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Environmentally Significant Operational Loss
Reduction on the Full GB Transmission NetworkPeter Macfie, Haibin Wan, Rachel Morfill, Martin Bradley
Data & Analysis, National Grid,
Gary Taylor, Malcolm Irving
Brunel Institute of Power Systems, Brunel University,
Abstract- A theoretical reduction of 1.4% in Great Britains(GB) transmission MW losses has been demonstrated on post-BETTA network simulations using Security ConstrainedOptimal Power Flow (SC-OPF) techniques. The simulatedefficiency saving applied to the live GB network could inprinciple save the power industry around 3.8 million and 45000tonnes of carbon dioxide over a full year. Such a saving wouldsupport with the European Union target of a 20% cut in carbondioxide emissions on 1990 levels [1]. Previous SC-OPF research[2] determined that between 1.1 and 1.7% transmission lossreduction is achievable on pre-BETTA networks. The SC-OPFalgorithm utilised voltage constraints that were consistent with
the GB Security and Quality of Supply Standards (SQSS) andincluded more than 50 of the worst credible contingencies on theGB network. The SC-OPF algorithm proceeded bymanipulating GB controls including the voltage target ofgenerators, Static VAR Compensators (SVC), and the status ofshunt capacitors and reactors to reduce the transmission MWloss objective function. Our optimised network simulationresults showed significant increases in system VAR gain, whichmeant that the reactive generation requirement was reduced inthese studies. This paper will present these results, and discusspractical issues with utilising SC-OPF on the GB network data.
I. INTRODUCTION
National Grid, which is based in the UK and has a large
business in the United States, is the system operator (SO) for
the high voltage electricity transmission system in Great
Britian (England, Wales, and Scotland). National Grid is also
the owner of the transmission system in England and Wales,
which comprises approximately 7200 kilometers of 400 and
275 kV overhead line, 677 kilometers of underground cable,
and 313 substations. National Grid is committed to its duty
under the Electricity Act of 1989 to develop and maintain an
efficient, coordinated and economical system. Since the
implementation of the British Electricity Trading and
Transmission Arrangements (BETTA) on 1st April 2005National Grids SO responsibilities were extended to include
the transmission network in Scotland, which has meant
around a 30% increase in the size of the network that needs to
be operated and managed in the GB energy balancing
mechanism.
Transmission losses are reported annually by National Grid
as the difference between electricity units entering and
leaving the system, these losses include fixed and variable
losses. During system design timescales, which occur around
7 years before real time, National Grids policy is to
implement network infrastructure that has been designed to
optimise lifetime operating costs including the expected cost
of transmission losses. This research however is concerned
with operational timescales, which occur close to real time.
National Grid considers losses while operating the
transmission system when deciding a secured network
configuration and voltage profile. National Grid has
commissioned this research, jointly with Brunel University,
to determine the potential for further reduction intransmission losses through changes in operational
procedures. The GB Security and Quality of Supply
Standards (SQSS) [3] set out the minimum requirements for
the planning and operation of the GB transmission system.
These standards include definitions of acceptable voltage
conditions that have to be maintained during normal
operation, and in the event of at least an n D contingency
criterion, where the D refers to the loss of a double circuit.
At present the transmission system relies on manual
adjustment of operational conditions, this is in contrast with
the perception of the power system community that optimal
power flow tools can be exploited to reduce losses. The longterm aim of this research is concerned with the direct or
indirect implementation of SC-OPF results to provide advice
in the reactive power operational planning and real-time
control of the live GB transmission system to reduce
transmission losses.
Loss minimisation OPF studies based on the Spanish
transmission network have recently been performed by
Ramos, Exposito, and Quintana [5], who demonstrated that
transmission MW loss reduction of around 3% on state
estimator data was achievable in theory using OPF with
reactive constraints, and generator voltage controls. Thesestudies indicated that OPF tools could be a valuable technique
in achieving transmission loss reduction on a large-scale
power system, but are possibly optimistic, as security
constraints were not included.
SC-OPF has been exploited in this research to manipulate
the voltage profile of the network by changing the state of
reactive control devices to reduce transmission losses.
Finding a feasible global minimum is a complex problem
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when minimising the non-separable MW loss objective
function, including discrete voltage control devices (such as
shunt capacitor and reactors), local controls and securing the
network against a large number of credible contingencies.
