Category Archives: Energy

The Complexity of a Zero Carbon Grid – Energy Insights

Energy Insights is a series highlighting interesting energy content from around the web.

Previous posts in this series include Automated Cars and How to save the energy system.


I’m excited to present this Energy Insights post. I’m highlighting a few interesting insights from the ‘The Complexity of a Zero Carbon Grid’ show.

This is very special as The Interchange podcast has only been publically relaunched recently.

The show considers what may be necessary to get to levels of 80-100% renewables. Stephen Lacey and Shayle Kann host the show with Jesse Jenkins as the guest.

The concept of flexibility

Jenkins observes that the concept of flexibility of electrical capacity appearing in literature. Flexibility means how quickly an asset is able to respond to change.

A combined cycle gas turbine plant is usually more flexible than a coal or nuclear generator. One reason for this is the ability to control plant electric output by modulating the supplementary burner gas consumption.


We will need flexibility on a second, minute, hourly or seasonal basis.

This concept of flexibility was also recently touched on by the excellent Energy Analyst blog. Patrick Avis notes that we need both flexibility (kW or kW/min) and capacity (kWh) for a high renewables scenario.

The post ‘Flexibility in Europe’s power sector’ could easily be enough material for a few Energy Insights posts. Well worth a read.

One investment cycle away

Jenkins observes that the investment decisions we make today will affect how we decarbonise in the future. Considering the lifetime of many electricity generation assets, we find that we are only a single investment cycle away from building plants that will be operating in 2050.

Most deep decarbonisation roadmaps include essentially zero carbon electricity by 2050. We need to ensure that when the next investment cycle begins we are not installing carbon intense generation as it would still be operating in 2050.

For both gas and coal the implied cutoff date for plant operation to begin is between 2010 – 2020.

Increasing marginal challenge of renewables deployment

The inverse relationship between the level of deployment of renewables and the marginal value added is well known. Jenkins notes that this relationship also applies to the deployment of storage and demand side response.

As renewable deployment increases the challenges for both storage and demand side response also increase.

Seasonal storage technologies

1 – Power to gas

Electricity -> hydrogen -> synthetic methane.

Figure 3 – Apros Power to Gas

Intermittency of the supply of excess renewable generation means that power to gas asset wouldn’t be fully utilized.

Didn’t cover the possibility of storage of electricity to allow a constant supply of electricity to the power to gas asset.

2 – Underground thermal

Limited to demonstration scale.

Didn’t cover the feasibility of generating electricity from the stored heat.

I would expect that the temperature of the stored heat is low.  Perhaps the temperature could be increased with renewable powered heat pumps.


Thanks for reading!

 

Energy Basics – Capacity Factor

All men & women are created equal. Unfortunately the same is not true for electricity generating capacity.
 
Capacity on it’s own is worthless – what counts the electricity generated (kWh) from that capacity (kW). If the distinction between kW and kWh is not clear this previous post will be useful.
 

Capacity factor is one way to quantify the value of capacity. It’s the actual electricity (kWh) generated as a percentage of the theoretical maximum (operation at maximum kW).

For example to calculate the capacity factor on an annual basis:
 
 

There are many reasons why capacity will not generate as much as it could.

Three major reasons are maintenance, unavailability of fuel and economics.

 
Maintenance
 
Burning fossil fuels creates a challenging engineering environment. The core of a gas turbine is high pressure & temperature gases rapidly rotating blazing hot metal. Coal power stations generate electricity by high pressure steam forcing a steam turbine to spin incredibly fast.
 
These high challenges mean that fossil fuel plants need a lot of maintenance. The time when the plant is being maintained is time the capacity isn’t generating electricity.
 
Renewables plants need a lot less maintenance than a fossil fuel generator. No combustion means there is a lot less stress on equipment.
 
Availability of fuel
 
Yet while renewables come ahead in terms of maintenance, they fall behind due to a constraint that fossil fuel generation usually doesn’t suffer from – unavailability of fuel.
 
This is why renewables like wind & solar are classed as intermittent. Fuel is often not available meaning generation is often not possible.
 
