. The method of claim 1, wherein the EG system comprises a photovoltaic inverter and ramping down the energy generation output level comprises changing an electrical characteristic of the photovoltaic inverter.

5. The method of claim 1, wherein the EG system comprises a plurality of photovoltaic inverters and ramping down the energy generation output level comprises changing an electrical characteristic of a photovoltaic inverter of the plurality of photovoltaic inverters.

6. The method of claim 1, wherein the first energy generation output level enables the EG system to match a corresponding load requirement during an overgeneration condition, and the overgeneration condition occurs when the EG system generates more power than the corresponding load requirement.

7. The method of claim 1, wherein the first energy generation output level enables the EG system to match the sum of a corresponding load requirement and an additional margin during an overgeneration condition, and the overgeneration condition occurs when the EG system generates more power than the sum of the corresponding load requirement and the additional margin.

10. The method of claim 1, wherein the predetermined rate varies based on the energy generation output level of the EG system.

11. The method of claim 1, wherein ramping down the energy generation output level to the predetermined failsafe output level takes between 10 seconds and 10 minutes.

12. The method of claim 1, further comprising: receiving, at the processor, a third dynamic control signal comprising an instruction to adjust the energy generation output level of the EG system to a second energy generation output level; and adjusting the energy generation output level of the EG system to the second energy generation output level.

13. The method of claim 1, wherein prior to receiving, at the processor, the first dynamic control signal, the energy generation output level of the EG system is at a second energy generation output level not equal to the first energy generation output level.

14. The method of claim 1, wherein the EG system is one EG system of a plurality of EG systems, and the method further comprises: maintaining the total energy generation output level of the plurality of EG systems at a first total energy generation output level for the predetermined period; if the second dynamic control signal is not received during the predetermined period: (i) ramping down the total energy generation output level at the predetermined rate after the predetermined period until a total predetermined failsafe output level for the plurality of EG systems is achieved, and (ii) maintaining the total predetermined failsafe output level until the third dynamic control signal is received by the processor.

15. A method of responding to a loss of communication for an energy generation (EG) system connected to an energy grid as part of a distributed generation system, the EG system method comprising: receiving, at a processor coupled to the EG system, dynamic control messages at a predetermined interval while the EG system is in normal communication with a control network; setting an energy generation output of the EG system to a first level based on a first dynamic control message; thereafter, receiving, at the processor, a second dynamic control message comprising an instruction to adjust an energy generation output level of the EG system to a second level; maintaining the energy generation output level of the EG system at the second level; and if a third dynamic control message is not received within the predetermined interval after the second dynamic control message:reducing the energy generation output level to a predetermined failsafe output level, andmaintaining the predetermined failsafe output level until communication with the control network is reestablished and a fourth dynamic control message is received by the processor to change the energy generation output level.

17. The method of claim 15, wherein reducing the energy generation output level to a predetermined failsafe output level comprises ramping down the energy generation output level at a predetermined rate until the predetermined failsafe output level is achieved.

19. A distributed generation management system comprising: a gateway; a control server; and an energy generation (EG) system configured to receive an instruction from the control server through the gateway to adjust an energy generation output level of the EG system and if the instruction is not received by the EG system in a predetermined period, the EG system is configured to ramp the energy generation output level at a ramp rate to a predetermined failsafe output level.

20. The distributed generation management system of claim 19, wherein the EG system comprises a memory storage device, and the ramp rate and the predetermined failsafe output level are stored on the memory storage device.


Techniques are disclosed for implementing a scalable hierarchical energy distribution grid utilizing homogeneous control logic are disclosed that provide distributed, autonomous control of a multitude of sites in an energy system using abstraction and aggregation techniques. A hierarchical energy distribution grid utilizing homogeneous control logic is provided that includes multiple control modules arranged in a hierarchy. Each control module can implement a same energy optimization scheme logic to directly control site energy resources and possibly energy resources of sites associated with control modules existing below it in the hierarchy. Each control module can act autonomously through use a similar set of input values to the common optimization scheme logic.

