Learned Behavior: Advances in machine-learning lead the way to true solar + storage profitability

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In the compelling short story “Lifecycle of Software Objects,” author Ted Chiang explores a world in which artificial intelligence (AI) is reared and cared for just as you would a child. He did this because he sees the commitment life-long learning and development as the only true way for AI to ever achieve conscious intelligence. Point being, there is only so much pre-programming can accomplish.

Advanced energy control software companies would agree with this philosophy. It’s not exactly full consciousness, but today’s energy storage controllers are using various machine-learning and artificial intelligence to make savvy decisions that balance priorities of asset generation, optimal storage use, tariff schedules, demand charges and miscellaneous grid responsibilities. For C&I solar + storage projects, these abilities aren’t a bonus, they are crucial to maximize the assets and hit key production and revenue goals. The main questions at hand here:

  • What’s the electrical tariff for the site, and how should the system maximize the cost avoidance or value captured under it over time?
  • What charge and discharge schedule for the asset makes sense for that moment in time?

Misjudging the answers to these questions is likely to be a $100,000 mistake or more in some cases.

Thinking fast and slow

Pason Power provides one of the top advanced energy storage control solutions on the market, and we asked Bryce Evans, head of customer and partner success, to give us a peek into how these systems think.

Pason Power’s autonomous control systems and machine-learning capabilities emerge first from two broad buckets of information processing — micro and macro — which can have conflicting agendas (all humans begin to nod).

Micro adaptations. Every second, the Pason system gathers data from a variety of real-time data sources — from the energy storage system’s power conversion systems (PCS), battery management system (BMS), meters at the site (for the current building load, on-site solar generation and sub-metering on machinery or sub-circuits), and other instrumentation data such as temperature sensors. It’s also consulting weather reports, cloud characteristics and maybe a weather station or irradiance sensor at the site (if applicable).

All of that is driven into a patent-pending forecasting engine, which produces two forecasts: the expected site demand over the next 24 hours and the expected output of the solar array. This is where the learned responses prove their worth as the system considers the forecasts within the context of various site constraints.

“Our system responds in real time to the instantaneous changes in site load among other factors and generates new forecasts every 15 minutes based on all of the information gathered,” Evans says.

Macro adaptations. Over a longer period of time (days, months and years), a model will need to be adjusted within the broader context of the evolving load of the site, potential changes to rate tariffs and new revenue stream opportunities. Pason handles the evolving energy usage patterns at the site by periodically deploying new AI models that are retrained to include the most recent gathered information from the site’s measurements. Most changes to rate tariffs are usually handled automatically by the AI control system, which has the capability to optimize battery operations for thousands of rate tariffs. Pason also deploys updates to the machine-learning model to explore other ways to best produce savings for this site given what’s been learned since the system was commissioned. This process is enabling continuous AI learning.

A primary application of C&I solar + storage is demand charge reduction. The most basic way to do this is to look at historical electrical consumption of the facility and configure the system with a fixed threshold. If site demand ever goes above X, the asset discharges. That requires no machine-learning sophistication.

But, what if you have a really hot month (an increasing likelihood in the age of global warming)? A fixed threshold approach might completely miss the peak demand charge.

“You might start to discharge the asset too soon because the threshold could be too low for this particular day,” Evans explains. “The HVAC system is going to run longer than usual because of the anomalous month, and because of how demand charges are billed, if you let one through the whole billing period is sunk from a cost avoidance standpoint.”

An adaptive system can assess such situations with more nuance.

“We see HVAC loads as a sweet spot for our system, which are highly correlated with weather,” Evans says. “We’ll be able to anticipate that increased need and be less aggressive in our demand charge reduction, counter intuitively, because raising the threshold makes sure we effectively shave the peak. If you discharge too soon, you miss part of the peak, and there’s no changing that now for the rest of the billing period.”

Consider the needs of others

Then there are the batteries themselves. Batteries need to be operated within specific use parameters to meet performance expectations and maintain their warranties, which is hugely important to buyers.

“We see a strong pull from the market for 10-year warranties for the batteries because customers want the modules warrantied in line with the system,” Evans says.

Demand for higher density battery modules at lower price points has driven rapid innovation and resulted in a market full of newer battery modules that don’t have enough testing behind them to know for certain how they will perform. To protect against this risk, battery warranties are getting more restrictive when specifying use conditions like state of charge, depth of discharge and temperature range.

It would be impossible for a non-adaptive control system to account for the unknowns of the battery’s performance while still adjusting the usage to not void the warranty.

“Our system has to keep all of that in mind while operating the asset and co-optimize all of these constraints,” Evans says.

Seeing the bigger picture

Intelligent energy storage control systems also mean more value stacking opportunities for system owners. Example: A system is designed with demand charge reduction as the primary application. Late in the monthly billing period, the system might reasonably surmise the highest peak for that month was already hit. Maybe instead of peak shaving that day, an intelligent system will perform PV self-consumption or TOU arbitrage instead.

