Category Archives: Energy

Getting to Zero: Pathways to Zero Carbon Electricity Systems – Jesse Jenkins

This Energy Insights posts looks Getting to Zero: Pathways to Zero Carbon Electricity Systems from Jesse Jenkins, a PhD candidate at MIT – references are these slides and this presentation.

Substitution with electricity versus substitution within low carbon resources

Electricity is a high quality form of energy. It can carry vast amounts of energy across continents or allow the fine grained control of information. The high quality nature of electricity makes it highly substitutable. Electricity allows decarbonization of transport and heating. Low carbon electricity can power cars or to heat our homes.

Unfortunately, low carbon electricity resources are themselves not easily substitutable. Different low carbon resources offer fundamentally different value to the grid. To use one resource in place of another is possible, but ends up being more expensive the more substitution you do.

Jenkins groups low carbon resources into three classes
– fuel saving variable – wind & solar
– fast-burst – energy storage & demand response
– flexible base – nuclear, carbon capture & storage, electricity to gas & seasonal storage

Wind and solar displace burning fossil fuels. Fast-burst resources displace capacity, avoiding the need to build, operate and maintain standby capacity. Flexible base resources offer both the ability to offset fossil fuels and to reduce capacity.

Jenkins reports that progress in variable renewables is roughly on track, but progress in flexible base resources is behind. Mature technologies such as nuclear or CCS are largely stagnant. Newer technologies such as underground storage or electricity to gas are unproven on the scales required to make a real difference.

The troubles of nuclear in the West are well documented. Today large scale nuclear plants are only being built in planned economies (i.e. China & the Middle East). Jenkins notes that investors struggle to handle the large absolute values of capital required to build nuclear in market based economies, even if the $/GW of a project is attractive.

Today’s low interest rates should make investing in nuclear more attractive, yet nuclear in the West is reducing in capacity. The West has largely forgotten how to build nuclear on time and on budget, with South Korea becoming the world leader in nuclear builds.

Smaller nuclear plants can help make the investment easier to swallow. Smaller plants also reduce construction and financing risks, and allow manufacturing to be done in factories (rather than be assembled on site).

Variable renewables peak

Jenkins shows the optimal relative mixture of low carbon resources as a function of carbon limits. Figure 1 below shows that initially it is optimal to focus on increasing the relative share of variable renewables on the grid.

It also shows that later on the grid requires other types of resources to deliver the lowest cost electricity supply. Eventually the costs of operating a variable renewable grid become so large that more expensive but dispatchable low carbon generation (i.e. nuclear) becomes the optimal economic decision.

Figure 1 – It’s not a straight line to zero

This means that we must continue to develop, support and improve fast-burst and flexible low carbon resources alongside variable renewables. Not because we need them today but because we need them tomorrow.

Value versus cost

Jenkins explains the historical mental model for supporting clean technology.  Subsidies allow a technology to reduce its costs and move up it’s experience curve. The path often includes developing economies of scale (i.e. in manufacturing or supply chains) and accumulation of iterative ‘learning by doing’ improvements. Once the cost of the technology is low enough then the technology can stand on it’s own two feet without subsidy.

The new mental model is that as renewable penetrations increase we see both decreases in cost and a concurrent decrease in value. Because renewables generate at the same time, the oversupply during these times drives down electricity prices. This driving down of prices is a signal that the grid isn’t valuing generation during these time periods. If the value of electricity generated by renewables is very low then even very cheap plants won’t get built.

Figure 2 – A race between declining cost and value

There are four reasons why the value of wind & solar decline as renewables penetration increases
– offsetting less fossil fuels
– offsetting less capacity requirements (i.e. avoided costs of building dispatchable generation)
– increased curtailment
– increased grid integration costs – reserve requirements & network costs

Diversification offers the lowest cost decarbonization

In the context of the type of optimization models Jenkins develops this makes complete sense. Introducing additional constraints onto a linear program will only ever end up with the same or a worse result.

Not allowing nuclear limits us to an equal or worse off situation than allowing nuclear. If a nuclear-free pathway was optimal then both models will find the same solution. The fact is that they don’t. Jenkins finds a strong consensus in literature that a diversified mix of low carbon resources offers cheapest deep decarbonization. Dispatchable low carbon resources significantly reduce the cost and technical resources of deep decarbonization.

Not using nuclear would require a massive (roughly double) build out of variable renewables capacity. Not only do we need more capacity but the global capacity factor [MWh / MWh maximum] will be low. The capacity will also have a very high cost per utilised output [$/MWh]. The low energy density nature of variable renewables also means greater land use impacts for a variable renewables scenario only solution.

A variable renewables only solution also requires long duration energy storage. In a review of literature, Jenkins finds a seasonal storage requirement for 8-16 weeks worth of US electricity consumption. To put this in context – the ten largest pumped hydro plants in the US currently have around 43 minutes of storage.

Thanks for reading!

The Four Inconvenient Truths of the Clean Energy Transition

All is not well with the clean energy transition. Positive commentary on the progress of the transition is frustrating. 2017 saw a 2% rise in global carbon emissions, and the concentration of CO2 has surpassed 400 ppm for the first time in several million years.

