Solar Energy
So you want to build a solar or wind farm? Here’s how to decide where

So you want to build a solar or wind farm? Here’s how to decide where
by David L. Chandler | MIT News
Boston MA (SPX) Dec 08, 2024
Deciding where to build new solar or wind installations is often left up to individual developers or utilities, with limited overall coordination. But a new study shows that regional-level planning using fine-grained weather data, information about energy use, and energy system modeling can make a big difference in the design of such renewable power installations. This also leads to more efficient and economically viable operations.
The findings show the benefits of coordinating the siting of solar farms, wind farms, and storage systems, taking into account local and temporal variations in wind, sunlight, and energy demand to maximize the utilization of renewable resources. This approach can reduce the need for sizable investments in storage, and thus the total system cost, while maximizing availability of clean power when it’s needed, the researchers found.
The study, appearing in the journal Cell Reports Sustainability, was co-authored by Liying Qiu and Rahman Khorramfar, postdocs in MIT’s Department of Civil and Environmental Engineering, and professors Saurabh Amin and Michael Howland.
Qiu, the lead author, says that with the team’s new approach, “we can harness the resource complementarity, which means that renewable resources of different types, such as wind and solar, or different locations can compensate for each other in time and space. This potential for spatial complementarity to improve system design has not been emphasized and quantified in existing large-scale planning.”
Such complementarity will become ever more important as variable renewable energy sources account for a greater proportion of power entering the grid, she says. By coordinating the peaks and valleys of production and demand more smoothly, she says, “we are actually trying to use the natural variability itself to address the variability.”
Typically, in planning large-scale renewable energy installations, Qiu says, “some work on a country level, for example saying that 30 percent of energy should be wind and 20 percent solar. That’s very general.” For this study, the team looked at both weather data and energy system planning modeling on a scale of less than 10-kilometer (about 6-mile) resolution. “It’s a way of determining where should we, exactly, build each renewable energy plant, rather than just saying this city should have this many wind or solar farms,” she explains.
To compile their data and enable high-resolution planning, the researchers relied on a variety of sources that had not previously been integrated. They used high-resolution meteorological data from the National Renewable Energy Laboratory, which is publicly available at 2-kilometer resolution but rarely used in a planning model at such a fine scale. These data were combined with an energy system model they developed to optimize siting at a sub-10-kilometer resolution. To get a sense of how the fine-scale data and model made a difference in different regions, they focused on three U.S. regions – New England, Texas, and California – analyzing up to 138,271 possible siting locations simultaneously for a single region.
By comparing the results of siting based on a typical method vs. their high-resolution approach, the team showed that “resource complementarity really helps us reduce the system cost by aligning renewable power generation with demand,” which should translate directly to real-world decision-making, Qiu says. “If an individual developer wants to build a wind or solar farm and just goes to where there is the most wind or solar resource on average, it may not necessarily guarantee the best fit into a decarbonized energy system.”
That’s because of the complex interactions between production and demand for electricity, as both vary hour by hour, and month by month as seasons change. “What we are trying to do is minimize the difference between the energy supply and demand rather than simply supplying as much renewable energy as possible,” Qiu says. “Sometimes your generation cannot be utilized by the system, while at other times, you don’t have enough to match the demand.”
In New England, for example, the new analysis shows there should be more wind farms in locations where there is a strong wind resource during the night, when solar energy is unavailable. Some locations tend to be windier at night, while others tend to have more wind during the day.
These insights were revealed through the integration of high-resolution weather data and energy system optimization used by the researchers. When planning with lower resolution weather data, which was generated at a 30-kilometer resolution globally and is more commonly used in energy system planning, there was much less complementarity among renewable power plants. Consequently, the total system cost was much higher. The complementarity between wind and solar farms was enhanced by the high-resolution modeling due to improved representation of renewable resource variability.
The researchers say their framework is very flexible and can be easily adapted to any region to account for the local geophysical and other conditions. In Texas, for example, peak winds in the west occur in the morning, while along the south coast they occur in the afternoon, so the two naturally complement each other.
