IMPERIAL COLLEGE LONDON
Faculty of Natural Sciences
Centre for Environmental Policy
The Electricity Supply Industry:
Past, Present, Future
By
Alex Whitney
A report submitted in partial fulfilment of the requirements for
the MSc and/or the DIC.
07/09/2011
2
DECLARATION OF OWN WORK
I declare that this thesis
The Electricity Supply Industry: Past, Present, Future
is entirely my own work and that where any material could be construed as the work of
others, it is fully cited and referenced, and/or with appropriate acknowledgement given.
Signature:.....................................................................................................
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Thesis title:
The Electricity Supply Industry: Past, Present, Future
Author:
Alex Whitney
I hereby assign to Imperial College London, Centre of Environmental Policy the right to hold
an electronic copy of the thesis identified above and any supplemental tables, illustrations,
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Date: __________________________
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Contents Introduction ......................................................................................................................... 6
Chapter One: A Brief History of the Grid ................................................................................. 7
1.1 Beginnings ...................................................................................................................... 7
1.2 Nationalisation ............................................................................................................... 8
1.3 Privatisation ................................................................................................................. 10
1.4 Re-Integration .............................................................................................................. 11
1.5 Competition ................................................................................................................. 15
1.6 NETA ............................................................................................................................. 17
1.7 In review ....................................................................................................................... 20
1.8 Renewables Investment ............................................................................................... 22
1.9 UK Policy ...................................................................................................................... 23
1.10 Electricity Market Reform .......................................................................................... 26
Chapter Two: The Electricity Generation Industry ................................................................ 28
2.1 Model Outline .............................................................................................................. 28
2.2 The Economics of Electricity Generation ..................................................................... 29
2.3 Levelised Cost Model ................................................................................................... 31
2.4 The Grid Today ............................................................................................................. 35
2.5 FPN and MEL Data ........................................................................................................ 36
2.6 Balancing Mechanism Data .......................................................................................... 39
2.7 Generation Mix ............................................................................................................ 43
2.8 Prices ............................................................................................................................ 46
2.9 The Big Six .................................................................................................................... 50
Chapter Three: Grid Model .................................................................................................... 53
3.1 Model Design ............................................................................................................... 53
3.2 Demand, Availability, Capacity .................................................................................... 54
3.3 Marginal Cost Curve ..................................................................................................... 54
3.4 Outcome ...................................................................................................................... 57
3.5 Scenarios ...................................................................................................................... 60
3.6 Results .......................................................................................................................... 61
3.7 Oversupply ................................................................................................................... 63
3.8 Storage ......................................................................................................................... 64
3.9 Modelling Storage ........................................................................................................ 65
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3.10 Results ........................................................................................................................ 68
Conclusion .............................................................................................................................. 71
References ............................................................................................................................. 72
A description of the main generation technologies .......................................................... 77
UK plant statistics ............................................................................................................... 78
List of Figures
Page
Fig 1.1 Takeovers and mergers of ex-publically-owned enterprises 1995-2011 14
Fig 1.2 Price support and costs for wind power by country 26
Fig 2.1 Levelised Cost model indicative results by technology 34
Fig 2.2 Interpreted FPN data 38
Fig 2.3 Interpolation rules 39
Fig 2.4 TGSD data and FPN data 42
Fig 2.5 Load factors by plant 2010 44
Fig 2.6 Load factors by plant, season and settlement period, 2010 46
Fig 2.7 BMU prices plant and settlement period, 2010 48 Fig 2.8 BMU prices by cumulative volume and by plant, 2010. Log-log plot 49
Fig 2.9 Detail of Fig 2.5, linear plot 49
Fig 3.1 Marginal cost curve components 55
Fig 3.2a Marginal cost curves by plant 56
Fing 3.2b Overall MCC 56
Fig 3.3a Sample model output 59
Fig 3.3b Sample model prices 59
Fig 3.4a Real-world output 59
Fig 3.4b Real-World prices 59
Fig 3.5 Load factors by year for each scenario 63
Fig 3.6 Oversupply by year 64
Fig 3.7 Modelling Storage 67
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Introduction
The UK’s electricity supply industry (ESI) is poised somewhere between flux and crisis. Our
deregulated industry structure has been so lauded and imitated it has become known as
the ‘British Model’, yet the industry is perpetually under investigation for price gouging and
anticompetitive behaviour. The regulator is frequently criticised as toothless and our
legislation is often byzantine, self-contradictory, self-defeating or all three. There are no
British utilities with anything like the international reach of EdF or E.on and a string of high
profile corporate failures have left the majority of our infrastructure in foreign hands.
Despite having the best renewable resources in the EU, our generation mix is 80%
dependent on coal and gas and investment has been steadily dropping. How did we get
into this mess and what happens next? This dissertation is a three-part attempt to answer
that question.
In the first chapter I trace the history of the grid from its beginnings, paying particular
attention to the effects, intended or otherwise, of the 1990 privatisation. I investigate
whether the particular structure of the ESI has helped or hindered attempts to kick-start
the ‘green revolution’. I contrast the UK’s approach with the rest of the EU and ask whether
the recent Energy Market Reform marks a change in direction.
In the second chapter I begin my empirical investigation. I obtain and process data from a
variety of sources in order to sketch a detailed picture of both the physical operation of the
grid and the underlying economic. I construct a ‘merit order’ of dispatch and create a
model to calculate the levelised cost of various technologies for use in the next chapter.
In the third chapter I create a model to estimate the generation mix and costs of electricity
through to 2025. Drawing upon section two, I create four possible scenarios for the grid
and quantify indicators such as costs, carbon emissions and reliability of supply. I extend
the model to investigate the effects of adding energy storage capabilities to the grid and
comment upon my results.
Finally, I offer some conclusions on the lessons of the past and the challenges of the future.
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Chapter One: A Brief History of the Grid
1.1 Beginnings
The history of the electricity supply industry (ESI) is a fascinating case study of the political
history of the UK. Since its inception the ebbs and flows of the industry have mirrored the
prevailing political winds, from laissez faire in the 19th and early 20th centuries to the post-
war consensus of embedded liberalism, and the post-Thatcher neoliberal ‘turn’. This review
will draw a sketch history of the grid with particular emphasis upon developments since
privatisation.
Up until the early 20th century, there was no national grid per se. In the mid-to-late 19th
century myriad small networks sprung up, privately owned and operated for profit (the first
public utility was a small hydro-electric facility established in Surrey in 1881). As a new
technology, electricity had only a few specific uses. The initial motivation came from
providing street (and later residential) lighting in competition with town gas. In the absence
of common standards relating to voltage, frequency and interconnection, these grids were
for the most part non-interoperable (Jamasb & Pollitt, 2007).
However it was in the early 20th century, as the domestic and commercial uses of electricity
began to multiply, that of the strategic importance of electrical power became evident, and
in 1926 the Central Electricity Board was formed to impose order upon the industry. The
CEB created operating standards, built high-voltage long-distance interconnects and
oversaw the construction of new capacity. The National Grid was created in 1933 to
oversee transmission infrastructure. Expansion, integration and technological
improvements began to increase the efficiency and reliability of the supply (Chick, 1995).
Yet at this point it is thought that there was still over 600 suppliers operating 400 power
stations at any of 19 different voltages (Chesshire, 1996) a proliferation attributed to a lack
of a cohesive central government programme for the development of utilities (Byatt, 1979).
The essential inefficiency of the existing system coupled with the fact that electricity
distribution had monopolistic economies of scale meant that public ownership was (in
retrospect) inevitable. This was finally realised in 1947, as part of the radical reinvention of
the state engineered by the post-war Labour government – who also nationalised coal,
transport, gas, iron and steel, healthcare, Cable and Wireless, British Airways and the Bank
of England.
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1.2 Nationalisation
Through this process the ESI was formalised into its current structure, which vertical links
together four components: generation, transmission, distribution and supply. Point-sources
of power generation scattered throughout the nation are interconnected by a high-voltage
transmission network, usually overhead pylons. The transmission network feeds into a
multitude of (non-interconnected) lower-voltage distribution networks which wind their
way capillary-like into every village and street corner in the country. Suppliers contract
local demand and oversee metering and billing. The whole ESI operates at 50Hz (three-
phase) and generators must synchronise at this frequency before they can transmit power.
Electricity flows roughly linearly through the system and substations are positioned
throughout the network to step the voltages down from 400kV to 275kV, 132kV,33kV,
11kV, 6kV, 450V and finally 240V (single phase) in the home (National Grid Electricity
Transmission, 2011)
The British Electrical Authority, later the Central Electricity Generating Board (CEGB), was
instituted as a vertical monopoly in control of generation and transmission. Distribution,
supply and customer services were the responsibilities of 14 regional Area Electricity
Boards (AEBs), and this arrangement remained relatively unchanged for the next 43 years.
Under nationalisation, integration and standards compliance advanced quickly. During the
two decades following the reform, the UK enjoyed unprecedented economic growth, and
this was coupled with a huge increase in demand for power as the generation that had
‘never had it so good’ filled their homes with electrical appliances. As a powerful and highly
centralised agency, the CEGB was well placed to meet this growing demand and embarked
on a huge plant-building program. Installed generation capacity increased from 16 GW to
65 GW between 1951 and 1971 (or from 0.3 kW to 1.2 kW per capita), whilst total
electricity generation grew from 57 to 221 TWh (MacLeay et al., 2011). Indeed a
government report from 1969 recommends scaling back the program due to oversupply.
(Truly, that was a different era - the same report states that the government expects
investment to “show a return of at least 8% in real terms” – and that “however careful the
forecasting... expenditure generally falls appreciably short of the approved figures.”)
(Nationalised Industries Review, 1969).
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This level of success was in large part due to the fact that the CEGB had the resources to
build power plants on a scale that had previously been technically and financially
unfeasible. In 1948, the largest power plant was 550 MW, yet by 1965 the average new
plant was 1300 MW (ibid). Of the 1960s power plants still in use, five are rated at over 2
GW. The UKs largest (and Europe’s second largest) power plant, Drax, was commissioned in
1974 and is rated at close to 4 GW (MacLeay et al., 2011). CEGB’s preferred technology was
the coal-fired steam turbine power plant, coupled with Open-Cycle Gas Turbines (OCGT),
and it played an important role in supporting the British coal industry. However the CEGB
was also in a position to invest in capital-intensive new technology and from 1970 onwards
- at the behest of the government - they constructed a total of ten nuclear power plants.
Although it was initially expected that nuclear would prove much cheaper than coal
(leading to the now-infamous tagline “Too Cheap To Meter”), enthusiasm waned after the
huge costs and poor reliability of nuclear became apparent (Chesshire, 1996).
In spite of this, the CEGB was for many years internationally renowned for its technical and
managerial competence. However, in some sense it was too big to last. It was by far the
largest of the UK state-owned enterprises and by 1987 its asset base totalled some £27bn
(the AEBs owned a further £15bn). The CEGB maintained a duopoly of suppliers for coal
plant, awarding contracts on the basis of ‘Buggin’s turn’; as a de facto monopoly (supplying
95% of generation in England and Wales) it was free to spend lavishly on R&D, through
which it exercised absolute control over the direction of UK ESI (Chesshire, 1996). From a
certain (Thatcherite) point of view it was a lumbering monolith that represented all that
was wrong with nationalised industry. In 1987, after two terms into office and fresh from
victory in the miners’ strike, the Tories took aim. Their manifesto from that year proclaims
that “We will continue the successful programme of privatisation... We will bring forward
proposals for privatising the electricity industry subject to proper regulation.” In May 1987
Thatcher was re-elected to her third term as Prime Minister and in 1988 the government
released the white paper Privatising Electricity.
The House of Commons Select Committee on Energy, in its review of government’s
proposals, commented nervously that “Reviews of international experience, particularly of
the USA and other European countries, do not reveal any strong, or indeed positive,
correlation between, on one hand, utility structure, form of ownership, and the degree of
competition, and the level of electricity prices and overall utility performance on the other”
(Chesshire, 1992).
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Nonetheless, the 1989 Electricity Act passed the broad framework for regulation, and so it
came to pass that in 1990 the CEGB was dissolved.
1.3 Privatisation
The neoclassical case against monopolies gives no quarter. A monopoly has no incentive to
drive down costs or improve efficiency, and can exploit its position to earn monopoly
profits since leaner, more efficient businesses are excluded from entry. Monopolies are not
only bad for consumers but bad for economic efficiency - and nationalised monopolies are
doubly bad because they gain preferential treatment from the government in place of
other worthier investments. The neoliberal school (to which Thatcher’s advisors
subscribed) further argues that the government has no business in business and if at all
possible national assets should be privatised and exposed to the unsentimental forces of
the market. They add, parenthetically, that this is always possible with appropriate
regulation (Harvey, 2005).
Despite Thatcher’s enthusiasm to privatise the system at all costs, the electricity market is
peculiarly resistant to marketization. Vickers and Yarrow (1991) highlight some of the
unusual economic characteristics of the ESI:
1) The ‘supply chain’ is tightly vertically integrated
2) Generation technologies are highly capital intensive with long lead times and high
sunk costs
3) Most generation technologies cause significant environmental externalities
4) To ensure security of supply, the ESI must operate with excess capacity in most
periods
5) Extremely tight technical demands – demand and supply must balance exactly at
every node across the whole network – mean that equilibration will always need
some central control regardless of the responsiveness of market mechanisms
6) Transmission and distribution are natural monopolies
The Government attempted to deal with the problem of monopolistic transmission and
distribution by instituting what became known as ‘unbundling’. The four sectors of the
industry were to be separated, with the transmission and distribution being privately held
but heavily regulated (to ensure that businesses did not abuse their monopoly position)
and with incentives to improve efficiency. The other two sectors, generation and supply,
were to be sold off ‘atomistically’ to ensure sufficient competition. A ‘pool’ system would
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operate whereby generators would compete amongst themselves for supply and suppliers
would compete amongst themselves for customers. A key feature was that no company
would be allowed to own both generation and supply assets, thus ensuring that anti-
competitive vertical integration did not re-emerge. Through strong competition, efficiency
would be increased and consumers would enjoy lower prices. It followed that no capacity
payment would be necessary, as inflated pool prices would alert businesses to a capacity
shortage and they would respond by investing in further generation (and if existing players
refused, a new entrant would step in to take advantage) (Thomas, 1996). Indeed it was
supposed that all key planning decisions could be left to the market once it had established
itself – a feature which would be particularly appealing to weary governments used to
shouldering the blame when things go wrong.