The motivation for the clean energy transition cannot be stronger – preventing dangerous climate change. Yet even the potential violence of climate change is not counteracting historical realities.

This post highlights four reasons why the clean energy transition is failing. All four reflect the experience of previous transitions. All four are unwelcome.

I’m not arguing against the need for the clean energy transition. It’s something that has to happen a lot faster. I’m showing some of the truths behind why this transition is (and will continue to be) difficult. Only by understanding these historical realities we can take action to counteract them.

The primary source for these ideas is Vaclav Smil’s excellent work on energy transitions (book, lecture and another lecture). Smil is my favorite energy writer – prolific, confidentially numeric and intelligently contrarian. Smil’s work is for me the best on energy ever written.  I’ve also previously written about Smil’s work on carbon capture and storage.

The Four Inconvenient Truths of Energy Transitions

Energy transitions are key to the development of civilization. Muscle and wood powered our early days – today we burn coal and gas to drive turbines, oil to drive cars and can harness energy that powers stars.

Now we are moving toward clean and smart technologies – wind turbines, solar panels, energy storage and intelligent operation. This transition is both very similar and very different to past transitions.

Three of the four inconvenient truths are how this transition is like the past
one – energy transitions are slow (and getting slower)
two – energy transitions are additive (old fuels don’t go away)
three – energy transitions are sequential and high variance (especially on small scales)

The fourth truth is one in which the clean energy transition is departing from previous transitions
four – energy transitions enable new utility

The First Inconvenient Truth – energy transitions are slow (and getting slower)

In Energy Transitions: History, Requirements, Prospects Smil notes subsequent transitions are taking longer each time. Coal took 35 years, oil 40 years and natural gas 55 years to move from 5% to 25% of global primary energy consumption.

There are two reasons for the slowing down. As the absolute size of our energy consumption increases, the relative effect of adding more is smaller. The massive growth in global energy consumption means that effort today has a smaller relative effect than it would have in the past.

Second, the technical challenges of using the new energy source increase. Moving from wood to coal was a reasonably easy transition – both are solid fuels that can be transported, handled and burnt using similar techniques. Using oil required building a massive global upstream and downstream infrastructure, cars and roads to drive on. Using gas required the development of gas turbines – one of the most complex machines humanity has ever created.

The clean energy transition is full of technical challenges. Clean energy generation is low power density (W/m2) – meaning we need to build wind & solar across vast areas of land. It also requires transmission lines, energy storage and intelligent operation, to counteract the disadvantages of geographically dispersed, low capacity factor and intermittent renewables.

What this means for the clean energy transition – it’s going to take a long time.

The Second Inconvenient Truth – energy transitions are additive

Of the four truths, this is the most inconvenient. As civilization progresses we increase both the amount and quality of energy we use. But each time we transition we don’t replace old energy sources – we add new energy sources on top. Older technologies take a long time to go away. We are still building coal-fired generators today and are recklessly likely to for a long time.

Figure 1 below shows the history of US primary fuel consumption. Note how US coal consumption has continued to rise all the way through to the start of the 21st century. Each energy transition has not displaced coal. Instead newer fuels add to existing coal consumption. Coal consumption has also continued to increase.

Figure 1 – U.S. Primary Energy Use over time in Quads from 1800 to the present by source (US Department of Energy Quadrennial Technology Review 2015)

As global energy demand increases, renewables be a significant part of the marginal increase. But it’s the older fossil fuel generation that needs to go – history shows us that this doesn’t happen quickly. One reason for this is that the economics of technology improves as it matures.

Improvements in core technology, building of supply chains and know-how mean that older technologies are often efficient, cheap to build and cheap to maintain.  A technology having a track record of performance and lifecycle cost also makes it more attractive for investors.

Diesel generators (an 1890’s technology) are a great example of this. Diesel generators are reasonably efficient, quick and cheap to build with a well-understood maintenance schedule.

What this means for the clean energy transition – fossil fuels aren’t going away.

The Third Inconvenient Truth – energy transitions are sequential and high variance (especially on small scales)

Smil notes that energy transitions “require a specific sequence of scientific advances, technical innovations and organizational actions” combined with “economic, political and strategic circumstances”.

The dependence doing the right things in the right order means progress is not guaranteed. Coal dominated China, nuclear powered France and hydro blessed New Zealand show that energy systems evolve very differently.

When we have specific requirements about where our energy system needs to go, getting what we want requires getting things right all across the board.

The inevitability of technological progress in the economics of wind & solar is sometimes confused with the inevitability deploying wind & solar. The reality is that even as clean technology improves, there is no guarantee that our energy system will decarbonize.

There are a multitude of other, equally important things that need to happen. For example, without the correct alignment of incentives through rate structures even very cheap batteries won’t have an impact. We cannot only rely on technology improving. Without everything else in the right place at the right time we won’t get where we need to be.

What this means for the clean energy transition – there is no guarantee things will move in the correct way.

The Fourth Inconvenient Truth – energy transitions enable new utility

The first three truths are ways in which the clean transition will be like the past. The final truth is a reality of the clean energy transition that moves against past trends.

