the journey to “green” energy - e.dso

The Journey to ‘Green’ Energyor ‘a Quest for Flexibility’

Eandis - 13 november 2015

The Journey to “Green” Energy

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This document was developed by the Smart Grid Program team of Eandis, managed by Patrick Reyniers. Questions? Please contact the team via [email protected].


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Table of Contents Introduction ................................................................................................................................ 5

Structure of this document: .................................................................................................. 6

PART ONE ................................................................................................................................ 7

Flexibility .................................................................................................................................... 9

Green Energy needs flexible behaviour .................................................................................. 9

A short overview of the possible solutions .............................................................................10

Everybody must contribute .................................................................................................10

Consumption / Demand ......................................................................................................11

Generation..........................................................................................................................11

Balancing the networks.......................................................................................................13

Inertia .................................................................................................................................14

Network architecture ...........................................................................................................14

Leveraging between energies .............................................................................................15

The biggest business opportunity ever ...............................................................................15

The 3 dimensions of Efficiency ..................................................................................................17

Energy Efficiency ..................................................................................................................17

Financial Efficiency ...............................................................................................................17

Green Efficiency ....................................................................................................................18

The Triangle of Efficiency ......................................................................................................18

The triangle test ..................................................................................................................18

1. Energy Efficiency ........................................................................................................19

2. Financial Efficiency .....................................................................................................19

3. Green Efficiency .........................................................................................................19

Conclusions ........................................................................................................................19

How can we solve these contradictions? ............................................................................19

PART TWO ...............................................................................................................................25

Typical reasoning errors in energy debates ...............................................................................27

Wrong timing .........................................................................................................................27

The market before “Gate Closure” ......................................................................................28

Real-time management and Active Network Management .................................................28

Settlement period: after the market. ....................................................................................28

Wrong extrapolations ............................................................................................................28

With regard to voltage levels ..............................................................................................28

With regard to customer types ............................................................................................29

With regard to business types.............................................................................................29


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The market is key ......................................................................................................................31

New roles in the market .........................................................................................................32

Aggregator..........................................................................................................................32

Storage Provider.................................................................................................................32

The Atrias Flex market model ................................................................................................35

Service Delivery Point model (SDP) ...................................................................................36

Flexibility Register ..............................................................................................................36

Activation register ...............................................................................................................37

A long term view on Metering ................................................................................................37

The market can’t solve everything .........................................................................................37

Energy control models ..............................................................................................................39

Basic principles of control systems ........................................................................................39

Closed Loop control ............................................................................................................39

Rebound effects .................................................................................................................40

Deterministic vs. stochastic control .....................................................................................41

Central vs. decentralized control .........................................................................................41

Controlling money is key .......................................................................................................42

The role of the Internet of Things ..........................................................................................43

Telecommunication backbone infrastructure or vulnerability..................................................43

CONCLUSIONS ........................................................................................................................45

Constant and accelerating change ........................................................................................45

Keep the triangle in mind .......................................................................................................45

Central but also local market models .....................................................................................45

Everybody must contribute ....................................................................................................45

Overview of detailed Eandis positions .......................................................................................47

Bibliography ..............................................................................................................................49


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Introduction

Our overall goal is to reduce greenhouse gas emissions, one of the causes of global

warming. One of the most important ways to obtain this reduction is to adapt our energy

generation and consumer behaviour.

Not only must electricity generation change to what we call “green” generation but also mass

energy consumption must be adapted so that we consume less and produce fewer

greenhouse gases.

Heating and Transport for example have to switch to other energy modes but this can lead to

substantial additional electricity consumption.

The transition to this “green” energy landscape can only be very gradual and it will be a very

long journey. The biggest challenge will be to act in the right way at the right time to facilitate

this transition and arrive at the final destination.

The goal of this paper is to give a view on the various aspects that will facilitate or hamper

this journey.

We’ll see that “Flexibility” will play a very important role. But we’ll also see that adding

flexibility into our energy landscape will only be possible if the right policies, control systems

and market mechanisms evolve at the same pace.

The conclusion will be that building a “Smart Grid” is a long and complex process.


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Structure of this document:

The different chapters of this document are building blocks to arrive at the general conclusions. Some important aspects are already highlighted within the chapters.

Besides the vision detailed in this document, Eandis adopts clear-cut positions referred to in the footnotes of the text and in an overview at the end of this document.

The document contains 3 parts:

PART ONE

Part One is intended for the general public and describes a number of general principles and facts concerning theprogressive transformation to a full “green” energy landscape.

We start by looking at general aspects of what’s now called Flexibility and

describe the needs and possible solutions.

It is important to verify solutions by considering their global and long-term impact.

This is in order to avoid solutions that have negative side-effects. We’ll use the

“Triangle of Efficiency” to describe these principles.

PART TWO

Part Two is intended for specialized audiences from the Energy Utility or Regulatory arena and describes some more detailed and technical aspects of our vision about the journey to a green energy world.

Discussions on these themes are sometimes situations of mutual

incomprehension because the same terms and solutions can be used for totally

different problems. Here we’ll discuss the typical reasoning errors in such

debates.

Before the conclusions, we’ll assess the role of the Market as key in the global

roadmap for the final “Green” energy landscape.

All these technical and market based solutions will only lead to our ultimate goal

if we keep some basic principles of “Control” models in mind.

CONCLUSIONS

This part presents the most important conclusions relative to our vision of the right path to take in order to obtain an energy landscape with the best ecological result.


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PART ONE


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Flexibility

Green Energy needs flexible behaviour

The transformation of our Energy Landscape from ”conventional” to ”green” is far more

than just replacing coal, gas and nuclear power plants by wind turbines, solar panels and

bio-gas CHP systems.

The impact on the whole energy landscape will be enormous and very tricky to achieve

due to the following factors, being the basic elements of what we’ll call further on

“Flexibility” and being basic components of what’s called a “Smart Grid”.

- Today, demand drives generation, but at the end of this transition it will be exactly

the opposite: generation will drive demand.

- It will take decades and cost a lot of money to progressively install new renewable

generation systems. The transition from “generation follows demand” to “demand

follows generation” will be gradual. And it will be very expensive because not all

components needed to achieve this will be present at the right time.

- Most of the renewable generation is intermittent and (apart from downwards) not

controllable. The more this type of generation will be connected to the grid, the

harder it will be to keep pace with demand by dispatchable generation. Conventional

power plants will gradually evolve to play a “gap filling” role, producing less at a

higher cost and as a consequence adding to the cost of the transition

- This change of role of fossil fuelled power plants will also cause a drop in their

financial efficiency and gradually push them out of the market. The management

challenge will be keeping them in the market as long as this additional highly flexible

energy is needed.

- Because not all demand is able to follow renewable, energy storage will play an

important role. This energy storage will be partly centralized, partly distributed. Local

storage systems will create additional challenges.