Claims which contain your search:

1. A distributed electrical grid system, comprising: a plurality of control modules arranged in a hierarchy and associated with a corresponding plurality of sites, wherein each control module of the plurality of control modules implements a same optimization scheme logic using a set of locally-determined input values to control one or more devices at the site to attempt to achieve an energy goal, the plurality of control modules including at least:a first control module configured as a child in the hierarchy, the first control module being associated with a first site and configured to determine an energy generation value of the site, an energy storage value of the site, and a load value of the site to be used as the set of input values for the optimization scheme logic; anda second control module configured as a parent in the hierarchy to a set of child control modules including the first control module, the second control module configured to determine an aggregate energy generation value of a set of sites associated with the set of child control modules, an aggregate energy storage value of the set of sites, and an aggregate load value of the set of sites to be used as the set of input values for the optimization scheme logic.

2. The distributed electrical grid system of claim 1, wherein the energy goal comprises minimizing deviations of a net flow of energy between the site and an external energy system.

3. The distributed electrical grid system of claim 1, wherein the energy goal comprises minimizing a net flow of energy between the site and an external energy system and deviations of the net flow of energy between the site and the external energy system.

4. The distributed electrical grid system of claim 1, wherein: the first control module is further configured to determine a plurality of energy cost values associated with exchanging energy with an external energy source at a corresponding plurality of times; the plurality of energy cost values are used in the set of input values of the optimization scheme logic; and the energy goal comprises minimizing a cost of energy provided to the site from the external energy source over a period of time.

5. The distributed electrical grid system of claim 1, wherein the second control module is associated with a second site including at least one of an energy generator device and an energy storage device, and wherein the second control module is further configured to: determine that a set of sites associated with the set of child control modules are utilizing, or will utilize, an increase in a net flow of energy into the set of sites; and in response to the determination, provide energy from the energy generator device or energy storage device of the second site to at least some of the set of sites.

6. The distributed electrical grid system of claim 1, wherein the one or more devices of the first site comprise a photo-voltaic (PV) device and an energy storage device.

7. The distributed electrical grid system of claim 1, wherein the first control module comprises a site gateway device communicatively coupled with the one or more devices of the first site using wireless communications.

8. A distributed electrical grid system, comprising: a plurality of control modules arranged in a hierarchy and associated with a corresponding plurality of sites, the plurality of control modules implementing a same optimization scheme logic to control one or more devices at the respective site to attempt to achieve an energy goal, the plurality of control modules including at least:a first control module configured as a child of a second control module in the hierarchy, the first control module being associated with a first site and configured to:the second control module configured as a parent to a set of child control modules including the first control module, the second control module being configured to:

9. The distributed grid system of claim 8, wherein each of the set of desired power curves indicates a desired net flow of energy into the corresponding site over time.

10. The distributed grid system of claim 8, wherein the set of desired power curves are determined such that, in aggregate, the set of desired power curves minimize deviations of a net flow of energy between the corresponding set of sites and an external energy system.

11. The distributed grid system of claim 8, wherein the set of desired power curves are determined so that, in aggregate, the set of desired power curves minimize a net flow of energy between the corresponding set of sites and an external energy system and deviations of the net flow of energy between the corresponding set of sites and the external energy system.

12. The distributed grid system of claim 8, wherein the first control module is further configured to transmit, to the second control module, a set of energy capabilities of the first site.

13. The distributed grid system of claim 12, wherein the set of energy capabilities of the first site includes an aggregate amount of energy storage capacity provided by one or more of the one or more devices at the first site.

14. The distributed grid system of claim 8, wherein first control module is further configured to: determine, for the first site, a projected energy utilization indicating a projected net flow of energy into the site; and transmit the projected energy utilization to the second control module.

15. A method, comprising: executing, by a control module configured as a parent to a plurality of child control modules of a plurality of control modules in a hierarchy, an optimization scheme logic to control one or more devices at a site associated with the control module to attempt to achieve an energy goal, wherein each of the plurality of control modules executes the same optimization scheme logic, the executing comprising:determining, by the control module, an aggregate energy generation value of a plurality of sites associated with the plurality of child control modules;determining, by the control module, an aggregate energy storage value indicating an amount of stored energy at the plurality of sites;determining, by the control module, an aggregate load value indicating an amount of energy utilized at the plurality of sites; anddetermining, by the control module based upon the aggregate energy generation value, the aggregate energy storage value, and the aggregate load value, whether to cause the one or more devices to provide energy to one or more of the plurality of sites.