1.4 Re-Integration
There were, however, severe practical problems with implementing these measures. Chief
among them, the issue of how to ensure that assets were sold off in such a way that they a)
would actually be bought (i.e. would be attractive propositions for investment) and b)
would provide the requisite amount of competition. At the time there were really 3
separate ‘grids’, two in Scotland and one in England & Wales, with little interconnection
between them. The government’s plans were compromised immediately when they
decided they would split England & Wales’ generation into just two businesses. Ostensibly
this was because the UK’s nuclear generation was uneconomically expensive compared to
coal and gas and could only survive by being ‘sheltered’ inside a large company – and only
another large company would be able to compete with the first. It was therefore conceived
that two thirds of the total capacity would go to newly-formed National Power plc (NP),
with Powergen plc (PG) taking the rest. However the nuclear assets were so unappealing to
investors that they had to be spun out at the last minute into the publicly-owned Nuclear
Electric – and then subsidised by consumers to the tune of £1bn/year via the ‘fossil fuel
levy’ (which perversely also subsidised power from the French interconnect). The asset sale
went ahead anyway. Nuclear Electric operated as a price-taker, meaning that the two
remaining generators formed a duopoly which, as we shall see, they weren’t shy about
exploiting (Thomas, 2010).
Furthermore, in a move designed to appease furious Scots, the Scottish ESI would remain
natively owned in the form of two vertically integrated monopolies, Scottish Power and
Scottish Hydro Electric. The England & Wales transmission grid was privatised to become
12
National Grid, who as the designated transmission system operator (TSO) had a very tightly
defined remit and was responsible for precisely balancing supply and demand. They soon
merged with the privatised gas industry SO to form Transco and have since bought assets in
New England. The 12 AEBs in England & Wales were privatised wholesale but since they
were both suppliers (regional electricity companies, RECs) and distributors (distribution
network operators, DNOs) they were forced to separate the two sides of their businesses.
In addition, they were allowed to procure up to 15% of their power from their own plant–
further violating the principles of de-integration, but at least (in theory) introducing some
competition into the generation market as they ordered 10GW of new plant in the early
1990s ‘dash for gas’, discussed below.
In 1993, in an attempt to stem the market power of the duopoly, the regulator (OFFER,
later OFGEM) required that the generators divest 6GW of capacity between them, which
was sold to the largest REC, Eastern Electricity (in violation of the 15% rule). Then in 1995
Scottish Power was allowed to take over the REC Manweb, and in 1998 National Power and
Powergen were granted permission to take over RECs provided they each divested a
further 4GW of plant. Two years later, the RECs were forced to demerge with their
respective DNOs, apparently to stop ‘cross-subsidy’ between them. With the principle of
de-integration already scuppered, a mass of acquisitions now took place. Since no one REC
or DNO had been allowed to become dominant, most were unable to fend off takeover
bids by large foreign utilities (who were able to leverage monopoly positions in their native
markets).
At this point there is a curious twist in the tale concerning 17 US utilities that suffered a
grievous case of groupthink and lost a lot of money as a result. The EU Electricity Directive
1996 (96/92/EC) had mandated the deregulation of EU ESIs, and this sparked a gold rush as
US utilities attempted to break into EU markets. Naturally they started in the UK where
deregulation was furthest advanced, whence they would launch into mainland Europe.
Between 1996 and 2000, seduced by the promise of fast profits on the back of a fast
trading market, they made many high-profile acquisitions at heavily inflated prices
including 9 RECs and 9 DNOs. But they found that the UK market was far less competitive
than imagined and that the rest of the EU was in no hurry to open up their ESI to
competition and buy-outs. One by one the utilities lost enthusiasm and dumped their
assets at fire-sale prices; by 2003 nearly all had left, nursing estimated losses in excess of
$20bn (Haar & Jones, 2008).
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In the wake of this fiasco, French, German and Spanish utilities swept in and cleaned up.
Figure 1.1 tracks the various fates of the 30 companies brought into existence by
privatisation. The apparent endgame of the flurry of mergers and takeovers is that the ESI
has contracted to just a handful of mostly foreign-owned corporations (some of which,
ironically, are publically held). One imagines that this is probably not what the government
had in mind when initiating the great experiment 21 years ago.
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Fig 1.1 Takeovers and mergers of ex-publically-owned enterprises 1995-2011
Top row colour coding: Red = Generator, Light Green = Distributor Dark Green = Supplier, Mid-Green = Suppler/Distributor, Orange = TSO Else: White box = US Utility, Coloured = Big Six, Grey = Other Note that it is not a full depiction of the UK ESI today as there have been a handful of new entrants in that time Sources: Haar,Laura N, 2008; author’s research
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1.5 Competition
The best laid plans go oft awry – whether or not the privatisation played out as intended,
the more pertinent question is: does the privatised grid deliver a competitive market for
power? And, taking the longer view: was it all worth it?
The ‘Pool’ system implemented in 1990 worked roughly as follows: each day was split into
48 half-hour ‘settlement periods’. For each settlement period, each owner of generation
placed a number of offers to sell electricity from each of their power plants, priced in
£/MWh (e.g. in one settlement period, a 1000MW coal-fired plant might offer to sell the
first 250 MWhs at £15/MWh and the next 250 MWhs at £20/MWh). The TSO took all of
these bids and constructed a marginal price curve for dispatch. The point at which the
curve met the predicted power demand was the System Marginal Price (SMP) and all
successful bidders were paid this price for their power (MacKerron & Segarra, 1996). (It
follows that every REC in fact bought their power from generators at the same price (the
SMP), which begs the question of how any REC was supposed to gain a significant cost
advantage over the others.)
In principle the Pool is quite a ‘pure’ implementation of marginalist economic theory - but
of course it relies heavily on there being sufficient competition to drive down prices for
suppliers. Since immediately after privatisation there was effectively only two generators
(National Power and Powergen), it is no surprise they were able to manipulate the SMP
simply by raising their offer prices. Reviewing the first year of operation, in which the SMP
had increased by 29%, OFFER concluded that ‘there is no doubt that the two major
generators have recently been able to increase Pool prices significantly’ (OFFER, 1991).
To increase competitiveness the market needed new entrants (termed Independent Power
Providers or IPPs). In theory, high Pool prices should have been sufficient to attract them,
but entering into competition with two giant incumbents in a new market was extremely
risky and no truly independent companies could obtain financing. The RECs, however, were
keen to obtain their own generation to avoid being ‘squeezed’ by NG and PG. The
government sought to encourage them by introducing Contracts for Differences (CfDs),
which effectively allowed power to be bought from IPPs at fixed prices regardless of the
SMP - giving IPPs a guaranteed income. RECs then eliminated all the risk by forming their
own (not really independent) IPPs and constructing new Combined Cycle Gas Turbine
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(CCGT) plant on the basis of ‘back-to-back’ deals : the gas prices were contracted at fixed
prices for 15 years and the power sold for fixed prices for 15 years (Thomas, 2006a). This
arrangement sparked the ‘dash for gas’ leading to a glut of new CCGT, causing the
government to place a moratorium on new projects in 1998.
The long-term contracts soon became a liability as the price of fuels dropped steadily
throughout the 1990s and RECs ended up overpaying for their generation. An REC
employee commented at the time that “from the RECs point of view it isn’t such a good
deal at the moment... but as a shareholder of the IPP we are doing very well thank you
since companies who buy from the IPP are buying at premium rates” (Branston, 2002).
Since the domestic market was not opened to competition until 1998, the RECs were in any
case able to pass their costs on to their customers. Businesses, however, were able to
negotiate and effectively ended up buying the cheaper coal and nuclear power. Studies
show that in 1993/1994, 61% of profits were from the domestic sector and 39% from the
industrial sector – in almost exact reverse to the relative size of the markets (Branston,
2000).
CfDs clearly violate the principle of an open and transparent market. The IPPs found that
since their generation was already ‘bought and sold’, Pool prices were irrelevant - but they
still had to ensure their plant was actually accepted for dispatch. IPPs therefore adopted
the policy of submitting very low offers to the Pool and effectively taking their generation
out of the market. But there were other distortions too: between 1990 and 1998, coal-fired
plants were forced to buy quotas of coal from British Coal at above-market prices in order
to postpone the collapse of the UK coal industry. RECs were obliged to buy this power at
contracted prices, effectively taking this generation off the market too; and at the same
time, nuclear power was being heavily subsidised and was also effectively off the market.
As Thomas notes, the net result was that for most of the Pool’s duration, “it is clear that
more than 95% of RECs’ needs were supplied from sources that were not required to
compete in the Pool” (Thomas, 2006a).
With such low liquidity, it is no wonder that manipulation was rife. Even while the 1990s
saw significant diversification in the generation market (the combined market share of NP
and PG fell from 77% in 1990 to 30% in 2000 (MacLeay et al., 2011), it appears that abuse
of market power actually increased in that time. A study by Sweeting (2007) estimates that
between 1995 and 2000 the SMP was inflated by an average of around £7/MWh translating
17
into an overpayment of £2.7B per year, most of which inevitably was passed onto domestic
consumers; moreover, the peak of exploitation did not occur until Q1 2009.
Despite attempting a raft of measures over the years to ‘fix’ the Pool, OFGEM eventually
admitted defeat. In 1999, citing “the continuing market power of a number of generators
and their willingness to exercise that market power at the expense of customers” (OFGEM,
1999), the regulator announced that the Pool was to be scrapped and replaced by the New
Energy Trading Arrangements, or NETA. The announcement of NETA amounted to an
acknowledgement that the Pool was not a fair and transparent market and that
‘unbundling’ had been a failure.
Whether the Pool could have worked if it had been better implemented is an open
question – but there is good evidence that the ‘British Model’ is fundamentally flawed in
practice if not necessarily in theory. I shall reflect on the experience of other countries at
the end of the chapter.
1.6 NETA
NETA is a complicated set of agreements but the fundamentals are simple. The most
significant innovation is that NETA abolished the mandatory pooling of generation and
replaced it with voluntary spot and futures markets. The vast majority of generation is now
sold to suppliers over-the-counter in confidential long-term contracts, and each supplier is
responsible for contracting its own supply.
One hour prior to each settlement period, the TSO calculates the total supply for that
period by summing the generation contracted for by each of the suppliers. It also calculates
a forecast of total demand using a model. The difference between supply and demand,
termed the Net Imbalance Volume (NIV), is settled via the Balancing Mechanism (BM)
(OFGEM, 1999).
The BM operates like a miniature pool, with generators placing offers to increase
generation or bids to decrease generation until demand is met. If the market is ‘short’
(there is an undersupply) the BM returns the system buy price (SBP); if the market is ‘long’
(oversupply) it returns the system sell price (SSP). There is one important difference – the
SBP/SSP is not the marginal price but the average price in the BM. The ‘reverse price’ (the
SSP if the market is short or the SBP if the market is long) is calculated from average spot
market price for that settlement period markets close. If the supplier under-forecasts its
18
demand it pays the SBP for the difference – if it over-forecasts it pays the SSP. In this way
each supplier has an incentive to calculate its demand as accurately as possible.
The SSP and SBP are very volatile and it is doubtful that they give a good indication of the
‘true’ price of electricity. For example, in December 2010 the SBP ranged from £40.25 to
£464 – a factor of 11. Even in July (a month of relatively flat demand) prices varied by a
factor of 5. The liquidity of the spot market is so low that those prices are equally
unreliable. I further explore these issues in chapter 2.
The net effect of NETA is to ‘black-box’ the vast majority of the electricity market. It heavily
favours vertically integrated generator/suppliers since the ability to buy energy ‘from
oneself’ greatly reduces ones exposure to the market – and therefore risk. An integrated
provider will utilise its own generation in the first instance and only enter the market if it
can negotiate a particularly favourable deal, thereby squeezing IPPs. This explains in part
why the UK ESI underwent marked contraction after 1999 (see Fig 1.1) and is now
dominated by the ‘Big Six’ integrated corporations: EdF, E.on, RWE, Iberdrola, SSE and
Centrica. Between them they account for around 70% of generation and over 99% of sales
(OFGEM, 2008). I look further into the effects of this oligopoly in section 2.9.
How exactly was NETA supposed to deliver economic efficiency and a good deal for
consumers? The logic appears to have been that since ‘unbundling’ had manifestly failed,
the regulators decided to go in completely the opposite direction. There are reasons for
thinking that vertically integrated suppliers are in fact more efficient due to lower
borrowing costs and ‘synergies’ (i.e. fewer staff). In that case, why not just encourage a
vertically integrated market in which the Big 6 compete for consumers on price?
Unfortunately there is a rather obvious flaw with this notion: healthy competition relies
upon a healthy rate of ‘switching’ between suppliers but consumers are notoriously ‘sticky’
(less than 10% consider changing their supplier year-on-year) (OFGEM, 2011) so there is an
incentive for suppliers to overcharge and only lower prices when customers threaten to
switch. Indeed the back-room costs incurred when a customer switches are so high that if
everyone simultaneously decided to switch supplier the cost to consumers would be
greater than the realised savings. It appears therefore that the regulator hoped that
suppliers would offer competitive prices even without the threat of switching.
