Researchers in Spain have created a novel method to select within a set of water bodies those where the investment in floating PV could be most beneficial. They combined geographic information systems, multi-criteria analysis, and intelligent optimization. The new approach reportedly results up to 8.4% better LCOE compared to conventional methods.
A group of Spanish scientists is proposing a new framework for stakeholders to assess and optimize floating PV (FPV) farms.
The proposed approach is intended for investors and policymakers, as it reportedly enables them to find the most beneficial bodies of water for FPV installation in a specific area or country while also optimizing their tilt angle at a later stage.
Spain was chosen as the first case study of the new method, enabling the researchers to identify the best sites in the country for FPV.
“Floating photovoltaic is in the early stages of implementation, so there are not many previous experiences to standardize decision-making,” they said. “In addition, the lack of specific design tools and production calculations is a barrier to understanding the real advantages. From the point of view of the investment, the stakeholders do not have a complete analysis of the profitability of their investment. From a technical, environmental, and legislative point of view, there is not enough information available to establish standards and criteria for the design and selection of the most suitable water bodies.”
The first step of the proposed method is integrating multi-source and multi-resolution geolocalized data geographic information systems (GIS) in a Javascript and Python-based Web-GIS environment.
Once all of the GIS data about the local bodies of water is collected, a multi-criteria analysis (MCDA) is performed, giving different values to different parameters to be considered in the decision-making. These parameters are the generation capacity factor, the water level variation, the levelized cost of energy (LCOE), the distance from the grid, the reduction of greenhouse gas (GHG) emissions, the legal water coverage rate, and the number of water bodies within a 25 km range.
“The MCDA’s objective is to obtain a set of solutions ordered from most to least suitable. Two methods have been selected from those used in the state of the art: COmplex PRoportional ASsessment (COPRAS) and the Weighted Aggregates Sum Product Assessment (WASPAS),” the scientists explained. “Results of the sensitivity and comparative analysis performed show that COPRAS presents a more stable ranking than WASPAS. For this reason, the COPRAS method is selected as more accurate.”
As the MCDA analysis yields the most beneficial bodies of water in a specific area, the method runs a tilt optimization artificial intelligence AI algorithm. Specifically, it uses genetic algorithms (GAs), which are widely used to solve optimization problems. GAs are metaheuristic methods that don’t guarantee the best solution but work well when finding exact solutions is too difficult or impossible.
Running the novel method on Spain, the group found that the total generation potential of all the masses consulted is 55.8 TWh, representing 22.3% of the country’s annual demand. However, they also found that out of hundreds of potential bodies of water, eleven represent about 32% of the total potential installed power. “This shows that those water bodies where the impact of the investment is greater are obtained,” the group explained.
Further, the group took the country’s top five bodies of water and ran the GA on them to find the best tilt angel. Then they compared it to six other tilt optimization methods from the literature. “In the case of LCOE, the improvements are between 2.1% and 8.4%, or in the case of GHG avoided, the improvements are between 0.66% and 10.3%,” they stated.
The framework was presented in “An innovative approach to assessing and optimizing floating solar panels,” published in Energy Conversion and Management. The research group included academics from Spain’s University of Salamanca and the scientific research and development services company Pudbuq.
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