The US solar market has weathered a few storms since the latest inauguration, from the trump tariffs shaking the market to the recent country’s somewhat turbulent policy landscape. However, the second quarter of 2018 witnessed more positive developments with respect to federal policy. In June, IRS issued a Notice, allowing projects to claim a greater ITC rate and ultimately a higher tax credit.
Navigating the nuances of solar risk management was the focus of a workshop, “Solar Risk Management: What Is It and Why It Matters to Investors, Sponsors & Asset Managers,” at the recent Solar Asset Management North America (SAMNA) conference in San Francisco.
In this report from the Solar Asset Management North America 2018 conference, Kiterocket’s Tom Cheyney finds that whether it’s managing large solar portfolios or nascent energy storage assets, repowering older solar installations or buttoning up residential O&M, the smart use of data is key.
As the installed capacity of solar grows by gigawatts every month, so too has the importance of asset management and operations and maintenance (O&M) services. With the addition of energy storage to the equation, the global services sector faces even more challenges—and opportunities—to optimize power plant performance for asset owners while reducing their costs.
By: Beau Blumberg and Dave Sheehan, Infiswift Solutions
How new IoT concepts can be used to seamlessly aggregate data across technologically diverse assets
Over the last 10 years, the North American PV market has grown exponentially. One result of this explosive growth is that more and more installed plants are passing their 3, 5 or 10 year age mark. With these maturing plants comes a new set of challenges in managing them. Operations are passing from the installation firms that built the plants to the long term owner-operators; they’ll continue to own the performance of the plants through the rest of their contract lifespan. The management needs of the plant constructors (who typically manage the plants for the first years of operation) are vastly different to the needs of the long term owner-operators. EPCs are driven by the performance guarantee (production above and beyond their guarantee usually does not result in a higher payment to the EPC), while owner-operators have an economic interest in maximizing plant performance.
The different needs of these stakeholders can result in an owner-operator inheriting a disparate portfolio of data acquisition systems and management softwares. These data acquisition systems are almost always built to communicate with the equipment at one specific plant, making data aggregation and analysis across portfolios very difficult. API communication between systems is also difficult without a homegrown system, and in some cases, security regulations make data export to the cloud very complex. Without easy access to their data, long term plant managers can spend more time collecting and normalizing data than analyzing plant performance. This lack of interoperability can significantly reduce the efficiency of data analysis, resulting in more time required to discover actionable information (or missing it altogether).
There are, however, opportunities to use new technologies to overcome many of these issues with interoperability. The Internet of Things (IoT) uses low cost hardware, wireless networks and modern architectures (see figure 1 below) to connect a broad variety of data points in the cloud. By aggregating data that has historically been siloed with new data sources that were previously not economical to collect, this allows users to make better decisions based on more complete data. Organizations like SunSpec and companies like infiswift are working together to define standards and deploy solutions in the solar space to improve on interoperability issues faced by the industry.
In the past, interoperability was limited to moving data between different devices within the network. Now, application level standards allow this data to have meaning and usability beyond a single plant extended through an entire portfolio. However, there is still an issue of how to handle legacy plants that don’t have a feasible way to support these new standards without substantial upgrades. With open platforms that adopt micro-services, such as those shown in the table below, it can be easy to ingest and normalize data from nearly anywhere yet easily swap data sources and formats if the plant is updated. This means provisioning plants can occur over time, eliminating a massive one-off IT project or deployment.
|Data Contextualization||Technology Micro-Service||In order for data and insights to be meaningful for all parts of a distributed system, it is critical that everyone speaks the same language and dialect. Data contextualization allows components to know what data they’ve been given (e.g.: back of module temperature) and characteristics of how it may be used.|
|Edge Computing||System||Advanced grid services and regulations such as Rule 21 require more intelligence at the edge. Rather than building these as monolithic software components, better interoperability is achieved by breaking them down into smaller, well-defined sub-systems.|
|OTA Updates||System||As software becomes more important in PV management, long-term interoperability can be maintained by enabling commodity hardware to be updated with over the air updates through the entire life of the plant.|
|Aggregation Services||Energy Micro-Service||When data has context across an entire portfolio, revenue streams and ancillary services such as virtual power plants become possible.|
|Wireless Communication||Technology||Without adding cost to gateways, it becomes possible to reliably interconnect all types of telemetry. Wireless communication can be used in parallel with existing wired networks to enable temporary or permanent use of new data sources.|
One owner-operator of a portfolio of utility-scale PV systems has already begun to use IoT architectures to address issues with data access. For a multi-MW plant, infiswift was engaged to make data previously confined to the local SCADA historian available remotely. A dedicated Cloud Historian was set up, and with no hardware deployed, data was extracted and made available via API to both a Financial Asset Management Suite and a Work Order Management System. Furthermore, the owner-operator was able to perform analyses which highlighted inconsistencies between the data and the monthly performance reports from the O&M provider. As a result, a number of underperforming strings were identified and fixed, resulting in increased performance.