Solar panels can’t generate at night. Wind turbines need wind speeds to be within a certain range – not too low, not too high – just right.
 
This means that wind & solar plants are often not able to generate at full capacity – or even to generate at all. This problem isn’t common for fossil fuel generation. Fossil fuels are almost always available through natural gas grids or on site coal storage.
 
Economics
 
The final reason for capacity to not generate is economics.
 
The relative price of energy and regulations change how fossil fuel capacity is dispatched. Today’s low natural gas price environment is the reason why coal capacity factors have been dropping.
 
Here renewables come out way ahead of fossil fuels. As the fuel is free renewables can generate electricity at much lower marginal cost than fossil fuels. Wind & solar almost always take priority over fossil fuel generation.
 
Typical capacity factors
 
The capacity factor wraps up and quantifies all of the factors discussed above.
 
Table 1 – Annual capacity factors (2014-2016 US average)

CoalCCGTWindSolar PVNuclear
Annual Capacity Factor56.13%53.40%33.63%26.30%92.17%

 

Table 1 gives us quite a bit of insight into the relative value of different electricity generating technologies. The capacity factor for natural gas is roughly twice as high as solar PV.

 

We could conclude that 1 MW of natural gas capacity is worth around twice as much as 1 MW of solar PV.

How useful is the capacity factor?

Yet the capacity factor is not a perfect measure of how valuable capacity is. Taking the average of anything loses infomation – capacity factor is no different.
 
Two plants operating in quite different ways can have the same capacity factor. A plant that operated 50% for the entire year and a plant that generated for half of the year at full capacity will both have an identical capacity factor.
 
The capacity factor loses infomation about the time of energy generation. The time of generation & demand is a crucial element in almost every energy system.
 
Generation during a peak can be a lot more valuable to the world than generation at other times. Because of the nature of dispatchable generation it is more likely to be running during a peak.
 
This leads us to conclude that low capacity factor generation could be more valuable than higher capacity factor generation.  This is especially true for solar in many countries as a) the peak often occurs when the sun is down and b) all solar generation is coincident.
 
The solution to the intermittency problem of renewables is storage. Storage will allow intermittent generation to be used when it’s most valuable – not just whenever it happens to be windy or sunny.
 
Thanks for reading!

Energy Insights – How to save the energy system – André Bardow

Energy Insights is a series where we highlight interesting energy content from around the web.

The previous post in this series was about the 2017 BP Annual Energy Outlook.


These three Energy Insights come from a TED talk titled ‘How to save the energy system’ given by André Bardow.   André is a fantastic speaker and his talk is well worth a watch.

Below are three of the many interesting things André discusses. I really enjoyed writing this – I find all of this fascinating.

Why peak oil won’t save the planet

As humanity burns more oil the amount of oil left to recover should decrease. This is logical – right?

Figure 1 below shows the opposite is true. Estimated global oil reserves have actually increased over time.  The key is to understand the definition of oil reserves.

Figure 1 – Estiamted global oil reserves Estimated global oil reserves (1980 – 2014)

Oil reserves are defined
 as the amount of oil that can be technically recovered at a cost that is financially feasible at the present price of oil.

Oil reserves are therefore a function of a number of variables that change over time:

  • Total physical oil in place – physical production of oil reduces the physical amount of oil in the ground.
  • Total estimated oil in place – the initial estimates are low and increased over time.
  • Oil prices – historically oil prices have trended upwards (Figure 2). Oil reserves defined as a direct function of the present oil price.
  • Technology – the oil & gas industry has benefited more than any other energy sector from improved technology.  Improved technology reduces the cost of producing oil.  This makes more oil production viable at a given price.
Figure 2 – Crude oil price (1950 – 2010)

Only the physcial production of oil has had a negative effect on oil reserves.

The other three (total oil estimate, technology & oil price) have all caused oil reserve estimates to increase.

We are not going to run out of oil any time soon. The limit on oil   production is not set by physcial reserves but by climate change.  I find this worrying – it would be much better if humanity was forced to swtich to renewables!

Wind & solar lack an inherent economy of scale

 A lot of the advantages in systems are from economies of scale – energy systems are no different.  Larger plants are more energy efficient and have lower specific capital & maintenance costs.

