Solar trackers have become important components of solar photovoltaic installations. Their ability to track the changing position of the sun in the sky can dramatically boost the energy gains of PV systems, by as much as 25 to 35 percent in some cases according to EnergySage.
The Internet of Things represents the biggest expansion of network connectivity in history. By connecting home appliances, embedded sensors and many other systems not conventionally regarded as "computers" to IP networks, the IoT could unlock many new interactions between devices and applications.
For example, Amazon announced in late 2018 a microwave that could be connected to an Alexa-powered speaker for voice controls, and which also featured an integrated dash button for easy reordering of popcorn. This appliance is pretty simple as IoT inventions go, but it illustrates the potential of connecting devices other than traditional PCs, servers, phones and tablets, in the delivery of innovative services.
Solar power solutions have an important role to play in the growth of the IoT. Providers of integrated solar platforms, like Trina Solar with TrinaPro, are particularly well-positioned to ensure that the IoT infrastructure of tomorrow can harness solar energy for enhanced reliable and resiliency.
Why solar and the IoT are a perfect match
IoT assets are not like most networked devices:
- They're more likely to have streamlined, space-optimized designs that exclude displays and other conventional components, and may even share power sources with the sensors connected to them. This is the case with some IoT weather stations.
- They may be built in unusual (compared to the familiar form factors of PCs and similar devices) shapes and with rugged materials. so that they can function in specific outdoor locations. However, these design decisions can complicate how they receive power.
With these requirements and restrictions in mind, solar power is often a good fit for IoT implementations. High-quality solar PV panels are rugged enough to withstand significant wind and snow loads, in addition to having certified waterproofing. The ongoing development of thin PV film may also boost solar's role in IoT, since lightweight panels could be installed in many possible locations.
Solar and IoT have a symbiotic relationship. Installing PV panels and batteries for solar energy storage can make certain types of IoT infrastructure more viable, while the wide-reaching connectivity of the IoT has benefits for solar power management. Indeed, the later has been proposed as a breakthrough IoT application – the use of smart sensors, software and algorithms could bring big changes to the monitoring of solar PV panels voltage, temperature, current and irradiance.
In other words, by connecting their solar infrastructures to the cloud, solar customers will be able to keep close tabs on performance degradations before they get out of hand. In a pre-IoT world, it's often necessary to send out technicians to address issues with causes that are difficult to pinpoint. With IoT solutions in place, issues within solar PV systems can be spotted in real-time and likely resolved with higher success rates.
Solar for utility-scale IoT projects
Moreover, integrated solar solutions, such as TrinaPro, can extend the possibilities for energy and utility projects in the IoT. Companies in these spaces have already tapped into the IoT's potential by monitoring critical infrastructure like pressure gauges, supporting preventative maintenance strategies and gathering data for business intelligence initiatives, but solar power opens new horizons.
With an all-in-one solar platform for utility projects, they gain convenient access to panels, inverters, batteries and trackers from a single provider. Modern trackers allow for higher-gain solar installations in a wider range of terrain than was available even a decade ago, when obstacles like pipelines and wetlands limited the feasibility of tracker implementation. The single-axis tracker in TrinaPro adjusts to the sun's east-west movement, providing significant energy gains that can channeled into IoT devices and applications.
Contributed by Trinasolar
TrinaPro is ideal for today's utility-scale projects and will continue to be a powerful platform as energy providers integrate more aspects of the IoT into their operations. Trina Solar combines high-quality components with attentive service to deliver the best possible experience for customers. Its scalability, reliability and overall are well-suited to unlocking the main advantages of the IoT, namely improved insight into operations and tighter integration between IT systems.
See-through solar materials that can be applied to windows represent a massive source of untapped energy and could harvest as much power as bigger, bulkier rooftop solar units, scientists report in Nature Energy.
As solar tariffs continue to fall and competition heats up, cost cutting on mounting structures is an obvious first choice because their under performance will not be noticed - until a major climatic event occurs.
Both technically advanced and low cost seems like a dichotomous notion that rarely coexists in a singular form. However, things are about to change in solar as one of major technical advancements, half-cell modules, hits the market. Many Tier 1 manufacturers have already been heavily focused on developing half-cut designs. Industry experts expect that half-cut cells will continue to gain market share over the next 10 years.
Take JinkoSolar as an example. Half-cell has been a technology that the company has been very excited about as the company finds that the performance gains by cutting the cells in half are well worth the extra manufacturing requirements. Compared to its conventional full-cell product, at JinkoSolar, half-cell products has a roughly 5-10 W output advantage based on different modules. The gains from half-cell technology are the most significant when applied to standard monocrystalline products.
Given the performance and economical strengths of half-cell products on monocrsytalline products, the conversation today when picking high performance modules is no longer about whether to pick monocrsytalline or polycrystalline module, but about figuring out what dollar per kW/h to opt for. Traditionally, you have the flagship Monocrsytalline PERC, which has extremely high output figures with a very substantial price-tag. Thus, if your project is not extremely space constraint, then the monocrystalline half-cell product may fit your high output needs without costing you an arm and a leg.
Taking a deeper dive, the range topping PERC products current has an output of over 305-310W. Guess what? So does the ranging toping half-cell products. JinkoSolar’s half-cell mono series puts out a respectable 300W and can reach peak as high as 310 W. Yet, the half-cell products are significantly cheaper than PERC modules. Half-cell mono module can achieve far more generation at a far less marginal cost. Beyond price, half-cell modules also have much better shade tolerance than that of full-sized modules, so if you’re doing a residential project where there may be a lot of shade, you might even getter better output from half-cell modules than PERC modules.