Why do we spend the time and money to transition to new energy sources? New sources of energy allow us to do things we couldn’t do before.

Coal enabled a revolution in manufacturing, oil & gas enabled revolutions in transportation. Energy transitions enable new utility by using higher quality fuels. One measurement of energy quality is energy density – how much energy we can squeeze into a given mass or volume.

Past transitions have been movements towards more energy dense fuels. Dry wood contains around 18 MJ/kg, anthracite coal 8-30 MJ/kg, oil 41-42 MJ/kg and methane (the primary component of natural gas) at 55 MJ/kg.

It is interesting that historical transitions have taken us from solids to liquids to gases. Volumetric energy density (MJ/m3) can be as important as energy density on a mass basis (MJ/kg). It’s difficult to compare renewables with fossil fuels on an energy density basis as renewables don’t consume fuel. Yet it is evident that water, sunlight and wind are less dense forms of energy than burning oil or gas.

Even if we ignore that clean technologies reverse the energy density trend, the electricity generated by clean technologies is still the same as what a gas turbine generates today. The clean energy transition lacks a killer app.

We aren’t getting any major new form of utility – only a cleaner version of what we already have. The cleaner nature of wind & solar are still worth working and paying for. But we are without a key driving force that helped to power previous transitions – the driving force of people wanting to heat their homes, power factories and fly around the globe.

The only thing that comes to mind is the role that inverter based renewables & storage can play using grid services such as fast frequency response.  It’s becoming clear that inverters are actually superior to synchronous generators in providing these services.  However this is a minor advantage compared to the coal fired revolution of manfuacturing and the oil fired revolution in mobility.

What this means for the clean energy transition – a key driving force that powered previous transitions won’t be helping this time.

I’m not arguing against the need for the clean energy transition. The need is urgent. The purpose of this post is to shine light on reality.

Only by acknowledging reality can we overcome the inconvenient and unwanted realities of energy transitions.

Thanks for reading.

References and further reading

Using reinforcement learning to control battery storage

If you just want to skip to the code
the example in this post was generated from this notebook
the DQN agent
the parameterization of Q(s,a) using Tensorflow
the battery environment

Using reinforcement learning to control energy systems is the application of machine learning I’m most excited about.

In this post I’ll show how an agent using the DQN algorithm can learn to control electric battery storage.

reinforcement learning in energy

importance of battery storage

One of the key issues with wind and solar is intermittency. Solar only generates when it’s sunny, wind only generates when it’s windy, but we demand access to electricity 24/7.

One way to manage this intermittency is with flexible demand. This is what we are doing at Tempus Energy, where we are using machine learning to unlock the value of flexible electricity assets.

Another way is with energy storage. The ability to store excess for when the grid needs it allows intermittent generation to be matched against demand.

But energy storage represents a double edged sword – it can actually end up doing more harm that good. The 2016 Southern California Gas company Advanced Energy Storage Impact report shows that commercial storage projects actually increased 2016 carbon emissions by 726 tonC.

Part of this is driven by misaligned incentives. Even with aligned incentives dispatching a battery is still challenging. Getting batteries supporting the grid requires progress in multiple areas:
– decreasing total system installation costs
– aligning incentives with prices that reflect the value of the battery to the grid
– intelligent operation

This work supports operating battery storage with intelligence.

reinforcement learning in energy

Reinforcement learning can be used to intelligently operate energy systems. In this post I show how a reinforcement learning agent based on the Deep Q-Learning (DQN) algorithm can learn to control a battery.

It’s a simplifed problem. The agent is given a perfect forecast of the electricity price, and the electricity price itself is a repetitive profile. It’s still very exciting to see the agent learn!

In reality an agent is unlikely to receive a perfect forecast. I expect learning a more realistic and complex problem would require a combination of:
– tuning hyper parameters
– a higher capacity or different structure neural network to approximating value functions and policies
– more steps of experience
– a different algorithm (AC3, PPO, TRPO, C51 etc.)
– learning an environment model

The agent and environment I used to generate these results are part of an open source Python library. energy_py is a collection of reinforcement learning agents, energy environments and tools to run experiments.

I’ll go into a bit of detail about the agent and environment below. The notebook used to generate the results is here.

the agent – DQN

DeepMind’s early work with Q-Learning and Atari games is foundational in modern reinforcement learning.  The use of a deep convolution neural network allowed the agent to learn from raw pixels (known as end to end deep learning).  The use of experience replay and target networks improved learning stability, and produced agents that could generalize across a variety of different games.

The initial 2013 paper (Mnih et. al 2103) was so significant that in 2014 DeepMind were purchased by Google for around £400M.  This is for a company with no product, no revenue, no customers and a few employees.

The DQN algorithm used in the second DeepMind Atari paper (Mnih et. al 2015) is shown below.

Figure 1 – DQN algorithm as given by Mnih et. al (2015) – I’ve added annotation in green.

In Q-Learning the agent learns to approximate the expected discounted return for each action. The optimal action is then selected by argmaxing over Q(s,a) for each possible action. This argmax operation allows Q-Learning to learn off-policy – to learn from experience generated by other policies.