Energy efficiency improves with size as the ratio of fixed energy losses to energy produced improves.   Figure 3 shows an example of this for spark ignition gas engines.
Figure 3 – Effect of gas engine size [kWe] on gross electric efficiency [% HHV]

This is also why part load efficiency is worse than full load efficiency.  Energy production reduces but fixed  energy losses remain constant.

Specific capital & operating costs also improve with size.  For example, a 10 MW and 100 MW plant may need the same land area at a cost of £10,000.  The specific capital cost of land for both projects is £1,000/MW versus £100/MW respectively.

Fossil fuel plants use their economy of scale to generate large amounts of electricity from a small number of prime movers.

Wind & solar plants are not able to do this. The problem is the prime movers in both wind & solar plants.

The maximum size of a wind or solar prime movers (wind turbines or solar panels) is small comapred with fossil fuel prime movers.  For example GE offer a 519 MWe gas turbine – the world’s largest wind turbine is the 8 MWe Vestas V164.

Figure 4 – The Vestas V164

A single gas turbine in CCGT mode is more than enough to generate 500 MWe.  A wind farm needs 63 wind turbines to generate the same amount.

The reason for the difference is fundamental to the technologies – the energy density of fuel.  Fossil fuels offer fantastic energy densities – meaning we can do a lot with less fuel (and less equipment).  Transportation favours liquid fossil fuels for this reason.

Wind & solar radiation have low energy densities. To capture more energy we need lots more blade or panel surface area.  This physical constraint means that scaling the prime movers in wind & solar plants is difficult. The physical size increases very fast as we increase electricity generation.

This means that wind turbines & solar panels need to very cheap at small scales. As wind & solar technologies improve there will be improvements in both the economy of scale & maximum size of a single prime mover.

But to offer a similar economy of scale as fossil fuels is difficult due to low energy density fuel.  It’s not that wind & solar don’t benefit from any economy of scale – for example grid connection costs can be shared. It’s the fact that fossil fuels:

  • share most of these economies of scale.
  • use high energy density fuels, which gives a fundamental advantage in the form of large maximum prime mover sizes.

We need to decarbonise the supply of heat & power as rapidly as possible.  Renewables are going to be a big part of that.  The great thing is that even with this disadvantage wind & solar plants are being built around the world!

Average German capacity factors
Andre gives reference capacity factors for the German grid of:

  • Solar = 10 %.
  • Wind = 20 %.
  • Coal = 80 %.

This data is on an average basis.  The average capacity factor across the fleet is usually more relevant than the capacity factor of a state of the art plant.

It is always good to have some rough estimates in the back if your mind.  A large part of engineering is using your own estimates based on experience with the inputs or outputs of models.

Thanks for reading!

CHP Scoping Model v0.2

See the introductory post for this model here.  

This is v0.2 of the CHP scoping model I am developing.  The model is setup with some dummy data.

If you want to get it working for your project all you need to do is change:

  • heat & power demands (Model : Column F-H)
  • prices (Model : Column BF-BH)
  • CHP engine (Input : Engine Library).

You can also optimize the operation of the CHP using a parametric optimization VBA script (Model : Column BW).

You can download the latest version of the CHP scoping model here.

Thanks for reading!

CHP Scoping Model v0.1

The most recent version of this model can be found here.

My motivation for producing this model is to give engineers something to dig their teeth into.

My first few months as an energy engineering graduate were spent pulling apart the standard CHP model used by my company.  A few years later I was training other technical teams how to use my own model.

I learnt a huge amount through deconstructing other peoples models and iterating through versions of my own.

So this model is mostly about education. I would love it to be used in a professional setting – but we may need a couple of releases to iron out the bugs!

In this post I present the beta version of a CHP feasibility model.  The model takes as inputs (all on a half hourly basis):

  • High grade heat demand (Model : Column F).
  • Low grade heat demand (Model : Column G).
  • Electricity demand (Model : Column H).
  • Gas, import & export electricity price (Model : Column BF-BH).

