solar Data-Files - Thunder Said Energy

Wind and solar: costs of grid inter-connection?

What are the costs of inter-connecting a utility-scale wind and solar project into the power grid, via a spur line, grid tie-in or feeder?

This data-file assesses twenty case studies of renewables assets in North America, based on published inter-connection documents.

Costs are highly variable. But a good baseline is to expect $100-300/kW of grid inter-connection costs, or $3-10/kW-km, over a 10-70 km typical distance (which includes the length of downstream lines that must be upgraded). Larger and higher voltage projects tend to have lower tie-in costs.

What is most surprising is how vastly the ranges can vary. The lowest-cost tie-in was $25/kW, tying in a solar asset to a 230kV power line with spare capacity that is a mere 1-mile away. Whereas the highest-cost tie-in was $1,250/kW (i.e., more than the 40MW solar project itself!) where the asset owner was asked to contribute an eye-watering c$50M to cover the costs of upgrading 500km of high-voltage transmission lines downstream of the inter-connection point.

Recent Commentary: to read more about costs of wind or solar grid connection, please see our article here. We are getting increasingly excited about opportunities in power transmission and power-electronics .

Global Energy Markets: 1750 to 2100

This model breaks down 2050 and 2100’s global energy market, based on a dozen core input assumptions.

You can ‘flex’ these assumptions, to see how it will affect future oil, coal and gas demand, as well as global carbon emissions.

Annual data are provided back to 1750 to contextualize the energy transition relative to prior transitions in history (chart below).

We are positive on renewables, but fossil fuels retain a central role, particularly natural gas, which could ‘treble’ in our base case.

A fully decarbonized energy market is possible by 2050, achieved via game-changing technologies that feature in our research.

Inter-correlations between solar assets?

This data-file aggregates granular data from seven solar assets around Western Europe (Netherlands, Germany, Belgium, Switzerland), to understand their volatility and inter-correlation, over a sample week in August-2021.

Across the entire week, the average solar plant generated at 12% of its nominal capacity, the 90th percentile was 40% and the maximum was 75%; which may suggest that the panels have been oversized relative to inverters or MPPT is not fully optimized.

Volatility and inter-correlations are quantified in the data-file, but are generally high. For example, over >500km distances, different solar generators’ 15-min by 15-min output is 60-80% correlated, which is even greater than for offshore wind (data here).

These issues suggest solar can provide a meaningful portion of decarbonizing grids, but surpassing 20% requires back-ups.

Solar power: decommissioning costs?

This data-file aims to break down the costs of decommissioning solar projects. Gross costs are estimated within a range of $0.03-0.20/W, which is around 3-20% of the initial installation costs.

This is better than nuclear, offshore wind and coal decommissioning costs, but worse than natural gas (data are shown in the file).

What might help the economics for solar is the ability to re-use old panels, in markets that are particularly price sensitive. In the best cases, this could allow zero-cost decommissioning of solar assets or possibly even a small profit.

Re-deploying old solar panels could also accelerate the global deployment of solar by c5%. Our notes, conclusions and numbers are built up in the data-file.

Solar costs: four horsemen?

Solar costs have deflated by an incredible 90% in the past decade to 4-7c/kWh. Some commentators now hope for 2c/kWh by 2050. Further innovations are doubtless. But there are four challenges, which could stifle future deflation or even re-inflate solar. Most debilitating would be a re-doubling of CO2-intensive PV-silicon. Our 15-page report explores re-inflation risks for solar developers.

Photovoltaic silicon: the economics?

The purpose of this model is to break down the most likely contribution of photovoltaic silicon to overall solar panel costs. The model starts from quartz, which is smelted into silicon metal, purified into polysilicon, upgraded into mono-crystalline poly-silicon and ultimately used in solar cells.

We estimate silicon explains $0.1/W of the cost of a $0.3/W panel. There is no way silicon producers are making economic returns below $12.5/kg mono-crystalline polysilicon prices.

If environmental costs are reflected as well, then PV-silicon price could double. Specifically, the average kg of PV silicon in a solar panel is most likely associated with 140kg of direct CO2 emissions.

In 2022, we have also updated the analysis to capture the costs of re-shoring a Chinese PV silicon facility to the West, which we think would increase prices by 2.5x in the US and 4x in Europe, re-inflating solar levelized costs by 1-2c/kWh (wholesale basis).

Energy Costs of Constructing Solar Assets

This data-file quantifies the energy costs of manufacturing solar panels, based on 10 studies and prior projects.

We see the average solar project requiring 5MWH/kW, with a 2.3-year energy payback, a c10x energy-return on energy-invested and CO2-intensity of 90kg/boe (for contrast, average oil is c440kg/boe and average gas is c350kg/boe).

The data-file contains calculations and data on different, individual studies, and estimates the net impact of solar on fossil fuel demand – past and present – after reflecting the net energy costs of solar manufacturing.

Comparable data for wind projects is linked here.

Solar power: what challenges?

Solar panel costs have been deflating at a rate of c20% per annum as the industry scales up into manufacturing mode. The IEA recently stated solar could thus provide the “cheapest electricity in history”.

What next? To answer this question, we reviewed 70 patents filed by leading solar manufacturers in 2020, in order to see what challenges they are aiming to resolve. We expect deflation to continue apace, while panels will also gain greater efficiency and longevity.

This data-file explains the conclusions, summarizing the findings from the patents and giving specific examples of gains in the offing.

Specific companies’ focuses can also be seen from the patents. Covered companies include Canadian Solar, Hanergy, Jinko, LG, Miasole, Panasonic, SunPower et al.

Tree database: forests to offset CO2?

Nature-based solutions are among the most effective ways to abate CO2. Forest offsets will cost $2-50/ton, decarboning liquid fuels for <$0.5/gallon and natural gas for <$1/mcf (chart below).

The data-file tabulates hundreds of data-points from technical papers and industry reports on different tree and grass types. It covers their growing conditions, survival rates, lifespans, rates of CO2 absorption (per tree and per acre) and their water requirements (examples below).

Gas power: decline rates?

This data-file tabulates the power generation profiles of 3,000 US natural gas-fired power plants, which have reported data to the US EIA, aggregated using in-house web-scraping software.

Unlike wind and solar assets, which exhibit clear decline rates of 1.5% and 2.5% per year, natural gas assets run at c44% of their peak utilization rates on average, which does not change materially over time, flexing within an interquartile range that spans from 14% to 74%.

In other words, gas power plants provide flexibility and long-term reliability in a grid, as they are dialled up and dialled down over time to meet demand. This is also illustrated by looking at the underlying data of individual power plants in the file (chart below).

The data-file also presents a cautionary tale from California. To accomodate 40TWH of new utility-scale renewables generation, we show that 35TWH of gas generation has now been permanently shuttered and another 11TWH has been idled. These closures are equivalent to 30% of California’s baseload and 17% of its peakload power capacity, providing one explanation for the State’s recent rolling black-outs. Full details are split out in the data-file.

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