- As storage solutions will only emerge progressively, balancing power will still be

needed. The role of natural gas fired power plants will be very important. This is not

only because of their very low emission factor but also because of the existing gas

storage capacity and the upcoming production of Synthetic Natural Gas from Power

to Gas production units.1

1 Gas: For Eandis, the role of the Natural Gas grid and its storage will gain importance. Natural Gas

and also Synthetic Natural Gas (produced by Power to Gas installations) or even with a portion of Hydrogen will be key to achieve the necessary balancing power for the future. It also ensures a way to store energy in an appropriate (and existing) way at a competitive cost.


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- The existing grids are designed to transport and distribute energy from central power

plants to local customers. Changing this principle by adding local generation and

storage systems will require a total review of their architecture.

- The “inertia” of conventional power plants will disappear and only partly be replaced

by the new systems. Preserving the balance of the network will be a bigger

challenge and calls for new technology to maintain frequency stability.

- The desire to reduce use of fossil fuels will also impact on other energy use such as

Transport and Heating. The result will be additional electricity consumption

increasing flows in the electricity grid. (e.g. Electric Vehicles and heat pumps.)

And again, all the problems already referred to will be heavily impacted by these

new types of demand!

- Traditional market models are not designed to handle an all renewable energy

landscape or even the transition to this goal. Intermediate and rapidly evolving

market models will be needed to support this change.

The reader will notice that the “Transition to Green” will be an immense and very

challenging journey that must be well prepared and planned.

But, rapid developments in different types of technology make it hard to plan and

organize well, because you cannot plan around something that is not invented yet.

One of the foundations of this transition will be supporting market models.

An incremental approach will be needed but the main challenge will be not to focus on

only one of the aspects without preparing for the others.

A short overview of the possible solutions

Everybody must contribute

As the path to “Green” will result mostly in a de-centralization of electricity generation, the solutions will also have to be found in a decentralized manner.

Every element (demand, generation and storage) should progressively start to participate in helping the system to remain balanced.

The shift to “generation drives demand” must be introduced progressively. Many ways exist to achieve this (and new solutions will emerge) but focusing on only one solution is certainly wrong.

We’ll see below some of the typical solutions that may enable demand to be changed or shifted in time. The impact of industrial demand or larger power plants is of course larger than in the residential domain. That’s why the focus should be on them first but also start trials and plan the other part to be ready for the next step.


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Consumption / Demand

Not all electricity demand can be shifted in time. Some are of the “direct” type, for example lighting. This is why we’ll also have to invest in storage to be able to cope with situations of neither wind nor sun.

As results of studies and pilot projects show, about 10-20% of demand can be time-shifted. However, in most cases this will cause a loss of energy efficiency (see section on the 3 dimensions of Efficiency).

See Bibliography (Oak Ridge National Laboratory 2013)

Certain pilot projects such as Address, Ecogrid and Linear have shown that the financial benefits for residential customers are currently very low. Therefore, the focus first is on industrial demand while preparing for the “change” in behaviour of residential customers.

See Bibliography (Address 2013) (Ecogrid-EU 2015) (Linear 2014)

So, shifting industrial demands will be the starting point. But, even if the technical cost of shifting is relatively lower, the impact on the cost of manpower must also be taken into account. A factory that in an extreme case produces only when there is wind and sun is almost impossible to realize. It will also require a lot of flexibility in terms of manpower and throw up some financial issues (e.g. adding additional production capacity) to be resolved.

A socio-cultural change will be necessary but we hope that the necessary technology (e.g. storage and load shifting automation) will allow us to achieve the necessary flexibility with minimized impact.

The way to steer this demand-shifting can be through financial signals (through tariff schemes for example) or controlled by any technical signal (centrally or locally created signals, based on the situation of the market or the network.

We’ll explain further in this paper the different types of control models and their pros and cons. The choice of control model types will have a huge impact on ways of handling a shift of demand in what we call “Demand Side Management”.

Generation

Green energy generation is mostly intermittent (wind and solar) as it depends entirely on weather situations. The only way to control it is downwards through curtailment.

The big shift from centralized conventional power plants to wind and solar is also a shift from power plants with a big operational cost to systems with almost no operational costs.

As mentioned before, conventional power plants will lose a lot in efficiency and their generation will become more expensive. A solution has to be found to allow natural gas and synthetic natural gas fired power plants to continue to play their balancing and gap-filling role. This is because of their low emission factor.

The reader will notice that biofuels are not discussed in this paper because much controversy surrounds this topic. Food-based biofuels are certainly not a good solution for the planet. We must also consider that the combustion of most biofuels produces perhaps less carbon dioxide but high levels ofnitrous oxide. Nitrous oxide (also called “laughing gas”) is also a major greenhouse gas and air pollutant.


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Considered over a 100-year period, it is calculated to have between 265 and 310 times more impact per unit mass (global-warming potential) than carbon dioxide.


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Balancing the networks

Going “Green” does not mean that we should allow all available green generation on our networks because we’ll still need for ex. the gas fired plants to fill the gaps between generation and demand. This until other technology can take over.

Indeed, we’ll still have to maintain the exact balance between generation and demand. This is the only way to keep the frequency on our networks at exactly 50Hz.

As a result of the intermittency of Wind and Solar production, we’ll need to provide extremely flexible additional power to fill the gap between the available green generation and the load demand and this has today a big cost.

The COST of producing energy from wind or sun is the SUM of the cost of the wind

turbines or solar panels + the cost of the necessary balancing power to maintain the

instantaneous and structural balance.

See Bibliography (Falko Ueckerdt, Lion Hirth, Gunnar Luderer, Ottmar Edenhofer 2013)

Today this is an additional cost of about 40%! So, all solutions must tend to lower the need for extra balancing power.

But it will not always be possible anymore to “just” balance the power. When for example green production exceeds demand, the only solution is curtailing green power or exporting to neighbouring countries. The cost of export is huge and always results in negative market prices. This means in fact that a green power plant will have to pay to inject power into the grid. The problem is that they will continue to produce as long as their income from subsidies exceeds the cost of injection.

The phenomenon of negative market prices is very interesting and should in theory be only temporary. At the end of our journey to “Green”, all the solutions will be in place to predict such moments and to shift demands or store excess energy. But in the meantime we unfortunately don’t have sufficient demands to take advantage of these negative prices.


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Inertia

One of the consequences of replacing large central power plants with small green ones is loss of Inertia.

Large generators tend to stabilize their rotational frequency just by the weight of their rotating parts. This is of considerable help in terms of coping with differences between energy supply and demand and so contributes greatly to keeping the frequency stable.

This loss of inertia also has to be replaced. Some solutions exist, for example based on flywheels and batteries as used today on some islands. But also bigger “Battery Power Plants” based on large battery setups are being tested and installed. They do not replace the physical weight of the rotors of the old generators but will do virtually the same thing electronically.

Investments in these types of solutions must not be forgotten in our overall plan to go “Green”.