Experience replay makes learning more independent and identically distributed by sampling randomly from the experience of previous policies. It is also possible to use human generated experience with experience replay. Experience replay can be used because Q-Learning is an off-policy learning algorithm.

A target network is used to improve learning stability by creating training Bellman targets from an older copy of the online Q(s,a) network. You can either copy the weights over every n steps or use a weighted average of previous parameters.

One of the issues with Q-Learning is the requirement of a discrete action space. In this example I discretize the action space into 100 actions. The balance with discretization is:
– too low = control is coarse
– too high = computational expense

I use a neural network to approximate Q(s,a). I’m using TensorFlow as the library to provide the machinery for using and improving this simple two layer neural network. Even though I’m using the DQN algorithm I’m not using a particularly deep neural network.

I make use of relu’s between the layers and no batch normalization. I preprocess the inputs (removing mean and scaling by standard deviation) and targets (min-max normalization) used with the neural network using energy_py Processor objects. I use the Adam optimizer with a learning rate of 0.0025.

The network has one output node per action – since I choose to discretize the action space with 5 discrete actions for each action, there are 10 total discrete actions and 10 output nodes in the neural network.

There are a number of other hyperparameters to tune such as the rate of decay of epsilon for exploration and how frequently to update the target network to keep learning stable. I set these using similar ratios to the 2015 DeepMind Atari paper (adjusting the ratios for the total number of steps I train for each experiment).

Figure 2 – DQN hyperparameters Mnih et. al (2015).

the environment – battery storage

The battery storage environment I’ve built is the application of storing cheap electricity and discharging when it’s expensive (price arbitrage.) This isn’t the only application of battery storage – Tesla’s 100 MW, 129 MWh battery in South Australia is being used for fast frequency response with impressive results.

I’ve tried to make the environment as Markov as possible – given a perfect forecast enough steps ahead I think battery storage problem is pretty Markov. The challenge using this in practice comes from having to use imperfect price forecasts.

The state space for the environment is the true price of electricity and the charge of the battery at the start of the step. The electricity price follows a fixed profile defined in state.csv.

The observation space is a perfect forecast of the electricity price five steps ahead. The number of steps ahead required for the Markov property will depend on the profile and the discount rate.

The action space is a one dimensional array – the first element being the charge and the second the discharge. The net effect of the action on the battery is the difference between the two.

The reward is the net rate of charge or discharge multiplied by the current price of electricity. The rate is net of an efficiency penalty applied to charging electricity. At a 90% efficiency a charge rate of 1 MW for one hour would result in only 0.9 MWh of electricity stored in the battery.


The optimal operating strategy for energy storage is very application dependent. Given the large number of potential applications of storage this means a large number of optimal operating patterns are likely to exist.

The great thing about using reinforcement learning to learn these patterns is that we can use the same algorithm to learn any pattern. Building virtual environments for all these different applications is the first step in proving this.

Below is a HTML copy of the Jupyter Notebook used to run the experiment. You can see the same notebook on GitHub here.

further work

Building the energy_py library is the most rewarding project in my career so far. I’ve been working on it for around one year, taking inspiration from other open source reinforcement learning libraries and improving my Python & reinforcement learning understanding along the way. My TODO list for energy_py is massive!

Lots of work to do to make the DQN code run faster. No doubt I’m making silly mistakes! I’m using two separate Python classes for the online and target network – it might be more efficient to have both networks be part of the same object. I also need to think about combining graph operations to reduce the number of Prioritized experience replay is another option to improve sample efficiency.

Less Markov & more realistic state and observation spaces – giving the agent imperfect forecasts. Multiple experiments across different random seeds.

Test ability to generalize to unseen profiles. This is the most important one. The current agent has the ability to memorize what to do (rather than understand the dynamics of the MDP).

I’ve just finished building a Deterministic Policy Gradient which I’m looking forward to playing around with.

Thanks for reading!

The State of Energy Storage in America – Energy Insights

Energy Insights highlights interesting energy content from around the web.

Previous posts include Demonetizing Everything: A Post Capitalism World and Vaclav Smil on Carbon Capture & Storage.

This Energy Insights posts summarizes a recent episode of the Greentech Media Interchange podcast.  The show discusses the State of Energy Storage in America.  It’s full of interesting insights into what’s going on right now with energy storage.

Below are my notes from the show.

In 2017 the US deployed around 700 MWh of battery storage. This corresponds to around 300 MW. This is a useful ratio to understand – the ratio of capacity to rate of charge/discharge. I think a useful rule of thumb could be around 2:1 (capacity : rate).

Smart meter deployment is sitting at around 50% in the US. Unlocking value of these smart meters requires smarter pricing (i.e. time of use).

Battery storage penetration depends both on technology and regulation. Regulation needs to support cost reflective and variable pricing.

Disappearance of gas peaking plants hurts gas turbine suppliers more than gas suppliers. Peaking units don’t consume a large amount of gas, they do consume a large amount of capital.

Importance of considering total project costs for batteries. Cost of battery storage technology is decreasing faster than balance of plant costs.