Irrespective of the theoretical merits (or otherwise) of NETA, what were the actual effects?
Initially NETA was widely judged a success for apparently lowering the wholesale and retail
19
price of electricity after introduction. However, closer inspection reveals that the
introduction of NETA coincided with cheaper coal (due to the expiry of expensive Coal
Board contracts) and a burst of new generation at the tail end of the ‘dash for gas’, so it
was likely that wholesale prices would have fallen anyway. In reality the wholesale market
decreased in price far more than the retail market, suggesting that the Big Six were
enriching themselves at the expense of both IPPs and consumers (Dağdeviren, 2009).
The fate of British Energy makes an interesting case study in this regard. The UK’s nuclear
plants were publically owned and supported by the Fossil Fuel Levy up until 1996, but when
technical advancements doubled plant availability they became viable businesses. With
wholesale prices in the Pool riding high, the government decided to remove the levy and
sell the 8 most modern plants under the title of British Energy plc (the privatisation was a
disastrous flop, raising only £1.7bn – roughly half the construction cost of a single plant)
(Thomas, 2010). British Energy prospered for a while but after the introduction of NETA its
income crashed by 30% and in 2003 it was bailed out by the public at a total cost of £10bn
(European Commission, 2004), much to the chagrin of the National Audit Office who
argued that the privatization should never have gone ahead to begin with (National Audit
Office, 2004). Yet by the middle of the decade wholesale prices had increased due to a
huge spike in the cost of gas, and British Energy was once again a viable business. Finally in
2009 it was acquired by EdF for £12bn – meaning that the endgame of this tussle between
state and private actors is that the UK nuclear industry is effectively the responsibility of
the French public.
Apart from this acquisition the ESI has been relatively static for the past few years, which
has given researchers a chance to revisit their assessments of NETA – and the results have
not been good. There was a sharp increase in retail prices around 2005 ostensibly due to
wholesale costs, but a study commissioned by the Right to Fuel campaign found that fully
half of the rise was simply due to increased supplier margins (Cornwall Energy Associates,
2008). Worse, when wholesale prices fell retail prices refused to follow suit. Last year a
comprehensive study was even more damning in its verdict of NETA, concluding that
“despite NETA’s stated intentions of reducing wholesale and thereby retail prices... instead
[NETA] merely rearranged where money was made in the system.” (Giulietti et al., 2010)
page number.
This assessment certainly rings true. Judging by the publications of OFGEM, they appear to
be locked in a perpetual battle with the utilities - both wholesale and retail markets are
20
currently being investigated for uncompetitive behaviour (the retail investigation has now
entered its fourth year) (OFGEM, 2008, 2010). Given that NETA is now ten years old and is
looking decidedly creaky, one wonders if another rule-change isn’t too far off.
1.7 In review
Now that we are up to date, it would be useful to put this remarkable history in context.
There is something very striking about the story of the UK ESI; though it has its own quirks,
curiosities, heroes and villains, in fact it gives the complete history of British capitalism in a
distilled form. In particular the reforms since 1990 are an excellent case study of ‘actually
existing neoliberalism’: the dispassionate hidden hand of the market shall be unleashed
upon the lumbering, monolithic state enterprises and deliver efficiency, growth and
prosperity for all – or at the very least send large profits to monopolistic private
corporations (Harvey, 2005).
In fact, there are good reasons why the ESI is not as amenable to competition to other
industries even in theory (adapted from Thomas (2006b)):
1) Inabiliy to store power: Storage in other industries allows one to balance out
fluctuations between supply and demand. In the absence of storage a free market
suffers huge price volatility
2) Supply and demand must balance perfectly: Some central control will always be
necessary to co-ordinate supply and demand. The free-market ideal of ‘free entry
and exit’ is clearly impossible
3) No substitutes: Most products are readily substitutable which effectively increases
competition in the marketplace and acts as a check on suppliers. No such check
exists for electricity suppliers
4) Vital to modern society: Unlike other (substitutable) products, a constant and
reliable power supply is essential to modern living. Under no circumstance would a
government allow the ESI to collapse – it is the ultimate “too big to fail”
5) Lack of investment: For security of supply it is necessary that there is excess
capacity in the system. A free market will underinvest in capacity because it will not
be able to turn a profit on plant that is only operated a few times a year
6) Unsustainable price structure: The power supply is by design completely
standardised, meaning that competition is based entirely on price. But if
21
competition pushes prices down to the marginal costs, generators will make a loss
on their capital investment (see section 2.2)
7) Environmental impacts: The environmental impacts of electricity generation are
substantial and regulation is necessary to ensure they are accounted for
Lohmann’s principle of ‘frame overflow’ (2009) provides a useful metaphor to explain the
difficulties that the UK ESI has faced since privatisation. The government attempted to put
the ESI into a ‘market’ frame, but the ESI is inherently resistant to such framing for the
reasons outlined above, so it generated frame overflows (e.g. collusion, underinvestment).
The government has tried to get the overflow back ‘in frame’ by introducing new rules and
regulations (e.g. divestment of assets, vertical integration) but this very process has
inevitably generated more overflows – therefore re-framing is a never-ending process. In
this context the new regulations to encourage renewable generation are another attempt
at reframing due to an environmental overflow. This is the topic of the next section.
An objective assessment of privatisation must surely deliver the verdict that it simply
wasn’t worth all the effort (Dağdeviren, 2009); nonetheless it is surprising the extent to
which the ‘British Model’ has been championed worldwide (Joskow, 2008) (though it is
perhaps less surprising when one recalls that the neoliberal mantra is TINA: There Is No
Alternative). In the mid-1990s the World Bank and the European Commission played
leading roles in exporting the British Model to countries around the world, both developing
and developed. The World Bank in particular has often made utility privatisation a
prerequisite of aid packages, most notoriously under the guise of Structural Adjustment
Programmes (Stiglitz, 2002). The twin principles of privatisation and deregulation form the
very lifeblood of the neoliberal project, so it has been necessary to tout the British Model
as a ‘success’ in the face of all evidence, as an act of ‘paradigm maintenance’ (Wade, 1996).
While the spread of the ‘British Model’ is a story of its own, suffice to say that when
applying the template to developing countries the results have tended to be far more
devastating than in the UK. This was an entirely predictable outcome given that such
countries generally have much weaker regulatory regimes (Dağdeviren, 2007) . Developed
countries, in contrast, are better able to resist and nations such as France, Germany and
Japan have effective control their ESIs even if there has been a degree of deregulation.
In the last decade the focus of energy policy has shifted from competition and markets to
cutting carbon emissions. I will now consider how government policy has influenced the
22
deployment of renewable energy sources. As we shall see, despite the notably ‘collectivist’
context (saving the planet), governments will always find a role for the market.
1.8 Renewables Investment
In the last decade successive British governments have pledged to various CO2 emission
reduction targets in an attempt to halt climate change. The Climate Change Act 2008 set a
legally-binding target of at least 80% cut in emissions by 2050 (against a 1990 baseline).
Compared to other sectors of the economy it is relatively straightforward to decarbonise
the ESI so it is expected to make a large contribution to the cuts. The EU Renewable
Directive 2009 set the “20 20 20” target that by 2020, renewable energy (RE) will source
20% of total energy consumption (15% in the UK’s case). This amounts to RE making up
some 40% of the UK electricity supply within the next 10 years (European Parliament and
the Council of the European Union, 2009).
The fundamental difficulty with RE - which must eventually be confronted by governments
- is that renewables are more expensive than conventional generation. (Economists have
clever ways of proving that actually they are cheaper if you take into account the
environmental benefits, but there is no escaping the impact on the bottom line.) In a
nationalised industry this would not present a problem: the state would just build the
things and pass the extra costs onto the consumer. But in a competitive market, a RE
generator has to be sure that a supplier will buy their expensive electricity; and a supplier
has to be sure that its competitors will also endure higher costs, or else it will lose market
share. Given that the ESI is inherently conservative in nature, it clearly needs a big incentive
to undertake investment in RE. (I should note at this point that in northern Europe,
‘renewable energy’ essentially means ‘wind energy’ – although sometimes one includes
nuclear too.)
In response, governments throughout the EU have introduced a raft of taxes, trading
schemes, tariffs and incentives to “send the correct price signals” to the market. Two
schemes have been most influential – Renewable Energy Feed-In Tariffs (REFITs) and
Tradable Green Certificates (TGCs).
REFITs simply give RE a guaranteed price in the wholesale electricity market; this price may
be fixed or may be pegged at a certain rate above the basic wholesale price. Energy
suppliers are forced to buy RE at this price whenever it becomes available (though the
precise rules vary). The benefit of this system (besides its simplicity) is that by guaranteeing
23
a fixed return in investment it eliminates capital risk - which is very important for
speculative new technologies (where even the perception of risk can push up the cost of
finance beyond what is affordable).
Because the government sets the tariff level, the REFIT affords the state a great deal of
control over the deployment of RE. While some see this as an advantage, others see it as a
weakness. Since the price has not been set by the market, it is not efficient i.e. the price
does not represent the marginal cost of generation, therefore REFITs in theory enable RE
generators to make profits at the expense of consumers. TGCs are mooted as a solution to
this problem. Under this scheme, RE generators are given certificates (TGCs) for the energy
they supply to the grid and suppliers are obligated to purchase a certain number of TGCs
per year. This sets up a separate market for RE on top of the normal electricity market and,
so the argument goes, that leads to efficient market outcomes (though at the cost of the
state surrendering control). In fact, some have argued that the above logic is flawed and
the TGC is no more efficient than the REFIT. As the EC commented in 2005, “both
instruments are equally market-based in that the regulatory body sets either the price or
the quantity and leaves the determination of the other to the market” (Commission of the
European Communities, 2005).
1.9 UK Policy
Almost every state in the EU has chosen one of the above two mechanisms, or some hybrid
of the two. It is interesting to note that the choice of policy appears to have been strongly
influenced by the ideological disposition of the state. The UK’s first announcement of RE
policy arrived in 1999 after years of political wrangling; unsurprisingly, given their
neoliberal bent, the New Labour government opted for a variant on TGCs, the Renewables
Obligation (RO), which was formally implemented in 2002 (Toke & Lauber, 2007). It will be
instructive to trace UK RE policy and compare the outcomes to those of other EU nations.
The RO policy set an obligation on suppliers to purchase Renewable Obligation Certificates
(ROCs) from RE generators equal to 10% of output by 2010, 15% by 2015 and 20% by 2020.
The alternative was to pay a ‘buy-out’ price of £30/MWh. Normally this would act as a cap
on the market price of ROCs, but there was an extra twist: the fines were recycled back to
the owners of ROCs so that the value of each ROC increases in proportion to the ROC
shortfall. Moreover, by design there was always at least 10% fewer ROCs available than
24
necessary for suppliers to meet their obligations. The net result of this is that the ROC price
had a price floor of £30 (Toke, 2010).
At the time there were complaints that the buy-out was too low to stimulate investment in
large scale wind, especially offshore wind, and yet too generous to other better-established
technologies. There were arguments that ROCs be ‘banded’ so that, for example, offshore
wind ROCs would be worth more than hydroelectric ROCs. The government defended its
position by stating that “it is no longer Government’s job to pick winners... the future role
of [Government] will be one of action but not direct intervention” (Department of Trade
and Industry, 2007).
It soon became clear that investment was indeed being directed away from large-scale
offshore wind projects and into small-scale onshore wind and landfill gas projects – indeed
landfill gas accounted for 44% of all ROCs issued between 2002 and 2005 – and it appeared
that the UK was set to miss its 2010 RE targets. Table 1.1 shows the volumes and ‘worth’ of
ROCs over time, indicating that by 2006 RE generation was at barely two thirds the target
amount. A 2006 government review paradoxically concluded that “the Obligation is
operating largely as anticipated” and therefore needed “amending”: the conclusion was
that the RO was to be banded after all (OFGEM, 2006). Thereafter the rules were changed
so that (e.g.) offshore wind was awarded 2 ROCs per MWh whereas landfill gas received
only 0.5 – an amendment that was labelled a “quasi-feed-in-tariff” by an irate British Wind
Energy Association (2009).
25
Year Obligation (MWh) Issued
(MWh)
% met Buyout/ROC
(£)
Worth
(£)
2002 8,393,972.00 4,973,091.00 59.2 15.9 45.9
2003 12,387,720.00 6,914,524.00 55.8 22.9 53.4
2004 14,315,784.00 9,971,851.00 69.7 13.7 45.1
2005 16,175,906.00 12,232,153.00 75.6 10.2 42.5
2006 19,390,016.00 12,868,408.00 66.4 16.0 49.3
2007 22,857,584.00 14,562,876.00 63.7 18.7 53.0
2008 25,944,763.00 16,813,731.00 64.8 18.6 54.4
2009 26,971,916.00 18,747,129.00 69.5 15.2 52.4
While this move did stimulate further investment in offshore wind, it was clear by now that
the RO was in any case forcing consumers to overpay massively for RE. The average auction
price for ROCs from 2002 to present was between £40 and £50 (e-ROC, 2011) which would
boost the revenue of onshore wind by at least 100%. That represents a huge premium for a
technology thought to be only around a third more expensive than CCGT (see section 2.3).
What’s more, the price of ROCs was not coming down over time as had been anticipated
(see Table 1.1). By the middle of the decade there was very good evidence that REFIT
systems, despite their alleged inefficiency, were achieving higher RE penetration at a lower
cost. Initially the EC were very pro-TGC schemes (Commission of the European
Communities, 1999) – by 2005 their own data was showing that (for wind) the countries
with TGC schemes were the least efficient and had the worst penetration rates - with the
UK the worst performer of all (see figure 1.2). They put this down to “higher risk premium
requested by investors, the administrative costs and the still immature green certificate
market” (Commission of the European Communities, 2005) page no).