Network architecture

At the beginning of this document, we spoke about the need to adapt the electrical networks to a progressively decentralized generation system and new types of demand.

One of the effects is that energy will circulate progressively in both directions on our networks. Copper (and today most aluminium) cables conduct energy in both directions so that is not an issue. The problems rather concern controlling the voltage and guaranteeing the security of a network with a highly fluctuating bi-directional load flows.

Technical solutions exist but the associated investments must be made. And here also they must be planned progressively along our journey to “Green”. The biggest challenge will be to invest at the right place, at the right time.

The second challenge will be to bring the demand electrically as close as possible to the new generation. This will reduce the amount of investment required and energy losses on the networks.


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Leveraging between energies

All flexibility solutions mostly tend to buffer energy and/or shift the energy demand in time.

We have to keep in mind that our final energy usage is mostly at the end of a long chain of conversion between energy forms. The start resource can be Oil, Gas, Wind, Solar, etc. to ultimately produce light, heat, kinetic power, etc. generally with many steps in between like electricity.

This means that we can perfectly well buffer energy somewhere else in this conversion chain. Preferably in energies that are easy or inexpensive to buffer. Gas and heat are a perfect example.

There is widespread use of heating energy in our countries. Heat is also relatively easy to store in water or more modern Phase Changing buffers. This method can be used to choose the time when we convert electricity to heat and thus shift the demand to a more appropriate moment.

These same types of levers can be an appropriate way of allocating an end-use to an overproduction of green energy, by creating heat for example instead of just curtailing the overproduction.

Also new types of levers are emerging. One of these is power-to-gas conversion. This is not only a way to store electrical energy (gas is far easier to store than electricity) but can also be used to assign an end-use to otherwise curtailed generation. The role of existing natural gas networks and storage will be important in terms of handling the synthetic natural gas produced.

The biggest business opportunity ever

As a result of this, the reader will notice that the only effective solution to achieve “Green” will be a combination of different solutions.

Solutions that can achieve the following four points will generate one of the biggest business opportunities. They will therefore have to participate in the whole balancing challenge:

- Flatten or stabilize the production output of green generation

- Control this output to follow the demand curve

- Reuse surplus/curtailed production to be stored and reused later instead of just

being lost

- Additionally provide certain ancillary services to the network to assist in Voltage and

Frequency control

This explains why we’ll have to look for a combination of solutions instead of considering each element separately.

It also explains that, for example, storing electricity in batteries or converting it into gas, even with low efficiency, can result in big benefits. This is not only because you store energy but also because you reuse energy that would otherwise be lost in unavoidable curtailment processes.


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Many other combined solutions are possible. Converter systems for Wind turbines, Photovoltaic panels, battery chargers, etc. can also be used to provide ancillary services such as reactive power voltage control and thus contribute to network stability.

The biggest challenge throughout the “journey to Green” will be to adjust the

combination of solutions at the right time.


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The 3 dimensions of Efficiency

In this chapter we’ll explain the 3 types of Efficiency that compose the global efficiency of the

Energy world. You’ll quickly see that in most cases these types are to be considered as

mutually exclusive or contradictory and that it will be hard to focus on one of them without

hampering at least one of the others.

At the end of this chapter we’ll look at some solutions for managing these contradictions and

some examples where the 3 dimensions are impacted positively.

Energy Efficiency

This is the first type of Efficiency and also the most commonly used definition of Energy

Efficiency. It is also the basis of the European Community’s Energy Efficiency Directive.

The idea is to focus on the efficiency of the processes that use energy with the goal of

using as little energy as possible.

Solutions such as power saving lighting and other low power consumption appliances are

positive results of this approach and will certainly contribute to a more efficient energy

landscape.

However, it is not always the case that because you consume less, you consume at the

right time. To maximize the efficiency of refrigeration or heating processes for example,

the control unit will try to keep the right balance between adding energy to and keeping

energy inside your process. Thermal inertia will play an important role in determining

optimal control parameters. This will not necessarily result in consuming in low-price

periods or in periods of available green Energy!

Efficiency of usage of the networks is also very important. The right network architecture

will allow local generation to flow to nearby demand points without undergoing a series of

voltage transformations. This will reduce losses on the network and on investments.

Financial Efficiency

To obtain maximal Financial Efficiency, we’ll try to consume electricity at the lowest

possible cost. But it is not always the case that because we consume less we will

consume at the lowest cost. Different tariff time schemes and the cost of different fuel

mixes are a good example of this.

To benefit from lower tariffs, we’ll have for example to wait longer before turning on

heating or cooling systems. This will always result in lower Energy Efficiency and we will

ultimately consume more energy to be able to obtain the same result at a lower cost.

The impact of the cost of the networks is also significant. Building a network that can

integrate all types of peaks of green energy will cost a lot more than trying to optimize


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usage of these networks. To lower the costs of the networks we’ll have to shave peak

loads and bring peak and base loads closer together. This might not necessarily be

compatible with periods of availability of green energy or the most efficient use of energy.

Green Efficiency

Green Efficiency demands that we should optimize and maximize usage of green energy

on the network with the goal to reduce CO² emissions.

As explained before, trying to go for “maximum green” will require storage and/or

additional flexible conventional power generation that costs a lot of money.

A good example is city heating based on waste heat. The cost-benefit analysis for this

type of heating is sometimes negative due to the need for large-scale investment in

transport networks. Although it does seem quite absurd to cool processes in one industry

and produce heat for other industries or residential usage just a few kilometres away.

The Triangle of Efficiency

All three efficiency types are necessary to achieve global efficiency of the new green

energy landscape we’ll be working on.

We see that these 3 types of efficiency are influencing each other but not necessarily in

the right direction. They interact in a Triangle type relationship.

Figure 1 Triangle of Efficiency

The triangle test

A solution designed to help on one of the 3 axes should always have no or only a

positive impact on the other axes. 2

2 Triangle Test: The position of Eandis is that every technical or regulatory solution should pass the

Triangle Test to ensure a sustainable effect on the greening of our Energy landscape. Every solution

Efficiency

Energy Finance

Green


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Let’s look again at some of the side effects discussed earlier and their impact on this

triangle.

1. Energy Efficiency

Consuming less energy necessarily leads some processes to consume more. Consuming less is also not necessarily consuming in “green” periods.

2. Financial Efficiency

Consuming at the lowest cost means shifting your consumption to a better tariff time. Currently, these tariff times do not necessarily correspond to periods of maximum green energy availability.

We can make the cost of the network as low as possible by optimizing its use (mostly by peak shaving). This network usage optimization is mostly independent from optimal green and energy efficiency times.

But, the right network architecture can bring demand closer to green generation and so optimize the networks. This demonstrates that solutions with a positive effect on more than one axis can exist.

3. Green Efficiency

Maximizing green production will automatically raise the cost of the extra balancing power. This will continue to apply while we await all the new solutions to cope with the intermittency of green generation.