The 2016 Southern California Gas company Advanced Energy Storage Impact report notes that commercial energy storage actually increased emissions by 726 tC in 2016. The main reason for this is the misalignment of incentives. The customer demand charges are not aligned with the carbon intensity of the grid.

As an industry we should demand that the upstream supply chains are run without child labour. Cobalt is a material used in battery storage today that involves too much child labour.

Thanks for reading!

Demonetizing Everything: A Post Capitalism World | Peter Diamandis – Energy Insights

Energy Insights highlights interesting energy content from around the web.

Previous posts include Getting Wind and Sun onto the Grid and The Complexity of a Zero Carbon Grid.

This post looks at Peter Diamandis’ talk Demonetizing Everything: A Post Capitalism World. The central premise of the talk is demonitization – technology is making utility cheaper.

Diamandis’ highlights demateralization as one driving force behind demonitization. Put simply – technology allows us to use less stuff to deliver more utility, making that utility cheaper.

Diamandis gives a great example of this demateralization leading to demonitization trend using the smart phone. Diamandis estimates that the functionality of a $50 smart phone of today would have cost millions 20 years ago. This is a direct result of the demateralization of functionality from hardware to software.

Yet when Diamandis got to his section on energy I was left quite frustrated. It’s not that I don’t agree with the central premise that demateralization doesn’t lead to demonitization. It does. I’m disagreeing that demateralization is occurring in our transition to renewables.

Because the energy density of renewable resources are so much less than fossil fuels, we actually require more steel, concrete and plastic per unit energy generated from wind & solar.

Table 1 – Range of materials requirements (fuel excluded) for various electricity generation technologies (DoE Quadrennial Technology Review 2015)

I also found talk too positive – Diamandis makes it sound like everything is OK, coal has been defeated and it’s all smooth sailing from here to a clean & decarbonized world. While we are making progress it is too slow – and carbon emissions are still rising.

Below I have a look at a few of the points Diamandis raised in more detail.

2016: Renewables Cheaper Than Coal

World Economic Forum Reports: Solar and wind now the same price or cheaper than new fossil fuel capacity in over 30 countries. Energy experts think coal will not recover.

I find a statement like this frustrating – it makes it seem as if the fight against coal is already won. The report that Diamandis refers to is the World Economic Forum’s Renewable Infrastructure Investment Handbook: A Guide for Institutional Investors.

You only need to look at one of the figures from that exact report (Figure 1 below) to see that it’s still happy days for coal. Coal is still by far the dominant fuel globally – does it really look like wind & solar have dealt a killing blow from which coal will never recover?

Figure 1 – World Energy Matrix (Renewable Infrastructure Investment Handbook)

It also doesn’t matter if wind & solar are cheap if we aren’t installing more capacity. The same WEF report shows that total investment ($USD billion per annum) in renewables has levelled off since 2011.

Due to the price decrease we still will be installing more renewables at the same level of investment, but it’s the combination of price and investment that gives us what we really care about – annual capacity installed.

Figure 2 – Investments in Clean Energy (USD bn) (Renewable Infrastructure Investment Handbook)

I also find the use of ’30 countries’ a misleading use of statistics. Are these 30 small, sunny countries which are perfect for solar? If the lessons learnt in these 30 countries don’t transfer to China, India and the USA then it doesn’t really matter in terms of fighting climate change.

It would be more relevant to look at in how many countries solar was cheaper than coal, then to weight each country by population or total energy consumption. This would give a more accurate picture of how solar is doing in displacing coal.  More accurate still would be to look at Figure 1.

The Global Status Report for Renewables states that renewable energy now accounts for 25% of the world’s power

The problem here is including all renewables together. The distortion comes from including hydropower with all other renewables.

While the statement Diamandis’ makes is true, it’s misleading to put this fact on the image of a solar panel. BP estimate that non-hydro renewables make up around 8% of global electricity generation. Indeed Ramez Naam (who spoke at the same Exponential Finance conference) estimates that wind & solar are around 7% of global electricity generation. This is a far cry from 25%!

The exact numbers here aren’t important – what’s important is that the viewer of the presentation is left thinking that renewables are doing fantastic when in fact wind & solar still make up a very small portion of global generation. The fight is not over – in fact we aren’t even winning.

Costa Rica operating on 100% renewables for over 300 days

I actually already addressed this misconception in an earlier post (Composition, not consumption). Costa Rica is lucky to have a very high penetration of hydroelectricity (around 80%). Hydroelectric dams have free energy storage built in – this allows the grid to easily deal with the intermittency of renewables.

Most countries do not have the luxury of a large hydro resource, so using Costa Rica as an example of how close we are to going 100% renewable globally is misleading.  We require different techniques and technologies to decarbonize the rest of the world.

Thanks for reading!

Jevon’s paradox – Energy Basics

Efficient use of energy must be the logical first step for anyone trying to slow carbon change. The benefits of not wasting energy are so evident that it should be a high priority for our civilization. Unfortunately it’s not quite that simple.

1865’s The Coal Question introduced what we now call Jevon’s paradox – that technological progress in the efficiency of using a resource leads to increases in resource consumption.