Ensuring that the final SC-OPF solution is feasible is an
important criterion that has to be met, before any control
switching result could be implemented, however a sub-
optimality in the solution could be tolerated if it led to an
improvement in the objective function. This paper presents
results showing that SC-OPF can be utilised on an already
well run large-scale power system to further reduce
transmission losses. Issues relating to the application of theseSC-OPF techniques for practical use by National Grid will
also be discussed in the context of this research.
II. OPERATIONAL REACTIVE POWER MANAGMENT
National Grid holds the sole licence to operate the
transmission network in England, Wales, and Scotland. The
Transmission Requirements (TR) group is responsible for
detailed operational planning from 13 weeks ahead to day
ahead, including preparing system access requirements to
allow maintenance and construction work on the GB
transmission system. This process includes optimisation of
the network voltage profile taking into account all planned
outages due to maintenance and construction work on the
transmission system. TR will ensure that the SQSS
requirements are satisfied and that ancillary services costs are
minimised [4]. One of the major TR deliverables is a secured
day-ahead peak demand network study, which is the focus of
this research. These studies are adjusted by TR for each day
to take account of changes in predicted demand, generation
and changes in outage patterns.
A hand-over document is prepared and delivered to thecontrol room, which includes network plans, outages, active
constraints on MW flows, and post-fault actions to be taken
in the event of fault outages. While the emphasis of the day-
ahead deliverable is on active power management there is an
obligation under the SQSS to secure voltage, which means
that reactive power management is also considered. The
SQSS includes regulatory requirements relating to generation
margin, frequency control, voltage condition, thermal
overload condition [3]. These regulatory voltage condition
requirements must be carefully considered in this research, as
reactive controls are manipulated by the optimisation
algorithm. It is essential to ensure that available dynamic
reactive reserves are maintained on SVCs and generators in
order to secure the post-fault system voltages in the event of
the most onerous credible fault. The steady state voltage
condition must be secured both pre and post fault, as shown
in Fig. 1 below. The figure shows the SQSS voltage
requirements at customer connected buses (including grid
supply points to the low voltage network), and also shows the
requirements at all other buses. The steady state voltage limit
information is indicated at each voltage level; the network
must be secured to meet these limits for both the intact
network and in the case of a fault outage. At customer
connections the voltage must not change more than the
regulated limits shown in the event of either a single circuit(SC) or double circuit (DC) outage.
The day-ahead deliverable from TR is presented to control
engineers, and is then used to derive a voltage profile target at
several key demand points during the day. The control room
transmission despatch engineer (TDE) then dynamically
switches reactive equipment and issues instructions to
generators in order to ensure system security and adequate
MVAR reserves, while using the voltage profile target as a
Fig. 1. Acceptable voltage conditions on the GB HV transmission system. SC=Single Circuit, DC=Double Circuit. Sourced from [11].
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guide. Control decisions include switching of shunt capacitors
and reactors, changes to automatic reactive switching control,
and issuing MVAR targets to generators. The reactive
switching decision will be heavily influenced by the
anticipated change in future demand. The peak demand point
study produced by TR will have been securely configured at a
snap-shot in time, but provides no information to the TDE on
how to evolve the network from one time period to the next.This highlights the key difference between off-line
snapshot studies used to ensure that the system is secure for
a given generation/demand/outage pattern, and the on-line
practical implementation of a secured network. The biggest
source of reactive power is the system network MVAR gain
(shunt gain BV2 minus series losses I2X ). This gain performs
a vital role in the reactive balance:
MVAR import + MVAR generated + MVAR gain =
MVAR absorbed by generators + consumer MVAR demand + MVAR export
(1)
The TDE will utilise this system gain to achieve the desiredvoltage profile by increasing system global voltage profile to
increase network gain prior to an increase in demand, so that
when demand does increase the voltage profile of the system
does not sag and lose MVAR gain. The snapshot day-ahead
study produced by TR is used by control engineers as a guide,
and represents the system at the peak demand point
reasonably accurately. The results presented in section 4 are
based on such day-ahead studies.