In the same way, consuming green is not always consuming less and today also not always cheaper. Trying to move demands to times of green energy availability will mostly lower the efficiency of these processes and as a consequence they may consume more energy.

Conclusions

It is very difficult to act on one of the axes without a negative impact on at least one of

the other two and obtain optimal Global Efficiency.

How can we solve these contradictions?

Each solution we consider should be subject to the “triangle test” described above.

Effective regulation can help work on the 3 axes together and achieve a global Efficiency gain.

must help to enhance the use of green energy, to lower the cost and enhance the overall energy efficiency.


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There is also the problem that solutions which can be helpful today can have an opposite effect some years later. The global energy landscape is evolving from all grey to all green and our solutions will have to evolve at the same pace.

New regulation will have to evolve at the same pace as the “greening” process of the

global energy landscape.

Let’s look at and analyse some types of solutions:

Network Architecture

Working on the right network architecture can have a financial benefit, a positive effect on losses and allow green energy to be more present on the network. The way to do this is to maximize integration of local generation on demand feeders. This will allow power to go via the shortest way to demand and result in fewer losses and voltage problems. An additional benefit is the ability to profit from the mostly higher reliability of demand feeders.

By adding automatic curtailment mechanisms to green productions, we can also start using the networks closer to their limits and achieve another part of the gain. These control systems can also allow use of the reactive control mechanisms of modern local productions and thus help solve a lot of Voltage problems.

In the case of degraded network upstream, these control systems will also enable maximized use of green energies to the remaining network capacity (instead of curtailing them or rejecting their installation for security reasons).

The location of renewable energy resources should also be carefully managed. In the same way as any factory should ideally be located close to transport links, all renewable energy generation resources should be located close to a strong grid connection to avoid unnecessary investment.

Subsidy mechanisms

The only way to obtain a positive impact on the 3 axes is to build a market model where Green energy is fully integrated in the market with other energies. Subsidies can of course help with this but as the landscape is evolving very fast, these subsidy mechanisms should also evolve fast.

That’s why subsidy mechanisms should be accompanied by an automatic dismantling mechanism as soon as their goal in the Efficiency Triangle described in this chapter is reached.

The result must be that in most cases “green” will be the cheapest on the market but we’ll also have to accept that there will be times when “green” is too expensive. By building the right components at the right time in the course of the evolving journey to “Green”, these times will disappear progressively.

Priority of Dispatch

Solutions like “priority of dispatch” for Green energy (as for any other flawed subsidy system) will only tend to enhance one of the axes and will generally lead to a negative impact on the other two. The effect of such mechanisms will only lead to a


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very small additional volume of green energy but at an extremely high cost in terms of handling.

See Bibliography (Falko Ueckerdt, Lion Hirth, Gunnar Luderer, Ottmar Edenhofer 2013)

Making Grey Energy more expensive

It might be an idea to promote green by making the “grey” generation based on fossil fuels more expensive. (By raising a CO² tax for example).

We must handle these types of solutions with care so as not to phase Grey energy out of the market too fast. This would indeed have dramatic consequences because of the lack of the necessary balancing power to fill the gap between demand and generation.

Special attention must be paid to conventional generation methods with a very low emission rate. Natural gas, Hydrogen and Synthetic Natural Gas (produced using Power to Gas technology) are the best examples of possible very useful methods for producing the necessary balancing power.

Tariff Schemes

Tariff schemes are a good way to handle the “pull” type of demand response. Just send out new tariffs and “hope” the consumer will react.

The most dynamic model is simply to communicate day-ahead spot prices to customers. In the Ecogrid project they even sent out in real time every 5 minutes a “balancing correction” to these prices.

The danger of tariff schemes is an overreaction resulting in a heavy unbalance or congestion, as there is not always a feedback loop. Each control model will only converge if you correct your actions based on the obtained results. This means that, based on the situation on the network, you should be able to adjust your tariffs more and more dynamically.

Since “greening” is now only in the early stages, such a dynamic type of feedback loop can still be avoided today in some regions. But as greening continues, this dynamic factor will have to increase.

A lot of pilot projects (like Linear, Ecogrid and Address for example) conducted at residential level showed that the financial result of “residential demand response” is today so low that business opportunity is still negative. However, as explained many times before, the journey to green will continue and at some point we will really also need residential demands to contribute to balancing the system. In the meantime we’ll first focus on industrial loads.

See Bibliography (Address 2013) (Ecogrid-EU 2015) (Linear 2014)

Local markets

The only way to handle congestion on a network is to act locally at the place of the congestion.

Because of the local aspect, there are generally only a limited number of actors. This is insufficient to obtain a liquid market and enabling gaming and extreme prices.


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Nevertheless we should find solutions to allow customers to profit from the availability of green energy or ask them to help by consuming it rather than curtailing it.

That’s why it will become more and more important to have good predictions: of productions but also of demands. Today’s predictions are mostly statistical. But as the impact of many additional factors such as the weather, market prices, etc. becomes more important, it will be harder to find the right patterns. We’ll soon be in what is called a “Big Data” situation.

Based on these predictions, possible congestions must be detected as swiftly as possible (at the latest day-ahead) and published. Based on this information, local agreements can take place between industries able to shift their demands and producers (who will avoid being curtailed at those times).

This is also the reason why regulation is very important for curtailment. As explained above and later in this paper, the market should resolve the biggest part of the problem.

In some cases, where the network operator has to do a lot of curtailment due to a lack of investment in the network, producers will ask the network operator to compensate the production loss. It is up to the network operator to run a profitable business but the lack of regulation may open the door to gaming.

This gaming can easily be avoided by fixing the curtailment compensation (if it exists) as

always based on market prices minus α. 3The factor α being a small correction so that

the compensation amount is always lower than the market price (this works also with

negative market prices). The producer is thus encouraged to find a market solution that

will enhance its profit rather than being compensated. The same principle can be used

in the future when Distribution System Operators (DSOs) try to bring congestion solving

products to the market. Such products can guarantee them compensation volumes

ready to be used in real-time during congestions.

Regulators should allow these local markets and prices to exist. Even at residential level we may have voltage congestion due to high Photovoltaic production. Why shouldn’t the neighbours help solve the problem by consuming at these times rather than at night? This can for example be achieved with a (network) bonus-malus tariff calculated in the substations.

The Ecogrid Project example

A nice example of a solution that acts on the 3 axes in a positive way is the real-time balancing market tested in the Ecogrid project.

See Bibliography (Ecogrid-EU 2015)

3 Curtailment Compensations: Eandis believes that the market should be able to solve also

congestion problems. Eandis can play an important role in facilitating this type of local markets by publishing predictions of network load and possible congestions. In case the market cannot solve the problems, Eandis must be able to intervene in real-time. In case compensations are agreed, they should always be lower than the concerning market prices, this to avoid gaming.


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Day-ahead price curves are sent to the participating customers. The consumers initially react via their controllers (or manual actions) and adapt their planning of consumption.