Jevon’s paradox is an inconvenient truth for energy efficiency. It’s not that efficiency doesn’t work – we do use less primary energy per unit of utility. It’s what happens afterwards where the gains in efficiency are cancelled out by more global effects.

Lets look at some of the possible first, second and third order effects (thanks Ray Dalio for this mental model). We will use gas fired heating as the example.

The first order effect of improving heating efficiency is that less gas is required to supply the same amount of heat. This effect is positive – we don’t burn as much gas to provide the same utility.

A secondary effect of improving heating efficiency could be that we now get more heat for the same amount of money. We spend the same amount, we get more heat – but no carbon saving. We can afford to heat bigger homes for the same amount of gas.

A third order effect could be that increased efficiency leads to less gas consumption – meaning saved carbon and money.  The question is then what does the economy do with the saved money?

If the saving is spent on taking a long haul holiday, we could actually see an increase in global carbon emissions. We improve the efficiency of supplying heat but overall as a civilization we burn more carbon. Alternatively if the saving is spent on building cleaner energy generation then even increases in utility could lead to a carbon saving.

It’s very difficult to generalize on what effect Jevon’s paradox has across different consumers, economics and technologies. Measuring the first order effects of energy efficiency projects is notoriously difficult – let alone any second or third order effects.

It’s important to note that energy efficiency is still worthwhile. It allows economic progress – this alone is worth doing. Yet for someone purely concerned with decarbonization, energy efficiency may not be the correct first option.

Jevon’s paradox is not guaranteed to occur. Any negative second or third order effects of energy efficiency can be smaller than the efficiency saving. It perhaps suggests that focusing on making sure any energy we use comes from as clean a primary source as possible is a safer bet than trying to use less dirty energy.

Further reading

Inertia in electricity systems – Energy Basics

Energy Basics is a series covering fundamental energy concepts.

Perhaps you’ve had critics of the energy transition shout “inertia” at you. Perhaps it’s keeping you up at night. Is our dream of a clean energy future impossible? This article will reassure you that losing inertia is something clean energy technologies can deal with.

Large, fossil fuel and synchronous generators have historically dominated our electricity system. Fossil fuels are burnt to force high temperature and pressure gases through turbines. These turbines rapidly spin shafts connected to alternators that generate AC electricity.

We are transitioning to a very different electricity system. We are building small-scale, clean and asynchronous generators. Wind turbines that spin at variable speeds, much slower than synchronous generators. Photovoltaic solar panels and batteries have no moving parts at all.

A key difference between these two systems is the inertia of the generators. Fossil fuel generators posses a lot of inertia due to the rapidly spinning & heavy turbine connected to the alternator. Once the turbine is spinning it’s hard to get it to stop – in the same way that it’s hard to stop a truck traveling at speed.

The speed at which the shaft & alternator needs to spin at is directly proportional to the desired grid frequency. In fact the grid frequency is the result of the speed that all these synchronous generators spin at. The frequency of electricity generated by a synchronous generator is given by

Poles refer to poles of the alternator.  120 is used to convert minutes to seconds and poles to pairs of poles.

The grid is an interconnected system – changing grid frequency requires changing the speed of every generator connected to the grid. This interrelationship becomes useful during times of supply & demand mismatches. Any imbalance needs to work to change the speed at which every generator on the grid spins. If these generators posses a lot of inertia, then the imbalance needs to work harder to change the grid frequency.

This is the value of inertia to the grid – it buys the grid operator time to take other actions such as load shedding or calling upon backup plant. These other actions are still needed – inertia won’t save the grid, just buy time for other actions to save the grid.

So now we understand that fossil fuel generators have inertia and how it is valuable to the grid (it buys the system operator time during emergency events). What does this mean for our energy transition? Do we need to keep around some fossil fuel generators to provide inertia in case something goes wrong? The answer is no.

Modern wind turbines can draw upon kinetic energy stored in the generator and blades to provide a boost during a grid stress. This ‘synthetic inertia’ has been used successfully in Canada, where wind turbines were able to supply a similar level of inertia to conventional synchronous generators.

Figure 1  –  Conceptual fast frequency response from a wind turbine

Photovoltaic solar and batteries also have a role to play. Both operate with inverters that convert DC into AC electricity. The solid-state nature of the devices means that they operate without any inertia. Yet this solid-state nature allows inverters the ability to quickly change operation in a highly controllable way. Inverters can quickly react to deliver whatever kind of support the grid needs during stress events.

Clean technologies are ready to create a new electricity system. Now we need to make sure we incentivize the technology that our grid needs. Market incentives should support technologies that can supply inertia on our cleaner grid. The level of support could be logically set so that the level of inertia on the grid will remain at the same level as our old fossil fuel based grid. That way, no one can complain.

Thanks for reading!

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.

insight one – 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.

insight two – 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.

insight three – 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.

Cheat sheet for gas engine & gas turbine CHP – Energy Basics

In my previous life at ENGIE I specialized in the technical modeling of combined heat & power (CHP) plants.

I developed models to support sales projects and to optimize operation of existing sites – such as the district heating scheme at the Olympic Park in London.

