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)
Advanced Technology from Huawei to Ensure a Better Harmonic Suppression
Preventative maintenance (PM) includes routine inspection and servicing of equipment to prevent malfunctions and unnecessary production loss.
"Key technical due diligence parameters, vendor selection process and Inspection criteria of Solar PV Module for Solar Projects"
A lot has been deliberated about module cleaning but it continues to a key area of interest at Solar Power Plants. The belief is cleaner Modules translate to better generations nos. and thereby higher revenues. However, it needs to be seen in this perspective – “Different Strokes for Different Plants”. Module cleaning using water cannot be a clinically driven process scheduled once or twice a month.
A more pragmatic approach is required to carry out this activity based on the dust levels on the panels, terrain, weather conditions, appreciable drop in performance ratio(PR) under no break down conditions. Many clients still go for a programmed schedule and expect the Operations & Maintenance(O&M) provider to do this activity religiously since it is a contractual obligation; the time has come to have alternate approaches and to use available technology to salvage a precious commodity, water. Yes, water, since Solar Plants may last 25 years but for how long will the water be available for this mundane activity across regions? Also, time should be devoted to inverter performance analysis to arrive at critical areas where performance is affected and to check whether it is attributed to deficiencies in module cleaning.Introduction: - A lot has been deliberated about module cleaning but it continues to a key area of interest at Solar Power Plants. The belief is cleaner Modules translate to better generations nos. and thereby higher revenues. However, it needs to be seen in this perspective – “Different Strokes for Different Plants”. Module cleaning using water cannot be a clinically driven process scheduled once or twice a month. A more pragmatic approach is required to carry out this activity based on the dust levels on the panels, terrain, weather conditions, appreciable drop in performance ratio(PR) under no break down conditions. Many clients still go for a programmed schedule and expect the Operations & Maintenance(O&M) provider to do this activity religiously since it is a contractual obligation; the time has come to have alternate approaches and to use available technology to salvage a precious commodity, water. Yes, water, since Solar Plants may last 25 years but for how long will the water be available for this mundane activity across regions? Also, time should be devoted to inverter performance analysis to arrive at critical areas where performance is affected and to check whether it is attributed to deficiencies in module cleaning.
Economics: A Silicon Polycrystalline module requires a conservative 2.5 litres of water/per module during module cleaning. So, a 10MWp plant typically will use 85,000 litres of water for every cycle. Add to this the cost of water, which is mostly bought in remote & barren sites and the cost of labour. For 9 cycles in dry months it can totally cost upwards of Rs3,00,000/annum. If water is scarce then the costs can be 25- 30% higher.
PROS & CONS of Module wet cleaning:
Cleaning with pressure hose can remove stubborn dirt & grime. However, the dirt can settle at the corners of the module if the water does not flow off properly. Modules at a larger tilt angle are less likely to hold the water due to gravity. Soft water is recommended to be used to avoid scaling on the modules, post evaporation. Availability of soft water cannot be guaranteed in remote areas with scarce resources and that puts a brake on the process at times. Reverse Osmosis(RO) plants installed at many sites also fail quickly because of the hardness of the water available in many parts of the country; this is an added cost to the client.
Wipers can be used to remove any settled dirt but the cloth wipers used should not scratch the surface, which in the long term can be detrimental. The design of the wipers should be fool proof that there is no deposit of lint on the modules while or after cleaning.
A couple of other constraints are there for wet cleaning - it can be done for a short time-period, mostly before 10 AM and after 4 PM when the module temperatures are in the 30-35 degrees C range in the tropics. Many clients shrink the time to a 2 hour window between 5-7 AM and 5-6 PM, which can affect the cleaning cycle and can prolong for more days in a month. It is better to align to the manufacturer guidelines for cleaning and follow a 15day cycle for cleaning at a medium size plant. Cleaning post dusk has its own risks of personal safety to the individuals and the cleaning quality may be erratic too though this is the most preferred time for clients.
Dust deposition pattern is to be studied in detail to organize the module cleaning activities better. It is usually dependent on weather conditions and the type of soil and vegetation at/near the site. Limited wet cleaning depending on the location can be carried out based on the assessment of the dust and the level of dip in performance ratio. If the O&M provider is meeting contractual obligations on Performance Ratio (PR) then a cycle can be staggered based on mutual consent between the O&M provider & the Plant developer. That can indirectly save the costs of bought out water / power costs of running pumps, if water is available at site.