Features of the model:

  • CHP is modeled as a linear function of load. Load can be varied from 50-100 %.
  • Can model either gas turbines or gas engines. No ability to model supplementary firing.  Engine library needs work.
  • The CHP operating profile can be optimized using a parametric optimization written in VBA.
    • Iteratively increases engine load from 50% – 100% (single HH period),
    • Keeps value that increased annual saving the most (the optimum),
    • Moves to next HH period,
    • Optimization can be started using button on (Model : Column BV). I reccomend watching it run to try understand it.  The VBA routine is called parametric.
  • Availability is modeled using a randomly generated column of binary variables (Model : Column C).
You can download the latest version of the CHP scoping model here.

Thanks for reading!

Monte Carlo Q-Learning to Operate a Battery

I have a vision for using machine learning for optimal control of energy systems.  If a neural network can play a video game, hopefully it can understand how to operate a power plant.

In my previous role at ENGIE I built Mixed Integer Linear Programming models to optimize CHP plants.  Linear Programming is effective in optimizing CHP plants but it has limitations.

I’ll detail these limitations in future post – this post is about Reinforcement Learning (RL).  RL is a tool that can solve some of the limitations inherent in Linear Programming.

In this post I introduce the first stage of my own RL learning process. I’ve built a simple model to charge/discharge a battery using Monte Carlo Q-Learning. The script is available on GitHub.

I made use of two excellent blog posts to develop this.  Both of these posts give a good introduction to RL:

Features of the script
 

As I don’t have access to a battery system I’ve built a simple model within Python.  The battery model takes as inputs the state at time t, the action selected by the agent and returns a reward and the new state.  The reward is the cost/value of electricity charged/discharged.

def battery(state, action):  # the technical model
    # battery can choose to :
    #    discharge 10 MWh (action = 0)
    #    charge 10 MWh or (action = 1)
    #    do nothing (action = 2)

    charge = state[0]  # our charge level
    SP = state[1]  # the current settlement period
    action = action  # our selected action
    prices = getprices()
    price = prices[SP - 1]  # the price in this settlement period

    if action == 0:  # discharging
        new_charge = charge - 10
        new_charge = max(0, new_charge)  
        charge_delta = charge - new_charge
        reward = charge_delta * price
    if action == 1:  # charging
        new_charge = charge + 10
        new_charge = min(100, new_charge)
        charge_delta = charge - new_charge
        reward = charge_delta * price
    if action == 2:  # nothing
        charge_delta = 0
        reward = 0

    new_charge = charge - charge_delta
    new_SP = SP + 1
    state = (new_charge, new_SP)
    return state, reward, charge_delta

The price of electricity varies throughout the day.
The model is not fed this data explicitly – instead it learns through interaction with the environment.
 
One ‘episode’ is equal to one day (48 settlement periods).  The model runs through thousands of iterations of episodes and learns the value of taking a certain action in each state.  
 
Learning occurs by apportioning the reward for the entire episode to every state/action that occurred during that episode. While this method works, more advanced methods do this in better ways.
def updateQtable(av_table, av_count, returns):
    # updating our Q (aka action-value) table
    # ********
    for key in returns:
        av_table[key] = av_table[key] + (1 / av_count[key]) * (returns[key] - av_table[key])
    return av_table
The model uses an epsilon-greedy method for action selection.  Epsilon is decayed as the number of episodes increases.
Results
 
Figure 1 below shows the the optimal disptach for the battery model after training for 5,000 episodes.  
Figure 1 – Electricity prices [£/MWh] and the optimal battery dispatch profile [%]
I’m happy the model is learning well. Charging occurs during periods of low electricity prices. It is also fully draining the battery at the end of the day – which is logical behavior to maximise the reward per episode.  
 

Figure 2 below shows the learning progress of the model.

Figure 2 – Model learning progress
Next steps
 
Monte Carlo Q-learning is a good first start for RL. It’s helped me to start to understand some of the key concepts.
 
Next steps will be developing more advanced Q-learning methods using neural networks.

Energy Insights – 2017 BP Energy Outlook

Energy Insights is a series where we pull out key points from energy articles around the web. This is not a full summary but a taste – if you like the ideas then please watch the presentation & read the report.  