Khorramfar says that this work “highlights the importance of data-driven decision making in energy planning.” The work shows that using such high-resolution data coupled with carefully formulated energy planning model “can drive the system cost down, and ultimately offer more cost-effective pathways for energy transition.”
One thing that was surprising about the findings, says Amin, who is a principal investigator in the MIT Laboratory of Information and Data Systems, is how significant the gains were from analyzing relatively short-term variations in inputs and outputs that take place in a 24-hour period. “The kind of cost-saving potential by trying to harness complementarity within a day was not something that one would have expected before this study,” he says.
In addition, Amin says, it was also surprising how much this kind of modeling could reduce the need for storage as part of these energy systems. “This study shows that there is actually a hidden cost-saving potential in exploiting local patterns in weather, that can result in a monetary reduction in storage cost.”
The system-level analysis and planning suggested by this study, Howland says, “changes how we think about where we site renewable power plants and how we design those renewable plants, so that they maximally serve the energy grid. It has to go beyond just driving down the cost of energy of individual wind or solar farms. And these new insights can only be realized if we continue collaborating across traditional research boundaries, by integrating expertise in fluid dynamics, atmospheric science, and energy engineering.”
Research Report:Decarbonized energy system planning with high-resolution spatial representation of renewables lowers cost
Related Links
Department of Civil and Environmental Engineering
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Solar Energy
Machine Learning Enhances Solar Power Forecast Accuracy

Machine Learning Enhances Solar Power Forecast Accuracy
by Simon Mansfield
Sydney, Australia (SPX) Feb 18, 2025
As solar power becomes a more significant component of the global energy grid, improving the accuracy of photovoltaic (PV) generation forecasts is crucial for balancing supply and demand. A recent study published in Advances in Atmospheric Sciences examines how machine learning and statistical techniques can enhance these predictions by refining errors in weather models.
Since PV forecasting depends heavily on weather predictions, inaccuracies in meteorological models can impact power output estimates. Researchers from the Institute of Statistics at the Karlsruhe Institute of Technology investigated ways to improve forecast precision through post-processing techniques. Their study evaluated three methods: adjusting weather forecasts before inputting them into PV models, refining solar power predictions after processing, and leveraging machine learning to predict solar power directly from weather data.
“Weather forecasts aren’t perfect, and those errors get carried into solar power predictions,” explained Nina Horat, lead author of the study. “By tweaking the forecasts at different stages, we can significantly improve how well we predict solar energy production.”
The study found that applying post-processing techniques to power predictions, rather than weather forecasts, yielded the most significant improvements. While machine learning models generally outperformed conventional statistical methods, their advantage was marginal in this case, likely due to the constraints of the available input data. Researchers also highlighted the importance of including time-of-day information in models to enhance forecast accuracy.
“One of our biggest takeaways was just how important the time of day is,” said Sebastian Lerch, corresponding author of the study. “We saw major improvements when we trained separate models for each hour of the day or fed time directly into the algorithms.”
A particularly promising approach involves bypassing traditional PV models altogether by using machine learning algorithms to predict solar power directly from weather data. This technique eliminates the need for detailed knowledge of a solar plant’s configuration, relying instead on historical weather and performance data for training.
The findings pave the way for further advancements in machine learning-based forecasting, including the integration of additional weather variables and the application of these methods across multiple solar installations. As renewable energy adoption accelerates, improving solar power forecasting will be key to maintaining grid stability and efficiency.
Research Report:Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning
Related Links
Institute of Atmosphere at CAS
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Solar Energy
The next-generation solar cell is fully recyclable

The next-generation solar cell is fully recyclable
by Robert Schreiber
Berlin, Germany (SPX) Feb 18, 2025
Researchers at Linkoping University have developed a groundbreaking method for recycling all components of a perovskite solar cell without the use of hazardous solvents. The process ensures that recycled solar cells maintain the same efficiency as newly manufactured ones, marking a significant step toward sustainable solar technology. The primary solvent used in this method is water, offering an environmentally friendly alternative to conventional recycling processes.
