How could microgrid and peer-to-peer energy networks work?


Why is the energy business so heavily controlled and regulated?

Mostly, its history.

When you have a few large generators and millions of consumers, its big business – and that leads to operators trying to control markets which triggers political oversight, which inevitably leads to questions of control.

So what we have across the world is a system of generation, transmission and distribution over a grid system that connects where energy is made and where it is used, and a parallel system of metering and accounting to bill users.

Microgrids and peer-to-peer systems want to change that

Imagine a new housing development where the developers have decided to create a private network of electricity wires that connect the homes instead of using the cables and equipment provided by the grid.

There may be a few connections to the main grid, but the rest of the properties are effectively off-grid.

At the same time, each house has solar panels for electricity and hot water, excellent insulation, low running requirements and perhaps a micro-chp unit and battery storage.

The independent network forms a microgrid.

The existence of housing units with the ability to generate electricity and heat from a variety of sources and a population that uses energy creates a network of peers – equal participants.

The concept of peer is sometimes forgotten – the households of the future will be both producers and users of energy – so called prosumers.

What they need to work are markets

In a microgrid peer-to-peer system, there will need to be some way of keeping everybody happy – and that is done by a price system and a market.

If people are free to set prices (or the trading is automated and the machines trade among themselves) then the market will result in a price that matches supply and demand.

It avoids the cost of routing energy through the grid, so it should be cheaper.

Experiments like the Brooklyn microgrid set up by LO3 Energy are showing how this could be done.

A peer-to-peer network does not have to be part of a microgrid

We could have renewable generators, like a solar farm, connected to the grid that want to directly sell all their output to a user connected somewhere else on the grid.

They can currently enter into a bilateral contract that is settled and billed by a supplier.

A true peer-to-peer system could eliminate the need for a supplier, and simply have a separate contract – based for example on a contract for differences model – although these are still complex to create and agree on a one-to-one basis.

A start in this direction is Open Utility’s Piclo platform that matches users with local generators.

We are still in the early stages of a transition

We’re a long way away from having solar PV on every roof and local networks of users have yet to spring up.

Will there be a revolutionary peer-to-peer change, or is it likely that the majority of the system will still be controlled by a few producers.

If history is anything to go by – network effects and scale matter.

We may have lots of committed, small players, but Google style companies for energy will still probably emerge – a few highly connected hub players that aggregate and influence how everything else works.

We still operate in a winner-takes-all ecosystem, and peer-to-peer is a small part of it.

Will it be different this time?

Do you have the skills needed for modern work?


Hedge funds could look very different in a few years.

The Financial Times published an article about the rise of DIY algorithmic traders – people who develop automated investment strategies.

These people don’t work for hedge funds or banks on Wall Street.

Instead, they are mathematicians, progammers, physicists and data experts who are using their skills and cheap, powerful computers to tackel investing.

And this is happening everywhere we look.

Online sales are hyper-competitive, and the companies with an algorithmic edge can squeeze out more profits from their platforms.

Recommendation engines are key to keeping users interested, as algorithms work out personalised offers.

The energy business is fuelled by data – from meters recording generation output to those working out who has consumed it and what their bill should be.

In a world of abundant, cheap money, projects have to work on razor thin margins.

Getting the numbers wrong, over time, will mean that the project makes negative returns.

So, who is going to succeed in this new world?

Drew Conway came up with the Data Science Venn Diagram to explore the key skills needed in the world of Data Science – the field that will most likely underpin modern work.

In adapted form, the key is having three sets of skills.

Hacking skills are an entry requirement – being able to deal with and clean text and numeric data is part of every project.

Excel won’t hack it anymore – we’re going to have to use better tools to deal with more and messier data.

Then we need some maths and stats knowledge.

Knowing how to draw and interpret charts and understand the relationships between sets of numbers makes the difference between guessing and having a theory.

And a scientific approach is based on having hypotheses and running experiments.

Finally – many people think they can simply waltz into a new field and take it over.

Domain knowledge often makes the difference between success and failure in a field – it’s very hard for someone to build a tool to solve a problem that they have never experienced themselves.

That’s why we get lots of tools that look pretty, but end up doing little.

Curious people with good tools are what we need for modern work.