In real-time, based on the unbalance of the system, the Transmission System Operator (TSO) sends out a positive or negative correction to these prices every 5 minutes. This helps the customers adjust their planning.

The balancing of the network is here fully transposed to a financial effect!

A statement from the final evaluation of the Ecogrid project:

The technical measurements evaluating the performance in terms of volume (kWh) or

evaluating the stability of the market have positive results. Wind power curtailment was

reduced by almost 80%, and the use of spinning reserves has been reduced by 5.5%. This means

that energy payments for spinning reserves would be cheaper and spinning reserve capacity

could also potentially be reduced.


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PART TWO


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Typical reasoning errors in energy

debates

Before talking about market models it is important to look at the most common reasoning

errors. In the working groups during definition of the new market models for Flexibility at

Atrias in Belgium, we encountered a lot of “mutual incomprehension” simply because we

were discussing different aspects of the same points but thinking we were discussing the

same issues.

We finally used the following drawing to clearly locate the different aspects under discussion.

This drawing helped us locate the discussions or the subject areas in their right context.

Figure 2 Timing and Level diagram

Wrong timing

Balancing the network is maximally dedicated to the market (Belgium market). This

means that Balance Responsible Parties will have to balance (supply = demand) their

portfolio on a 15 minutes basis and deliver a well-balanced portfolio by the Day-Ahead

closure of the market. However they are able to correct their portfolio on an intraday basis

through acting on the Intraday Hub by exchanging unbalances with other parties.

Timing

Voltage le

vel


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When the gate closes one hour before real-time, the TSO (Elia) takes over and handles

the residual unbalances by using the balancing resources they contracted through a

market sourcing process.

The market before “Gate Closure”

The market starts far before real-time. It starts with the buying and selling of Futures years ahead and ends with intraday exchanges.

The discussion is totally different when discussing solutions on a year ahead, day ahead or intraday basis.

But as soon as the gate closes, we’ll talk only about real-time management.

Real-time management and Active Network Management

After the gate closure, the TSO handles the residual unbalances and manages the exceptions through the use of their contracted balancing resources and frequency containment reserves.

The TSO and DSO will use what we call “active network management” control systems to ensure the reliability and security of the networks they are responsible for.

The available curtailing systems on green energy generation for example are part of these mechanisms. The network operator will only use them when the market failed to assume the balance and during possible congestions of the network. These mechanisms will control the power on the network to prevent the conventional protection systems from cutting them fully off.

The fact that “flexibility” can be used before closure to balance a Balance Responsible Party’s (BRP) portfolio as well as for solving balancing or congestion problems in real-time is one of the most common discussion points. It is almost the same flexibility but the use is totally different.

Settlement period: after the market.

Afterwards, all actions must be settled and the money exchanged between partners. Unbalances must be paid, flexibility actions rewarded (depending on type of contract) as of course also the energy produced and consumed.

Wrong extrapolations

With regard to voltage levels

Power plants and very large consumers connected directly to the high voltage grid are mostly already participating in different types of reserves and balancing activities. These types of contracts are always one-to-one between the TSO and the partner.

When we start using balancing power (generation or demand) on distribution networks, the number of participants increases greatly (because of smaller impacts).

For this reason Aggregators start to play an important role but this also means converting the one-to-one relationship to a one-to-many relationship. An aggregator


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will have to contract a lot more customers to ensure continuous availability of the flexibility resources he is selling to a TSO for example. Depending on the time of activation, he has to activate the right clients to obtain the desired flexibility.

As the number of clients of an aggregator will increase, it will not be possible anymore to have a deterministic view on what’s happening on the network when an aggregator acts for balancing purposes.

Residential and smaller clients will at some time also start participating in the market. However it will not be possible to settle their actions in a deterministic way nor control them all directly. Signals will be sent out but reactions from the market will come on a stochastic base, not only in volume but also in reaction time.

With the coming of the “Internet of Things”, flexibility can be made available that will not be measurable anymore. Control systems will have to act in a totally different way. Central control will not be possible anymore. The role of local automation systems, that react to local signals, together perhaps with global broadcasted information, will become more important and even crucial.

With regard to customer types

Deregulation of the energy market started at the highest industrial level and drilled down step by step. The last step was just an extrapolation of the industrial models to the residential ones. But was this the right step? Free choice of energy supplier is today a duty for the residential customer instead of just an entitlement.

In some cases better optimizations will exist for residential customers through combining services. For example, the manager of a big residential building will be able to get better price conditions for his energy than each occupant separately. He can also lower his rental rates by installing solar power or hot water for the whole building. We still have to keep the “triangle test” described above in mind!

We conclude that we must take care that new models don’t hamper the possibility of bundling services of different types together.

With regard to business types

When looking for new solutions, people tend to extrapolate known models instead of really starting to think out of the box.

Good examples of this type of wrong extrapolation are charging models for Electrical Vehicles (EV). The combination of deregulated electricity and transport fuel is not evident. Consumers have the choice of retailer for their electricity supply at home. This doesn’t mean that you have to be able to charge your car at any charging station using this same retailer. This would be as difficult as being able to refuel from any petrol retailer at any petrol station.

Also roaming models for EV charging were tested in pilot projects as an extrapolation of Mobile telecom models. The only difference is that for an EV, there is nothing to roam! You have no mobile number to connect! All you need is charging power and in most cases you’ll get better tariffs at your parking area or charging station than at home, just because they are much bigger clients than you are and they will automatically have better conditions.


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The market is key

The energy market model in Belgium has evolved over the last decade, bringing the

balancing responsibility progressively more and more to the market.

The Balancing Responsible Party (BRP) must offer at market closure a full balanced portfolio

on a 15 min basis (supply = demand for every quarter of an hour). After the day-ahead

closure, BRPs can still exchange unbalances through the Intraday Hub up until Gate Closure

The TSO only acts on the residual imbalance after gate closure by using their balancing and

reserve resources. They contracted these resources through a separate sourcing process on

the market and use them on a bid ladder principle. The TSO manages the “Imbalances

market” and charges the cost to balance the network to the responsible BRPs. BRPs who

help adjust the balance in the right direction are remunerated for this through the same

market mechanism.

These market models will have to evolve progressively to enable the (progressive) greening

of the energy landscape. Upcoming green energy generation and new demands in the

distribution networks create additional problems to resolve. One of these is the possibility of

congestion on the networks. We also have to be careful with the new balancing products as

they could easily be at the origin of new congestion.

As the network operator is responsible for security of the network, it must always be

able to act on the network in real-time.

A market solution through appropriate flexibility products might be possible to prevent

congestion problems. However, congestion is a local problem and there are locally

usually not enough clients to create a market with enough liquidity. The regulator will

play an important role to prevent gaming and help the DSOs fully assume their role of

market facilitators.

We strongly believe that the role of the DSO in a first stage is limited to publishing

possible congestion predictions and leaving it to the market to act on the information.