In India, the dust levels during the pre-monsoon months (Mar- May) are high and the cleaning will have to be more regular and practically daily whereas in the monsoon months it will not be required, and in the other months it can decided on a case to case basis.
In a Rajasthan plant, it was noticed that module cleaning, when not done for 2 months in a row did not affect energy generation considerably; the loss of generation of about 3 percent in 2 months could easily amount to at least 70% costs of doing module cleaning itself. If more cycles were planned each month, as some clients may insist, then then it will be reasonable business sense to not clean at all!
Data with respect to module cleaning for a Thin Film plant shown below is for a 6-month period where generation for 10 non-break down days have been considered. No Module cleaning was done in Months Sep & Oct and months Jan & Feb. Only during the Nov & Dec months two cycles of module cleaning was carried out in each month. The results showed up that there was only a 1.5% dip in generation /month on an average. This is at least 50% lower deviation than usually noticed if a plant is not cleaned once in a month. A general thumb rule in the industry is that Soiling losses are at 3% -4% between a plant not cleaned Vs the same plant being cleaned regularly.
A point to note is that there is always a possibility of 1.5% -2% difference of generation on daily basis between an inverter having the maximum generation of the day and the median generation across all inverters in a medium / large size plant where module cleaning is regularly happening. This is because the cleaning cycle will be block wise and will cover associated inverters day by day. So, a minimum 0.5% - 1% generation loss may be inherently seen at a plant level with inverters’ performance variations (as an example for a 10Mwp plant) even if module cleaning is regular.A point to note is that there is always a possibility of 1.5% -2% difference of generation on daily basis between an inverter having the maximum generation of the day and the median generation across all inverters in a medium / large size plant where module cleaning is regularly happening. This is because the cleaning cycle will be block wise and will cover associated inverters day by day. So, a minimum 0.5% - 1% generation loss may be inherently seen at a plant level with inverters’ performance variations (as an example for a 10Mwp plant) even if module cleaning is regular.
If plants have seasonal tilt it is better to concentrate more on the module cleaning in the seasons where the tilt is 3-5 degrees with an eye still on the Performance ratios. The trigger points can be decided mutually as to when to do the module cleaning rather than adhere to a schedule which starts on 1st of every month and ends on 15th/20th.Depending on the type of soil at site the cleaning cycle can be altered as some areas have clay like soil where the dust particles can stick more to the glass and it will be imperative to do cleaning regularly whereas in other areas where the soil or dust is hard sand and it may not stick.
If there are unseasonal rains during each month the module cleaning cycles can be abandoned or continued a case to case basis. It is better to review site conditions after unseasonal rains as it leaves behind a lot of unwanted dust on the modules. Selective cleaning can be done on affected modules alone. Inverter wise PR measurements can indicate the blocks that may require cleaning. Miscellaneous Issues:
While doing module cleaning the pyranometers must be cleaned as per a desired frequency. Usually these are cleaned once at the beginning of each cycle. This frequency should not be tampered since this can affect performance ratio measurements at the plant. Cleaning it daily is not advisable as it may lead to disturbing the inclination settings, lead to inaccuracies in measurements and may indicate poor plant performance, which may not be the case.
Additional Module cleaning may be required to be done if the bird population at the plant is high since bird droppings on solar panels will be very common. This is more serious as the acidic nature of the droppings can affect performance significantly by shading and hot spot creation. Water is still the best agent for cleaning bird droppings as solvents are not usually recommended by module manufacturers. Changing bird behaviour is the best approach or some deterrent is to be applied. Use of bird scare mechanism is suggested and has been seen to be an apt deterrent.
Dragon flies are other creatures that can affect Solar plant aesthetics a lot. These flies that thrive near water bodies can lay thousands of eggs on Solar panels, many a time mistakenly considering the panels to be water bodies. Cleaning the egg ridden panel is a tough task though the effect on plant performance has been noticed to be limited.
Way forward for Module cleaning: Use of technology should be stepped up like use of drones for monitoring dust levels at the plants or dry cleaning using robots. The advantages of using robotic cleaning are significant as a consistent 3-3.5% higher output is possible daily over conventionally wet cleaned modules where cleaning schedules are staged over a fortnight /month. The pay back on the investment can be within a decent 5-7 years. Developers can think of working on these lines as it has always been a classic complaint in the industry on the quality and pace of wet cleaning of modules in large size plants. The suspicion mostly is that PRs are low because of inadequacy of Module cleaning.