Table 1.1 Value of ROCs 2002-2009 Source: Ofgem (2007, 2011)
26
1.10 Electricity Market Reform
It seems probable that OFGEM’s continued promotion of markets at all costs – even
thoroughly dysfunctional ones – has imposed huge net costs on consumers. However when
even the World Bank is (supposedly) rethinking its position (Thomas, 2006b) there is reason
to think that the healing has begun. In 2010 the Energy Minister Mike O’Brien stated that
“in order to ensure that we were able to make an energy revolution ... we had to get
OFGEM to stop being so pedantically market driven” (quoted in (Toke, 2011)) and the
regulator has become notably more interventionist in recent years. The latest White Paper,
entitled Energy Market Reform, was released by DECC in July 2011 (Department of Energy
and Climate Change, July 2011). As the title hints, it calls for a rethink of RE policy. Among
its proposals are: phasing out the RO; replacing it with ‘contracts for differences’ feed-in-
tariffs (CfD FITs);a carbon price floor; an emissions standard; capacity payments.
The mechanism for capacity payments has yet to be announced but will be geared towards
keeping the lights on rather than encouraging RE investment. The emissions can basically
be thought of as a ban on new coal-fired (but not gas-fired) plant. From the point of view of
RE, CfD FITs represent the most significant change. From 2014 RE generators will be able to
sign long-term contracts with an as-yet unspecified counterparty (possibly the government)
Fig 1.2 Price support and costs for wind power by country From Commission of the European Communities, 2005
27
to pay them a fixed price per MWh, the ‘strike’ price. The energy will still be sold on the
wholesale market – at the market price - but the counterparty will make up the difference
to the strike price. The logic is that this will guarantee a return on investment, thereby
reducing the risk-premium for RE developers, without distorting the rest of the market.
Many details are still to be settled, however, most notably the level of the strike price.
There are also concerns that the liquidity of the wholesale market is too low to deliver a
stable price, as is necessary for such a mechanism. OFGEM is conducting a separate review
on how to increase liquidity in the wholesale markets (OFGEM, 2010).
The carbon price floor is designed to reduce uncertainty surrounding the EUETS carbon
price, which is notoriously volatile. The floor has been set at £16/tonne CO2 in 2012 rising
to £30/tonne by 2020 and £70/tonne by 2030. Though the price floor will increase the cost
of gas and (particularly) coal plants, it alone is unlikely to increase prices enough to support
RE (and it will in any case be nullified by CfD FITs). For this reason the price floor has been
widely interpreted as a subsidy for nuclear power: according to the treasury, the price floor
will results in the nuclear industry (read: EdF) benefitting by an average of £50m/year to
2030.
Industry reactions to the EMR have been mixed. Unsurprisingly EdF were delighted, stating
that “This is good news for customers, policy makers and investors” (EDF Energy, 2011),
whereas most environmental groups expressed disappointment. Greenpeace criticized the
EMR for lacking ambition and ignoring the structural problems of the industry, commenting
“there are six winners from today's white paper and millions of losers”. Overall the
response was muted with most commentators suspending judgement until further details
are released. This was typified by the Association of Electricity Producers who responded
that they had “some concerns” but that “there is a great deal of detail to be agreed before
all this takes effect.” (Carrington, 2011).
Undoubtedly the industry is right to be cautious; nonetheless it is certainly possible to
speculate on how the EMR will affect the ESI in the future. That topic forms the basis of the
remainder of this paper.
28
Table 2.2 Electricity costs by source
Chapter Two: The Electricity Generation Industry
I will now begin the empirical part of the investigation, the eventual objective of which is to
create a simple model of electricity generation in the UK. From there I will attempt to
project into the future taking into account the UKs renewables policy as described in
section 1.10.
Why am I focussing on generation? Put simply, because it is ‘where the action is’; it is the
sector which accounts for most investment and the sector which is mooted to undergo a
‘green revolution’ (indeed in 2009 the Minister for Energy encouraged a roomful of
offshore wind developers to “imagine you are pin-striped revolutionaries in the spirit of
Che Guevara on the Sierra Madre” (quoted in (Toke, 2011)). This is in contrast with the rest
of the supply chain - essentially just the means by which electricity is delivered to the plug -
which will remain relatively static for the foreseeable future. However, we should not
overestimate the significance of generation to consumers (provided the lights don’t go
out): a recent Ofgem report reveals that on average consumers pay 13.4p/kWh or
£134/MWh for electricity (c.f. £39/KWh for gas) (Ofgem, 22 June 2011), of which
generation accounts for only 42% (see Table 2.1) (although we should expect this fraction
to increase in the future).
2.1 Model Outline
The model I will use is simple but still flexible enough to investigate a range of future
scenarios. It will be useful to outline it now, though I will elaborate in Section 3.1. My
guiding principle in this investigation is that the UK ESI is driven by cost – that the cost
Contribution £/MWh
Generation 56.25
Operating costs 16.25
VAT & other costs 52.5
Supplier Margin 8.75
Total 133.75
29
determines which power plants get built in the first place and which plants are given
priority for dispatch (the so-called ‘merit order’). Suppose that one knows both the total
power required at a given moment in time and the costs of power generation for each
power plant on the grid. The model is a ‘ladder’ model: it will meet the power demand
simply by activating the plants one-by-one in ‘merit order’ (i.e. starting with the cheapest)
until the electricity demand is met. This will deliver the least-cost way of meeting electricity
demand for a given set of power plants. By adjusting various parameters – e.g. carbon
prices, fuel prices, fixed costs, electricity demand – one can then explore a number of
possible scenarios and speculate on what DECC likes to call Our Energy Future.
Clearly for such a model to be accurate it is crucial to get the costs right - therefore the first
step will be to gain an understanding the economics of the industry.
2.2 The Economics of Electricity Generation
Broadly speaking, the economics of the power industry are not that dissimilar to any other
industry: a product (electricity) is made a factory (power plant) and revenue is generated
through the sale of said product. The costs of plant can be broken down into capital
expenditure (CAPEX, the up-front costs) and operating expenditure (OPEX, the running
costs) (Berrie, 1983). The subtlety is that there are numerous plant types each with a
different balance of capex and opex making them suitable for different roles within the ESI.
Roughly speaking, if you were to build a plant tomorrow your choice would be between an
expensive machine with cheap fuel or a cheap machine with expensive fuel. The former
includes renewables such as nuclear, wind, solar and tidal. The latter includes fossil-fuel
burning plants such as coal, CCGT, OCGT, diesel and gas CHP. A brief profile of the most
common plant types is given in Appendix A.
For any new plant, the main contributor to the capex is Engineering, Procurement and
Construction (EPC): the actual building of the structure. To this we add related costs such as
ancillary equipment, land purchase, planning, legal fees and network connection. Arguably
we should include decommissioning costs, though these occur at the end of plant lifespan.
Since all these costs are ‘one-offs’ it is possible to subsume capex into a single
representative figure for each type of plant, commonly quoted in £/kW capacity (this
assumes that economies of scale are already taken into account). It is important to note,
however, that capex is an inherently uncertain metric and cost escalations are common.
Commodity prices have shot up in the past few years to the extent that the real capex of a
30
coal or nuclear plant has more than doubled. It remains to be seen whether this price shift
is permanent.
Opex can be broken down into costs that are constant year-on-year (fixed costs) and costs
that vary depending upon mode of operation (variable costs). The main drivers of fixed
costs are labour, business rates, insurance, network charges and financing. The main
variable costs are fuel purchase, carbon taxes, fuel disposal and handling of by-products.
The distinction between fixed and variable costs is not always clear; maintenance will have
both a fixed and variable components. The fixed opex can be subsumed into a headline
figure similarly to capex – it quoted in £/KW/year. The variable opex can be quoted in
£/MWh. This figure is particularly important as it is the marginal cost (MC) of electricity for
a given plant. I will discuss the implications of this figure later in this section.
Assuming that for a given plant one can calculate these three figures – capex, fixed opex,
variable opex – with reasonable accuracy, how can we combine them into a single
indicative “cost of electricity generation”? The standard way to do so is to introduce the
Levelised Cost (LC). The LC can be thought of as the (constant) price at which a generator
would have to sell electricity if it were to exactly break even on its complete lifetime
investment. It is defined as the net present value of all costs (in £s) divided by the net
present value of energy generated (in MWhs). It is calculated by summing the expected
costs for each year for the lifetime of the plant, applying a discount rate to each year’s
costs, summing the discounted costs across all years and dividing by discounted lifetime
energy generated to end up with a levelised cost of electricity in £/MWh. Expressed
algebraically,
∑
∑
where T is the lifespan of the plant, Ct, Ft and Vt are the capex, fixed opex and variable opex
in year t, K is the plant capacity, r is the discount rate and Et is energy generated in year t.
The LC provides a common metric against which one can compare power plants with wildly
different cost structures - e.g. heavily front-loaded investment versus uniform investment.
However it does not necessarily denote a lower bound on the price which the plant will sell
electricity for – that is the MC. To elaborate: suppose that a nuclear power plant has a LC of
£50/MWh, but this is mostly due to very high up-front capital costs. The cost of actually
31
operating the plant, including fuel - the MC - is just £15/MWh (uranium is cheap). Now
suppose that due an excess of cheap CCGT, the wholesale price of electricity is £35/MWh.
Should the nuclear plant operate? Clearly it should – at the moment it is losing (LC – MC) =
£35 every hour for every MW of capacity, just in capital and fixed costs (so a 1000 MW
plant will be losing £35,000/hour). If it generates at full capacity it is now losing (LC – MC +
MC – price) = £15 per MW capacity per hour – so it is better off generating even if it is still
losing money. Therefore we should not expect the LC to necessarily reflect the sale price
that any given plant achieves.
It is worth noting that this situation in itself is not that different to any other market: if I
manufacture a trinket for £30 but the market price is £20, I’m still better off selling it and
taking a £10 loss rather than not and losing £30. If this continued for long in a free market, I
would go bust and the world would be better off. But remember that the power supply is
unique: it must never fail and it cannot be stored. Therefore it is always necessary to have
excess capacity in the system, so even non-profit-making plants cannot be allowed to close.
This is in fact precisely what happened in the British Energy debacle (section 1.6), resulting
in a £10bn public bailout. It does appear that there is a fundamental contradiction between
free electricity markets and the need for capacity. At the moment the UK’s oligopolistic
market is just about ‘unfree’ enough to deliver stability in that regard (the integrated
suppliers essentially subsidise their own unprofitable peaking plant), but as mentioned in
section 1.10, DECC have recently acknowledged the need to introduce a new capacity
mechanism to address this.
2.3 Levelised Cost Model
Let us look more closely at formula (1). Though it is by no means simple, behind the
symbols hides considerable further complexity. The lifespan of a plant can be anything
from 15 to 50 years – the levelised cost calls for one to know or calculate the opex and
energy output for every year in that time (capex is less problematic since it is heavily front-
loaded). This may require, for example, predicting the price of fuel 20 years hence -
including carbon taxes. Et is a function of plant load therefore depends upon the total
energy demand, generation mix and merit order of the plant in the tth year. In the future
there may be technical advances, government interventions or changes in market structure
which could work in favour of or against any particular plant. Moreover the results are
sensitive to the discount rate, but discount rate does not have a well-defined value, rather
it requires some hybrid of financial and political judgement (low discount rates imply we
32
‘care’ more about the future and favours front-loaded investment; conversely high
discount rates favour uniform or tail-loaded investment).
Nonetheless it will be very useful to calculate approximate LC values to use as a basis for
ascribing prices to technologies in the grid model. Rather than try to account for all of these
details explicitly, my approach was to reconstruct the model used in a Mott MacDonald in a
recent study for the UK government (Mott MacDonald, June 2010). The LC model works
according the principles outlined above: taking each technology in turn, one inputs a host
of parameters such as lifespan, efficiency, construction time, EPC cost – around 40 in all –
and the model calculates the costs for each year of operation broken down by capex, opex,
fuel and carbon costs. A sample list of parameters – for the base CCGT case – are
reproduced in Table 2.2. It also calculates total energy generated per year. Each cost was
discounted by the appropriate amount and then summed over all years and divided by
summed discounted output to give the LC.
My objective was firstly to program my own version of the model in MATLAB and use the
supplied parameters to replicate the results; secondly to adjust the parameters to explore a
range of different scenarios. Unfortunately the paper does not disclose the Mott
MacDonald model in enough detail for the results to be replicated with perfect accuracy;
however I was able to reverse-engineer a model that produced results within 1-2 % of the
original findings, which is good enough for our purposes. From this point on all references
to the LC model will refer to my own.
33
CCGT Parameter Unit Value
Timings Pre-develop period years 2
Construction period years 2.5
Plant Lifespan years 30
Operational Gross power MW 830
Parameters Gross Efficiency % 59
Ave Degradation % 3.5
Ave Availability % 91.2
Ave Load Factor % 90
Aux Power % 2.3
CO2 Removal % 0
Capex Pre-license costs £/kw 25
Pre-license costs £m 20.8
Reg/license/enquiry £/kw 25
Reg/license/enquiry £m 20.8
EPC £/kw 656.3
EPC £m 544.7
Infrastructure £/kw 12
Infrastructure £m 10
Dev as share of EPC % 7.6
Total CAPEX £/kw 718.3
Opex O&M fixed £/MW/year 15000
O&M fixed £m/year 12.5
O&M variable £/MWh 2.2
O&M variable £m/year 13.1
Total O&M £m/year 25.6
Insurance £/MW/year 5000
Insurance £m/year 4.2
Connection/UoS £/MW/year 6000
Connection/UoS £m/year 5
CO2 trans storage £/MWh 0
CO2 trans storage £m/year 0
Total fixed/year £/MW/year 26000
Total Opex £m/year 34.7
Table 2.2 Example Input Parameters for Levelised Cost model Taken from Mott MacDonald June 2010 Note not all parameters are shown e.g. Carbon price, Load Profile
34
The central results from the LC model are shown in Figure 2.1, split into fixed and variable
costs (including a carbon tax). We can have reasonable confidence in these results as they
are in agreement not only with the original paper but with other similar studies, for
example (UK Energy Research Centre (UKERC), 2007), (Arup, 2011), (PB Power, 2004).