So, if you thinking about installing a conventional mono PERC module, it might be time to give take a look at half-cell mono. Put both products through a comprehensive set of benchmarks to find out which is best – take a deep dive into output, degradation, prices and LCOE too. While some Mono PERC users will continue to use Mono PERC if they face heavy space constraints, I see Mono half-cell launching a very strong campaign against to unseat Mono PERC as the high-efficiency module of choice.
However don’t worry too much, as JinkoSolar has been raising the bar when it comes to monocrystalline module. Both of JinkoSolar’s Mono PERC and Mono half-cell have been a hit. So if quality panels with a great price-performance ratio are what you seek, you won’t go wrong with either of JinkoSolar’s modules.
Impractical high dead load required to counter wind uplift remains Achilles heel of ballasted rooftop solar installations.
You’re a developer or engineering firm who is working on a large solar project and you’re deciding which solar resource dataset to use. You know you need to use a high-quality resource dataset to get the project across the line. How do you make a good choice on the resource data, which will be the backbone of your project?
Three key questions will help you make a wise choice:
- Are hourly data available, or only monthly means?
- How old is the dataset?
- What is the dataset’suncertainty?
If you can answer these questions about the resource data used in your projects, then you’re already on the path to making better choices then many. Based on these answers and how much risk you are willing to live with, you can make an informed tradeoff between schedule, accuracy, and cost. This article focuses on the first question.
Long-term monthly mean meteorological data is sometimes used as a basis for predicting the yield of photovoltaic systems, since such data is widely available at little or no cost. Also, software tools like PVsyst and Plant Predict allow users to input monthly mean data, and then generate from this a year of synthetic hourly data that can be used as an input to photovoltaic yield modeling.
At very early stages of project design, this can be a quick way to get indicative numbers. The problem with this approach isthat – depending on the location and type of project - it can add too much resource risk, even in the early prospecting stages of project development. This is especially true for tracking PV plants and locations - such as India - with high resource variability.
In a recent Vaisala study, PVsyst was used to compare the P50 energy results derived from using multiple years of Vaisala’s 3TIER Services hourly meteorological data (Vaisala, 2017) to those derived using two types of synthetic years based on the same data: a PVsyst synthetic year (hereafter: PVsyst-SYN) and a Typical Meteorological Year (TMY). The same underlying resource dataset was used, and the only difference in the projects was the temporal resolution.
Modeling was performed for ten Megawatt-scale photovoltaic projects in six different countries including India. For each project, simulations were run for two configurations: an equator-facing fixed tilt orientation and one with horizontal single-axis East-West trackers with backtracking.
PVsyst was used to run all simulations, with only the meteorological data varying between the three approaches.
- Full timeseries approach: The hourly timeseries for the ten project locations covered roughly 20 years of individual simulations run separately in PVsyst to derive the corresponding annual yield. The full timeseries yield estimate (P50) was then taken to be the median of the individual annual yields.
- PVsyst-SYN approach: In this approach, the long-term monthly means of global horizontal irradiance (GHI) are first used to stochastically derive synthetic hourly GHI data as described by Meteotest in 2017.
- TMY approach: Vaisala creates TMY datasets using an empirical approach that selects four-day samples from the full timeseries to create a “typical year” of data with 8760 hours, while conserving the monthly and annual mean of GHI. The process is iterated until the annual means of all solar variables in the TMY dataset match the means of the full timeseries to within 1% or less.
Finally, the simulated yields from the PVsyst-SYN and TMY approaches were compared to the P50 yields from the full timeseries approach for each of the ten PV systems, for fixed and tracking configurations.
For all project types and locations, we saw significantly more deviation from the PVsyst-SYN approach. Notably, it was difficult to predict whether the bias was high or low – this means it cannot be treated as a consistent known bias.
The deviation between the energy yields from the long-term hourly timeseries and the TMY files relates to how well the TMY averages match the long-term dataset. If the TMY was not well matched for GHI we would expect these energy yield biases to be larger. Vaisala’s TMY creation process is designed to minimize these deviations for that very reason.
For tracking plants, the average difference in the hourly vs monthly average approach was about 2%, but the maximum difference was over 7%!For tracking plants, the monthly averagePVsyst-SYN approach has too much uncertainty to use even in the early prospecting stages of project development. A 7% difference in energy from predicted to actual production is more than enough to make or a break a project; thus, we recommend hourly data at all project phases if you’re developing tracking plants.
The news is better for fixed PV. The energy differences between the models was less than 1%, and the maximum difference was around 2%. That is a deviation that’s generally acceptable at early project stages. In later development stages, you should incorporating hourly timeseries data to reduce the uncertainty, especially if you are in a competitive financing situation.
A Proactive Approach
So as a proactive project developer or engineer how can you make sure you are making the best resource choices for your projects? Vaisala and other providers cover most geographies with satellite-based datasets at and hourly or even sub-hourly resolution at a reasonable cost.
If you have received a report from an engineering firm and are wondering what resource methodology they have used in order to understand the accuracy of the estimates, you should look past the name of the data provider. Many prominent engineering firms use the low-cost monthly means but not the hourly time series. Any time the words “synthetic” or “generated” are used in regards to the resource data, that is an indication that hourly values are derived and not native to the resource timeseries.
Given the increase in availability and the decrease in costs for hourly timeseries we would encourage others in the industry to drop the synthetic data creation practice so we can all build projects we will be proud of.
Authors: Gwendalyn Bender, Sophie Pelland Ph.D,Louise Leahy Ph.D. and Rajni Umakanthan
Vaisala, Seattle (U.S.A.) and Bangalore (India)
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