Previous posts in this series include the IEA 2016 World Outlook and Automated Cars.

Often people jump to the conclusion that anything released by an oil major is self-serving.  Don’t be like this!  If you ignore a report like the Outlook it is only you that is missing out.

The search for truth requires humility.  You need to be honest about your own ignorance.  You need to be open to learning from any source of infomation.  You need to be confident that you can judge the quality of that infomation.

Below I highlight BP’s view on passenger cars and the effect of climate policies on natural gas over the course of the Outlook (2015-2035).

Oil consumption for passenger cars

Figure 1 – Net change in car oil consumption

BP project a doubling of the global passenger car fleet due to the continued emergence of the middle class.

Luckily the increased oil consumption associated with double the number of cars is almost entirely offset by a 2.5 % annual improvement in fuel efficiency.

This fuel efficiency assumption seems quite small – but actually it is a strong break with the past.  The average for the last twenty years is only 1 %.

Even small improvements in fuel efficiency have a large effect on oil consumption due to the size of the combustion engine fleet.

The opposite is true with electric cars.  BP are projecting the number of electric cars increasing from 1.2 million to 100 million.  This is a compounded annual growth rate of around 25 %!

Unlike with fuel efficiency this relative increase has very little effect.  Electric car deployment increasing by 100 times leads to only a 6 % reduction versus 2015 oil consumption.

Electric cars are a sexy topic that gets a lot of media attention – yet vehicle fuel efficiency may be more important if we care about climate change.

What we need to remember is that large relative increases can be dwarfed by small relative increases.  It’s important to take everything back to the absolute value (in this case oil consumption) that we care about.

Risks to gas demand

Oil majors and clean energy professionals are both interested in the future of natural gas.  In the Outlook BP take a look at how climate policy could affect the growth of natural gas.

Strong climate policies pose a risk to all fossil fuels – natural gas included.  Strong climate policies lead to the reduction of all fossil fuels in favour of low carbon energy generation.

However the Outlook shows that actually both strong and weak climate policies pose risks to natural gas consumption.

Figure 2 – The effect of climate policy strength on natural gas consumption growth

Weak climate policies will favour fossil fuels but also benefit coal over natural gas.  BP expect the net effect of this would be a reduction in gas growth versus their base case.

This is quite a nice example of a Laffer curve.  The Laffer curve is traditionally used for demonstrating the relationship between tax revenue and the tax rate.  The curve shows there is an optimum somewhere in the middle.

Figure 3 – The Laffer Curve

BP are showing that natural gas consumption likely follows a Laffer curve with respect to climate policy.

I hope you found these two insights as interesting as I did.  I encourage you to check out either the presentation or the report for further interesting insights.

Energy Basics – Average vs Marginal Carbon Emissions

Carbon savings might seem like a simple calculation – yet many professionals are getting it wrong. I know because I was making this mistake in my previous job!

Accurate calculation of carbon savings is crucial in the fight against climate change.  Knowing how much we save from a project can be compared to other projects such as renewable generation.

So where are people going wrong?  The key is to understand the difference between average and marginal carbon emissions.

Average carbon emissions are calculated using the total carbon emissions and total amount of electricity generated:

Table 1 – Calculation of average carbon intensity (for Base Case – see below)
Carbon emissionstC83,330
Electricity generatedMWh182,827
Carbon intensitytC/MWh0.456
This average intensity can be used to calculate carbon savings.  For example if we had a project that saved 2 MWh we would calculate 2 * 0.456 = 0.912 tC as the saving.  This is wrong!

To understand why we need to the concept of the marginal generator.  In reality as electricity is saved the reduction in generation is not spread across each generator.  The reduction occurs in one plant – the marginal generator.  Let’s run through an example.

Suppose we have a grid where electricity is supplied by either wind or coal (the Base Case).  If we save 1 GW of electricity, the generation of the coal plant will reduce by 1 GW (Case 1).

The wholesale mechanism operating in most electricity markets will reduce output on the most expensive plant, not reduce the output of all plants equally.

