Category Archives: Energy Insights

Getting Wind and Sun onto the Grid – Energy Insights

Energy Insights highlights interesting energy content from around the web.

Previous posts include Elon Musk on autonomous cars and the CAISO Stage 1 Grid Emergency.


This post highlights three interesting insights from the 2017 IEA report Getting Wind & Sun onto the Grid.

1 – decorrelation of risks to grid stability

An electricity system operator faces different sources of uncertainty when managing a grid. Three of the major sources of uncertainty are:

1 – risk of losing generators or transmission infrastructure – either a large power plant tripping out or loss of part of the transmission system

2 – demand forecasting – electricity supply must be balanced with demand in real time. Backup plant will balance the grid in case demand is higher than forecast

3 – intermittent renewables – the weather dependent nature of wind & solar means that future generation is uncertain. Sudden changes in the weather can lead to dramatic changes in power generated by wind & solar plants. Backup plant must be kept spinning to make up for the lost renewables generation.

The IEA make the point that these three sources of uncertainty are not correlated. I would argue there is a degree of correlation between demand forecasting and the intermittent nature of wind & solar. Generation by low voltage plant like residential solar appears to the system operator as a reduction in demand.

I love the idea of optimizing over all three sources of uncertainty together rather than in isolation. By being smarter about how de-risk these uncertainties we can reduce the grid’s reliance on backup plant.

End consumers of electricity ultimately pay for all grid costs. Avoiding the installation, operation & maintenance of backup plant will mean cheaper & cleaner electricity for us all.

2 – dilemma of resource strength, co-location with grid and geographical smoothing

Optimizing the location of wind & solar plants requires balancing three factors:

1 – renewable resource strength

2 – cost of connecting to the grid

3 – geographical smoothing

Optimizing for resource strength means placing renewables where the wind is strongest or the sun shines brightest. A stronger or more consistent resource availability improves economics by increasing electricity generated. Unfortunately the best wind & solar resources often in remote areas. The cost of connecting to remote resources to the grid is higher.

Co-location with the grid minimizes the investment cost for renewables projects. The IEA quote a study that the cost of expanding the grid had a median value of around 15% of the cost of the generation capacity. Co-location with the grid will keep this cost to a minimum. It will also reduce electricity lost as heat during transmission.

Geographical smoothing is about spreading renewable generation over different areas. Short term variations in renewable generation in different locations tend to cancel out. Concentrating wind turbines in one place means that when the wind stops blowing there it has a major effect on the grid. By spreading the generation geographically, the output is more consistent.

Another concept related to geographical smoothing is technological diversification. This is the smoothing effect gained by diversifying between wind and solar. The IEA make the point that generation from wind & solar is often negatively correlated. This means that changes in solar generation are often balanced by changes in wind generation.

Optimizing the location of a renewables plant requires optimizing all three of these factors together. At first glance we should put wind turbines where it’s windiest – but perhaps a less windy location closer to the grid would be a better use of capital.

3 – technical tools to strengthen the grid

One of the major costs of integrating renewables is grid infrastructure. Renewables plants are often located where the resource is strongest – which can be far away from the transmission system.

The IEA highlight a few techniques to strengthen the grid at minimum investment cost. I’m going to detail two of them.

1 – dynamic line ratings

The capacity of cables to carry electricity is limited by temperature – if the line gets too hot you’re gonna have a bad time. This capacity is also known as the line rating.

Currently the rating of cables are often based on a fixed temperature. Fixing the temperature limits the capacity of the line to carry electricity. A dynamic line rating takes into account the changing ambient temperature. When it gets cold a dynamic line rating would increase the capacity of the cable to carry electricity.

The IEA quote a Swedish project where dynamic line ratings were used to identify a 60% increase in colder periods. These colder periods correlate with windier periods – meaning the increased capacity often coincides with lots of available wind power. I’m not sure if ‘identify’ means these savings were actually realized or just identified as potential savings. Nevertheless – 60% is a significant increase.

Interestingly enough it seems that forecasting for dynamic line rating is an active area of research. It’s another potential application of machine learning – similar to the forecasting of electricity demands or prices I discuss here.

2 – flexible AC transmission systems

This is the use of power electronic devices to improve grid power factor.

Power factor is the ratio of active to reactive power. Active power is useful – reactive power is not useful. But the cables in our electricity systems need to carry both simultaneously.

Reducing reactive power means more space for active power in the cables that make up the grid. The investment required is the solenoids or condensers that are installed midway on the line to absorb or inject reactive power. This is a lot cheaper than building new transmission lines.