One can see that offshore wind in particular is very expensive relative to traditional
technologies, although onshore wind is roughly competitive. We can also see, for example,
that with this modelled carbon price CCS (carbon capture and storage) is actually more
expensive to install than the savings it would deliver. I will discuss the findings further at
the modelling stage, but for now it is important to note, however, that these are LCs for
new-build plants and since EPC costs have spiked in recent years they very likely
overestimate the LC of any existing plant and it would be unwise to rely uniquely on
modelled LCs to determine ESI pricings. And, of course, LCs tell us little about how power
0.0
50.0
100.0
150.0
200.0
250.0
Var Costs
Fixed Costs
Fig 2.1 Levelised Cost model indicative results by technology
35
plants are actually utilised on a day to day basis. To draw a more complete picture of the
industry, it will be necessary to analyse real-world data.
2.4 The Grid Today
This brings us to our next section, a close look at how the various generation assets are
operated and traded in today’s ESI - information which will be invaluable when formulating
the grid model. I found that when attempting such an analysis one hits an immediate
roadblock - since most intimate dealings within the ESI (i.e. who buys what from whom for
how much) are now ‘trade secrets’, it is very resistant to outside scrutiny. Indeed just
working out ‘who owns what’ is non-trivial since assets change hands frequently.
Much of the time I have relied upon government reports and independent analyses - the
Digest of UK Energy Statistics (DUKES) proved particularly useful (though the 2010 data was
not published until early August (MacLeay et al., 2011)). However my main source has been
the mass of raw data available on the website www.bmreports.com, from where it is
possible to download data relating to each Balancing Mechanism Unit (BMU) in the UK
going back several years in half-hour intervals. (A Balancing Mechanism Unit is National
Grid’s term for any piece of infrastructure which puts power into or takes power out of the
grid – here I use it to refer to any generation asset i.e. one that puts power into the grid.)
The main dataset of interest was the power output of each BMU at a given moment in
time, known as the Final Physical Notification (FPN). I was also interested in a sister
dataset, the Maximum Export Limit (MEL). The MEL is the maximum power that a BMU is
capable of generating in a given time period, and by comparison with the actual output
(FPN), allows one to calculate the load factor of the BMU. One might expect the MEL to be
a constant equal to the capacity of the power plant, and much of the time it is, but there
are plenty of occasions when a BMU might not be able to operate at maximum capacity,
for example due to planned maintenance. Finally, I was interested in the full results of the
balancing mechanism- which means the Net Imbalance Volume (NIV) and all the offers and
bids put in by each BMU to increase or decrease their output to meet the NIV. This data
can be used to infer the marginal cost of generation for each BMU (with caveats).
I decided that my data range would be the entirety of 2010, which amounts to some
365*48 = 17520 settlement periods. In terms of sheer quantity of data this is perhaps
overkill, but any shorter time period would risk missing out seasonal variations.
36
2.5 FPN and MEL Data
The bmreports website, administered by National Grid subsidiary Elexon, allows one to
download raw system data via the TIBCO relay service. TIBCO relay data is released daily in
the form of a ~50Mb comma separated value (CSV) text file. Buried within this file is the
FPN, MEL and balancing mechanism information for each BMU for each settlement period.
Helpfully the website also pre-extracts the FPN and MEL data from the relay file ready for
download. The date and data type are specified entirely within the URL – for example to
download FPN data for 1st January 2010, one queries the following URL:
http://www.bmreports.com/tibcodata/2010-01-01/tib_messages_FPN.2010-01-
01.gz
The format of the URL lends itself to scripting. To obtain the data for all of 2010 I wrote a
script which generates each date of the year in turn, downloads from the corresponding
URL and extracts the file, resulting in 365 CSV files for each data type (FPN and MEL).
The FPN and MEL data were in the same format. A sample of raw FPN data (to which I have
added headers) is shown in Table 2.3:
Data
Type
BMU Settlement
Period
Start Date/Time Output End Date/Time Output
PN T_DRAXX-1 3 20100101010000 645 20100101013000 645
PN T_DRAXX-2 3 20100101010000 645 20100101010100 635
PN T_DRAXX-2 3 20100101010100 635 20100101010200 631
PN T_DRAXX-2 3 20100101010200 631 20100101013000 631
Taking the columns in turn-
Data Type: PN stands for Physical Notification
BMU: indentifies the balancing mechanism unit via a unique ID. Here the data
refers to Drax Power Station units one and two. Some large power stations, such as
Drax, have multiple turbines which can be operated independently; hence they
have more than one BMU (Drax in fact has nine).
Table 2.3 Sample FPN
data
37
Settlement period: tells us which half-hour period the data concerns. ‘3’ refers to
the third half-hour period of the day i.e. 01:00 – 01:30.
Start Date/Time: The start date and time of this particular relay entry – a numerical
string in the format yyyymmddhhmm
Output: The MW output from the BMU at the start time
End Date/Time: the end date and time of the entry
Output: The MW output at the end time
It is good practise that each BMU reports its output at the start and the end of each
settlement period regardless of whether its output changes in that time. If a particular
entry gives different start and end outputs (i.e. if output changes), one assumes a constant
rate of change between the two times. If there is a gap in the time series one linearly
interprets between known data points.
The full interpretation of Table 2.3 is that on the 1st Jan 2010, Drax unit 1 output 645MW
continuously between 01:00 and 01:30 whereas Drax unit 2 output went from 645MW at
01:00 to 635 MW at 01:01 to 631 MW at 01:02, where it stayed until 01:30. This is shown
schematically in Fig X. MEL data would be interpreted in the same way except that it would
represent the maximum power that a BMU was capable of outputting as opposed to the
power actually output. This could plausibly be constrained by fuel or staffing availability,
maintenance, planned and unplanned downtime or other circumstances.
38
A number of steps were required to put the data into a usable form. I wrote a series of
scripts to perform the following operations:
Scan each day’s data into MATLAB
Output the data from each individual settlement period into its own .mat file
Download a list of all generator BMUs from the bmreports website and scan into
MATLAB
Take each BMU from the list in turn, search each of the 17520 settlement period
files for the corresponding BMU entry and collate the data for each BMU into a
new output file
The net result was a file for each generator BMU (266 in number) containing the FPNs for
the whole year – in other words a complete record of the power generated by each power
station in the country. Similar data was aggregated for the BMU MELs.
Having so processed the data, a degree of cleaning was necessary. There are a number of
ways that the data for a given BMU can be inconsistent:
1) Consecutive records show a discontinuity of output
2) Two records overlap in time but with the same output
3) Two records overlap in time with different outputs
4) Data ‘missing’ i.e. a temporal gap
Fig 2.2 Interpreted FPN data Numbers indicate corresponding row entry in Table 2.3
39
All these inconsistencies were indeed found to occur with various frequencies. I opted for
the following solutions:
1) The second record is assumed to take precedence. The first record is shortened to
achieve consistency with the second
2) As in 1), the first record is shortened. This leaves us with a situation as in 3)
3) No change. It is assumed that the BMU changes its output ‘very quickly’ in a way
that I will not attempt to quantify
4) A third record is created which interpolates across the gap
The inconsistencies and their solutions are shown schematically in Fig 2.3. A series of
scripts were run to carry out the modifications to the data for both FPNs and MELs.
2.6 Balancing Mechanism Data
The BM data is a little different. There are a large number of bids and offers that are put in
for each settlement period, but a quick perusal of the data shows that many of them are
not ‘competitive’. That is, since it costs nothing to make a bid/offer, BMUs often place
unrealistic ones on the off chance that they get accepted due to some miscalculation or
system failure. (This has happened on a handful of occasions, most famously on 10th Dec
2002 when two large power plants failed at short notice causing the marginal system buy
price to ‘top out’ at £9,999/MWh (ERI, 2004).)
Fig 2.3 Interpolation rules – see text
40
The uncompetitive bids/offers are not particularly relevant to my work, so to simplify
things I opted to download just the bids and offers ‘selected’ by the TSO. In effect this
means just the cheapest 10-20 such bids and offers, depending upon the imbalance
volume. To download such data one queries the ‘soapserver’ via the following URL:
http://www.bmreports.com/bsp/additional/soapfunctions.php?output=CSV&dT=YYY
Y-MM-DD&SP=#N&element=DETSYSPRICE&submit=Invoke
replacing YYYY-MM-DD with the date and #N with the settlement period of interest. A
script was written to cycle through the 17520 different datasets. The format of the datasets
is a little more complicated but an edited snapshot is reproduced in Table 2.4 (once again
with my own headers).
Bid/Offer Date SP Index BMU ID Offer
Price
Offer
Volume
Imbalance
Volume
BID 20100101 2 1 T_RATS-4 24.7 -0.283 0
BID 20100101 2 2 T_RATS-3 24.65 -0.283 0
OFFER 20100101 2 1 T_CDCL-1 35 2.15 2.15
OFFER 20100101 2 2 T_COSO-1 36.58 87.5 87.5
Bid/Offer: Indicates whether it is a bid (to reduce a BMU’s output) or an offer (to
increase output)
Date: in the format YYYYMMDD
SP: Settlement period number
Index: The merit order of the bid/offer in the ‘stack’. i.e. if the system is ‘short’
(undersupplied) then the TSO will accept offers in order of their index, whereas if it
is ‘long’ the TSO will accept bids in index order. The index is found by sorting the
bids and offers in ascending price order
BMU ID: As before. Here the bids come from Ratcliffe-on-Soar coal-fired plant units
4 and 3. The offers come from Cottam Development Centre CCGT plant and
Coryton CCGT plant
Offer price: The price the plant will pay (per MWh) to decrease (bid) or increase
(offer) their power output. It may seem strange that a plant would pay to reduce
Table 2.4 Sample BMU data
41
their output – but if they have already contracted that power for (say) £30/MWh
then it makes sense to pay £24 to lower output and pocket the difference
Offer Volume: Offer/bid volume in MWh. Since it is for a half-hour period, double
this to work out the actual MW output
Imbalance Volume: The volume that was ‘accepted’ i.e. did in fact end up getting
used. This information is calculated retroactively once metering is completed. In
this case the system turned out to be ‘short’ so the bids went unused and the two
offers were contracted for their full respective volumes (if they had been lower in
the ‘stack’ they may have had a smaller – or indeed zero – volume accepted).
Once the imbalance volumes have been measured, the SBP (if short) or SSP (if long) is
calculated by
∑
∑
where Pi is the ith offer price and Vi is the ith imbalance volume.
The BM data was analysed in much the same way as the FPN and MEL data to produce a
separate file for each BMU detailing all the offers and bids it made during 2010. Each entry
that was ultimately accepted by the system operator was flagged.
I also undertook a statistical analysis of the BM data for each SP, calculating the maximum,
minimum, mean, median and standard deviation for each set of bids and offers, performing
separate calculations for the set of all bids/offers and the subset of only those which were
accepted.
To summarise then, my main data sets are FPNs, MELs and BM bids and offers for every
generator BMU on National Grid’s system from 1st Jan 2010 to 31st Dec 2010. How
representative is this dataset? It is important to note that all data comes from the BM, and
that not every power source is a participant. There are many small power sources (for
example, local CHP or wind power schemes) which are either not connected to the grid or
are ‘too small’ to be significant for system balancing. However the vast majority of larger
plants are participants, including every plant of capacity greater than 50MW. It is probably
fair to say that no one knows exactly how much generation capacity there is in the UK - the
2011 DUKES attempts to identify every generator over 1 MW capacity and lists a total of
370 plants with a total capacity of 85GW. Of that, the BM accounts for 80GW with just 114
42
plants (266 BMUs), showing that the vast majority of UK capacity is concentrated within the
subset for which I possess data.
I was also able to check the data against the NG Total Gross System Demand (TGSD) data,
which is declared separately. TGSD is simply the sum of power output of every station
connected to the grid and is reported for each settlement period. I calculated the sum of all
BMU FPNs and compared them with the TGSD for each settlement period and found that
they differed by an average of 1.3GW or about 3.5%. Since the FPNs are issued before final
load balancing (via the BM), this is more or less what we would expect. Therefore from
here on I shall assume that the dataset is ‘complete’ even if that is not strictly the case.
For comparison, Fig 2.4 shows the two demands shown side by side for a typical week in
March (chosen at random). Interestingly we can see that the BMU FPNs tend to
overestimate peak demand and underestimate trough demand – this may be due to the
effect of the French interconnect which tends to import at the former and export at the
latter time (thereby requiring the BMUs to lower and increase their output respectively).
Fig 2.4 TGSD data (red) and FPN data (blue)
43
2.7 Generation Mix
After cleaning the data, each BMU was assigned a fuel type from the categories of Coal,
CCGT, OGCT, Nuclear, Hydroelectric, Pumped Storage, Wind and Oil, allowing similar BMUs
to be grouped together. I also assigned the ownership of each BMU from the options of
Centrica, EdF, EoN, SSE, Iberdrola, RWE and Independent.
Two particularly important plant statistics are the load factor (current power output as a
fraction of possible output) and availability (possible output as a fraction of rated capacity).
A high load means a plant is being well-utilised; a high availability means a plant is very
reliable. I was able to calculate the load for each power-plant at each moment in time by
dividing each FPN entry by its corresponding MEL entry. I calculated the BMU availability at
each moment in time by dividing the MEL by the maximum MEL reported during the year.
I was now in a position to answer a very wide range of queries. Average CCGT BMU size?
