EBOOK FUTURE POWER SYSTEMS

EBOOK
FUTURE POWER SYSTEMS
Stephen Browning
January 2015
ECI Publication No Cu0213
Available from www.leonardo-energy.org

Publication No Cu0213
Issue Date: January 2015
Page i
Document Issue Control Sheet
Document Title: eBook – Future Power Systems
Publication No: Cu0213
Issue: 03
Release: Public
Author(s): Stephen Browning
Reviewer(s): Hans De Keulenaer
Document History
Issue Date Purpose
1 Jan 2008 Initial release
2 Oct 2014 Update
3 Jan 2015 Release as eBook
Disclaimer
While this publication has been prepared with care, European Copper Institute and other contributors provide
no warranty with regards to the content and shall not be liable for any direct, incidental or consequential
damages that may result from the use of the information or the data contained.
Copyright© European Copper Institute.
Reproduction is authorised providing the material is unabridged and the source is acknowledged.

Page ii
CONTENTS
Introduction.................................................................................................................................................... 1
1. Electricity System Operation - Fundamentals of Matching.......................................................................... 2
2. Demand Profile........................................................................................................................................... 9
3. Fossil Plant Heat consumption.................................................................................................................. 17
4. Renewable and Distributed Generation.................................................................................................... 25
5. Distribution Configuration and Sizing........................................................................................................ 38
6. More Distributed Generation.................................................................................................................... 40
7. Active Distribution Management.............................................................................................................. 44
8. The ‘Active’ Customer............................................................................................................................... 46
9. Configure for DER Management................................................................................................................ 50
10. The Customer and the Industry............................................................................................................... 53
11. New data from the customer to the Industry.......................................................................................... 57
12. New data from the Industry to the customer.......................................................................................... 59
13. Intelligent Buildings and Processes ......................................................................................................... 61
14. Premises Power Profile and DER control................................................................................................. 66
15. Data Logistics.......................................................................................................................................... 71
16. Data Structure, Metering and Settlement............................................................................................... 75
17. DER, Market and Matching ..................................................................................................................... 79
18. DER Participation in Market Timescales.................................................................................................. 84
19. DER Participation in Operator Timescales............................................................................................... 90
20. The Smart Enterprise, its Objective and Forecasting ............................................................................... 98
21. The Smart Customer ............................................................................................................................. 103
22. Strategy and Value................................................................................................................................ 114

Page 1
INTRODUCTION
"The Future is out there somewhere; we just have to make sure we get the best one"
"There are an infinite number of ways of running an Electricity Supply system badly"
When the GB System Demand Peaks at 60GW, we are pushing 85 million Brakehorsepower through a quite
fragile set of wires.
The way in which electricity is to be supplied is subject to radical change. Distributed and Renewable
Generation, together with Demand Management, is being promoted to reduce the use of central fossil fired
plant, increase efficiency in delivery of energy and reduce emissions.
However, this will only be achieved if all resources are properly monitored and controlled within a new
framework for electricity supply management. Any electricity supply system is always in instantaneous Power
balance; the wires hold no storage and electricity moves at the speed of light from Alternator to Appliance
across the system. We need to recognise the need for continuous tight matching of generation power to
demand power, the associated requirement for accurate prediction. Both power and time are crucial factors.
The future system, comprising Central Generation (Big) and Distributed Resources (Little), needs to work as a
disaggregated but co-ordinated unit to make major improvements. This is a combination of the Wholesale
(Big) and Retail (Little) Markets, with System Operator functions, to reduce the output requirement from fossil
fired main plant while at the same time making sure the remaining output of such plant is generated at the
most efficient level (full load). This will ensure an effective reduction in fuel burn and emissions.
All in all what we require is not just a ‘Smart Grid’ but a ‘Smart Enterprise’.
The latest versions of my 22 articles on Future Power Systems are up in the ether at
http://eleceffic.com/Future Power Systems
FPS 1-3 look at the basics of matching and generating plant characteristics. FPS 1 now has a Gas Power
demand diagram which shows the awesome level of storage they have, as against Electricity which has none.
FPS 4 covers renewables impact and has a new diagram to show the effect of forecasting uncertainty on the
big ramps caused by wind variability (Page 7). This is crucial to demonstrate that these movements are much
more difficult (if not impossible) to handle than the regular demand ramps.
FPS 5-7 tackle future distribution (esp active), FPS8-14 the customer to utility interface while FPS15-19
examine customer data and participation issues.
The potential for storage and the ability of ICT to provide effective monitoring and trading/control of
distributed resources (DER - covers customer demand, generation and storage) and maintain network security
is covered here.
FPS 20 looks at the Smart Enterprise as regards Objective (flatten the fossils) and Forecasting impact (existing
Top-Down methods rendered useless)
In FPS 21 I go through Customer Engagement in some detail where I develop a proposal for 'empathic
constructive dialogue' and a staged approach to introducing more dynamic pricing. Go to the end first to see
the salient points.
Finally, FPS 22 asks the big question; what is the value of each Future Electricity and Future Energy strategy.

Page 2
1. ELECTRICITY SYSTEM OPERATION - FUNDAMENTALS OF MATCHING
Electricity flows from Alternator to Appliance at the speed of light and there is no storage in the wires. Thus,
each Electricity system is always in perfect balance. Sum of Generation Power = Sum of Demand Power.
On an AC system, this rule is maintained in real time by the frequency, whose deviation from nominal (50 or 60
Hz) represents the difference between 'required demand' and 'delivered demand'.
The demand varies continually by time, day, week and season.

Page 3
The frequency has to be kept within strict limits to avoid system degradation; usually +/- 1% for normal
operation. If the frequency deviates beyond 2%, automatic disconnection and measures are necessary to
arrest the slide and avoid collapse.
Therefore, within each system, Total Generation Power must be closely matched to Total Demand Power.
This means that both Generation and Demand must be predictable and an adequate level of control is
required to allow the match to be set and adjusted for all times.
When frequency changes, Synchronous Generators will instantaneously release or absorb inertial energy and
some (resistive) demand will reduce. Extra generation (and increasingly demand) is set to provide additional
response and backup for same so that any event, including the loss of the largest infeed, can be compensated
without an excessive frequency deviation.
The transport system must be secure - under both steady state conditions and for any credible fault, both
transmission and distribution must not be overloaded, have unacceptable voltage excursions or be unstable.

Page 4
We can try to show the overall deviation limits in terms of maximum excursion from 'generation requirement
= required demand'. This assumes that demand is 40% frequency sensitive. On a large power system only
resistive load will react. Motors and other inductive loads are not frequency sensitive. This shows that the
mismatch of generation delivered to that required has to be tightly controlled.

Page 5

Page 6
The level of frequency response to demand change varies, depending on the size of the interconnected
system.
So, we need to be able to predict both demand and generation, and ensure that the match is kept within
tolerance, for all timescales from immediate out to planning while ensuring the transport system is secure.
To do this, it is necessary to predict and model the demand (with its continuous variation) overall and by
location. We need to model generation (prices, dynamic constraints), also by location, to be able to set the
match and predict network loadings and voltage/stability conditions in detail.
Electricity vs Gas - Operating Logistics
These two main energy delivery systems are totally different. Electricity moves from alternator to appliance
at the speed of light with no storage whatsoever in the wires. Gas can be compressed and decompressed.
Whenever gas is pumped into a section of pipe, a doubling of the pressure would mean that the stored energy
in that section of pipe has also increased by a factor of 2. Thus the pipework (linepack) and gasometers hold
an incredible amount of storage.
The GB Gas system delivers @1100TWh/annum with variations from @5TWh/day on a Cold Winters day down
to 1TWh/day in Summer. The linepack sits at @4TWh but the 'end of day' level is kept reasonably constant
from day to day to ensure that stability and the correct pressure gradients are maintained. There are also
large storage caverns both onshore and offshore (@33TWh and @3TWh respectively).
Like Electricity, the Gas demand will vary across each day and the 'Supply' (Beach and Interconnector Imports
plus Storage withdrawal) have to be flexed to match Offtake (Demand + Interconnector Export + Storage
Injection). Varying Gas production from 'wet' (Gas+Oil) wells is tricky as it is preferable to run the associated
Oil production at a constant rate. Dry wells (Gas only) can in theory modulate their output more easily.
Balancing Trades are executed each day to ensure the Linepack stays within its 'End of Day' Target range but
the inherent storage allows for less frequent instructions.

Page 7
For Electricity the Power (rate of energy delivery) Input to Demand must be tightly matched as described
above. Many instructions are issued (10+ per half hour) to ensure stability is maintained.
Day to day Gas energy demand variation in the winter can also be quite marked as temperatures change.
Linepack variations within day of @190GWh can occur, with maximum difference between start and end day
Linepack values held below 65Gwh.

Page 8
Here is a view of the Gas energy demand variation across a number of years, broken down by source. The
large scale regulating duty has shifted from the UK Continental Shelf wells to the Norwegian Interconnector.
You can see the extent to which storage stabilises the supply as the demand varies.
ANNUAL GAS DEMAND CYCLE 2000-2010
Thick Dashed Line is the UK Demand 1mcm = @10.5GWh
And here is an attempt to compare half hourly Gas and Electricity Power demand and supply (infeeds and
generation respectively ) across a December Peak period.
The slightly erratic nature of the Gas demand curve is due to it being derived from '2 minute' records of Supply
and hourly 'Linepack change' data.