16. The method of claim 15, wherein the determining whether to provide the energy comprises: determining, by the control module, that a net flow of energy between the plurality of sites and an external energy system exceeds a threshold amount.

17. The method of claim 16, wherein: the determining whether to provide the energy further comprises determining an energy need amount based upon a difference between the net flow of energy and the threshold amount; and the executing further comprises providing the threshold amount of energy from the one or more devices of the site to the one or more of the plurality of sites.

18. A method, comprising: receiving, at a control module configured as a child of a parent control module and as a parent of one or more child control modules, a desired load curve from the parent control module, wherein the control module, the parent control module, and the one or more child control modules are members of a plurality of control modules of a hierarchy, the plurality of control modules each executing a same optimization scheme logic; receiving, at the control module, a capability report message from each of the one or more child control modules indicating energy capabilities of one or more sites associated with the one or more child control modules and any descendant control modules thereof; determining, by the control module using the optimization scheme logic utilizing a set of input values comprising the desired load curve and the indicated energy capabilities, one or more desired child load curves; and transmitting, by the control module to the one or more child control modules, the respective desired child load curves.

21. A distributed electrical grid system comprising: a plurality of interconnected nodes, wherein each of the plurality of interconnected nodes includes:an aggregate net load profile including all local loads and immediate child loads;an aggregate energy generation (EG) resource including all local EG resources and immediate child EG resources; andan aggregate storage capacity including a local storage capacity and immediate child storage capacities, wherein each of the plurality of interconnected nodes is configured to normalize its aggregate net load profile by using its aggregate EG resource and aggregate storage capacity.


Patent
SolarCity | Date: 2015-11-13

We describe a modular adjustable power factor renewable energy inverter system. The system comprises a plurality of inverter modules having a switched capacitor across its ac power output, a power measurement system coupled to a communication interface, and a power factor controller to control switching of the capacitor. A system controller receives power data from each inverter module, sums the net level of ac power from each inverter, determines a number of said capacitors to switch based on the sum, and sends control data to an appropriate number of the inverter modules to switch the determined number of capacitors into/out of said parallel connection across their respective ac power outputs.

Claims which contain your search:

21. A distributed energy generation inverter system for applying volt-ampere reactive (VAR) control to an alternating current (AC) output, the distributed energy generation inverter system comprising: a plurality of individual inverters, each individual inverter comprising a direct current (DC) power input for receiving DC power from a DC source, an AC power output for supplying AC power to a common AC circuit, and a reactive element switchable to be connected in parallel to the AC output; and a controller communicatively coupled to each of the plurality of inverters, the controller configured to monitor the individual power output of each inverter, determine a fraction of the maximum total potential power being supplied by the plurality of inverters, and when the fraction is above a pre-determined threshold, selectively controlling one or more of the individual inverters to switch their respective reactive elements into parallel connection across the respective AC outputs.

22. The distributed energy generation inverter system of claim 21, wherein the controller employs phase control to selectively control the one or more of the individual inverters to switch the respective reactive elements in parallel for a portion of an AC cycle in which the reactive element is switched in.

23. The distributed energy generation inverter system of claim 21, wherein the common AC circuit is a three phase AC power feed, and wherein an inverter of the plurality of inverters is for coupling to each phase of the three phase AC power feed, the controller configured to control a power factor of each phase of the three phase AC power feed.

24. The distributed energy generation inverter system of claim 21, wherein the reactive element includes a capacitor.

25. The distributed energy generation inverter system of claim 21, wherein the controller is communicatively coupled to each of the plurality of inverters by way of a wireless communication channel.

26. The distributed energy generation inverter system of claim 21, wherein the DC power source is a solar photovoltaic panel.

27. The distributed energy generation inverter system of claim 21, wherein the pre-determined threshold is greater than or equal to 0.5.

28. The distributed energy generation inverter system of claim 21, wherein the controller is configured to access a lookup table that stores power factor compensation data defining a number of reactive elements to switch in parallel to the fraction of the maximum total potential power being supplied by the plurality of inverters.

29. The distributed energy generation inverter system of claim 21, wherein monitoring the individual power output of each inverter includes sensing a current provided by each of the inverters to the common AC circuit.