CHP is an attractive technology for maximizing the recovery of heat & electricity from fuel. The technologies are mature and will deliver carbon benefits in most electricity grids.

This post aims to give concise practical details about the two most commons forms of gas based CHP. Together the gas engine and gas turbine are around 90 % of installed capacity of CHP in the UK.

Table 1 – CHP in the UK (from DECC)

This post focuses on the facts that matter in the day to day world of energy management.

common for both gas engines & gas turbines
  • Both have total efficiencies (electric + thermal) roughly around 80 % HHV.
  • Both operate with a maximum electric efficiency at full load.
  • In part load operation total efficiency remains around 80 % HHV – reductions in electric efficiency are counteracted by increases in thermal efficiency.
gas engines

Figure 1 – a simple gas engine schematic
Key practical advantages
  • High electric efficiency.
  • Cheap maintenance cost.
  • Cheap capital cost.
Key practical disadvantages
  • Half of the recoverable heat is generated as low quality (<100 °C).
  • Usually only economic at sizes below 5 MWe.

A gas engine has a high electric efficiency (30-38 % HHV).

A gas engine generates roughly the same amount of electricity and heat – i.e. the heat to power ratio is around 1:1.

The recoverable heat generated in a gas engine is split roughly half high grade (>500 °C), half low grade (<100 °C).

Gas engines are typically economic up until 5-6 MWe.  Beyond that size gas turbines become competitive.

Gas engine CHP is low maintenance (0.6 – 1.2 p/kWh @ 8,000 hours/yr) and captial (500 – 1,500 /kWe total project) cost.

Having half of the heat generated as low quality means that a low-grade heat sink is required.

Many industrial processes only require high-temperature heat (typically served using steam). Without a low-grade heat sink for the low-grade heat the economics of a gas engine will suffer.

Typical low-grade heat sinks include space heating, boiler feedwater heating on sites with low condensate return rates and low-temperature process heating. This makes district heating and hospitals good applications of gas engine CHP.

gas turbines
Figure 2 – a simple gas turbine schematic
Key practical advantages
  • All of the heat generated is high quality.
  • Supplementary firing can be used to generate more heat at high efficiency.
  • Potential to combine with steam turbines to generate more power.
Key practical disadvantages
  • Lower electric efficiency.
  • Complex emissions control systems.
  • Usually limited to sizes above 5 MWe.

A gas turbine operates with a lower electric efficiency (25-35% HHV) than a gas engine.

A gas turbine generates roughly twice as much heat as power – ie the heat to power ratio is around 2:1.

Unlike a gas engine, all of the heat generated by a gas turbine is high grade (>500 C).  This makes gas turbines ideal for industrial sites that need high-temperature steam to run their processes.

This also allows gas turbines to be used in combined cycle mode (steam is generated off the exhaust and used to drive a steam turbine).  More gas can be fired into the exhaust to further increase steam generation (known as supplementary firing).

This can be a key advantage of gas turbines, as the marginal efficiency of supplementary firing is higher than generating heat in a shell or water tube boiler.

blockchain in energy – part two – blockchain and the grid

This is the second of a two part series on blockchain in the energy system.

blockchain in energy – part one – what is blockchain
introduction, advantages, description of the system, proof of stake vs. proof of work, smart contracts, bitcoin & Ethereum

blockchain in energy – part two – blockchain and the grid
how far can we go with blockchain and the grid, perspectives of the system operator, consumers, generators, machines & regulators

The first part of this series introduced blockchain and concepts such as smart contracts and proof of work.  In this second post I will theorize on how far we could go with blockchain and the electricity grid.

Today the most successful applications of blockchain are in cryptocurrencies such as bitcoin. Using blockchain to support the grid will offer additional challenges – some of which may necessitate keeping third parties in place.

It’s unclear how far we can go with blockchain and the grid.  Currently applications of blockchain in the energy industry are limited to small pilot scale projects.  Even in more widespread cryptocurrency applications the technology is still not considered mature.

Our transition towards a physically decentralized, small scale and clean electricity system is well underway. Blockchain is not necessary for this transition to happen. Yet it could be a useful tool in developing a more secure, efficient and open electricity grid.

The transmission & distribution system operator

It would be a waste to just use the blockchain to just do meter billing – Joi Ito

I’m excited about the potential for blockchain to be the transaction and control layer for the grid. Blockchain could enable a living, self-organizing grid powered by smart contracts. Smart contracts would automatically match generators with consumers, balancing the grid in near real time. Virtual power plants and storage could also be dispatched using smart contracts.

A blockchain could make smaller scale (in both time and volume) transactions more viable. This would make a more diverse range of resources available to the system operator for real time balancing. It would also reduce the length of the settlement process – no need for complex and costly processes such as profiling.

This blockchain would provide an excellent database for managing grid operations. Data would be available for every transaction – price, quantity and time all down to a fine scale. This dataset would also allow more accurate forecasts of generation & demand. More accurate forecasts will reduce spinning reserve and imbalance costs.

Blockchain is the first technology that can identify of the origin of electricity. This would simplify certification of renewable generation or tracking carbon emissions.