Elon Musk on autonomous cars – Energy Insights

Energy Insights highlights interesting energy content from around the web.

Previous posts include The Complexity of a Zero Carbon Grid and the CAISO Stage 1 Grid Emergency.


In this Energy Insights post I highlight an interesting insight from a TED talk with Elon Musk. The talk is as wide ranging as Musk’s talents – well worth a watch.

Impact of autonomous cars

Musk highlights that shared autonomous vehicles will likely become more affordable than a bus, leading to an increase in miles driven in cars.

“the amount of driving that will occur will be greater with shared autonomy and traffic will get far worse” – Elon Musk

Increasing traffic is not as significant a problem for passengers in autonomous cars. Time in an autonomous car can be spent productively. But what will be the impact on transport carbon emissions?

Let’s have a look at one positive and one neutral scenario. Both of these scenarios require a clean electricity grid power electric vehicles.

If the increased miles driven are spread across the fleet, then the only way for autonomous cars to lead to a global carbon saving is for electric cars to replace fossil fuels. The absolute number of fossil fuel cars must go down.

If all the increased miles driven are taken up by electric cars, then carbon emissions would stay stagnant. For me this seems like the route we must take – making sure that the bulk of the driving load is being done by electric vehicles. Ideally all autonomous cars are electric.

The number of fossil fuel cars will increase. BP expect an increase from 1 billion to 2 billion from now until 2050. This trend can’t be stopped. But we can smartly operate our passenger fleet to favour electric cars over fossil fuels.

Thanks for reading!

CAISO Stage 1 Grid Emergency – Energy Insights

Energy Insights highlights interesting energy content from around the web.

Previous posts include The Complexity of a Zero Carbon Grid and the 2017 BP Energy Outlook.


California Grid Emergency Comes Days After Reliability Warning


On May 3rd 2017 the California grid experienced its first Stage 1 grid emergency in nearly a decade.

The reasons for this emergency notice were:
– a 330 MW gas-fired plant outage
– 800 MW of imports that were unavailable
– a demand forecasting error of 2 GW

A Stage 1 grid emergency doesn’t mean a blackout – it forces the ISO to dip into reserves and slip below required reserve margins.  It allows CAISO to access interruptible demand side managment programs.

I wanted to highlight two features of this event I found interesting.

1 – The demand forecasting error of 2 GW

This is a massive error in absolute terms – equivalent to a large power station!

To put this error in perspective demand on the 11th of May for the same time period was around 28 GW – giving a relative error of around 7%.

It’s important to note that this error isn’t actually an error in forecasting the actual demand – it’s distributed & small scale solar that is appearing to the ISO as reduced demand.

2 – Lack of flexibility

It was unusual that the issues began developing around the peak, and demand wasn’t ramping down much, but solar was ramping off faster than what the thermal units online at the time could keep up with in serving loadCAISO spokesperson Steven Greenlee

In a previous post I highlighted the concept of flexibility.  This event demonstrates why flexibility is so important for managing a modern electric grid.

Even if you have the capacity (MW) you might not have the flexibility (MW/min) to cope with the intermittent nature of renewables.

It’s also made clear in the RTO article that interruptible demand side management programs are only called upon in a Stage 1 emergency.  Prior to this thermal units are used to balance the system.

Using flexible demand side assets as a first step to balance the grid could be a more optimal way to deal with this problem.

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!

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 Insights – Automated Cars

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 listen to the podcast.

Previous posts in this series include the IEA 2016 World Outlook and Vaclav Smil on Carbon Capture & Storage.

This insight comes from the Energy Gang podcast “Autonomous Car Fleets: A Dystopian or Utopian Future?”  If you don’t already listen to the Energy Gang podcast, you should be.

The podcast guest Joshua Goldman quotes a study that increased automation could lead to a 60% increase in miles driven in cars.  Without improvements in fuel efficiency this would lead to an increase in carbon emissions and pollutants from transportation.

Automation will allow us to spend the time in our cars how we want to.  While this is great for us it may not be great for the environment!

 

 

 

Energy Insights – Vaclav Smil on Carbon Capture & Storage

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 pick up a copy of the book.

Previous posts in this series include A Look at Wind and Solar and the IEA 2016 World Outlook.

This post is from the excellent book Energy Myths and Realities: Bringing Science to the Energy Policy Debate by Vaclav Smil.

In the chapter ‘Technical Fixes’, Smil discusses Carbon Capture & Storage (CCS).  I would like to highlight two insights that I found fascinating.