30. The distributed energy generation inverter system of claim 21, wherein each of the individual inverters includes a switching device coupled to switch their respective reactive element, and wherein the controller is configured to switch the switching device at a peak voltage point of the AC power.

31. The distributed energy generation inverter system of claim 29, wherein each of the switching devices includes a triac.

32. The distributed energy generation inverter system of claim 21, wherein the controller includes a field-programmable gate array (FPGA).

33. The distributed energy generation inverter system of claim 21, wherein each of the individual inverters includes a two-stage power converter, wherein a first stage of the two stage power converter is electrically isolated from the second stage of the two stage power converter.

34. The distributed energy generation inverter system of claim 33, wherein the first stage is to be coupled to the DC power source, and wherein the first stage include a switching DC-to-AC converter.

35. The distributed energy generation inverter system of claim 21, wherein each of the plurality of individual inverters includes a second reactive element switchable to be connected in parallel to the AC output, and when the fraction is above a pre-determined threshold, the controller is further configured to selectively control one or more of the individual inverters to switch their respective second reactive elements into parallel connection across the respective AC outputs.

36. An controller for a distributed energy generation system for applying volt-ampere reactive (VAR) control to an alternating current (AC) output, the controller comprising: a communication interface for communicating with a plurality of inverters; processing logic coupled to the communication interface; and a computer-readable medium accessible to the processing logic, the computer-readable medium storing instructions, when executed by the processing logic will cause the controller to perform a method comprising: monitoring, via the communication interface, the individual power output of each of the plurality of inverters; determining a fraction of the maximum total potential power being supplied to a common AC power grid by the plurality of inverters; selectively switching, via the communication interface, reactive elements in one or more of the plurality of inverters when the fraction is above a pre-determined threshold, wherein selectively switching the reactive elements puts the reactive elements in parallel connection with the AC power grid to apply volt-ampere reactive (VAR) control to an AC output to the AC power grid.


Patent
SolarCity | Date: 2015-12-04

Techniques for controlling a distributed generation management system may be provided. Real-time power generation information may be collected from sensors of energy generation systems that make up a grid of controlled systems. An aggregate real-time power generation requirement may be determined for the grid based on the real-time power generation information. Using the aggregate requirements, a power profile may be calculated for the grid that indicates a level of power generation for the grid. In some examples, a control signal to control power generation may be generated and provided to the controlled systems.

Claims which contain your search:

1. A method for controlling, by a server, a plurality of energy generation systems, comprising: detecting the plurality of energy generation systems over a wireless network, the plurality of energy generation systems forming a grid network; requesting sensor data from the plurality of energy generation systems of the grid network, the sensor data including real-time power generation data; receiving the sensor data from at least a subset of the plurality of energy generation systems; determining real-time power generation requirements for at least the subset of the plurality of energy generation systems based at least in part on the sensor data; determining an aggregate real-time power generation requirement for the grid network based at least in part on the real-time power generation requirements for at least the subset of the plurality of energy generation systems; calculating a power profile for the grid network based at least in part on the aggregate real-time power generation requirement; generating a control signal to control power generation for one or more of the plurality of energy generation systems of the grid network, the control signal corresponding to the power profile; and transmitting the control signal to the one or more energy generation systems.

2. The method of claim 1, wherein the power profile is calculated to reconcile the aggregate real-time power generation requirement for the grid network.

4. The method of claim 1, wherein the control signal is configured to instruct a power generation asset of the one or more energy generation systems.

5. The method of claim 1, wherein the sensor data includes asset data that identifies one or more assets of the grid network, wherein the one or more assets of the grid network are configured to manage real-time power generation of at least one of the plurality of energy generation systems, and wherein the control signal is configured to control the one or more assets of the grid network.

6. A system, comprising a server computer with a processor configured to execute computer-executable instructions, stored in a memory of the server computer, to: identify a plurality of energy generation systems over a network, the plurality of energy generation systems forming a grid network; receive power data from each sensor of at least a subset of the plurality of energy generation systems of the grid network; determine an aggregate real-time power requirement for the grid network based at least in part on the power data; calculate a power profile for the grid network based at least in part on the aggregate real-time power generation requirement; generate a control signal to control power generation for one or more of the plurality of energy generation systems of the grid network, the control signal corresponding to the power profile; and transmit, over the network, the control signal to the one or more of the plurality of energy generation systems.