Page 9
2. DEMAND PROFILE
The demand is continually changing, thus generation has to be scheduled and dispatched to track it, plus
provide adequate response and spare, which can react in the appropriate timescales to cater for inaccuracies
in demand prediction or unexpected generation output. Here are some examples of different weekday
demand patterns in Great Britain (GB).
The metering from all generation sources +/- interconnector flows will of course summate to the demand as
the system is always in balance. The system operator will maintain continuous and integrated metering for the
main plant, transmission system and interconnection flows. Total and nodal demand histories are derived from
this and stored.

Page 10
Demand Prediction
Demand prediction is normally carried out from analysis of total historical demand data against weather for
each cardinal point periods (time of day) in the relevant groups (day of the week, season of the year). This
gives a set of weather coefficients and a base profile across each season for each Cardinal point. Using the
base patterns levels and forecast weather, future demands for each Cardinal point can be derived.
20 October 2010 ©Stephen R Browning 12

Page 11
20 October 2010 ©Stephen R Browning 14
21 January 2015 ©Stephen R Browning 15

Page 12
Industry Structure
Operation of conventional main generation is of course under the plant operator's control, with output
committed and dispatched through market and system operation mechanisms. The instructed profile is
compared with the demand prediction and plant ordering and dispatch adjusted to match across all lead
timescales. This diagram shows the business elements of the 'unbundled' industry in Great Britain.

Page 13
Transmission design/modelling and Distribution design
The Transmission System is 'Active', with Power flows changing in magnitude and direction with as demand
and generation output changes. Thus detailed fast metering of Power flow, voltage and other data is
required. Predictive modelling of flows, voltage and stability are needed to ensure stable and secure
operation.
To do this we need the predicted loading profile on the wires. This is derived by application of nodal
(substation) demand data derived from nodal demand history and ratioed to match forecast total demand.
Instructed generation output is applied at each connection node and the resulting nodal profile is applied to
the grid technical data to calculate load flows. The system is then analysed to ensure it will be secure -
loading, voltage and stability in the steady state and after fault.
To this data we need to add the Interconnector Imports and Exports at their points of connection.
Passive distribution systems are designed and customer connections analysed to ensure the system will be
secure at the peak and trough conditions in each year. Thus the passive system is always sized to meet the
maximum demand on it.

Page 14
Generation and Demand 'accounting' for matching
A large system will carry a range of generating units, from big main units (500/660MW individual), right down
to (increasing amounts of) microgeneration. To adequately and efficiently 'match' Generation to Demand the
market and operator do not need a precise view of all the very small plant, as long as, individually or in
aggregate, it does not form a large percentage of demand or always runs in a 'stable' manner.
However, for total and nodal demand prediction to be accurate, the metering must not be 'distorted' by
omission of large amounts of embedded generation meters from the generation summation.
We model the 'match point' as the output required from the all significant Generating plant, plus
Interconnector Imports less Interconnector Exports and Pumped Storage Demand, to meet the GB Customer
and Power Station demands plus Transmission losses. We model Active against Passive.
So, the 'actual' view of the Generation-demand 'matching point' looks as follows. Note that the scales are
always 'level' as the system is always in balance.

Page 15
Synchronism
Each system is of course in 'dynamic' balance. All generating units are 'locked together'; the voltage on each
phase must of course maximise then minimise at the exactly the same time at all points on the system. Thus
the 'magnets' within the rotors on all synchronous generating units are always at the same relative position.
09 December 2011 ©Stephen R Browning 21
Alternator – Stator Coils and Rotor Magnet
N
S
+VE
-VE
Rotation
Single pole Synchronous machine
Speed = 3000 revolutions per minute
= 50 revolutions (cycles) per second
Stator windings
Red, Yellow and Blue phases at
120degrees to each other.
As the North pole passes each +ve
winding the voltage in that phase is at
maximum. As North passes each –ve
winding the phase voltage is at minimum
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
Synchronism
All units at all stations must be at the same rotor position

Page 16
It is a sobering thought that, at the annual peak of 60GW, the combined set of running generating units is
pushing over 85 million brake horsepower into the wires as the demand appliances 'pull' exactly the same
amount out.
All those Generating Units and the Demand are dynamically (Electromagnetically) 'locked' together.
Thus Electricity systems are the biggest machines on the planet.

Page 17
3. FOSSIL PLANT HEAT CONSUMPTION
Most thermal fossil fired generation is designed to be most efficient at full load. Large coal and oil units are
typically 36% efficient at max ouput dropping to 32% at half load. CCGTs can be 55% efficient at maximum, but
only 40% when at half load.
The fuel burn and thus the emissions, per unit output, follow the same pattern.
In addition, each unit will consume start up heat to bring it on load, which increases with the time the
generator has been shut down. Again the fuel burn and emissions lines follow the same characteristic.

Page 18
Generating Plant Dynamics
If a Unit is Off then it cannot again synchronise until its minimum shutdown time has elapsed since the time it
was last desynchronised and the notice to synchronise time has elapsed from the time it was instructed to
come back on. Note that the NTS increases with time off load (see above). Also, each station may have a
restriction on how many units can be rolled up to synchronise at the same time. This can be due to a
combination of works power supply limitations, staffing requirements while rolling or make up water plant
capability. A generator cannot synchronise until the defined interval time has elapsed since the previous unit
was synchronised.
Once synchronised, the unit must increase output at its run up rate until it has reached the lower of its
minimum stable generation level, its minimum output profile (inflexibility) or its maximum output profile
(availability). It can then operate between this level and availability with ramping speed limited by its run up
and run down rates.
When the unit is due to come off it must deload from the lower of minimum stable generation, inflexibility and
availability to desynchronise, at its run down rate. The desynchronisation time must be at least its minimum on
time from the synchronisation time. A unit cannot be shut down and start back up on each day more than the
permitted number of shutdowns. Also, at each station, a unit cannot desynchronise until the defined interval
has elapsed from the desynchronisation of the previous unit at the same station.
Maximum ramp rates are around 10MW/minute on a large machine. Coal fired units may be able to ramp
faster, once hot, by changing the logistics of mill operation.

Page 19
If there are two or more units at a station, there may be interval restrictions on coming off and on. You can
'stagger' the order (FIFO, LILO).
16 February 2012 ©Stephen R Browning 30
0
200
400
600
800
-20 10 40 70 100 130 160 190 220 250 280 310
MWoutput
time minutes
2 Genset Dynamic Profile
Max Run Up
Rates
Max Run Down Rates
Notice to
Sync
Min On time
Max Output Level
Min Shut Down Time
Min Stable output
Notice to
Sync
Staggered Sync/Desync
Sync Interval
DeSync Interval

Page 20
So, for a 4 genset station the envelope could look like this
0
500
1000
1500
2000
2500
3000
-20 10 40 70 100 130 160 190 220 250 280 310 340 370 400
MWoutput
time minutes
4 Genset Station Output Profile
Max Output Level
All Dynamics in
Notice to
Sync
Min Stable output
Envelope with Ramp rates
& Sync/Desync Intervals
ignored
Response and Reserve
In general fossil units are only able to provide upward and downward regulation while operating between
minimum stable generation and availability, with the level of that regulation bounded by those same limits.
03 November 2011 ©Stephen R Browning 25
-100
-80
-60
-40
-20
0
20
40
60
80
100
0 50 100 150 200 250 300 350 400 450 500 550 600 650
ReserveMW
MW Output Gen
Genset Reserve Capability
UP Down
MSG

Page 21
Scheduling and Dispatch
The variations in the daily demand curve dictate that a number of generators start up for the plateau and peak
periods of the day. Some demand rises are so fast (up to 3000MW/hhr in GB) that a number of units will be
ramping simultaneously. At all times, some units are also part-loaded for response, reserve and spare duty, to
cover unexpected demand or generation changes. Units have to be ordered far enough in advance that they
will synchronise at the correct time. The Transmission flows and voltage/stability condition has to be analysed
for each timestep using the predicted generation and demand data. The plant selection (and any variable
demand) is adjusted to ensure Transmission security is maintained.
It is vital that the demand curve is accurately predicted and generation is reliably operated to avoid
unnecessary part loading, allocation of excess reserve or ordering of generators that aren't actually needed in
the event. Prediction, reliability and timing are the key to efficient operation.
The conventional power plant is designed to be controllable for instruction following. Thus, its output is
predictable for the purpose of Generation-Demand matching. Even so, allowances have to be made to cover
the risk of plant breakdown; response, reserve and spare output is carried to cover the anticipated level of
generation shortfall and failure as against the instructed output.
For efficient operation, Generation is 'stacked' into the load curve in on load merit order, each tranche of plant
running for shorter periods as merit order cost increases. Start up costs are also taken into account. A unit
with high start up cost may not be selected for a short run, if there is other plant with higher on load cost but
much lower startup cost which can cover the run period at a cheaper overall cost. Likewise, slow or inflexible
units may be rejected for short runs if they incur high 'inefficiency' penalties as a result.
When not in merit, units can either shut down, incurring start up costs and having to stay off for minimum
shutdown time, or run through part , incurring extra per unit costs due to operating at lower efficiency. To
accommodate their minimum output other, cheaper, plant also has to be deloaded and run inefficiently.
Again start up vs running costs and flexibility all have to be taken into account.
At all time Reserve has to be provided, either by part loading generating plant or by response from the retail
customer side (supplier market).
Unit Commitment, Scheduling and Dispatch is thus a complex Mixed Integer-Linear time series problem. In
addition, any uncertainty about predicted Generation Availability or Demand take will require the Matching
process to be continuously re-run and strategies adjusted.
A simplified view of Generating Plant Tranche stacking (without the part loading effects, assuming constant
and predictable availability) is shown below