Future developments for this owner-operator would likely include deploying additional software at other plants. Aggregating data from a full portfolio of plants together with external systems such as Financial Asset Management, Work Order Management, Grid Data Feeds, and Asset Tracking will allow the owner to visualize and access data in a central location from multiple inverter vendors, streamline analysis and make significant operational improvements in managing the full portfolio. Without proper data interoperability, significant overhead is added to completing these critical operational tasks.
As the PV industry matures, new problems have surfaced that require modern solutions to overcome them. Interoperability is the gating issue that can hold all sorts of other innovation back if not handled properly. Once resolved, however, more advanced services can be implemented that use accessible and complete data to optimize production and change the industry.
by Mina Mesbahi
As solar plants grow in size and complexity, system operators are required to combine data gathered from multiple sources to make decisions to optimise the performance of their assets. In this webinar (of which the full recordings are now available), Rob Andrews, CEO of Heliolytics and Hugo Lapie, Director of Sales of QOS will discuss different cases of investigation using real-time data, aerial inspections, and prediction tools to identify issues, improve diagnosis and shorten resolution times.
1. Advanced data analytics
In terms of data exchange methods, there are not any standard practices. This leads to greater challenges when managing a higher number of assets since the data needs to be aggregated from multiple systems and then centralised. There are multiple factors to consider in the realm of advanced data analytics, the most important of which is data accuracy/quality. The accuracy of data can be improved using the QOS via:
Data cleansing: Despite implementing the best processes, the data received can be incoherent at times. According to Hugo Lapie of QOS Energy, in such instances, Machine Learning (ML) can be a robust and scalable tool to filter the inaccurate/ incoherent data.
Data filling: In addition to data cleansing, ML can be utilised to fill in any gaps of missing data (i.e. due to communication errors) using external data sources such as weather forecast.
All in all, detection of under-performances across a portfolio, or a specific plant, is feasible only when the accuracy of data is enhanced. Upon detection, a more in-depth investigation can be conducted using multiple sources of data including on-site inspection, weather forecast, aerial inspection, satellite radiation data, etc.
2. Aerial inspection for portfolio asset optimisation
As stated earlier, a number of data sources can be used in advanced data analytics to optimise plant performance. One of the newest data sources being introduced is aerial inspection. In this webinar, Rob Andrews, CEO of Heliolytics, discusses how aircraft PV inspections can result in an industry leading level automation in PV systems. According to Andrews, automation is valuable in terms of improving existing processes as well as enabling new processes.
2.1 Aircraft inspection process
The aircraft inspection process begins with creation of a digital as-builts, which is a digital asset registry, and includes the location of every module and component in the system. Secondly, aerial inspection is carried out by the aircraft. Heliolytics uses visible and infrared images to collect data, which allows for cross-correlation. The next step is very crucial, which is data analysis analytics. Heliolytics has developed an artificial intelligence toolset, which examines the data collected and identifies location classification faults. The last step is reporting, which can take different forms. One other critical point is the ability to distinguish between hotspots created by soiloing and hotspots due to inherent module defect.
2.1.1 Data capture
Heliolytics uses a manned aircraft to capture data, enabling them to place high-quality sensors onto the aircraft. This increases the quality of the data and the speed, at which data is gathered. Another important factor regarding data capture is that the system should be at a relative thermal equilibrium when the data is captured to avoid false positives in the analysis.
2.1.2 Data Analysis
The next step in the inspection process is the analytics side. Artificial Intelligence (AI) can be used to analyse the data capture and locate the defects. Overall, AI tools provide accurate loclalisation and classification of defects detected. Another important aspect of data analytics is the conversion from an analogue dataset to a digital one.
2.2 Aircraft inspection for asset optimisation
According to Andrews, The standard practice up until a few months ago has been manual DC preventive maintenance, which historically translates to manual I-V curve or electrical inspections. The cost structure associated with this practice is linear and therefore not a scalable proposition, in addition to imposing some health risks and safety hazards. The non-digital nature of the inspection calls for inconsistencies in accuracy. Heliolytics conducted a field study to compare manual electrical inspection with aircraft inspection and found out that manual electrical tests identified only 22% of faults detected by aircraft inspection. Therefore, aerial inspection is a more plausible method for asset optimisation.
2.2.1 Improve existing processes
Classification of faults: Digital aircraft inspection provides a unified view and a standardised classification system in identifying and disaggregating defect types.
Location of faults: A detailed site-mapping can be achieved when using aircraft inspection, which provides accurate site location and geolocation of system faults. This is particularly valuable in terms of remediation action planning and early identification of warranty defects.