7. The system of claim 6, wherein the processor is further configured to execute the computer-executable instructions to request the power data from each sensor at least in response to identifying the plurality of energy generation systems.

9. The system of claim 6, wherein the power data received from the sensor comprises real-time power generation data.

10. The system of claim 9, wherein the processor is further configured to execute the computer-executable instructions to determine real-time power generation requirements for at least the subset of the plurality of energy generation systems based at least in part on real-time power generation data.

11. The system of claim 6, wherein the processor is further configured to execute the computer-executable instructions to: request asset data from the plurality of energy generation systems, wherein the asset data identifies one or more assets in the plurality of energy generation systems; and receive the asset data from the plurality of energy generation systems, wherein the control signal is configured to control the one or more assets in the plurality of energy generation systems.

12. The system of claim 11, wherein the one or more assets are configured to control real-time power generation of the plurality of energy generation systems.

14. The method of claim 6, wherein the sensor data is received from a gateway device that collects the sensor data from at least the subset of the plurality of energy generation systems.

16. The method of claim 14, wherein the gateway device is configured to receive the sensor data from at least one of a meter coupled to an asset of one energy generation system of the subset of the plurality of energy generation systems, the one energy generation system of the subset of the plurality of energy generation systems, a set of energy generation systems of the subset of the plurality of energy generation systems, or a secondary gateway.

17. A computer-readable storage medium configured to store computer-readable instructions that, when executed by one or more processors, configure the one or more processors to perform operations comprising: identifying a plurality of energy generation systems over a network, the plurality of energy generation systems forming a grid network; receiving sensor data from at least a subset of the plurality of energy generation systems, the sensor data including real-time power generation data; determining real-time power generation requirements for at least the subset of the plurality of energy generation systems based at least in part on the sensor data; determining an aggregate real-time power generation requirement for the grid network based at least in part on the real-time power generation requirements for at least the subset of the plurality of energy generation systems; calculating a power profile for the grid network based at least in part on the aggregate real-time power generation requirement; providing, to the one or more energy generation systems, a control signal to control power generation for one or more of the plurality of energy generation systems of the grid network based at least in part on the power profile.

18. The computer-readable storage medium of claim 17, wherein the power profile is calculated to ensure maintenance of the aggregated real-time power generation requirement for the grid network.

20. The computer-readable storage medium of claim 17, wherein the sensor data includes asset data that identifies one or more assets of the grid network, wherein the one or more assets of the grid network are configured to manage real-time power generation of at least one of the plurality of energy generation systems, and wherein the control signal is configured to control the one or more assets of the grid network.