Page 22
25/5/05 ©Stephen R Browning 8
Generation regime - GB Winter Weekdays
37813 38701 38761
60000 59244 58844
35000
40000
45000
50000
55000
60000
00:00
03:00
06:00
09:00
12:00
15:00
18:00
21:00
00:00
03:00
06:00
09:00
12:00
15:00
18:00
21:00
00:00
03:00
06:00
09:00
12:00
15:00
18:00
21:00
00:00
Time
DemandMW
Demand Baseload Wkdy Base Wkday n1 Wkday n2 Wkday 15 Wkday 12
Wkday plat var Wkday Pk 5 Wkday Pk 4 Wkday Pk 3.5 Wkday Pk 2.5 Wkdy Peak 1.5 Wkdy Peak 0.5
Gen Plant
Tranches

Page 23
AC Alternator output
The alternator rotor carries a number of electromagnets (poles) rotating within a stator having one output
winding per phase per pole around the circumference. Here is the picture of a single pole 50Hz machine with
the phase voltage info.
N
S
+VE
-VE
Rotation
Single Pole synchronous machine
Rotor Speed = 3000 revolutions per minute
= 50 revolutions (cycles) per second
As the North pole passes each +ve
winding the voltage in that phase is at
maximum. As North passes each –ve
winding the phase voltage is at minimum
Stator windings
Red, Yellow and Blue phases at
120degrees to each other.
In one revolution the rotor passes each of the coils as follows.
One revolution
N
S
Rotor direction
Phase Volts
Red Max
N
S
+
-
1
Blue Min
+
-
2
Yellow Max
+
-
3
Red Min
+
-
4
Blue Max
+
-
5
Yellow Min
+
-
6

Page 24
Which produces the following this waveform in each phase (two revolutions shown).
-1
-0.5
0
0.5
1
0
15
30
45
60
75
90
105
120
135
150
165
180
195
210
225
240
255
270
285
300
315
330
345
360
375
390
405
420
435
450
465
480
495
510
525
540
555
570
585
600
615
630
645
660
675
690
705
720
Red Phase Yellow Phase Blue Phase
CoilVoltage-PerUmitAmplitude
Rotor angle – Degrees from ‘top dead centre’ (Magnet Vertical - North up, Red Phase at Max)
Three Phase Generation
Stator Coil Output Voltages – 2 revolutions
Rotor Position (from previous diagram)
1 2 3 4 5 6 1 2 3 4 5 6 1
As we noted at the end of Future Power Systems 2, the voltage maxima and minima on each phase occur at
the same time everywhere on the system. All the synchronous generators are 'locked together' dynamically.

Page 25
4. RENEWABLE AND DISTRIBUTED GENERATION
Renewable Generation replaces fossil fuel burn and consequent emissions. Distributed generation is more
efficient at providing electricity near the point of consumption and multi-energy generation systems (heat,
cooling and power) can provide that energy more efficiently than conventional methods, although still using
fossil fuel.
The latest Distributed Generation at premises level comprises micro wind, photovoltaic and combined heat
and power (sometimes with cooling) installations. Separate large wind generation is accommodated at higher
distribution voltages although with careful rules for operation if the system becomes stressed.
The problem with any renewable generation is predictability and the fact that there is gross variation from day
to day. Both irradiance (for PV) and wind speed are difficult to estimate at the lead times relevant to
committing main generation.
A quick summary of Generation types, 'drivers' and predictability:

Page 26
Wind
Wind Turbine output is roughly a cube law characteristic from cut-in speed up to max, then flat at max output
up to cut out. Cut in is @4m/s, 8kts and full output is normally achieved at 13m/s, 26kts. Cut out speed is
between 25m/s, 50kts for onshore turbines and 30m/s, 60kts for offshore units.
0
20
40
60
80
100
0 4 8 12 16 20 24
%
M/s
Wind Turbine O/P vs wind speed
Type 1 Type 2
In the UK, the wind is 'synoptic' and thus variable; caused by depressions circling around. Direction, Track and
Intensity is 'controlled' by the Jet stream.
Jet Stream and Surface Pressure forecasts
The above shows the different wind patterns that can appear over a five day period as systems intensify and
then fill. They normally move from west to east although they can be 'held' in a particular position by the Jet
Stream. Wind output 'picks up' as the packed isobars cross the turbines.
Most of the time when not calm, speeds normally fall within the pickup range. In certain places, higher speeds
will occur with gales which will be into the max output plateau or, with high gusts, beyond the cut-out point.

Page 27
Sets of Turbines are configured in farms. Initially, these were in small dispersed groups with low total output
and therefore little impact on the generation requirement.
In 2009, the round 3 UK Offshore wind sites were announced.
There are 9 proposed sites with plans for a total of 6400 towers carrying 5MW heads. This totals to @32GW
which is about 40% of the current, conventional installed capacity. Allowing for cutout under high wind
conditions, the fleet will probably get up to a max output of @25GW.
Here is a map of the locations

Page 28
Analysis of actual wind speeds has been used to determine the likely output profile of the whole offshore fleet
by ratioing. Wind output over a 4 day period can look like this.
0
5000
10000
15000
20000
25000
30000
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
Demand
Time
Wind Output - 4 days. Capped at 25k
If we subtract actual wind speed from demand over a 4 day period (Thursday through Sunday in this case), the
remaining curve, being the demand on other installed plant (Nuclear, conventional steam, Hydro, etc), can
look like this.
10000
15000
20000
25000
30000
35000
40000
45000
50000
55000
60000
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
Demand
Time
GB demand and Conventional plant demand - 4 days
Demand Conv Plant Dmd
Conventional Plant Demand = GB demand less Wind output wind capped at 25k
Assume 30GW wind installed
As you can see, the timing and levels of the 'other plant' Peaks, Troughs and ramps have been considerably
altered. The maximum ramp up has changed from @14GW in 4 hours on the Day 2 (Thursday) morning to
@25GW in 5.5 hours on Day 4 (Saturday) morning. Wind penetration by Power is varying from 6% to 63%, the
latter at the demand minimum on the third day. At that time we have 25GW of Wind and less than 15GW of
Nuclear and Conventional plant; stable operation of the system will not be possible especially with extra short
timescale wind output variation.

Page 29
And this is the of course the impact assuming perfect forecasting of wind output.
Wind can be difficult to predict, being mainly caused by the passage of cyclonic weather systems. As these
farms are clustered in discrete locations, the passage of a depression will hit them in order, depending on
track. Getting the track and timing right is difficult but crucial to output prediction accuracy.
My earlier analysis of forecast versus actual data for a particular location now follows.. This is based on
definitive forecasting and more modern ensemble and other methods are improving the accuracy.Here is an
example of wind predictability for one area and the effect on the residual requirement for main generation in
Great Britain.
For this example we have installed 5000MW in the sea to the Northwest of Manchester and used that city’s
forecast and actual data over two days.
First here are the Day Ahead and 0500 forecasts.
807
1039
1960
1575
4750
4100
3500
3001
0
1000
2000
3000
4000
5000
05:00 11:00 17:00 23:00 05:00 11:00 17:00
MW
Wind Output - Predicted - Day Ahead and 0500
D/A Fcast MW 0500 Fcast MW
Wind speed offshore simulated
Assumes 5000MW installed.
At Day ahead, the wind speed prediction was pessimistic – 4-8 m/s which is wind turbine cut-in. Wind farm
operators would have given low estimates to suppliers who would in turn have purchased extra conventional
plant.
The 0500 forecasts show very different profiles; it looks like a weather system has now been identified as
‘incoming’.

Page 30
Now we move on to 0900 and add the actual wind speed based outputs
807
1039
1960
1575
4100
3500
3001
4750
2786
1039
0
1000
2000
3000
4000
5000
05:00 11:00 17:00 23:00 05:00 11:00 17:00
MW
Wind Output - Predicted and Actual at 0900
D/A Fcast MW 0500 Fcast MW Last Fcast MW Act MW
Oh dear, it looks like the wind didnt arrive (the low dark blue value is the actual)
And a new forecast appears (Last fcast red line); and prediction for 1200 is revised downwards.
Now let's see what happened at 1200 and thereafter!!
807
1039
1960
1575
4100
3500
3001
4750
2786
1274
1039
4250
4650
3411
0
1000
2000
3000
4000
5000
05:00 11:00 17:00 23:00 05:00 11:00 17:00
MW
Wind Output - Predicted and Actual
D/A Fcast MW 0500 Fcast MW Last Fcast MW Act MW
The wind arrived with a vengeance!! In fact, for the 1700 Daily demand peak the forecast still stayed at @50%
assuming a drop off whereas the speed actually increased; the system came through later than expected!!