Figure 1 & 2 – The effect of saving 1 GW of electricity.  Note that the generation from wind is unchanged.
Table 2 – The daily results for the Base Case & Case 1 (you can download the model below)
Base CaseCase 1Saving
WindMWh91,25691,2560
CoalMWh91,57167,57124,000
TotalMWh182,827158,82724,000
Carbon emissionstC83,32961,48921,840
Carbon intensitytC/MWh0.4560.3870.910
Our carbon saving is equal to 1 GW multiplied by the carbon intensity of the marginal plant.

If we were to use the average grid carbon intensity (0.456 tC/MWh) we calculate a daily carbon saving of only 21,480 tC.

You might be asking – how do we know what the marginal generator will be?  It’s likely to be the most expensive generator at that time (it may not be if the plant needs to be kept on for technical reasons).   As renewables are characterized by low marginal costs they are the unlikely to be pushed off the grid.

Luckily high marginal cost generators like open cycle gas turbines are usually also carbon intense – so your saved electricity is likely doing valuable work – and potentially more than you previously thought!

You can download a copy of the model to see my assumptions here:
average-vs-marginal-emissions-2017-02-02.xlsx

A Simple Look at the Efficiency Penalty Cost of Renewables

This article is a response to a post called Wind Integration vs. Air Emission Reductions: A Primer for Policymakers.  This article claims the efficiency penalty of turning down fossil fuel power stations offsets the benefit of renewable power generation.
 
I found this an interesting idea – so I developed a simple model to understand what could be happening.
 
Renewable power generation brings a carbon benefit by generating less power in fossil fuel power stations.  Yet there is a factor working in opposition to this. Generating less in fossil fuel power stations can incur an efficiency penalty.
 
I don’t have data for the relationship between fossil fuel power station generation and efficiency. Instead I look at what the breakeven efficiency penalty would be to offset the benefit of renewable generation.
 
I created a simple model supplying 1 GW of electricity.  Renewable output is varied from 0 GW to 0.5 GW with coal supplying the balance.
 

I assumed the fossil fuel power station operates at a 50 % HHV efficiency at full load.  I then looked at various efficiency penalty factors in the form of reduced efficiency per reduction in load (% HHV / % load).   The efficiency penalty was modeled as linear.  You can download a copy of the model here.

 

Figure 1 – The effect of various assumed efficiency penalties on fossil fuel consumption
For this simple model 5 % HHV / % load is the break even.  If the efficiency really reduces at this rate then generating electricity from renewables is giving us no carbon benefit.
 
The real question is what is the actual relationship between fossil fuel power station output and efficiency.   It’s likely to be non-linear.  I also expect it would not be as harsh as 5 % HHV/% load – so likely renewables are providing a carbon benefit.
 
Is it is useful to know that this is a carbon penalty we could be paying somewhere on the system as renewables penetration increases This penalty will net off some of the maximum benefit that renewable generation could supply.
 
However if we can actually start permanently shutting down fossil fuel power stations then this effect doesn’t occur.  This suggests that the number of fossil fuel power stations operating could be a good metric to keep track of.

Renewables Confusion – Renewable versus Clean

 Introduction to the Series Renewables Confusion
 
Renewable energy is the fastest moving and most discussed topic in energy today. Unfortunately with a large amount of discussion comes a large amount of confusion. Even the educated and impartial observer struggles to get a full understanding.
 
This series isn’t going to inform you about the truth about renewables – because I don’t have it. A topic so fast moving requires a humble approach.
 
For the record – human-caused climate change is the biggest problem we face. My professional mission is to contribute towards fighting it.
 
Yet a mission to fight climate change does not mean blindly supporting renewables.
 
This series is not about bashing renewables. It’s about highlighting some of the inherent problems or areas where confusion arises.
 
Problems don’t mean that renewable deployment is not the way to go. In choosing a path forward we don’t need renewables to be perfect – only to be superior to the alternatives.
 
Highlighting problems allows us to address them. If we can see issues that may occur with high levels of renewables penetration we can start to address them now.
 
Confusion One – Renewable versus Clean
 
People often point out that no resource is renewable. When looked at on a cosmic scale even the energy provided by the sun will one day not be available.
 
