Growing Pain #1: Maximising efficiency and minimising risk in large-scale project design
Ten years ago, an average solar PV project was a relatively simple affair, comprising 10MW of fixed, monofacial modules. The market has moved on a long way since then. Now, typical solar farms approach 100MW in size and may use a range of technologies such as bifacial, intelligent tracking and floating modules, creating new possibilities for more efficient energy production.
Solar designers have a broader scope than ever before to create optimised projects. But the increased complexity of larger, more technologically varied projects requires smarter project design than ever. Adding to the pressure, the increasingly competitive market environment globally means that there is little margin for error for solar developers.
On larger projects still, at the gigawatt level, these challenges are amplified. Investor expectations are higher, technical risks are greater, and the project is more exposed to environmental conditions that impact performance.
To arm solar project designers with the tools they need to deliver on the full potential of the modern ‘megaproject’, developers need the right software. Using advanced simulation technology, they can identify the best configuration of modules and ensure a project generates the most energy for the lowest cost. However, advances in software have often failed to keep pace with the rapid proliferation of project types and module technologies.
And most importantly, this increased diversity puts key assumptions made by project designers under strain – particularly around the strength of their data inputs.
In this instalment of ‘growing pains’, we will explore the ingredients of a profitable large scale solar project and empower solar developers to use data to build resilience into their project design.
How are new technologies transforming large-scale project design?
The drive towards larger and larger projects, has been accompanied – and supported by – significant cost reductions and advances in PV module technology. In recent years, the following trends have been particular drivers of growth:
- The price difference between monofacial and bifacial installations has shrunk significantly, increasing adoption for bifacial modules on a broader range of solar projects.
- Trackers have also become more affordable, while building in improvements such as intelligent algorithms, cloud movement forecasting data, and shading.
- Larger projects can now benefit from trackers which rotate individual sections of modules to ensure that overall asset efficiency is optimised.
- Floating solar installations are becoming increasingly common, opening up a whole range of potential new sites for PV projects.
However, these trends also bring new technical challenges for project designers. For example, the reduction in thermal losses due to the water-cooling effect for floating solar installations is still not well understood. Designers also need to factor in new variables such as water temperature and wave heights.
Fundamentally, the problem designers face as they look to enable the effective roll out of exciting new technologies for new solar projects is a lack of suitable data. Unfortunately, the majority of free or low-cost resource datasets do not account for the increased range of environmental parameters that impact the performance of these systems.
Key considerations when assessing the suitability of datasets for these technologies are:
- Bifacial modules – does your dataset include albedo data?
- Intelligent trackers – is your resource data sufficiently granular to show variations across a large-scale project site, or to sub-hourly resolution?
- Floating solar – does your data include the full range of complex meteorological inputs such as water temperature and wind speeds at the water’s surface?
These are critical inputs that must be integrated into simulation software and project design tools, before the full potential of these technologies can be realised.
Better environmental and resource data will also empower developers to independently validate and verify the claims made by manufacturers of bifacial modules and tracking systems. Many independent software tools are not as accurate as those used by manufacturers, so solar developers often trust the statistics without the ability to interrogate what they are told.
In this context, it is clear that data standards across the industry must be improved, giving developers and designers the knowledge they need to make the most of advances in technology.
How can developers manage financial risk on large-scale projects?
The larger a project gets, the higher the financial stakes. Underperformance costs more. Damage is more expensive to repair. The move away from feed-in-tariffs and subsidies towards a merchant, PPA-backed market has increased the risk falling on developers – driving more aggressive auction bids and squeezing margins.
Furthermore, although unit costs have fallen around 70% compared to ten years ago, with around $500,000 of investment needed per MW now compares to $2 million per MW then, Capital Expenditure (CAPEX) has increased by a factor of 3 as projects scale up. This means that financial risks of developing and operating a solar project are no less significant.
A successful solar project has a good ROI for investors. A poorly designed project can become a liability.
Underestimation of future production during the design phase may lead to significant technical difficulties in the operational phase, increasing the risk of expensive module and inverter failures. Overestimation will lead to consistent underperformance, leading to financial losses during periods of below-par solar irradiance.
By ensuring accurate resource data is used throughout all stages of project design, developers can minimise their exposure to financial risks and stand to benefit from:
- Increased confidence in the potential profitability of projects
- Valuations that stand up to scrutiny from investors
- Setting the terms of contracts reliably by accurately calculating energy yields
- Bidding lower with more certainty in competitive auctions
Which data inputs are needed for best practice project design?