Page 31
In tabular form here are the Forecasts and Actuals for each demand Peak and Trough time
For the first day, the day ahead forecast predicted low output levels of 20% in the moring and 40% for the
evening Peak. The 0500 forecast then predicted 96% at the 0900 Peak, reducing to 80% at the 1200 Peak then
72% at 1500 and 65% at the 1700 Daily peak. This is evidence that a depression was now expected to sweep
across the area and move on.
However, at 0900 the actual output was in fact 20%!! The final forecast for 1200 was then revised down to
@55%.
But then the depression hit the fans – Actual output at 1200 was 86%. The final estimate for 1500 was set at
at 55%.
However, by 1500 the wind reached max strength giving 96% output. The final estimate for the 1700 Daily
Peak was set at 55%.
And at the 1700 Daily Peak, the wind was still at 72%
This magnitude of difference between the forecasts and the error to the actual makes the job of committing,
scheduling and dispatching the other generation to match the remaining demand both difficult and inefficient.
It is also difficult to ensure Transmission operates in a stable state.
Time Day Ahead 0500 Last Actual
Peak 0900 16.15 95.00 95.00 20.79
Peak 1200 20.79 82.00 55.71 85.00
Trough 1500 39.20 70.00 55.71 93.00
Peak 1700 32.26 63.00 26.14 75.00
Forecasts
Wind Percentage Outputs - Day 1

Page 32
60098
52132
59244
52035
57097
58824
47774
48935
51021
57395
36000
40000
44000
48000
52000
56000
60000
05:00 11:00 17:00 23:00 05:00 11:00 17:00
MW
GB Dmd D/A Fcast MW 0500 Fcast MW Last Fcast MW Act MW
GB Demand then less Predicted and Actual Wind output
This is just the uncertainty due to one 5MW site; in all we have 9 proposed sites with a total of @32GW of
Wind Generation. However I was using 'definitive' which give the large errors. More modern forecasting
methods, including Ensemble forecasting and better wind speed to turbine output models, plus the effects of
geographical 'distribution' will reduce the error range. However, accuracy of only 50% at 4 hours ahead is
currently assumed but it is expected that better modelling and location diversity should reduce the error to
30%.
As we showed earlier, new fast ramps will appear in the residual conventional plant demand. Although we
currently cope with fast pickups and dropoffs in normal demand, the timing and magnitude of those events is
always predictable. For example, as 'Day follows Night' the GB morning weekday pickup always starts @0530
and is mainly complete by finishes by 0815. Thus conventional plant commitment, scheduling and dispatch is
always taking place over the same time period and at the same rate. Across the demand rise, 10 large units (or
equivalent capacity) will always be ramping up at their maximum rate to maintain the Generation to Demand
match.

Page 33
The new ramps introduced by wind variation will also have a large degree of uncertainty as regards magnitude
and timing. If we superimpose a wind forecasting error of +/-15% (lower end of the 4 hour ahead error band
as stated above) on the big (32GW) fleet wind output data and cap max output to 25000MW this is the result.
0
5000
10000
15000
20000
25000
30000
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
Demand
Time
Wind Forecasting Error - 4 days. Wind Capped at 25k
Wind Forecast Upper Wind Forecast Lower
And here is the resulting uncertainty in the residual conventional plant demand. The normal Demand
forecasting error range is also shown, being 1% at 4 hours ahead.
10000
15000
20000
25000
30000
35000
40000
45000
50000
55000
60000
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
00:30…
06:30…
12:30…
18:30…
Demand
Time
GB demand and Conventional plant demand bands
4 hour ahead forecast errors Demand 1% Wind output 30%
Demand Upper Demand Lower Conv Plant Upper Conv Plant Lower
Conventional Plant Demand = GB demand less Wind output. Wind Capped at 25k
Assume 30GW wind installed
It will be difficult to commit, schedule and dispatch conventional plant effectively or efficiently against such a
'moving uncertain target'.

Page 34
It is now recognised that high speed bulk storage is required to enable large wind penetration. The question is,
however, whether this will be able to sufficiently 'smooth' the variability and buffer the uncertainty. There is
also the issue of steady state stability of the Transmission system if storage is not co-located with the
generation, as has been mooted by the proponents of Customer Demand reaction and the Electric Vehicle
charge/discharge strategy. Big swings of Wind Generation in offshore locations and onshore compensatory
storage could put Transmission into 'Unsteady State' instability.
Note that the above example shows the wind upper error range with a cutout cap. With diverse location of
the farms the fleet is unlikely to get up to full output (32GW). When some farms are at full load the wind
speed at other farms will be above cutout (25-30m/s). Here is an example of 'cutout' with the current GB
fleet. The line shows actual output and the bars are predicted output.
Wind Cutout – Dec 13 2011

Page 35
Photovoltaics
Here is an example of 3 days of PV output superimposed on an 'average' domestic load curve.

Page 36
Micro CHP and CCHP
In general, CHP and CCHP systems are driven by the heating or cooling requirements of the premises, or
the process for which the thermal energy is required. Temperature fluctuates less frequently than other
weather variables and plant output is more in tune with demand, which increases with low and high
temperatures.
Also, assuming the CHP runs continuously when the weather is cold, the domestic premises profile might
appear as follows:

Page 37
Note that both the PV and CHP systems may tend to export at times of low premises demand.
Within the current operational framework, it is assumed that more spare and reserve output on conventional
generation will need to be carried to meet the increased level of uncertainty introduced by renewable and
other distributed generation.
Operational Overview - Observation, Prediction and Control
• Keep Generation and Demand separate – metering and prediction.
• Distributed Generation prediction – process control driven (industrial CHP)
• Distributed Generation prediction – weather variable driven.
maintain capacity register by plant type by area (PV, wind, marine, CHP).
Monitor area weather variables and prediction – widespread continuous.
• Distributed Generation prediction - Controllable sites (CHP).
Metering of control elements and trading interface.
• Site control and observation elements
• Electrical Storage.
• Site and micro-grid – demand/gen/storage - Import/Export control.
• Cheap communications and ‘distributed aggregated’ trading – energy and reserve.
Operational Issues
• Operation is all about ‘Timing’ – deliver it ‘Now’
(not a second more, not a second less).
• Observation, Predictability, and Reliability is the key to efficiency minimising market imbalance and
unnecessary reserve/spare plant.
• ROCs do not encourage improved forecasting techniques.
• Reducing renewable generation output to improve predictability reduces already low Load Factor.
• Improve Demand Efficiency – reduce variation
Change the tarriff attitude from ‘one price at any time’.
• Storage – the great issue – Low loss
Buffer variable Export and Import. Can we make a breakthrough???
• Cheap advanced ICT systems to monitor/control distributed systems and communicate dynamic tariffs or
trading data automatically.

Page 38
5. DISTRIBUTION CONFIGURATION AND SIZING
Conventional distribution system management is based on supplying demand to customers on a discrete
network connected to a transmission grid supply point. Some conventional, observable system instruction
following generation is also accommodated at the higher distribution voltages, able to regulate active and
reactive power export (and reactive import) to meet system matching and transmission and distribution
security requirements. Such generation is carefully controlled to avoid Power Quality issues at adjacent
customer premises.
Design of the network is carried out by simple analysis of maximum and minimum demand - Max Gen and Min
Gen - Min demand conditions to determine system capacity and quality. Because the generation is
controllable, output can be intertripped or limited if necessary at low demand periods to avoid the need for
major reinforcements to accommodate excess export at such times. This simple analysis will cover all expected
loading conditions with supply transformer tap changing and generator control maintaining a valid voltage
profile.
The loading pattern is predominantly a power flow from the grid supply point, decreasing by distance from
that supply point with the voltage profile behaving in a similar manner. On feeders with generation, control is
exercised to ensure security and quality is maintained. This design method means connections are geared to
maximum demand conditions without any provision at the lower (domestic) levels for customer action to
reduce the Peak leadings which only occur a few times a year. As a result, the systems are heavily sized, which
does increase the customer connection charge; a large proportion of the final delivered cost of electricity.

Page 39
GB domestic feeders at 240V are 60A/phase 14kW and 100A/phase 25kW.
Distribution Charging
In the UK, Transmission, Distribution and Balancing services Use of System charges are a fixed annual levy on
the Suppliers and Generator Owners. For demand, it is based on supplier (wholesale) take at the three
chargeable (Triad) system peaks; each Peak must be at least 10 days away from the others. The retail
customer is billed via the supplier; they don't see the UoS element explicitly unless the tariff has a Standing
Charge (p/day) separate from the Energy rate (p/kWh). Otherwise the UoS is recovered as a p/kWh figure
rolled into the Energy charge. Thus the wires charges are correctly defined as an infrastructure (capacity
overhead) charge, not as an energy based component.