Why we don’t understand how we fit into reality


Science has been more successful at making life easier for us than any other system of thinking so far.

We have learned to control and adapt the material world to ourselves.

As George Bernard Shaw said, The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man.

That’s worked for a few hundred years because of a particular way of thinking.

The positivist approach looks at reality and sees it as something that is independent of anything in it, including ourselves.

If we drop a stone, it falls the same way it will when dropped by anyone else.

That means we can look at objects, measure their properties and build concepts and ideas that exist independently of us.

Gravity would exist whether there was life or not. Once a building is constructed, the designer is no longer needed for people to live and use the building.

In the positivist’s world, there are things and other fuzzy things like people that don’t really compute.

We get into trouble when we try and apply positivist thinking to social structures like organisations and companies.

These structures exist because humans.

We can argue that if people didn’t exist, then there would still be moon rocks.

If people didn’t exist, there would be no companies to work for or carbon emissions to reduce.

Interpretivists see people as inseparable from reality. They are part of the world.

What we see around is constructed from what we see and the ideas we have – and how we interpret that.

This is why the assembly line organisation constructed by Ford and the lean manufacturing system constructed by Toyota both, on the surface, make cars – but have fundamentally different organisational philosophies.

Positivists run into trouble when they try and apply principles that work very well for things in the real world to organisations.

It’s easy to fix a problem in a machine – apply grease to a stuck part and it gets going.

An organisation’s equivalent of grease is harder to grasp – is it a meeting, a study, a team that works on a problem?

The extreme positivist approach says that everything can be fixed with a hammer and a spanner.

The extreme interpretive approach says everything is in our minds so nothing really matters.

A pragmatic view is somewhere in between.

There are technical solutions to some problems.

In many other situations, however, we need to have a model for how people fit in as well.

Without that, we can fool ourselves that we understand reality more than we actually do.

Is information enough to spur action?


The domestic sector uses nearly 30% of the total energy used in the UK, and 80% of that is used for space and water heating.

Reducing energy use in this sector would clearly help reduce emissions and help the UK move towards its carbon targets.

Several approaches have been used to do this – from carrying out performance measurements using the Standard Assessment Procedure (SAP) and providing Energy Performance Certificates (EPCs) to dispensing written and face to face advice and information on how to save energy.

There are very few studies on whether any of these approaches actually result in savings.

A study published by the Behavioural Insights Team (BIT) in late 2017commissioned by NEST and Npower found that the predicted results from models such as the SAP varied widely from actual performance.

For example, the SAP predicted that savings from loft insulation in a medium home would be £120 and pay back in 2.5 years.

Real world data had a saving of £21, raising the payback period to 11 years.

On a day to day basis, however, the way in which people use the controls and settings in their homes has a greater impact on the amount of energy they use.

Does providing advice improve how they use their controls?

Another study in 2014 found that written information or advice in the home had no impact in the amount of energy used.

In some cases, showing people how to use their thermostats may have increased energy usage as they now used them to increase temperatures and get more comfortable.

This could be because of a number of reasons, and include the common problems with behaviour such as forgetting, ingrained habits and just not wanting to deal with the effort or hassle or doing something.

The purpose of the NEST and Npower commissioned study was to see if there was a statistically significant saving to be had from using a system like the NEST learning thermostat, which uses sensors and machine learning to optimise the heating schedule.

Once installed, NEST uses occupancy and weather data that is collected over time to figure out when it should turn the heating up to ensure comfort levels are maintained and when it can be reduced without impact.

Four studies – the most rigorous of their kind so far – showed that compared to homes having a programmable timer, thermostat and radiator valves, the NEST system could save 4.5 – 5% of total gas consumption.

Adding in an optional feature that does seasonal savings by tweaking winter use adds another 3.3% to the savings figure, taking the total to nearly 8%.

It can also nudge users – giving them leaves if they turn down the heating and act in an energy efficient way.

The thermostat is around £280 installed, with a payback of 6.5 to 11 years.

And this is still where the problem lies.

Even at a relatively low capital cost, the payback is going to be on the order of 10 years.

And that makes it hard to create a simple business case for change – especially for operators of large portfolios that may quickly have to spend hundreds of thousands of pounds to retrofit a few thousand homes.