514MW. Average load factor for coal plants? 58%. Correlation coefficient between BMU
capacity and load factor? 0.19. Hours of downtime for Drax turbine 1? 91. Hours of
operation for Pembroke power station? Zero (it isn’t built yet).
Obviously there are a huge number of angles from which one could attack the dataset, so it
is worth outlining my objectives in a little more detail. The overall aim is to ascertain the
merit order of plant in the UK. In the Section 2.2 it was explained that the costs of
generating electricity can be roughly broken down into fixed and variable costs. The
variable costs are equal to the marginal costs of production (MC) and (I argued) it is this
value that should determine the merit order of dispatch.
If there was a strict merit order by plant type – e.g. Nuclear < CCGT < Coal < Hydro < OCGT
– then one would expect the load factors to be something like this: Nuclear 100%, CCGT
100%, Coal 45%, Hydro 0%, OCGT 0% i.e. certain plant types would never get used as they
would be too low down the merit order. In reality, as any economist will tell you, MC
curves are not flat- particularly in this case where each plant type is made up of many
different plants each with their own characteristics. One would expect the curves from
plants types to overlap such that it might be cheaper e.g to increase Coal load from 0% to
10% rather than increase CCGT load from 80% to 90%.
44
Fig 2.5 shows the load factors for each plant type throughout 2010, calculated as 4-week
moving averages. This allows one to follow the seasonal changes in demand while ignoring
the short-term variations that depend on time-of-day and day-of-week. The dotted line
shows the moving average of power demand, normalised by dividing by the mean demand
for the year; it indicates how the total load varies within a year.
The graph allows us to comment on the overall merit order. Taking each plant type in turn:
Nuclear: consistently operated at full load irrespective of other factors, implying that it is
top of the merit order. This agrees with what we know about nuclear i.e. that it has very
high capital costs and low marginal costs.
Coal: operates at between 40% and 85% and appears to follow the shape of the normalised
demand curve. This implies that most of the time coal is the ‘marginal’ plant in the merit
order.
OCGT and Oil: practically zero load throughout the year. This is because they are right at
the bottom of the merit order and are only used as ‘peaking’ plant at times of exceptional
demand.
Wind: achieves average loads of between 20% and 40%, but the output fluctuates wildly
and at random. No surprises there – wind of course is not dispatchable and indeed is
notoriously unpredictable, and the grid must absorb it whenever it is available. The results
Fig 2.5 Load factors by plant 2010
45
are in line with other studies of wind which shows that you can expect an average capacity
factor of roughly 30% (depending upon location).
Hydro: appears to be closely correlated with wind. Here then is proof that when it is windy,
it is rainy! Though hydro plants have a limited ability to choose when to dispatch power
over the course of the day/week, those variations are not visible on the graph so it appears
to follow the load of wind. In fact if you look closely you can see that hydro load seems to
lag behind wind by a few days; this could be evidence that the hydro plants wait a little
before choosing the optimum time to empty their reservoirs.
CCGT: a flattish load curve implying that it is not strongly influenced by total demand.
However it does not operate at close to maximum, rather between about 60% and 75% - it
appears that the majority of CCGT operates at baseload (always-on) but the rest is rarely
used. The reasons for this are a little more complicated. A plausible explanation is that a lot
of CCGT is still tied to long-term baseload contracts dating from the mid-1990s (see Section
1.5) and otherwise would be lower down the merit-order than coal plant. This is supported
by the fact that the fuel cost of coal is currently much lower than gas so one would expect
the MC of coal power to be lower than that of CCGT (IMF, 2011).
Figure 2.6 shows the data in a slightly different way, breaking down average load factors
for each plant type by time of day. The data is shown in 3-month blocks to allow seasonal
comparisons. The dotted line shows the system load profile. One can see that system load
shares many features across all seasons: a low load at night, a peak in the morning, a slight
tailing off and plateau followed by a second peak in the evening. The chief difference
between seasons (besides the absolute magnitude of output) is in the relative prominence
of the peaks and the plateau.
46
Here the differences in load profiles between plant types are starker. Nuclear and wind are
flat; hydro shows very pronounced peaks coinciding with demand peaks, indicating that
dispatch is being controlled to maximise profit; oil and OCGT are again barely visible. CCGT
load looks like a more ‘flattened’ version of the system load, whereas Coal follows the
system load in an exaggerated fashion. While there are many more features which one
could comment upon, the important point is that the data presented in this way basically
supports the conclusions made above.
2.8 Prices
So what can we say about that actual prices that suppliers pay for wholesale electricity?
This part is tricky because of aforementioned ‘trade secrets’. In the vast majority of cases,
suppliers (i.e. the Big Six) buy their electricity ‘from themselves’ or else from IPPs through
long-term bilateral contracts. In each case the sale price is confidential.
There are spot and futures markets in electricity but liquidity is very low. I calculated that in
2010 the average trading volume per settlement period was just 554MWh or roughly 3% of
all electricity (in a healthy market this number would be at least 200%). In such a situation
the spot prices are likely to be more volatile and probably systematically biased relative to
Fig 2.6 Load factors by plant, season
and settlement period, 2010
47
the ‘true’ system price (since only ‘desperate’ firms participate). Moreover the actual bid
and offer prices are, once again, confidential: the exchange (AP ENDEX) only releases the
average price per settlement period, which is unhelpful for anyone trying to distinguish
between different power sources.
There are other snatches of information available. OFGEM issues occasional reports on
wholesale and retail prices (for example Table 2.1) and forces the Big Six to publish yearly
‘segmental accounts’ which purport to break down the costs of generation and supply (see
the next section) – though it seems to me that some of the numbers are dubious: does
Scottish Power really spend three times as much as Scottish and Southern to generate 1
MWh?
However our main source of pricing information remains the Balancing Mechanism bids
and offers. To reiterate: these are bids/offers that BMUs make every settlement period to
balance supply and demand - each entry consists of BMU ID, bid/offer volume, bid/offer
price and a flag (accepted/declined). In theory each bid/offer should represent the
marginal cost of electricity for each power plant at that moment in time. However the
dataset, while very large (10-20 entries for each of the 17520 settlement periods), was
never really supposed to ascertain the true marginal cost of electricity so should be used
with care. Firstly, not every BMU chooses to make bids and offers so it is an
unrepresentative (self-selecting) dataset. Secondly, the balancing mechanism only settles
relatively small volumes – in 2010 the average (absolute) NIV was just 294 MWh – and tells
us very little about the other 98% of generation. Finally there is the problem of bids/offers
themselves. Bids are always lower than offers, so which price best represents the marginal
cost of generation? Suppose that a power plant is on a long-term contract whereby it sells
electricity at its marginal cost of £30/MWh. It may then choose to use the BM to place an
offer of £35/MWh and a bid of £25/MWh and bag itself a £5/MWh profit if either is
accepted (otherwise it may as well not bother). So we expect that the ‘true’ marginal cost
for a plant actually lies somewhere between the average bid and average offer price.
With these caveats in mind, let us look at the data. Fig 2.7 shows the average lowest,
highest and mean accepted bids and offers for each settlement period. Unsurprisingly the
offer price profiles follow the load profile through the day, featuring the same twin-peaked
characteristics. The bid price profiles are very flat indicating that it is always relatively
cheap to lower ones electricity production. Note also that there is a consistent gap
48
between the bid and offer prices. The mean offer is always at least £10/MWh above the
mean bid, rising to £40+/MWh during periods of peak demand.
Fig 2.8 is a little more complicated. It shows a plot of cumulative offer volume versus offer
price for each plant type, sorted ascending; the axes are logarithmic in order to show all
data at once. The bid data has much the same shape but with slightly lower prices overall.
There is no nuclear or wind data as they do not participate in the balancing mechanism. We
can make a few observations. The coal and CCGT curves are near-identical, suggesting they
have very similar costs – or alternatively that some sort of gaming behaviour is occurring,
given coal marginal costs are thought to be lower. The other plant types are priced in the
order predicted in the previous section. Every curve has a prominent ‘flick’ in its tail (even
on a logarithmic scale): these are indicative of speculative bids that were accepted at a
time when the grid was operating very close to full capacity; hence they are not ‘marginal’
offers, indeed they likely deliver windfall profits to generators.
Fig 2.9 shows a section of Fig 2.8 with linear scale allowing one to better make out the
gradient of CCGT and coal curves.
Fig 2.7 BMU prices plant and
settlement period, 2010
49
Fig 2.8 BMU prices by cumulative volume and by plant, 2010. Log-log plot Fig 2.9 Detail of Fig 2.8, linear plot
50
It is important to recognise that Figs 2.5 and 2.6 are not marginal cost curves – nonetheless
they obviously tell us something about electricity prices. Indicative statistics are shown in
Table 2.4. I shall use these results in the next section when choosing prices for the grid
model.
2.9 The Big Six
As a slight digression before moving on to the final section, it is worth looking briefly at
wholesale market structure. As mentioned in section 1.6, the ‘Big Six’ integrated suppliers
have achieved very dominant positions in the ESI, accounting for 70% of UK capacity and
99% of supply. I have summarised key corporate indicators for their UK and international
operations in Tables 2.5a and 2.5b. One can see that the UK-based companies are notably
smaller by asset base and it would not be surprising at some point to see them being
bought out or perhaps even merging. The other four are truly corporate behemoths - EdF is
the world’s largest utility.
Section 1.6 mentioned that the Big Six are currently under review for abusing their
oligopolistic market positions. There is clear evidence of this in the retail market where
high margins are indicative of market power, and NETA would seem to lend itself to abuse
Plant Min Mean Median Stand. Dev.
CCGT 27 60.7 57.5 19.5
Coal 30 61.9 58 21.2
Hydro 45 136.9 125 58.5
Pumped Storage 45 147.6 144 34.8
Oil 75 339.2 345 141.7
OCGT 180 277.7 270 41.5
Nuclear N/A
Wind N/A
Table 2.4 BMU Statistics by plant type
51
at the wholesale end too. I was interested to see if the Big Six leverage their positions to
‘squeeze out’ IPPs.
Company
Generation
Capacity
(MW)
Gen
Revenue
(£m)
Gen
costs
(£m)
Gen
2010
(TWh)
Sales
2010
(TWh)
Gen Costs
(£/MWh)
Gen Margin
(£/MWh)
EdF 14087 3574 2433 71.6 63.6 49.92 15.94
E.on 10170 1575 1330 29.8 48.3 52.85 8.22
RWE 11751 733 392 32.6 49.8 22.48 10.46
Iberdrola 5889 1643.5 1233 26.9 23.1 61.10 15.26
SSE 9270 841 424 33 60 25.48 12.64
Centrica 4363 1075 893 22.8 45.1 47.15 7.98
Total 55530 9441.5 6705 216.7 289.9 43.57 12.63
Company Ownership
UK
revenue
(£m)
UK
profits
(£m)
Group
revenue
(£m)
Group
profits
(£bn)
Group
Assets
(£bn)
Market
Cap
(£bn)
EdF
French (85%
State) 5.36 -0.18 57.36 0.90 211.27 33.54
E.on German 4.48 0.21 81.75 5.15 133.80 24.82
RWE German 4.49 -0.09 46.94 2.91 81.87 12.06
Iberdrola Spanish 2.32 0.00 26.79 2.53 82.48 29.05
SSE UK 5.78 0.27 28.33 1.42 21.45 11.30
Centrica UK 4.63 0.23 22.40 1.30 19.28 14.91
One way to do this would be for the Big Six to put a downward pressure on wholesale
prices (while cross-subsidising their own wholesale businesses from retail), thus making
IPPs less profitable and therefore vulnerable to takeovers. The number of IPPs that have
Table 2.5a Big Six UK statistics
Table 2.5b Big Six Group statistics
52
gone bankrupt and/or been bought out in the last decade suggests this may well have
occurred (c.f. the fate of British Energy, Section 1.6).
However in the absence of reliable wholesale prices, this behaviour is difficult to prove;
instead, I decided to look at plant utilisation. If, for example, it turned out that IPPs were
achieving lower average plant loads than the Big 6, that might be evidence that the Big 6
have a preference for using their own plant i.e. IPPs are getting ‘cut out’ of the market. To
that end, I calculated the average load for CCGT and coal plants owned by the Big Six and
IPPs respectively. The results (shown in Table 2.6) actually suggest the opposite – that IPPs
achieve considerably higher loads overall. Could this be evidence that the IPPs are super-
efficient, proof that market forces are working their magic? A more likely explanation is
that since IPPs are by nature financially precarious it is very important for them to achieve
a high utilisation. Whereas the Big Six can afford to ‘shop around’ for their wholesale
power, IPPs will to keep the turbines turning 'at any cost’ to bring in revenue. One would
expect this to result in lower per-MWh income for IPPs i.e. a ‘price squeeze’. However in
the absence of price data, this is just speculation.
It occurred to me that if IPPs are being squeezed, they might make a more aggressive effort
to exploit the BM to top-up their earnings. This would manifest itself as IPPs making a
disproportionately large number of offers and bids and taking a disproportionate amount
of BM revenue. However I found that IPPs accounted for 27% of BM bids/offers (by
volume) and took 27% of BM revenue, which is close proportion to their ~30% market
share.
Overall, then, the dataset gives evidence that IPPs and the Big Six do have different modes
of operation, but in the absence of detailed price information it is not possible to prove
abuse of market power.
Big6 IPPs
CCGT 0.43 0.60
Coal 0.35 0.60
Table 2.6 Load factors
by plant and owner
53
Chapter Three: Grid Model
In this final chapter I develop a (relatively) simple model of electricity supply which enables
me to investigate various possible futures of the Grid. Whilst a fully realistic model would
be an unfeasibly immense undertaking, I believe that as long as I capture the key features
of the industry I can still contribute a useful analysis. Moreover, I am aware that this work
is not ‘original’ and many others have attacked the problem with far greater resources, for
example Poyry (2010); therefore I see the modelling in part as an intellectual exercise –
how far can you get with just a computer, a coffee machine and a deadline? However, I
believe that my method and results do offer some novelty, particularly in the final section.