An important point is that any financial compensation for curtailment owed by a DSO

must always be lower than the market prices. This should incentivize participants to

resolve predicted congestion problems before the real-time systems intervene.

However, the discussion about financial compensation for curtailment will perhaps no

longer be relevant in cases where a DSO can allow more green generation on the

network by using these control systems (e.g. allowing green generation on a degraded

network based on real-time estimation of remaining capacity).


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That is why green energy should be able to fully participate in the market as soon as

possible. Temporary financial assistance can exist but should always tend to help them only

to be more competitive instead of creating some kind of parallel market system4.

The basis of the Atrias study was to extend existing market models to allow a new Flexibility

market to be fully integrated. The goal was to organize the necessary processes around the

new upcoming roles.

New roles in the market

New roles are emerging in the market. In recent years we have seen a strong upcoming

presence of Aggregators offering balancing services to our TSOs. Other roles are also

being discussed, for example the role of Storage Provider.

Aggregator

The TSOs have adapted and developed new products for balancing and reserves to allow aggregators to participate in this market with resources on the medium Voltage networks.

The DSOs were also involved in order to create a specific “network study” product to analyse possible side effects of the offered flexibility (significant reductions in demand in regions with very high local production can for example lead to congestion upstream to the TSO network).

The role of the DSO is also very important to help with the settlement of these actions and offering the necessary data services (aggregated in many ways: per BRP, per Aggregator, per network node, etc.) to monitor the effect on the energy system.

Storage Provider

Some new market roles are not yet defined and are still under discussion.

Belgium is equipped with a large storage plant (the Coo Hydro Power Plant) with a capacity of over 1000MW. This capacity is currently used to store the overcapacity of nuclear power at night and re-use it for daily peaks and emergency situations.

This storage power plant is fully managed today by an energy producer but we see a debate starting about the possible role of a network operator in operating storage plants. Some pilot projects are being conducted with for example battery storage and Power-to-Gas.

The type of use of storage will be important in order to define the right roles. The question of whether a network operator can also be a storage manager depends on the type of use of the storage and also the time factor. Very short time storage or

4 Green Generation stimulation: Eandis believes stimuli to enhance the volume of green generation

must be built on helping this green generation to fully participate in the market to gain his market share


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storage designed to offer voltage regulation or frequency control can indeed be defined as “tools” for and from a network operator.

But as soon as storage is deployed in a market role, the network operator should act

as a market facilitator and not a market player.5

The complexity will start at the point when we start to use storage for both types of services. A power-to-gas plant for example can act as ancillary services provider but the gas produced must be sold in the market.

In all cases however, the location of the storage plants should be defined together with the network operators. Storage can indeed offer a number of services to the network and these services will in most cases be the biggest money-maker for these storage plants only if located on the right node of the network.

Local storage

Batteries are becoming cheaper and more efficient. As long as a battery is used only at a customer’s premises to store at cheaper time periods (or times when there is too much local production) and for re-use at peak or more expensive times, this does not have too much impact on the market. Only the customer’s demand profile on the network (and for the retailer) will change.

We should be aware that lower exchange with the network will also result in lower grid fees for individual customers. But caution: if all customers do this, there will be no gain, because the total cost of network operation must still be distributed to the customers.

So, a couple of questions arise:

- Will the lower investment needs be fully offset by lower (capacity based) grid fees?

- Will the role of a DSO be transformed into a kind of Emergency connection operator?

District storage

A few pilot projects have already shown that district storage through batteries generates mostly negative business opportunities.

Indeed, if batteries are only used to store in cheaper time periods and re-use is in expensive time periods (thus just playing a market role), there is no business opportunity.

Furthermore, batteries are becoming cheaper, but batteries can also offer many other services to the network.

Chargers and converters can indeed be used to contribute to Voltage regulation and for primary frequency control. They should focus on these ancillary services for the network and degrade their “market” role to a marginal one.

However, the need for these types of services on the network is far more important in industrial areas, so we should again focus first on these.

5 Storage: The role of a DSO should be in the first place a market facilitator but the ownership of

storage installation able to deliver network ancillary services should be allowed by regulation


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Industrial Area storage

See below the graph of a typical demand and injection curve in an industrial zone (Port of Antwerp).

Figure 3 industrial demand and wind curve

See Bibliography (Sweco 2015)

We see that the demand curve (red) is fairly flat but that the injection curve (purple: wind production) is highly volatile.

The sum of both (the green curve) is as a result more volatile with significant and rapid drops in demand. This means that the balancing power has to be able to “follow” these new demand curves. It will not only become very tricky for the balancing power to follow these demand curves but also very expensive.

When storage can be used to flatten this curve and also enable the curve to follow demand it can be of huge benefit to help build our green energy landscape. If it can offer supplementary voltage control and perhaps also primary frequency control, we really will have the right solutions. In this case, all marginal market handling of storage capacity is simply extra profit.


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The Atrias Flex market model

We made a distinction between roles and parties. One party can have more than one

role.

Initially, we have even split the role of aggregator into 2 roles: the Flexibility Service

Provider (FSP) and the Balancing Service Provider (BSP). This is because aggregated

flexibility can be offered to parties other than the TSO in order to resolve the residual

imbalance of the network.

When an aggregator offers its flexibility to a TSO, it acts as a BSP. But, an FSP can also

offer flexibility for example to a Balance Responsible Party (BRP) directly, to help it

balance its portfolio before market closure.

Figure 4 The Atrias Flex market model

The developed model helps facilitate the open energy market and prevents a vendor lock-

in. Settlement and compensation models were set-up to handle all kinds of different

cases.

We also analysed and integrated the possibility for an FSP to offer flexibility (coming from

clients from one BRP) to another BRP. The compensation model resolves this and

furthermore, recent papers from ACER show that this cross-BRP offering is also an EC

goal.

Special attention was also paid to protection against gaming.


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Three toolsets are necessary to achieve this model: the Service Delivery Point Model, a

Flexibility Register and an Activation Register.

Service Delivery Point model (SDP)

One of the bases is the “Service Delivery Point” model. This model allows a grid user to act with more than one meter on the network and buy its energy from different retailers but also to offer its flexibility or local production to different parties.

A service delivery point is a combination of the master meter (“head-point”) together with a sub-meter. In this scenario, more than one SDP is possible at one customer’s premises.

Through this SDP model, the structuring processes allow choice (and switching) of different retailers and aggregators for different parts of the client’s demand or generation.

Figure 5 Service Delivery Point model

You can for example have one retailer for your direct demand (lights, etc.) and another for your flexible demand where this retailer can switch on and off or modulate your car charging or a part of your industrial process.

Note that this doesn’t mean that all meters must be defined as a supplementary SDP. Flexibility providers can of course install all kind of tools behind the meters to steer the demand without the need to define additional SDPs. The right balance to facilitate the market, without making it too complex and without allowing too much vendor

lock-in) will have to be found.6

Flexibility Register

The flexibility register is based on the existing Access Register. The role of the FSP is added in as an additional relationship to an SDP of a grid user. A service delivery point can be connected to only one FSP as it can also be connected to only one retailer.