New homes will probably get systems like NEST fitted as standard and, when we do a major refurb, it will be a small part of the overall cost and easy to justify.

But, in summary, the evidence shows that we get better results when we automate how choices are made rather than if we ask people to change.

How to develop a product someone will buy


The acid test of a product is whether people will buy it.

It makes sense to have a systematic approach to testing our assumptions about how customers will respond, and a new canvas from the folks that developed the Business Model Canvas called the Value Proposition Canvas could help.

There are a couple of versions of this floating around, but the type in the picture above is easy to draw and work with.

The Value Proposition canvas has two main sections – what we do and think it does for customers, and our assumptions about the jobs our customers need doing and what that means for them.

Starting from the customer end, if they get their jobs done, we assume that they get certain gains.

These might simply be lower costs or greater sales.

Or they might be operational – better quality, intangible – greater corporate social responsibility scores or brand recognition.

If that is the case, they why aren’t they doing more about it already?

It’s usually because there are things in the way – pains that stop them from moving forward.

This then leads to a simple matching exercise that we need to do.

When we look at the products and services that we offer, which ones are gain creators and which ones are pain relievers?

If we can match gain creators to the gains that customers want and pain relievers to the pains that they have, then we have a better chance of creating value that a customer will be willing to pay for.

Let’s take an example of a company that does data analysis for customers – providing an Analysis as a Service proposition.

Many companies collect large amounts of data – from sales and product information to energy usage and cost data.

We might assume that if they could use this data more effectively, targeting areas where there are hidden costs or by using it to better target their sales efforts, then they could reduce costs or increase sales.

The problem is that there is more data than can be analysed using tools such as Excel, it takes time and many organisations can’t spare the skilled people that it needs to do this.

So, the service provider might see an opportunity to provide trained staff on a consulting basis, perhaps Devops engineers who can do both development and operations and work closely with managers and existing technical people to extend and develop the tools needed to do this.

If the area we are working in is core to the business, then it could be run as a partnership between the service provider and the company.

If it’s non-core, it could be outsourced.

A simple canvas such as this quickly makes the assumptions we have about the product and customer visible.

The next step is to get out of the building, as Steve Blank says, and talk to potential customers and test our assumptions.

We refine our model based on the conversations we have and iterate until we have something that is market ready.

And that has a better chance of passing our acid test.

Are you being biased without knowing it?


Modern society functions on information.

It’s the basis of much of the activity that takes place every day – from markets where price discovery depends on the kind of information used by participants to which treatment option we should choose.

By default, information is not value or bias free. It carries the views and opinions of the people giving it to us.

These days we are particularly susceptible to the biases built into the large tech companies that dominate the flow of information we get.

We look for information using a search engine. Social media filters and serves up what it thinks we will like. Recommendation engines on shopping sites suggest what we should read next.

This creates problems.

Safiya Umoja Noble writes about how racism and sexism is built into search engines in her book, Algorithms of Oppression.

We see a filtered set of results from what is out there, and nothing suggests that what we see is biased in any way or not representative of the larger information cloud.

Noble’s analysis shows this isn’t the case, and that the nature of the internet business and the companies that dominate it shows up in results that discriminate between people and races.

To some extent, the search engines show up a mirror to society – the algorithms are learning from what we do online.

We could see the results not as a brave attempt to classify and bring us the best of what is out there, but a reflection of how human society is right now.

And what that tells us is that we still have a long way to go in getting fairness and equality into global society.

Some people believe that the problem can be solved with more data and better learning algorithms.

That may well be the case, but right now we have problems with information and content on the internet that range from graphic displays of terrorism to online bullying.

The tech companies are starting their response by adding more humans into the process to help filter and curate appropriate content.

The volumes that are generated every day, however, means that bad stuff will inevitably get through.

To protect ourselves we need more than filters, we need frameworks to organise and question the information that gets through.

We need to learn to think for ourselves.

Do you see things as they really are?


Look at the picture above – what do you see?

We are far more influenced by small things than we realise.

It turns out, for example, that we are more likely to be generous after lunch than before, think a person is more warm and approachable if handed a warm cup of coffee by them than a cold drink or be affected positively or negatively by the words we hear.