3.1 Model Design
The model treats all UK power as originating from just 6 generalised sources: Nuclear,
CCGT, Coal, OCGT, Onshore Wind and Offshore Wind. Other sources are ignored as they
make up an insignificant fraction of generation (e.g. hydro) or are similar enough to be
subsumed into another source (e.g Oil). In outline, the model works as follows: Taking each
settlement period one-by-one and a given set of input parameters (in particular, the power
demand to be met), it generates a marginal cost curve of electricity. From this curve the
model estimates the order of dispatch, returning, amongst other things, the power output
and revenue for each type of power plant. From this one can calculate, for example,
average electricity cost, average plant loads, or total carbon emissions. By looping through
several days or weeks one can simulate the output and costs over a period of time. The
model is constructed so that every parameter can be specified at every point in the time
period – or just a handful of times, or just once. By changing the input parameters (for
example, the balance of nuclear and wind energy, or the cost of CCGT) one can explore a
range of hypothetical scenarios.
Costs are calculated separately for ‘baseload’ and ‘variable’ generation. For each plant type
one specifies the fraction that is ‘baseload’ and fraction that is ‘variable’; baseload
generation operates at a fixed price and output regardless of power demand or marginal
price. The power generated through baseload is subtracted from system demand before
the marginal cost curve is calculated, then ‘added back in’ when calculating that final
outcomes. For each plant I chose the cost/MWh assigned to baseload generation to be
54
equal to the median cost of variable generation, or else (if there is no variable generation)
equal to coefficient c1 (see below).
3.2 Demand, Availability, Capacity
The demand profile is an important input. I used the profile from 2010 as the starting point.
The demand profile is then modified for future years by multiplying throughout by a
constant in proportion to the expected change in demand (which may be positive or
negative). This approach succeeds in changing overall demand while maintaining the same
generic shape for each year (important to capture daily, weekly and seasonal variations).
The plant capacity – the total possible output of a plant if it is fully functional – also draws
upon 2010 data as a starting point (see appendix A). By modifying the inputs one can map
alternative scenarios: for example one might wish to steadily increase the amount of wind
power and decrease coal as time progresses.
The availability – the fraction of plant which is operable at a given moment in time – can
arguably be ignored in some circumstances (i.e. simply set to 100%, or 85% or some other
constant). However, analysis of the MEL data (section X) showed that availability does tend
to go through a seasonal cycle, decreasing at times of lower demand - presumably because
plants are taken offline and/or scheduled for maintenance at times when they are less
likely to be needed. Therefore I created a profile for each plant based on the MEL data
(smoothed by taking the 4-week rolling average). The profile was duplicated for each year
of simulation. The exception was wind power, for which I assigned a randomly-chosen
weeks’ worth of availability data from 2010 to each week of simulation data one-by-one.
This is a simple way of capturing the extreme variability inherent in wind power.
3.3 Marginal Cost Curve
The marginal cost curve (MCC) is the key component of the model as it determines the
variable outputs. It is generated first by creating a load-price curve for each power plant
(running from 0 to 1). The load curve is a sum of linear and exponential curves, obeying the
equation:
where p is the price, x is the load and c1 – c5 are coefficients to be specified. I chose this
form as it allows one to emulate the shape of the curves in Fig 2.8, i.e. smoothly increasing
55
from a constant but with a sharp flick at the tail. Fig 3.1 shows a such a curve achieved by
setting c1 – c5 = 15, 10, 5, 0.03 and 12 respectively .
Next each load curve is scaled by multiplying by the available capacity of its respective
power plant yielding a series of six curves of varying ranges. The curves are combined
together and sorted by price to give a single marginal cost curve.
Getting the shape of the MCC is important but also a matter of judgement. The intention is
to get the load curves the correct shape to start with and then explore different scenarios
mainly by changing the constant c1, i.e. changing only the intercept. The logic behind the
shape of each plant is as follows:
Nuclear: Completely flat. Will be run at 100% baseload so the only important coefficient is
c1.
CCGT: Curving gradually upwards according to the principles of increasing marginal cost.
Sharply rising at near to 100% load to model the fact that the system prefers to have
reserve capacity, and in circumstances where supply is stretched, marginal prices go
Fig 3.1 Marginal cost curve
components
56
through the roof. CCGT will be run at 40% baseload in the ‘present day’ scenario as per
Section 2.7 but will be run at 0% baseload in most other cases.
Coal: Similar to CCGT; the main point of differentiation between them is the intercept c1
with opportunity to increase c2 to reflect the ratio different ramping ratios. 0% baseload.
OCGT: Linear load curve with high intercept and sharp gradient. A ‘get out’ valve which
stops the price rising far beyond a certain point (£200-£300/MWh) when supply is severely
stretched.
Wind: A special case. Will be run at 100% baseload, to reflect that it is always absorbed by
the grid when operational. The variability of wind will be modelled by varying the
availability (above), not the load factor. Therefore as with Nuclear, c1 is the only relevant
coefficient; it should be set to equal the price paid for wind by the new REFIT mechanism.
Onshore and Offshore Wind are differentiated to allow for two-tier REFITs.
Fig 3.2a which needs labelling shows example load curves for each of the plants and Fig3.2b
shows the corresponding cost curves along with the overall marginal cost curve.
Fig 3.2a Marginal cost curves by plant Fig 3.2b Overall MCC (blue)
57
3.4 Outcome
How realistic is this model? Clearly it is highly stylised, a ‘toy’ model. Some of the most
glaring simplifications and omissions include:
No geographical model of the Grid – therefore no accounting for transmission
bottlenecks and losses
No model of market structure – assumes perfectly efficient market, always
choosing least-cost first
No model of demand side – demand is a model input
Time resolution limited to every 30 minutes – no treatment of fine variations in
demand
Similarly, no treatment of plant ramp rates – assume plants can increase and
decrease output without penalty
All variations between plants subsumed into a single cost curve. Several
technologies omitted completely, most notably hydro
Monotonically increasing MCC – in reality running a plant at a lower load factor is
often less efficient, therefore sometimes reducing output would increase marginal
cost
In spite of this I believe the model captures enough detail to make it useful to look at some
loosely-sketched future scenarios, provided the input parameters are chosen wisely. Let us
look at a sample set of outputs. Fig 3.3a shows a set of results from a week picked more-or-
less at random, the week starting 25th Nov 2010. The parameters are close to real-life
except that the amount of Wind has been doubled to provide a little interest. One can see
that Wind fluctuates semi-randomly contributing occasional bursts of energy to the grid,
whereas Nuclear is completely flat operating completely in baseload. CCGT and coal make
up the rest of the supply in roughly equal proportion; CCGT is running at 40% baseload but
is slightly more expensive than Coal, so Coal shows greater variability. At times of
maximum demand, OCGT steps in to contribute the final few GWs of supply. Fig3.3b shows
the corresponding prices calculated by the model. The peak variable price (i.e. the marginal
price) usually follows the supply curve closely but we can see large peaks at times of
58
exceptionally high demand. The average variable price is lower and much less variable but
shows the same basic shape, as one would expect. The overall price takes into account the
payments made to wind and nuclear too; in this example Nuclear, Onshore Wind and
Offshore Wind are receiving payments of £30/MWh, £70/MWh and £140/MWh
respectively. This means that at periods of high wind the overall price is pushed up
somewhat; otherwise it is pushed slightly down.
Comparing the model outputs with the real-world data in Fig 3.4a, the similarities are
encouraging. The shapes are slightly different because the model uses the TSGD as its
demand input which does not match the FPN data exactly (Section 2.7 ); nonetheless the
generation mix is a close match and could be further improved by tweaking parameters.
Comparing prices (Fig 3.4b), it appears the model marginal price is a good proxy for market
spot price. Of course the whole point is that the market spot price is a very flawed metric
and we do not know ‘real’ prices very accurately, so we needn’t be too concerned with
discrepancies as long as the model results are robust.
59
Fig 3.3a Sample model output Fig 3.3b Sample model prices
Fig 3.4a and 3.4b Corresponding real-world data
60
3.5 Scenarios
Having outlined the model and established that the results are plausible, let us put it to
use. Obviously there are potentially thousands of input parameters – 37 for each cycle –
so to impose some logic on the process I have developed four scenarios for the future. Each
scenario sketches out a possible path for development of the UK ESI. Each scenario runs
from 2010 until 2025 and the simulation will run for the entirety of that period (a total of
48*365*15 = 262800 cycles - taking roughly 15 minutes to execute). I have assumed that
the basic costs of building plant remains the same in all scenarios - now is finally the time to
confront what prices to choose. As mentioned above, the load curves have been chosen
such that I need only pick the minimum price c1. However there is a rather glaring
inconsistency between my data sources. The BM prices (Table 2.4) suggest that some CCGT
and Coal plants have a marginal price as low as £30/MWh, and the OFGEM data gives an
overall average of £56/MWh (Table 2.1). However the levelised cost data suggests that
new-builds will have to charge at least £80/MWh to break even with Offshore Wind costing
a boggling £190/MWh. To quote a consultant at Poyry (2011), electricity costs are “going
up... way up!” How to reconcile the data sources?
As a test I ran the simulation with today’s parameters and values of c1 equal to the
minimum values in Table 2.4, and found that the overall average price came out at
£57/MWh, very close to OFGEM’s value. Therefore I will use these as the 2011 prices. My
strategy is then to increase prices linearly until they reach the LC levels by 2020, after
which they will remain flat. This is not the most elegant solution but clearly increased
capital costs cannot just be ignored. However, (here it gets a little tricky), the LC levels will
not necessarily be the ones calculated in Section 2.3. The LC model has several input
parameters which should for consistency be obtained from the grid model – for example,
plant load factor – but the output from the LC model also affects the grid model, i.e. there
is a feedback loop between the LC model and the grid model. However there should be a
stable equilibrium where the price gives the output that gives the price, so I constructed a
script to continually change the parameters until this equilibrium is found.
With the input prices settled, let’s take a look at the scenarios:
“Base Case”: Based on government projections from 2010 before the EMR was announced.
‘Low case’ taken on the assumption that business-as-usual policies
Total demand to increase by 6%; capacity to increase by 14%
61
Wind to increase to 30% of capacity; CCGT to 40%; others to decrease
Carbon prices to increase from 16 to 49 EUR/tonne
Wind energy payments same as today: £50/MWh onshore, £100/MWh offshore
(eroc auction)
Wind 50/50 onshore and offshore
“Energy Market Reform”: As above but implementing new EMR policies.
Lower payments for Wind through REFIT : £40/MWh onshore, £80/MWh offshore
Nevertheless, more Wind built – increasing to 40% capacity
Higher Coal and CCGT prices due to carbon price floor
More Nuclear built: capacity decreases then increases at end of decade, 10%
capacity by 2025
“Go For Green”: An alternative which attempts to aggressively cuts carbon by introducing
wind power at an accelerated rate.
Demand-side reduction: demand falls by 10%
Higher carbon price, increasing faster and further from 16 to 100 EUR/tonne
Coal and Nuclear phased out
CCS on new CCGT plants reduces emissions by 30% but increases costs by 40%
Higher offshore REFIT: £110/MWh
More Wind built: increasing to 50% of capacity
Greater fraction of Wind built onshore (66%)
“Too Cheap To Meter”: An alternative which brings lots of Nuclear online, backed up by
CCGT
Nuclear construction increases linearly to 35% of capacity by 2025
Coal phased out
Slow Wind uptake, increasing to 20% of capacity
CCS on new CCGT plants reduces emissions by 30% but increases costs by 40%
3.6 Results
Summary results (across 15 years) are shown in Table 3.1.
62
This result does not appear to vary much between scenarios – the difference between in
price highest and lowest is only 15%. This is likely due to the fact that all scenarios have the
same start point and there is a limit to how far they can evolve for that point in 15 years. In
all scenarios the fractional price increase over today is substantial – ranging roughly from
20-40%. The EMR price is notably higher than others, which I believe is due mainly to the
effect of the carbon price floor pushing up baseload prices - and at least it does achieve a
10% reduction in CO2 emissions versus BC. How is it that the GFG and TCTM scenarios
achieve lower prices? Simply because they reduce both CO2 by a substantial amount, and
carbon prices are expected to be a significant cost in the future, adding around £30/MWh
to the price of Coal and £12.50 to CCGT by 2025. The GFG also benefits from the
enlightened populace putting up with lots of onshore wind which is substantially cheaper
than offshore. However the intermittency of Wind means that CCGT still has a large role to
play in the GFG scenario. This is less of an issue with the TCTM scenario – between Wind
and substantial Nuclear generation, it achieves the highest carbon reductions of all and at
relatively low cost (perhaps this is something for environmentalists consider?).
Fig 3.5 shows the generation mix by year for each scenario. The most striking thing is how
similar they all look, especially wind. The GFG scenario ends up with a huge 47GW of Wind
capacity by 2025, enough in theory to deliver the vast majority of our electricity. But the
load factor averages only around 30%, so even in the end it is still only providing 40% of
energy (and a fraction of this will be ‘unwanted’ – see below). The overall view is that
CCGT dominates generation at present and will continue to do so in the future – perhaps
even more so as Coal plants are retired. Consequently the most important developments
from a consumer’s point of view is anything which impacts the price of CCGT, be it gas
prices, a carbon tax or mandatory CCS.