Page 40
6. MORE DISTRIBUTED GENERATION
What we would expect to see under the current development framework is an increased penetration of
smaller 'fuel or customer requirement' driven, unobservable, distributed generation of different types at
different levels.
At domestic level we have Micro CHP, PV or Wind technology. PV is expensive, due to local turbulence Wind
gives low yield at roof height and CHP systems (boiler + heat recovery turbines) are still being
commercialised. However, the penetration of Micro Generation is forecast to increase.
At commercial/industrial level we have an effective market for CHP and CCHP, albeit mainly based on fossil
fuels. Use of PV and some mini wind is also being applied.
Larger stand alone Wind Generation parks are separately connected to Distribution feeders.
Uncertainty of Wind output has already been shown previously. As a further example, application of say 3m
domestic CHP units in Great Britain, all working to heat requirement would have the following impact on a
winter's day.

Page 41
With a cold, uniform external temperature the CHP will run continuously day and night. This is not the most
efficient way to reduce fossil generation output.
Commercial CHP only runs in the daytime period and should produce a better impact profile.

Page 42
Distributed domestic Photovoltaic systems will produce maximum output during summer daylight hours while
domestic premises demand is not at its maximum. This will cause the premises to export.
There have already been cases of resulting local high voltage causing the inverter to trip. As regards the
National position, domestic PV output can contribute to reducing higher load levels but leaves an evening
Peak. You need a lot of capacity to make a significant impact; 3 million 1.1 kWP panels against the Great
Britain demand in this example.

Page 43
On commercial premises, maximum PV output occurs during the building maximum demand period and is
synergistic with any electrical cooling load.
The overall impact on the main system of large DG penetration would mean that generation output would
have to be made observable, albeit aggregated over suitable groups; say by supply point and then by defined
transmission area and Nationally. Local and aggregate prediction mechanisms will be required.
At the same time the customer 'attitude' to demand is changing. Energy use reduction and the development
of energy efficient premises and processes is being progressed. Also non-time critical Electricity demand is
being identified and appliance operation coordinated for use as efficient short term reserve.
To get true electricity efficiency, the need to recognise the inefficiency introduced by large demand variations
over time and the need for accurate prediction and operation is crucial.

Page 44
7. ACTIVE DISTRIBUTION MANAGEMENT
Although the overall distribution energy supplied at a grid supply point will decrease with distributed
generation, the supply point and the individual feeders will experience variable power flow patterns
depending on the amount, type and distribution and location of generation connected.
Renewable generation output will of course vary depending on weather (irradiance, wind speed,) while CHP
and CCHP will run at a constant output depending on the heat requirement.
The result will be variations in flow patterns by weather, time of day, day of the week and time of the year, all
of which will be hard to predict. Such variations will need to be managed to avoid voltage and stability
excursions on the distribution system.
Customers are also actively trying to reduce their energy demand. In addition, Non-time-critical demand is
being identified and proposed for use as a short term reserve.
In modern low energy and passive housing, the residual electrical demand will be cooking, lighting and
entertainment plus the small ventilation system and heating load; demand will probably peak during darkness.
At this level, premises CHP is inappropriate (low heating load), although communal heating/cooling CHP may
be appropriate. Distributed generation for such premises will probably comprise PV or Microwind.
As regards Power Quality, more modern devices such as compact fluorescent lights and switched mode power
supplies in electronic and entertainment equipment are introducing increased levels of harmonic 'pollution' at
distribution level. The demand in low energy houses will comprise a higher percentage of such devices. DC-AC
micro-generation inverters also introduce harmonic distortion into the supply.
Customers need to be made more aware of the impact of their demand and embedded generation at different
times. Instantaneous delivery of electrical power matched to demand, not just energy over time, has to be
securely managed to avoid interruption, overloading of circuits, voltage excursions and inefficient and
unnecessary running of a main fossil fired plant. The resulting need for accurate and separate forecasting of
generation and demand at this level needs to be made clear.

Page 45
So, to maintain a secure active distribution network with its changing flow patterns, it is necessary to monitor
embedded generation, demand and remote line flows and voltage levels to a greater extent than with a pure
passive system. Levels of control also need to be exercised to maintain delivery and quality within prescribed
limits. The correct level of control can also reduce peak flows and thus allow more efficient Network design
without unnecessary excess capacity. This in turn leads to a cheaper but weaker and thus more volatile
network, where both generation and demand need careful control and active power quality conditioning may
need to be applied. The use of storage to buffer fluctuations may also be beneficial as an alternative to more
capacity.
This leads to the conclusion that all 'Distributed Electricity Resources' (DER) on an active system, generation,
demand and storage must be monitored and the appropriate level of control by 'trading' applied to ensure
secure operation. The operator of the passive distribution network has to become more active - a distribution
system operator.

Page 46
8. THE ‘ACTIVE’ CUSTOMER
Distributed resources need careful handling for Distributed Network Security and to ensure that fossil fuel
burn is reduced most efficiently. Clever management of 'Distributed Electricity Resources' (DER) is the
key. The new Active Network now carries sometimes unpredictable generation and some controllable
demand. There is also the possibility that storage could be applied to facilitate flow management on the
distribution system and avoid fluctuations and also reduce the system import at times of high demand when
the most inefficient fossil fired plant has to start up and run.
We need to persuade customers that the 'Fit and Forget' approach to distributed generation isnt going to
achieve the best results. At the same time we must remember that the customer's main activity is getting on
with life (domestic), carrying out business (commercial) and manufacturing (industrial). They do not want to
dedicate time or expensive resources to good power profile management; the process has to be automatic.
Let us look at the Customer Demand and Distributed Generation profiles in more detail.
First, here is 7 days of 2 hour average demand for a busy house.
0
0.5
1
1.5
2
2.5
3
01/12/201100:00
01/12/201112:00
02/12/201100:00
02/12/201112:00
03/12/201100:00
03/12/201112:00
04/12/201100:00
04/12/201112:00
05/12/201100:00
05/12/201112:00
06/12/201100:00
06/12/201112:00
07/12/201100:00
07/12/201112:00
08/12/201100:00
AveragekWper2hrinterval
House Demand - Avg kW per 2 hr block

Page 47
The spot demand is even more erratic.
The domestic customer has a basic refrigeration demand, a smooth lighting and entertainment load which
peaks morning and evening then a large but highly erratic heating appliance demand (e.g cooking, hair dryers)
which puts large spikes onto the profile. A large laundry equipment heating load will appear when the
machines are operated. Note that domestic distribution connections are rated at least 12kW. Although this
historically would be to accommodate some direct heating load, coincident heavy cooking demand with other
demands peaking still needs to be catered for. In addition, Eco house designs can include instantaneous
electric water heating. This will cause new demand spikes at time of general peak demand as against tanked
hot water storage systems using gas or off peak electricity as the energy source.
If the domestic customer adds some renewable generation, we would expect to see an 'erratic' generation
pattern overlay for Wind (turbulence effect at low levels) and a more consistent generation pattern for PV,
depending on cloud movements across the sun. This would probably lead to overall daytime export and
morning/evening import. CHP systems would generate in blocks dependent on the outside temperature;
however such technology is not appropriate for high efficiency houses with a low thermal and cooling
demands are supplied by heat recovery, heat pumps and solar thermal panels, plus heat stores. Overall there
is a considerable level of 'unpredictability' at individual domestic premises level, both generation and
demand, which limits the potential benefit of control.
0
500
1000
1500
2000
2500
3000
3500
00:00 04:00 08:00 12:00 16:00 20:00 00:00
Time
Powerfromgrid(W)

Page 48
Moving up to commercial level, assuming some heating load will be met by larger scale CCHP (20kWe)and with
a day-night temperature variation on the building, we could get the following profile shape in Winter.
The demand is less erratic for a large commercial building but shows a large day-night variation. The CHP
would however cut in before and cut out after main occupancy times. On a premises basis predictability is
better than domestic. Generation varies with temperature while demand shows a higher ‘basic’ level plus
some light and temperature based variations. Again generation and demand need to be monitored separately
to ensure records of each are accurate and some level of control could be applied.

Page 49
For a commercial building with a large PV array we might get this profile in summer
The residual site import is reduced in the morning but comes back up in the afternoon before work finishes.
At industrial level, large CHP is geared to providing heat and electricity for major processes. The generation
will normally operate when the process demand is applied. The sizing of such CHP will normally be limited so
as not to exceed the heat or electrical demands to avoid unprofitable export under simple tariffs or production
of unnecessary heat. The operation of the plant and the demand should be predictable against manufacturing
process operation timetables.
The more predictable and controllable Generation and Demand is, the more scope there is for control to assist
with system management by operating outside normal premises requirement. At individual domestic level
where there seems limited scope for control, some 'non time critical' demand (e.g. Laundry) can be usefully set
to operate at appropriate times (low National Demand). Commercial and Industrial locations may be more
suited to premises level control.