It would also allow a system operator to see which parts of the grid were used to supply electricity. This would allow charging based on how the transmission and distribution system was actually used.

Theoretically a blockchain allows a fully decentralized system. In the context of an electricity grid it’s hard to see how the centralized role of transmission and distribution system operators disappearing.

Electricity consumers

A blockchain could create a more dynamic and competitive marketplace. A smart contract could purchase electricity from many different sources within the same day. An eaiser ability to switch should mean more competition and lower electricity prices. Combine this with the efficiency improvements of removing third parties and we would expect that electricity would get cheaper for consumers.

Smart contracts would also give consumers more power over where their electricity comes from. Environmentally conscious consumers could purchase 100% renewable electricity. Community focused consumers could prefer locally generated electricity. Price concious consumers could minimize costs by always purchasing the cheapest electricity available.

Allowing consumers more control over their electricity mix could increase customer engagement. Getting consumers engaged with where their energy comes from would be a powerful ally in speeding up our energy transition.  It would also be fantastic for utilities looking for loyal customers.

A blockchain could also be powerful in developing communities. Imagine knowing all of the electricity you used was generated on your street. Money would be kept in the local economy, improving the business case for local generation. It’s a virtuous cycle that could accelerate our energy transition.

Using our energy system to develop a community spirit is potentially one of the most powerful impacts a blockchain could have. Technology often leads to more isolated communities – it would be great for technology to bring communities together.

It’s not clear exactly how far a blockchain system could go in completely removing third parties from the grid. A number of duties currently fulfilled by suppliers would need to be done by consumers themselves.

Take forecasting for example. Today electricity suppliers submit forecasts and get charged based on their imbalance. These forecasts are crucial for the system operator to schedule generation. How would this forecasting process be done when there is no supplier responsible for consumers?

Blockchain could be a major positive for electricity consumers in third world countries. Third world economies are often high inflation and low trust – a blockchain using prepaid smart meters could help with both these issues.  Investements in solar panels can be made using cryptocurrencies with the generation supported by a grid blockchainm

Establishing blockchains in a third world countries could allow that economy to skip over the dirty & centralized systems historically used in developed countries.

Electricity generators

A blockchain system could be very powerful for small scale generators. Removing third parties and their inefficiencies should improve the economics of all generators. We are already seeing impressive reductions in the cost of renewables and storage – an increased share of the value of electricity generated kept by investors will only make these technologies more lucrative.

A blockchain would democratize access to the marketplace, removing barriers that currently hurt small scale generators, flexibility, and storage. Today small scale generators often get a very unattractive fixed export rate. A blockchain would allow small scale generators to compete on equal footing in the same marketplace as a 2 GW power station.

Other technologies such as flexibility and storage are often locked out of the market through high minimum capacities or out of market subsidies. A blockchain is the antithesis of this.

The blockchain would also provide an excellent data source for investors in electricity generation. An investor could accurately understand the historical value of electricity generated through the transaction history of the blockchain.


The first role for machines in a blockchain system is to put the smart into smart contracts. The basis for this intelligence could be either a set of human designed heuristics or a narrow machine learning system. Reinforcement learning agents could be deployed as smart contracts, built to provide useful functions such as controlling voltage or frequency.

Blockchain also allows machines to do something quite interesting – own property. A machine with access to the private key of a blockchain account essentially owns any property associated with that private key. A blockchain could lead to the rise of a machine to machine economy, with machines trading and building assets within their blockchain accounts.

Regulators & the legal system

Regulators have a lot of work to do to prepare for a blockchain marketplace. Three broad areas require attention:

  1. protecting consumers,
  2. the physical nature of the electricity system,
  3. nurturing a technology to maturity.

Our current legal framework protects customers in centralized marketplaces. Additional protection is needed for a decentralized system.

The major challenge is the lack of a central authority.  Today’s legal system involves a clear allocation of organizational & legal responsibility – this doesn’t really exist in a system where consensus is reached by the majority. It’s one reason why an electricity grid would likely be supported by a private blockchain.

As discussed in Part One the optimal mechanism for verifying blockchain truth is not clear.  Mechanisms such as proof of work or proof of stake all have drawbacks.  Another implication of verification mechanism design is the balance of power between miners and other nodes.  Paradigms such as the tangle have evolved to adjust this balance.

Another issue is the boundary between the blockchain and the grid (the smart meter). Today suppliers appoint meter operators to install and maintain electricity meters. In a fully decentralized blockchain system it’s unclear who would be responsible for calibrating meters. A blockchain is excellent at ensuring only valid transactions are accepted, but there is still potential for fraud at the interface between the real and blockchain world.

The second area relates to the physical nature of the electricity system. The electricity grid is critical infrastructure that has additional layers of electricity supply security and physical safety.

Using blockchain in an electricity system requires an additional level of care over pure data systems such as cryptocurrencies. Managing the electricity grid involves not only data but the physical delivery of electricity.

Finally regulators will need to be careful in dealing with a technology that is not fully mature. Any immature technology can give surprising results as it develops and deployed at scale.

Thanks for reading!

Sources and further reading

The tangle whitepaper