Scenario Total
Supply
Average supply
(GW)
Revenue
(£bn)
£/MWh CO2 Emssions
(Mtonnes)
Average
Error (%)
BC 5173.62 39.37 626.27 60.53 1766.62 0.30
EMR 5203.34 39.60 727.21 69.88 1560.40 0.87
GFG 4877.75 37.12 642.85 65.90 1380.36 1.12
TCTM 5297.34 40.31 663.69 62.64 950.67 2.62
Table 3.1 Model summary results by scenario
63
3.7 Oversupply
The GFG and TCTM scenarios may seem appealing but they have a distinct problem, as we
shall see. The final column of Table 2.1 shows the “Average Error”; this is the average
percentage difference between supply and demand, calculated as where S is
total supply and D is total demand. In a well-functioning grid supply and demand are in
perfect balance at all times (otherwise the system frequency drops and customers may
experience brownouts or blackout) – but in the future this may not be the case unless there
is careful planning. If there is too much wind and not enough dispatchable generation then
when wind drops to near zero (it happens on a UK-wide basis several times a year) there
will be insufficient power supply. Equally, if there is a burst of wind the grid will struggle to
absorb the excess power - electricity prices may even go negative as suppliers have pay
businesses to increase power consumption or else risk damaging the grid infrastructure.
Nuclear is also a liability because for technical reasons it can only change its output the
course of weeks, not intra-day as load balancing requires.
I have included average error as a measure of how much of a problem this is likely to be.
Given that the supply and demand are in balance most of the time, an average error of 1-
Fig 3.5 Load factors by year for each scenario
64
2% conceals some quite severe individual imbalances. As we can see, this problem is bad
enough with lots of wind (GFG scenario) but with potentially disastrous with wind and
nuclear and not much else (TCTM). Figure 3.6 illustrates this point further. It shows the
‘oversupply’ each year i.e the number of GWh which were generated beyond what was
required (the undersupply is tiny by comparison). It shows, unsurprisingly, that the more
wind and/or nuclear, the more likely you are to have a oversupply problems, whereas CCGT
alleviates the problem - it is no coincidence that the TCTM scenario peaks at the point
where there is maximum nuclear and minimum CCGT.
Though the GFG and TCTM scenarios are ‘unrealistic’ in that they call for rapid deployment
of new technologies that (to put it bluntly) simply won’t happen, they highlight the point
that sometime soon intermittency will become a serious problem. I will spend the
remainder of this chapter with a brief investigation of the mooted solution to these
problems: storage.
3.8 Storage
In the past five or so years there has been increasing interest in the energy storage as a
possible solution to these problems (MacKay, 2008). Actually there has always been a small
subset of the ESI concerned with energy storage, even before the prospect of significant
non-dispatchable loads. The rationale is simple: The UK power supply can vary in
magnitude by up to 100% within 24 hours. At peak periods this means activating the less
efficient, more expensive and more polluting peaking plants. However, if one could store
Fig 3.6 Oversupply by year
65
energy from baseload sources at times of low demand and offload it at peak periods, the
need for peaking plant could be reduced. Not only is this potentially cheaper, it puts less
stress on the grid (since the supply curve is smoothed) and reduces carbon emissions.
Moreover, by ‘buying low and selling high’, storage operators can make a tidy profit. When
factors in the need to level loads from wind and other intermittent sources, one can see
why storage has become a very important - and potentially very valuable – commodity. (I
should add that there are also many ancillary ways that storage can add value e.g. load
balancing, black start, backup power - a recent report identified 17 separate uses (Eyer &
Corey, 2010). However here I will focus on the arbitrage aspect.)
The real stumbling block to energy storage is technology. The only viable large-scale
technology is pumped hydroelectric, where energy is stored by pumping water uphill and
released by sending it back through turbines. Storage in this way is relatively cheap and
achieves round-trip efficiencies of 70-80%; the problem is that it requires a spare mountain
(with planning permission). Therefore pumped-storage projects tend to be few and far
between. In anticipation of future need, there are a host of other technologies vying to be
the next big thing in storage, for example Compressed Air, Pumped Heat, Flow Battery and
Flywheel technologies, but as yet nothing competitive with Pumped Hydro (Walawalkar,
2008).
The UK grid has four pumped-hydro plants but capacity is dominated by Dinorwig, an
amazing structure set inside a mountain in North Wales. It was initiated in 1974,
supposedly for the ‘nuclear revolution’ which never arrived, has a peak output of 1.8 GW
and a maximum capacity of 9.4 GWh (MacLeay et al., 2011). There are currently no plans
for new pumped hydro to be constructed in the future, which begs the question – do we
have enough?
3.9 Modelling Storage
This is a complicated question (storage is a complicated topic) so I will aim to provide an
answer in a fairly narrow way. I will use my model to see a) how the introduction of storage
would alter the above scenarios and b) how much money a storage provider could make in
the process.
I chose to model storage as follows: prior to running the grid model, the power demand
profile and baseload output are supplied to a separate script. Given a store capacity (MWh)
and efficiency (%), this script will optimise the demand so that the store exactly fills and
66
exactly empties once every day, or else ‘flattens’ the load profile – whichever would create
least overall demand. In the case where there is too much baseload power to be fully
absorbed, the script simply optimises as best it can (some energy will still have to be
dumped).
The model is then executed as before with the new demand profile. The marginal price for
each settlement period is taken to be the price at which storage provider buys/sells power.
By multiplying the list of marginal prices by storage electricity purchases/sales and
summing, we obtain the total profit (or loss) made by the storage provider. By comparing
the overall simulation indicators (e.g. revenue, CO2 emissions etc) with the zero-storage
case we can see what kind of effect the introduction of storage has had. This method is
summarised schematically in Fig 3.7.
67
Fig 3.7 Modelling Storage
68
3.10 Results
I chose to model 3 storage scenarios on top of the four previous scenarios. The first
scenario has 10,000 MWh storage (“Store10K”), the second has 100,000 MWh
storage(“Store100K”) and the third has infinite storage (“StoreInf”). The third one is also
slightly different in that it balances the loads across four days rather than just one.
Efficiency is set equal to 80% in all cases. Store10K is a “realistic” scenario (c.f Dinorwig),
Store100K represents the limits of what could plausibly happen, and StoreInf is the best
possible scenario to test the limits of the usefulness of the concept.
The results are shown in Table 3.2 alongside the original simulation results for comparison.
Three columns have been added. Store profit is straightforward, but note that it excludes
the costs of building and operating the storage. Benefit to Grid is the reduction in revenue
(i.e. reduced cost) to the ESI as a whole due to using fewer peaking plants etc. Total benefit
is the sum of these two measures and is equal to the total welfare benefit to society.
69
Scenario Total Supply (TWh)
Ave supply (GW)
Revenue £bn £/MWh CO2 (MT) Average Error (%)
Store Profit (£bn)
Benefit to Grid (£bn)
Total Benefit (£bn)
BC 5173.62 39.37 626.27 60.53 1766.62 0.30 0.00 0.00 0.00 BCS10k 5179.29 39.42 623.35 60.18 1757.13 0.11 0.28 2.92 3.20
BCS100k 5087.70 38.72 608.76 59.83 1686.31 0.06 4.55 17.51 22.07 BCSInf 5067.22 38.67 605.91 59.79 1675.74 0.05 4.75 20.36 25.11
EMR 5203.34 39.60 727.21 69.88 1560.40 0.87 0.00 0.00 0.00
EMRS10k 5205.04 39.61 725.57 69.70 1554.91 0.20 0.68 1.64 2.32 EMRS100k 5105.94 38.86 705.40 69.08 1478.62 0.05 5.62 21.81 27.43 EMRSInf 5074.34 38.72 699.86 68.96 1463.33 0.05 6.35 27.35 33.71
GFG 4877.75 37.12 642.85 65.90 1380.36 1.12 0.00 0.00 0.00
GFGS10k 4871.41 37.07 639.28 65.62 1379.37 0.5 0.34 3.57 3.92 GFGS100k 4784.64 36.41 626.35 65.45 1337.68 0.06 4.94 16.50 21.44 GFGSInf 4750.01 36.25 620.11 65.28 1325.59 0.06 6.24 22.74 28.98
TCTM 5297.34 40.31 663.69 62.64 950.67 2.62 0.00 0.00 0.00
TCTMS10k 5283.20 40.21 661.06 62.56 945.23 0.11 1.24 2.64 3.88 TCTMS100k 5166.79 39.32 644.36 62.36 893.70 0.06 3.77 19.34 23.11 TCTMSInf 5124.39 39.11 638.53 62.30 877.36 0.06 4.38 25.16 29.54
Table 3.2 Storage model summary results by scenario
70
There is a wealth of information here. The simulations confirm most of the trends one
would expect: more storage means lower costs, less CO2 and lower errors (i.e. better grid
reliability). We can see plenty of interesting features : for example, it appears that the two
grid scenarios with most nuclear (EMR and TCTM) also benefit the most from storage. This
suggests, somewhat contrary to received wisdom, that storage is even more important for
nuclear than for wind – perhaps simply because nuclear loads are consistently higher than
wind and so when they do cause problems, they cause big problems.
I don’t want to dwell on all the features of the results. The general message is what is
important: storage is going to be big. If we can increase our storage tenfold from where we
are today (roughly, Store10K to Store100K), the potential benefit is enormous – in most
scenarios (including EMR) averaging well over £1bn/year. The overall conclusion is that it is
essential to invest in storage, the more, and the sooner, the better.
I would add one point: the storage profits tend make up only around a fifth of the total
welfare benefit. Is a large enough slice of the pie to encourage investment? There has been
some work that suggests it is (Sioshansi et al., 2009), but I am not so sure. Is this set to be
the big next failure of the free market in the UK ESI? At the very least, we can say that it
would have good pedigree.
71
Conclusion
In this dissertation I have drawn to sketch history of the UK ESI, detailled the current ‘state
of play’ of the industry, commented on the latest incentive scheme and speculated on the
future direction. Given the vastness of the subject it has been necessary to gloss over and
simplify many important points. Nevertheless, I will now draw some overall conclusions
from the study.
Firstly, the UK has not been well served by privatisation. The state has lost control of a vital
economic and strategic asset, customers have been overcharged, many businesses have
gone bust, and been the cause of who-knows how many headaches for the regulator. The
state should not be afraid to become more interventionist, perhaps even creating a new
state-owned integrated supplier, in an attempt to wrest back control of the industry.
Secondly, the state faces a huge challenge in the next decade to marshal the expertise and
investment necessary for the ‘Green Revolution’ (while also keeping the lights on). Recently
it has made some progress in this area but I am still not convinced it understands the scale
of the task it has set itself. The EMR is a good start, but now it needs to think bigger.
Finally, energy prices are going to rise in the future, of that there is no doubt – but with
careful planning, and particularly with shrewd strategic investment in storage R&D and
assets, the costs to the consumer will be minimised – and we might even create a few
‘green jobs’ in the process.
Overall, the ESI has had an interesting couple of decades - and it is about to have at least a
couple more.
72
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Appendix A
A description of the main generation technologies
Plant Type Description
Coal
Coal-fired steam boilers are the old 'work horses' of generation. The standard configuration is to pulverise the coal, burn it in a boiler and use heat exchangers to create high-pressure superheated steam (around 500C and 150 bar). The steam generates power by passing through a series of turbines before being released into a cooling tower. All but one of the UKs coal was built in the CEGB era and most of it is large, between 2 and 4GW. Coal is by far the most polluting energy source and many plants are scheduled for closure - others have installed expensive flue-gas desulpherisers. Coal designs are flexible and some can also accept gas, oil and more recently biomass as a feedstock.
OCGT
Open Cycle Gas Turbines are a relatively old technology that have mostly been superseded by CCGTs (below). In their simplest implementation they consists of a compressor, combusion chamber, expander and turbine on a single shaft. Air is passed through the compressor, mixed with a natural gas or vapourised oil feedstock, ignited and expanded to drive the turbine. Since OCGT are cheap, small and inefficient they nowadays exist mainly as 'peaking' plants, providing power only in times of exceptional demand. Some operate only a handful of times every year - others are mothballed for years at a time.
CCGT
Combined Cycle Gas Turbines are the 'state of the art' in conventional fossil fuel generation. Based on the OCGT, the hot gas leaving the expander is passed through a heat-exchanger to create steam which is passed through a second expander on the same shaft, greatly enhancing efficiency. Compared to coal, CCGT has a smaller footprint, lower capital costs, is more flexible and emits around 60% less CO2 per MWh (however it requires more maintenance and fuel costs are significantly higher). Since privatisation almost all new plants have been CCGT.
Nuclear
The UK is one of the few countries that embraced nuclear technology, building the world's first commercial station in 1954 and amassing a stock of 16 by 1990, though enthusiasm has waned due to cost and safety concerns. There are many different implementations and the UK's are particularly idiosyncratic, but the basic principle is to initiate the fission of Uranium-235, causing a chain reaction which gives off large quantities of heat. The heat is used to run steam turbines similarly to a coal boiler. Unlike other technologies, nuclear is very inflexible and is run at a constant load for weeks or months at a time. Though once labeled "too cheap to meter", it is currently the most expensive type of generation by some margin - but also the only renewable technology with proven capacity.
Wind
The preferred power source of environmentalists, being non-hazardous and zero-carbon, wind is perhaps the UKs best bet for a 'green revolution'. Essentially just a windmill joined to a turbine, wind power is expensive relative to conventional plants at present but this may change if/when carbon taxes are introduced. The biggest problem is land - of all technologies discussed here, wind farms have by far the largest footprint for a given power capacity and planning permission can be a problem. Offshore wind may be the answer, but it is even more expensive.
78
UK plant statistics
Source: DUKES
Plant
Type Number
Smallest
(MW)
Largest
(MW)
Median
(MW)
Total
Capacity
(MW)
Oldest
(Year)
Newest
(Year)
Indicative
Thermal
Efficiency
Coal 18 363 3870 1940 28766 1918 2000 38%
OCGT 30 10 140 41 1580 1952 2006 20%
CCGT 38 50 1750 665 25429 1991 2010 55%
Nuclear 10 434 1210 1040 10170 1967 1995 N/A
Wind 157 1 322 13 4845 1992 2011 N/A
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