Page 50
9. CONFIGURE FOR DER MANAGEMENT
The main issue with DER management will be monitoring, trading and control at all levels. Let us look at the
overall objective again:
From the point of view of the market and the operator, there is a need to monitor by location and time what
the demand is expected to be and what generation outputs are programmed, together with data on the ability
to instruct changes to generation and demand power profile, with energy and notice restrictions as
appropriate, plus reserve capability so that timely instructions can be made to ensure demand and generation
match with adequate reserve and spare to cover the error margins.
All this needs to be managed within a framework of continually changing demand as in these examples of
different Great Britain weekday profiles.
The objective is to both reduce and smooth the power output of fossil fired generation while making the
residual requirement for such plant predictable. This will not only reduce the energy requirement, but also,
when such a plant is required, ensure it runs at peak efficiency to avoid unnecessary fuel burn and emissions.
As we get more generation at distribution level and variable DER that can participate, a system matching a
two-way communication system is required to monitor and also trade where feasible.
At DER level, RES generation normally operates at full achievable (albeit variable) output, except where
distribution or transmission security and quality limitations apply. To do otherwise for system matching
purposes is inappropriate as we are simply reducing 'free' output, which has zero pollution/emission effects.
It is appropriate to vary CCHP unit output for system matching, but the degree of action may be limited by the
associated heat or cooling requirement.
Electrical storage can be employed to smooth out excursions in the import or export profile to assist system
matching and where located appropriately, to avoid overloading or assist with maintaining voltage

Page 51
levels. However, this adds additional cost and some energy loss. For CCHP, heat stores can also be used to
permit variation of plant electrical output and can be very efficient.
Domestic premises loads and RES generation, with inherently fluctuating profiles may not be suitable for major
participation in power profile management, except for large time variable demand such as laundry.
Businesses and community CCHP systems are suitable for electricity or heat storage, to benefit the customer
and the system. Distributed Generation must disconnect from the distribution system if supply is lost.
Therefore, electricity storage at premises level can also be configured to provide UPS support and allow the
premises generation to keep running.
So, with large DG penetration, we have the need to monitor and be able to exercise control, where available,
over a large range of premises and devices below each supply point. We have premises with demand,
generation and/or storage, individual generation sites (e.g. wind farms) and possibly system connected storage
and power conditioning. This combination of premises, individual generating plants and devices, forms a
microgrid.

Page 52
A lot of individual data is required for distribution system (microgrid) security management, and the
aggregated information by supply point is then required by the market and the system operator for demand-
generation matching and to maintain transmission integrity.
From the customer's perspective, there needs to be a considerable change in their relationship with the
electricity supply business to achieve a tariff benefit from DER control.

Page 53
10. THE CUSTOMER AND THE INDUSTRY
The current relationship between the customer and the electricity supply business looks as follows. This
diagram is based on the unbundled electricity supply structure in Great Britain (GB). With the exception of
balance trading and settlement, all the business elements will be present in any electricity supply structure.
This is the case even in countries with full or partial vertical integration.
In Great Britain, each supplier party is responsible for trading by the half hour to ensure that a viable profile of
generation is purchased to meet forecast demand. The resultant power profiles (by generating unit and
supplier-demand grouping) are submitted to the system operator so that the overall match can be checked
and adjusted and transmission security maintained. At 1.5 hours ahead, the operator takes over, making
specific instruction to individual generation units.
Each customer deals with their supplier, who has to charge for both energy and use of system. Normally, this is
on a per unit consumed basis by period billing, even though the system charges relate to capacity and
maximum demand. Distribution security is maintained on the 'passive' model; the customer only contacts the
distributor in cases of supply failure.
Demand falls into one of three types:-
Time Critical
Non Time Critical
Unnecessary!!!
The latter of these three is obviously being tackled vigorously as public awareness of energy costs rises. Use of
power efficient light bulbs and recognition of the fact that empty rooms and inanimate objects are not
frightened of the dark is being recognised; a change from the acceptance of 'passive energy waste' we have
grown up with. However, more automatic systems may be necessary as 'manual' operation can be tedious and
the requirement tends to be forgotten over time.
GB ESI
Generation
Supply
Transmission
Distribution
Customers
System
OperationSettlementTrading

Page 54
Tackling the non-time critical element is important to improve the operating profile of residual fossill fired
plant. However, this has to be done in a predictable manner.
Under a new model, some customers are active participants in a short-term market system.
The classic commercial interface between the customer and the system has always been limited by the
capability of the metering and logistics of obtaining meter readings. Historically, a simple electro-mechanical
integrating energy meter was the only practical option. This was read at set intervals and the energy consumed
charged at a pre-set tariff. A separate standing charge was levied to cover connection and use of system
charges. Where electrical storage heating was appropriate, a second register and a simple clock switch was
added to allow this load to be energised overnight at a lower tariff rate. Larger premises could justify some
more sophisticated metering with such facilities as maximum power demand tariffs and alarms.
Modern data acquisition and storage technology, together with cheap communications, can make the
customer to utility interface more dynamic. This has the potential to allow a wider range of premises to have
demand and energy use monitored more frequently and to enable DER to have a more active role in
generation to demand matching. However, suitable commercial mechanisms need to be developed to enable
this effectively.
A variable tariff model that reacts to real time and short term predicted system conditions is one possibility,
with price signals generated from the operator and the market respectively. However, this subjects the
customer to uncertainty as regards future energy costs and makes budgeting difficult. Premises with
generation will have justified the installation against a forward analysis of energy rates. For a large installation,
the owner (industrial or commercial) will have secured a power purchase agreement to fix the value of the
energy in their project plan.
Setting the price signals correctly is a tricky business. The objective here is to correct generation-demand
mismatch and remove expensive and inefficient operation by main plant; smoothing and peak reduction.
Marginal pricing mechanisms can show large swings and the application of raw data could give excessive
inappropriate changes to power profile. Average prices will give the wrong message and may cause adverse
Premises, Micro-Grids and System
Demand
Demand
M M
MM
M
Demand
EMS
Supplier
Control
Distributor
Control
System
Operator
Distribution
Network
Weather

Page 55
behavior to that required. The prices need to be set so that the customers deliver the level of power change
required. Time staggering the application of price changes by customer groups (generic) can also give more
precise results. Geographic control is also of course required to maintain transmission and distribution security
under this model. The issue of differential treatment of customers, especially as regards charging due to
transmission/distribution congestion has to be carefully managed.
The second method is to enable trades in 'variations' from the expected power profile using incremental and
decremental offers; this is similar to the way in which the Great Britain operator matching mechanism
works. It should be a more accurate way of adjusting the generation-demand match. However, variation
trading requires a pre-declaration of expected power profile with prices for increasing or reducing import-
export. If changes to profile are instructed, the final premises metering needs to be compared with the
declared profile. This facilitates calculation of energy charging/payment for the instructed difference and any
penalties for non-delivery. Again, to be effective this process needs to be carried out in market and operator
timescales; with small premises it is difficult to predict the power profile.
It is possible to consider other variants, such as capped/collared tariff pricing to reduce the level of price
variations the customer sees. Also, as was made clear earlier, the process needs to be automatic; the
customer does not want to be actively involved in power management on a continuous basis, unless there is a
potential financial consequence, (say a short-term high price,) which could be avoided by simple manual
action.
System demand and Prices
20000
25000
30000
35000
40000
45000
50000
55000
60000
00:30
01:30
02:30
03:30
04:30
05:30
06:30
07:30
08:30
09:30
10:30
11:30
12:30
13:30
14:30
15:30
16:30
17:30
18:30
19:30
20:30
21:30
22:30
23:30
Time
Demand
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
Price£/Mwh
Demand Operator Sell Operator Buy Market Index
Forward Market and Short-term Operator

Page 56
Customer Use of System charging (Wires and services)
Under FPS 5 we outlined the existing logistics of the GB UoS charges for demand, being an annual levy on
Suppliers at wholesale level based on their Peak Power take.
However, at retail level it is difficult for the UoS annual charge to be correctly apportioned on the retail side;
the problems are metering to determine customer Peak Power usage and the capability of the Suppliers' back
office systems. In the future, some customers may have more large demands (such as Electric Vehicles) and
fair apportionment of the charges will be more important. If retail customers were to be correctly charged
UoS based on their maximum Power demand that will also influence them to control their appliances more
efficiently and thus smooth the Distribution and Transmission system loadings.
Customer Trading and Market Prices
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
13:30
14:30
15:30
16:30
17:30
18:30
19:30
20:30
21:30
22:30
23:30
Time
Demandreduction-CustomerOffer&
resultingSupplierInstructionkW
0
20
40
60
80
100
120
Price£/Mwh
Appliance Running Customer Offer Supplier Instruct Market DER Price

Page 57
11. NEW DATA FROM THE CUSTOMER TO THE INDUSTRY
Proper data communication between the active customer and the industry is vital for efficient operation of the
system, to reduce and smooth out the operation of the remaining fossil-fired plant while maintaining adequate
security of transmission and distribution systems.
The industry requires data for the following processes.
Market and System Operator - data to ensure accurate matching of Generation with Demand with adequate
reserve capability.
System Operator - data to ensure the Transmission system is secure and stable (steady state and after credible
fault) and that delivered Power quality is adequate.
Distribution operator - data to ensure maintenance of end user power quality and security of supply
This all requires accurate forward predictions and metered actuals for Generation and Demand Power, by time
and by location.
At the lower levels, Distribution group loadings with feeder and voltage data is required, together with
predictions of projected import/export and possible changes to same by participation.
For matching and transmission security, location aggregated
data is appropriate; again both the intended trajectory and capability to alter same are required.
For extensive distributed resources, effective aggregation is of paramount importance.
The distributor needs an accurate view of his system conditions but not the full detail of each individual
premises contribution. Any control action will probably be automatic at the lower levels, to alter active
resources import/exports (demand, generation, storage) and any system compensation equipment fitted.
The market and operator require multi-megawatt aggregated data for demand and generation and variable
resources capability. The market requires this information aggregated by supplier for forward bi-lateral
trading. The operator requires totals by grid supply point and overall.
Both operator and market need predictions of timescale and persistance information on variable resource
capability - the lead time to activate a change and the duration that can be sustained. Various pilot initiatives
are already being carried out for done on provision of short notice short term Demand management and
backup Generation use to provide ancillary (reserve) services to the operator.