We also don’t realise how important the situation is in affecting what happens next.

This is called context blindness and it happens all the time.

We ascribe people’s actions often to who they are as people – their personalities and traits – rather than the situation they find themselves in.

We believe murderers are bad people, while the murderers themselves think of their crime as something they had to do in that situation.

We see Warren Buffett and Bill Gates as super-smart people who created their own futures, rather than average people who had a series of helpful experiences that set them up for stock investment and technology startups.

This is called the fundamental attribution error and, in his book Mindware: Tools for Smart Thinking, Richard Nisbett describes how culture may have an impact on how often we make the error.

Going back to the picture, if you’re from the West (particularly American), you probably noticed the three big fish swimming to the left, then the smaller fish, vegetation and some of the other parts of the picture.

If you’re from the East (particularly Japanese), you probably noticed the stream, vegetation, rocks and shells and three big fish swimming to the left.

Apparently, people from the East pay more attention to context than those from the West, and Nisbett speculates that this, as with many things, goes back to the Greeks.

The Greeks have a tradition of intellectual independence, possibly developed as a result of their geography and economic features, and laid the foundation of scientific thinking as we know it now.

The East had a more holistic approach – with interdependent economics and value systems that required a large population to cooperate and subsume their personal interests to that of society as a whole.

Independent Western thinkers, as a result, believe that people have complete control over their actions, and so if they get it right or wrong do so as a result of who they are.

Easterners, on the other hand, are more likely to give people the benefit of the doubt and take their situation into consideration.

This small difference in mindset has a large impact in many areas.

People who believe that criminals are inherently bad and should be locked up may be unable to see the benefits of how changing the environment those people are in could change their future.

People may have become conditioned to think that they can only do certain jobs, but find new opportunities when they move cities or find new friends.

Life isn’t static – just because things are the way they are now that doesn’t mean that is the way they always will be.

Changing our context is the first step to changing our reality.

How to think critically


Thinking critically about something is not the same as criticising it – just like making an argument is very different from having an argument.

In day-to-day usage it appears negative or destructive – but from an academic point it’s simply a common sense approach.

How can we approach a new situation, idea or information with our eyes open and do something meaningful as a result?

This starts with being able to consider the situation critically – questioning rather than blindly accepting the things that are put in front of us.

This is especially important in a world where we face complex choices – from whether we should act quickly or slowly on our own contribution to climate change to enthusiastically adopt the latest technology fad.

Quite often we default to doing nothing – and that may be the worst of all outcomes. That leads to atrophy and failure.

John Mingers identified four aspects of critical thinking that act as a useful checklist for us.

The first is to be wary of rhetoric.

Is the argument fair, balanced and logical or is the speaker using language in a way that could appeal to emotions or mislead us?

Is it a sales pitch rather than an insight?

It’s not always easy to tell, because we can be swayed by passionate people who believe in what they are saying – but we need to try.

The second aspect is to question tradition.

Tradition can involve unquestioned assumptions that are made by people or the culture and practices that have sprung up around an idea.

This can be a difficult thing to approach as the existing position, or status quo, is something people will cling strongly to and resist changing.

It’s easier to go along with them – but that might not be the right thing to do.

The third aspect is not to accept authority unthinkingly.

Arthur C. Clarke wrote – If an elderly but distinguished scientist says that something is possible, he is almost certainly right; but if he says that it is impossible, he is very probably wrong.

Things change – and sometimes people that have built their reputation on a particular set of ideas find it difficult or impossible to accept that their contributions could now be overturned.

If they have power, they can direct resources and attention to other areas instead.

We see examples of this everywhere – most notably in politics across the world.

The final aspect is to question the objectivity of the people involved.

Robert Pirsig in Zen and the art of motorcycle maintenance writes about how scientific work can be like sorting grains of sand on the beach into piles.

The piles represent related ideas, concepts, theories. It’s the way that we approach and classify the world.

The thing we cannot forget is that the piles do not exist on their own.

There is a person kneeling there on the beach making them.

And we need to consider how value-free and objective that person is about the issue.

For example, we would not give a news report from a state that routinely censors information the same weight as a report from a respected investigative reporter.

So, in summary, critical thinking is not about criticising.