Other technologies to be looked at here are dust sensors and a self-cleaning mechanism. Different methods – one as a trigger point to clean when dust reaches a threshold can be thought about; those arrays or blocks can be marked for wet cleaning b) to auto clean the panel by creating electro static charge to repel dust. Suitable technology needs to be scouted for and may be commercially available.
Developers can also explore buying more superior modules with good Antireflection and Anti-Soiling properties for any future investments; it can surely cut the recurring costs of wet cleaning.
Author- Ganesh H, AVP – Analytics.
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.
King Abdullah City for Atomic and Renewable Energy (K∙A∙CARE) announces its plan to launch the Machine-Assisted Cleaning Solutions (MACS) project.
A solar PV project is divided into various essential aspects of infrastructure such as: electrical engineering, civil engineering, module mounting structures, weather monitoring stations, and supervisory controls. In order for a solar power plant to achieve the desired generation values, it is vital that the design conceptualization translates into implementation on the ground. This means that every aspect of engineering and construction needs to be properly validated, documented, and easily accessible during the project cycle.
Th article provides a high-level overview on the importance of independent monitoring during the construction phases and its implementation techniques with the help of site quality assurance documentation. The presence of independent engineers during various project phases such as initiation, planning, and execution will help solar power plant developers and engineering, procurement, and construction (EPC) companies achieve acceptable solar power plant quality standards.
Solar PV power plants are often considered to be simple construction projects, but the reality is that essential methodology and implementation techniques are often ignored because of delays starting the project or the need to meet the commercial operational date (COD) deadline in the power purchase agreement (PPA).
Basic health, safety and environment (HSE) site quality documentation such as the field quality plan (FQP), installation checklist, pre-commissioning checklist, and testing checklist are frequently treated as being unimportant. Even imperative guidelines that detail project construction activities such as the method of statements (MOS) or technical work procedures (TWP) are regularly ignored or neglected by the EPC. These plans and procedures can be implemented and adhered to as a project progresses with the help of independent engineers.
These actual images from project sites illustrate low quality civil engineering work that was executed in the absence of supervision or construction monitors.
Lack of supervision and underutilization of implementation quality checklists has resulted in structural post pile cap erosion after heavy rains in Figure (A). Improper grouting and insufficient finish to the foundation bolts of the structural pole can be observed in Figure (B).
Figure (C) shows deformed structural bracing members for the installation of module mounting structures that is the result of implementation quality checklists not being used along with no independent oversight. Deformed bracing can be replaced, but in Figure (D) the support post has been installed at an incline, which will result in instability to modules. These low quality mistakes are repairable, but at a high cost and the rework will consume precious time towards meeting project completion deadlines. Such issues have been observed to be accepted by developers as defects. Quality lapses are commonly observed and noted in the electrical infrastructure as well. Modules can be damaged or even broken beyond acceptable limits, leading to higher costs for project installations. Compromised quality can lower energy generation, causing a project to fall short of the target output for which it was selected.
To mitigate the above concerns, solar PV project construction should typically progress through the following stages: planning, conceptualization, schematic design, construction design and drawings, and most importantly construction administration to ensure that quality control and assurance is implemented throughout the project lifecycle illustrated in Figure (E).
Figure (E): Project Life Cycle
Appointing a designated team of engineers for construction monitoring during the project phases to confirm the implementation of quality control and assurance systems would achieve acceptable quality plant standards at delivery. These engineers would principally help contractors follow consultant, developer, and manufacturer recommendations during installation, testing, and commissioning for all the project components. They would compile records of all the installation and testing results in the form of checklists, which will in turn make the project documentation stronger and more useful for future O&M.
Construction monitoring of solar PV plants needs to be performed in the interest of quality control and to resolve issues that arise due to non-conformance to standard industry practices. In this model, every task is completed in a sequential manner and the steps include resolving common problem or lapses as shown in Figure (F).
Project construction monitoring teams would apply their knowledge, skills, tools, and techniques to project activities to assure they meet established project safety and quality requirements. They would also be proficient at helping EPCs in implementation aspects such as following quality plans, utilizing check lists, completing compliance reports, resource management, etc. It is essential for the solar industry in India to appoint construction monitoring teams so that a standard level of project construction can be achieved and maintained.
- Author: Mr. Dhiraj Madje, Divisional Manager- Projects and Construction Management, Sgurrenergy India Pvt. Ltd.
- Article Link: https://www.linkedin.com/pulse/solar-pv-projects-case-independent-construction-monitoring-madje/
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.