The size of infrastructure required to handle CO2

Smil calculates that to sequester 15% of 2008 emissions would require handling around 6 billion m3 of CO2.  In the same year total global crude oil extraction was around 4.6 million m3.

For me this puts into perspective how unrealistic large scale CCS is.

The amount of time and capital it would take to develop a system on the same scale as today’s crude oil extraction is immense.  To think that building such a massive system would still leave over 80% of our emissions untouched is a killing blow to CCS as anything more than a small part of the decarbonisation solution.

Projections for future price declines

Smil also comments on the potential for future cost declines of CCS technology.

Smil points out that we often make the mistake of assuming that the impressive cost declines seen in electronics will occur in other industries.  Producing advanced microprocessors is an automated process with low labour and material inputs.

This is contrasted with the massive amounts of labour and materials required for the construction and maintenance of CCS infrastructure (capture plants, pipelines, compressors, injection sites).  He also makes the point that materials needed to produce this infrastructure would put upward pressure on the cost of materials such as steel, aluminium, plastics and concrete.

Smil concludes:

Consequently we cannot exclude increased, rather than decreased, unit costs with a future mass adoption of carbon capture and storage.

I hope you enjoyed my short summary of two interesting insights made by the excellent Vaclav Smil.  His book Energy Myths and Realities: Bringing Science to the Energy Policy Debate is one of his best and I cannot recommend it or any of his other works highly enough.

Energy Insights – IEA 2016 World 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 full presentation.

Previous posts in this series include A Look at Wind and Solar.

This post highlights some of the key points from the 2016 IEA World Energy Outlook presentation. The presentation hosted by the Center for Strategic & International Studies is well worth a watch.   I also recommend subscribing to the CSIS channel on YouTube – it is full of interesting energy content.

Middle East oil production at a maximum of last 40 years (35% of global production).

Middle East output is at a historical high due to:

  • A period of relative calm with countries like Iraq producing oil.
  • Low price of oil incentivising oil producers to maximize output to maintain cash flow.

This period of high production could come to an end with any instability that the Middle East is historically known for.

Approval of new oil projects lowest since 1950s – same for discoveries

The low oil price environment is leading to project financing drying up. It will likely take a few years for the effects of the lack of investment

Combining this with the potential for Middle East supply to reduce we could see a quite interesting oil market in a few years.

Oil investment is required for:

– 2/3 for decline in existing fields.

– 1/3 for growth in oil demand.

This is an interesting point – it is not only the demand in growth but the decline in existing fields that requires investment.

Slowdown in oil growth – but no peak yet. Still growth.

– Decline in power generation (still used a lot in Middle East).

– Decline in heating and passenger cars (double of fleet size by 2040 but improved efficiency -> small reduction in passenger car oil use).

BUT

– Growth in trucks, aviation and petrochemicals.

This is a somewhat controversial part of the IEA 2016 Outlook – that the peak in oil demand is yet to come. Many other forecasters predict a decline in oil demand.

This also highlights that electrification of passenger cars alone won’t slow down our oil demand growth.  Efforts in other sectors like aviation and petrochemicals are crucial to reducing oil consumption.

Chinese gas consumption – global = 25%, China = 5%

This statistic gives me hope – there is still a large low hanging fruit in China of converting coal to gas. In this low natural gas price environment projects like this should be attractive. I would expect that it is supply of gas that is the issue – which the growing LNG market can help support.

Thanks for reading!

Energy Insights – A Look at Wind and Solar

Energy Insights is a series where we pull out key points from energy articles around the web.  This is not a summary but a taste.   The full articles contain even more interesting insights!  

This post covers a series of two articles.  If you are short of time I recommend reading Part Two – a brilliant article that highlights some issues that may limit renewable penetrations.  Part One is also worth a read, detailing the progress of renewables so far.

Part One – How Far We’ve Come

Together, wind and solar increased from 1.1 percent to 3.3 percent of global electricity over that same period (2008 – 2016)

This shows both the impressive relative growth (300% increase) and not so impressive amount of wind & solar in the entire global mix.

Of the power generation growth (TWh) between 2003 and 2016, 10.9% came from wind and solar

2003 to 2016 is a long period of time – it would be interesting to understand how this percentage has changed from 2003 to 2016.

Part Two – Is There An Upper Limit To Intermittent Renewables?

It is increasingly difficult for the market share of variable renewable energy sources at the system-wide level to exceed the capacity factor of the energy source.

This idea of a limit on renewables deployment based on the capacity factor is based not on technical but economic constraints.

The marginal value of variable renewable energy to the grid declines as the penetration rises.

This declining value is what leads to limiting renewables penetration via the merit order effect in wholesale markets.