We can ignore this facetious and unhelpful reasoning. Let’s use two simple definitions as the basis for discussion.
 
There are many different renewable energy technologies. We will focus on solar and wind in this series.
 
We define renewable as not depleting the natural resource.
 
We define clean as not contributing to negative environmental effects such as climate change, poisoning of rivers or loss of biodiversity.
 
There’s a third definition I could have included here – sustainability. Sustainability is meeting the needs of the today without compromising the ability of future generations to meet their own needs.
 
While I like the idea of sustainability actually applying it becomes difficult. What exactly are the needs of today? Are our needs different from the needs of future generations?
 
Now let’s look solar and wind in the context of the definitions above. We will take a look at both the operation and manufacture of these plants.
 
Solar
 
The operation of solar plants makes use of the sun for fuel and water for washing panels. Both are renewable and use of the sun as a fuel is clean.
 
The negative environmental effects generating water for panel cleaning is site specific. Best practice for generating this water will most likely have minimal environmental impact. The operation of solar panels is renewable and clean.
 
The manufacture of solar panels involves processing of quartz in electric furnaces to remove oxygen. The electricity used in silicon production today is non-renewable. Where the silicon production occurred will determine how non-renewable it was.
 
Silicon tetrachloride is a hazardous chemical that is a by-product of silicon purification. Production of hazardous wastes is not unusual in chemical processing. Best practice for disposal or reprocessing will limit environmental damage.
 
Solar panel manufacture uses rare earth elements such as silver, tellurium or indium. Unless we are at a position of 100% recycling then use of these will be depleting the natural resource. This makes solar panel manufacture non-renewable.
 
The use of carbon-intense electricity makes solar panel manufacture unclean. While this may change in the future with high levels of renewables penetration it’s not the case today – and probably won’t be for a long time.
 
Wind
 
Operation of wind plants appears renewable. Yet Vaclav Smil notes in his excellent book Energy Myths and Realities: Bringing Science to the Energy Policy Debate:
 

very large-scale extraction of wind (requiring installed capacities on a TW scale needed to supply at least a quarter of today’s demand) reduces wind speeds and consequently lowers the average power density of wind-driven generation to around 1 W/m2 (from 2 W/m2)

 If Smil is correct then by our definition the operation of wind turbines is not renewable. Large scale deployment of wind depletes the resource.
 
How clean large turbine scale deployment is an unknown. Changing wind speeds may do environmental damage but who knows?
 
Now let’s take a look at wind turbine manufacture. Wind turbine blades are made from fiberglass. Producing fiberglass requires non-renewable petrochemicals.
 
A significant amount of steel and concrete is used in building wind turbine plants. Fossil fuels are used in the production of both.
 
It’s not only the energy content of fossil fuels that is required. Fossil fuels are an inherent part of the chemistry of steel and concrete manufacture.
 

In steel production fossil fuels are required for iron smelting. Concrete production uses coal to remove carbon from calcium carbonate.

 

Fe2O3 + 3CO → 2Fe + 3CO2
CaCO3 → CaO + CO2
 
In both reactions above carbon dioxide is produced. Not only is wind turbine manufacture non-renewable it is also inherently unclean.
 
What does this actually mean?
 
At this point in the article supporters of solar and wind may be a bit upset. It looks a lot like renewables bashing!
 
The truth is all energy generation technologies have their problems.
 
In choosing a way forward, it’s not about if a technology has problems. It’s about what those problems are versus the alternative.
 
This is a function not only of the problem but of societies attitude towards the problem. Perhaps we don’t mind depleting our resources of rare earth elements if it means reducing carbon emissions.
 
To quantify the environmental benefit of renewables versus fossil fuels is a project with a massive scope. The analysis is full of uncertainty and is location & time specific.
 
Yet but only highlighting some of the inherent problems with solar & wind we can think about ways to address them in the future.
 
Perhaps we need to focus on understanding what impact large-scale wind deployment will have on wind speeds, or what quantities of rare earth elements we have left for use in solar panels.
 
By addressing these issues we can move forward into a brighter (but maybe less windy) future.
 
Our next post in this series will be about Capacity versus Generation.