News Article | February 3, 2016
Site: www.greentechmedia.com

Marco Mangelsdorf is president of Hawaiian PV installer ProVision Solar and a director of the Hawaii Island Energy Cooperative. He spoke at a recent EUCI event on getting Hawaii to 100 percent renewables and other topics. "Oahu is the biggest market in the state, and it's very interesting to see the ups and downs on what's been known as the 'solar coaster.' We've hit our peak already, as you can see, in 2012, went down in 2013, went down again in 2014 and went back up last year by approximately 15 percent, so there's been one heck of ride for people in my business." Maps of the island grid from HECO show "darker colors are representative of those circuits which have the highest penetration of rooftop solar," said Mangelsdorf, adding that Hawaii is "reaching levels of PV penetration that are unprecedented, and unprecedented for any other part of the United States." But it's not the island of Oahu that is the harbinger of the solar industry to come, according to this installer. "Molokai may be the future for us and the rest of the state," said Mangelsdorf, "because Molokai has reached a level of distributed generation [and] DER penetration where for all intents and purposes, Maui Electric, since the beginning of last year...has allowed very, very few new distributed-generation PV systems to be approved." He continued, "My concern is...how much longer do we have in the rest of the state...until we reach levels seen on Molokai in terms of the door coming down and [being] closed to more distributed generation allowed to be exporting power to the grid?" The veteran installer explained the new tariff system, in which the Customer Grid Supply (CGS) option credits surplus power provided to the utility at "something roughly half of [the] retail" rate. "What effect is it having on people like me and businesses like mine?" asked Mangelsdorf. "It's not having any effect quite yet. Why? Because PV contractors did a fantastic job last year in scaring the bejesus out of a lot of homeowners after Hawaiian Electric [proposed the new tariff]. They literally went door-to-door to sign people up, trying to sign up homeowners who had not done NEM -- 'No money down, you don't pay anything, just sign the form, let us get you in the program.'" He noted that the new tariff increases simple payback for grid-connected PV systems by 50 percent to 100 percent. He said, "Depending on one's consumption, it can push it out to maybe six to nine years, so that's clearly not in the best interest of the homeowner." "There's also the question of how long the caps are going to last. The caps under CGS are 25 megawatts for [Oahu], 5 megawatts for the Big Island, 5 megawatts on the big islands of Maui," he said, adding that "the big question" is what happens after the caps have been reached. Mangelsdorf also suggested that the three of the biggest installers on the islands -- Sunrun, SolarCity and Vivint -- have seen their value propositions "extremely diminished" under the CGS and CSS interconnect agreements. He said, "These three companies don't have much, if anything, in terms of a product offering today to replace the killing they were making under the NEM program." "There's so much buzz about [energy storage]. It will be wonderful and glorious when it does happen, but when that's going to happen, no one really knows. As far as I can tell, looking at the data, it's not happening now...and it's not going to happen in the near term." He challenged the audience: "Anybody want to take a guess at how many customer self-supply applications HECO, MECO and HELCO have received as of today? 500? 250? 100?" According to Mangelsdorf, the total is one. "That's one customer self-supply application for all three companies." "That to me does not indicate a gold rush or a rush of any kind for homeowners going for battery-based systems," he said. According to a company spokesperson, SolarCity hasn’t begun offering the self-supply products yet, but will begin this spring. Ravi Manghani, GTM's senior storage analyst, confirms that a payback period of five to seven years is about right for the grid supply tariff. He notes, "Self-supply using solar-plus-storage only marginally improves economics for certain segment of residential customers that have large evening loads. Adding storage can add another level of complexity that very few customers will be comfortable with, so it's part of natural adoption behavior." He added, "The self-supply tariff went into place only in October. It will take a few months for vendors and installers to set up the necessary infrastructure to start actively deploying." "The gospel according to Marco is that utility companies will always, always, always be criticized for not doing more when it comes to renewable energy. Unless and until they were to open up the grid to everybody with little or no restrictions, and that simply can never happen, because the utility is a public good that must be maintained properly for the benefit of everybody who [relies on] the utility company. Just like you can't have everybody [who wants to] go on a freeway with their bicycle, their skateboard, their horse and buggy, on horseback -- that is simply not practical," he said. As for the threat of grid defection, he said, "Defecting from the grid is a hell of lot more hype than it is reality. I've lived off-grid, and it's a vastly different lifestyle. I'm absolutely convinced that there is only a...[very] small minority of people who would be willing to put the energy and effort into living off-grid. Grid defection -- it's not going to happen in the near term" Mangelsdorf remains rooted in reality. "I'm a lot more concerned as a business owner about near-term revenue, about paying my bills, paying my employees in the months and the years to come than about whether the state is reaching 100 percent renewables...29 years from now." "In the near term, I think there's going to be a substantial drop-off in the number of new system sales this year based on a number of factors, not least of which is that we're now in the post-NEM world. I think...a number of these finance companies will all be leaving our shores...in the weeks and months to come," Mangelsdorf said. He added, "SolarCity lost several hundred million dollars in one quarter last year. I can't afford to lose $300 million in a quarter. I can't afford to lose $300,000 in a year, so I'm competing against companies that can afford to lose hundreds of millions of dollars a quarter. On the flip side of that, how long can a business model last where these companies are losing such sums of money?" He stressed that "battery storage on either side of the meter will not make much of a difference in the near term," adding, "the reduced post-NEM value proposition will cause a significant drop in new system sales in 2016 compared to the 2015 uptick." He concluded, "You can't just talk the talk about renewable energy being a great and wonderful thing. It has to be good for everybody, it has to be cost-effective -- and even though my business is suffering from a drop in sales, it's still the right thing to do."