2.2.2 Enabling new processes
Workflow integration: Having a digital product facilitates integration and exportation into existing workflows such as SCADA, asset management, case management systems.
Incorporation of disparate datasets: A serial number batch with a statistically higher level of module faults can be identified through correlation of module serial numbers. This also allows for spotting the early warning signs for warranty issues.
Fault evolution tracking: System evolution over time supplies crucial metrics on site or operations performance, which lays the groundwork for predictive analytics.
Portfolio level analytics: The standardised datasets across the entire portfolio allows a broader level portfolio analysis.
To learn more about advanced data analytics and aerial inspection as well as other fundamental and cutting-edge topics, join the 5th annual edition of Solar Asset Management North America; a pre-eminent conference dedicated to optimisation of the operational phase of solar assets taking place in the high-powered ambitious city of San Francisco on March 13&14. More information can be found on the website: https://solarassetmanagement.us/
In addressing PV plant repowering for the upcoming 2018 Solar Asset Management Conference, we will discuss PV repowering in two sessions. The panels will be look beyond simply refurbishing, repairing, restoring or renovating the power plant. The focus is on delivering improved lifecycle performance, cost and risk reduction while extending a profitable plant’s economic life.
I hate to break it to you, but your data is dumb. In the renewable energy industry, we collect a ton of data, but most of it is without context. This not only leaves our insights into projects pretty rudimentary but also limits our ability to optimize our asset management processes. This makes you less efficient than you could be.
The recent leaps and bounds in artificial intelligence technology and machine learning have ushered a new era driven by automation. Artificial Intelligence (AI) is defined as a branch in computer science emphasising the creation of intelligent machines and computers similar to human beings in terms of behaviour and response capabilities.
Sports, due to their ability to reach enormous number of fans worldwide, have long been platforms for value promotion. One of such values have been the concept of sustainability and energy efficiency that have become the subject of increased focus lately. We've compiled an overview of the 50 biggest solar systems connected to stadiums and sports venues worldwide.
Last week of March marked the 4th annual Solar Asset Management conference organized by Solarplaza and held at the Grand Hyatt in San Francisco. Many of the most active asset owners, project developers, EPCs, developers, and monitoring and data analytic companies attended.
If players like NextEra, Southern Power, NRG, and now Brookfield continue to dominate the ownership landscape, they could keep a large share of the AM and O&M market captive, i.e. performed in-house and inaccessible to service providers.
On 16th February 2017, Solarplaza organized the webinar “Potential Induced Degradation (PID): Mechanisms, Recognition and Mitigation”. Jenya Meydbray, Vice President of strategy and business development at DNV GL, and Adrián Ramos, Senior Business Developer at OMRON joined the webinar as speakers and shared their expertise in mitigating the threat of potential induced degradation. The entire video recording of the webinar and the speakers’ slides can be freely accessed here.
We'd like to highlight a webinar that will be organized by NREL, the National Renewable Energy Laboratory and will go in-depth on the topic of soiling.
Following our recent review of the Top U.S. Utility PV Asset Owners, let’s look at the competitive landscape in asset management and operations & maintenance (O&M).
On 24th January 2017, Solarplaza organized the webinar “Facing the Challenges of a Booming Solar Portfolio”. Chris Franz, Vice President of asset management at Cypress Creek, and Edmee Kelsey, CEO of 3megawatt joined the webinar as speakers and shared their expertise in dealing with the challenges of maintaining a growing solar asset portfolio.
In preparation of the fourth edition of Solar Asset Management North America we spoke to Chad Sachs, CEO of RadianGen, a leading asset management service provider and software developer in the North American market, and one of the companies profiling themselves as sponsor of the event.
The U.S. market experienced its busiest year in 2016, largely driven by a spectacular amount of construction in the utility segment. Per GTM Research, when the dust settles and all the numbers are in, the cumulative capacity of utility PV operating in the U.S. will have increased by more than 70% in a single year. But who owns these assets?
We're glad to be able to share with you the GTM Research data-overview of the 25 largest US utility-scale solar PV plant portfolios. We've published the top 5 here, along with some insightful observations by GTM's Colin Smith. The full top 25 is freely available upon completing the short form at the bottom of the article.
Consolidation is a recurring theme in many solar markets as they mature, but how fragmented or concentrated are each of these markets? How does the U.S. compare to Germany, Italy, or the U.K.? The latest edition of GTM Research’s annual report, Megawatt-Scale PV O&M and Asset Management 2016-2021, authored by yours truly, provides answers to this question for utility-scale plants. The chart below represents the share of the top 10 owners, asset managers, and O&M providers in 9 of the largest PV markets, as a percentage of the total installed base of PV plants 1 MWdc and above (at the end of Q3 2016).