III. PROBLEM FORMULATION
Finding the minimum losses by manipulating reactivecontrols is a reactive management problem, which can be
formulated as a security constrained optimal reactive
dispatch. The solution is a state of the system that provides
optimal settings of reactive controls, while maintaining
system security in the event of any of the credible
contingencies. The SC-OPF technique utilised in this
research is the same as that presented by Dandachi [6], which
is essentially a linear program formulated in terms of only
control variables in a compact form. In this research the
starting point for the SC-OPF is a day-ahead study, which is a
feasible solution; this study also forms a reference to
determine any improvement in the objective function. Thealgorithm evolves toward the final solution through
successive iterations, which act to reduce the MW losses
objective function, while alleviating all constraint violations
that maybe encountered. Convergence is achieved when:
There are no constraint violations.
Changes in the objective function are within a user
defined tolerance.
The control movements are within user definedtolerances.
A. Objective FunctionThis research is based on minimising a transmission MW
losses objective. Such an objective is highly non-separable
[7], as it cannot be approximated using separate active and
reactive power components. This makes finding a global
solution more difficult, because of the increased complexity.
An additional challenge arises because the transmissionlosses objective function is not usually convex, which means
that it can have many minima. The SC-OPF solution is
therefore typically a minimum, but not necessarily a unique
global minimum.
B. Control VariablesThe modelled control variables within the SC-OPF are:
Generator voltage targets.
Static VAR compensator (SVC) voltage targets.
Discrete shunt capacitors on/off
Discrete shunt reactors on/off.
The voltage targets were only defined for reactive controls
which possess a wide enough reactive range to support the
voltage (typically >100 MVAR range). These reactive
controls were set-up to control the voltage level at the high-
voltage side to achieve the voltage target determined by the
SC-OPF.
C. System ConstraintsThe system constraints included [6]:
Control limits (e.g. generator, and SVCs). MVAR interchange between user defined reactive
power areas.
Voltage limits. Generator MVAR reserves relaxed for
contingency cases.
When contingencies are included in the optimization, the
resulting reactive generation pattern will have adequate
MVAR reserves, so that the voltage can be secured in theevent of any of these occurring. A large number of included
contingencies should therefore imply widespread MVAR
reserves, which would make the need to define reserve
constraints redundant. MVAR reserve constraints are
however included for several reasons including:
To hold back the voltage profile, as this could be
raised to the limit of feasibility by the objective
function.
To allow for error in the network data.
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The system constraints that define the SC-OPF problem
include all the intact system constraints, and all the modelled
security constraints for the around 50 of the worst credible
contingencies on the network. Since the day-ahead data
forms the starting point for the SC-OPF no constraints are
initially violated in either the intact system or in contingency
cases.
D. Data model formulationSC-OPF algorithms have been extensively researched [6],
but practical applications of reactive SC-OPF have not been
successfully implemented within National Grid on a
permanent basis. Therefore the underlying algorithms are
relatively mature, and the challenge now lies in
implementation and documentation of these techniques to
solve current practical issues on large scale power systems.
Network data preparation and appropriate setting up of OPF
parameters are necessary to achieve successful and
meaningful results. Ensuring that that a well conditionednetwork is utilised is important, because OPF is much more
sensitive to network data inaccuracies than conventional
power flow.
This research was based on the National Grid tool Coldstart
[12], which utilised SC-OPF to remove infeasibilities and set-
up a voltage profile to give a feasible and economic solution.
This tool was modified so that it could be used for secure
MW transmission loss reduction. The Coldstart process
converts day-ahead network data into an appropriate format
for analysis, which included adding OPF related data such as:
Acceptable voltage conditions - See figure 1.
Flagging voltage controlled nodes
Flagging shunts as optimisable
Reactive reserve requirements
MVAR interchanges
Contingency information - 50 cases
The initial voltage targets and the initial shunt switching
pattern were set-up to be identical to the day-ahead study.
The in-service shunts needed to be accurately represented in
the data, so that they can be switched in as required to
achieve the objective. An intact network power flow, and
contingency power flows, were performed on this initial
converted day-ahead data to form a reference for the MW
losses objective, and to form a starting point for the OPF
process. Many of the difficulties encountered achieving
convergence during the SC-OPF process were due to
inaccuracies in the network data, or inappropriate settings in
the OPF.