News Article | September 19, 2016
Site: cleantechnica.com

Originally published on Nexus Media. By Courtney St. John and Steve Hargreaves Nobody said the third industrial revolution would be easy. As is often the case with systemic change, it’s hard to see radical progress when you’re in the thick of a transition — and the energy transition is no different. Some players recognize the trend towards clean energy and are getting on board. Others are resisting progress at every step. Just this week, an Inside Climate News report revealed that a northeastern utility organization invited a climate denier to speak at their annual conference. The utility industry no doubt feels threatened by a shift to distributed energy, which could render their monopolies obsolete. But even that was a pretty desperate move. When a trade association for one of the nation’s largest industries continues to spread misinformation about climate change, and prominent political leaders continue to ignore established climate science, the energy transition begins to feel like a series of fits and starts that may never get us to the emission reductions needed. But it’s all about the long-term trends. And those trends point to a fundamental shift happening in the way energy is produced. This week, the International Energy Agency announced that renewable energy generation attracted more than 2.5 times more expenditures than fossil fuels in 2015. An analysis of coal plants in Texas found that cheaper wind and solar on the grid is contributing to a market transformation where coal is no longer cost competitive. Total energy generated by coal plants in most of the state declined from about 40 percent in 2010 to about 25 percent in the first half of 2016. Meanwhile, costs for wind energy will continue to drop over the next few decades as taller turbines and wider rotors produce more energy per turbine. The shift to a clean energy economy requires policymakers, utilities and businesses to work through complex decisions about how energy should be generated and sold. While some utilities are behaving badly, others are coming to the table to encourage fair policy on distributed generation. On Tuesday, power utility NV Energy and solar manufacturer SolarCity reached a deal on net metering that will maintain higher reimbursement rates for the power produced by Nevada’s current 32,000 rooftop solar customers. More than anything, the energy revolution presents no shortage of opportunities for anyone who wants to be involved. The benefits — whether they are cost savings, cleaner air, safer cars or more energy choices — are undeniable. We need the expertise of policymakers, businesses, engineers, artists and yes, even utilities, to navigate our way through this transition. It couldn’t be a more exciting time to jump on board — we need all hands on deck. Buy a cool T-shirt or mug in the CleanTechnica store!   Keep up to date with all the hottest cleantech news by subscribing to our (free) cleantech daily newsletter or weekly newsletter, or keep an eye on sector-specific news by getting our (also free) solar energy newsletter, electric vehicle newsletter, or wind energy newsletter.