Knowing a controller has the smarts to put every asset to optimal use, an asset owner might want to plan for it in system design by adding a little more storage than a system would need for just demand reduction.

Evans hints that Pason is preparing for that with a new application based on value stacking. The good news is, you don’t need to jump right in and buy those batteries now. The machine-learning of these systems will only improve over time as more data is compiled, and all of those smarts will be pushed back into the brains of your controller (software updates are included with the annual subscription fee).

Playing nice with others

Deploying all of this is becoming easier for novice, mid-market solar installers too. Consider Stem, another big name in machine-learning storage systems. Since 2012, Stem has captured data from its systems on a one-second basis to feed its predictive analytics, machine-learning and grid-edge computing AI. The company has largely functioned as its own project developer, becoming one of the top storage companies in California by combining its advanced AI Athena platform with the SGIP rebate to uncover big savings for customers.

Solar installers can now put all of this accumulated knowledge to use as Stem is now looking to partner with solar companies to deploy even more systems in the C&I mid-market through its Stem Partner Network.

“Through the Stem Partner Network, we deliver end-to-end partner support and services, such as training, project development advisory services, marketing and lead generation, deal support and access to a partner portal with educational resources,” Christy Martell says. “We worked with a few developers to help them understand how to design storage into their projects and ultimately bring more value to their end customers.”

Stem partners with the strongest names among solar providers across the United States to unlock new value from solar projects for their customers, backed by performance guarantees. To that end it has partnered with Energy Toolbase to provide developers an efficient path to design systems and map out projected savings.

Typically, Stem will work with the developer to suggest a combination of value streams, and this is especially valuable going forward as the market grows for complex grid services or utility programs such as demand response.

“We have seen an uptick in deal size going into 1 to 4 MW, and that’s continuing to grow,” says Than Tran, VP of global demand generation and marketing for Stem. “As we grow larger, we want to bring in other strategic providers to help us build a comprehensive solution to address all C&I customers.”

Pason Power recently partnered with Chint Power Systems (CPS), which integrates Pason Power’s software into its Energy Storage System as the exclusive platform for commercial and industrial (C&I) customers.

Smaller storage systems

Smaller storage systems leave less room for testing integrations in the field, which is why Pason Power partnered with CPS to pre-integrate their systems.

“CPS is bringing the entire solution to the commercial segment,” says Casey Miller, VP of products and business development for CPS America. “We are enabling solar installers to get in the storage game by offering their commercial building customers a turnkey storage solution with clear economic benefits and managed risk.”

This integrated energy storage solution fosters a simplified, single-source procurement process for customers rather than having to rely on multiple vendors. Hardware and software will arrive pre-built and pre-configured, making it easy for developers to install so customers can quickly begin seeing the benefits. Why this integration is important:

  1. The bankability of the firms involved is a huge factor in decision making when procuring storage components. Chint is a strong brand with a reputation for reliable products and excellent service, and Pason has been around for 40 years.
  2. The practical advantages. “By partnering with Chint, we can pre-integrate our control system into their product, do all of our testing in our facility and then sell a turnkey product that can be installed in half a day,” Evans says. This means decreased install costs, decreased commissioning costs, higher reliability and no misfires in the early billing periods that equate to missed savings opportunities.
  3. Because Pason’s modeling and energy management software were designed together, they use the same logic which ensures a truly integrated system. This enables users to pre-select the hardware which also improves accuracy when modeling the system and economics.

“Pason Power is doing the little things right to make the solution easy for customers,” Miller says “The net-net is our customers get the predicted economic benefits over the full life of the solution, not just year one or two.”

— Solar Builder magazine

Solmetric now shipping its new PVA-1500 V2 IV curve tracer

solmetric curve tracer

Solmetric has begun shipping its new PVA-1500 V2 IV curve tracer for commissioning, maintaining, and troubleshooting PV systems. This latest model of the popular 1500Volt/30Amp PV Analyzer product line adds some key new features, including:

• Charging/charged LED indicators on the I-V unit and SolSensor
• In-the-field firmware upgrade capability so that your unit won’t need to come back to factory for future firmware feature enhancements and patches
• Ability to pair/re-pair SolSensor to I-V unit in the field
• Enhanced protection against inadvertent I-V sweeps while string is connected to an inverter

And, of course, it still includes all the other PVA-1500 features that have made the earlier model so popular, including:

• Measures strings up to 1500 Volts at 30 Amps
• WiFi connectivity
• Highest accuracy
• Highest measurement throughput
• Largest display with best array troubleshooting features
• 300 ft wireless sensor range

— Solar Builder magazine

Interplay Learning to provide virtual reality solar training with Global Sustainable Energy Solutions

virtual reality solar training

Interplay Learning, a provider of online training for skilled trades utilizing virtual reality (VR) and 3D simulations, formed a partnership with Global Sustainable Energy Solutions (GSES) last month to provide its solar training and education. By implementing the Virtual Reality and 3D simulations of Interplay, GSES will offer training courses such as site surveying, installation, commissioning and trouble-shooting processes.