Structuring processes can occur in the same way as a retailer switchover.

6 Metering and sub-metering: Eandis believes that market faced metering managed by a DSO can

strongly contribute in facilitating the upcoming market models and avoid vendor lock-in.


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Managing this register helps network operators maintain the relationship between FSPs, customers and their BRP and thus allows them to pass information on a correct aggregated basis to the right partners.

Activation register

Each Activation of flexibility on a Flex SDP is entered in the Activation Register by the activating party. The content of this register allows network operators to assume their data distribution and settlement role.

Note: flexibility achieved through tariff schemes is not “activation” and thus not taken account of in this register.

This model is intended to be used for large-scale flexibility only. Any extrapolation to residential demands is not yet foreseen and will perhaps not be possible.

The initial scopes of this mechanism are medium and high voltage levels. It would be not realistic to also include activation of residential customers through broadcasting signals because the response is not deterministic anymore. (See later with regard to control systems).

A long term view on Metering

Currently, and in the Atrias model, all “market faced” meters are handled by the network

operator who is also the data-manager. But we have to consider possible wrong

extrapolations.

We can for example imagine that a lot of flexibility can be acquired without the possibility

of measuring it exactly. Suppose that every hot water boiler or even every laptop charger

is equipped with a very small “Internet of Things” connection that allows a flexibility

provider to switch it off for a time. This means enormous flexibility for the energy system

but we will only be able to guess, based on statistical information, how much demand will

react because not all these demands are necessarily active at the moment we need to

shut them off.

We’ll see straight away that these types of “stochastic” responses are not exactly

measurable anymore and that we should focus first on more important and deterministic

flexibility that we will mostly find among larger customers.

See more in “control models” about this type of demand shifting and the danger of what

are called the “rebound” effects afterwards.

The market can’t solve everything

We must remember that in the whole market flow there is a short time span that is not

under control of the market; between gate closure and real-time.

The market mechanisms must continue to evolve to allow market players to resolve the

maximum number of balancing problems and in the future also congestion problems, and


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this before gate closure. The network operators play a key role in facilitating these market

mechanisms.

However we must not forget that in real-time handling, network operators must be able to

intervene. This is not only to resolve the residual imbalances by using the contracted

balancing resources, but also the congestion problems by managing the resources

connected to these networks.

In all the evolutions of market models, we must keep the “Triangle Test” in mind. So,

every solution on the Energy, Financial or Green axis must at least not hamper the other

axes but if possible help them in the right direction.

These market mechanisms must also be “gaming proofed”. Each instance of gaming on

the market will help the gaming party only in one of the 3 Efficiency axes and have a

negative effect on the other two.


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Energy control models

Basic principles of control systems

The base goal while controlling a system is to obtain a well-defined output. In our case,

the output is a well-balanced energy landscape where input and output are at all times

the same and which results in an exact 50Hz frequency.

Additional control might be needed to keep the load on some network sections under the

physical limits of the network components. In most cases, this is typically their physical

temperature resulting from the electricity current.

We have two dimensions to control on the network: balancing and local congestion.

Both may be contradictory because an action to limit overload of a local segment

(congestion) may have an influence on the global balancing of our system. The opposite

is also true. If we try to bring the system in balance by adding demand or supply, this may

also cause local congestion problems.

In all cases “closed loop control” is needed but as explained, in this case there are many

loops and one loop can influence another.

Closed Loop control

Only a continuous correction of the input, based on the difference between real output

and desired output will help to converge to the desired result.

Figure 6: basic control loop

The size of this correction however is not always easy to define. If we correct too much it

can lead to an overreaction. Finding the right control parameters is sometimes very hard,

especially in these very complex energy systems with a lot of changes in time and

interacting parameters.


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Figure 7 Control Parameter effects

Tariff schemes for example, with a too strong incentive to consume at certain times can

lead to an overreaction which leads to congestion or imbalances.

In the Ecogrid project, the system imbalances are translated into a real-time price

correction. The automatic control systems in the connected homes and factories react to

these signals and try to adapt the behaviour of the consuming appliances to minimize the

cost of use and thus participate in correction of the imbalance. These price corrections

are continuously adapted, based on the result on the system from the reaction of the

demand. This helps to converge as soon as possible to a system balance. We saw that

the price corrections varied a lot according to time of day, outside temperature, etc.

simply depending on how much demand was able to participate in the adjustments.

To fine-tune such control systems, system reaction to past corrections but also a lot of

forecast information is very helpful. Even forecasting of demand reactions to price

corrections is very important.

We conclude that if we try to control balancing only through tariff schemes, they will

have to be more and more dynamically and even locally oriented as our journey to

“green” evolves.

Rebound effects

Energy-consuming processes have an additional behaviour that must be handled with care. In most cases, cutting off demand will, for example, lead to cooling down (or warming up) of the processes. When demand reconnects, depending on the downtime, an extra load demand will be necessary to resume the process. If all this extra load demand occurs together, this creates a peak that is possibly higher than the peak we tried to resolve!

This phenomenon is called “rebound” and can lead to the solution of one problem on the network, but the creation of a new one when resuming.


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Deterministic vs. stochastic control

Unlike deterministic control, stochastic control deals with uncertainty in the model.

As long as we act on high and medium voltage levels and thus larger industrial demands or generation, we can act deterministically (and measure exactly the result of our actions).

Bus as we try to control and act on smaller demands that are scattered among the network and voltage levels, it will become very hard to have direct control over them. We can send out signals to all customers but we will never be able to know exactly the status of all these appliances: consuming, almost at the end of a cycle, ready to start, etc.

Measuring the impact of stochastically reacting control models will have to be statistical. But as the reactions of these processes will evolve in time, depending on market prices, outside temperature, holidays, etc. a lot of prediction work is necessary. Today these influences are minimal but as our journey to “green” continues, the influences will become more dynamic and interacting.

Central vs. decentralized control

It is not possible to steer the world from one point. Even nature is not organized like that so how would a human being be capable of doing what nature can’t.

Nature is organized in Ecosystems which are combinations of elements that have reached a certain equilibrium. And this equilibrium evolves in time based on many factors. These factors can be very local and small like combinations of molecules, but can also be global like temperature, daylight, etc.

The nicest example of a human-built ecosystem is the Internet. It is an equilibrium that evolves but nobody controls it centrally.

We can also look at the “swarm model” as an example. Bees have very simple behaviour patterns. One of these is to assemble themselves in a swarm around a queen. But as soon as the swarm exists, this swarm has its own behaviour pattern that is not programmed in any of the bees.

Even for this we have a human example: traffic jams. A traffic jam has a behaviour that is virtually the same in most countries but is absolutely not programmed in the cars/drivers building it.