With the anticipated surge in electricity demand due to the expansion of artificial intelligence and the electrification of transportation, sustainable energy sources must advance to prevent further environmental impact. Solar power has long been considered a viable renewable energy source, with silicon-based panels dominating the market for over three decades. However, as first-generation silicon panels reach the end of their lifespan, waste management poses a major challenge.
“There is currently no effective technology to handle the waste from silicon solar panels. As a result, outdated panels are being discarded in landfills, leading to vast amounts of electronic waste,” explained Xun Xiao, postdoctoral researcher at Linkoping University’s Department of Physics, Chemistry, and Biology (IFM).
Feng Gao, a professor of optoelectronics at the same department, emphasized the importance of considering recyclability in emerging solar technologies: “If we don’t have a recycling solution in place, perhaps we shouldn’t introduce new solar cell technologies to the market.”
Perovskite solar cells are among the most promising alternatives for next-generation solar technology. These cells are lightweight, flexible, and transparent, making them suitable for various surfaces, including windows. Additionally, they achieve energy conversion efficiencies of up to 25 percent, rivaling silicon-based solar cells.
“Many companies are eager to commercialize perovskite solar cells, but we must ensure that they do not contribute to landfill waste. Our project introduces a method where all components of perovskite solar cells can be reused without sacrificing performance,” said Niansheng Xu, postdoctoral researcher at Linkoping University.
Although perovskite solar cells have a shorter lifespan than their silicon counterparts, it is crucial to develop an efficient and environmentally friendly recycling process. Additionally, these cells contain a small amount of lead, essential for high efficiency but requiring proper handling to prevent environmental contamination. In many parts of the world, manufacturers are legally obligated to recycle end-of-life solar cells sustainably.
Existing recycling methods for perovskite solar cells often rely on dimethylformamide, a toxic and potentially carcinogenic solvent commonly found in paint removers. The Linkoping researchers have devised an innovative approach that replaces this hazardous chemical with water, significantly reducing environmental risks. This method enables the recovery of high-quality perovskite materials from the water-based solution.
“We can recover every component-the glass covers, electrodes, perovskite layers, and charge transport layers,” Xiao added.
The next phase of research will focus on scaling up this process for industrial applications. In the long term, scientists believe that perovskite solar cells will become a key component of the global energy transition, particularly as supporting infrastructure and supply chains evolve.
Research Report:Aqueous based recycling of perovskite photovoltaics
Related Links
Linkoping University
All About Solar Energy at SolarDaily.com
Solar Energy
China to further shrink renewables subsidies in market reform push

China to further shrink renewables subsidies in market reform push
by AFP Staff Writers
Shanghai (AFP) Feb 9, 2025
China’s top economic planner said on Sunday it would reduce some renewable energy subsidies in reforms intended to open the booming sector to market forces.
China has sought to scale back government support for renewable energy companies in recent years as the sector reaches critical mass.
It installed a record amount of renewable energy last year and has already surpassed a target to have at least 1,200 gigawatts of solar and wind capacity installed by 2030.
New clean energy projects completed after June 1 must sell electricity at rates determined by the market rather than at preferential rates previously used to support China’s energy transition, the National Development and Reform Commission (NDRC) said in a statement.
The NDRC urged energy producers to “push forward clean energy’s participation in market transactions”.
The commission also said it “encourages electricity providers and electricity buyers to sign multi-year purchase agreements and pre-emptively manage market risks”.
Beijing invested more than $50 billion in new solar supply capacity from 2011 to 2022, according to the International Energy Agency.
It has built almost twice as much wind and solar capacity as every other country combined, according to research published last year.
However, China’s grid is struggling to keep up.
Renewable supply is increasingly being blocked to prevent the grid from becoming overwhelmed, a process known as curtailment.
Beijing has rolled out a series of measures over the past decade aimed at weaning renewable energy providers off state financial support.
It ended subsidies for new solar power stations and onshore wind power projects in 2021.
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