News Article | February 15, 2017
Site: www.greentechmedia.com

California Gov. Jerry Brown gave an impassioned speech on Tuesday, rejecting many of the conservative policies championed by the Trump administration and Republicans in Washington, D.C. “California is not turning back. Not now, not ever,” said Brown during his state-of-the-state address. He focused his speech on fighting for immigrant rights, protecting healthcare and continuing to act on climate change. “Our state is known the world over for the actions we have taken to encourage renewable energy and combat climate change,” said Brown. “We cannot fall back and give in to the climate deniers,” he said. “The science is clear. The danger is real.” Many in the environmental community and cleantech industry will be looking to California, which represents the sixth-largest economy in the world, to continue leading the clean energy transition during Trump’s presidency. The new administration has already pledged to undo federal regulations on climate change and proposed budget cuts for the Environmental Protection Agency and the Department of Energy . Trump also took executive action on Tuesday to advance the approval of the Keystone XL and Dakota Access pipelines. Brown’s state-of-the-state address isn’t the first time the governor has spoken out against Trump. In December, he told scientists attending the American Geophysical Union conference that he would protect University of California science labs, and if Trump plans to turn off the NASA satellites monitoring Earth’s climate, California would “launch its own damn satellite.” California has already taken major steps to act on climate, including the approval of a 50 percent renewable energy target. The only flaw with that target is that it wasn’t ambitious enough, California Senate leader Kevin de León recently told The Los Angeles Times. California is already moving toward a clean energy future faster than expected and should “explore the idea” of a 100 percent renewable energy target, he said. In addition to California’s renewable energy portfolio standard, Governor Brown has set a goal of installing 12,000 megawatts of distributed generation in the state, defined as projects under 20 megawatts. As of October 31, 2016, nearly 9,400 megawatts of distributed generation capacity was operating or installed in California, with an additional 900 megawatts pending. That total includes almost 5,100 megawatts of solar self-generation capacity, which far exceeds the state’s goal of installing 3,000 megawatts of solar energy residential and commercial sites by 2017, according to a recent report by the California Energy Commission. California’s programs to support renewable distributed generation could add another 1,800 megawatts if fully subscribed, the report states. Distributed energy resources have become an important element of California’s fight against climate change -- as well as an important part of the state's economy. To facilitate growth in the sector, state lawmakers and regulators have introduced myriad programs and policies aimed at the integration of distributed energy resources. At the same time, the private sector has come up with new technologies and business models to better manage distributed energy resources on the grid. On March 8-9 in San Francisco, Greentech Media is hosting a conference on the future of electricity in California -- one of America’s most innovative states. California’s Distributed Energy Future (CDEF) will kick off on March 8 with a pre-conference workshop in collaboration with More Than Smart. The workshop will give attendees the opportunity to review the stakeholders in California’s clean energy sector, catch up on the latest policy developments, and discuss distributed energy terms and concepts in an interactive half-day session. On March 9, GTM will host a full day of panel sessions, taking a deep dive on topics such as rate design, community-choice aggregation, electric vehicle infrastructure, and distributed energy financing. The conference will feature insights from GTM Research and industry experts, including the California Public Utilities Commission, Southern California Edison, SolarCity, Siemens and many more. California can act on clean energy and climate change on its own, and in partnership with like-minded states, said Gov. Brown in his address. “Make no mistake -- we’re going to do exactly that,” he said. Register to attend CDEF here to be a part of the conversation on the future of distributed energy resources in the Golden State.


News Article | January 5, 2016
Site: www.reuters.com

A wind turbine installed by United Wind is shown in a field in Millerton, New York in this March 2013 publicity photo released to Reuters on January 4, 2016. A wind turbine installed by United Wind is shown at the Double A Vineyard in Fredonia, New York in this September 2014 publicity photo released to Reuters on January 4, 2016. The funding is a vote of confidence not only in the Brooklyn, New York-based startup, but in the nascent market for wind energy that is both produced and used on site. Distributed generation, or power that is produced where it is consumed, has taken off in recent years as governments have pushed for more renewable sources of energy and have sought to scale back on fossil fuels like coal and natural gas that are traditionally used in utility-owned power plants. Small wind turbines work best on large properties, generally an acre or more, where wind is abundant. The turbines are far smaller than the massive ones seen in a typical industrial wind farms. United Wind customers include farms, industrial facilities and rural homes. The $200 million will fund about 1,000 projects in the Northeast and Midwest, United Wind told Reuters, allowing the privately-held company to expand significantly beyond the 26 projects it developed since 2013. Forum includes rooftop solar in its portfolio of investments and sees United Wind as a "first mover" in a market with major opportunity for growth, Chief Executive Richard Abboud said. The funding from Forum adds to the $13.5 million United Wind received from New York's state-sponsored NY Green Bank and U.S. Bancorp late last year. "The capital is coming in," United Wind CEO Russell Tencer said in an interview. "We expect to see that same hockey stick (growth) curve that we've seen in the solar industry." The Distributed Wind Energy Association, an industry trade group, expects distributed wind capacity to reach 30 gigawatts by 2030, up from 1 GW in 2015. "To have a leasing model like what United Wind is coming up with is huge," said Jennifer Jenkins, executive director of the DWEA. "You see where solar is now and they are there because of this model." Much like popular solar lease programs that have underpinned the rapid growth of companies like SolarCity Corp and Sunrun Inc, United Wind's lease program allows farmers or other rural property owners, with no upfront cost, to power their homes or businesses with small wind turbines, producing between 50 and 100 percent of their electricity needs while lowering their overall energy costs. United Wind's primary source of revenue comes from monthly payments by its customers. Distributed wind is able to use federal tax credits and state incentives that make it cost-competitive with power from the grid. The small and medium-sized turbines United Wind uses for its leases would start around $80,000 to buy outright, Tencer added. "I saw an opening in the market where all the ingredients to make distributed wind work were there," Tencer said. "But no company had pulled all that together into a package."

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