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The aggregation of Distributed Energy Resources (DER) forms a Virtual Power Pool (VPP). This can comprise all
active elements (Generation, Demand Management, Storage and Reactive control).
A VPP can offer network services to maintain stability, security and power quality at local level.
VPP aggregation forms multi-MW blocks.
Supplier aggregated blocks can be used for short term energy trading in the market to meet the half hourly
energy requirements. (Commercial VPP)
Separate location aggregation is used to provide services to the system operator. (Technical VPP).
Blocks of dispatchable power are used to maintain system demand-generation matching
Blocks for ancillary service provision (power/reaction time/duration capability) can be used to provide
response to cover unexpected changes in the generation-demand match at near real-time.
There are currently a number of experiments being carried out with VPPs and as noted above there are
separate initiatives and mechanisms for provision of ancilliary services by DER management. It is imperative
that the overall interface framework and the data content requirements are clearly defined for each
purpose. There are different requirements to support local security, provide ancillary services, dispatchable
power and marketable energy. It is important that a single set of data from the premises level can be
configured to meet each requirement by clever filtering and aggregation.

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12. NEW DATA FROM THE INDUSTRY TO THE CUSTOMER
Now, we need to look at the data flow in the opposite direction.
This will mainly comprise instructions and signals to change the intended Import-Export profile of DER
premises in response to the data offered to do same.
The important thing to remember is that because the system is always in balance, everything affects
everything else!
The reverse route comprises a series of disseminators in parallel with each aggregator unit. When the
distributor, the system operator, or a market supplier accepts an 'offer' from a block of DER resource, the
block instruction has to be disseminated back to the original premises and then to the individual equipment
that will make the necessary response.
In addition, the instruction needs to be accommodated by the other parties. For example, if the system
operator instructs a DER block power change by location, the resulting action will impact each supplier party
who has a contract with one or more of the component premises. In Great Britain, the resultant energy
change needs to be aggregated by the supplier by half an hour to avoid distorting the supplier's contracted
energy within the settlement process. There are already mechanisms in place to do this for dispatch and
ancillary service provision on large units. In addition, the power change may only be achievable if the
distribution operator takes (albeit automatic) action to maintain network stability.
It is important to note the change in role for the distribution operator who now starts to be a (partly
automatic) distribution system operator. The concept of control of active DER resources is being tried out in
various locations, to permit larger amounts of distributed generation to be connected as long as local security
can be maintained by intertripping or other active output management schemes if fault conditions occur. In
Great Britain, these initiatives are being configured within specific areas know as local dispatch zones (LDZs.)

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The combined control of variable demand, storage and generation and aggregation/dissemination of
instructions is being tried out within the virtual power pool concept as part of smart networks research.
For an instructed change, which will comprise power and duration, this is all fairly simple to
manage. However, the easiest way to cause DERs to respond is by simple tariff price switching as with the
ENEL (Italian) Telegstore system and other simple distributed demand switching methods. The impact of a
price change on a group power and voltage profile and supplier energy can be more difficult to guage.

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13. INTELLIGENT BUILDINGS AND PROCESSES
To look at a communication strategy from the bottom up, we need to start at premises level.
There is a considerable move to increase the level of communications within domestic premises for various
uses.
At domestic level entertainment, computing and security are driving initiatives for both wired and wireless
connections internally. Communication based on digital internet protocol (IP) is increasingly being adopted,
apart from simpler analogue signals and commands. IP packet addressing obviously allows more flexibility in
handling signals and data between different devices on a single network. There are a number of initiatives
supporting home automation and the US HAN project (Home Area Network) is concerned with configuration
and coordination of energy management systems. There are a large number of companies offering different
solutions to a number of areas of home automation.
This gives rise to a number of different communication systems and it is imperative that standards for data
traffic are developed. This will allow a single communication backbone and facilitate interoperability of
communication and control units with peripheral sensors and controllers from various manufacturers. A single
'network' needs to be configured from the wired and wireless elements.
The data content standards for energy management need to be defined carefully. Segregated information on
power flow for different demand, generation and storage appliances and control signals to same need to be
managed to ensure simple analysis of current/predicted states and the ability to vary.
This all leads us to facilitation of intelligent control at premises level, within the overall framework of
electricity supply and able to react to current and predicted generation-demand conditions. This can only
really be handled by automatic systems and communication through to industry.

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High demand (industrial) users can already enter into various schemes with their suppliers to reduce their
tariff rates in exchange for participation in demand reduction, but these have tended to be simplistic in the
past.
At domestic level, the capability to vary premises import-export power profile needs to be analysed by device
type and ability to vary output or input to determine the capability for control. What we are looking at here is
the ability to 'time-shift' demand and possibly generation.
Lighting is time-critical and cannot really have its operating time period altered at domestic level. Likewise,
instantaneous water heating, and in the main, cooking and entertainment are also fixed. It is interesting to
speculate whether on-demand entertainment might alter time usage patterns, but that is unlikely. On
weekdays, only the evening period is normally available to people for relaxation.
Fridges and freezers can have their duty cycles delayed to give some short duration demand reduction shift.
However, it has to be remembered that that reconnection will cause a larger overall demand increase as more
units simultaneously operate rather than the normal time diversity that would be expected. Control of
refrigeration load is only really practical for short term ancillary services provision. The achievable reduction
will of course depend on the appliance demand cycle which is in turn related to the temperature at its
location.
Laundry is a non-time critical load and has been an ideal target for domestic demand shifting initiatives (as in
Italy.) The start time can be delayed by time or price signal, but once started, it is not efficient to interrupt
operation of the appliances.
In hot climates air conditioning and air cooling are the most important loads to consider for time shifting. The
peak demand will occur just after sundown, (combined lighting/cooling,) although some tests with price-
varying thermostats have set the high price for a four hour afternoon block. The result of this is a large
reduction at the start of the time block, then a gradual decay in the demand reduction over time. At the end of
the period, there will be an increase above expected demand as delayed cooling comes back on. This results in
a less than optimal reduction at the peak time with a sharper residual peak.
Here is a possible example of the effect of fixed period priced reduction in Great Britain. This is based on the
average domestic load shape and includes the increased demand effect at the end of the period.

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It requires a large number of households of this type to have a large cumulative impact on Great Britain's
demand (see below.)
The main contribution the domestic sector can make is to shift the use of high energy non-time critical devices,
primarily laundry to the off peak periods, which is already forming the focus of early smart metering
applications. The use of dynamic pricing by sector, supplier group or geographical area allows more precise
control of the demand to be time shifted (more below) as against simple timed techniques.

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Small-scale renewable generation needs to be allowed to operate at maximum level; to curtail output is a
waste of free energy and an extra control complication. However, as we saw earlier, high levels of generation
in the low demand daytime period (especially PV) can cause voltage rise and the generation will trip as
required by the distribution operator. Some intelligent compensation may be needed, either in terms of
optional demand or intelligent voltage control. Storage at individual premises level may be appropriate, but
again adds cost and control complications. Microgrid level equipment may be more appropriate.
Commercial premises have a steady daytime demand, mainly lighting and office equipment. The size and scale
of larger commercial premises with renewable generation may make storage and intelligent control effective
at this level. Intelligent control of lighting at the ends of the working day will also help alleviate local and
national demand peaks at these times, caused by the cumulative effect of commercial and domestic demand.
Let us say that we have a large commercial premises with CHP. The following graph shows the premises
Import-Export profile, CHP output, and the modified remises I-O profile without and with smoothing (storage).
The storage removes export and the peak spikes of the remaining import, which thus alleviates strain on the
local distribution system which will allow it to accommodate more customers.

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However, against the Great Britain demand profile, the simple smoothing at local level does not improve the
load shape. In fact, it actually increases the level of the demand rise for the peak itself, as against the
unsmoothed condition.
This all goes to illustrate that dynamic control is necessary to improve the overall demand profile as each
sector has a different influence on the load shape. As such, efficient external communication is important.
The industrial sector can control the production loading to some degree, depending on the nature of the
manufacturing process. Some trials are already in place as regards short term interruption of heavy electric
(induction) heating loads to provide operator ancillary services. Large scale changes to the timing of
production runs will need carefully managed communication to co-ordinate.
The most critical area is handling the information on premises consumption and tariff rates and making the
owner aware of critical periods, without overburdening and causing disinterest. Automatic monitoring of
appliance power, storage and generation states allows estimation of what changes to the forward
import/export profile are possible.
Dynamic pricing can improve the load shape further by grading the level of reduction over time. Also, applying
price changes on an area by area basis over time will also avoid gross over-reactions. From the customer
perspective, predictive price information in advance is also vital. When high prices are forecast, the customer
systems can take anticipatory compensating action both before and after the high price period. This will avoid
violent changes to the overall load shape across price switches and prevent too much decay in price related
demand reduction over the period of application.