It’s about not blindly following persuasive, traditional, authoritarian or seemingly objective points of view.

How to make your way to higher profits


Most of us are stuck in a zone where we make hardly any money.

It turns out that the profits made by organisations fall along a power curve.

This means that most fall into a zone where their returns on capital employed are tiny.

They make net returns of less than 2% on invested capital, making 10% gross and paying out 8% to lenders.

Chris Bradley, Martin Hirt and Sven Smit from McKinsey explain how in this article, showing that companies at the top make around 30 times as much as the ones in the middle.

The ones at the bottom make losses of over 14 times.

This works out to around $50m in the middle, nearly $1,500m at the top and losses of just under $700m at the bottom.

They found that around half of where we sit on the curve depends on the industry we’re in – Tobacco is at the top, paper is languishing in the middle.

As the saying goes – before you climb the ladder, make sure it’s leaning against the right wall.

It’s better to be average in a great industry than great in a poor one.

This has echoes of Warren Buffett who wrote when a management with a reputation for brilliance tackles a business with a reputation for bad economics, it is the reputation of the business that remains intact.

As this McKinsey article shows, returns are highest in pharmaceuticals, household personal goods and software, and lowest in utilities, telecommunications and transport.

So what’s the answer?

It turns out that to move up the curve, we need to do the basics plus a bit.

The authors come up with five points.

The first is to have a disciplined acquisition process. We need to evaluate and buy businesses that are a good fit for us.

The other four are basic good business practice.

We should focus on the areas that are doing best, moving more resources to them.

We need to invest in our capability, putting capital into the business so that we have the technology and resources to respond to the market.

Becoming more productive goes without saying – doing more with less.

We need to stand out – differentiate ourselves by innovating and creating new business models.

Interestingly, this power curve could be used to describe individuals and their careers just as much as organisations.

A few make a lot of money, the vast majority get by and some lose a lot.

The question that we need to answer is what moves we’re going to make next.

The authors argue that these need to be big ones – just doing what everyone else is doing isn’t enough.

We need to do more and go further if we want to break out of where we are and move towards a more profitable position.

Doing nothing is a bad idea.

How to optimise only the things that matter


Much of what we do can be described in the form of a process flow – and we often assume that if we can improve performance by improving parts of the process.

To improve traffic flow, for example, we could have all cars drive at the same speed – surely that will help?

That doesn’t turn out to be the case.

We can see this effect when something happens on the motorway that causes a lane to be shut.

It doesn’t matter how well everyone drives individually.

The flow rate of the vehicles is set by the capacity of the number of lanes available and so, when we lose one, everyone slows down as the same number of vehicles now has to pass through the lesser number of lanes.

Eliyahu M. Goldratt, in his books Goal and Theory of Constraints, sets out how the throughput from a process is going to result from one constraint or bottleneck.

To improve the throughput – the number of things coming out of the process – we need to figure out where the bottleneck is and what we need to do to improve its performance.

It’s a waste of time spending effort optimising any other part of the process, because the performance of the system overall will still be set by the bottleneck.

Goldratt sets out a five-step process for dealing with constraints. In adapted form, these suggest we should:

  1. Figure out where they are.
  2. Decide what to do about them.
  3. Decide how everything else works based on the impact on 1 and 2.
  4. If, in doing all this, the constraint is no longer the limiting one, then go after the next one.
  5. A warning – we need to keep repeating this, as the limiting constraint will move around.

The way we often figure out where constraints are is because they have piles of work-in-progress (WIP) in front of them.

The same thing applies with knowledge work.

A person can be a bottleneck if the work they do is slower than the rest of the work carried out by others, and so they become the limiting factor in the operation.

Aligning how we work with bottlenecks has a number of benefits:

  1. We know that throughput is set at the capacity of the bottleneck. To increase output, we need to work on the bottleneck.
  2. This means that we can minimise inventory to the level required by the bottleneck. Working any other part of the operation simply piles up money in stuff that will take time to be processed.
  3. We can also reduce operating expenses because we don’t need more people in areas that don’t directly contribute to the bottleneck activity.

In summary – when we try and optimise an activity we often try and speed up parts of the system.

What we need to do instead is improve flow through the system.

And that starts by focusing on bottlenecks.

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