Combining those two examples we can conclude that the only way to control energy landscapes efficiently is also a combination of some central information together with very locally created signals. The use of very local control systems that react to local facts and some global signals will have to create a kind of swarm that creates a well-balanced energy system. Even this equilibrium will evolve over time as will an ecosystem.

Here are some examples of local control systems.

Voltage based control on low voltage

On a low voltage feeder, each instance of congestion leads generally to a voltage problem. Too much local production will lead to over-voltage, too much demand to under-voltage.


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Acting based on a local voltage measurement may be a solution. Controllers starting up some appliances when local production is too high can reabsorb this. The problem is the lack of market models for operating this system from one client to another.

Another example can be a home-charger for an Electrical Vehicle that reduces the charging current based on the voltage drop it causes.

Frequency relays on low voltage loads.

While the previous voltage based example suffers from a lack of market models and appropriate regulation, we can envisage some compulsory solutions, such as the use of local frequency relays on heavy consuming appliances like hot-water boilers or heat-pumps.

When significant problems occur with high impact on the energy system like the loss of a big power-plant or an important transmission line, frequency will drop. Frequency containment and restoring reserves will operate automatically but why shouldn’t a lot of very small appliances participate?

If the trigger to shut off is set to a frequency just above the trigger for frequency containment reserves (49.8Hz), this will cut off a large part of demand that may prevent a further drop or at least help limit the impact of the frequency drop.

The remaining problem will be to decide on how long they have to be cut off. If we cut off all electrical boilers at the same moment for the same time-lap, we will certainly have a very big problem when they all come back on together. This rebound will perhaps lead to another frequency drop? A random defined time is perhaps a solution to be tested.

Controlling money is key

It’s a regrettable fact that consumers react mostly only on money. Every control action

must correspond to a certain amount of money if we want a consumer to react. So, using

tariff and price systems is the key to changing their behaviour and building the

progressive journey to the “green” energy landscape.

But this doesn’t mean that very complex and detailed tariff and price mechanisms are the

only way. We saw in the Telco markets a change to these very detailed models but then

we were very soon back to packaged service offerings. The big question is whether we

can skip this back and forth step or not.

In the Telco markets they soon came to the conclusion that their customers’ behaviour

was predictable and so they could offer service packages. The big difference between

Telco and energy is the absence of balancing needs in Telco.

A number of pilot projects showed that the range of actual market prices for residential

consumers is too narrow and thus this also applies to their benefits for participating in

flexibility markets. The benefits will not offset the cost of home control tools today.


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These control tools, such as “energy boxes”, are necessary to reduce the impact on user

comfort for customers participating in flexibility. And at some point in time during our

journey to “green”, we’ll need their participation.

Once retailers or aggregators are in a position to be able to offer packaged services

together with the necessary automation tools, then we will be able to move forward. This

will be the time when they reap the benefits of this approach.

The role of the Internet of Things

The number of connected “things”, (people and devices) is increasing extremely rapidly.

We can conjecture that one day our household appliances will exchange information with

the network (through for example a smart meter) and start adapting their behaviour.

As long as we are talking about “houses” this might not be a problem but in contrast to

what we have said up until now, this will not be the same for industrial or network

appliances.

One of the characteristics of the modern Internet of Things (IOT) is the very short lifecycle

of these devices but also of their connection method.

We can easily set up update mechanisms for software in our smart meters for example,

but replacing millions of meters after a couple of years is unfeasible or too expensive.

The way connected devices are connected is an even more serious risk. Wireless

connection systems also evolve very rapidly but are even more affected by environmental

disturbances.

Telecommunication backbone infrastructure or

vulnerability

The use of flexibility in our energy landscape will require a lot of information to be

communicated between parties.

We also see a lot of papers about smart cities. A smart city is more than a city with a lot of

smart grid equipment installed all together. Traffic, waste transport, etc. are also building

blocks of a smart city. The result is an even more complex need for connectivity between

numerous tools, devices and parties.

The connection backbone infrastructure in our energy landscape or smart city is possibly

the biggest vulnerability of all. Not only can “conventional hackers” cause big problems

but as we go more and more wireless, these new vulnerabilities will appear.

We conclude that we must be very careful in choosing the right combination of

connectivity methods and technologies. Not only because of their shortening lifecycle

but also because they will increasingly become the heart of our systems.


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CONCLUSIONS

Constant and accelerating change

The journey to our ultimate “all green” energy landscape will be a continual step by step

building process. It will be very hard to plan the right steps at the right moments. This is

true for technical solutions but also for market models and regulation.

On top of this, some of the actions we take will necessarily have only temporary effects,

simply because the landscape changes and the effect of certain actions can be very

quickly reversed.

Keep the triangle in mind

Every step we take should be verified using the described “triangle test”. Every solution

that acts on Energy, Financial or Green efficiency must at least not impact or must have a

positive effect on the other axes.

Central but also local market models

We will not be able to control everything centrally. We will also not be able to let the

market solve all our problems. Nevertheless the market is the key to the solution and will

have to evolve very fast.

As not all problems will be resolved by the market, network operators will still have to be

able to act in real-time to resolve residual imbalance or congestion and to cope with

incidents.

Everybody must contribute

Progressively, every producer and consumer in our energy landscape will have to

contribute in order to obtain a fully green landscape. Business opportunities are currently

not positive with regard to small consumers but are starting to be positive for industrial

consumers.

There is a lot of work to be done in terms of building the technology to minimize the

impact of this evolution on our behaviour and comfort!


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Overview of detailed Eandis

positions

C

Curtailment Compensations: Eandis believes that the market should be able to solve also congestion problems.

Eandis can play an important role in facilitating this type of local markets by publishing predictions of network

load and possible congestions. In case the market cannot solve the problems, Eandis must be able to intervene

in realtime.In case compensations are agreed, they should always be lower than the concerning market prices,

this to avoid gaming. ............................................................................................................................................... 22

G

Gas: For Eandis, the role of the Natural Gas grid and its storage will gain on importance. Natural Gas and also

Synthetic Natural Gas (produced by Power to Gas installations) or even with a acceptable proportion of

Hydrogen will be key to achieve the necessary balancing power for the future. It also ensures a way to store

energy in an appropriate (and existing) way at the lowest cost ............................................................................... 9

Green Generation stimulation: Eandis believes stimuli to enhance the volume of green generation must be built

on helping this green generation to fully participate in the market to gain his market share ............................. 32

M

Metering and sub-metering: Eandis believes that market faced metering managed by a DSO can strongly

contribute in facilitating the upcoming market models and avoid vendor lock-in. ............................................. 36

S

Storage: The role of a DSO should be in the first place a market facilitator but the ownership of storage

installation able to deliver network ancillary services should be allowed by regulation ...................................... 33

T

Triangle Test: The position of Eandis is that every technical or regulatory solution should pass the Triangle Test

to ensure a sustainable effect on the greening of our Energy landscape. Every solution must help to enhance

the use of green energy, to lower the cost and enhance the overall energy efficiency. .......................................... 19


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