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14. PREMISES POWER PROFILE AND DER CONTROL
The Power profiles for different DER sectors, with and without generation, need to be considered carefully.
As we mentioned earlier, domestic premises' level of electrical demand is highly erratic. The relative level of
short term variability could increase in modern high efficiency houses, even though the overall energy
consumption will decrease. To this could be added generation of an 'erratic' nature such as wind (turbulence
at low height) and PV under changing light conditions (fast moving broken cloud).
PV under a clear sky or constant cloud level will give a smoother, but variable profile, rising to a peak then
dropping again as the sun traverses. CHP will give periods of constant output depending on the outside
temperature and thermal cycling requirement. However, in more efficient homes CHP will not be needed; CHP
schemes on some pilot, low energy domestic developments can be seriously underutilised. Lower heating
requirements, a move to instantaneous electric water heating (new high erratic demand) and heat pumps
obviate the need for fossil fired systems. Against this, note that heat pumps may not be viable in high density
developments!
On working days, PV tends to operate before the peak demand and thermal (rather than synoptic) wind can
drop when darkness falls. As we noted before and above, demand in high efficiency dwellings will be
dominated by lighting, entertainment, cooking and water heating while occupied; thus the weekday peak
demand always occurs in the evening.
Implications
Loading for residential customers
0
500
1000
1500
2000
2500
3000
3500
00:00 04:00 08:00 12:00 16:00 20:00 00:00
Time
Powerfromgrid(W)

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3m panels operating on a bright day in Great Britain (GB) will actually shift the peak time to the evening.
Commercial premises have a steadier load during the working day period, comprising lighting, water heating,
cooling and office equipment plus some (relatively) minor cooking load. Again, application of renewable
generation is subject to the same observations as above; wind will be erratic but PV output will synergise with
the highest demand level. Commercial space (especially high rise) in dense urban areas will again not be
suitable for heat pump installations and natural cooling, due to lack of open ground and density of occupation.
Thus, CHP for both heating and to drive cooling may be appropriate, as illustrated in the previous article (13).

Page 68
For electricity generation alone, PV is also being increasingly installed on modern commercial buildings. The
resulting profile for the premises can look as follows.
When scaled up on a GB basis, again the peak gets shifted to the evening

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Industrial demand is highly 'bespoke' and driven by the requirements of the individual production processes. It
is usually more controllable within time periods and notice limits. Some large demand can be interruptible at
short notice while other processes can have their schedules adjusted with some notice, but are
'uninterruptible' when in progress. Where heat and electricity are used by processes, fossil-fired CHP has been
found be efficient and cost reducing. Renewables will make some reduction.
We need to consider what level of control is appropriate at premises level. As we said before Demand falls into
one of three types:
Time critical
Non time critical
Unnecessary!
The latter of these three is obviously being tackled vigorously as public awareness of energy costs rises. Use of
power efficient light bulbs and recognition of the fact that empty rooms and inanimate objects are not
frightened of the dark (turn lights off in unoccupied areas) is being recognised; a change from the acceptance
of 'passive energy waste' we have grown up with.
Manual actions are relatively time consuming and tend to be forgotten after a while. What we must
remember is that each customer group is primarily concerned with
Making Widgets (Industrial)
Making Money (commercial) and
Getting on with life (Domestic)
Thus automatic monitoring and management is the key to ensuring efficient premises energy management.
Tackling the non-time critical demand is more complex to handle; remember that predictability is vital - power,
time and location. There are considerable gains to be made by smoothing and reducing the peak demands on -
fossil fired generation. However, poorly controlled load movement can give rise to worse demand shapes as
was experienced in the early days of fixed time off-peak domestic electrical heating. The remnants of this can
still be seen as an artificial trough around midnight on the Great Britain (GB) Spring demand profile
below. There are also examples of 'bad shaping' in the previous article (13) on intelligent buildings.

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As we have already said, careful, ramped application of dynamic electricity prices (export and import) by time
and group (supplier, geographical and/or sector) can influence premises Import/Export by changes to Demand,
Generation and Storage. This should be able to effect compensation for unpredictable renewable plant output
and produce a more efficient load profile for the remaining fossil plant which needs to run, rather than simple
timer or advance time block pricing methods. Having said this it is important to recognise that forecasts
of prices by time are important for effective control of customer DER resources.
However, any level of 'change' to the demand (and distributed generation/storage) profile gives rise to issues
of predictability for the market and system/distribution operator. We will explore this in more detail later.

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15. DATA LOGISTICS
As we have already stated, data traffic for distributed resource management will require the use of high speed
aggregation and dissemination mechanisms between the customer and the commercial and operator sections
of the industry.
Within premises we are seeing increased levels of data traffic and external interfaces - computing and
entertainment in the domestic sector and business traffic in the commercial sector. The commercial sector
also has buildings energy and facilities management systems while the industrial sector has large process
management applications.
Digital audio/video, business and process management applications are all data intensive and are mainly
communicated by IP protocol packets. Power management data for future power systems is reasonably sparse
and should not impose a great extra data burden at premises level, although some specialised equipment will
be necessary. The main issue here is to define the data framework most applicable to each premises type and
how that can be aggregated and disseminated at the higher levels.
Monitoring is important at device level for large premises demands, generation and storage with non time
critical elements being managed directly. However, it is certainly not necessary to monitor every lamp bulb
separately; presence and environmental sensors on a zone basis are already available to detect usage and
control/override lighting, heating/cooling levels and appliance operation as appropriate. The individual
demand for each large appliance and the other more general loads from zones should be monitored.

Page 72
From the premises, simple data for demands, generation output and storage condition (kwH capacity and
charge level) and any programmed activity are required. For controllable elements, timescales are required.
Refrigeration can be interrupted for short periods at short notice while laundry loads can be timed in advance,
but must normally run the cycle uninterrupted once started.
Renewable generation should not be interrupted except to maintain network stability but storage can be
programmed at short or long notice.
The next level in the control sequence is the microgrid.

Page 73
The premises controllers interface to a microgrid controller, which monitors import/export and exercises
control over premises variable components. This system ensures real time and lead timescale secure, stable
operation of the microgrid within power quality and any commercially applied limits. It also facilitates
management of 'power variation' data from individual premises (generation, demand, storage) and
instructions resulting from the acceptance of these offers by the market or operators (distribution and
system). This offer/acceptance process again requires analysis of the microgrid integrity as a result of the
instructions.
Premises data, comprising generation and demand power, storage power, energy and offers to change the
same need to be aggregated in total for the microgrid (technical aggregation) and also by supplier (commercial
aggregation) to support market activity. Any variation instructions will be on an aggregated basis for the
microgrid and have to be disseminated back to the individual premises.
So we come to the operators and the market. The suppliers will use any offers to vary within forward market
timescales and may operate trades to increase or decrease their total contracted energy in half hour blocks.
The distribution operator systems will aggregate the data for the microgrids by supply point to give totals for
the system operator. The system operator may use offers in the short term matching mechanism and for
ancillary service purposes.

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All resulting instructions will be disseminated back by supply point, microgrid and then customer premises
with operational instructions re-aggregated by supplier and market instructions aggregated by microgrid and
supply point. This ensures supplier contracted energy is correct within settlement and that security, stability
and power quality is maintained. In the case of ‘trigger’ instructions (ancillary services activation or
intertrip/restriction in case of fault), any execution of the associated action must be recorded for commercial
and technical evaluation of the resultant power and energy change.

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16. DATA STRUCTURE, METERING AND SETTLEMENT
We now need to explore the requirements for the content of different types of messages used between the
customer premises and the industry.
We have already said that an IP protocol communications structure is appropriate for the transport of this
data. Because of the number of businesses involved in the data chain and the suppliers of equipment to
support it, a set of standards for the structure of messages needs to be defined. This not only affects
equipment in the electricity management chain, but also those control systems, such as home automation or
buildings management, within which energy management is incorporated.
The data structures for the main utility communications between supply and generation and the market and
the operator are well defined. These carry data on energy (market timescale) and power/response profiles
(operator timescale) with trades (market) and instructions (operator) to adjust same so that demand matches
generation to an acceptable tolerance in real time. However, although the same principal data requirements
also exist for communication with Distributed Energy Resources (DER), different data structures are
appropriate. It will necessary for the aggregation and dissemination tools to handle any translations required.
The first thing to look at is the metering data streams and their uses on the Great Britain (GB) Power System.
Operator metering
The system operator uses continuous spot power metering of all the critical circuits. This covers all
transmission circuits, supergrid supply transformers, main generators and interconnections. Because the
power system is always in balance, summating the generation output gives the demand less that embedded
generation which does not have operational metering. The operator uses the raw and calculated data in real
time to monitor generation output versus instruction, also total and supply point demands with the latter used
in on line system security analysis. Spot demand history is also used as a basis for demand shape prediction.

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