“Our partnership with Interplay Learning allows us to take a massive step forward with the solar training courses we offer by utilizing Virtual Reality,” said Geoff Stapleton, Managing Director at GSES. “It will exponentially benefit those who take our courses in Australia, New Zealand, Asia, Africa and the Pacific Islands. Traditionally, we delivered courses online and face-to-face in various countries. Even if we had hundreds of trainers on staff, we could not achieve the penetration or benefits that Virtual Reality offers. This new partnership will allow us to expand our global reach and serve a worldwide market.”

“Interplay Learning is thrilled to partner with GSES as their multinational reach helps us impact the skilled trades gap globally,” said Doug Donovan, CEO of Interplay Learning. “Our solutions allow GSES to deliver effective solar training using interactive video courses and 3D simulations created by top solar industry experts. Course materials are accessible by desktop, laptop, tablet, mobile phone, or VR-headset and provide an immersive learning experience for engagement and a field-like experience. It’s a tremendous opportunity to elevate the learning process,” stated Donovan.

— Solar Builder magazine

DNV GL launches SolarFarmer PV plant modeling software to handle complex terrain

As the demand for solar energy increases, solar plant design are becoming more challenging as the terrain becomes more complex. This will require PV plant design software that can perform more reliable modeling for accurate energy calculations. This makes DNV GL’s new software, SolarFarmer, intriguing. Launched at Intersolar Europe this year, the SolarFarmer software models, designs and analyzes solar PV plants, but is especially aimed at accurately and efficiently handling layouts in increasingly complex terrain.

“It’s exciting to be supporting the PV industry with SolarFarmer,” says Tony Mercer, Head of Department for renewables software at DNV GL. “The software is built from the ground up with scale in mind and brings together layout, energy calculation and automation, giving our customers new and efficient ways to explore and optimize their PV project development,” he says.

SolarFarmer can be used for conceptual and detailed design and analysis for solar PV plants. It combines thoroughly validated PV simulation algorithms with a user-friendly, modern user interface allowing quick configuration of PV plant designs and simulation of PV layouts.

SolarFarmer offers:

• Design and analysis of development of solar PV plants, from conceptual model to detailed solar plant design
• Efficient and traceable method for modelling in complex terrain including our model for submodule electrical mismatch
• Design time savings using automated layout for fixed tilt and trackers
• Thoroughly-validated models for accurate energy production calculations
• Expert modelling; SolarFarmer engineers are working directly with DNV GL experts to improve modelling of components during the design phase, such as modelling for thin-film modules
• Sub-hourly energy assessments – we know as experts that hourly energy assessment can lead to errors e.g. underestimating inverter clipping losses during cloud coverage. SolarFarmer provides sub-hourly energy assessment calculations

— Solar Builder magazine

PV startup Raycatch (backed by BayWa r.e.) debuts automated AI Diagnostic DeepSolar system

Raycatch_BayWa r.e._Solar

Innovative startup Raycatch, which is backed by global renewable energy developer, service provider and wholesaler BayWa r.e., developed the next-generation of DeepSolar, its AI-based Software as a Service (SaaS) solution. DeepSolar is a diagnostic system that enables large-scale PV plant owners and operators to optimize solar assets and maximize their return on investment.

“DeepSolar 2.0 is our answer to the challenges PV plant owners and operators face every day. The new developments are based on our extensive expertise in PV software, which has already been implemented in a variety of projects that in total add up to a volume of 1.5 GW,” explains Haggai Hofland, Raycatch CEO and Founder.

DeepSolar 2.0 comprises features that make it possible to successfully identify and break down all components affecting solar plant performance – and group them back together to create an extensive, coherent picture. The software supports solar plant owners by providing them with comprehensive ROI information and data-driven operational insights. In addition, the diagnostic system can identify the sources behind technical issues, outline issue resolutions, evaluate costs and make prioritized recommendations based on plant owners’ respective needs.

“At BayWa r.e., we are excited by the new features of DeepSoar 2.0. It is the perfect addition to our comprehensive O&M portfolio, which currently amounts to a total of 6,100 MW of renewable power,” states Tobias Bittkau, Global Head of Services at BayWa r.e. “AI-based solutions like DeepSolar 2.0 are key to the O&M market, as they make the production of renewable energy easier and more efficient and help drive the energy transition.”

Founded in 2015, Raycatch is an innovative startup that uses cutting-edge AI technology to apply accurate diagnostics to the complex, noisy, high-volume data that characterizes solar projects.
By tapping into existing plant data, Raycatch provides actionable insights that help plant owners and operators extract more energy from their existing installations, reduce operational costs, and benefit from larger profit margins.

— Solar Builder magazine