Using reliable resource data in project design has a significant role to play in mitigating both technical and financial risks to a large-scale solar installation. But what does ‘reliable’ look like in practice? And which variables need to be considered?
Generally speaking, a more accurate data inputs used in the project design phase will result in a more profitable project. Well informed simulations and truly optimised design configurations of large-scale projects require key solar and meteorological data parameters – the more up to date and granular, the better. These include:
- Historical subhourly (15 minute or more granular) time series covering extensive period of time, to better calculate long-term averages, while shining a spotlight on variability and the occurrence of extreme phenomena.
- Normal operational parameters such as Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), Diffuse Irradiance (DIF), and Global Tilted Irradiance (GTI) – the foundation of production.
- Environmental characteristics, including Albedo (ALB), Air Temperature (TEMP), Relative Humidity (RH), Wind Speed (WS), and Precipitation (PREC) to estimate risks effectively and account for increased degradation and the risk of damage from wind gusts, waves, salty conditions and sandstorms.
The best projects are designed by considering all possible environmental inputs, even in the pre-feasibility stage, then maintaining continuity in the use of data tools in the design, development and operation stages. This produces a bank of data from each stage of the project, which can be used to analyse current conditions to drive better decision making.
Ultimately, this saves time and resources while improving long-term reliability. Project lifetime can be extended by effective design, adequate sizing of components, optimization of construction and operation, and preventive maintenance. Accurate data underpins all of this.
What advances in project design tools will further enhance large-scale solar profitability?
As we have mentioned, the solar sector has seen an increase in project diversity. At the design stage, there are several different options available. Selecting the best design involves running thousands of simulations to identify the optimal configuration.
The majority of desktop-based tools currently available to project developers do not have the capacity for the number of simulations necessary for design optimisation. Cloud-based design optimisation tools often do, but they typically rely on suboptimal low resolution datasets, covering limited period of time, with insufficient validation and low reliability. Inputting a validated, high resolution bankable data source into project design tools is vital.
Inaccurate data can influence designers to opt for a particular combination of technologies, over another, more suitable option. Decisions developers make in the design stage determine profitability over the lifetime of the project – and for large scale solar, even a 1% increase in production can have a huge effect on profitability.
The future of large-scale solar project design rests on the adoption of advanced software tools which can work effectively with full-time series data to simulate and visualise results. Access to statistical and mapping tools will also be essential to ensure all relevant factors and weather situations are taken into account.
Project designers will also be supported in future by adaptive simulation tools, able to provide quick preliminary results where needed, or a full, detailed simulation integrating multiple data sets.
The increased size of solar projects will play a key role in creating economies of scale that keep the industry competitive. It is vital to ensure that the opportunity is not wasted. This means equipping solar designers with the data tools they need to deliver optimised, profitable projects.
Solargis works with solar developers globally to optimise their designs by providing industry-leading solar resource data and expert consultancy during the prospecting and evaluation phases of the asset.
Get in touch at https://solargis.com/about-us/contact to find out more about how Solargis can help your large-scale project reach its full potential.
Case study: using sub-hourly solar resource data to future proof a large-scale design
The advent of smart, more efficient inverters has changed the calculations made during project design. Mega projects already push the envelope on what is possible using existing components. Increasingly competitive auctions have spurred developers to seek extra efficiencies and make the best use of current technology.
For example, inverters convert electricity produced by solar modules as direct current into alternating current. The ideal DC/AC ratio is approximately 1.05 – 1.15, depending on inverter specification, to maximise inverter performance. By using fewer inverters, project designs can change the ratio of power, so that the asset supplies more useful energy to the grid. However, as the ratios of DC/AC edge towards 1.5, large-scale projects can test the limits of inverters.
This strategy helps to counter reductions in efficiency due to aging modules or partially compensate for short-term solar resource variability. At the start of the project lifecycle, this can result in ‘clipping’, where excess energy in peak hours is cut off. This is acceptable up to certain levels, as it means extra production in off peak hours. However, above these levels it causes overload, which is challenging for inverters to handle. In extreme cases inverters can be damaged, and the project risks curtailment and grid penalties.
Crucial to enabling effective design at this finely balanced margin is accurate, high-resolution resource data and forecasting. Project owners can then understand potential losses at a 1 or 5 minute level, and ensure that projected production figures in peak times are correct.