E. Issues with implementing OPF on a large-scale networkSpecific problems with using the MW losses objective
function have already been discussed. Some general
problems with optimal power flow (OPF) were outlined by
Tinney [8]. These included the use of equivalent networks
causing errors. For example, a problem could be encountered
if a reduced section of network had a negative impedancebranch. In this case the minimise MW losses objective would
attempt to maximise the flow in this branch, which would
lead to a misleading reduction in the total MW losses. To
avoid this problem in the network models presented in this
research we only flagged non-equivalent branches as
optimisable to be included in the loss minimization objective.
A second difficulty discussed by Tinney relates to the
discrete variable sub-problem in the OPF whereby some
variables can only be adjusted in discrete steps, which can
cause a problem when all variables are treated as continuous
in the optimisation procedure. The SC-OPF procedure treatsall variables as continuous, and then rounds all the discrete
variables to their nearest discrete value. This not only causes
sub-optimality, but can cause constraint violations making the
final solution infeasible. There are several methods of
dealing with this problem described in [13].
IV. LOSS MINIMISATION STUDIES ON GB NETWORKS
A. ResultsAll studies presented are based on 2007 weekday day-ahead network studies produced by TR. These studies have
been manually configured around one of the demand peaks of
the day occurring between 10:30-13:00, so that they
represented the system state using the best information
available at the time. Figure 2 shows the day-ahead MW
losses and optimised MW losses for Tuesday studies at
monthly intervals across the year of 2007 referenced against
the scale on the right hand side (RHS), and are represented
with solid square blocks. The difference between the day-
ahead MW losses and optimised MW losses is the MW loss
reduction. Figure 2 also shows the MW demand, day-ahead
MVAR generation and day-ahead MVAR gain, as well as the
optimised MVAR generation and optimised MVAR gain
referenced against the scale on the left hand side (LHS). The
total demand has been scaled down to 20% of its value, so
that it can be included on the LHS scale. Figure 3 highlights
the relationship between the percentage MW loss reduction,
which is the success of the SC-OPF at achieving lower losses,
and the percentage MVAR gain change for the network upon
optimisation of the weekday 2007 day-ahead networks.
Figure 3 also shows the sum total lagging MVAR reserve
percentage change between the day-ahead and the optimised
studies. The lagging MVAR reserve is the amount of MVAR
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generating capability that is remaining on generators and
SVCs on the system.
B. Conclusions and Further WorkMW loss reductions of between 0.51 and 2.56%, with a
mean average of 1.4%, have been achieved by SC-OPF on a
selection of 2007 weekday day-ahead network studiesproduced by TR shown in Figure 2. These results indicate
that the GB electricity transmission network is already well
managed, so exhibits low losses; however a small but
significant savings could still be achieved if the optimised
solutions derived by SC-OPF could be implemented in
practice. A 1.4% MW loss saving would have saved around
45000tCO2 based on the 6.10TWh 2006/07 outturn
transmission losses [9, 10]. This represents a 3.8 million
saving to the power industry as a whole based on a 45/MWh
average generation cost [9]. Efficiency savings play a key
role in the UK governments commitment to tackling climate
change, so any successful implementation of SC-OPFtechnology to reduce GB transmission losses in National
Grids network operations process would be beneficial for all
stakeholders. These efficiency savings can be compared to
the result of Bansal et al [2], which showed that up to 1.7%
MW loss savings can be achieved on a single pre-BETTA
day-ahead network. The results in figure 2 cover more day-
ahead networks than previous research, with a variety of
demands spread across the year for post-BETTA day-ahead
data covering the whole of GB. Figure 2 also shows that the
minimising MW losses objective achieved not only an
improvement in MW losses, but also increased system gain,
and reduced the MVAR generation. Both these
improvements (MW losses and MVAr generation) would
save industry money, and this would be partly shared with
National Grid through the incentive scheme described in [2].
Figure 3 shows a strong correlation between lower MW
losses and increased MVAR system gain. Figure 3 also
shows that the SC-OPF is also achieving a beneficial increase
in lagging MVAR reserves on the system with the minimise
MW losses objective. This figure confirms, the expected
relationship, that greater MVAR gain implies greater MVAR
reserves. It is likely that the MW losses objective is reducing
losses through reduced line currents by increasing the systemvoltage profile. The increased voltage profile increases the
system MVAR gain, which reduces the MVAR generation
requirement in the reactive balance, which in turn implies
greater MVAR reserves.
Further studies are needed to deepen our understanding
about the state changes made by SC-OPF when applied to the
National Grid transmission network when utilising a
minimise transmission losses objective function. These
studies are required to derive useful advice detailing optimal
control patterns, which securely minimise transmission
losses, the later part of this research will need to be carried
out closely with TR in order to get continuous feedback and
adapt advice accordingly. Future SC-OPF research is likely
to investigate alternative objective functions such as reactive
losses, and reactive generation costs. The results from these
differing objectives will be used to assess their suitability forminimising transmission losses, and their effects on reactive
reserve and system security. Effective solution procedures
for optimization problems involving such multi-objective
functions will also be explored.
ACKNOWLEDGMENT
The authors acknowledge the use of and user support for
Nexants SCOPE software, which was used to perform the
SC-OPF analysis in this research. The authors also wish to
acknowledge the assistance and support of National Grid and
the EPSRC.
REFERENCES
[1] DTI, Meeting the energy challenge - A White Paper on Energy 2007,published May 2007, p9, retrieved 6th December 2007 from
www.berr.gov.uk.[2] J. Bansal, G.A. Taylor, Y.H. Song, H.B. Wan, A.M. Chebbo and M.E.
Bradley, The scope for further loss minimisation on the National Gridtransmission system, UPEC 2006, Newcastle upon Tyne, UK, 6-8
September 2006.[3] Ofgem, GB Security and Quality of Supply Standard, Version 1.0,
published September 22 2004, retrieved 14th February 2008 fromwww.ofgem.gov.uk.
[4] Ofgem, National Grid Electricity Transmission System Operator
Incentives from 1 April 2007, published 27 February 2007, ref 35/07,retrieved 23rd January 2008 from www.ofgem.gov.uk.
[5] J. Ramos, A. Gomez Exposito, V.H. Quintana, Transmission power
loss reduction by interior-point methods: implementation issues andpractical experience, IEE Proceedings-Generator Transmission
Distribution, Vol 152, No 1, pp 90-98, 2005.[6] N. Dandachi, Improved algorithm for the voltage/VAR management
on the NGC system, IEE Colloqium, Issue 24, pp4/1 pp 4/6,1997.[7] O. Alsa, J. Bright, M. Praise, B. Stott, Further Developments in LP-
Based Optimal Power Flow, IEEE Transaction on Power Systems,Vol 5, pp.697 711, 1990.
[8] W.F. Tinney, Some Deficiencies In Optimal Power Flow, IEEETransactions on Power Systems, Vol 3, No 2, pp 676 683, 1988.
[9] Ofgem, Zonal transmission losses assessment of proposals to modifythe Balancing and Settlement Code, published 23 February 2007,
retrieved 21st February 2008 from www.ofgem.gov.uk.[10] R. Price, Network Operations Energy Requirements Transmission
Losses Report, Internal National Grid document, 2007.[11] National Grid, Application of Security and Quality of Supply
Standards in Operational Timescales, Internal National Grid document,National Grid, BP 1883 Issue 6 18 March 2005.
[12] F. Ali, ELLA and COLDSTART User Guide, Internal National Griddocument, National Grid, Issue 2 Draft 1, November 2002.
[13] Y. Song, M. Irving, Optimisation techniques for electrical powersystems Part 2 Heuristic optimisation methods, Tutorial:
Optimisation techniques, IEE Power Engineering Journal, Vol.15,No.3, pp.151-160, 2001.
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2000
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8000
10000
12000
14000
0 10 20 30 40 50 60
We ek Number
MV
ARgeneration,gainandMW
Demand
0
200
400
600
800
1000
1200
1400
MWlosses
MVAR gain
optimised
MVAR gain
MVAR
generation
optimised
MVAR
generation
MW Demand
- Scaled
down
MW Losses
optimised
MW losses
Fig. 2. This graph shows Tuesday day-ahead results from SC-OPF studies spread at monthly intervals across 2007.
-10
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Week Number
%MV
ARgainand%
MVARreservechange
uponoptimisation
0
0.5
1
1.5
2
2.5
3
%M
W
losseschangeuponoptimisation
% MVARgain change
% MVARReservechange
% MW losssavings
Fig. 3. Shows the same set of SC-OPF studies from figure 2. MVAR gain and MVAR reserve change % are on LHS axis, and MW loss change %
on RHS axis. The changes relate the percentage change between the day-ahead study and the optimised study.